Bidirectional User Throughput Maximization Based on Feedback Reduction in LiFi Networks

OpenAIRE

Soltani, Mohammad Dehghani; Wu, Xiping; Safari, Majid; Haas, Harald

2017-01-01

Channel adaptive signalling, which is based on feedback, can result in almost any performance metric enhancement. Unlike the radio frequency (RF) channel, the optical wireless communications (OWCs) channel is fairly static. This feature enables a potential improvement of the bidirectional user throughput by reducing the amount of feedback. Light-Fidelity (LiFi) is a subset of OWCs, and it is a bidirectional, high-speed and fully networked wireless communication technology where visible light ...

Decorrelation of Neural-Network Activity by Inhibitory Feedback

Science.gov (United States)

Einevoll, Gaute T.; Diesmann, Markus

2012-01-01

Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between

Neural network based online simultaneous policy update algorithm for solving the HJI equation in nonlinear H∞ control.

Science.gov (United States)

Wu, Huai-Ning; Luo, Biao

2012-12-01

It is well known that the nonlinear H∞ state feedback control problem relies on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that has proven to be impossible to solve analytically. In this paper, a neural network (NN)-based online simultaneous policy update algorithm (SPUA) is developed to solve the HJI equation, in which knowledge of internal system dynamics is not required. First, we propose an online SPUA which can be viewed as a reinforcement learning technique for two players to learn their optimal actions in an unknown environment. The proposed online SPUA updates control and disturbance policies simultaneously; thus, only one iterative loop is needed. Second, the convergence of the online SPUA is established by proving that it is mathematically equivalent to Newton's method for finding a fixed point in a Banach space. Third, we develop an actor-critic structure for the implementation of the online SPUA, in which only one critic NN is needed for approximating the cost function, and a least-square method is given for estimating the NN weight parameters. Finally, simulation studies are provided to demonstrate the effectiveness of the proposed algorithm.

Disseminating Innovations in Teaching Value-Based Care Through an Online Learning Network.

Science.gov (United States)

Gupta, Reshma; Shah, Neel T; Moriates, Christopher; Wallingford, September; Arora, Vineet M

2017-08-01

A national imperative to provide value-based care requires new strategies to teach clinicians about high-value care. We developed a virtual online learning network aimed at disseminating emerging strategies in teaching value-based care. The online Teaching Value in Health Care Learning Network includes monthly webinars that feature selected innovators, online discussion forums, and a repository for sharing tools. The learning network comprises clinician-educators and health system leaders across North America. We conducted a cross-sectional online survey of all webinar presenters and the active members of the network, and we assessed program feasibility. Six months after the program launched, there were 277 learning community members in 22 US states. Of the 74 active members, 50 (68%) completed the evaluation. Active members represented independently practicing physicians and trainees in 7 specialties, nurses, educators, and health system leaders. Nearly all speakers reported that the learning network provided them with a unique opportunity to connect with a different audience and achieve greater recognition for their work. Of the members who were active in the learning network, most reported that strategies gleaned from the network were helpful, and some adopted or adapted these innovations at their home institutions. One year after the program launched, the learning network had grown to 364 total members. The learning network helped participants share and implement innovations to promote high-value care. The model can help disseminate innovations in emerging areas of health care transformation, and is sustainable without ongoing support after a period of start-up funding.

MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion

Science.gov (United States)

Zhang, Yunong; Chen, Ke; Ma, Weimu; Li, Xiao-Dong

This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.

PEER-FEEDBACK AND ONLINE INTERACTION: A CASE STUDY

Directory of Open Access Journals (Sweden)

Martha Isabel Espitia

2013-08-01

Full Text Available The implementation of information and communication technologies (ICTs in the English as a Foreign Language (EFL classroom has led to different practices and types of interaction. Online interaction allows teachers and students to use the target language beyond the classroom and provides students with more time to be exposed to and use the language. This case study aimed at understanding how a group of twelve students at Universidad de la Sabana, who participated in online forums as part of the requirements of a blended EFL course, interacted online to provide peer-feedback on written compositions. It also analyzed how online interaction was undertaken when using online forums. Findings suggest that participants raised awareness about the relevance of editing to avoid possible language problems by reviewing their peers' products and that the implementation of online peer feedback as an assessment strategy reveals students' beliefs towards language assessment.

Video-based peer feedback through social networking for robotic surgery simulation: a multicenter randomized controlled trial.

Science.gov (United States)

Carter, Stacey C; Chiang, Alexander; Shah, Galaxy; Kwan, Lorna; Montgomery, Jeffrey S; Karam, Amer; Tarnay, Christopher; Guru, Khurshid A; Hu, Jim C

2015-05-01

To examine the feasibility and outcomes of video-based peer feedback through social networking to facilitate robotic surgical skill acquisition. The acquisition of surgical skills may be challenging for novel techniques and/or those with prolonged learning curves. Randomized controlled trial involving 41 resident physicians performing the Tubes (Da Vinci Intuitive Surgical, Sunnyvale, CA) simulator exercise with versus without peer feedback of video-recorded performance through a social networking Web page. Data collected included simulator exercise score, time to completion, and comfort and satisfaction with robotic surgery simulation. There were no baseline differences between the intervention group (n = 20) and controls (n = 21). The intervention group showed improvement in mean scores from session 1 to sessions 2 and 3 (60.7 vs 75.5, P feedback subjects were more comfortable with robotic surgery than controls (90% vs 62%, P = 0.021) and expressed greater satisfaction with the learning experience (100% vs 67%, P = 0.014). Of the intervention subjects, 85% found that peer feedback was useful and 100% found it effective. Video-based peer feedback through social networking appears to be an effective paradigm for surgical education and accelerates the robotic surgery learning curve during simulation.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

Science.gov (United States)

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

An information spreading model based on online social networks

Science.gov (United States)

Wang, Tao; He, Juanjuan; Wang, Xiaoxia

2018-01-01

Online social platforms are very popular in recent years. In addition to spreading information, users could review or collect information on online social platforms. According to the information spreading rules of online social network, a new information spreading model, namely IRCSS model, is proposed in this paper. It includes sharing mechanism, reviewing mechanism, collecting mechanism and stifling mechanism. Mean-field equations are derived to describe the dynamics of the IRCSS model. Moreover, the steady states of reviewers, collectors and stiflers and the effects of parameters on the peak values of reviewers, collectors and sharers are analyzed. Finally, numerical simulations are performed on different networks. Results show that collecting mechanism and reviewing mechanism, as well as the connectivity of the network, make information travel wider and faster, and compared to WS network and ER network, the speed of reviewing, sharing and collecting information is fastest on BA network.

PROFICIENT CONSTITUTION FOR ONLINE EDUCATIONAL ENVIRONMENT USING FEEDBACK ANALYSIS

Directory of Open Access Journals (Sweden)

D. Rajagopal Devarajan

2014-06-01

Full Text Available Modern world learners want to get more knowledge about their respective field which they like. The Learner just has to implement the knowledge using which they are learnt. This implementation has been used in the modern world. Learners are like to study in within the term they like to learn supplementary bunch. This requirement has been fulfilled by the online education system. The online Educational System provides the Notes, Books, Audio and Video Lectures, Manuals, Presentations etc., learners are expecting more apart from the contents or the learners are not willing to study which were provided by the online Educational System. The online Educational system provider has to know about the Learners satisfaction level, which has been evaluated by the Learners feedback. The feedback analysis has been evaluated by the selected criteria. Each criterion has some scoring options. Using this option, the learners will give the feedback to the particular subject trainer through the scoring options. Finally, feedback scores are evaluated and  give the advice to the trainer to implement the new techniques for giving the training. This operation is instructing the trainer to implement and update their teaching skills.

Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

Science.gov (United States)

Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

2010-09-01

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

Tailoring of feedback in online assessment: Lessons learnt

NARCIS (Netherlands)

Vasilyeva, E.; De Bra, P.M.E.; Pechenizkiy, M.; Bonk, C.J.; et al., xx

2008-01-01

Feedback plays an important role in the learning process. Particularly, the functions of the elaborated feedback (EF) in online assessment may include assisting students in understanding their mistakes and misconceptions, motivating the student for further learning, suggesting directions for

A game theory-based trust measurement model for social networks.

Science.gov (United States)

Wang, Yingjie; Cai, Zhipeng; Yin, Guisheng; Gao, Yang; Tong, Xiangrong; Han, Qilong

2016-01-01

In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks. We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively. We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.

Impact of Interactive Video Communication Versus Text-Based Feedback on Teaching, Social, and Cognitive Presence in Online Learning Communities.

Science.gov (United States)

Seckman, Charlotte

A key element to online learning is the ability to create a sense of presence to improve learning outcomes. This quasi-experimental study evaluated the impact of interactive video communication versus text-based feedback and found a significant difference between the 2 groups related to teaching, social, and cognitive presence. Recommendations to enhance presence should focus on providing timely feedback, interactive learning experiences, and opportunities for students to establish relationships with peers and faculty.

Exploring the value of peer feedback in online learning for the provider

NARCIS (Netherlands)

Marijke Kral; Gino Camp; Esther van Popta; Robert Jan Simons; Rob L. Martens

2017-01-01

This paper reviews studies of peer feedback from the novel perspective of the providers of that feedback. The possible learning benefits of providing peer feedback in online learning have not been extensively studied. The goal of this study was therefore to explore the process of providing online

Diffusion of influence in energy awareness campaigns on the online social networking site of facebook

Energy Technology Data Exchange (ETDEWEB)

Samaha, Kimberly

2010-09-15

The era of government jurisdiction based on separate and autonomous entities has been replaced with an intergovernmental and intersectoral network of industry, regulators, special interest groups and individual citizens. New forms of regulatory feedback will be inspired more by the concepts of networks- they will be flatter, leaner, and more flexible. An evaluation of new methods for the diffusion of public awareness regarding energy technologies, policies and projects, was conducted using the technology platform of Facebook. This paper reports on the results of an eighteen month formal study of the Diffusion of Influence in Online Social Networks.

Managing Trust in Online Social Networks

Science.gov (United States)

Bhuiyan, Touhid; Josang, Audun; Xu, Yue

In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Recommendations may be received through a chain of friends network, so the problem for the user is to be able to evaluate various types of trust opinions and recommendations. This opinion or recommendation has a great influence to choose to use or enjoy the item by the other user of the community. Collaborative filtering system is the most popular method in recommender system. The task in collaborative filtering is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users. Because of the different taste of different people, they rate differently according to their subjective taste. If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster. But the collaborative filtering system performs poor when there is insufficient previous common rating available between users; commonly known as cost start problem. To overcome the cold start problem and with the dramatic growth of online social networks, trust based approach to recommendation has emerged. This approach assumes a trust network among users and makes recommendations

Multiple Description Coding with Feedback Based Network Compression

DEFF Research Database (Denmark)

Sørensen, Jesper Hemming; Østergaard, Jan; Popovski, Petar

2010-01-01

and an intermediate node, respectively. A trade-off exists between reducing the delay of the feedback by adapting in the vicinity of the receiver and increasing the gain from compression by adapting close to the source. The analysis shows that adaptation in the network provides a better trade-off than adaptation...

A feedback-retransmission based asynchronous frequency hopping MAC protocol for military aeronautical ad hoc networks

Directory of Open Access Journals (Sweden)

Jinhui TANG

2018-05-01

Full Text Available Attacking time-sensitive targets has rigid demands for the timeliness and reliability of information transmission, while typical Media Access Control (MAC designed for this application works well only in very light-load scenarios; as a consequence, the performances of system throughput and channel utilization are degraded. For this problem, a feedback-retransmission based asynchronous FRequency hopping Media Access (FRMA control protocol is proposed. Burst communication, asynchronous Frequency Hopping (FH, channel coding, and feedback retransmission are utilized in FRMA. With the mechanism of asynchronous FH, immediate packet transmission and multi-packet reception can be realized, and thus the timeliness is improved. Furthermore, reliability can be achieved via channel coding and feedback retransmission. With theories of queuing theory, Markov model, packets collision model, and discrete Laplace transformation, the formulas of packet success probability, system throughput, average packet end-to-end delay, and delay distribution are obtained. The approximation accuracy of theoretical derivation is verified by experimental results. Within a light-load network, the proposed FRMA has the ability of millisecond delay and 99% reliability as well as outperforms the non-feedback-retransmission based asynchronous frequency hopping media access control protocol. Keywords: Ad hoc networks, Aeronautical communications, Frequency hopping, Media Access Control (MAC, Time-sensitive

Information diffusion in structured online social networks

Science.gov (United States)

Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

2015-05-01

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

Towards benchmarking citizen observatories: Features and functioning of online amateur weather networks.

Science.gov (United States)

Gharesifard, Mohammad; Wehn, Uta; van der Zaag, Pieter

2017-05-15

Crowd-sourced environmental observations are increasingly being considered as having the potential to enhance the spatial and temporal resolution of current data streams from terrestrial and areal sensors. The rapid diffusion of ICTs during the past decades has facilitated the process of data collection and sharing by the general public and has resulted in the formation of various online environmental citizen observatory networks. Online amateur weather networks are a particular example of such ICT-mediated observatories that are rooted in one of the oldest and most widely practiced citizen science activities, namely amateur weather observation. The objective of this paper is to introduce a conceptual framework that enables a systematic review of the features and functioning of these expanding networks. This is done by considering distinct dimensions, namely the geographic scope and types of participants, the network's establishment mechanism, revenue stream(s), existing communication paradigm, efforts required by data sharers, support offered by platform providers, and issues such as data accessibility, availability and quality. An in-depth understanding of these dimensions helps to analyze various dynamics such as interactions between different stakeholders, motivations to run the networks, and their sustainability. This framework is then utilized to perform a critical review of six existing online amateur weather networks based on publicly available data. The main findings of this analysis suggest that: (1) there are several key stakeholders such as emergency services and local authorities that are not (yet) engaged in these networks; (2) the revenue stream(s) of online amateur weather networks is one of the least discussed but arguably most important dimensions that is crucial for the sustainability of these networks; and (3) all of the networks included in this study have one or more explicit modes of bi-directional communication, however, this is limited to

Online Social Networking: A Primer for Radiology

OpenAIRE

Prasanna, Prasanth M.; Seagull, F. Jacob; Nagy, Paul

2011-01-01

Online social networking is an immature, but rapidly evolving industry of web-based technologies that allow individuals to develop online relationships. News stories populate the headlines about various websites which can facilitate patient and doctor interaction. There remain questions about protecting patient confidentiality and defining etiquette in order to preserve the doctor/patient relationship and protect physicians. How much social networking-based communication or other forms of E-c...

Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks

KAUST Repository

Elkhalil, Khalil

2015-05-01

User/relay selection is a simple technique that achieves spatial diversity in multiuser networks. However, for user/relay selection algorithms to make a selection decision, channel state information (CSI) from all cooperating users/relays is usually required at a central node. This requirement poses two important challenges. Firstly, CSI acquisition generates a great deal of feedback overhead (air-time) that could result in significant transmission delays. Secondly, the fed-back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. Motivated by the aforementioned challenges, we propose a limited feedback user/relay selection scheme that is based on the theory of compressed sensing. Firstly, we introduce a limited feedback relay selection algorithm for a multicast relay network. The proposed algorithm exploits the theory of compressive sensing to first obtain the identity of the “strong” relays with limited feedback air-time. Following that, the CSI of the selected relays is estimated using minimum mean square error estimation without any additional feedback. To minimize the effect of noise on the fed-back CSI, we introduce a back-off strategy that optimally backs-off on the noisy received CSI. In the second part of the thesis, we propose a feedback reduction scheme for full-duplex relay-aided multiuser networks. The proposed scheme permits the base station (BS) to obtain channel state information (CSI) from a subset of strong users under substantially reduced feedback overhead. More specifically, we cast the problem of user identification and CSI estimation as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, we first obtain the identity of the strong users and then estimate their CSI using the best linear unbiased estimator (BLUE). Moreover, we derive the

Towards a deep understanding of malware propagation in online social networks

Energy Technology Data Exchange (ETDEWEB)

Yan, Guanhua [Los Alamos National Laboratory; Eidenbenz, Stephan [Los Alamos National Laboratory; Chen, Guanling [U OF MASSACHUSETTS LOWELL; Li, Nan [U OF MASSACHUSETTS LOWELL

2009-01-01

Online social networks, which have been expanding at a blistering speed in the recent years, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically targets these online social networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in online social networks. Our study is based on a dataset collected from a real-world location-based online social network. We analyze the social structure and user activity patterns of this network. We further use extensive trace-driven simulation to study the impact of initial infection, user click probability, social structure, and activity patterns on malware propagation in online social networks. The results from this work has greatly deepened our understanding of the nature of online social network malware and also shed light on how to defend against them effectively.

An Enhanced Feedback-Base Downlink Packet Scheduling Algorithm for Mobile TV in WIMAX Networks

Directory of Open Access Journals (Sweden)

Joseph Oyewale

2013-06-01

Full Text Available With high speed access network technology like WIMAX, there is the need for efficient management of radio resources where the throughput and Qos requirements for Multicasting Broadcasting Services (MBS for example TV are to be met. An enhanced  feedback-base downlink Packet scheduling algorithm  that can be used in IEEE 802.16d/e networks for mobile TV “one way traffic”(MBS is needed to support many users utilizing multiuser diversity of the  broadband of WIMAX systems where a group of users(good/worst channels share allocated resources (bandwidth. This paper proposes a WIMAX framework feedback-base (like a channel-awareness downlink packet scheduling algorithm for Mobile TV traffics in IEEE806.16, in which network Physical Timing Slots (PSs resource blocks are allocated in a dynamic way to mobile TV subscribers based on the Channel State information (CSI feedback, and then considering users with worst channels with the aim of improving system throughput while system coverage is being guaranteed. The algorithm was examined by changing the PSs bandwidth allocation of the users and different number of users of a cell. Simulation results show our proposed algorithm performed better than other algorithms (blind algorithms in terms of improvement in system throughput performance. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso

Students' Informal Peer Feedback Networks

Science.gov (United States)

Headington, Rita

2018-01-01

The nature and significance of students' informal peer feedback networks is an under-explored area. This paper offers the findings of a longitudinal investigation of the informal peer feedback networks of a cohort of student teachers [n = 105] across the three years of a UK primary education degree programme. It tracked the dynamic nature of these…

On the Feedback Reduction of Relay Multiuser Networks using Compressive Sensing

KAUST Repository

Elkhalil, Khalil; Eltayeb, Mohammed; Kammoun, Abla; Al-Naffouri, Tareq Y.; Bahrami, Hamid Reza

2016-01-01

This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks employing opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory

Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks.

Science.gov (United States)

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-11-11

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer-Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.

Persistent disturbance rejection via state feedback for networked control systems

Energy Technology Data Exchange (ETDEWEB)

Yue Dong [Institute of Information and Control Engineering Technology, Nanjing Normal University, 78 Bancang Street, Nanjing, Jiangsu 210042 (China)], E-mail: medongy@njnu.edu.cn; Lam, James [Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road (Hong Kong); Wang Zidong [Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH (United Kingdom)], E-mail: Zidong.Wang@brunel.ac.uk

2009-04-15

The problem of persistent disturbance rejection via state feedback for networked control systems is concerned based on the Lyapunov function method. The effect of the network conditions, such as network-induced delay and data dropout, is considered in the modeling of the system. It is assumed that the state and the control signals are individually quantized by quantizers on the sensor side and the controller side. The feedback gain and the quantizer parameters that guarantee the internal stability and the disturbance rejection performance of the closed-loop system are obtained by solving some linear matrix inequalities. To illustrate the effectiveness of the proposed method, a numerical example is provided for the design of the feedback gain and the quantizer parameters.

Persistent disturbance rejection via state feedback for networked control systems

International Nuclear Information System (INIS)

Yue Dong; Lam, James; Wang Zidong

2009-01-01

The problem of persistent disturbance rejection via state feedback for networked control systems is concerned based on the Lyapunov function method. The effect of the network conditions, such as network-induced delay and data dropout, is considered in the modeling of the system. It is assumed that the state and the control signals are individually quantized by quantizers on the sensor side and the controller side. The feedback gain and the quantizer parameters that guarantee the internal stability and the disturbance rejection performance of the closed-loop system are obtained by solving some linear matrix inequalities. To illustrate the effectiveness of the proposed method, a numerical example is provided for the design of the feedback gain and the quantizer parameters.

Count Your Calories and Share Them: Health Benefits of Sharing mHealth Information on Social Networking Sites.

Science.gov (United States)

Oeldorf-Hirsch, Anne; High, Andrew C; Christensen, John L

2018-04-23

This study investigates the relationship between sharing tracked mobile health (mHealth) information online, supportive communication, feedback, and health behavior. Based on the Integrated Theory of mHealth, our model asserts that sharing tracked health information on social networking sites benefits users' perceptions of their health because of the supportive communication they gain from members of their online social networks and that the amount of feedback people receive moderates these associations. Users of mHealth apps (N = 511) completed an online survey, and results revealed that both sharing tracked health information and receiving feedback from an online social network were positively associated with supportive communication. Network support both corresponded with improved health behavior and mediated the association between sharing health information and users' health behavior. As users received greater amounts of feedback from their online social networks, however, the association between sharing tracked health information and health behavior decreased. Theoretical implications for sharing tracked health information and practical implications for using mHealth apps are discussed.

Mining Trust Relationships from Online Social Networks

Institute of Scientific and Technical Information of China (English)

Yu Zhang; Tong Yu

2012-01-01

With the growing popularity of online social network,trust plays a more and more important role in connecting people to each other.We rely on our personal trust to accept recommendations,to make purchase decisions and to select transaction partners in the online community.Therefore,how to obtain trust relationships through mining online social networks becomes an important research topic.There are several shortcomings of existing trust mining methods.First,trust is category-dependent.However,most of the methods overlook the category attribute of trust relationships,which leads to low accuracy in trust calculation.Second,since the data in online social networks cannot be understood and processed by machines directly,traditional mining methods require much human effort and are not easily applied to other applications.To solve the above problems,we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically.We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing.We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships.We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort.We perform experiments on real-life data extracted from Epinions.The experimental results verify the effectiveness and wide application use of our proposed method.

Online Advertising in Social Networks

Science.gov (United States)

Bagherjeiran, Abraham; Bhatt, Rushi P.; Parekh, Rajesh; Chaoji, Vineet

Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising. This chapter focuses on the role of social network information in online display advertising.

Stability, gain, and robustness in quantum feedback networks

International Nuclear Information System (INIS)

D'Helon, C.; James, M. R.

2006-01-01

In this paper we are concerned with the problem of stability for quantum feedback networks. We demonstrate in the context of quantum optics how stability of quantum feedback networks can be guaranteed using only simple gain inequalities for network components and algebraic relationships determined by the network. Quantum feedback networks are shown to be stable if the loop gain is less than one--this is an extension of the famous small gain theorem of classical control theory. We illustrate the simplicity and power of the small gain approach with applications to important problems of robust stability and robust stabilization

The Stratonovich formulation of quantum feedback network rules

Science.gov (United States)

Gough, John E.

2016-12-01

We express the rules for forming quantum feedback networks using the Stratonovich form of quantum stochastic calculus rather than the Itō or SLH (J. E. Gough and M. R. James, "Quantum feedback networks: Hamiltonian formulation," Commun. Math. Phys. 287, 1109 (2009), J. E. Gough and M. R. James, "The Series product and its application to quantum feedforward and feedback networks," IEEE Trans. Autom. Control 54, 2530 (2009)) form. Remarkably the feedback reduction rule implies that we obtain the Schur complement of the matrix of Stratonovich coupling operators where we short out the internal input/output coefficients.

Feedback Reduction in Broadcast and two Hop Multiuser Networks: A Compressed Sensing Approach

KAUST Repository

Shibli, Hussain J.

2013-05-21

In multiuser wireless networks, the base stations (BSs) rely on the channel state information (CSI) of the users to in order to perform user scheduling and downlink transmission. While the downlink channels can be easily estimated at all user terminals via a single broadcast, several key challenges are faced during uplink (feedback) transmission. Firstly, the noisy and fading feedback channels are usually unknown at the base station, and therefore, channel training is usually required from all users. Secondly, the amount of air-time required for feedback transmission grows linearly with the number of users. This domination of the network resources by feedback information leads to increased scheduling delay and outdated CSI at the BS. In this thesis, we tackle the above challenges and propose feedback reduction algorithms based on the theory of compressive sensing (CS). The proposed algorithms encompass both single and dual hop wireless networks, and; i) permit the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead, ii) are agnostic to the statistics of the feedback channels, and iii) utilize the apriori statistics of the additive noise to identify strong users. Numerical results show that the proposed algorithms are able to reduce the feedback overhead, improve detection at the BS, and achieve a sum-rate close to that obtained by noiseless dedicated feedback algorithms.

The use of personalized behavioral feedback for online gamblers: An empirical study

Directory of Open Access Journals (Sweden)

Michael Mario Auer

2015-09-01

Full Text Available Over the last few years, online gambling has become a more common leisure time activity. However, for a small minority, the activity can become problematic. Consequently, the gambling industry has started to acknowledge their role in player protection and harm minimization and some gambling companies have introduced responsible gambling tools as a way of helping players stay in control. The present study evaluated the effectiveness of mentor (a responsible gambling tool that provides personalized feedback to players among 1,015 online gamblers at a European online gambling site, and compared their behavior with matched controls (n=15,216 on the basis of age, gender, playing duration, and theoretical loss (i.e., the amount of money wagered multiplied by the payout percentage of a specific game played. The results showed that online gamblers receiving personalized feedback spent significantly less time and money gambling compared to controls that did not receive personalized feedback. The results suggest that responsible gambling tools providing personalized feedback may help the clientele of gambling companies gamble more responsibly, and may be of help those who gamble excessively to stay within their personal time and money spending limits.

Efficient Online Learning Algorithms Based on LSTM Neural Networks.

Science.gov (United States)

Ergen, Tolga; Kozat, Suleyman Serdar

2017-09-13

We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.

Modelling Users` Trust in Online Social Networks

Directory of Open Access Journals (Sweden)

Iacob Cătoiu

2014-02-01

Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.

The ESID Online Database network.

Science.gov (United States)

Guzman, D; Veit, D; Knerr, V; Kindle, G; Gathmann, B; Eades-Perner, A M; Grimbacher, B

2007-03-01

Primary immunodeficiencies (PIDs) belong to the group of rare diseases. The European Society for Immunodeficiencies (ESID), is establishing an innovative European patient and research database network for continuous long-term documentation of patients, in order to improve the diagnosis, classification, prognosis and therapy of PIDs. The ESID Online Database is a web-based system aimed at data storage, data entry, reporting and the import of pre-existing data sources in an enterprise business-to-business integration (B2B). The online database is based on Java 2 Enterprise System (J2EE) with high-standard security features, which comply with data protection laws and the demands of a modern research platform. The ESID Online Database is accessible via the official website (http://www.esid.org/). Supplementary data are available at Bioinformatics online.

Learners' Interpersonal Beliefs and Generated Feedback in an Online Role-Playing Peer-Feedback Activity: An Exploratory Study

Science.gov (United States)

Ching, Yu-Hui; Hsu, Yu-Chang

2016-01-01

Peer feedback affords interaction and critical thinking opportunities for learners in online courses. However, various factors prevent learners from taking advantage of these promising benefits. This study explored learners' perceptions of the interpersonal factors in a role-playing peer-feedback activity, and examined the types of peer feedback…

The role of feedback and social presence in an online peer coaching program for student teachers

NARCIS (Netherlands)

Thurlings, M.C.G.; Vermeulen, M.; Bastiaens, T.J.; Stijnen, P.J.J.

2014-01-01

Feedback is essential in any kind of learning. This study focused on feedback in online learning and conceptualized feedback as a social interaction process. Online learning rests on social interaction, which is affected by feelings of social presence. Therefore, we investigated received and

The role of feedback and social presence in an online peer coaching program for student teachers

NARCIS (Netherlands)

Thurlings, Marieke; Vermeulen, Marjan; Bastiaens, Theo; Stijnen, Sjef

2018-01-01

Feedback is essential in any kind of learning. This study focused on feedback in online learning and conceptualized feedback as a social interaction process. Online learning rests on social interaction, which is affected by feelings of social presence. Therefore, we investigated received and

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

Science.gov (United States)

Mai, Huanhuan; Song, Gangbing; Liao, Xiaofeng

2013-01-01

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.

Online social networking for radiology.

Science.gov (United States)

Auffermann, William F; Chetlen, Alison L; Colucci, Andrew T; DeQuesada, Ivan M; Grajo, Joseph R; Heller, Matthew T; Nowitzki, Kristina M; Sherry, Steven J; Tillack, Allison A

2015-01-01

Online social networking services have changed the way we interact as a society and offer many opportunities to improve the way we practice radiology and medicine in general. This article begins with an introduction to social networking. Next, the latest advances in online social networking are reviewed, and areas where radiologists and clinicians may benefit from these new tools are discussed. This article concludes with several steps that the interested reader can take to become more involved in online social networking. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Analysis of Feedback processes in Online Group Interaction: a methodological model

Directory of Open Access Journals (Sweden)

Anna Espasa

2013-06-01

Full Text Available The aim of this article is to present a methodological model to analyze students' group interaction to improve their essays in online learning environments, based on asynchronous and written communication. In these environments teacher and student scaffolds for discussion are essential to promote interaction. One of these scaffolds can be the feedback. Research on feedback processes has predominantly focused on feedback design rather than on how students utilize feedback to improve learning. This methodological model fills this gap contributing to analyse the implementation of the feedback processes while students discuss collaboratively in a specific case of writing assignments. A review of different methodological models was carried out to define a framework adjusted to the analysis of the relationship of written and asynchronous group interaction, and students' activity and changes incorporated into the final text. The model proposed includes the following dimensions: 1 student participation 2 nature of student learning and 3 quality of student learning. The main contribution of this article is to present the methodological model and also to ascertain the model's operativity regarding how students incorporate such feedback into their essays.

Modeling online social signed networks

Science.gov (United States)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Scaling-Up Youth-Led Social Justice Efforts through an Online School-Based Social Network.

Science.gov (United States)

Kornbluh, Mariah; Neal, Jennifer Watling; Ozer, Emily J

2016-06-01

The exploration of social networking sites (SNS) in promoting social change efforts offers great potential within the field of community psychology. Online communities on SNS provide opportunities for bridging across groups, thus fostering the exchange of novel ideas and practices. Currently, there have only been limited efforts to examine SNS within the context of youth-led efforts. To explore the potential of SNS to facilitate the diffusion of social justice efforts between distinct youth groups, we linked three school-based youth-led participatory action research projects involving 54 high school students through a SNS. This study offers an innovative methodological approach and framework, utilizing social network analysis and strategic sampling of key student informants to investigate what individual behaviors and online network features predict student adoption of social change efforts. Findings highlight prospective facilitators and barriers to diffusion processes within a youth-led online network, as well as key constructs that may inform future research. We conclude by providing suggestions for scholars and practitioners interested in examining how SNS can be used to enhance the diffusion of social justice strategies, youth-led engagement efforts, and large-scale civic organizing. © Society for Community Research and Action 2016.

Social and place-focused communities in location-based online social networks

Science.gov (United States)

Brown, Chloë; Nicosia, Vincenzo; Scellato, Salvatore; Noulas, Anastasios; Mascolo, Cecilia

2013-06-01

Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users' habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users' visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate place-focused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about place-based groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.

Two-Layer Feedback Neural Networks with Associative Memories

International Nuclear Information System (INIS)

Gui-Kun, Wu; Hong, Zhao

2008-01-01

We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly. (general)

On the Feedback Reduction of Relay Multiuser Networks using Compressive Sensing

KAUST Repository

Elkhalil, Khalil

2016-01-29

This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks employing opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory are introduced and their effect on the system performance is analyzed. The problem of joint user identity and signal-tonoise ratio (SNR) estimation at the base-station is casted as a block sparse signal recovery problem in CS. Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated using the best linear unbiased estimator (BLUE). To minimize the effect of feedback noise on the estimated SNRs, a back-off strategy that optimally backs-off on the noisy estimated SNRs is introduced, and the error covariance matrix of the noise after CS recovery is derived. Finally, closed-form expressions for the end-to-end SNRs of the system are derived. Numerical results show that the proposed techniques drastically reduce the feedback air-time and achieve a rate close to that obtained by scheduling techniques that require dedicated error-free feedback from all network users. Key findings of this paper suggest that the choice of half-duplex or full-duplex SNR feedback is dependent on the channel coherence interval, and on low coherence intervals, full-duplex feedback is superior to the interference-free half-duplex feedback.

A last updating evolution model for online social networks

Science.gov (United States)

Bu, Zhan; Xia, Zhengyou; Wang, Jiandong; Zhang, Chengcui

2013-05-01

As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.

Online Identities and Social Networking

Science.gov (United States)

Maheswaran, Muthucumaru; Ali, Bader; Ozguven, Hatice; Lord, Julien

Online identities play a critical role in the social web that is taking shape on the Internet. Despite many technical proposals for creating and managing online identities, none has received widespread acceptance. Design and implementation of online identities that are socially acceptable on the Internet remains an open problem. This chapter discusses the interplay between online identities and social networking. Online social networks (OSNs) are growing at a rapid pace and has millions of members in them. While the recent trend is to create explicit OSNs such as Facebook and MySpace, we also have implicit OSNs such as interaction graphs created by email and instant messaging services. Explicit OSNs allow users to create profiles and use them to project their identities on the web. There are many interesting identity related issues in the context of social networking including how OSNs help and hinder the definition of online identities.

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

Directory of Open Access Journals (Sweden)

Soha Saleh

2017-01-01

Full Text Available Mirror visual feedback (MVF is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical or opposite (mirror hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with

Numerical Feedback Stabilization with Applications to Networks

Directory of Open Access Journals (Sweden)

Simone Göttlich

2017-01-01

Full Text Available The focus is on the numerical consideration of feedback boundary control problems for linear systems of conservation laws including source terms. We explain under which conditions the numerical discretization can be used to design feedback boundary values for network applications such as electric transmission lines or traffic flow systems. Several numerical examples illustrate the properties of the results for different types of networks.

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

International Nuclear Information System (INIS)

Mai, Huanhuan; Liao, Xiaofeng; Song, Gangbing

2013-01-01

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller. (paper)

READING COMPREHENSION EXERCISES ONLINE: THE EFFECTS OF FEEDBACK, PROFICIENCY AND INTERACTION

Directory of Open Access Journals (Sweden)

Philip Murphy

2007-02-01

Full Text Available This paper describes an ongoing project to create an online version of a reading programme, a custom-designed English language proficiency course at a university in Japan. Following an interactionist view of second language acquisition, it was hypothesised that comprehension of a reading passage could be enhanced by online materials promoting interaction between students as they completed a multiple-choice reading comprehension exercise. Interaction was promoted: (a through pair work at a single computer and (b by providing Elaborative feedback in the form of hints about incorrect answers as a means of stimulating discussion about corrections. Students were randomly selected from upper and lower levels of English proficiency, as determined by the Kanda English Proficiency Test (Bonk & Ockey, 2003, to receive either Elaborative feedback or Knowledge of Correct Response feedback (which supplies the correct answers. Within these groups, some students worked in pairs and some alone. Quantitative results show that the interaction between Type of feedback and Manner of study (individual or pair work was statistically significant; students performed best on a follow-up comprehension exercise when in pairs and having been provided with Elaborative feedback. Furthermore, qualitative analysis of transcribed interactions also shows that Elaborative feedback was conducive to quality interaction.

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

Science.gov (United States)

Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

2018-03-01

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

The effects of online social networks on tacit knowledge transmission

Science.gov (United States)

Zhu, Hong-Miao; Zhang, Sheng-Tai; Jin, Zhen

2016-01-01

Due to the popular use of online social networks in today's world, how to propagate employees' tacit knowledge via online social networks has attracted managers' attention, which is critical to enhance the competitiveness of firms. In this paper, we propose a tacit knowledge transmission model on networks with even mixing based on the propagation property of tacit knowledge and the application of online social networks. We consider two routes of transmission, which are contact through online social networks and face-to-face physical contact, and derive the threshold that governs whether or not a kind of tacit knowledge can be shared in an organization with few initial employees who have acquired it. The impact of the degree distribution of the users' contact network on the transmission is investigated analytically. Some numerical simulations are presented to support the theoretical results. We perform the sensitivity analysis of the threshold in terms of the propagation parameters and confirm that online social networks contribute significantly to enhancing the transmission of tacit knowledge among employees.

Identifying online user reputation of user-object bipartite networks

Science.gov (United States)

Liu, Xiao-Lu; Liu, Jian-Guo; Yang, Kai; Guo, Qiang; Han, Jing-Ti

2017-02-01

Identifying online user reputation based on the rating information of the user-object bipartite networks is important for understanding online user collective behaviors. Based on the Bayesian analysis, we present a parameter-free algorithm for ranking online user reputation, where the user reputation is calculated based on the probability that their ratings are consistent with the main part of all user opinions. The experimental results show that the AUC values of the presented algorithm could reach 0.8929 and 0.8483 for the MovieLens and Netflix data sets, respectively, which is better than the results generated by the CR and IARR methods. Furthermore, the experimental results for different user groups indicate that the presented algorithm outperforms the iterative ranking methods in both ranking accuracy and computation complexity. Moreover, the results for the synthetic networks show that the computation complexity of the presented algorithm is a linear function of the network size, which suggests that the presented algorithm is very effective and efficient for the large scale dynamic online systems.

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

Science.gov (United States)

Rovniak, Liza S; Sallis, James F; Kraschnewski, Jennifer L; Sciamanna, Christopher N; Kiser, Elizabeth J; Ray, Chester A; Chinchilli, Vernon M; Ding, Ding; Matthews, Stephen A; Bopp, Melissa; George, Daniel R; Hovell, Melbourne F

2013-08-14

High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA. A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants' online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

Science.gov (United States)

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

Directory of Open Access Journals (Sweden)

Waddah Waheeb

Full Text Available Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN and the Dynamic Ridge Polynomial Neural Network (DRPNN. Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

SEIR Model of Rumor Spreading in Online Social Network with Varying Total Population Size

International Nuclear Information System (INIS)

Dong Suyalatu; Deng Yan-Bin; Huang Yong-Chang

2017-01-01

Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network . (paper)

Exploring the use of tactile feedback in an ERP-based auditory BCI.

Science.gov (United States)

Schreuder, Martijn; Thurlings, Marieke E; Brouwer, Anne-Marie; Van Erp, Jan B F; Tangermann, Michael

2012-01-01

Giving direct, continuous feedback on a brain state is common practice in motor imagery based brain-computer interfaces (BCI), but has not been reported for BCIs based on event-related potentials (ERP), where feedback is only given once after a sequence of stimuli. Potentially, direct feedback could allow the user to adjust his strategy during a running trial to obtain the required response. In order to test the usefulness of such feedback, directionally congruent vibrotactile feedback was given during an online auditory BCI experiment. Users received either no feedback, short feedback pulses or continuous feedback. The feedback conditions showed reduced performance both on a behavioral task and in terms of classification accuracy. Several explanations are discussed that give interesting starting points for further research on this topic.

Effects of Response-Driven Feedback in Computer Science Learning

Science.gov (United States)

Fernandez Aleman, J. L.; Palmer-Brown, D.; Jayne, C.

2011-01-01

This paper presents the results of a project on generating diagnostic feedback for guided learning in a first-year course on programming and a Master's course on software quality. An online multiple-choice questions (MCQs) system is integrated with neural network-based data analysis. Findings about how students use the system suggest that the…

Studies on the population dynamics of a rumor-spreading model in online social networks

Science.gov (United States)

Dong, Suyalatu; Fan, Feng-Hua; Huang, Yong-Chang

2018-02-01

This paper sets up a rumor spreading model in online social networks based on the European fox rabies SIR model. The model considers the impact of changing number of online social network users, combines the transmission dynamics to set up a population dynamics of rumor spreading model in online social networks. Simulation is carried out on online social network, and results show that the new rumor spreading model is in accordance with the real propagation characteristics in online social networks.

Stabilization of model-based networked control systems

Energy Technology Data Exchange (ETDEWEB)

Miranda, Francisco [CIDMA, Universidade de Aveiro, Aveiro (Portugal); Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); Abreu, Carlos [Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); CMEMS-UMINHO, Universidade do Minho, Braga (Portugal); Mendes, Paulo M. [CMEMS-UMINHO, Universidade do Minho, Braga (Portugal)

2016-06-08

A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

The Effectiveness of Instructor Personalized and Formative Feedback Provided by Instructor in an Online Setting: Some Unresolved Issues

Science.gov (United States)

Planar, Dolors; Moya, Soledad

2016-01-01

Formative feedback has great potential for teaching and learning in online undergraduate programmes. There is a large number of courses where the main source of feedback is provided by the instructor. This is particularly seen in subjects where assessments are designed based on specific activities which are the same for all students, and where the…

Identifying Gatekeepers in Online Learning Networks

Science.gov (United States)

Gursakal, Necmi; Bozkurt, Aras

2017-01-01

The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

Network feedback regulates motor output across a range of modulatory neuron activity.

Science.gov (United States)

Spencer, Robert M; Blitz, Dawn M

2016-06-01

Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5-35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. Copyright © 2016 the American Physiological Society.

Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation.

Directory of Open Access Journals (Sweden)

Salvador Dura-Bernal

Full Text Available Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance. Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom

A Study on the Impact of Collective Feedback in the Online Technical and Professional Communication Classroom

Science.gov (United States)

Singleton, Meredith

2016-01-01

This dissertation study seeks to determine whether feedback in the online Technical and Professional Communication classroom impacts student performance. This dissertation proposes that online Technical and Professional Communication instructors consider adopt such a feedback methodology in order to engage students with writing practices that…

Online Particle Detection by Neural Networks Based on Topologic Calorimetry Information

CERN Document Server

Ciodaro, T; The ATLAS collaboration; Damazio, D; de Seixas, JM

2011-01-01

This paper presents the last results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction over the ATLAS calorimetry information (energy measurements). Later, the extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time in 59%. Also, the payload necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount

SEIR Model of Rumor Spreading in Online Social Network with Varying Total Population Size

Science.gov (United States)

Dong, Suyalatu; Deng, Yan-Bin; Huang, Yong-Chang

2017-10-01

Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network. Supported by National Natural Science Foundation of China under Grant Nos. 11275017 and 11173028

A framework for online social networking features

Directory of Open Access Journals (Sweden)

Mohsen Shafiei Nikabadi

2014-06-01

Full Text Available Social networks form a basis for maintaining social contacts, finding users with common interests, creating local content and sharing information. Recently networks have created a fundamental framework for analyzing and modeling the complex systems. Users' behavior studies and evaluates the system performance and leads to better planning and implementation of advertising policies on the web sites. Therefore, this study offers a framework for online social networks' characteristics. In terms of objective, this survey is practical descriptive. Sampling has been done among 384 of graduate students who have good experiences of membership in online social network. Confirmatory factor analysis is used to evaluate the validity of variables in research model. Characteristics of online social networks are defined based on six components and framework's indexes are analyzed through factor analysis. The reliability is calculated separately for each dimension and since they are all above 0.7, the reliability of the study can be confirmed. According to our research results, in terms of size, the number of people who apply for membership in various online social networking is an important index. In terms of individual preference to connect with, people who are relative play essential role in social network development. In terms of homogeneity variable, the number of people who visit their friends’ pages is important for measuring frequency variable. In terms of frequency, the use of entertainment and recreation services is more important index. In terms of proximity, being in the same city is a more important index and index of creating a sense of belonging and confidence is more important for measuring reciprocity variable.

Evolution of Various Library Instruction Strategies: Using Student Feedback to Create and Enhance Online Active Learning Assignments

Directory of Open Access Journals (Sweden)

Marcie Lynne Jacklin

2013-06-01

Full Text Available This case study traces the evolution of library assignments for biological science students from paper-based workbooks in a blended (hands-on workshop to blended learning workshops using online assignments to online active learning modules which are stand-alone without any face-to-face instruction. As the assignments evolved to adapt to online learning supporting materials in the form of PDFs (portable document format, screen captures and screencasting were embedded into the questions as teaching moments to replace face-to-face instruction. Many aspects of the evolution of the assignment were based on student feedback from evaluations, input from senior lab demonstrators and teaching assistants, and statistical analysis of the students’ performance on the assignment. Advantages and disadvantages of paper-based and online assignments are discussed. An important factor for successful online learning may be the ability to get assistance.

The Endogenous Feedback Network

DEFF Research Database (Denmark)

Augustenborg, Claudia Carrara

2010-01-01

proposals, it will first be considered the extents of their reciprocal compatibility, tentatively shaping an integrated, theoretical profile of consciousness. A new theory, the Endogenous Feedback Network (EFN) will consequently be introduced which, beside being able to accommodate the main tenets...... of the reviewed theories, appears able to compensate for the explanatory gaps they leave behind. The EFN proposes consciousness as the phenomenon emerging from a distinct network of neural paths broadcasting the neural changes associated to any mental process. It additionally argues for the need to include a 5th...

Managing Written and Oral Negative Feedback in a Synchronous Online Teaching Situation

Science.gov (United States)

Guichon, Nicolas; Betrancourt, Mireille; Prie, Yannick

2012-01-01

This case study focuses on the feedback that is provided by tutors to learners in the course of synchronous online teaching. More specifically, we study how trainee tutors used the affordances of Visu, an experimental web videoconferencing system, to provide negative feedback. Visu features classical functionalities such as video and chat, and it…

The Deep Structure of Organizational Online Networking

DEFF Research Database (Denmark)

Trier, Matthias; Richter, Alexander

2015-01-01

While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon...... of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation...... of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi-dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers...

Adaptive online state-of-charge determination based on neuro-controller and neural network

Energy Technology Data Exchange (ETDEWEB)

Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)

2010-05-15

This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.

News from the Library: Two new online services available at CERN: thank you for your feedback!

CERN Multimedia

CERN Library

2012-01-01

The Library has activated two different new online services, as a result of very successful trial periods and thanks to your positive feedback and strong support: PressDisplay and Nucleonica.   PressDisplay is an online portal where one can browse and read online articles from more than 1,900 newspapers from 95 countries, as soon as they are published. Le Monde, International Herald Tribune and many more titles are available in their original layout including text and images. Thanks to RSS feeds, users can directly receive news from their favorite newspapers, but articles can also be printed and sent via email. Based on the very positive feedback we got from the community, the CERN Library has activated a one-year subscription to PressDisplay, and a wealth of information is now available at everyone's fingertips at CERN. In addition to that, the latest Library news about Nucleonica, the nuclide and isotope database derived from the Karlsruhe Nuclide Chart, generated so much interest ...

Online social networking: a primer for radiology.

Science.gov (United States)

Prasanna, Prasanth M; Seagull, F Jacob; Nagy, Paul

2011-10-01

Online social networking is an immature, but rapidly evolving industry of web-based technologies that allow individuals to develop online relationships. News stories populate the headlines about various websites which can facilitate patient and doctor interaction. There remain questions about protecting patient confidentiality and defining etiquette in order to preserve the doctor/patient relationship and protect physicians. How much social networking-based communication or other forms of E-communication is effective? What are the potential benefits and pitfalls of this form of communication? Physicians are exploring how social networking might provide a forum for interacting with their patients, and advance collaborative patient care. Several organizations and institutions have set forth policies to address these questions and more. Though still in its infancy, this form of media has the power to revolutionize the way physicians interact with their patients and fellow health care workers. In the end, physicians must ask what value is added by engaging patients or other health care providers in a social networking format. Social networks may flourish in health care as a means of distributing information to patients or serve mainly as support groups among patients. Physicians must tread a narrow path to bring value to interactions in these networks while limiting their exposure to unwanted liability.

How Does Early Feedback in an Online Programming Course Change Problem Solving?

Science.gov (United States)

Ebrahimi, Alireza

2012-01-01

How does early feedback change the programming problem solving in an online environment and help students choose correct approaches? This study was conducted in a sample of students learning programming in an online course entitled Introduction to C++ and OOP (Object Oriented Programming) using the ANGEL learning management system platform. My…

Improvement in dose escalation using off-line and on-line image feedback in the intensity modulated beam design for prostate cancer treatment

International Nuclear Information System (INIS)

Yan, D.; Birkner, M.; Nuesslin, F.; Wong, J.; Martinez, A.

2001-01-01

Purpose: To test the capability of dose escalation in the IMRT process where the organ/patient temporal geometric variation, measured using either off-line or on-line treatment CT and portal images, are adapted for the optimal design of intensity modulated beam. Materials and Methods: Retrospective study was performed on five prostate cancer patients with multiple CT scans (14∼17/patient) and daily portal images obtained during the treatment course. These images were used to determine the displacements of each subvolume in the organs of interest caused by the daily patient setup and internal organ motion/deformation. The temporal geometric information was processed in order of treatment time and fed into an inverse planning system. The inverse planning engine was specifically implemented to adapt the design of intensity modulated beam to the temporal subvolume displacement and patient internal density changes. Three image feedback strategies were applied to each patient and evaluated with respect to the capability of safe dose escalation. The first one is off-line image feedback, which designs the beam intensity based on the patient images measured within the first week of treatment. The second is an on-line 'the target of the day' strategy, which designs the beam intensity in daily bases by using 'the image of the day' alone. The last one is also the on-line based. However, it designs the instantaneous beam intensity based on also dose distribution in each organ of interest received prior to the current treatment. For each of the treatment strategies, the minimum dose delivered to the CTV was determined by applying the identical normal tissue constraints of partial dose/volumes. This minimum dose was used to represent the treatment dose for each patient. Results: The off-line strategy appears feasible after 5 days of image feedback. The average treatment dose among the patients can be 10% higher than the one in the conventional IMRT treatment where the inverse

A Social Learning Management System Supporting Feedback for Incorrect Answers Based on Social Network Services

Science.gov (United States)

Son, Jiseong; Kim, Jeong-Dong; Na, Hong-Seok; Baik, Doo-Kwon

2016-01-01

In this research, we propose a Social Learning Management System (SLMS) enabling real-time and reliable feedback for incorrect answers by learners using a social network service (SNS). The proposed system increases the accuracy of learners' assessment results by using a confidence scale and a variety of social feedback that is created and shared…

Online body schema adaptation based on internal mental simulation and multisensory feedback

Directory of Open Access Journals (Sweden)

Pedro eVicente

2016-03-01

Full Text Available In this paper, we describe a novel approach to obtain automatic adaptation of the robot body schema and to improve the robot perceptual and motor skills based on this body knowledge. Predictions obtained through a mental simulation of the body are combined with the real sensory feedback to achieve two objectives simultaneously: body schema adaptation and markerless 6D hand pose estimation. The body schema consists of a computer graphics simulation of the robot, which includes the arm and head kinematics (adapted online during the movements and an appearance model of the hand shape and texture. The mental simulation process generates predictions on how the hand will appear in the robot camera images, based on the body schema and the proprioceptive information (i.e. motor encoders. These predictions are compared to the actual images using Sequential Monte Carlo techniques to feed a particle-based Bayesian estimation method to estimate the parameters of the body schema. The updated body schema will improve the estimates of the 6D hand pose, which is thenused in a closed-loop control scheme (i.e. visual servoing, enabling precise reaching. We report experiments with the iCub humanoid robot that support the validity of our approach. A number of simulations with precise ground-truth were performed to evaluate the estimation capabilities of the proposed framework. Then, we show how the use of high-performance GPU programming and an edge-based algorithm for visual perception allow for real-time implementation in real world scenarios.

Interaction in Spoken Word Recognition Models: Feedback Helps

Science.gov (United States)

Magnuson, James S.; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D.

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis. PMID:29666593

Interaction in Spoken Word Recognition Models: Feedback Helps.

Science.gov (United States)

Magnuson, James S; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.

Interaction in Spoken Word Recognition Models: Feedback Helps

Directory of Open Access Journals (Sweden)

James S. Magnuson

2018-04-01

Full Text Available Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.

Use of online social networking sites among pre-service information technology teachers

Directory of Open Access Journals (Sweden)

Elif Buğra Kuzu

2013-12-01

Full Text Available The current study aimed to investigate the current status and perceptions of pre-service information technology (IT teachers regarding the use of online social networking sites (SNSs. The investigation was further supported through participant feedback regarding the design and implementation of a blended learning environment to embrace online SNSs in instructional settings. The study had a qualitative nature and employed a focus group interview to collect data. Participants were ten fourth graders who were randomly selected from voluntary undergraduate students enrolled at an IT education department of a Turkish state university. Researchers resorted to content analysis through an inductive coding process, provided themes addressing student perceptions and needs, and proposed implications and suggestions for further instructional practices.

The impact of online video lecture recordings and automated feedback on student performance

NARCIS (Netherlands)

Wieling, M. B.; Hofman, W. H. A.

To what extent a blended learning configuration of face-to-face lectures, online on-demand video recordings of the face-to-face lectures and the offering of online quizzes with appropriate feedback has an additional positive impact on the performance of these students compared to the traditional

Output feedback control of a quadrotor UAV using neural networks.

Science.gov (United States)

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Internet based personalized feedback interventions for gamblers in Singapore: First results.

Science.gov (United States)

Zhang, Melvyn W B; Yi, Yang; Cheok, Christopher C S

2016-01-01

Problem or pathological gambling has been a worldwide concern in the recent years, especially so with the advances in the technology, facilitating easier access to various means of gambling. Along with the advances in web-based and smartphone technologies, these technologies have been recently applied as adjunctive clinical tools to help gamblers. Taking into careful consideration the existing evidence base for Internet based interventions for pathological gambling, it seemed that the current published literature has demonstrated largely the efficacy of a personalized feedback intervention for pathological gambling; and further studies are still under-going to try and demonstrate the clinical feasibility of online web-based cognitive behavioral interventions for pathological gamblers. Given this, the aims of the current study are to (a) replicate an online personalized feedback intervention and determine its receptiveness in an Asian cohort of gamblers; and (b) to identify the demographics and characteristics of Asian gamblers who would utilize an online intervention. The workgroup at the National Addiction Management Service, Singapore conceptualized the online personalized feedback intervention for gamblers. The English version was launched on the 31st of March 2014 and the Chinese version was launched on the 30th of September 2014. A cumulative total of 708 participants took part with rhe mean age of the participants being 32.70 (SD = 11.638), with 89.1% males and 10.9% females. The mean problem gambling severity score (PGSI) was 10.80 (SD = 8.13), with the vast majority participating in Casino gambling on board a cruise (36.0%). Of significance, approximately 59.2% of the participants who sought help with our online e-intervention did have a diagnosis of problem gambling. This is one of the first few studies to demonstrate and replicate the potential use of an Internet based intervention for non-problem and problem gamblers. The current study has demonstrated

Observer-based output feedback control of networked control systems with non-uniform sampling and time-varying delay

Science.gov (United States)

Meng, Su; Chen, Jie; Sun, Jian

2017-10-01

This paper investigates the problem of observer-based output feedback control for networked control systems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.

Ontology-based topic clustering for online discussion data

Science.gov (United States)

Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

2013-03-01

With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

Semantically Enhanced Online Configuration of Feedback Control Schemes.

Science.gov (United States)

Milis, Georgios M; Panayiotou, Christos G; Polycarpou, Marios M

2018-03-01

Recent progress toward the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability, and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This paper addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.

A Feedback-Based Secure Path Approach for Wireless Sensor Network Data Collection

Science.gov (United States)

Mao, Yuxin; Wei, Guiyi

2010-01-01

The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose. PMID:22163424

A feedback-based secure path approach for wireless sensor network data collection.

Science.gov (United States)

Mao, Yuxin; Wei, Guiyi

2010-01-01

The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.

A Feedback-Based Secure Path Approach for Wireless Sensor Network Data Collection

Directory of Open Access Journals (Sweden)

Guiyi Wei

2010-10-01

Full Text Available The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.

The effect of regulation feedback in a computer-based formative assessment on information problem solving

NARCIS (Netherlands)

Timmers, Caroline; Walraven, Amber; Veldkamp, Bernard P.

2015-01-01

This study examines the effect of regulation feedback in a computer-based formative assessment in the context of searching for information online. Fifty 13-year-old students completed two randomly selected assessment tasks, receiving automated regulation feedback between them. Student performance

A Comparison of Online Social Networks and Real-Life Social Networks: A Study of Sina Microblogging

Directory of Open Access Journals (Sweden)

Dayong Zhang

2014-01-01

Full Text Available Online social networks appear to enrich our social life, which raises the question whether they remove cognitive constraints on human communication and improve human social capabilities. In this paper, we analyze the users' following and followed relationships based on the data of Sina Microblogging and reveal several structural properties of Sina Microblogging. Compared with real-life social networks, our results confirm some similar features. However, Sina Microblogging also shows its own specialties, such as hierarchical structure and degree disassortativity, which all mark a deviation from real-life social networks. The low cost of the online network forms a broader perspective, and the one-way link relationships make it easy to spread information, but the online social network does not make too much difference in the creation of strong interpersonal relationships. Finally, we describe the mechanisms for the formation of these characteristics and discuss the implications of these structural properties for the real-life social networks.

Discrete Opinion Dynamics on Online Social Networks

International Nuclear Information System (INIS)

Hu Yan-Li; Bai Liang; Zhang Wei-Ming

2013-01-01

This paper focuses on the dynamics of binary opinions {+1, −1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-affirmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks. (general)

Discrete Opinion Dynamics on Online Social Networks

Science.gov (United States)

Hu, Yan-Li; Bai, Liang; Zhang, Wei-Ming

2013-01-01

This paper focuses on the dynamics of binary opinions {+1, -1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-affirmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks.

A New Feedback-Analysis based Reputation Algorithm for E-Commerce Communities

Directory of Open Access Journals (Sweden)

Hasnae Rahimi

2014-12-01

Full Text Available Dealing with the ever-growing content generated by users in the e-commerce applications, Trust Reputation Systems (TRS are widely used online to provide the trust reputation of each product using the customers’ ratings. However, there is also a good number of online customer reviews and feedback that must be used by the TRS. As a result, we propose in this work a new architecture for TRS in e-commerce application which includes feedback’ mining in order to calculate reputation scores. This architecture is based on an intelligent layer that proposes to each user (i.e. “feedback provider” who has already given his recommendation, a collection of prefabricated feedback to like or dislike. Then the proposed reputation algorithm calculates the trust degree of the user, the feedback’s trustworthiness and generates the global reputation score of the product according to his ‘likes’ and ‘dislikes’. In this work, we present also a state of the art of text mining tools and algorithms that can be used to generate the prefabricated feedback and to classify them into different categories.

Online networks destroy social trust

OpenAIRE

Sabatini, Fabio; Sarracino, Francesco

2014-01-01

Studies in the social capital literature have documented two stylised facts: first, a decline in measures of social participation has occurred in many OECD countries. Second, and more recently, the success of social networking sites (SNSs) has resulted in a steep rise in online social participation. Our study adds to this body of research by conducting the first empirical assessment of how online networking affects two economically relevant aspects of social capital, i.e. trust and sociabilit...

Blending Formal and Informal Learning Networks for Online Learning

Science.gov (United States)

Czerkawski, Betül C.

2016-01-01

With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

Output Feedback-Based Boundary Control of Uncertain Coupled Semilinear Parabolic PDE Using Neurodynamic Programming.

Science.gov (United States)

Talaei, Behzad; Jagannathan, Sarangapani; Singler, John

2018-04-01

In this paper, neurodynamic programming-based output feedback boundary control of distributed parameter systems governed by uncertain coupled semilinear parabolic partial differential equations (PDEs) under Neumann or Dirichlet boundary control conditions is introduced. First, Hamilton-Jacobi-Bellman (HJB) equation is formulated in the original PDE domain and the optimal control policy is derived using the value functional as the solution of the HJB equation. Subsequently, a novel observer is developed to estimate the system states given the uncertain nonlinearity in PDE dynamics and measured outputs. Consequently, the suboptimal boundary control policy is obtained by forward-in-time estimation of the value functional using a neural network (NN)-based online approximator and estimated state vector obtained from the NN observer. Novel adaptive tuning laws in continuous time are proposed for learning the value functional online to satisfy the HJB equation along system trajectories while ensuring the closed-loop stability. Local uniformly ultimate boundedness of the closed-loop system is verified by using Lyapunov theory. The performance of the proposed controller is verified via simulation on an unstable coupled diffusion reaction process.

A network-based system of simulation, control and online assistance for HTR-10

Energy Technology Data Exchange (ETDEWEB)

Zhu Shutang [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China)], E-mail: zhust@tsinghua.edu.cn; Luo Shaojie; Shi Lei [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China)

2008-07-15

A network-based computer system has been developed for HTR-10. This system integrates three subsystems: the simulation subsystem (SIMUSUB), the visualized control designed subsystem (VCDSUB) and the online assistance subsystem (OASUB). The SIMUSUB consists of four functional elements: the simulation calculating server (SCS), the main control client (MCC), the data disposal client (DDC) and the results graphic display client (RGDC), all of which can communicate with each other via network. It is intended to analyze and calculate physical processes of the reactor core, the main loop system and the steam generator, etc., as well as to simulate the normal operational and transient accidents. The result data can be dynamically displayed through the RGDC. The VCDSUB provides a platform for control system modeling where the control flow systems can be automatically generated and graphically simulated. Based on the data from the field bus, the OASUB provides some of the reactor core parameters, which are difficult to measure. This integrated system can be used as an educational tool to understand the design and operational characteristics of the HTR-10, and can also provide online support for operators in the main control room, or as a convenient powerful tool for the control system design.

A network-based system of simulation, control and online assistance for HTR-10

International Nuclear Information System (INIS)

Zhu Shutang; Luo Shaojie; Shi Lei

2008-01-01

A network-based computer system has been developed for HTR-10. This system integrates three subsystems: the simulation subsystem (SIMUSUB), the visualized control designed subsystem (VCDSUB) and the online assistance subsystem (OASUB). The SIMUSUB consists of four functional elements: the simulation calculating server (SCS), the main control client (MCC), the data disposal client (DDC) and the results graphic display client (RGDC), all of which can communicate with each other via network. It is intended to analyze and calculate physical processes of the reactor core, the main loop system and the steam generator, etc., as well as to simulate the normal operational and transient accidents. The result data can be dynamically displayed through the RGDC. The VCDSUB provides a platform for control system modeling where the control flow systems can be automatically generated and graphically simulated. Based on the data from the field bus, the OASUB provides some of the reactor core parameters, which are difficult to measure. This integrated system can be used as an educational tool to understand the design and operational characteristics of the HTR-10, and can also provide online support for operators in the main control room, or as a convenient powerful tool for the control system design

Quantifying Users' Interconnectedness in Online Social Networks - An Indispensible Step for Economic Valuation

Science.gov (United States)

Gneiser, Martin; Heidemann, Julia; Klier, Mathias; Landherr, Andrea; Probst, Florian

Online social networks have been gaining increasing economic importance in light of the rising number of their users. Numerous recent acquisitions priced at enormous amounts have illustrated this development and revealed the need for adequate business valuation models. The value of an online social network is largely determined by the value of its users, the relationships between these users, and the resulting network effects. Therefore, the interconnectedness of a user within the network has to be considered explicitly to get a reasonable estimate for the economic value. Established standard business valuation models, however, do not sufficiently take these aspects into account. Thus, we propose a measure based on the PageRank-algorithm to quantify users’ interconnectedness in an online social network. This is a first but indispensible step towards an adequate economic valuation of online social networks.

Online professional networks for physicians: risk management.

Science.gov (United States)

Hyman, Jon L; Luks, Howard J; Sechrest, Randale

2012-05-01

The rapidly developing array of online physician-only communities represents a potential extraordinary advance in the availability of educational and informational resources to physicians. These online communities provide physicians with a new range of controls over the information they process, but use of this social media technology carries some risk. The purpose of this review was to help physicians manage the risks of online professional networking and discuss the potential benefits that may come with such networks. This article explores the risks and benefits of physicians engaging in online professional networking with peers and provides suggestions on risk management. Through an Internet search and literature review, we scrutinized available case law, federal regulatory code, and guidelines of conduct from professional organizations and consultants. We reviewed the OrthoMind.com site as a case example because it is currently the only online social network exclusively for orthopaedic surgeons. Existing case law suggests potential liability for orthopaedic surgeons who engage with patients on openly accessible social network platforms. Current society guidelines in both the United States and Britain provide sensible rules that may mitigate such risks. However, the overall lack of a strong body of legal opinions, government regulations as well as practical experience for most surgeons limit the suitability of such platforms. Closed platforms that are restricted to validated orthopaedic surgeons may limit these downside risks and hence allow surgeons to collaborate with one another both as clinicians and practice owners. Educating surgeons about the pros and cons of participating in these networking platforms is helping them more astutely manage risks and optimize benefits. This evolving online environment of professional interaction is one of few precedents, but the application of risk management strategies that physicians use in daily practice carries over

Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease

Science.gov (United States)

Gorzelic, P.; Schiff, S. J.; Sinha, A.

2013-04-01

Objective. To explore the use of classical feedback control methods to achieve an improved deep brain stimulation (DBS) algorithm for application to Parkinson's disease (PD). Approach. A computational model of PD dynamics was employed to develop model-based rational feedback controller design. The restoration of thalamocortical relay capabilities to patients suffering from PD is formulated as a feedback control problem with the DBS waveform serving as the control input. Two high-level control strategies are tested: one that is driven by an online estimate of thalamic reliability, and another that acts to eliminate substantial decreases in the inhibition from the globus pallidus interna (GPi) to the thalamus. Control laws inspired by traditional proportional-integral-derivative (PID) methodology are prescribed for each strategy and simulated on this computational model of the basal ganglia network. Main Results. For control based upon thalamic reliability, a strategy of frequency proportional control with proportional bias delivered the optimal control achieved for a given energy expenditure. In comparison, control based upon synaptic inhibitory output from the GPi performed very well in comparison with those of reliability-based control, with considerable further reduction in energy expenditure relative to that of open-loop DBS. The best controller performance was amplitude proportional with derivative control and integral bias, which is full PID control. We demonstrated how optimizing the three components of PID control is feasible in this setting, although the complexity of these optimization functions argues for adaptive methods in implementation. Significance. Our findings point to the potential value of model-based rational design of feedback controllers for Parkinson's disease.

Popularity Evaluation Model for Microbloggers Online Social Network

Directory of Open Access Journals (Sweden)

Xia Zhang

2014-01-01

Full Text Available Recently, microblogging is widely studied by the researchers in the domain of the online social network (OSN. How to evaluate the popularities of microblogging users is an important research field, which can be applied to commercial advertising, user behavior analysis and information dissemination, and so forth. Previous studies on the evaluation methods cannot effectively solve and accurately evaluate the popularities of the microbloggers. In this paper, we proposed an electromagnetic field theory based model to analyze the popularities of microbloggers. The concept of the source in microblogging field is first put forward, which is based on the concept of source in the electromagnetic field; then, one’s microblogging flux is calculated according to his/her behaviors (send or receive feedbacks on the microblogging platform; finally, we used three methods to calculate one’s microblogging flux density, which can represent one’s popularity on the microblogging platform. In the experimental work, we evaluated our model using real microblogging data and selected the best one from the three popularity measure methods. We also compared our model with the classic PageRank algorithm; and the results show that our model is more effective and accurate to evaluate the popularities of the microbloggers.

Distributed User Selection in Network MIMO Systems with Limited Feedback

KAUST Repository

Elkhalil, Khalil; Eltayeb, Mohammed E.; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.

2015-01-01

We propose a distributed user selection strategy in a network MIMO setting with M base stations serving K users. Each base station is equipped with L antennas, where LM ≪ K. The conventional selection strategy is based on a well known technique called semi-orthogonal user selection when the zero-forcing beamforming (ZFBF) is adopted. Such technique, however, requires perfect channel state information at the transmitter (CSIT), which might not be available or need large feedback overhead. This paper proposes an alternative distributed user selection technique where each user sets a timer that is inversely proportional to his channel quality indicator (CQI), as a means to reduce the feedback overhead. The proposed strategy allows only the user with the highest CQI to respond with a feedback. Such technique, however, remains collision free only if the transmission time is shorter than the difference between the strongest user timer and the second strongest user timer. To overcome the situation of longer transmission times, the paper proposes another feedback strategy that is based on the theory of compressive sensing, where collision is allowed and all users encode their feedback information and send it back to the base-stations simultaneously. The paper shows that the problem can be formulated as a block sparse recovery problem which is agnostic on the transmission time, which makes it a good alternative to the timer approach when collision is dominant.

Distributed User Selection in Network MIMO Systems with Limited Feedback

KAUST Repository

Elkhalil, Khalil

2015-09-06

We propose a distributed user selection strategy in a network MIMO setting with M base stations serving K users. Each base station is equipped with L antennas, where LM ≪ K. The conventional selection strategy is based on a well known technique called semi-orthogonal user selection when the zero-forcing beamforming (ZFBF) is adopted. Such technique, however, requires perfect channel state information at the transmitter (CSIT), which might not be available or need large feedback overhead. This paper proposes an alternative distributed user selection technique where each user sets a timer that is inversely proportional to his channel quality indicator (CQI), as a means to reduce the feedback overhead. The proposed strategy allows only the user with the highest CQI to respond with a feedback. Such technique, however, remains collision free only if the transmission time is shorter than the difference between the strongest user timer and the second strongest user timer. To overcome the situation of longer transmission times, the paper proposes another feedback strategy that is based on the theory of compressive sensing, where collision is allowed and all users encode their feedback information and send it back to the base-stations simultaneously. The paper shows that the problem can be formulated as a block sparse recovery problem which is agnostic on the transmission time, which makes it a good alternative to the timer approach when collision is dominant.

Social networking in online support groups for health: how online social networking benefits patients.

Science.gov (United States)

Chung, Jae Eun

2014-01-01

An increasing number of online support groups (OSGs) have embraced the features of social networking. So far, little is known about how patients use and benefit from these features. By implementing the uses-and-gratifications framework, the author conducted an online survey with current users of OSGs to examine associations among motivation, use of specific features of OSG, and support outcomes. Findings suggest that OSG users make selective use of varied features depending on their needs, and that perceptions of receiving emotional and informational support are associated more with the use of some features than others. For example, those with strong motivation for social interaction use diverse features of OSG and make one-to-one connections with other users by friending. In contrast, those with strong motivation for information seeking limit their use primarily to discussion boards. Results also show that online social networking features, such as friending and sharing of personal stories on blogs, are helpful in satisfying the need for emotional support. The present study sheds light on online social networking features in the context of health-related OSGs and provides practical lessons on how to improve the capacity of OSGs to serve the needs of their users.

Online Social Networks - Opportunities for Empowering Cancer Patients.

Science.gov (United States)

Mohammadzadeh, Zeinab; Davoodi, Somayeh; Ghazisaeidi, Marjan

2016-01-01

Online social network technologies have become important to health and apply in most health care areas. Particularly in cancer care, because it is a disease which involves many social aspects, online social networks can be very useful. Use of online social networks provides a suitable platform for cancer patients and families to present and share information about their medical conditions, address their educational needs, support decision making, and help to coping with their disease and improve their own outcomes. Like any other new technologies, online social networks, along with many benefits, have some negative effects such as violation of privacy and publication of incorrect information. However, if these effects are managed properly, they can empower patients to manage cancer through changing behavioral patterns and enhancing the quality of cancer patients lives This paper explains some application of online social networks in the cancer patient care process. It also covers advantages and disadvantages of related technologies.

Feedback authoring possibilities in web-based learning systems

NARCIS (Netherlands)

Vasilyeva, E.; De Bra, P.M.E.; Pechenizkiy, M.; Bonk, C.J.; et al., xx

2008-01-01

This paper surveys and analyses the feedback authoring possibilities in online assessment modules of the most popular Learning Management Systems (LMS) including Moodle, Sakai, and Blackboard. We consider the problem of authoring and support of tailored and personalized feedback and demonstrate how

The Structure of Online Consumer Communication Networks

NARCIS (Netherlands)

B.G.C. Dellaert (Benedict); M.J.W. Harmsen-van Hout (Marjolein); P.J.J. Herings (Jean-Jacques)

2006-01-01

textabstractIn this paper we study the structure of the bilateral communication links within Online Consumer Communication Networks (OCCNs), such as virtual communities. Compared to the offline world, consumers in online networks are highly flexible to choose their communication partners and little

Does Feedback Influence Student Postings to Online Discussions?

Directory of Open Access Journals (Sweden)

Katrina A. Meyer, Ph.D.

2007-07-01

Full Text Available Feedback theory proposes that feedback influences the behavior of a system and its parts and that is governed by rules. This exploratory study attempts to test this theory in a graduate-level class on leadership theory. Twelve students were asked to participate in five online discussions, each lasting one week. The questions for each discussion were selected to be provocative and rich, without having any correct answer. At the end of the discussion, students were asked to indicate which posting and poster they felt was “best” or most valuable and why they felt the posting was “best.” There is mixed evidence that the voting influenced subsequent postings; some individuals did improve while others were consistently good or poor posters. Students selected postings that were (in declining frequency of occurrence: thoughtful or thought-provoking, well written or justified, uplifting, presented new information, same as their own opinions, changed me, or complex. These reasons are similar to those of the instructor, although the students’ difficulty in choosing and rationalizing a choice and the frequency at which students chose posts that captured their own thoughts and opinions are of some concern. This study provides some evidence that these graduate students could evaluate their own discussions without the instructor intruding or dictating an evaluation scheme, although this may not be true for other groups of online students.

How social network analysis can be used to monitor online collaborative learning and guide an informed intervention.

Science.gov (United States)

Saqr, Mohammed; Fors, Uno; Tedre, Matti; Nouri, Jalal

2018-01-01

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content

Online social network data as sociometric markers.

Science.gov (United States)

Binder, Jens F; Buglass, Sarah L; Betts, Lucy R; Underwood, Jean D M

2017-10-01

Data from online social networks carry enormous potential for psychological research, yet their use and the ethical implications thereof are currently hotly debated. The present work aims to outline in detail the unique information richness of this data type and, in doing so, to support researchers when deciding on ethically appropriate ways of collecting, storing, publishing, and sharing data from online sources. Focusing on the very nature of social networks, their structural characteristics, and depth of information, we provide a detailed and accessible account of the challenges associated with data management and data storage. In particular, the general nonanonymity of network data sets is discussed, and an approach is developed to quantify the level of uniqueness that a particular online network bestows upon the individual maintaining it. Using graph enumeration techniques, we show that comparatively sparse information on a network is suitable as a sociometric marker that allows for the identification of an individual from the global population of online users. The impossibility of anonymizing specific types of network data carries implications for ethical guidelines and research practice. At the same time, network uniqueness opens up opportunities for novel research in psychology. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Online social networking in people with psychosis: A systematic review.

Science.gov (United States)

Highton-Williamson, Elizabeth; Priebe, Stefan; Giacco, Domenico

2015-02-01

Online social networking might facilitate the establishment of social contacts for people with psychosis, who are often socially isolated by the symptoms and consequences of their disorder. We carried out a systematic review exploring available evidence on the use of online social networking in people with psychosis. The review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Included studies examined the use of the online social networking by people with an a priori diagnosis of psychosis (inclusive of bipolar disorder). Data from included studies were extracted and narratively synthesised. A total of 11 studies, published between 2005 and 2013, reported data on online social networking in people with psychosis. People with psychosis seem to spend more time in chat rooms or playing online games than control groups. The use of other online tools, such as Facebook or communication through e-mail, is lower or the same than controls. Online social networking was used by patients with psychosis for establishing new relationships, maintaining relationships/reconnecting with people and online peer support. Online social networking, in the form of forums or online chats, could play a role in strategies aimed at enhancing social networks and reduce the risk of isolation in this population. © The Author(s) 2014.

Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.

Directory of Open Access Journals (Sweden)

Miaolei Zhou

Full Text Available As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.

Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.

Science.gov (United States)

Zhou, Miaolei; Zhang, Qi; Wang, Jingyuan

2014-01-01

As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.

Does Sentiment Among Users in Online Social Networks Polarize or Balance Out?

DEFF Research Database (Denmark)

Trier, Matthias; Hillmann, Robert

2017-01-01

Users express and share sentiments electronically when they communicate within online social network applications. One way to analyze such interdependent data is focusing on the inter-user relationships by applying a sociological perspective based on social network analysis. Existing studies exam...... examined the existence or distribution of sentiments in online communication at a general level or in small observed groups....

Online feedback assessments in physiology: effects on students' learning experiences and outcomes.

Science.gov (United States)

Marden, Nicole Y; Ulman, Lesley G; Wilson, Fiona S; Velan, Gary M

2013-06-01

Online formative assessments have become increasingly popular; however, formal evidence supporting their educational benefits is limited. This study investigated the impact of online feedback quizzes on the learning experiences and outcomes of undergraduate students enrolled in an introductory physiology course. Four quiz models were tested, which differed in the amount of credit available, the number of attempts permitted, and whether the quizzes were invigilated or unsupervised, timed or untimed, or open or closed book. All quizzes were composed of multiple-choice questions and provided immediate individualized feedback. Summative end-of-course examination marks were analyzed with respect to performance in quizzes and were also compared with examination performance in the year before the quizzes were introduced. Online surveys were conducted to gather students' perceptions regarding the quizzes. The vast majority of students perceived online quizzes as a valuable learning tool. For all quiz models tested, there was a significant relationship between performance in quizzes and end-of-course examination scores. Importantly, students who performed poorly in quizzes were more likely to fail the examination, suggesting that formative online quizzes may be a useful tool to identify students in need of assistance. Of the four quiz models, only one quiz model was associated with a significant increase in mean examination performance. This model had the strongest formative focus, allowing multiple unsupervised and untimed attempts. This study suggests that the format of online formative assessments is critical in achieving the desired impact on student learning. Specifically, such assessments are most effective when they are low stakes.

Hierarchical feedback modules and reaction hubs in cell signaling networks.

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical feedback modules and reaction hubs in cell signaling networks.

Directory of Open Access Journals (Sweden)

Jianfeng Xu

Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

The Effect of Online Gaming, Cognition and Feedback Type in Facilitating Delayed Achievement of Different Learning Objectives

Science.gov (United States)

Cameron, Brian; Dwyer, Francis

2005-01-01

Online and computer-based instructional gaming is becoming a viable instructional strategy at all levels of education. The purpose of this study was to examine the effect of (a) gaming, (b) gaming plus embedded questions, and (c) gaming plus questions plus feedback on delayed retention of different types of educational objectives for students…

Curation-Based Network Marketing: Strategies for Network Growth and Electronic Word-of-Mouth Diffusion

Science.gov (United States)

Church, Earnie Mitchell, Jr.

2013-01-01

In the last couple of years, a new aspect of online social networking has emerged, in which the strength of social network connections is based not on social ties but mutually shared interests. This dissertation studies these "curation-based" online social networks (CBN) and their suitability for the diffusion of electronic word-of-mouth…

How Will Online Affiliate Marketing Networks Impact Search Engine Rankings?

NARCIS (Netherlands)

D. Janssen (David); H.W.G.M. van Heck (Eric)

2007-01-01

textabstractIn online affiliate marketing networks advertising web sites offer their affiliates revenues based on provided web site traffic and associated leads and sales. Advertising web sites can have a network of thousands of affiliates providing them with web site traffic through hyperlinks on

Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

Science.gov (United States)

Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2017-08-24

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

Evolution of a large online social network

International Nuclear Information System (INIS)

Hu Haibo; Wang Xiaofan

2009-01-01

Although recently there are extensive research on the collaborative networks and online communities, there is very limited knowledge about the actual evolution of the online social networks (OSN). In the Letter, we study the structural evolution of a large online virtual community. We find that the scale growth of the OSN shows non-trivial S shape which may provide a proper exemplification for Bass diffusion model. We reveal that the evolutions of many network properties, such as density, clustering, heterogeneity and modularity, show non-monotone feature, and shrink phenomenon occurs for the path length and diameter of the network. Furthermore, the OSN underwent a transition from degree assortativity characteristic of collaborative networks to degree disassortativity characteristic of many OSNs. Our study has revealed the evolutionary pattern of interpersonal interactions in a specific population and provided a valuable platform for theoretical modeling and further analysis

Online social networking issues within academia and pharmacy education.

Science.gov (United States)

Cain, Jeff

2008-02-15

Online social networking sites such as Facebook and MySpace are extremely popular as indicated by the numbers of members and visits to the sites. They allow students to connect with users with similar interests, build and maintain relationships with friends, and feel more connected with their campus. The foremost criticisms of online social networking are that students may open themselves to public scrutiny of their online personas and risk physical safety by revealing excessive personal information. This review outlines issues of online social networking in higher education by drawing upon articles in both the lay press and academic publications. New points for pharmacy educators to consider include the possible emergence of an "e-professionalism" concept; legal and ethical implications of using online postings in admission, discipline, and student safety decisions; how online personas may blend into professional life; and the responsibility for educating students about the risks of online social networking.

Online reputation systems: The effects of feedback comments and reactions on building and rebuilding trust in online auctions

NARCIS (Netherlands)

Utz, S.; Matzat, U.; Snijders, C.

2009-01-01

Previous research on reputation systems primarily focused on their trust-building function. The present research addresses their trust-rebuilding function-specifically, the role of the short text comments given in reaction to negative feedback. Rebuilding trust is often necessary because on-line

Link prediction in multiplex online social networks

Science.gov (United States)

Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

Personalized behavioral feedback for online gamblers: A real world empirical study

Directory of Open Access Journals (Sweden)

Michael Mario Auer

2016-11-01

Full Text Available Some gambling companies around the world have started to utilize responsible gambling tools to support their clientele gamble more responsibly (e.g., limit-setting tools, pop-up messages, personalized feedback. However, relatively few studies have evaluated whether such tools actually work. The present study examined whether the use of three types of information (i.e., personalized feedback, normative feedback, and/or a recommendation could enable players to gamble more responsibly as assessed using three measures of behavior, i.e., theoretical loss (TL, amount of money wagered, and gross gaming revenue (GGR. By manipulating the three forms of information, data from six different groups of players were analyzed. The participant sample drawn from the population were those that had played at least one game for money on the Norsk Tipping online platform (Intaspill during April 2015. A total of 17,452 players were randomly selected from 69,631 players that fulfilled the selection criteria. Gambling activity among the control group (who received no personalized feedback, normative feedback or no recommendation was also compared with the other five groups that received information of some kind (personalized feedback, normative feedback and/or a recommendation. Results clearly showed that overall gambling behavior (as assessed by the three measures was significantly higher in the control group than the other five groups. Players that received both personalized feedback and a recommendation decreased gambling behavior the most. It is concluded that personalized behavioral feedback can enable behavioral change in gambling but that normative feedback does not appear change behavior significantly more than personalized feedback.

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Directory of Open Access Journals (Sweden)

Zhisheng Zhang

2016-01-01

Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

Links between real and virtual networks: a comparative study of online communities in Japan and Korea.

Science.gov (United States)

Ishii, Kenichi; Ogasahara, Morihiro

2007-04-01

The present study explores how online communities affect real-world personal relations based on a cross-cultural survey conducted in Japan and Korea. Findings indicate that the gratifications of online communities moderate the effects of online communities on social participation. Online communities are categorized into a real-group-based community and a virtual-network-based community. The membership of real-group-based online community is positively correlated with social bonding gratification and negatively correlated with information- seeking gratification. Japanese users prefer more virtual-network-based online communities, while their Korean counterparts prefer real-group-based online communities. Korean users are more active in online communities and seek a higher level of socializing gratifications, such as social bonding and making new friends, when compared with their Japanese counterparts. These results indicate that in Korea, personal relations via the online community are closely associated with the real-world personal relations, but this is not the case in Japan. This study suggests that the effects of the Internet are culture-specific and that the online community can serve a different function in different cultural environments.

Privacy in Online Social Networks

NARCIS (Netherlands)

Beye, Michael; Jeckmans, Arjan; Erkin, Zekeriya; Erkin, Zekeriya; Hartel, Pieter H.; Lagendijk, Reginald; Tang, Qiang; Abraham, A.

Online Social Networks (OSNs) have become part of daily life for millions of users. Users building explicit networks that represent their social relationships and often share a wealth of personal information to their own benefit. The potential privacy risks of such behavior are often underestimated

From "Hello" to Higher-Order Thinking: The Effect of Coaching and Feedback on Online Chats

Science.gov (United States)

Stein, David S.; Wanstreet, Constance E.; Slagle, Paula; Trinko, Lynn A.; Lutz, Michelle

2013-01-01

This exploratory study examined the effect of a coaching and feedback intervention in teaching presence and social presence on higher-order thinking in an online community of inquiry. Coaching occurred before each chat, and feedback was provided immediately afterwards. The findings suggest that over time, the frequency of higher-order thinking…

Low-Feedback Opportunistic Scheduling Schemes for Wireless Networks with Heterogenous Users

KAUST Repository

Rashid, Faraan

2012-07-01

Efficient implementation of resource sharing strategies in a multi-user wireless environment can improve the performance of a network significantly. In this thesis we study various scheduling strategies for wireless networks and handle the problem of opportunistically scheduling transmissions using channel aware schemes. First we propose a scheme that can handle users with asymmetric channel conditions and is opportunistic in the sense that it exploits the multi-user diversity of the network. The scheme requires the users to have a priori knowledge of their channel distributions. The associated overhead is limited meaning it offers reduced feedback load, that does not scale with the increasing number of users. The main technique used to shrink the feedback load is the contention based distributed implementation of a splitting algorithm that does not require explicit feedback to the scheduler from every user. The users find the best among themselves, in a distributed manner, while requiring just a ternary broadcast feedback from the scheduler at the end of each mini-slot. In addition, it can also handle fairness constraints in time and throughput to various degrees. Next we propose another opportunistic scheduler that offers most of the benefits of the previously proposed scheme but is more practical because it can also handle heterogenous users whose channel distributions are unknown. This new scheme actually reduces the complexity and is also more robust for changing traffic patterns. Finally we extend both these schemes to the scenario where there are fixed thresholds, this enables us to handle opportunistic scheduling in practical systems that can only transmit over finite number of discrete rates with the additional benefit that full feedback session, even from the selected user, is never required.

Recommendation in evolving online networks

Science.gov (United States)

Hu, Xiao; Zeng, An; Shang, Ming-Sheng

2016-02-01

Recommender system is an effective tool to find the most relevant information for online users. By analyzing the historical selection records of users, recommender system predicts the most likely future links in the user-item network and accordingly constructs a personalized recommendation list for each user. So far, the recommendation process is mostly investigated in static user-item networks. In this paper, we propose a model which allows us to examine the performance of the state-of-the-art recommendation algorithms in evolving networks. We find that the recommendation accuracy in general decreases with time if the evolution of the online network fully depends on the recommendation. Interestingly, some randomness in users' choice can significantly improve the long-term accuracy of the recommendation algorithm. When a hybrid recommendation algorithm is applied, we find that the optimal parameter gradually shifts towards the diversity-favoring recommendation algorithm, indicating that recommendation diversity is essential to keep a high long-term recommendation accuracy. Finally, we confirm our conclusions by studying the recommendation on networks with the real evolution data.

Online and Offline Social Networks: Use of Social Networking Sites by Emerging Adults

Science.gov (United States)

Subrahmanyam, Kaveri; Reich, Stephanie M.; Waechter, Natalia; Espinoza, Guadalupe

2008-01-01

Social networking sites (e.g., MySpace and Facebook) are popular online communication forms among adolescents and emerging adults. Yet little is known about young people's activities on these sites and how their networks of "friends" relate to their other online (e.g., instant messaging) and offline networks. In this study, college students…

Partially blind instantly decodable network codes for lossy feedback environment

KAUST Repository

Sorour, Sameh

2014-09-01

In this paper, we study the multicast completion and decoding delay minimization problems for instantly decodable network coding (IDNC) in the case of lossy feedback. When feedback loss events occur, the sender falls into uncertainties about packet reception at the different receivers, which forces it to perform partially blind selections of packet combinations in subsequent transmissions. To determine efficient selection policies that reduce the completion and decoding delays of IDNC in such an environment, we first extend the perfect feedback formulation in our previous works to the lossy feedback environment, by incorporating the uncertainties resulting from unheard feedback events in these formulations. For the completion delay problem, we use this formulation to identify the maximum likelihood state of the network in events of unheard feedback and employ it to design a partially blind graph update extension to the multicast IDNC algorithm in our earlier work. For the decoding delay problem, we derive an expression for the expected decoding delay increment for any arbitrary transmission. This expression is then used to find the optimal policy that reduces the decoding delay in such lossy feedback environment. Results show that our proposed solutions both outperform previously proposed approaches and achieve tolerable degradation even at relatively high feedback loss rates.

Neural network based approach for tuning of SNS feedback and feedforward controllers

International Nuclear Information System (INIS)

Kwon, Sung-Il; Prokop, Mark S.; Regan, Amy H.

2002-01-01

The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

Modern Social Support Structures: Online Social Networks and their Implications for Social Workers

Directory of Open Access Journals (Sweden)

Kala Chakradhar

2009-03-01

Full Text Available Mapping and assessing social networks and the quality of their social support is a valuable intervention strategy for social workers. These networks have now spread onto the digital realm in the form of Online Social Networks (OSNs. This study investigated the nature of social support provided by such networks to their users in a rural mid-South University (USA and explored parallels with the current understanding of social support in conventional social networks. A web-based survey administered to college students revealed that users of these online networks were predominantly undergraduate first year students, female, single, unemployed and from a variety of academic disciplines. The examination of the components of OSNs appears to mirror those of offline networks. They also seem to complement the effects of each other while contributing to an individual's support system. The paper concludes with critical implications of such online social networking for University students and social workers in practice and education.

Modulation of dynamic modes by interplay between positive and negative feedback loops in gene regulatory networks

Science.gov (United States)

Wang, Liu-Suo; Li, Ning-Xi; Chen, Jing-Jia; Zhang, Xiao-Peng; Liu, Feng; Wang, Wei

2018-04-01

A positive and a negative feedback loop can induce bistability and oscillation, respectively, in biological networks. Nevertheless, they are frequently interlinked to perform more elaborate functions in many gene regulatory networks. Coupled positive and negative feedback loops may exhibit either oscillation or bistability depending on the intensity of the stimulus in some particular networks. It is less understood how the transition between the two dynamic modes is modulated by the positive and negative feedback loops. We developed an abstract model of such systems, largely based on the core p53 pathway, to explore the mechanism for the transformation of dynamic behaviors. Our results show that enhancing the positive feedback may promote or suppress oscillations depending on the strength of both feedback loops. We found that the system oscillates with low amplitudes in response to a moderate stimulus and switches to the on state upon a strong stimulus. When the positive feedback is activated much later than the negative one in response to a strong stimulus, the system exhibits long-term oscillations before switching to the on state. We explain this intriguing phenomenon using quasistatic approximation. Moreover, early switching to the on state may occur when the system starts from a steady state in the absence of stimuli. The interplay between the positive and negative feedback plays a key role in the transitions between oscillation and bistability. Of note, our conclusions should be applicable only to some specific gene regulatory networks, especially the p53 network, in which both oscillation and bistability exist in response to a certain type of stimulus. Our work also underscores the significance of transient dynamics in determining cellular outcome.

Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

Directory of Open Access Journals (Sweden)

Yuzheng Yang

2014-01-01

Full Text Available An adaptive sliding controller using radial basis function (RBF network to approximate the unknown system dynamics microelectromechanical systems (MEMS gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

Accelerator and feedback control simulation using neural networks

International Nuclear Information System (INIS)

Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.

1991-05-01

Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the ''Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This ''Accelerator'' network is then used to train a second ''Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs

Synchronization of cellular neural networks of neutral type via dynamic feedback controller

International Nuclear Information System (INIS)

Park, Ju H.

2009-01-01

In this paper, we aim to study global synchronization for neural networks with neutral delay. A dynamic feedback control scheme is proposed to achieve the synchronization between drive network and response network. By utilizing the Lyapunov function and linear matrix inequalities (LMIs), we derive simple and efficient criterion in terms of LMIs for synchronization. The feedback controllers can be easily obtained by solving the derived LMIs.

Online Social Networking, Sexual Risk and Protective Behaviors: Considerations for Clinicians and Researchers.

Science.gov (United States)

Holloway, Ian W; Dunlap, Shannon; Del Pino, Homero E; Hermanstyne, Keith; Pulsipher, Craig; Landovitz, Raphael J

2014-09-01

Online social networking refers to the use of internet-based technologies that facilitate connection and communication between users. These platforms may be accessed via computer or mobile device (e.g., tablet, smartphone); communication between users may include linking of profiles, posting of text, photo and video content, instant messaging and email. This review provides an overview of recent research on the relationship between online social networking and sexual risk and protective behaviors with a focus on use of social networking sites (SNS) among young people and populations at high risk for sexually transmitted infections (STIs). While findings are mixed, the widespread use of SNS for sexual communication and partner seeking presents opportunities for the delivery and evaluation of public health interventions. Results of SNS-based interventions to reduce sexual risk are synthesized in order to offer hands-on advice for clinicians and researchers interested in engaging patients and study participants via online social networking.

Physical limits of feedback noise-suppression in biological networks

International Nuclear Information System (INIS)

Zhang, Jiajun; Yuan, Zhanjiang; Zhou, Tianshou

2009-01-01

Feedback is a ubiquitous control mechanism of biological networks, and has also been identified in a variety of regulatory systems and organisms. It has been shown that, for a given gain and with negligible intrinsic noise, negative feedback impairs noise buffering whereas positive feedback enhances noise buffering. We further investigate the influence of negative and positive feedback on noise in output signals by considering both intrinsic and extrinsic noise as well as operator noise. We find that, while maintaining the system sensitivity, either there exists a minimum of the output noise intensity corresponding to a biologically feasible feedback strength, or the output noise intensity is a monotonic function of feedback strength bounded by both biological and dynamical constraints. In both cases, feedback noise-suppression is physically limited. In other words, noise suppressed by negative or positive feedback cannot be reduced without limitation even in the case of slow transcription

Online Reconstruction and Calibration with Feedback Loop in the ALICE High Level Trigger

Directory of Open Access Journals (Sweden)

Rohr David

2016-01-01

at the Large Hadron Collider (LHC at CERN. The High Level Trigger (HLT is an online computing farm, which reconstructs events recorded by the ALICE detector in real-time. The most computing-intensive task is the reconstruction of the particle trajectories. The main tracking devices in ALICE are the Time Projection Chamber (TPC and the Inner Tracking System (ITS. The HLT uses a fast GPU-accelerated algorithm for the TPC tracking based on the Cellular Automaton principle and the Kalman filter. ALICE employs gaseous subdetectors which are sensitive to environmental conditions such as ambient pressure and temperature and the TPC is one of these. A precise reconstruction of particle trajectories requires the calibration of these detectors. As our first topic, we present some recent optimizations to our GPU-based TPC tracking using the new GPU models we employ for the ongoing and upcoming data taking period at LHC. We also show our new approach to fast ITS standalone tracking. As our second topic, we present improvements to the HLT for facilitating online reconstruction including a new flat data model and a new data flow chain. The calibration output is fed back to the reconstruction components of the HLT via a feedback loop. We conclude with an analysis of a first online calibration test under real conditions during the Pb-Pb run in November 2015, which was based on these new features.

Improvement of Hand Hygiene Quality and Compliance Using Bioburden Measurement and Online Feedback in Germany.

Science.gov (United States)

Günther, Frank; Rudolph, Kai; Frank, Uwe; Mutters, Nico T

2017-01-01

To improve compliance with hand hygiene, a novel method with inclusion of an online reporting system was developed, comprising measurement of total hand bioburden, anonymous online feedback, and onsite training. The intervention significantly improved both compliance and quality of hand hygiene and reduced Staphylococcus aureus incidence. Infect Control Hosp Epidemiol 2016;1-4.

How a feedback system enhances vendor reputation, mitigates product complexity and facilitates online purchase decision-making : insights from B2C transaction logs.

OpenAIRE

Bartels, Julia

2015-01-01

Despite the growing population of Internet customers, purchasing online can still be a confusing and overwhelming activity. Perceived risk plays a crucial role in online buying decisions. The mechanism of online customer feedback has been identified to improve trust and to reduce risk in online marketplaces. Feedback from previous customers greatly builds online vendor reputation and establishes trust, which positively influence the intent to purchase. This study aims to find out how a feedba...

Feedback surveys for transnational social change networks : a step ...

International Development Research Centre (IDRC) Digital Library (Canada)

Feedback surveys are an assessment exercise that differs from conventional evaluation by creating a comparative data set. Transnational social change networks are international networks with members spread across multiple countries working to collectively organize towards a common long-term goal that would not be ...

Adaptive control of nonlinear system using online error minimum neural networks.

Science.gov (United States)

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Measurement of Online Social Networks

Science.gov (United States)

Gjoka, Mina

2010-01-01

In recent years, the popularity of online social networks (OSN) has risen to unprecedented levels, with the most popular ones having hundreds of millions of users. This success has generated interest within the networking community and has given rise to a number of measurement and characterization studies, which provide a first step towards their…

Optimal Channel Selection Based on Online Decision and Offline Learning in Multichannel Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Mu Qiao

2017-01-01

Full Text Available We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.

Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

Directory of Open Access Journals (Sweden)

Bahita Mohamed

2011-01-01

Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

Evaluating the feasibility of using online software to collect patient information in a chiropractic practice-based research network.

Science.gov (United States)

Kania-Richmond, Ania; Weeks, Laura; Scholten, Jeffrey; Reney, Mikaël

2016-03-01

Practice based research networks (PBRNs) are increasingly used as a tool for evidence based practice. We developed and tested the feasibility of using software to enable online collection of patient data within a chiropractic PBRN to support clinical decision making and research in participating clinics. To assess the feasibility of using online software to collect quality patient information. The study consisted of two phases: 1) Assessment of the quality of information provided, using a standardized form; and 2) Exploration of patients' perspectives and experiences regarding online information provision through semi-structured interviews. Data analysis was descriptive. Forty-five new patients were recruited. Thirty-six completed online forms, which were submitted by an appropriate person 100% of the time, with an error rate of less than 1%, and submitted in a timely manner 83% of the time. Twenty-one participants were interviewed. Overall, online forms were preferred given perceived security, ease of use, and enabling provision of more accurate information. Use of online software is feasible, provides high quality information, and is preferred by most participants. A pen-and-paper format should be available for patients with this preference and in case of technical difficulties.

LHCb Online Networking Requirements

CERN Document Server

Jost, B

2003-01-01

This document describes the networking requirements of the LHCb online installation. It lists both quantitative aspects such as the number of required switch ports, as well as some qualitative features of the equipment, such as minimum buffer sizes in switches. The document comprises both the data acquisition network and the controls/general-purpose network. While the numbers represent our best current knowledge and are intended to give (in particular) network equipment manufacturers an overview of our needs, this document should not be confused with a market survey questionnaire or a formal tendering document. However the information contained in this document will be the input of any such document. A preliminary schedule for procurement and installation is also given.

Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

OpenAIRE

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2015-01-01

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solv...

Content Propagation in Online Social Networks

NARCIS (Netherlands)

Blenn, N.

2014-01-01

This thesis presents methods and techniques to analyze content propagation within online social networks (OSNs) using a graph theoretical approach. Important factors and different techniques to analyze and describe content propagation, starting from the smallest entity in a network, representing a

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Relations between scripted online peer feedback processes and quality of written argumentative essay

NARCIS (Netherlands)

Noroozi, Omid; Biemans, Harm; Mulder, Martin

2016-01-01

Teachers often complain about the quality of students' written essays in higher education. This study explores the relations between scripted online peer feedback processes and quality of written argumentative essay as they occur in an authentic learning situation with direct practical relevance.

On-line reputation systems : the effects of feedback comments and reactions on building and rebuilding trust in on-line auctions

NARCIS (Netherlands)

Utz, S.; Matzat, U.; Snijders, C.C.P.

2009-01-01

Previous research on reputation systems primarily focused on their trust-building function. The present research addresses their trust-rebuilding function-specifically, the role of the short text comments given in reaction to negative feedback. Rebuilding trust is often necessary because on-line

Academics and Social Networking Sites: Benefits, Problems and Tensions in Professional Engagement with Online Networking

OpenAIRE

Jordan, Katy; Weller, Martin

2018-01-01

The web has had a profound effect on the ways people interact, with online social networks arguably playing an important role in changing or augmenting how we connect with others. However, uptake of online social networking by the academic community varies, and needs to be understood. This paper presents an independent, novel analysis of a large-scale dataset published by Nature Publishing Group detailing the results of a survey about academics use of online social networking services. An ope...

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

Science.gov (United States)

Kwak, Doyeon; Kim, Wonjoon

2017-01-01

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

Directory of Open Access Journals (Sweden)

Doyeon Kwak

Full Text Available It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.

Online Social Networking: Usage in Adolescents

Science.gov (United States)

Raju, Nevil Johnson; Valsaraj, Blessy Prabha; Noronha, Judith

2015-01-01

Online social networking (OSN) has played a significant role on the relationship among college students. It is becoming a popular medium for socializing online and tools to facilitate friendship. Young adults and adolescents are the most prolific users of OSN sites. The frequent use of OSN sites results in addiction toward these sites and…

Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators.

Science.gov (United States)

Yang, Qinmin; Jagannathan, Sarangapani

2012-04-01

In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

Followers are not enough: a multifaceted approach to community detection in online social networks.

Science.gov (United States)

Darmon, David; Omodei, Elisa; Garland, Joshua

2015-01-01

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a 'community' as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of 'community.' In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.

Foraging Online Social Networks

NARCIS (Netherlands)

Koot, G.; Huis in ’t Veld, M.A.A.; Hendricksen, J.; Vries, A. de; Broek, E.L. van den

2014-01-01

A concise and practical introduction is given on Online Social Networks (OSN) and their application in law enforcement, including a brief survey of related work. Subsequently, a tool is introduced that can be used to search OSN in order to generate user profiles. Both its architecture and processing

Community Structure in Online Collegiate Social Networks

Science.gov (United States)

Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

2009-03-01

Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

Feedback topology and XOR-dynamics in Boolean networks with varying input structure.

Science.gov (United States)

Ciandrini, L; Maffi, C; Motta, A; Bassetti, B; Cosentino Lagomarsino, M

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter gamma. We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying gamma , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.

Feedback topology and XOR-dynamics in Boolean networks with varying input structure

Science.gov (United States)

Ciandrini, L.; Maffi, C.; Motta, A.; Bassetti, B.; Cosentino Lagomarsino, M.

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter γ . We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying γ , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.

Vaccine Hesitancy and Online Information: The Influence of Digital Networks.

Science.gov (United States)

Getman, Rebekah; Helmi, Mohammad; Roberts, Hal; Yansane, Alfa; Cutler, David; Seymour, Brittany

2017-12-01

This article analyzes the digital childhood vaccination information network for vaccine-hesitant parents. The goal of this study was to explore the structure and influence of vaccine-hesitant content online by generating a database and network analysis of vaccine-relevant content. We used Media Cloud, a searchable big-data platform of over 550 million stories from 50,000 media sources, for quantitative and qualitative study of an online media sample based on keyword selection. We generated a hyperlink network map and measured indegree centrality of the sources and vaccine sentiment for a random sample of 450 stories. 28,122 publications from 4,817 sources met inclusion criteria. Clustered communities formed based on shared hyperlinks; communities tended to link within, not among, each other. The plurality of information was provaccine (46.44%, 95% confidence interval [39.86%, 53.20%]). The most influential sources were in the health community (National Institutes of Health, Centers for Disease Control and Prevention) or mainstream media ( New York Times); some user-generated sources also had strong influence and were provaccine (Wikipedia). The vaccine-hesitant community rarely interacted with provaccine content and simultaneously used primary provaccine content within vaccine-hesitant narratives. The sentiment of the overall conversation was consistent with scientific evidence. These findings demonstrate an online environment where scientific evidence online drives vaccine information outside of the vaccine-hesitant community but is also prominently used and misused within the robust vaccine-hesitant community. Future communication efforts should take current context into account; more information may not prevent vaccine hesitancy.

Deep and Surface Processing of Instructor's Feedback in an Online Course

Science.gov (United States)

Huang, Kun; Ge, Xun; Law, Victor

2017-01-01

This study investigated the characteristics of deep and surface approaches to learning in online students' responses to instructor's qualitative feedback given to a multi-stage, ill-structured design project. Further, the study examined the relationships between approaches to learning and two learner characteristics: epistemic beliefs (EB) and…

Online social network sites and social capital: a case of facebook

OpenAIRE

Naseri, Samaneh

2017-01-01

The present study is a theoretical and literary review of online social network sites and their impact on social capital. In this review, the Facebook is selected as one popular and important online social networking site in the world today. To This end, first two main concepts of social capital, bridging and bonding social capital has been provided. Next, the concept of online social networks and the impact of FB on social networks are discussed.

Characterizing interactions in online social networks during exceptional events

Science.gov (United States)

Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

2015-08-01

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

Proprioceptive feedback and brain computer interface (BCI based neuroprostheses.

Directory of Open Access Journals (Sweden)

Ander Ramos-Murguialday

Full Text Available Brain computer interface (BCI technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1 motor imagery of the hand movement without any overt movement and without feedback, (2 motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3 passive (the orthosis passively opens and closes the hand without imagery and (4 active (overt movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants. Group 1 (n = 9 received contingent positive feedback (participants' sensorimotor rhythm (SMR desynchronization was directly linked to hand orthosis movements, group 2 (n = 8 contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements and group 3 (n = 7 sham feedback (no link between brain oscillations and orthosis movements. We observed that proprioceptive feedback (feeling and seeing hand movements improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and

Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses.

Science.gov (United States)

Ramos-Murguialday, Ander; Schürholz, Markus; Caggiano, Vittorio; Wildgruber, Moritz; Caria, Andrea; Hammer, Eva Maria; Halder, Sebastian; Birbaumer, Niels

2012-01-01

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive

Multivoxel Patterns Reveal Functionally Differentiated Networks Underlying Auditory Feedback Processing of Speech

DEFF Research Database (Denmark)

Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.

2013-01-01

The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection, and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations...... within a multivoxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was used to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while...... human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during...

Coupling between feedback loops in autoregulatory networks affects bistability range, open-loop gain and switching times

International Nuclear Information System (INIS)

Tiwari, Abhinav; Igoshin, Oleg A

2012-01-01

Biochemical regulatory networks governing diverse cellular processes such as stress-response, differentiation and cell cycle often contain coupled feedback loops. We aim at understanding how features of feedback architecture, such as the number of loops, the sign of the loops and the type of their coupling, affect network dynamical performance. Specifically, we investigate how bistability range, maximum open-loop gain and switching times of a network with transcriptional positive feedback are affected by additive or multiplicative coupling with another positive- or negative-feedback loop. We show that a network's bistability range is positively correlated with its maximum open-loop gain and that both quantities depend on the sign of the feedback loops and the type of feedback coupling. Moreover, we find that the addition of positive feedback could decrease the bistability range if we control the basal level in the signal-response curves of the two systems. Furthermore, the addition of negative feedback has the capacity to increase the bistability range if its dissociation constant is much lower than that of the positive feedback. We also find that the addition of a positive feedback to a bistable network increases the robustness of its bistability range, whereas the addition of a negative feedback decreases it. Finally, we show that the switching time for a transition from a high to a low steady state increases with the effective fold change in gene regulation. In summary, we show that the effect of coupled feedback loops on the bistability range and switching times depends on the underlying mechanistic details. (paper)

On-line plant-wide monitoring using neural networks

International Nuclear Information System (INIS)

Turkcan, E.; Ciftcioglu, O.; Eryurek, E.; Upadhyaya, B.R.

1992-06-01

The on-line signal analysis system designed for a multi-level mode operation using neural networks is described. The system is capable of monitoring the plant states by tracking different number of signals up to 32 simultaneously. The data used for this study were acquired from the Borssele Nuclear Power Plant (PWR type), and using the on-line monitoring system. An on-line plant-wide monitoring study using a multilayer neural network model is discussed in this paper. The back-propagation neural network algorithm is used for training the network. The technique assumes that each physical state of the power plant can be represented by a unique pattern of instrument readings which can be related to the condition of the plant. When disturbance occurs, the sensor readings undergo a transient, and form a different set of patterns which represent the new operational status. Diagnosing these patterns can be helpful in identifying this new state of the power plant. To this end, plant-wide monitoring with neutral networks is one of the new techniques in real-time applications. (author). 9 refs.; 5 figs

Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

KAUST Repository

Alharbi, Basma Mohammed; Zhang, Xiangliang

2017-01-01

Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals's mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users' check-ins and their network of friends, without impairing the model's complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

KAUST Repository

Alharbi, Basma Mohammed

2017-02-07

Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals\\'s mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users\\' check-ins and their network of friends, without impairing the model\\'s complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

Mining human mobility in location-based social networks

CERN Document Server

Gao, Huiji

2015-01-01

In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to ""check in"" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely l

Block compressed sensing for feedback reduction in relay-aided multiuser full duplex networks

KAUST Repository

Elkhalil, Khalil

2016-08-11

Opportunistic user selection is a simple technique that exploits the spatial diversity in multiuser relay-aided networks. Nonetheless, channel state information (CSI) from all users (and cooperating relays) is generally required at a central node in order to make selection decisions. Practically, CSI acquisition generates a great deal of feedback overhead that could result in significant transmission delays. In addition to this, the presence of a full-duplex cooperating relay corrupts the fed back CSI by additive noise and the relay\\'s loop (or self) interference. This could lead to transmission outages if user selection is based on inaccurate feedback information. In this paper, we propose an opportunistic full-duplex feedback algorithm that tackles the above challenges. We cast the problem of joint user signal-to-noise ratio (SNR) and the relay loop interference estimation at the base-station as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated. Numerical results show that the proposed technique drastically reduces the feedback overhead and achieves a rate close to that obtained by techniques that require dedicated error-free feedback from all users. Numerical results also show that there is a trade-off between the feedback interference and load, and for short coherence intervals, full-duplex feedback achieves higher throughput when compared to interference-free (half-duplex) feedback. © 2016 IEEE.

Block compressed sensing for feedback reduction in relay-aided multiuser full duplex networks

KAUST Repository

Elkhalil, Khalil; Eltayeb, Mohammed; Kammoun, Abla; Al-Naffouri, Tareq Y.; Bahrami, Hamid Reza

2016-01-01

Opportunistic user selection is a simple technique that exploits the spatial diversity in multiuser relay-aided networks. Nonetheless, channel state information (CSI) from all users (and cooperating relays) is generally required at a central node in order to make selection decisions. Practically, CSI acquisition generates a great deal of feedback overhead that could result in significant transmission delays. In addition to this, the presence of a full-duplex cooperating relay corrupts the fed back CSI by additive noise and the relay's loop (or self) interference. This could lead to transmission outages if user selection is based on inaccurate feedback information. In this paper, we propose an opportunistic full-duplex feedback algorithm that tackles the above challenges. We cast the problem of joint user signal-to-noise ratio (SNR) and the relay loop interference estimation at the base-station as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated. Numerical results show that the proposed technique drastically reduces the feedback overhead and achieves a rate close to that obtained by techniques that require dedicated error-free feedback from all users. Numerical results also show that there is a trade-off between the feedback interference and load, and for short coherence intervals, full-duplex feedback achieves higher throughput when compared to interference-free (half-duplex) feedback. © 2016 IEEE.

Users structure and behavior on an online social network during a political protest

Science.gov (United States)

Morales, A. J.; Losada, J. C.; Benito, R. M.

2012-11-01

Over the past years, new technologies and specially online social networks have penetrated into the world’s population at an accelerated pace. In this paper we analyze collected data from the web application Twitter, in order to describe the structure and dynamics of the emergent social networks, based on complexity science. We focused on a Venezuelan protest that took place exclusively by Twitter during December, 2010. We found a community structure with highly connected hubs and three different kinds of user behavior that determine the information flow dynamics. We noticed that even though online social networks appear to be a pure social environment, traditional media still holds loads of influence inside the network.

Online Customization and Enrollment Application Network (OCEAN

Directory of Open Access Journals (Sweden)

E. Kongar

2007-09-01

Full Text Available This paper introduces the Online Customization and Enrollment Application Network (OCEAN, developed in the School of Engineering at the University of Bridgeport. OCEAN is an interactive web-based application for graduate programs, concentrations, certificates and courses across the Schools of Engineering, Business and Education that allows prospective and current students to customize their preferences in the course selection process depending on the targeted graduate concentrations, degrees, and/or dual degree programs.

Recommender systems for location-based social networks

CERN Document Server

Symeonidis, Panagiotis; Manolopoulos, Yannis

2014-01-01

Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of t...

Engineering Online and In-person Social Networks for Physical Activity: A Randomized Trial

Science.gov (United States)

Rovniak, Liza S.; Kong, Lan; Hovell, Melbourne F.; Ding, Ding; Sallis, James F.; Ray, Chester A.; Kraschnewski, Jennifer L.; Matthews, Stephen A.; Kiser, Elizabeth; Chinchilli, Vernon M.; George, Daniel R.; Sciamanna, Christopher N.

2016-01-01

Background Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. Purpose To conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively-measured outcomes. Methods Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3% male, 83.4% overweight/obese) were randomized to 1 of 3 groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking, and prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Results Participants increased their MVPA by 21.0 mins/week, 95% CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Conclusions Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. Trial Registration Number NCT01142804 PMID:27405724

Composition and structure of a large online social network in The Netherlands.

Directory of Open Access Journals (Sweden)

Rense Corten

Full Text Available Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization. The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in The Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for The Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.

Composition and structure of a large online social network in The Netherlands.

Science.gov (United States)

Corten, Rense

2012-01-01

Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization). The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in The Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for The Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.

Qualitätssicherung von Online-Kursen in E-Learning Portalen

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Stefan Iske

2009-12-01

Full Text Available Dieser Artikel fokussiert den Bereich des Qualitätsmanagements im e-Learning am Beispiel der Qualitätssicherung von Online-Kursen in einem E-Learning-Portal. Dargestellt wird ein Work-Flow spezifischer Qualitätsmanagement-Prozesse: (1 allgemeine Unterstützung der Autoren von Online-Kursen, (2 Evaluation erstellter Online-Kurse, (3 Feedback der Lernenden (Kursteilnehmer an den Kurs-Autor, (4 Feedback der Lernenden an das Qualitätssicherungs-Team (5 Feedback der Kurs-Autoren an das Qualitätssicherungs-Team, sowie (6 die zusammenfassende Evaluation des Lernenden- und Experten-Feedback durch das Qualitätssicherungs-Team. Dieser Work-Flow wurde empirisch getestet und in den europäischen E-Learning-Marktplatz "Columbus-Portal" implementiert, welcher im Rahmen des von der Europäischen Kommission (eTEN-Programm finanzierten Projektes " Electronic Learning and Assistan­ce Network" (ELAN entwickelt wurde. This article describes and reflects quality management in the field of e-learning, especially the assurance of the quality of provided on-line courses of an e-learning portal. A work flow of specific processes of quality management is presented, which consist of (1 general support of authors creating on-line courses, (2 evaluation of created on-line courses, (3 feedback of learners (course users to the author, (4 learner feedback to the quality assurance team (5 author feedback to the quality assurance team and (6 the evaluation of learner and expert-feedback. This work flow was empirically tested and implemented in the European e-learning marketplace “Columbus-Portal” which was developed in the project “Electronic Learning and Assistan­ce Network” (ELAN funded by the European Commission (eTen-programme.

Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design

Science.gov (United States)

Ang, Chee Siang; Zaphiris, Panayiotis

We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.

Methods of Profile Cloning Detection in Online Social Networks

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Zabielski Michał

2016-01-01

Full Text Available With the arrival of online social networks, the importance of privacy on the Internet has increased dramatically. Thus, it is important to develop mechanisms that will prevent our hidden personal data from unauthorized access and use. In this paper an attempt was made to present a concept of profile cloning detection in Online Social Networks (OSN using Graph and Networks Theory. By analysing structural similarity of network and value of attributes of user personal profile, we will be able to search for attackers which steal our identity.

Data Quality in Online Health Social Networks for Chronic Diseases

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Venkatesan, Srikanth

2017-01-01

Can medical advice from other participants in online health social networks impact patient safety? What can we do alleviate this problem? How does the accuracy of information on such networks affect the patients?. There has been a significant increase , in recent years, in the use of online health social network sites as more patients seek to…

Emergence, evolution and scaling of online social networks.

Science.gov (United States)

Wang, Le-Zhi; Huang, Zi-Gang; Rong, Zhi-Hai; Wang, Xiao-Fan; Lai, Ying-Cheng

2014-01-01

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Emergence, evolution and scaling of online social networks.

Directory of Open Access Journals (Sweden)

Le-Zhi Wang

Full Text Available Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Online social networks for patient involvement and recruitment in clinical research.

Science.gov (United States)

Ryan, Gemma Sinead

2013-01-01

To review current literature and discuss the potential of online social networking to engage patients and the public and recruit and retain participants in clinical research. Online social networking is becoming a large influence on people's daily lives. Clinical research faces several challenges, with an increasing need to engage with patients and the public and for studies to recruit and retain increasing numbers of participants, particularly in under-served, under-represented and hard to reach groups and communities. Searches were conducted using EMBASE, BNI, ERIC, CINAHL, PSYCHinfo online databases and Google Scholar to identify any grey or unpublished literature that may be available. Review methods This is a methodology paper. Online social networking is a successful, cost-effective and efficient method by which to target and recruit a wide range of communities, adolescents, young people and underserved populations into quantitative and qualitative research. Retention of participants in longitudinal studies could be improved using social networks such as Facebook. Evidence indicates that a mixed approach to recruitment using social networking and traditional methods is most effective. Further research is required to strengthen the evidence available, especially in dissemination of research through online social networks. Researchers should consider using online social networking as a method of engaging the public, and also for the recruitment and follow up of participants.

Pharmacists on Facebook: online social networking and the profession.

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Mattingly, T Joseph; Cain, Jeff; Fink, Joseph L

2010-01-01

To provide a brief history of Facebook and online social networking and discuss how it has contributed and can contribute in the future to a paradigm change in social communications. When student pharmacists complete school and enter practice, they encounter enhanced expectations to act appropriately and professionally. Facebook expands the dilemma of separating private and public life--a challenge for individuals in all professions. From the standpoint of a professional association, Facebook provides a tremendous opportunity to reach out to members in an unprecedented way. Pharmacy organizations are beginning to use these new tools to increase communication and dissemination of information. The popularity of Facebook has brought the issue of online social networking to the forefront of professional and organizational discussions. The issues of privacy, identity protection, and e-professionalism are likely to reappear as pharmacists and student pharmacists continue to communicate via online networks. The potential exists for organizations to harness this organizational and communication power for their own interests. Further study is needed regarding the interaction between online social networking applications and the profession of pharmacy.

Synchronization of chaotic recurrent neural networks with time-varying delays using nonlinear feedback control

International Nuclear Information System (INIS)

Cui Baotong; Lou Xuyang

2009-01-01

In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme

Enhanced online convolutional neural networks for object tracking

Science.gov (United States)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

Dynamic Output Feedback Control for Nonlinear Networked Control Systems with Random Packet Dropout and Random Delay

Directory of Open Access Journals (Sweden)

Shuiqing Yu

2013-01-01

Full Text Available This paper investigates the dynamic output feedback control for nonlinear networked control systems with both random packet dropout and random delay. Random packet dropout and random delay are modeled as two independent random variables. An observer-based dynamic output feedback controller is designed based upon the Lyapunov theory. The quantitative relationship of the dropout rate, transition probability matrix, and nonlinear level is derived by solving a set of linear matrix inequalities. Finally, an example is presented to illustrate the effectiveness of the proposed method.

Mobile robot nonlinear feedback control based on Elman neural network observer

Directory of Open Access Journals (Sweden)

Khaled Al-Mutib

2015-12-01

Full Text Available This article presents a new approach to control a wheeled mobile robot without velocity measurement. The controller developed is based on kinematic model as well as dynamics model to take into account parameters of dynamics. These parameters related to dynamic equations are identified using a proposed methodology. Input–output feedback linearization is considered with a slight modification in the mathematical expressions to implement the dynamic controller and analyze the nonlinear internal behavior. The developed controllers require sensors to obtain the states needed for the closed-loop system. However, some states may not be available due to the absence of the sensors because of the cost, the weight limitation, reliability, induction of errors, failure, and so on. Particularly, for the velocity measurements, the required accuracy may not be achieved in practical applications due to the existence of significant errors induced by stochastic or cyclical noise. In this article, Elman neural network is proposed to work as an observer to estimate the velocity needed to complete the full state required for the closed-loop control and account for all the disturbances and model parameter uncertainties. Different simulations are carried out to demonstrate the feasibility of the approach in tracking different reference trajectories in comparison with other paradigms.

Exploring Self-Disclosure in Online Social Networks

Science.gov (United States)

Velasco-Martin, Javier

2013-01-01

This project explores how experienced adult users of social media disclose personal information over online social networks (OSN). This work introduces a four-dimensional model to serve as a foundational framework for the study of online self-disclosure (OSD); these four dimensions are personal, social, technological and contextual, and support…

Scheduling for Multiuser MIMO Downlink Channels with Ranking-Based Feedback

Science.gov (United States)

Kountouris, Marios; Sälzer, Thomas; Gesbert, David

2008-12-01

We consider a multi-antenna broadcast channel with more single-antenna receivers than transmit antennas and partial channel state information at the transmitter (CSIT). We propose a novel type of CSIT representation for the purpose of user selection, coined as ranking-based feedback. Each user calculates and feeds back the rank, an integer between 1 and W + 1, of its instantaneous channel quality information (CQI) among a set of W past CQI measurements. Apart from reducing significantly the required feedback load, ranking-based feedback enables the transmitter to select users that are on the highest peak (quantile) with respect to their own channel distribution, independently of the distribution of other users. It can also be shown that this feedback metric can restore temporal fairness in heterogeneous networks, in which users' channels are not identically distributed and mobile terminals experience different average signal-to-noise ratio (SNR). The performance of a system that performs user selection using ranking-based CSIT in the context of random opportunistic beamforming is analyzed, and we provide design guidelines on the number of required past CSIT samples and the impact of finite W on average throughput. Simulation results show that feedback reduction of order of 40-50% can be achieved with negligible decrease in system throughput.

Benford's Law Applies to Online Social Networks.

Science.gov (United States)

Golbeck, Jennifer

2015-01-01

Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.

Benford's Law Applies to Online Social Networks.

Directory of Open Access Journals (Sweden)

Jennifer Golbeck

Full Text Available Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal, we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.

Nursing Librarians Cultivating Evidence-Based Practice Through an Asynchronous Online Course.

Science.gov (United States)

Mears, Kim; Blake, Lindsay

2017-09-01

In response to a request from the Nursing Shared Governance Evidence-Based Practice Council, librarians created an online evidence-based practice (EBP) continuing education course for clinical nurses. The curriculum was adapted from a previously created face-to-face course and was offered online through a learning management system. Although many nurses registered for the course, only a small sample was able to complete all modules. Feedback revealed that nurses appreciated the ease of online use, but they experienced technical barriers. Overall, nurses completing the course agreed that all learning objectives were met. An online asynchronous course for nurses is a viable option for teaching EBP, but hospital computer limitations must be taken into account to allow for participants' full immersion into the material. J Contin Educ Nurs. 2017;48(9):420-424. Copyright 2017, SLACK Incorporated.

Effective seeding strategy in evolutionary prisoner's dilemma games on online social networks

Science.gov (United States)

Xu, Bo; Shi, Huibin; Wang, Jianwei; Huang, Yun

2015-04-01

This paper explores effective seeding strategies in prisoner's dilemma game (PDG) on online social networks, i.e. the optimal strategy to obtain global cooperation with minimum cost. Three distinct seeding strategies are compared by performing computer simulations on real online social network datasets. Our finding suggests that degree centrality seeding outperforms other strategies regardless of the initial payoff setting or network size. Celebrities of online social networks play key roles in preserving cooperation.

The impact of problem solving strategy with online feedback on students’ conceptual understanding

Science.gov (United States)

Pratiwi, H. Y.; Winarko, W.; Ayu, H. D.

2018-04-01

The study aimed to determine the impact of the implementation of problem solving strategy with online feedback towards the students’ concept understanding. This study used quasi experimental design with post-test only control design. The participants were all Physics Education students of Kanjuruhan University year 2015. Then, they were divided into two different groups; 30 students belong to experiment class and the remaining 30 students belong to class of control. The students’ concept understanding was measured by the concept understanding test on multiple integral lesson. The result of the concept understanding test was analyzed by prerequisite test and stated to be normal and homogenic distributed, then the hypothesis was examined by T-test. The result of the study shows that there is difference in the concept understanding between experiment class and control class. Next, the result also shows that the students’ concept understanding which was taught using problem solving strategy with online feedback was higher than those using conventional learning; with average score of 72,10 for experiment class and 52,27 for control class.

Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial.

Science.gov (United States)

Rovniak, Liza S; Kong, Lan; Hovell, Melbourne F; Ding, Ding; Sallis, James F; Ray, Chester A; Kraschnewski, Jennifer L; Matthews, Stephen A; Kiser, Elizabeth; Chinchilli, Vernon M; George, Daniel R; Sciamanna, Christopher N

2016-12-01

Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes. Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. The trial was registered with the ClinicalTrials.gov (NCT01142804).

Impaired Feedforward Control and Enhanced Feedback Control of Speech in Patients with Cerebellar Degeneration.

Science.gov (United States)

Parrell, Benjamin; Agnew, Zarinah; Nagarajan, Srikantan; Houde, John; Ivry, Richard B

2017-09-20

The cerebellum has been hypothesized to form a crucial part of the speech motor control network. Evidence for this comes from patients with cerebellar damage, who exhibit a variety of speech deficits, as well as imaging studies showing cerebellar activation during speech production in healthy individuals. To date, the precise role of the cerebellum in speech motor control remains unclear, as it has been implicated in both anticipatory (feedforward) and reactive (feedback) control. Here, we assess both anticipatory and reactive aspects of speech motor control, comparing the performance of patients with cerebellar degeneration and matched controls. Experiment 1 tested feedforward control by examining speech adaptation across trials in response to a consistent perturbation of auditory feedback. Experiment 2 tested feedback control, examining online corrections in response to inconsistent perturbations of auditory feedback. Both male and female patients and controls were tested. The patients were impaired in adapting their feedforward control system relative to controls, exhibiting an attenuated anticipatory response to the perturbation. In contrast, the patients produced even larger compensatory responses than controls, suggesting an increased reliance on sensory feedback to guide speech articulation in this population. Together, these results suggest that the cerebellum is crucial for maintaining accurate feedforward control of speech, but relatively uninvolved in feedback control. SIGNIFICANCE STATEMENT Speech motor control is a complex activity that is thought to rely on both predictive, feedforward control as well as reactive, feedback control. While the cerebellum has been shown to be part of the speech motor control network, its functional contribution to feedback and feedforward control remains controversial. Here, we use real-time auditory perturbations of speech to show that patients with cerebellar degeneration are impaired in adapting feedforward control of

How Will Online Affiliate Marketing Networks Impact Search Engine Rankings?

OpenAIRE

Janssen, David; Heck, Eric

2007-01-01

textabstractIn online affiliate marketing networks advertising web sites offer their affiliates revenues based on provided web site traffic and associated leads and sales. Advertising web sites can have a network of thousands of affiliates providing them with web site traffic through hyperlinks on their web sites. Search engines such as Google, MSN, and Yahoo, consider hyperlinks as a proof of quality and/or reliability of the linked web sites, and therefore use them to determine the relevanc...

Adaptation of elaborated feedback in e-learning

NARCIS (Netherlands)

Vasilyeva, E.; Pechenizkiy, M.; De Bra, P.M.E.; Nejdl, W.; Kay, J.; Pu, P.; Herder, E.

2008-01-01

Design of feedback is a critical issue of online assessment development within Web-based Learning Systems (WBLSs). In our work we demonstrate the possibilities of tailoring the feedback to the students’ learning style (LS), certitude in response and its correctness. We observe in the experimental

Student and Instructor Responses to Emotional Motivational Feedback Messages in an Online Instructional Environment

Science.gov (United States)

Sarsar, Firat

2017-01-01

The purpose of this study was to investigate the effectiveness of Emotional Motivational Feedback Message (EMFEM) in an online learning environment. This exploratory research was conducted using mixed method single case study design. Participants were 15 undergraduate students enrolled in an instructional technology course in a large state…

Essential elements of online information networks on invasive alien species

Science.gov (United States)

Simpson, A.; Sellers, E.; Grosse, A.; Xie, Y.

2006-01-01

In order to be effective, information must be placed in the proper context and organized in a manner that is logical and (preferably) standardized. Recently, invasive alien species (IAS) scientists have begun to create online networks to share their information concerning IAS prevention and control. At a special networking session at the Beijing International Symposium on Biological Invasions, an online Eastern Asia-North American IAS Information Network (EA-NA Network) was proposed. To prepare for the development of this network, and to provide models for other regional collaborations, we compare four examples of global, regional, and national online IAS information networks: the Global Invasive Species Information Network, the Invasives Information Network of the Inter-American Biodiversity Information Network, the Chinese Species Information System, and the Invasive Species Information Node of the US National Biological Information Infrastructure. We conclude that IAS networks require a common goal, dedicated leaders, effective communication, and broad endorsement, in order to obtain sustainable, long-term funding and long-term stability. They need to start small, use the experience of other networks, partner with others, and showcase benefits. Global integration and synergy among invasive species networks will succeed with contributions from both the top-down and the bottom-up. ?? 2006 Springer.

Online Questionnaires Use with Automatic Feedback for e-Innovation in University Students

OpenAIRE

Remesal, Ana; Colomina, Rosa M.; Mauri, Teresa; Rochera, M. José

2017-01-01

Technological tools have permeated higher education programs. However, their mere introduction does not guarantee instructional quality. This article presents the results of an innovation project aimed at fostering autonomous learning among students at a Pre-School and Primary Teacher Grade. For one semester all freshmen students used a system for autonomous learning embedded in the institutional online platform (Moodle), which included automatic formative feedback. The system was part of a c...

Control of a local neural network by feedforward and feedback inhibition

NARCIS (Netherlands)

Remme, M.W.H.; Wadman, W.J.

2004-01-01

The signal transfer of a neuronal network is shaped by the local interactions between the excitatory principal cells and the inhibitory interneurons. We investigated with a simple lumped model how feedforward and feedback inhibition in.uence the steady-state network signal transfer. We analyze how

Academics and Social Networking Sites: Benefits, Problems and Tensions in Professional Engagement with Online Networking

Science.gov (United States)

Jordan, Katy; Weller, Martin

2018-01-01

The web has had a profound effect on the ways people interact, with online social networks arguably playing an important role in changing or augmenting how we connect with others. However, uptake of online social networking by the academic community varies, and needs to be understood. This paper presents an independent, novel analysis of a…

Online Academic Networks as Knowledge Brokers: The Mediating Role of Organizational Support

Directory of Open Access Journals (Sweden)

Elena-Mădălina Vătămănescu

2018-04-01

Full Text Available Placing online academic networks in the framework of social, cultural and institutional “deterritorialization,” the current paper aims at investigating the functionality of these new forms of transnational and trans-organizational aggregations as knowledge brokers. The emphasis is laid on the influence of human collective intelligence and consistent knowledge flows on research innovation, considering the role of organizational support within higher education systems. In this respect, the research relied on a questionnaire-based survey with 140 academics from European emerging countries, the data collected being processed via a partial least squares structural equation modelling technique. Evidence was brought that, as knowledge brokers, online academic networks are systems aimed to support the access to human collective intelligence and consistent knowledge flows which exert a positive influence on research innovation, both directly and indirectly, by means of formal and informal organizational support. As facilitators of collaborative environments for individuals with specialized knowledge, competence, expertise and experience, online academic networks have set themselves up as an agora for academics worldwide and as an outlet for their acumen and literacy.

Bridging online and offline social networks: Multiplex analysis

Science.gov (United States)

Filiposka, Sonja; Gajduk, Andrej; Dimitrova, Tamara; Kocarev, Ljupco

2017-04-01

We show that three basic actor characteristics, namely normalized reciprocity, three cycles, and triplets, can be expressed using an unified framework that is based on computing the similarity index between two sets associated with the actor: the set of her/his friends and the set of those considering her/him as a friend. These metrics are extended to multiplex networks and then computed for two friendship networks generated by collecting data from two groups of undergraduate students. We found that in offline communication strong and weak ties are (almost) equally presented, while in online communication weak ties are dominant. Moreover, weak ties are much less reciprocal than strong ties. However, across different layers of the multiplex network reciprocities are preserved, while triads (measured with normalized three cycles and triplets) are not significant.

Delay reduction in lossy intermittent feedback for generalized instantly decodable network coding

KAUST Repository

Douik, Ahmed S.

2013-10-01

In this paper, we study the effect of lossy intermittent feedback loss events on the multicast decoding delay performance of generalized instantly decodable network coding. These feedback loss events create uncertainty at the sender about the reception statues of different receivers and thus uncertainty to accurately determine subsequent instantly decodable coded packets. To solve this problem, we first identify the different possibilities of uncertain packets at the sender and their probabilities. We then derive the expression of the mean decoding delay. We formulate the Generalized Instantly Decodable Network Coding (G-IDNC) minimum decoding delay problem as a maximum weight clique problem. Since finding the optimal solution is NP-hard, we design a variant of the algorithm employed in [1]. Our algorithm is compared to the two blind graph update proposed in [2] through extensive simulations. Results show that our algorithm outperforms the blind approaches in all the situations and achieves a tolerable degradation, against the perfect feedback, for large feedback loss period. © 2013 IEEE.

Delay reduction in lossy intermittent feedback for generalized instantly decodable network coding

KAUST Repository

Douik, Ahmed S.; Sorour, Sameh; Alouini, Mohamed-Slim; Ai-Naffouri, Tareq Y.

2013-01-01

In this paper, we study the effect of lossy intermittent feedback loss events on the multicast decoding delay performance of generalized instantly decodable network coding. These feedback loss events create uncertainty at the sender about the reception statues of different receivers and thus uncertainty to accurately determine subsequent instantly decodable coded packets. To solve this problem, we first identify the different possibilities of uncertain packets at the sender and their probabilities. We then derive the expression of the mean decoding delay. We formulate the Generalized Instantly Decodable Network Coding (G-IDNC) minimum decoding delay problem as a maximum weight clique problem. Since finding the optimal solution is NP-hard, we design a variant of the algorithm employed in [1]. Our algorithm is compared to the two blind graph update proposed in [2] through extensive simulations. Results show that our algorithm outperforms the blind approaches in all the situations and achieves a tolerable degradation, against the perfect feedback, for large feedback loss period. © 2013 IEEE.

Competition between global and local online social networks

Science.gov (United States)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-01

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

Competition between global and local online social networks.

Science.gov (United States)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-27

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

Dynamic Intelligent Feedback Scheduling in Networked Control Systems

Directory of Open Access Journals (Sweden)

Hui-ying Chen

2013-01-01

Full Text Available For the networked control system with limited bandwidth and flexible workload, a dynamic intelligent feedback scheduling strategy is proposed. Firstly, a monitor is used to acquire the current available network bandwidth. Then, the new available bandwidth in the next interval is predicted by using LS_SVM approach. At the same time, the dynamic performance indices of all control loops are obtained with a two-dimensional fuzzy logic modulator. Finally, the predicted network bandwidth is dynamically allocated by the bandwidth manager and the priority allocator in terms of the loops' dynamic performance indices. Simulation results show that the sampling periods and priorities of control loops are adjusted timely according to the network workload condition and the dynamic performance of control loops, which make the system running in the optimal state all the time.

Regular network model for the sea ice-albedo feedback in the Arctic.

Science.gov (United States)

Müller-Stoffels, Marc; Wackerbauer, Renate

2011-03-01

The Arctic Ocean and sea ice form a feedback system that plays an important role in the global climate. The complexity of highly parameterized global circulation (climate) models makes it very difficult to assess feedback processes in climate without the concurrent use of simple models where the physics is understood. We introduce a two-dimensional energy-based regular network model to investigate feedback processes in an Arctic ice-ocean layer. The model includes the nonlinear aspect of the ice-water phase transition, a nonlinear diffusive energy transport within a heterogeneous ice-ocean lattice, and spatiotemporal atmospheric and oceanic forcing at the surfaces. First results for a horizontally homogeneous ice-ocean layer show bistability and related hysteresis between perennial ice and perennial open water for varying atmospheric heat influx. Seasonal ice cover exists as a transient phenomenon. We also find that ocean heat fluxes are more efficient than atmospheric heat fluxes to melt Arctic sea ice.

Distribution of Feedback among Teacher and Students in Online Collaborative Learning in Small Groups

Science.gov (United States)

Coll, Cesar; Rochera, Maria Jose; de Gispert, Ines; Diaz-Barriga, Frida

2013-01-01

This study explores the characteristics and distribution of the feedback provided by the participants (a teacher and her students) in an activity organized inside a collaborative online learning environment. We analyse 853 submissions made by two groups of graduate students and their teacher (N1 = 629 & N2 = 224) involved in the collaborative…

Cloud-based Networked Visual Servo Control

OpenAIRE

Wu, Haiyan; Lu, Lei; Chen, Chih-Chung; Hirche, Sandra; Kühnlenz, Kolja

2013-01-01

The performance of vision-based control systems, in particular of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control, which integrates networked computational resources for cloud image processing, is considered in this article. The main contributions of this article are i) a real-time transport protocol for transmitti...

Competitive diffusion in online social networks with heterogeneous users

Science.gov (United States)

Li, Pei; He, Su; Wang, Hui; Zhang, Xin

2014-06-01

Online social networks have attracted increasing attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. However, most research on diffusion dynamics in epidemiology cannot be applied directly to characterize online social networks, where users are heterogeneous and may act differently according to their standpoints. In this paper, we propose models to characterize the competitive diffusion in online social networks with heterogeneous users. We classify messages into two types (i.e., positive and negative) and users into three types (i.e., positive, negative and neutral). We estimate the positive (negative) influence for a user generating a given type message, which is the number of times that positive (negative) messages are processed (i.e., read) incurred by this action. We then consider the diffusion threshold, above which the corresponding influence will approach infinity, and the effect threshold, above which the unexpected influence of generating a message will exceed the expected one. We verify all these results by simulations, which show the analysis results are perfectly consistent with the simulation results. These results are of importance in understanding the diffusion dynamics in online social networks, and also critical for advertisers in viral marketing where there are fans, haters and neutrals.

Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system

International Nuclear Information System (INIS)

Sabahi, Kamel; Teshnehlab, Mohammad; Shoorhedeli, Mahdi Aliyari

2009-01-01

In this study, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL strategy consists of intelligent and conventional controllers in feedforward and feedback paths, respectively. In this strategy, a conventional feedback controller (CFC), i.e. proportional, integral and derivative (PID) controller, is essential to guarantee global asymptotic stability of the overall system; and an intelligent feedforward controller (INFC) is adopted to learn the inverse of the controlled system. Therefore, when the INFC learns the inverse of controlled system, the tracking of reference signal is done properly. Generally, the CFC is designed at nominal operating conditions of the system and, therefore, fails to provide the best control performance as well as global stability over a wide range of changes in the operating conditions of the system. So, in this study a supervised controller (SC), a lookup table based controller, is addressed for tuning of the CFC. During abrupt changes of the power system parameters, the SC adjusts the PID parameters according to these operating conditions. Moreover, for improving the performance of overall system, a recurrent fuzzy neural network (RFNN) is adopted in INFC instead of the conventional neural network, which was used in past studies. The proposed FEL controller has been compared with the conventional feedback error learning controller (CFEL) and the PID controller through some performance indices

Human-Centered Development of an Online Social Network for Metabolic Syndrome Management.

Science.gov (United States)

Núñez-Nava, Jefersson; Orozco-Sánchez, Paola A; López, Diego M; Ceron, Jesus D; Alvarez-Rosero, Rosa E

2016-01-01

According to the International Diabetes Federation (IDF), a quarter of the world's population has Metabolic Syndrome (MS). To develop (and assess the users' degree of satisfaction of) an online social network for patients who suffer from Metabolic Syndrome, based on the recommendations and requirements of the Human-Centered Design. Following the recommendations of the ISO 9241-210 for Human-Centered Design (HCD), an online social network was designed to promote physical activity and healthy nutrition. In order to guarantee the active participation of the users during the development of the social network, a survey, an in-depth interview, a focal group, and usability tests were carried out with people suffering from MS. The study demonstrated how the different activities, recommendations, and requirements of the ISO 9241-210 are integrated into a traditional software development process. Early usability tests demonstrated that the user's acceptance and the effectiveness and efficiency of the social network are satisfactory.

Measuring structural similarity in large online networks.

Science.gov (United States)

Shi, Yongren; Macy, Michael

2016-09-01

Structural similarity based on bipartite graphs can be used to detect meaningful communities, but the networks have been tiny compared to massive online networks. Scalability is important in applications involving tens of millions of individuals with highly skewed degree distributions. Simulation analysis holding underlying similarity constant shows that two widely used measures - Jaccard index and cosine similarity - are biased by the distribution of out-degree in web-scale networks. However, an alternative measure, the Standardized Co-incident Ratio (SCR), is unbiased. We apply SCR to members of Congress, musical artists, and professional sports teams to show how massive co-following on Twitter can be used to map meaningful affiliations among cultural entities, even in the absence of direct connections to one another. Our results show how structural similarity can be used to map cultural alignments and demonstrate the potential usefulness of social media data in the study of culture, politics, and organizations across the social and behavioral sciences. Copyright © 2016 Elsevier Inc. All rights reserved.

The effects of online peer feedback and epistemic beliefs on students’ argumentation-based learning

NARCIS (Netherlands)

Noroozi, Omid; Hatami, Javad

2018-01-01

Although the importance of students’ argumentative peer feedback for learning is undeniable, there is a need for further empirical evidence on whether and how it is related to various aspects of argumentation-based learning namely argumentative essay writing, domain-specific learning, and

Social networking and online recruiting for HIV research: ethical challenges.

Science.gov (United States)

Curtis, Brenda L

2014-02-01

Social networking sites and online advertising organizations provide HIV/AIDS researchers access to target populations, often reaching difficult-to-reach populations. However, this benefit to researchers raises many issues for the protections of prospective research participants. Traditional recruitment procedures have involved straightforward transactions between the researchers and prospective participants; online recruitment is a more complex and indirect form of communication involving many parties engaged in the collecting, aggregating, and storing of research participant data. Thus, increased access to online data has challenged the adequacy of current and established procedures for participants' protections, such as informed consent and privacy/confidentiality. Internet-based HIV/AIDS research recruitment and its ethical challenges are described, and research participant safeguards and best practices are outlined.

Social Networking and Online Recruiting for HIV Research: Ethical Challenges

Science.gov (United States)

Curtis, Brenda L.

2015-01-01

Social networking sites and online advertising organizations provide HIV/AIDS researchers access to target populations, often reaching difficult-to-reach populations. However, this benefit to researchers raises many issues for the protections of prospective research participants. Traditional recruitment procedures have involved straightforward transactions between the researchers and prospective participants; online recruitment is a more complex and indirect form of communication involving many parties engaged in the collecting, aggregating, and storing of research participant data. Thus, increased access to online data has challenged the adequacy of current and established procedures for participants’ protections, such as informed consent and privacy/confidentiality. Internet-based HIV/AIDS research recruitment and its ethical challenges are described, and research participant safeguards and best practices are outlined. PMID:24572084

Online networks, social interaction and segregation: An evolutionary approach

OpenAIRE

Antoci, Angelo; Sabatini, Fabio

2018-01-01

There is growing evidence that face-to-face interaction is declining in many countries, exacerbating the phenomenon of social isolation. On the other hand, social interaction through online networking sites is steeply rising. To analyze these societal dynamics, we have built an evolutionary game model in which agents can choose between three strategies of social participation: 1) interaction via both online social networks and face-to-face encounters; 2) interaction by exclusive means of face...

Exploring Co-studied Massive Open Online Course Subjects via Social Network Analysis

Directory of Open Access Journals (Sweden)

Katy Jordan

2014-06-01

Full Text Available Massive Open Online Courses (MOOCs allow students to study online courses without requiring previous experience or qualifications. This offers students the freedom to study a wide variety of topics, freed from the curriculum of a degree programme for example; however, it also poses a challenge for students in terms of making connections between individual courses. This paper examines the subjects which students at one MOOC platform (Coursera choose to study. It uses a social network analysis based approach to create a network graph of co-studied subjects. The resulting network demonstrates a good deal of overlap between different disciplinary areas. Communities are identified within the graph and characterised. The results suggests that MOOC students may not be seeking to replicate degree-style courses in one specialist area, which may have implications for the future moves toward ‘MOOCs for credit’.

Estimating Ads’ Click through Rate with Recurrent Neural Network

Directory of Open Access Journals (Sweden)

Chen Qiao-Hong

2016-01-01

Full Text Available With the development of the Internet, online advertising spreads across every corner of the world, the ads' click through rate (CTR estimation is an important method to improve the online advertising revenue. Compared with the linear model, the nonlinear models can study much more complex relationships between a large number of nonlinear characteristics, so as to improve the accuracy of the estimation of the ads’ CTR. The recurrent neural network (RNN based on Long-Short Term Memory (LSTM is an improved model of the feedback neural network with ring structure. The model overcomes the problem of the gradient of the general RNN. Experiments show that the RNN based on LSTM exceeds the linear models, and it can effectively improve the estimation effect of the ads’ click through rate.

An evolving model of online bipartite networks

Science.gov (United States)

Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang

2013-12-01

Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

Science.gov (United States)

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2015-10-01

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case-study network as virtual reality.

Entrepreneur online social networks: structure, diversity and impact on start-up survival

NARCIS (Netherlands)

Song, Y.; Vinig, T.

2012-01-01

In this paper, we discuss the results of a pilot study in which we use a novel approach to collect entrepreneur online social network data from LinkedIn, Facebook and Twitter. We studied the size and structure of entrepreneur social networks by analysing the online network industry and location

Structural Changes in Online Discussion Networks

DEFF Research Database (Denmark)

Yang, Yang; Medaglia, Rony

2014-01-01

Social networking platforms in China provide a hugely interesting and relevant source for understanding dynamics of online discussions in a unique socio-cultural and institutional environment. This paper investigates the evolution of patterns of similar-minded and different-minded interactions ov...

Communication, collaboration and identity: factor analysis of academics’ perceptions of online networking

Directory of Open Access Journals (Sweden)

Katy Jordan

2018-04-01

Full Text Available Since the advent of online social networking sites, much has been written about their potential for transforming academia, as communication and collaboration underpin many scholarly activities. However, the extent to which these benefits are being realised in practice is unclear. As the uptake of tools by academics continues to grow, there is a question as to whether differences exist in their use and if any patterns or underlying factors are at play. This article presents the results of an online survey addressing this gap. A disciplinary divide was evident in terms of preferred academic social networking platforms, while perceptions about how academics use online networking for different purposes are linked to job position. Exploratory factor analysis identified four components representing different strategies used by academics in their approaches to online networking, including maintaining a personal learning network, promoting the professional self, seeking and promoting publications, and advancing careers.

Can Social Comparison Feedback Affect Indicators of Eco-Friendly Travel Choices? Insights from Two Online Experiments

Directory of Open Access Journals (Sweden)

Rouven Doran

2017-01-01

Full Text Available Two online experiments explored the effects of social comparison feedback on indicators of eco-friendly travel choices. It was tested whether the chosen indicators are sensitive to the information conveyed, and if this varies as a function of in-group identification. Study 1 (N = 134 focused on unfavourable feedback (i.e., being told that one has a larger ecological footprint than the average member of a reference group. People who received unfavourable feedback reported stronger intentions to choose eco-friendly travel options than those who received nondiscrepant feedback, when in-group identification was high (not moderate or low. Perceived self- and collective efficacy were not associated with the feedback. Study 2 (N = 323 extended the focus on favourable feedback (i.e., being told that one has a smaller ecological footprint than the average member of a reference group. Neither unfavourable nor favourable feedback was associated with behavioural intentions, self- or collective efficacy. This means that Study 2 failed to replicate the finding of Study 1 that behavioural intentions were associated with unfavourable feedback, given that in-group identification is high. The findings are discussed in light of the existing literature. Suggestions are made for future studies investigating social comparison feedback as a means to motivate people to make eco-friendly travel choices.

A neural network based artificial vision system for licence plate recognition.

Science.gov (United States)

Draghici, S

1997-02-01

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

Spreading in online social networks: the role of social reinforcement.

Science.gov (United States)

Zheng, Muhua; Lü, Linyuan; Zhao, Ming

2013-07-01

Some epidemic spreading models are usually applied to analyze the propagation of opinions or news. However, the dynamics of epidemic spreading and information or behavior spreading are essentially different in many aspects. Centola's experiments [Science 329, 1194 (2010)] on behavior spreading in online social networks showed that the spreading is faster and broader in regular networks than in random networks. This result contradicts with the former understanding that random networks are preferable for spreading than regular networks. To describe the spreading in online social networks, a unknown-known-approved-exhausted four-status model was proposed, which emphasizes the effect of social reinforcement and assumes that the redundant signals can improve the probability of approval (i.e., the spreading rate). Performing the model on regular and random networks, it is found that our model can well explain the results of Centola's experiments on behavior spreading and some former studies on information spreading in different parameter space. The effects of average degree and network size on behavior spreading process are further analyzed. The results again show the importance of social reinforcement and are accordant with Centola's anticipation that increasing the network size or decreasing the average degree will enlarge the difference of the density of final approved nodes between regular and random networks. Our work complements the former studies on spreading dynamics, especially the spreading in online social networks where the information usually requires individuals' confirmations before being transmitted to others.

HIV/AIDS, social capital, and online social networks.

Science.gov (United States)

Drushel, Bruce E

2013-01-01

The prospects for online social networks as sites of information-gathering and affiliation for persons with AIDS and others concerned about HIV/AIDS not only represent the latest development in a trend toward circumventing traditional media and official information sources, but also may offer hope for a revitalization of HIV/AIDS discourse in the public sphere. This article provides an overview of three decades of information-seeking on the pandemic and its social and personal implications, as well as case studies of three examples of social networking surrounding HIV/AIDS. It finds preliminary evidence of the formation of strong and weak ties as described in Social Network Theory and suggests that the online accumulation of social capital by opinion leaders could facilitate dissemination of messages on HIV/AIDS awareness and testing.

Online Social Networks and the New Organizational Spaces

Directory of Open Access Journals (Sweden)

Cintia Rodrigues de Oliveira Medeiros

2013-04-01

Full Text Available We analyzed the ‘virtuality’ of the social space and the boundaries of organizations from the emergence and dissemination of online social networking. The purpose is to identify how the use of social networks by 10 Brazilian companies enables the redefinition and expansion of organizational space. For the analysis of the data, we used the theory of social space of Lefebvre (2004, which defines three moments of space social production: the imagined space, the lived space and the perceived space. The methodological qualitative approach is done by document analysis from the websites of the companies. We show that the organizational space has new contours with the adoption of online social networks and we analyzed four spatial metaphors: the square, the museum, the temple and the market.

Indigenous development and networking of online radon monitors in the underground uranium mine

International Nuclear Information System (INIS)

Gaware, J.J.; Sahoo, B.K.; Sapra, B.K.; Mayya, Y.S.

2011-01-01

There has been a long standing demand for online monitoring of radon level in various locations of underground Uranium mine for taking care of radiological protection to workers. Nowadays, radon ( 222 Rn) monitors, based on electrostatic collection of charged progeny and subsequent detection by semiconductor detector are increasingly employed for radon monitoring in environment. However, such instruments have some limitations such as (i) requirement of additional dryer since sensitivity is dependent on the humidity (ii) cannot be connected to a network and (iii) not cost effective etc. Hence use of such instruments in underground uranium mine (humidity level >90), may not be reliable. Towards this end, we have indigenously developed radon monitor based on electrostatic collection and scintillation technology for the online monitoring in uranium mine. This instrument overcomes the above mentioned limitation of commercial radon monitors and based on custom made features. Different tests and measurements were carried out and compared with commercial instruments. It was found to be in an excellent agreement with the commercial instruments. A few such instruments have been installed in different locations of uranium mine at Turamdih and connected to a network system for online monitoring and display. (author)

Three Principles for the Design of Energy Feedback Visualizations

DEFF Research Database (Denmark)

Brewer, Robert S.; Xu, Yongwen; Lee, George E.

2013-01-01

, online educational activities, and real-world activities such as workshops and excursions. We describe our experiences developing energy feedback visualizations in the Kukui Cup based on in-lab evaluations and field studies in college residence halls. We learned that energy feedback systems should...

The use of online word of mouth opinion in online learning: a questionnaire survey.

Science.gov (United States)

Sandars, John; Walsh, Kieran

2009-04-01

There is increasing use of online word of mouth opinion (user feedback) systems for general services but its use in online learning has not been previously investigated. To understand why users of BMJ Learning provide and read word of mouth feedback, and whether this feedback influences uptake of modules by prospective users. Online questionnaire of users of BMJ Learning who had completed online user feedback. 109 questionnaires were completed (response rate 25%). The main motivation to contribute was to influence the authors of the module (66%), and 43% stated that they wanted to help other users to make an informed choice. 16% stated that they wanted to develop an online community of learners. The main motivation to read the user feedback was to see if they agreed with the comments (56%). Online word of mouth opinion (user feedback) appears to be useful for online learners. There are also system design considerations since the attempt to create an online community of learners that is desired by some users will not be appreciated by others. Further research with a larger number of users is recommended to confirm the findings.

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

1997-12-01

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

Development of and feedback on a fully automated virtual reality system for online training in weight management skills.

Science.gov (United States)

Thomas, J Graham; Spitalnick, Josh S; Hadley, Wendy; Bond, Dale S; Wing, Rena R

2015-01-01

Virtual reality (VR) technology can provide a safe environment for observing, learning, and practicing use of behavioral weight management skills, which could be particularly useful in enhancing minimal contact online weight management programs. The Experience Success (ES) project developed a system for creating and deploying VR scenarios for online weight management skills training. Virtual environments populated with virtual actors allow users to experiment with implementing behavioral skills via a PC-based point and click interface. A culturally sensitive virtual coach guides the experience, including planning for real-world skill use. Thirty-seven overweight/obese women provided feedback on a test scenario focused on social eating situations. They reported that the scenario gave them greater skills, confidence, and commitment for controlling eating in social situations. © 2014 Diabetes Technology Society.

Assessing Online Textual Feedback to Support Student Intrinsic Motivation Using a Collaborative Text-Based Dialogue System: A Qualitative Study

Science.gov (United States)

Shroff, Ronnie H.; Deneen, Christopher

2011-01-01

This paper assesses textual feedback to support student intrinsic motivation using a collaborative text-based dialogue system. A research model is presented based on research into intrinsic motivation, and the specific construct of feedback provides a framework for the model. A qualitative research methodology is used to validate the model.…

The Online Social Networking of Cyberspace: A Study on the Development of an Online Social Network Project and the Sport Industry's Perception of Its Relative Advantage

Science.gov (United States)

Liptrap, Timothy John

2011-01-01

This exploratory case study examined online social networking (OSN), and the perceptions of Sport Marketing students and sport industry professional as to the relative advantage of the OSN tools in the marketplace. The conceptual framework for this study was based on Boyer's (1990) concepts of Scholarship of Teaching and Learning (SoTL), and the…

The Relationship Between Online Social Networking and Depression: A Systematic Review of Quantitative Studies.

Science.gov (United States)

Baker, David A; Algorta, Guillermo Perez

2016-11-01

Online social networking sites (SNSs) such as Facebook, Twitter, and MySpace are used by billions of people every day to communicate and interact with others. There has been increasing interest in the potential impact of online social networking on wellbeing, with a broadening body of new research into factors associated with both positive and negative mental health outcomes such as depression. This systematic review of empirical studies (n = 30) adds to existing research in this field by examining current quantitative studies focused on the relationship between online social networking and symptoms of depression. The academic databases PsycINFO, Web of Science, CINAHL, MEDLINE, and EMBASE were searched systematically using terms related to online social networking and depression. Reporting quality was critically appraised and the findings discussed with reference to their wider implications. The findings suggest that the relationship between online social networking and symptoms of depression may be complex and associated with multiple psychological, social, behavioral, and individual factors. Furthermore, the impact of online social networking on wellbeing may be both positive and negative, highlighting the need for future research to determine the impact of candidate mediators and moderators underlying these heterogeneous outcomes across evolving networks.

Exploration of digital entrepreneurship – online home based businesses through empirical analysis

OpenAIRE

Anwar, Naveed

2015-01-01

Digital entrepreneurship is a broad domain and includes businesses predominantly operating online, such as online retailers, portals, community sites and also businesses providing services to enable other businesses to operate online, such as web designers, platform providers and operators. This research focused online home based businesses on any stage of business development, for example start-up or grown, and focus on any aspect, such as raising finance, establishing networks or developing...

ASSESSMENT OF LIBRARY USERS’ FEEDBACK USING MODIFIED MULTILAYER PERCEPTRON NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

K G Nandha Kumar

2017-07-01

Full Text Available An attempt has been made to evaluate the feedbacks of library users of four different libraries by using neural network based data mining techniques. This paper presents the results of a survey of users’ satisfactory level on four different libraries. The survey has been conducted among the users of four libraries of educational institutions of Kovai Medical Center Research and Educational Trust. Data were collected through questionnaires. Artificial neural network based data mining techniques are proposed and applied to assess the libraries in terms of level of satisfaction of users. In order to assess the users’ satisfaction level, two neural network techniques: Modified Multilayer Perceptron Network-Supervised and Modified Multilayer Perceptron Network-Unsupervised are proposed. The proposed techniques are compared with the conventional classification algorithm Multilayer Perceptron Neural Network and found better in overall performance. It is found that the quality of service provided by the libraries is highly good and users are highly satisfied with various aspects of library service. The Arts and Science College Library secured the maximum percent in terms of user satisfaction. This shows that the users’ satisfaction of ASCL is better than the other libraries. This study provides an insight into the actual quality and satisfactory level of users of libraries after proper assessment. It is strongly expected that the results will help library authorities to enhance services and quality in the near future.

Are health behavior change interventions that use online social networks effective? A systematic review.

Science.gov (United States)

Maher, Carol A; Lewis, Lucy K; Ferrar, Katia; Marshall, Simon; De Bourdeaudhuij, Ilse; Vandelanotte, Corneel

2014-02-14

The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change. The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions. Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where "population" included child or adult populations, including healthy and disease populations; "intervention" involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; "comparator" was either a control group or within subject in the case of pre-post study designs; "outcomes" included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and "study design" included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen's d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included

Incorporating profile information in community detection for online social networks

Science.gov (United States)

Fan, W.; Yeung, K. H.

2014-07-01

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

Sentiment Polarization and Balance among Users in Online Social Networks

DEFF Research Database (Denmark)

Hillmann, Robert; Trier, Matthias

2012-01-01

Communication within online social network applications enables users to express and share sentiments electronically. Existing studies examined the existence or distribution of sentiments in online communication at a general level or in small-observed groups. Our paper extends this research...... by analyzing sentiment exchange within social networks from an ego-network perspective. We draw from research on social influence and social attachment to develop theories of node polarization, balance effects and sentiment mirroring within communication dyads. Our empirical analysis covers a multitude...... of social networks in which the sentiment valence of all messages was determined. Subsequently we studied ego-networks of focal actors (ego) and their immediate contacts. Results support our theories and indicate that actors develop polarized sentiments towards individual peers but keep sentiment in balance...

Interaction patterns of nurturant support exchanged in online health social networking.

Science.gov (United States)

Chuang, Katherine Y; Yang, Christopher C

2012-05-03

Expressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. To build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. We identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. Nurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. The computer-mediated communication format may have the greatest influence on the supportive interactions

Application of Online Multimedia Courseware in College English Teaching Based on Constructivism Theory

Science.gov (United States)

Li, Zhenying

2012-01-01

Based on Constructivism Theory, this paper aims to investigate the application of online multimedia courseware to college English teaching. By making experiments and students' feedback, some experience has been accumulated, and some problems are discovered and certain revelations are acquired as well in English teaching practice, which pave the…

Confinement and diffusion modulate bistability and stochastic switching in a reaction network with positive feedback

International Nuclear Information System (INIS)

Mlynarczyk, Paul J.; Pullen, Robert H.; Abel, Steven M.

2016-01-01

Positive feedback is a common feature in signal transduction networks and can lead to phenomena such as bistability and signal propagation by domain growth. Physical features of the cellular environment, such as spatial confinement and the mobility of proteins, play important but inadequately understood roles in shaping the behavior of signaling networks. Here, we use stochastic, spatially resolved kinetic Monte Carlo simulations to explore a positive feedback network as a function of system size, system shape, and mobility of molecules. We show that these physical properties can markedly alter characteristics of bistability and stochastic switching when compared with well-mixed simulations. Notably, systems of equal volume but different shapes can exhibit qualitatively different behaviors under otherwise identical conditions. We show that stochastic switching to a state maintained by positive feedback occurs by cluster formation and growth. Additionally, the frequency at which switching occurs depends nontrivially on the diffusion coefficient, which can promote or suppress switching relative to the well-mixed limit. Taken together, the results provide a framework for understanding how confinement and protein mobility influence emergent features of the positive feedback network by modulating molecular concentrations, diffusion-influenced rate parameters, and spatiotemporal correlations between molecules

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

DEFF Research Database (Denmark)

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

2015-01-01

correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...... dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...

Addiction to Internet Use, Online Gaming, and Online Social Networking Among Young Adults in China, Singapore, and the United States.

Science.gov (United States)

Tang, Catherine So-Kum; Koh, Yee Woen; Gan, YiQun

2017-11-01

The current study investigated the rates of addictions to Internet use, online gaming, and online social networking as well as their associations with depressive symptoms among young adults in China, Singapore, and the United States. A total of 3267 undergraduate students were recruited. Psychological instruments were used to assess various Internet-related addictions and depressive symptoms. Male students were more addicted to Internet and online gaming whereas female students were more addicted to online social networking. Compared with students in the United States, Chinese and Singaporean students were more addicted to Internet use and online social networking but less to online gaming. The odds of depression among students with addiction to various Internet-related addictions were highest in China. Internet-related addiction is a new public health concern of young adults, especially in the Asia-Pacific regions. It is found to associate with depressive symptoms. Strategies should address this phenomenon with attention to specific needs of gender and region while managing mood disturbances.

Event-Based Stabilization over Networks with Transmission Delays

Directory of Open Access Journals (Sweden)

Xiangyu Meng

2012-01-01

Full Text Available This paper investigates asymptotic stabilization for linear systems over networks based on event-driven communication. A new communication logic is proposed to reduce the feedback effort, which has some advantages over traditional ones with continuous feedback. Considering the effect of time-varying transmission delays, the criteria for the design of both the feedback gain and the event-triggering mechanism are derived to guarantee the stability and performance requirements. Finally, the proposed techniques are illustrated by an inverted pendulum system and a numerical example.

Event-triggered output feedback control for distributed networked systems.

Science.gov (United States)

Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa

2016-01-01

This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem's local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Online social networks for crowdsourced multimedia-involved behavioral testing: An empirical study

Directory of Open Access Journals (Sweden)

Jun-Ho eChoi

2016-01-01

Full Text Available Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk, which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods.

Online Social Networks for Crowdsourced Multimedia-Involved Behavioral Testing: An Empirical Study

Science.gov (United States)

Choi, Jun-Ho; Lee, Jong-Seok

2016-01-01

Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk), which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods. PMID:26793137

Online Social Networks for Crowdsourced Multimedia-Involved Behavioral Testing: An Empirical Study.

Science.gov (United States)

Choi, Jun-Ho; Lee, Jong-Seok

2015-01-01

Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk), which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods.

A Bayesian matching pursuit based scheduling algorithm for feedback reduction in MIMO broadcast channels

KAUST Repository

Shibli, Hussain J.

2013-06-01

Opportunistic schedulers rely on the feedback of all users in order to schedule a set of users with favorable channel conditions. While the downlink channels can be easily estimated at all user terminals via a single broadcast, several key challenges are faced during uplink transmission. First of all, the statistics of the noisy and fading feedback channels are unknown at the base station (BS) and channel training is usually required from all users. Secondly, the amount of network resources (air-time) required for feedback transmission grows linearly with the number of users. In this paper, we tackle the above challenges and propose a Bayesian based scheduling algorithm that 1) reduces the air-time required to identify the strong users, and 2) is agnostic to the statistics of the feedback channels and utilizes the a priori statistics of the additive noise to identify the strong users. Numerical results show that the proposed algorithm reduces the feedback air-time while improving detection in the presence of fading and noisy channels when compared to recent compressed sensing based algorithms. Furthermore, the proposed algorithm achieves a sum-rate throughput close to that obtained by noiseless dedicated feedback systems. © 2013 IEEE.

Dimensionality and effects of information motivation on users’ online social network advertising acceptance

Directory of Open Access Journals (Sweden)

IMRAN ANWAR MIR

2018-04-01

Full Text Available Social media has produced substantial changes in the communication landscape. Online social network sites (SNS grew as a common platform for online social interaction. SNS firms generate revenue from the advertising appearing on SNS. Their survival depends on users’ approval of such social network advertising (SNA. Marketing literature indicates that users accept advertising if it is consistent with their motivations for using social media. Information seeking is the most recognized SNS motivation. Yet, research on evaluating the influence of SNS information motivation on users’ approval of SNA is scarce. Based on SNS uses and gratifications theory, this study proposes a multidimensional model that shows the influence of SNS information motivation on users’ approval of SNA.

Choosing your network: social preferences in an online health community.

Science.gov (United States)

Centola, Damon; van de Rijt, Arnout

2015-01-01

A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals' behavior in various contexts. However little is known about how people select their health contacts in these virtual domains. This is because selection preferences in peer networks are notoriously difficult to detect. In existing networks, unobserved pressures on tie formation--such as common organizational memberships, introductions to friends of friends, or limitations on accessibility--may mistakenly be interpreted as individual preferences for interacting/not interacting with others. We address these issues by adopting a social media approach to studying network formation. We study social selection using an in vivo study within an online exercise program, in which anonymous participants have equal opportunities for initiating relationships with other program members. This design allows us to identify individuals' preferences for health contacts, and to evaluate what these preferences imply for members' access to new kinds of health information, and for the kinds of social influences to which they are exposed. The study was conducted within a goal-oriented fitness competition, in which participation was greatest among a small core of active individuals. Our results show that the active participants displayed indifference to the fitness and exercise profiles of others, disregarding information about others' fitness levels, exercise preferences, and workout experiences, instead selecting partners almost entirely on the basis of similarities on gender, age, and BMI. Interestingly, the findings suggest that rather than expanding and diversifying their sources of health information, participants' choices limited the value of their online resources by selecting contacts

A Study of Malware Propagation via Online Social Networking

Science.gov (United States)

Faghani, Mohammad Reza; Nguyen, Uyen Trang

The popularity of online social networks (OSNs) have attracted malware creators who would use OSNs as a platform to propagate automated worms from one user's computer to another's. On the other hand, the topic of malware propagation in OSNs has only been investigated recently. In this chapter, we discuss recent advances on the topic of malware propagation by way of online social networking. In particular, we present three malware propagation techniques in OSNs, namely cross site scripting (XSS), Trojan and clickjacking types, and their characteristics via analytical models and simulations.

Empirical analysis of online social networks in the age of Web 2.0

Science.gov (United States)

Fu, Feng; Liu, Lianghuan; Wang, Long

2008-01-01

Today the World Wide Web is undergoing a subtle but profound shift to Web 2.0, to become more of a social web. The use of collaborative technologies such as blogs and social networking site (SNS) leads to instant online community in which people communicate rapidly and conveniently with each other. Moreover, there are growing interest and concern regarding the topological structure of these new online social networks. In this paper, we present empirical analysis of statistical properties of two important Chinese online social networks-a blogging network and an SNS open to college students. They are both emerging in the age of Web 2.0. We demonstrate that both networks possess small-world and scale-free features already observed in real-world and artificial networks. In addition, we investigate the distribution of topological distance. Furthermore, we study the correlations between degree (in/out) and degree (in/out), clustering coefficient and degree, popularity (in terms of number of page views) and in-degree (for the blogging network), respectively. We find that the blogging network shows disassortative mixing pattern, whereas the SNS network is an assortative one. Our research may help us to elucidate the self-organizing structural characteristics of these online social networks embedded in technical forms.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Science.gov (United States)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

Directory of Open Access Journals (Sweden)

Zhicong Zhang

2018-01-01

Full Text Available We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.

Traffic Route Guidance using Feedback of Predicted Travel Times : Improving Travel Times in the Berlin Traffic Network

OpenAIRE

Bergsten, Arvid; Zetterberg, Daniel

2008-01-01

Traffic congestions constitute a problem in many large cities. Congestions can be handled by reducing the network demand, expanding the infrastructure, or by utilizing the road network more efficiently. This master thesis presents a methodology for route guidance, based on automatic feedback control from the current traffic situation. Through variable direction signs or individual in-car devices, all vehicles with a certain origin and destination (which are both normally intermediate) are gui...

LHCb: Time structure analysis of the LHCb Online network

CERN Multimedia

Antichi, G; Campora Perez, D H; Liu, G; Neufeld, N; Giordano, S; Owezarski, P; Moore, A

2013-01-01

The LHCb Online Network is a real time high performance network, in which 350 data sources send data over a Gigabit Ethernet LAN to more than 1500 receiving nodes. The aggregated throughput of the application, called Event Building, is more than 60 GB/s. The protocol employed by LHCb makes the sending nodes transmit simultaneously portions of events to one receiving node at a time, which is selected using a credit-token scheme. The resulting traffic is very bursty and sensitive to irregularities in the temporal distribution of packet-bursts to the same destination or region of the network. In order to study the relevant properties of such a dataflow, a non-disruptive monitoring setup based on a networking capable FPGA (NetFPGA) has been deployed. The NetFPGA allows order of hundred nano-second precise time-stamping of packets. We study in detail the timing structure of the Event Building communication, and we identify potential effects of micro-bursts like buffer packet drops or jitter.

Parameter estimation in space systems using recurrent neural networks

Science.gov (United States)

Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

1991-01-01

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

Facebook and romantic relationships: intimacy and couple satisfaction associated with online social network use.

Science.gov (United States)

Hand, Matthew M; Thomas, Donna; Buboltz, Walter C; Deemer, Eric D; Buyanjargal, Munkhsanaa

2013-01-01

Online social networks, such as Facebook, have gained immense popularity and potentially affect the way people build and maintain interpersonal relationships. The present study sought to examine time spent on online social networks, as it relates to intimacy and relationship satisfaction experienced in romantic relationships. Results did not find relationships between an individual's usage of online social networks and his/her perception of relationship satisfaction and intimacy. However, the study found a negative relationship between intimacy and the perception of a romantic partner's use of online social networks. This finding may allude to an attributional bias in which individuals are more likely to perceive a partner's usage as negative compared to their own usage. Additionally, it was found that intimacy mediates the relationship between online social network usage and overall relationship satisfaction, which suggests that the level of intimacy experienced in a relationship may serve as a buffer that protects the overall level of satisfaction.

Brain-actuated gait trainer with visual and proprioceptive feedback

Science.gov (United States)

Liu, Dong; Chen, Weihai; Lee, Kyuhwa; Chavarriaga, Ricardo; Bouri, Mohamed; Pei, Zhongcai; Millán, José del R.

2017-10-01

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension. Approach. We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects. Main results. (i) For real-time classification, the average accuracy was 62.33 +/- 4.95 % and 63.89 +/- 6.41 % for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback (69.45 +/- 9.95 %) was significantly better than with visual feedback (62.89 +/- 9.20 %), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn. Significance. The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that

Router Agent Technology for Policy-Based Network Management

Science.gov (United States)

Chow, Edward T.; Sudhir, Gurusham; Chang, Hsin-Ping; James, Mark; Liu, Yih-Chiao J.; Chiang, Winston

2011-01-01

This innovation can be run as a standalone network application on any computer in a networked environment. This design can be configured to control one or more routers (one instance per router), and can also be configured to listen to a policy server over the network to receive new policies based on the policy- based network management technology. The Router Agent Technology transforms the received policies into suitable Access Control List syntax for the routers it is configured to control. It commits the newly generated access control lists to the routers and provides feedback regarding any errors that were faced. The innovation also automatically generates a time-stamped log file regarding all updates to the router it is configured to control. This technology, once installed on a local network computer and started, is autonomous because it has the capability to keep listening to new policies from the policy server, transforming those policies to router-compliant access lists, and committing those access lists to a specified interface on the specified router on the network with any error feedback regarding commitment process. The stand-alone application is named RouterAgent and is currently realized as a fully functional (version 1) implementation for the Windows operating system and for CISCO routers.

Opportunistic Relay Selection With Limited Feedback

KAUST Repository

Eltayeb, Mohammed E.

2015-08-01

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Generally, relay selection algorithms require channel state information (CSI) feedback from all cooperating relays to make a selection decision. This requirement poses two important challenges, which are often neglected in the literature. Firstly, the fed back channel information is usually corrupted by additive noise. Secondly, CSI feedback generates a great deal of feedback overhead (air-time) that could result in significant performance hits. In this paper, we propose a compressive sensing (CS) based relay selection algorithm that reduces the feedback overhead of relay networks under the assumption of noisy feedback channels. The proposed algorithm exploits CS to first obtain the identity of a set of relays with favorable channel conditions. Following that, the CSI of the identified relays is estimated using least squares estimation without any additional feedback. Both single and multiple relay selection cases are considered. After deriving closed-form expressions for the asymptotic end-to-end SNR at the destination and the feedback load for different relaying protocols, we show that CS-based selection drastically reduces the feedback load and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback. © 1972-2012 IEEE.

Discovering the influential users oriented to viral marketing based on online social networks

Science.gov (United States)

Zhu, Zhiguo

2013-08-01

The target of viral marketing on the platform of popular online social networks is to rapidly propagate marketing information at lower cost and increase sales, in which a key problem is how to precisely discover the most influential users in the process of information diffusion. A novel method is proposed in this paper for helping companies to identify such users as seeds to maximize information diffusion in the viral marketing. Firstly, the user trust network oriented to viral marketing and users’ combined interest degree in the network including isolated users are extensively defined. Next, we construct a model considering the time factor to simulate the process of information diffusion in viral marketing and propose a dynamic algorithm description. Finally, experiments are conducted with a real dataset extracted from the famous SNS website Epinions. The experimental results indicate that the proposed algorithm has better scalability and is less time-consuming. Compared with the classical model, the proposed algorithm achieved a better performance than does the classical method on the two aspects of network coverage rate and time-consumption in our four sub-datasets.

Analyzing the Social Networks of High- and Low-Performing Students in Online Discussion Forums

Science.gov (United States)

Ghadirian, Hajar; Salehi, Keyvan; Ayub, Ahmad Fauzi Mohd

2018-01-01

An ego network is an individual's social network relationships with core members. In this study, the ego network parameters in online discussion spaces of high- and low-performing students were compared. The extent to which students' ego networks changed over the course were also analyzed. Participation in 7 weeks of online discussions were…

Impact of Mental Health Screening on Promoting Immediate Online Help-Seeking: Randomized Trial Comparing Normative Versus Humor-Driven Feedback.

Science.gov (United States)

Choi, Isabella; Milne, David N; Deady, Mark; Calvo, Rafael A; Harvey, Samuel B; Glozier, Nick

2018-04-05

Given the widespread availability of mental health screening apps, providing personalized feedback may encourage people at high risk to seek help to manage their symptoms. While apps typically provide personal score feedback only, feedback types that are user-friendly and increase personal relevance may encourage further help-seeking. The aim of this study was to compare the effects of providing normative and humor-driven feedback on immediate online help-seeking, defined as clicking on a link to an external resource, and to explore demographic predictors that encourage help-seeking. An online sample of 549 adults were recruited using social media advertisements. Participants downloaded a smartphone app known as "Mindgauge" which allowed them to screen their mental wellbeing by completing standardized measures on Symptoms (Kessler 6-item Scale), Wellbeing (World Health Organization [Five] Wellbeing Index), and Resilience (Brief Resilience Scale). Participants were randomized to receive normative feedback that compared their scores to a reference group or humor-driven feedback that presented their scores in a relaxed manner. Those who scored in the moderate or poor ranges in any measure were encouraged to seek help by clicking on a link to an external online resource. A total of 318 participants scored poorly on one or more measures and were provided with an external link after being randomized to receive normative or humor-driven feedback. There was no significant difference of feedback type on clicking on the external link across all measures. A larger proportion of participants from the Wellbeing measure (170/274, 62.0%) clicked on the links than the Resilience (47/179, 26.3%) or Symptoms (26/75, 34.7%) measures (χ 2 =60.35, PWellbeing measures. Participants with a previous episode of poor mental health were less likely than those without such history to click on the external link in the Symptoms measure (P=.003, odds ratio [OR] 0.83, 95% CI 0.02-0.44), and

A multimodal interface device for online board games designed for sight-impaired people.

Science.gov (United States)

Caporusso, Nicholas; Mkrtchyan, Lusine; Badia, Leonardo

2010-03-01

Online games between remote opponents playing over computer networks are becoming a common activity of everyday life. However, computer interfaces for board games are usually based on the visual channel. For example, they require players to check their moves on a video display and interact by using pointing devices such as a mouse. Hence, they are not suitable for visually impaired people. The present paper discusses a multipurpose system that allows especially blind and deafblind people playing chess or other board games over a network, therefore reducing their disability barrier. We describe and benchmark a prototype of a special interactive haptic device for online gaming providing a dual tactile feedback. The novel interface of this proposed device is able to guarantee not only a better game experience for everyone but also an improved quality of life for sight-impaired people.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

Science.gov (United States)

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

Testing Quick Response (QR) Codes as an Innovation to Improve Feedback Among Geographically-Separated Clerkship Sites.

Science.gov (United States)

Snyder, Matthew J; Nguyen, Dana R; Womack, Jasmyne J; Bunt, Christopher W; Westerfield, Katie L; Bell, Adriane E; Ledford, Christy J W

2018-03-01

Collection of feedback regarding medical student clinical experiences for formative or summative purposes remains a challenge across clinical settings. The purpose of this study was to determine whether the use of a quick response (QR) code-linked online feedback form improves the frequency and efficiency of rater feedback. In 2016, we compared paper-based feedback forms, an online feedback form, and a QR code-linked online feedback form at 15 family medicine clerkship sites across the United States. Outcome measures included usability, number of feedback submissions per student, number of unique raters providing feedback, and timeliness of feedback provided to the clerkship director. The feedback method was significantly associated with usability, with QR code scoring the highest, and paper second. Accessing feedback via QR code was associated with the shortest time to prepare feedback. Across four rotations, separate repeated measures analyses of variance showed no effect of feedback system on the number of submissions per student or the number of unique raters. The results of this study demonstrate that preceptors in the family medicine clerkship rate QR code-linked feedback as a high usability platform. Additionally, this platform resulted in faster form completion than paper or online forms. An overarching finding of this study is that feedback forms must be portable and easily accessible. Potential implementation barriers and the social norm for providing feedback in this manner need to be considered.

Developing an online professional network for veterinary education: the NOVICE project.

Science.gov (United States)

Baillie, Sarah; Kinnison, Tierney; Forrest, Neil; Dale, Vicki H M; Ehlers, Jan P; Koch, Michael; Mándoki, Mira; Ciobotaru, Emilia; de Groot, Esther; Boerboom, Tobias B B; van Beukelen, Peter

2011-01-01

An online professional network for veterinarians, veterinary students, veterinary educationalists, and ICT (Information and Communication Technology) educationalists is being developed under the EU (European Union) Lifelong Learning Programme. The network uses Web 2.0, a term used to describe the new, more interactive version of the Internet, and includes tools such as wikis, blogs, and discussion boards. Focus groups conducted with qualified and student veterinarians within the project's five founding countries (The Netherlands, Germany, United Kingdom, Hungary, Romania) demonstrated that online professional communities can be valuable for accessing information and establishing contacts. Online networks have the potential to overcome common challenges to face-to-face communities-such as distance, cost, and timing-but they have their own drawbacks, such as security and professionalism issues. The Network Of Veterinary ICt in Education (NOVICE) was developed using Elgg, an open-source, free social networking platform, after several software options had been considered. NOVICE aims to promote the understanding of Web 2.0, confidence to use social software tools, and participation in an online community. Therefore, the Web site contains help sections, Frequently Asked Questions, and access to support from ICT experts. Five months after the network's launch (and just over one year into the project) 515 members from 28 countries had registered. Further research will include analysis of a core group's activities, which will inform ongoing support for and development of informal, lifelong learning in a veterinary context.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

Science.gov (United States)

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Security and trust in online social networks

CERN Document Server

Carminati, Barbara; Viviani, Marco; Viviani, Marco; Carminati, Barbara

2013-01-01

The enormous success and diffusion that online social networks (OSNs) are encountering nowadays is vastly apparent. Users' social interactions now occur using online social media as communication channels; personal information and activities are easily exchanged both for recreational and business purposes in order to obtain social or economic advantages. In this scenario, OSNs are considered critical applications with respect to the security of users and their resources, for their characteristics alone: the large amount of personal information they manage, big economic upturn connected to thei

A Bayesian matching pursuit based scheduling algorithm for feedback reduction in MIMO broadcast channels

KAUST Repository

Shibli, Hussain J.; Eltayeb, Mohammed E.; Al-Naffouri, Tareq Y.

2013-01-01

challenges are faced during uplink transmission. First of all, the statistics of the noisy and fading feedback channels are unknown at the base station (BS) and channel training is usually required from all users. Secondly, the amount of network resources

Analysis of context dependence in social interaction networks of a massively multiplayer online role-playing game.

Science.gov (United States)

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

OpenAIRE

Cancelier, A.; Claumann, C. A.; Bolzan, A.; Machado, R. A. F.

2016-01-01

Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, ...

Effect of online social networking on employee productivity

OpenAIRE

A. Ferreira; T. du Plessis

2009-01-01

The popularity of social networking sites is relatively recent and the effect of online social networking (OSN) on employee productivity has not received much scholarly attention. The reason most likely lies in the social nature of social networking sites and OSN, which is assumed to have a negative effect on employee productivity and not bear organisational benefit. This reseach investigated recent Internet developments as seen in the social Web and specifically investigated the effect of OS...

Your Health Buddies Matter: Preferential Selection and Social Influence on Weight Management in an Online Health Social Network.

Science.gov (United States)

Meng, Jingbo

2016-12-01

A growing number of online social networks are designed with the intention to promote health by providing virtual space wherein individuals can seek and share information and support with similar others. Research has shown that real-world social networks have a significant influence on one's health behavior and outcomes. However, there is a dearth of studies on how individuals form social networks in virtual space and whether such online social networks exert any impact on individuals' health outcomes. Built on the Multi-Theoretical Multilevel (MTML) framework and drawing from literature on social influence, this study examined the mechanisms underlying the formation of an online health social network and empirically tested social influence on individual health outcomes through the network. Situated in a weight management social networking site, the study tracked a health buddy network of 709 users and their weight management activities and outcomes for 4 months. Actor-based modeling was used to test the joint dynamics of preferential selection and social influence among health buddies. The results showed that baseline, inbreeding, and health status homophily significantly predicted preferential selection of health buddies in the weight management social networking site, whereas self-interest in seeking experiential health information did not. The study also found peer influence of online health buddy networks on individual weight outcomes, such that an individual's odds of losing weight increased if, on average, the individual's health buddies were losing weight.

Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

Science.gov (United States)

Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun

2017-07-01

In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.

Recruiting migrants for health research through social network sites: an online survey among chinese migrants in australia.

Science.gov (United States)

Hu, Jie; Wong, Kam Cheong; Wang, Zhiqiang

2015-04-27

Traditionally, postal surveys or face to face interviews are the main approaches for health researchers to obtain essential research data. However, with the prevalence of information technology and Internet, Web-based surveys are gaining popularity in health research. This study aims to report the process and outcomes of recruiting Chinese migrants through social network sites in Australia and to examine the sample characteristics of online recruitment by comparing the sample which was recruited by an online survey to a sample of Australian Chinese migrants collected by a postal survey. Descriptive analyses were performed to describe and compare the process and outcomes of online recruitment with postal survey questionnaires. Chi square tests and t tests were performed to assess the differences between the two samples for categorical and continuous variables respectively. In total, 473 Chinese migrants completed the online health survey from July to October 2013. Out of 426 participants recruited through the three Chinese social network sites in Australia, over 86.6% (369/426) were recruited within six weeks. Participants of the Web-based survey were younger, with a higher education level or had resided in Australia for less time compared to those recruited via a postal survey. However, there was no significant difference in gender, marital status, and professional occupation. The recruitment of Chinese migrants through social network sites in our online survey was feasible. Compared to a postal survey of Chinese migrants, the online survey attracted different group of Chinese migrants who may have diverse health needs and concerns. Our findings provided insightful information for researchers who are considering employing a Web-based approach to recruit migrants and ethnic minority participants.

Systemic risk in multiplex networks with asymmetric coupling and threshold feedback

Science.gov (United States)

Burkholz, Rebekka; Leduc, Matt V.; Garas, Antonios; Schweitzer, Frank

2016-06-01

We study cascades on a two-layer multiplex network, with asymmetric feedback that depends on the coupling strength between the layers. Based on an analytical branching process approximation, we calculate the systemic risk measured by the final fraction of failed nodes on a reference layer. The results are compared with the case of a single layer network that is an aggregated representation of the two layers. We find that systemic risk in the two-layer network is smaller than in the aggregated one only if the coupling strength between the two layers is small. Above a critical coupling strength, systemic risk is increased because of the mutual amplification of cascades in the two layers. We even observe sharp phase transitions in the cascade size that are less pronounced on the aggregated layer. Our insights can be applied to a scenario where firms decide whether they want to split their business into a less risky core business and a more risky subsidiary business. In most cases, this may lead to a drastic increase of systemic risk, which is underestimated in an aggregated approach.

Online social networking and the experience of cyber-bullying.

Science.gov (United States)

O'Dea, Bridianne; Campbell, Andrew

2012-01-01

Online social networking sites (SNS) are popular social tools used amongst adolescents and account for much of their daily internet activity. Recently, these sites have presented opportunities for youth to experience cyber-bullying. Often resulting in psychological distress, cyber-bullying is a common experience for many young people. Continual use of SNS signifies the importance of examining its links to cyber-bullying. This study examined the relationship between online social networking and the experience of cyber-bullying. A total of 400 participants (Mage=14.31 years) completed an online survey which examined the perceived definitions and frequency of cyber-bullying. Users of SNS reported significantly higher frequencies of stranger contact compared to non-users. Spearman's rho correlations determined no significant relationship between daily time on SNS and the frequency of stranger contact. This suggests that ownership of a SNS profile may be a stronger predictor of some cyber-bullying experiences compared to time spent on these sites. Findings encourage continued research on the nature of internet activities used by young adolescents and the possible exposure to online victimization.

An Online Social Networking Approach to Reinforce Learning of Rocks and Minerals

Science.gov (United States)

Kennelly, Patrick

2009-01-01

Numerous and varied methods are used in introductory Earth science and geology classes to help students learn about rocks and minerals, such as classroom lectures, laboratory specimen identification, and field trips. This paper reports on a method using online social networking. The choice of this forum was based on two criteria. First, many…

Tweacher: New proposal for Online Social Networks Impact in Secondary Education

Directory of Open Access Journals (Sweden)

Sebastián ROMERO

2013-05-01

Full Text Available This paper presents and analyzes the potential uses and motivations of online social networks in education, with special emphasis on secondary education. First, we show several previous researches supporting the use of social networking as an educational tool and discuss Edmodo, an educative online social network. The work carried out during two academic years with senior students of primary and secondary schools is also analyzed. After that we present Tweacher an educative social network application and evaluate its use in the classroom to prove its useful use between teachers and students. This research has allowed us to see the reality of social network use among young people and identify the challenges of its application to education environment.

Students' Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

Science.gov (United States)

Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook

2018-01-01

This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…

Neural cryptography with feedback.

Science.gov (United States)

Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

2004-04-01

Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

Research and Teaching: Exploring the Use of an Online Quiz Game to Provide Formative Feedback in a Large-Enrollment, Introductory Biochemistry Course

Science.gov (United States)

Milner, Rachel; Parrish, Jonathan; Wright, Adrienne; Gnarpe, Judy; Keenan, Louanne

2015-01-01

In a large-enrollment, introductory biochemistry course for nonmajors, the authors provide students with formative feedback through practice questions in PDF format. Recently, they investigated possible benefits of providing the practice questions via an online game (Brainspan). Participants were randomly assigned to either the online game group…

Support for Protests in Latin America: Classifications and the Role of Online Networking

Directory of Open Access Journals (Sweden)

Rachel R. Mourão

2016-09-01

Full Text Available In recent years, Latin Americans marched the streets in a wave of protests that swept almost every country in the region. Yet few studies have assessed how Latin Americans support various forms of protest, and how new technologies affect attitudes toward protest tactics. Using data from the Latin American Public Opinion Project (N = 37,102, cluster analyses grouped citizens into four distinct groups depending on their support for protests. Most Latin Americans support moderate forms of protest, rejecting more radical tactics. Online networking is associated with support for both moderate and radical protests. But those who support only moderate protests use online networking sites more than Latin Americans as a whole, while those who support radical protests use online networking sites significantly less. Our findings suggest that only peaceful and legal demonstrations have been normalized in the region, and online networking foments support for moderate protest tactics.

Gender differences in collaborative learning over online social networks: Epistemological beliefs and behaviors

Directory of Open Access Journals (Sweden)

Rosanna Y.-Y. Chan

2013-09-01

Full Text Available Online social networks are popular venues for computer-supported collaborative work and computer-supported collaborative learning. Professionals within the same discipline, such as software developers, often interact over various social network sites for knowledge updates and collective understandings. The current study aims at gathering empirical evidences concerning gender differences in online social network beliefs and behaviors. A total of 53 engineering postgraduate students were engaged in a blogging community for collaborative learning. Participants’ beliefs about collaboration and nature of knowledge and knowing (i.e. epistemological beliefs are investigated. More specifically, social network analysis metrics including in-degree, out-degree, closeness centrality, and betweenness centrality are obtained from an 8-interval longitudinal SNA. Methodologically speaking, the current work puts forward mixed methods of longitudinal SNA and quantitative beliefs survey to explore online social network participants’ beliefs and behaviors. The study’s findings demonstrate significant gender differences in collaborative learning through online social networks, including (1 female engineering postgraduate students engage significantly more actively in online communications, (2 male engineering postgraduate students are more likely to be the potential controllers of information flows, and (3 gender differences exist in belief gains related to social aspects, but not individual's epistemic aspects. Overall, participants in both genders demonstrated enhanced beliefs in collaboration as well as the nature of knowledge and knowing.

Employment relations: A data driven analysis of job markets using online job boards and online professional networks

CSIR Research Space (South Africa)

Marivate, Vukosi N

2017-08-01

Full Text Available Data from online job boards and online professional networks present an opportunity to understand job markets as well as how professionals transition from one job/career to another. We propose a data driven approach to begin to understand a slice...

Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System

Directory of Open Access Journals (Sweden)

Yu-Te eWang

2014-10-01

Full Text Available In America, sixty percent of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-realty environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory feedback was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing feedback to subjects suffering momentary cognitive lapses, and assess the efficacy of the feedback in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.

Neural network for adapting nuclear power plant control for wide-range operation

International Nuclear Information System (INIS)

Ku, C.C.; Lee, K.Y.; Edwards, R.M.

1991-01-01

A new concept of using neural networks has been evaluated for optimal control of a nuclear reactor. The neural network uses the architecture of a standard backpropagation network; however, a new dynamic learning algorithm has been developed to capture the underlying system dynamics. The learning algorithm is based on parameter estimation for dynamic systems. The approach is demonstrated on an optimal reactor temperature controller by adjusting the feedback gains for wide-range operation. Application of optimal control to a reactor has been considered for improving temperature response using a robust fifth-order reactor power controller. Conventional gain scheduling can be employed to extend the range of good performance to accommodate large changes in power where nonlinear characteristics significantly modify the dynamics of the power plant. Gain scheduling is developed based on expected parameter variations, and it may be advantageous to further adapt feedback gains on-line to better match actual plant performance. A neural network approach is used here to adapt the gains to better accommodate plant uncertainties and thereby achieve improved robustness characteristics

Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network

Science.gov (United States)

Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao

2009-10-01

A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.

Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

Science.gov (United States)

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior. PMID:22496771

Analysis of context dependence in social interaction networks of a massively multiplayer online role-playing game.

Directory of Open Access Journals (Sweden)

Seokshin Son

Full Text Available Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs, here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

Collective iteration behavior for online social networks

Science.gov (United States)

Liu, Jian-Guo; Li, Ren-De; Guo, Qiang; Zhang, Yi-Cheng

2018-06-01

Understanding the patterns of collective behavior in online social network (OSNs) is critical to expanding the knowledge of human behavior and tie relationship. In this paper, we investigate a specific pattern called social signature in Facebook and Wiki users' online communication behaviors, capturing the distribution of frequency of interactions between different alters over time in the ego network. The empirical results show that there are robust social signatures of interactions no matter how friends change over time, which indicates that a stable commutation pattern exists in online communication. By comparing a random null model, we find the that commutation pattern is heterogeneous between ego and alters. Furthermore, in order to regenerate the pattern of the social signature, we present a preferential interaction model, which assumes that new users intend to look for the old users with strong ties while old users have tendency to interact with new friends. The experimental results show that the presented model can reproduce the heterogeneity of social signature by adjusting 2 parameters, the number of communicating targets m and the max number of interactions n, for Facebook users, m = n = 5, for Wiki users, m = 2 and n = 8. This work helps in deeply understanding the regularity of social signature.

A low noise single-transistor transimpedance preamplifier for Fourier-transform mass spectrometry using a T feedback network.

Science.gov (United States)

Lin, Tzu-Yung; Green, Roger J; O'Connor, Peter B

2012-09-01

A novel single-transistor transimpedance preamplifier has been introduced for improving performance in Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry. A low noise junction field-effect transistor (JFET), BF862, is used as the main amplification stage of this trans-impedance preamplifier, and a T-shaped feedback network is introduced as both the feedback and the gate biasing solutions. The T feedback network has been studied using an operational amplifier (Op Amp), AD8099. Such a feedback system allows ~100-fold less feedback resistance at a given transimpedance, hence preserving bandwidth, which is beneficial to applications demanding high gain. The single-transistor preamplifier yields a tested transimpedance of ~10(4) Ω (80 dBΩ) in the frequency range between 1 kHz and 1 MHz (mass-to-charge ratio, m/z, of around 180-180k for a 12-T FT-ICR system), with a low power consumption of ~6 mW, which implies that this preamplifier is well suited to a 12-T FT-ICR mass spectrometer. In trading noise performance for higher trans-impedance, an alternative preamplifier design, an AD8099 preamplifier with the T feedback network, has also been studied with a capability of ~10(6) Ω (120 dBΩ) transimpedance in the same frequency range. The resistive components in the T feedback network reported here can be replaced by complex impedances, which allows adaptation of this feedback system to other frequency, transimpedance, and noise characteristics for applications not only in other mass spectrometers, such as Orbitrap, time-of-flight (TOF), and ion trap systems, but also in other charge/current detecting systems such as spectroscopy systems, microscopy systems, optical communication systems, or charge-coupled devices (CCDs).

Online teaching of inflammatory skin pathology by a French-speaking International University Network.

Science.gov (United States)

Perron, Emilie; Battistella, Maxime; Vergier, Béatrice; Fiche, Maryse; Bertheau, Philippe; Têtu, Bernard

2014-01-01

Developments in technology, web-based teaching and whole slide imaging have broadened the teaching horizon in anatomic pathology. Creating online learning material including many types of media such as radiologic images, whole slides, videos, clinical and macroscopic photographs, is now accessible to most universities. Unfortunately, a major limiting factor to maintain and update the learning material is the amount of resources needed. In this perspective, a French-national university network was initiated in 2011 to build joint online teaching modules consisting of clinical cases and tests. The network has since expanded internationally to Québec, Switzerland and Ivory Coast. One of the first steps of the project was to build a learning module on inflammatory skin pathology for interns and residents in pathology and dermatology. A pathology resident from Québec spent 6 weeks in France and Switzerland to develop the contents and build the module on an e-learning Moodle platform under the supervision of two dermatopathologists. The learning module contains text, interactive clinical cases, tests with feedback, virtual slides, images and clinical photographs. For that module, the virtual slides are decentralized in 2 universities (Bordeaux and Paris 7). Each university is responsible of its own slide scanning, image storage and online display with virtual slide viewers. The module on inflammatory skin pathology includes more than 50 web pages with French original content, tests and clinical cases, links to over 45 virtual images and more than 50 microscopic and clinical photographs. The whole learning module is being revised by four dermatopathologists and two senior pathologists. It will be accessible to interns and residents in the spring of 2014. The experience and knowledge gained from that work will be transferred to the next international resident whose work will be aimed at creating lung and breast pathology learning modules. The challenges of sustaining a

General Voltage Feedback Circuit Model in the Two-Dimensional Networked Resistive Sensor Array

Directory of Open Access Journals (Sweden)

JianFeng Wu

2015-01-01

Full Text Available To analyze the feature of the two-dimensional networked resistive sensor array, we firstly proposed a general model of voltage feedback circuits (VFCs such as the voltage feedback non-scanned-electrode circuit, the voltage feedback non-scanned-sampling-electrode circuit, and the voltage feedback non-scanned-sampling-electrode circuit. By analyzing the general model, we then gave a general mathematical expression of the effective equivalent resistor of the element being tested in VFCs. Finally, we evaluated the features of VFCs with simulation and test experiment. The results show that the expression is applicable to analyze the VFCs’ performance of parameters such as the multiplexers’ switch resistors, the nonscanned elements, and array size.

Stochastic Online Learning in Dynamic Networks under Unknown Models

Science.gov (United States)

2016-08-02

The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for

Online social networking services in the management of patients with diabetes mellitus: systematic review and meta-analysis of randomised controlled trials.

Science.gov (United States)

Toma, Tania; Athanasiou, Thanos; Harling, Leanne; Darzi, Ara; Ashrafian, Hutan

2014-11-01

Social networking services (SNS) can facilitate real-time communication and feedback of blood glucose and other physiological data between patients and healthcare professionals. This systematic review and meta-analysis aims to summarise the current evidence surrounding the role of online social networking services in diabetes care. We performed a systematic literature review of the Medline, EMBASE and PsychINFO databases of all studies reporting HbA1c (glycated haemoglobin) as a measure of glycaemic control for social networking services in diabetes care. HbA1c, clinical outcomes and the type of technology used were extracted. Study quality and publication bias were assessed. SNS interventions beneficially reduced HbA1c when compared to controls, which was confirmed by sensitivity analysis. SNS interventions also significantly improved systolic and diastolic blood pressure, triglycerides and total cholesterol. Subgroup analysis according to diabetes type demonstrated that Type 2 diabetes patients had a significantly greater reduction in HbA1c than those with Type 1 diabetes. Online SNS provide a novel, feasible approach to improving glycaemic control, particularly in patients with Type 2 diabetes. Further mechanistic and cost-effectiveness studies are required to improve our understanding of SNS and its efficacy in diabetes care. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Enforcement of Privacy Policies over Multiple Online Social Networks for Collaborative Activities

Science.gov (United States)

Wu, Zhengping; Wang, Lifeng

Our goal is to tend to develop an enforcement architecture of privacy policies over multiple online social networks. It is used to solve the problem of privacy protection when several social networks build permanent or temporary collaboration. Theoretically, this idea is practical, especially due to more and more social network tend to support open source framework “OpenSocial”. But as we known different social network websites may have the same privacy policy settings based on different enforcement mechanisms, this would cause problems. In this case, we have to manually write code for both sides to make the privacy policy settings enforceable. We can imagine that, this is a huge workload based on the huge number of current social networks. So we focus on proposing a middleware which is used to automatically generate privacy protection component for permanent integration or temporary interaction of social networks. This middleware provide functions, such as collecting of privacy policy of each participant in the new collaboration, generating a standard policy model for each participant and mapping all those standard policy to different enforcement mechanisms of those participants.

The Method of Building a Network of Online Showcases on the Basis of the MVC Architecture

Directory of Open Access Journals (Sweden)

Pursky Oleg I.

2017-10-01

Full Text Available A method to build a network of online showcases that support a large number of customer orders and visits, which meets the current performance standards and the reliability of Internet solutions in the sphere electronic commerce, has been developed. The method involves the creation of a typical showcase and the implementation for the information management system of a showcases network of an own database operating on the data from the central management information system with the two-way data replication. A mechanism for «cloning» the online showcases, which are part of the network, and their quick integration with the business processes of enterprise and a management system based on a typical showcase, has been proposed. The development of typical online showcases is implemented on the basis of MVC concept (Model-View-Controller, the ASP.NET MVC Framework Technology, and the visual templates of web pages, thus ensuring that the algorithms for the behavior of objects are independent of both the objects themselves and their visual representation. This enhances the development of e-commerce projects significantly, speeds up the implementation process, and provides a high degree of flexibility and functionality of the online showcases.

Similarity between community structures of different online social networks and its impact on underlying community detection

Science.gov (United States)

Fan, W.; Yeung, K. H.

2015-03-01

As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

Using the Internet, Online Social Networks and Potentially Incurred Risk: Student Opinions

Directory of Open Access Journals (Sweden)

Gintautė Žibėnienė

2013-08-01

Full Text Available Regulation of harmful content on the Internet is a soaring problem in the expansion of the information society, and it is being discussed in different European countries. Therefore, it is important to discuss the issue of being safe online: to do research on what children think about when using the Internet, threats experienced while online, to discuss ways of recognising and protecting children from online threats (such as cyber bullying, bullying, abuse, temptations with purpose of sexual harassment, leaking of personal information, spreading, harmful and illegal Internet content, etc.. The research objective of this paper is to present the opinion of gymnasium students on using the Internet, online social networks and likely experienced threats. The methodology—opinion research of the gymnasium students, which was carried out 17–21 December 2012; an analysis of professional target publications and a questionnaire survey were also applied. Analysis of the research findings was based on the analysis methods of quantity analysis (descriptive and analysis of quality content. Statistical findings were analysed applying the software “Statistical Package for the Social Sciences (SPSS 21.0 for Windows.” According to the research of student opinion, it was revealed that the most common student online activities are browsing of chat websites, exchange of videos and other material, viewing, listening and participating in social networks, whereas erotica and viewing of pornography are the rarest activities and are unimportant for students. Online social networks are especially popular among students, as most interviewed students (95.3% have their own profile in social networks. Students mostly visit social networks to communicate, to make friends (77.1% of respondents, however, every second student (60.7% visits them just to spend time, which can be assumed about the problems of students’ leisure time. The vast majority of students think that

The effects of online peer feedback and epistemic beliefs on students’ argumentation-based learning

OpenAIRE

Noroozi, Omid; Hatami, Javad

2018-01-01

Although the importance of students’ argumentative peer feedback for learning is undeniable, there is a need for further empirical evidence on whether and how it is related to various aspects of argumentation-based learning namely argumentative essay writing, domain-specific learning, and attitudinal change while considering their epistemic beliefs which are known to be related to argumentation. In this study, a pre-test–post-test design was conducted with 42 higher education students who wer...

On-line validation of feedwater flow rate in nuclear power plants using neural networks

International Nuclear Information System (INIS)

Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.

1994-01-01

On-line calibration of feedwater flow rate measurement in nuclear power plants provides a continuous realistic value of feedwater flow rate. It also reduces the manpower required for periodic calibration needed due to the fouling and defouling of the venturi meter surface condition. This paper presents a method for on-line validation of feedwater flow rate in nuclear power plants. The method is an improvement of the previously developed method which is based on the use of a set of process variables dynamically related to the feedwater flow rate. The online measurements of this set of variables are used as inputs to a neural network to obtain an estimate of the feedwater flow rate reading. The difference between the on-line feedwater flow rate reading, and the neural network estimate establishes whether there is a need to apply a correction factor to the feedwater flow rate measurement for calculation of the actual reactor power. The method was applied to the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant. The venturi meters used for flow measurements are susceptible to frequent fouling that degrades their measurement accuracy. The fouling effects can cause an inaccuracy of up to 3% relative error in feedwater flow rate reading. A neural network, whose inputs were the readings of a set of reference instruments, was designed to predict both feedwater flow rates simultaneously. A multi-layer feedforward neural network employing the backpropagation algorithm was used. A number of neural network training tests were performed to obtain an optimum filtering technique of the input/output data of the neural networks. The result of the selection of the filtering technique was confirmed by numerous Fast Fourier Transform (FFT) tests. Training and testing were done on data from TMI-1 nuclear power plant. The results show that the neural network can predict the correct flow rates with an absolute relative error of less than 2%

An information search model for online social Networks - MOBIRSE

Directory of Open Access Journals (Sweden)

Miguel Angel Niño Zambrano

2015-09-01

Full Text Available Online Social Networks (OSNs have been gaining great importance among Internet users in recent years.  These are sites where it is possible to meet people, publish, and share content in a way that is both easy and free of charge. As a result, the volume of information contained in these websites has grown exponentially, and web search has consequently become an important tool for users to easily find information relevant to their social networking objectives. Making use of ontologies and user profiles can make these searches more effective. This article presents a model for Information Retrieval in OSNs (MOBIRSE based on user profile and ontologies which aims to improve the relevance of retrieved information on these websites. The social network Facebook was chosen for a case study and as the instance for the proposed model. The model was validated using measures such as At-k Precision and Kappa statistics, to assess its efficiency.

Neural node network and model, and method of teaching same

Science.gov (United States)

Parlos, Alexander G. (Inventor); Atiya, Amir F. (Inventor); Fernandez, Benito (Inventor); Tsai, Wei K. (Inventor); Chong, Kil T. (Inventor)

1995-01-01

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

Material matters for learning in virtual networks: a case study of a professional learning programme hosted in a Google+ online community

Directory of Open Access Journals (Sweden)

Aileen Ackland

2015-08-01

Full Text Available In this paper, we draw on Actor–Network Theories (ANT to explore how material components functioned to create gateways and barriers to a virtual learning network in the context of a professional development module in higher education. Students were practitioners engaged in family learning in different professional roles and contexts. The data comprised postings in the Google+ community, email correspondence, meeting notes, feedback submitted at the final workshop and post-module evaluation forms. Our analysis revealed a complex set of interactions, and suggests multiple ways human actors story their encounters with non-human components and the effects these have on the learning experience. The aim of this paper is to contribute to a more holistic understanding of the components and dynamics of social learning networks in the virtual world and consider the implications for the design of online learning for continuous professional development (CPD.

Linear-control-based synchronization of coexisting attractor networks with time delays

International Nuclear Information System (INIS)

Yun-Zhong, Song

2010-01-01

This paper introduces the concept of linear-control-based synchronization of coexisting attractor networks with time delays. Within the new framework, closed loop control for each dynamic node is realized through linear state feedback around its own arena in a decentralized way, where the feedback matrix is determined through consideration of the coordination of the node dynamics, the inner connected matrix and the outer connected matrix. Unlike previously existing results, the feedback gain matrix here is decoupled from the inner matrix; this not only guarantees the flexible choice of the gain matrix, but also leaves much space for inner matrix configuration. Synchronization of coexisting attractor networks with time delays is made possible in virtue of local interaction, which works in a distributed way between individual neighbours, and the linear feedback control for each node. Provided that the network is connected and balanced, synchronization will come true naturally, where theoretical proof is given via a Lyapunov function. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme. (general)

Blessed Oblivion? Knowledge and Metacognitive Accuracy in Online Social Networks

Science.gov (United States)

Moll, Ricarda; Pieschl, Stephanie; Bromme, Rainer

2015-01-01

In order to reap the social gratifications of Online Social Networks (OSNs), users often disclose self-related information, making them potentially vulnerable to their online audiences. We give a brief overview of our theoretical ideas and empirical research about additional cognitive and metacognitive factors relevant for the perception of risk…

Multi-Stratum Networks: toward a unified model of on-line identities

DEFF Research Database (Denmark)

Rossi, Luca; Magnani, Matteo

2012-01-01

One of the reasons behind the success of Social Network Analysis is its simple and general graph model made of nodes (representing individuals) and ties. However, when we focus on our daily on-line experience we must confront a more complex scenario: people inhabitate several on-line spaces...... interacting to several communities active on various technological infrastructures like Twitter, Facebook, YouTube or FourSquare and with distinct social objectives. This constitutes a complex network of interconnected networks where users' identities are spread and where information propagates navigating...... through different communities and social platforms. In this article we introduce a model for this layered scenario that we call multi-stratum network. Through a theoretical discussion and the analysis of real-world data we show how not only focusing on a single network may provide a very partial...

Social Networking Sites: A premise on enhancement

OpenAIRE

MANINDERPAL SINGH SAINI; GYEWON MOON

2013-01-01

This article address five constructs that are paramount toward continued evolution of social networking sites (SNS`s) they include, - stabilisation, visual, language, security and flexibility. These constructs add to our proposed framework. Firmly grounded research on social networking sites and literature, we propose that user feedback, is the critical component that stimulates the development and growth of social networking sites online. We offer a framework that can aid new and current soc...

Silicon nanophotonics for scalable quantum coherent feedback networks

International Nuclear Information System (INIS)

Sarovar, Mohan; Brif, Constantin; Soh, Daniel B.S.; Cox, Jonathan; DeRose, Christopher T.; Camacho, Ryan; Davids, Paul

2016-01-01

The emergence of coherent quantum feedback control (CQFC) as a new paradigm for precise manipulation of dynamics of complex quantum systems has led to the development of efficient theoretical modeling and simulation tools and opened avenues for new practical implementations. This work explores the applicability of the integrated silicon photonics platform for implementing scalable CQFC networks. If proven successful, on-chip implementations of these networks would provide scalable and efficient nanophotonic components for autonomous quantum information processing devices and ultra-low-power optical processing systems at telecommunications wavelengths. We analyze the strengths of the silicon photonics platform for CQFC applications and identify the key challenges to both the theoretical formalism and experimental implementations. In particular, we determine specific extensions to the theoretical CQFC framework (which was originally developed with bulk-optics implementations in mind), required to make it fully applicable to modeling of linear and nonlinear integrated optics networks. We also report the results of a preliminary experiment that studied the performance of an in situ controllable silicon nanophotonic network of two coupled cavities and analyze the properties of this device using the CQFC formalism. (orig.)

Silicon nanophotonics for scalable quantum coherent feedback networks

Energy Technology Data Exchange (ETDEWEB)

Sarovar, Mohan; Brif, Constantin [Sandia National Laboratories, Livermore, CA (United States); Soh, Daniel B.S. [Sandia National Laboratories, Livermore, CA (United States); Stanford University, Edward L. Ginzton Laboratory, Stanford, CA (United States); Cox, Jonathan; DeRose, Christopher T.; Camacho, Ryan; Davids, Paul [Sandia National Laboratories, Albuquerque, NM (United States)

2016-12-15

The emergence of coherent quantum feedback control (CQFC) as a new paradigm for precise manipulation of dynamics of complex quantum systems has led to the development of efficient theoretical modeling and simulation tools and opened avenues for new practical implementations. This work explores the applicability of the integrated silicon photonics platform for implementing scalable CQFC networks. If proven successful, on-chip implementations of these networks would provide scalable and efficient nanophotonic components for autonomous quantum information processing devices and ultra-low-power optical processing systems at telecommunications wavelengths. We analyze the strengths of the silicon photonics platform for CQFC applications and identify the key challenges to both the theoretical formalism and experimental implementations. In particular, we determine specific extensions to the theoretical CQFC framework (which was originally developed with bulk-optics implementations in mind), required to make it fully applicable to modeling of linear and nonlinear integrated optics networks. We also report the results of a preliminary experiment that studied the performance of an in situ controllable silicon nanophotonic network of two coupled cavities and analyze the properties of this device using the CQFC formalism. (orig.)

Myths on Bi-direction Communication of Web 2.0 Based Social Networks: Is Social Network Truly Interactive?

Science.gov (United States)

2011-03-10

more and more social interactions are happening on the on-line. Especially recent uptake of the social network sites (SNSs), such as Facebook (http...Smart phones • Live updates within social networks • Facebook & Twitters Solution: WebMon for Risk Management Need for New WebMon for Social Networks ...Title: Myths on bi-direction communication of Web 2.0 based social networks : Is social network truly interactive

Designing Dialogic E-Learning in Pharmacy Professionalism Using Calibrated Feedback Loops (CFLs

Directory of Open Access Journals (Sweden)

Sue Roff

2013-09-01

Full Text Available The feedback analytics of online software including Articulate and Bristol Online Surveys can be used to facilitate dialogic learning in a community of practice such as Pharmacy and, thereby, promote reflective learning by the creation of formative calibrated feedback loops. Based on work with medical, dental, nursing, osteopathic, and social work students, trainees, and registrants, the paper shows how an online learning community can be created along the continuum from undergraduate to registrant to develop authentic dialogic e-learning around standards of Professionalism. The Dundee PolyProfessionalism inventories and Situational Judgement Scenarios (SJSs can be customised for Pharmacy Professionalism learning to support evidence-based curriculum design along benchmarked learning curves and to profile Professionalism learning in individuals and cohorts.

Online social networks that connect users to physical activity partners: a review and descriptive analysis.

Science.gov (United States)

Nakhasi, Atul; Shen, Album Xiaotian; Passarella, Ralph Joseph; Appel, Lawrence J; Anderson, Cheryl Am

2014-06-16

The US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways. The aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face. In September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword "find" coupled with 1 of 4 prefix terms "health," "fitness," "workout," or "physical" coupled with 1 of 2 stem terms "activity partners" or "activity buddies." We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword "find" coupled with 1 of 2 stem terms "activity partners" and "activity buddies." Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free. Of the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely

Professional Online Presence and Learning Networks: Educating for Ethical Use of Social Media

Science.gov (United States)

Forbes, Dianne

2017-01-01

In a teacher education context, this study considers the use of social media for building a professional online presence and learning network. This article provides an overview of uses of social media in teacher education, presents a case study of key processes in relation to professional online presence and learning networks, and highlights…

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

Science.gov (United States)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

The strategic impact of social networks on the online gaming industry : strategic use of technology

OpenAIRE

Sousa, Sofia Taveira de

2012-01-01

This dissertation focuses on assessing the strategic potential of social networks by answering the following research question: Is there any strategic impact of social networks on the online gaming industry? In order to analyze the strategic potential of social networks for online games, we identify the main factors that online players consider as crucial for them to keep playing. These factors can either be related to the game’s strategy itself, such as all the details, graphics and ambig...

Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

Science.gov (United States)

Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

2016-09-14

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking.

Science.gov (United States)

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2016-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992-1998), global regime formation through the FCTC negotiations (1999-2005), and philanthropic funding through the Bloomberg Initiative (2006-2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999-2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network.

Applying social network analysis to understand the knowledge sharing behaviour of practitioners in a clinical online discussion forum.

Science.gov (United States)

Stewart, Samuel Alan; Abidi, Syed Sibte Raza

2012-12-04

Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment--an online discussion forum--for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional

Cognitive Benefits of Online Social Networking for Healthy Older Adults.

Science.gov (United States)

Myhre, Janelle W; Mehl, Matthias R; Glisky, Elizabeth L

2017-09-01

Research suggests that older adults who remain socially active and cognitively engaged have better cognitive function than those who are isolated and disengaged. This study examined the efficacy of learning and using an online social networking website, Facebook.com, as an intervention to maintain or enhance cognitive function in older adults. Forty-one older adults were assigned to learn and use Facebook (n = 14) or an online diary website (active control, n = 13) for 8 weeks or placed on a waitlist (n = 14). Outcome measures included neuropsychological tests of executive functions, memory, and processing speed and self-report questionnaires about social engagement. The Facebook group showed a significant increase in a composite measure of updating, an executive function factor associated with complex working memory tasks, compared to no significant change in the control groups. Other measures of cognitive function and social support showed no differential improvement in the Facebook group. Learning and using an online social networking site may provide specific benefits for complex working memory in a group of healthy older adults. This may reflect the particular cognitive demands associated with online social networking and/or the benefits of social engagement more generally. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

On-line learning in radial basis functions networks

OpenAIRE

Freeman, Jason; Saad, David

1997-01-01

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...

Political campaigning 2.0: The influence of online news and social networking sites on attitudes and behavior

Directory of Open Access Journals (Sweden)

Montathar Faraon

2014-11-01

Full Text Available This study aimed to examine differences in influence between online news (e.g., New York Times and social networking sites (e.g., Facebook and Twitter on attitudes in political campaigns. In a web-based experiment, campaign, polls and election between two fictitious candidates were simulated. Participants’ explicit and implicit attitudes as well as voting behavior were assessed using self-report items and the Implicit Association Test (IAT. The results reveal that information emanating from online news had a significant influence on explicit and implicit attitudes while that of social networking sites did not. Overall, negative items had a stronger impact than positive ones, more so in online news compared to social networking sites. Negative information from either type of media was more likely to change participants’ explicit attitudes in a negative direction and as a consequence also change their vote. Practical implications of the findings and limitations of the study are discussed.

Online unsupervised formation of cell assemblies for the encoding of multiple cognitive maps.

Science.gov (United States)

Salihoglu, Utku; Bersini, Hugues; Yamaguchi, Yoko; Molter, Colin

2009-01-01

Since their introduction sixty years ago, cell assemblies have proved to be a powerful paradigm for brain information processing. After their introduction in artificial intelligence, cell assemblies became commonly used in computational neuroscience as a neural substrate for content addressable memories. However, the mechanisms underlying their formation are poorly understood and, so far, there is no biologically plausible algorithms which can explain how external stimuli can be online stored in cell assemblies. We addressed this question in a previous paper [Salihoglu, U., Bersini, H., Yamaguchi, Y., Molter, C., (2009). A model for the cognitive map formation: Application of the retroaxonal theory. In Proc. IEEE international joint conference on neural networks], were, based on biologically plausible mechanisms, a novel unsupervised algorithm for online cell assemblies' creation was developed. The procedure involved simultaneously, a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilized the cell assemblies by learning the feedforward input connections. Here, we first quantify the role played by the retroaxonal feedback mechanism. Then, we show how multiple cognitive maps, composed by a set of orthogonal input stimuli, can be encoded in the network. As a result, when facing a previously learned input, the system is able to retrieve the cognitive map it belongs to. As a consequence, ambiguous inputs which could belong to multiple cognitive maps can be disambiguated by the knowledge of the context, i.e. the cognitive map.