Query: VELNET (Virtual Environment for Learning Networking)

Relevant passages:
Web-Based Virtual Learning Environments: A Research Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Computer Self-Efficacy: Development of a Measure and Initial Test <=SEP=> This paper discusses the role of individuals’ beliefs about their abilities to competently use computers (computer self-efficacy) in the determination of computer use. A survey of Canadian managers and professionals was conducted to develop and validate a measure of computer self-efficacy and to assess both its impacts and antecedents. Computer selfefficacy was found to exert a significant influence on individuals’ expectations of the outcomes of using computers, their emotional reactions to computers (affect and anxiety), well as their actual computer use. An individual’s self-efficacy and outcome expectations were found to be positively influenced by the encouragement of others in their work group, as well as others’ use of computers. Thus, self-efficacy represents an important individual trait, which moderates organizational influences (such as encouragement and support) on an individual’s decision to use computers. Understanding self-efficacy, then, is important o the successful implementation of systems in Introduction Understanding the factors that influence an individual’s use of information technology has been a goal of MIS research since the mid-1970s, when organizations and researchers began to find that adoption of new technology was not living up to expectations. Lucas (1975, 1978) provides some of the earliest evidence of the individual or behavioral factors that influenced IT adoption. The first theoretical perspective to gain widespread acceptance in this research was the Theory of Reasoned Action (Fishbein and Ajzen, 1975). This theory maintains that individuals would use computers if they could see that there would be positive benefits (outcomes) associated with using them. This theory is still widely used today in the IS literature and has demonstrated validity. However, there is also a growing recognition that additional explanatory variables are needed (e.g., Thompson, et al., 1991; Webster and Martocchio, 1992). One such variable, examined in this research, comes from the writings of Albert Bandura and his work on Social Cognitive Theory (Bandura, 1986). Self-efficacy, the belief that one has the capability to perform a particular behavior, is an important construct in social psychology. Self-efficacy perceptions have been found to influence decisions about what behaviors to undertake (e.g., Bandura, et al., 1977; Betz and Hackett, 1981), the effort exerted and persistence in attempting those behaviors (e.g., Barling and Beattie, 1983; Brown and Inouye, 1978), the emotional responses (including stress and anxiety) of the individual performing the behaviors (e.g., Bandura, et al., 1977; Stumpf, et a1.,1987), and the actual performance attainments of the individual with respect to the behavior (e.g., Barling M/S Quarterly/June 1995 189 Computer Self-Efficacy---Measurement and Beattie, 1983; Collins, 1985; Locke, et al., 1984; Schunk, 1981; Wood and Bandura, 1989). These effects have been shown for a wide variety of behaviors in both clinical and managerial settings. Within a management context, self-efficacy has been found to be related to attendance (Frayne and Latham, 1987; Latham and Frayne, 1989), career choice and dev,elopment (Betz and Hackett, 1981; Jones, 1986), research productivity (Taylor, et al., 1984), and sales performance (Barling and Beattie, 1983). Several more recent studies (Burkhardt and Brass, 1990; Gist, et a1.,1989; Hill, et al., 1986; 1987; Webster and Martocohio, 1992; 1993) have examined the relationship between self-efficacy with respect to using computers and a variety of computer behaviors. These studies found evidence of a relationship between selfefficacy and registration in computer courses at universities (Hill, et al., 1987), adoption of high technology products (Hill, et al., 1986) and innovations (Burkhardt and Brass, 1990), as well performance in software training (Gist, et al., 1989; Webster and Martocchio, 1992; 1993). All of the studies argue the need for further research to explore fully the role of self-efficacy in computing behavior. This paper describes the first study in a program of research aimed at understanding the impact of self-efficacy on individual reactions to computing technology. The study involves the development of a measure for computer self-efficacy and a test of its reliability and validity. The measure was evaluated by examining its performance in a nomological network, through structural equations modeling. A research model for this purpose was developed with reference to literature from social psychology, as well as prior IS research. The paper is organized as follows. The next section presents the theoretical foundation for this research. The third section discusses the development of the self-efficacy measure. The research model is described and the hypotheses are presented in the following section. Then, the research methodology is outlined, and the results of the analyses are presented. The paper concludes with a discussion of the implications of these findings and the strengths and limitations of the research. Theoretical Background Social Cognitive Theory Social Cognitive Theory (Bandura, 1977; 1978; 1982; 1986) is a widely accepted, empirically validated model of individual behavior. It is based on the premise that environmental influences such as social pressures or unique situational characteristics, cognitive and other personal factors including personality as well as demographic haracteristics, and behavior are reciprocally determined. Thus, individuals choose the environments in which they exist in

Judo strategy. The competitive dynamics of Internet time. <=SEP=> Competition on the Internet is creating fierce battles between industry giants and small-scale start-ups. Smart start-ups can avoid those conflicts by moving quickly to uncontested ground and, when that's no longer possible, turning dominant players' strengths against them. The authors call this competitive approach judo strategy. They use the Netscape-Microsoft battles to illustrate the three main principles of judo strategy: rapid movement, flexibility, and leverage. In the early part of the browser wars, for instance, Netscape applied the principle of rapid movement by being the first company to offer a free stand-alone browser. This allowed Netscape to build market share fast and to set the market standard. Flexibility became a critical factor later in the browser wars. In December 1995, when Microsoft announced that it would "embrace and extend" competitors' Internet successes, Netscape failed to give way in the face of superior strength. Instead it squared off against Microsoft and even turned down numerous opportunities to craft deep partnerships with other companies. The result was that Netscape lost deal after deal when competing with Microsoft for common distribution channels. Netscape applied the principle of leverage by using Microsoft's strengths against it. Taking advantage of Microsoft's determination to convert the world to Windows or Windows NT, Netscape made its software compatible with existing UNIX systems. While it is true that these principles can't replace basic execution, say the authors, without speed, flexibility, and leverage, very few companies can compete successfully on Internet time.

Forward Reasoning and Dependency-Directed Backtracking in a System for Computer-Aided Circuit Analysis <=SEP=> We present a rule-based system for computer-aided circuit analysis. The set of rules, called EL, is written in a rule language called ARS. Rules are implemented by ARS as pattern-directed invocation demons monitoring an associative data base. Deductions are performed in an antecedent manner, giving EL's analysis a catch-as-catch-can flavour suggestive of the behavior of expert circuit analyzers. We call this style of circuit analysis propagation of constraints. The system threads deduced fact~ with justifications which mention the antecedent facts attd the rule used. These justifications may be examined by the user to gain in right into the operation of the set of rules as they apply to a problem. ~q~e same justifications are used by the system to determine the currently active dat~-base context Jor reasoning in hypothetical situations. They are also used by the system in the at~alysis of failures to reduce the search space. This leads to effective control of combinatorial search which we call dependency-directed backtracking.

Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model <=SEP=> This study extends Davis' (1989) TAM model and Straub's (1994) SPIR addendum by adding gender to an IT diffusion model. The Technology Acceptance Model (TAM) has been widely studied in IS research as an explanation of the use of information systems across IS-types and nationalities. While this line of research has found significant cross-cultural differences, it has ignored the effects of gender, even though in socio-linguistic research, gender is a fundamental aspect of culture. Indeed, sociolinguistic research that has shown that men tend to focus discourse on hierarchy and independence while women focus on intimacy and solidarity. This literature provides a solid grounding for conceptual extensions to the IT diffusion research and the Technology Acceptance Model. Testing gender differences that might relate to beliefs and use of computer-based media, the present study sampled 392 female and male responses via a cross-sectional survey instrument. The sample drew from comparable groups of knowledge workers using E-mail systems in the airline industry in North America, Asia, and Europe. Study findings indicate that women and men differ in their perceptions but not use of E-mail. These findings suggest that researchers should include gender in IT diffusion models along with other cultural effects. Managers and co-workers, moreover, need to realize that the same mode of communication may be perceived differently by the sexes, suggesting that more favorable communications environments might be created, environments that take into account not only organizational contextual factors, but also the gender of users. The creation of these environments involves not only the actual deployment of communication media, but also organizational training on communications media.

EDUCATIONAL DATA MINING <=SEP=> Computer-based learning systems can now keep detailed logs of user-system interactions, including key clicks, eye-tracking, and video data, opening up new opportunities to study how students learn with technology. Educational Data Mining (EDM; Romero, Ventura, Pechenizkiy, & Baker, 2010) is concerned with developing, researching, and applying computerized methods to detect patterns in large collections of educational data – patterns that would otherwise be hard or impossible to analyze due to the enormous volume of data they exist within. Data of interest is not restricted to interactions of individual students with an educational system (e.g., navigation behavior, input to quizzes and interactive exercises) but might also include data from collaborating students (e.g., text chat), administrative data (e.g., school, school district, teacher), and demographic data (e.g., gender, age, school grades). Data on student affect (e.g., motivation, emotional states) has also been a focus, which can be inferred from physiological sensors (e.g., facial expression, seat posture and perspiration). EDM uses methods and tools from the broader field of Data Mining (Witten & Frank, 2005), a sub-field of Computer Science and Artificial Intelligence that has been used for purposes as diverse as credit card fraud detection, analysis of gene sequences in bioinformatics, or the analysis of purchasing behaviors of customers. Distinguishing EDM features are its particular focus on educational data and problems, both theoretical (e.g., investigating a learning hypothesis) and practical (e.g., improving a learning tool). Furthermore, EDM makes a methodological contribution by developing and researching data mining techniques for educational applications. Typical steps in an EDM project include data acquisition, data preprocessing (e.g., data “cleaning”), data mining, and validation of results.

Why Do Men Get More Attention? Exploring Factors Behind Success in an Online Design Community <=SEP=> Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (e.g., recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users’ position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.

Removing ECG noise from surface EMG signals using adaptive filtering <=SEP=> Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularization factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions.

Forecasting the behavior of multivariate time series using neural networks <=SEP=> Abstract--This pal~er presents a neural network approach to multivariate time-series anal.lwis. Real world observations ~?/flottr /.'ices in three cities have been used as a benchmark in our ev~eriments, l"eed/orward connectionist networks have bt,t,tl ~k,.signed to model flottr l , ices over the period/iom ..lltgttsl 1972 to Novt,mher 1980./or the cities ~?[Blt[]ith~. :lliltneapoli.s, and Kansas Cit): Remarkable sttccess has heen achieved ill training the lletworks to leatvt the price CllrlV [i," each t?/these cities and in m a k i l l g acc l t ra le price predictions. Our r¢,~lt/l.s show that the it¢ltral network al~proat'h is a leading contender with the statistical modeling apl,oaches.

Dose-response relationship in music therapy for people with serious mental disorders: systematic review and meta-analysis. <=SEP=> Serious mental disorders have considerable individual and societal impact, and traditional treatments may show limited effects. Music therapy may be beneficial in psychosis and depression, including treatment-resistant cases. The aim of this review was to examine the benefits of music therapy for people with serious mental disorders. All existing prospective studies were combined using mixed-effects meta-analysis models, allowing to examine the influence of study design (RCT vs. CCT vs. pre-post study), type of disorder (psychotic vs. non-psychotic), and number of sessions. Results showed that music therapy, when added to standard care, has strong and significant effects on global state, general symptoms, negative symptoms, depression, anxiety, functioning, and musical engagement. Significant dose-effect relationships were identified for general, negative, and depressive symptoms, as well as functioning, with explained variance ranging from 73% to 78%. Small effect sizes for these outcomes are achieved after 3 to 10, large effects after 16 to 51 sessions. The findings suggest that music therapy is an effective treatment which helps people with psychotic and non-psychotic severe mental disorders to improve global state, symptoms, and functioning. Slight improvements can be seen with a few therapy sessions, but longer courses or more frequent sessions are needed to achieve more substantial benefits.

Javalanche: efficient mutation testing for Java <=SEP=> To assess the quality of a test suite, one can use mutation testing - seeding artificial defects (mutations) into the program and checking whether the test suite finds them. Javalanche is an open source framework for mutation testing Java programs with a special focus on automation, efficiency, and effectiveness. In particular, Javalanche assesses the impact of individual mutations to effectively weed out equivalent mutants; it has been demonstrated to work on programs with up to 100,000 lines of code.

Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer <=SEP=> Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club’s performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season’s economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.

Dynamic Traffic Diversion in SDN: testbed vs Mininet <=SEP=> In this paper, we first propose a simple Dynamic Traffic Diversion (DTD) algorithm for Software Defined Networks (SDN). After implementing the algorithm inside the controller, we then compare the results obtained under two different test environments: 1) a testbed using real Cisco equipment and 2) a network emulation using Mininet. From the results, we get two key messages. First, we can clearly see that dynamically diverting important traffic on a backup path will prevent packet loss and reduce jitter. Finally, the two test environments provide relatively similar results. The small differences could be explained by the early field trial image that was used on the Cisco equipment and by the many setting parameters that are available in both environments.

Embedded Trench Redistribution Layers (RDL) by Excimer Laser Ablation and Surface Planer Processes <=SEP=> This paper reports the demonstration of 2-5 µm embedded trench formation in dry film polymer dielectrics such as Ajinomoto build-up film (ABF) and Polyimide without using chemical mechanical polishing (CMP) process. The trenches in these dielectrics were formed by excimer laser ablation, followed by metallization of trenches by copper plating processes and overburden removal with surface planer tool. The materials and processes integrated in this work are scalable to large panel fabrication at much higher throughput, for interposers and high density fan-out packaging at lower cost and higher performance than silicon interposers.

ESPRIT-estimation of signal parameters via rotational invariance techniques <=SEP=> High-resolution signal parameter estimation is a problem of significance in many signal processing applications. Such applications include direction-of-arrival (DOA) estimation, system identification, and time series analysis. A novel approach to the general problem of signal parameter estimation is described. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC.

Assessment of Knowledge , Perception and Practice of Maternal Nutrition Among Pregnant Mother Attending Antenatal Care in Selected Health <=SEP=> Nutrition during preconception as well as throughout pregnancy has a major impact on the outcome of pregnancy. Therefore, the aim of this study was designed to assess knowledge, perception and practices of maternal nutrition among pregnant women during antenatal care in selected health center of Horo Guduru Wollega zone. A facility-based cross-sectional study design was conducted on total 405 pregnant mothers from January to June, 2017. A semi structured interview and questionnaires was used to collect information in the areas of socio-demographic, knowledge, perception, and practices towards maternal nutrition among pregnant mothers. Statistical Package for Social Sciences (SPSS) version 20.0 was used to perform descriptive statistics. The result obtained during study indicates that 63.5%, 70.6% and74.6% of pregnant mother good knowledge, perception and practices respectively while 36.5%, 29.4% and 25.4% was poor knowledge, perception and practice respectively. This study clearly indicated that less than half of pregnant mother’s attending antenatal care in the study area had poor knowledge, perception and practices. Therefore, nutrition education should be intensified to improve the overall knowledge, perception, and practices of pregnant mothers towards maternal nutrition in different villages, health centers, health posts and hospitals.

Novelty and diversity metrics for recommender systems: choice, discovery and relevance <=SEP=> There is an increasing realization in the Recommender Systems (RS) field that novelty and diversity are fundamental qualities of recommendation effectiveness and added-value. We identify however a gap in the formalization of novelty and diversity metrics –and a consensus around them– comparable to the recent proposals in IR diversity. We study a formal characterization of different angles that RS novelty and diversity may take from the end-user viewpoint, aiming to contribute to a formal definition and understanding of different views and meanings of these magnitudes under common groundings. Building upon this, we derive metric schemes that take item position and relevance into account, two aspects not generally addressed in the novelty and diversity metrics reported in the RS literature.

Comparative evaluation of soft-switching concepts for bi-directional buck+boost dc-dc converters <=SEP=> Soft-switching techniques are an enabling technology to further reduce the losses and the volume of automotive dc-dc converters, utilized to interconnect the high voltage battery or ultra-capacitor to the dc-link of a Hybrid Electrical Vehicle (HEV) or a Fuel Cell Vehicle (FCV). However, as the performance indices of a power electronics converter, such as efficiency and power density, are competing and moreover dependent on the underlying specifications and technology node, a comparison of different converter topologies naturally demands detailed analytical models. Therefore, to investigate the performance of the ARCP, CF-ZVS-M, SAZZ and ZCT-QZVT soft-switching converters, the paper discusses in detail the advantages and drawbacks of each concept, and the impact of the utilized semiconductor technology and silicon area on the converter efficiency. The proposed analytical models that correlate semiconductor, capacitor and inductor losses with the component volume furthermore allow for a comparison of power density and to find the η-ρ-Pareto-Front of the CF-ZVS-M converter.

Development of a robotic finger with an active dual-mode twisting actuation and a miniature tendon tension sensor <=SEP=> A robot finger is developed with enhanced grasping force and speed using active dual-mode twisting actuation, which is a type of twisted string actuation. This actuation system has two twisting modes (Speed Mode and Force Mode) with different radii of the twisted string, and the twisting mode can be changed automatically. Therefore, the actuator operates like an automatic transmission having two transmission ratios, and the robot finger can generate a large grasping force or fast motion depending on the twisting mode. In addition, a miniature tendon tension sensor is developed and embedded in the fingertip for automatic mode change and grasping force control. A relation between the fingertip force and the tension of the tendon is derived by kinematic and kinetic analyses of the robot finger. The performance of the robotic finger and the tension sensor are verified by experimental results.

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM <=SEP=> Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

A Look into 30 Years of Malware Development from a Software Metrics Perspective <=SEP=> During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. Malware plays a key role in most of today’s cyber attacks and has consolidated as a commodity in the underground economy. In this work, we analyze the evolution of malware since the early 1980s to date from a software engineering perspective. We analyze the source code of 151 malware samples and obtain measures of their size, code quality, and estimates of the development costs (effort, time, and number of people). Our results suggest an exponential increment of nearly one order of magnitude per decade in aspects such as size and estimated effort, with code quality metrics similar to those of regular software. Overall, this supports otherwise confirmed claims about the increasing complexity of malware and its production progressively becoming an industry.

Maturity Models for Systems Thinking <=SEP=> Recent decades have seen a rapid increase in the complexity of goods, products, and services that society has come to demand. This has necessitated a corresponding growth in the requirements demanded of organizational systems and the people who work in them. The competence a person requires to be effective in working in such systems has become an area of increased interest to scholars and practitioners in many disciplines. How can we assess the degree to which a person is executing the competencies required to do good systems work? Several industries now utilize maturity models in the attempt to evaluate and cultivate people’s ability to effectively execute complex tasks. This paper will examine current thought regarding the value and pitfalls of maturity models. It will identify principles and exemplars that could guide the development of a Maturity Model of Systems Thinking Competence (MMSTC) for the varied roles people inhabit in systems contexts.

Learning Attractor Dynamics for Generative Memory <=SEP=> A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well-founded solution to robust retrieval is given by attractor dynamics, which iteratively clean up patterns during recall. However, incorporating attractor dynamics into modern deep learning systems poses difficulties: attractor basins are characterised by vanishing gradients, which are known to make training neural networks difficult. In this work, we avoid the vanishing gradient problem by training a generative distributed memory without simulating the attractor dynamics. Based on the idea of memory writing as inference, as proposed in the Kanerva Machine, we show that a likelihood-based Lyapunov function emerges from maximising the variational lower-bound of a generative memory. Experiments shows it converges to correct patterns upon iterative retrieval and achieves competitive performance as both a memory model and a generative model.

Artificial intelligence theory (Basic concepts) <=SEP=> Taking the bionic approach as a basis, the article discusses the main concepts of the theory of artificial intelligence as a field of knowledge, which studies the principles of creation and functioning of intelligent systems based on multidimensional neural-like growing networks. The general theory of artificial intelligence includes the study of neural-like elements and multidimensional neural-like growing networks, temporary and long term memory, study of the functional organization of the “brain” of the artificial intelligent systems, of the sensor system, modulating system, motor system, conditioned and unconditioned reflexes, reflexes arc (ring), motivation, purposeful behavior, of “thinking”, “consciousness”, “subconscious and artificial personality developed as a result of training and education”.

Dual-band bandpass filter using helical resonators <=SEP=> This paper demonstrates non-uniform pitch helical resonators for realization of dual-band bandpass filters. By modifying the pitch and coil diameter of conventional uniform pitch helical resonators, the desired dual-band frequencies can be obtained. The external coupling and inter-resonator coupling structure are also illustrated. A non-uniform pitch helical resonator was designed and fabricated with unloaded quality-factor (>900) for both passbands. A 2nd order Butterworth filter that has passbands at 840 MHz and 2590 MHz has been designed. The simulation results show that the filter has a good selectivity with 6% and 4.3% fractional bandwidth at the passbands, respectively. The good power handling capability also suggests that the filter is applicable for microcell/picocell LTE base stations.

Automation of Attendances in Classrooms using RFID <=SEP=> This paper automates the design and implementation of students’ attendance management system, taken into consideration easy access and time saving. The system will be used in faculty of computing and information system (FCIT) at King Abdulaziz University (KAU). Radio Frequency Identification (RFID) and wireless technology are two technologies which will be applied as infrastructure in the indoor environment. University Based Services (UBS) and tag IDs of the RFID are being used in this paper, in order to determine the attendance list for academic staff. Once the academic staff enters a classroom, he will be able to register student’s presence. Therefore, an academic system for identifying and tracking attendance at computing college at KAU will be described, and hybrid RFID and Wireless LAN (WLAN) technologies will be implemented in FCIT academic environment.

Got Flow?: Using Machine Learning on Physiological Data to Classify Flow <=SEP=> As information technologies (IT) are both, drivers of highly engaging experiences and sources of disruptions at work, the phenomenon of flow - defined as "the holistic sensation that people feel when they act with total involvement" [5, p. 36] - has been suggested as promising vehicle to understand and enhance user behavior. Despite the growing relevance of flow at work, contemporary measurement approaches of flow are of subjective and retrospective nature, limiting our possibilities to investigate and support flow in a reliable and timely manner. Hence, we require objective and real-time classification of flow. To address this issue, this article combines recent theoretical considerations from psychology and experimental research on the physiology of flow with machine learning (ML). The overall aim is to build classifiers to distinguish flow states (i.e., low and high flow). Our results indicate that flow-classifiers can be derived from physiological signals. Cardiac features seem to play an important role in this process resulting in an accuracy of 72.3%. Hereby, our findings may serve as foundation for future work aiming to build flow-aware IT-systems.

Measuring Universal Intelligence in Agent-Based Systems Using the Anytime Intelligence Test <=SEP=> This paper aims to quantify and analyze the intelligence of artificial agent collectives. A universal metric is proposed and used to empirically measure intelligence for several different agent decision controllers. Accordingly, the effectiveness of various algorithms is evaluated on a per-agent basis over a selection of abstracted, canonical tasks of different algorithmic complexities. Results reflect the different settings over which cooperative multiagent systems can be significantly more intelligent per agent than others. We identify and discuss some of the factors influencing the collective performance of these systems.

Creating Constructivist Learning Environments on the Web: The Challenge in Higher Education <=SEP=> Australian universities have traditionally relied on government funding to support undergraduate teaching. As the government has adopted the ‘user-pays’ principle, universities have been forced to look outside their traditional market to expand the undergraduate, post-graduate and international offerings. Alternate delivery methods in many universities have utilised web-based instruction as a basis for this move because of three perceptions: access by the target market is reasonably significant, it is a cost-effective method of delivery, and it provides global access. Since the mid sixties, the trend for both on-campus teaching and teaching at a distance has been to use behaviourist instructional strategies for subject development, which rely on the development of a set of instructional sequences with predetermined outcomes. These models, whilst applicable in a behaviourist environment, are not serving instructional designers well when the theoretical foundation for the subject outcomes is based on a constructivist approach to learning, since the constructivist group of theories places less emphasis on the sequence of instruction and more emphasis on the design of the learning environment. (Jonassen, 1994. p 35). In a web-based environment this proves to be even more challenging. This paper will review current research in design goals for web-based constructivist learning environments, and a move towards the development of models. The design of two web-based subjects will be explored in the context of the design goals developed by Duffy and Cunningham (1996 p 177) who have produced some basic assumptions that they call “metaphors we teach by”. The author seeks to examine the seven goals for their relevance to the instructional designer through the examination of their relevance to the web-based subjects, both of which were framed in constructivist theory.

Robust road marking detection using convex grouping method in around-view monitoring system <=SEP=> As the around-view monitoring (AVM) system becomes one of the essential components for advanced driver assistance systems (ADAS), many applications using AVM such as parking guidance system are actively being developed. As a key step for such applications, detecting road markings robustly is a very important issue to be solved. However, compared to the lane marking detection methods, detection of non-lane markings, such as text marks painted on the road, has been less studied so far. While some of methods for detecting non-lane markings exist, many of them are restricted to roadways only, or work poorly on AVM images. In this paper, we propose an algorithm which can robustly detect non-lane road markings on AVM images. We first propose a difference-of-Gaussian based method for extracting a connected component set, followed by a novel grouping method for grouping connected components based on convexity condition. For a classification task, we exploit the Random Forest classifier. We demonstrate the robustness and detection accuracy of our methods through various experiments by using the dataset collected from various environments.