Query: MODEL AS A DESCRIPTIVE TOOL IN EVALUATING A VIRTUAL LEARNING ENVIRONMENT

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.

Usability, quality, value and e-learning continuance decisions <=SEP=> Previous research suggests that an eventual information technology (IT) success depend on both its initial adoption (acceptance) and subsequent continued usage (continuance). Expectancy disconfirmation theory (EDT) has been successfully used to predict users intention to continue using information technologies. This study proposed a decomposed EDT model to examine cognitive beliefs and affect that influence users continuance decision in the context of e-learning service. The proposed model extended EDT by decomposing the perceived performance component into usability, quality, and value. Research hypotheses derived from this model are empirically validated using the responses to a survey on e-learning usage among 183 users. The results suggest that users continuance intention is determined by satisfaction, which in turn is jointly determined by perceived usability, perceived quality, perceived value, and usability disconfirmation. 2004 Elsevier Ltd. All rights reserved.

Measuring information systems success: models, dimensions, measures, and interrelationships <=SEP=> Received: 30 December 2006 Revised: 12 July 2007 2nd Revision: 16 December 2007 3rd Revision: 29 April 2008 Accepted: 15 May 2008 Abstract Since DeLone and McLean (D&M) developed their model of IS success, there has been much research on the topic of success as well as extensions and tests of their model. Using the technique of a qualitative literature review, this research reviews 180 papers found in the academic literature for the period 1992–2007 dealing with some aspect of IS success. Using the six dimensions of the D&M model – system quality, information quality, service quality, use, user satisfaction, and net benefits – 90 empirical studies were examined and the results summarized. Measures for the six success constructs are described and 15 pairwise associations between the success constructs are analyzed. This work builds on the prior research related to IS success by summarizing the measures applied to the evaluation of IS success and by examining the relationships that comprise the D&M IS success model in both individual and organizational contexts. European Journal of Information Systems (2008) 17, 236–263. doi:10.1057/ejis.2008.15

The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach <=SEP=> 

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

Broadcasting Intermediate Blocks as a Defense Mechanism Against Selfish-Mine in Bitcoin <=SEP=> Although adopted by many cryptocurrencies, the Bitcoin mining protocol is not incentive-compatible, as the selfish mining strategy enables a miner to gain unfair mining rewards. Existing defenses either demand fundamental changes to block validity rules or have little effect on an attacker with more than one third of the total mining power. This paper proposes an effective defense mechanism against resourceful selfish miners. Our defense requires miners to publish intermediate blocks, the by-products of the mining process, then build on each other’s work. By adding transparency to the mining process, block forks are resolved by comparing the amount of work of each branch. Moreover, this mechanism has the advantages of backward compatibility, low communication and computational costs, accelerating block propagation, and mitigating double-spending attacks on fast payments. To evaluate our design, we computed the most profitable mining strategy within our defense with analysis and simulation. Our simulation showed that within our defense, a selfish miner with almost half of the total mining power can only gain marginal unfair block rewards.

Beyond information seeking: towards a general model of information behaviour <=SEP=> Introduction. The aim of the paper is to propose new models of information behaviour that extend the concept beyond simply information seeking to consider other modes of behaviour. The models chiefly explored are those of Wilson and Dervin. Argument A shortcoming of some models of information behaviour is that they present a sequence of stages where it is evident that actual behaviour is not always sequential. In addition, information behaviour models tend to confine themselves to depictions of information seeking. Development. A model of 'multi-directionality' is explored, to overcome the notion of sequential stages. Inspired by authors such as Chatman, Krikelas, and Savolainen, modes of information behaviour such as creating, destroying and avoiding information are included. Conclusion. New models of information behaviour are presented that replace the notion of 'barriers' with the concept of 'gap', as a means of integrating the views of Wilson and Dervin. The proposed models incorporate the notion of multi-directionality and identify ways in which an individual may navigate 'gap' using modes of information behaviour beyond information seeking.

Mention-anomaly-based Event Detection and tracking in Twitter <=SEP=> The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (Mention-Anomaly-Based Event Detection), a novel method that leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect important events and estimate the magnitude of their impact over the crowd. The main advantages of MABED over prior works are that (i) it relies solely on tweets, meaning no external knowledge is required, and that (ii) it dynamically estimates the period of time during which each event is discussed rather than assuming a predefined fixed duration. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Last, we show that MABED helps with the interpretation of detected events by providing clear and precise descriptions.

Reduced Number of Hypocretin Neurons in Human Narcolepsy <=SEP=> Murine and canine narcolepsy can be caused by mutations of the hypocretin (Hcrt) (orexin) precursor or Hcrt receptor genes. In contrast to these animal models, most human narcolepsy is not familial, is discordant in identical twins, and has not been linked to mutations of the Hcrt system. Thus, the cause of human narcolepsy remains unknown. Here we show that human narcoleptics have an 85%-95% reduction in the number of Hcrt neurons. Melanin-concentrating hormone (MCH) neurons, which are intermixed with Hcrt cells in the normal brain, are not reduced in number, indicating that cell loss is relatively specific for Hcrt neurons. The presence of gliosis in the hypocretin cell region is consistent with a degenerative process being the cause of the Hcrt cell loss in narcolepsy.

Working Memory: A View from Neuroimaging <=SEP=> We have used neuroimaging techniques, mainly positron emission tomography (PET), to study cognitively driven issues about working memory. Two kinds of experiments are described. In the first kind, we employ standard subtraction logic to uncover the basic components of working memory. These studies indicate that: (a) there are different working-memory systems for spatial, object, and verbal information (with the spatial system localized more in the right hemisphere, and the verbal system more in the left hemisphere); (b) within at least the spatial and verbal systems, separable components seem to be responsible for the passive storage of information and the active maintenance of information (with the storage component being localized more in the back of the brain, and the maintenance component in the front); and (c) there may be separate components responsible for processing the contents of working memory (localized in prefrontal cortex). In our second kind of experiment we have focused on verbal working memory and incrementally varied one task parameter-memory load-in an effort to obtain a more fine-grained analysis of the system's operations. The results indicate that all relevant components of the system show some increase in activity with increasing memory load (e.g., the frontal regions responsible for verbal rehearsal show incremental increases in activation with increasing memory load). In contrast, brain regions that are not part of the working-memory system show no effect of memory load. Furthermore, the time courses of activation may differ for regions that are sensitive to load versus those that are not. Taken together, our results provide support for certain cognitive models of working memory (e.g., Baddeley, 1992) and also suggest some distinctions that these models have not emphasized. And more fundamentally, the results provide a neural base for cognitive models of working memory.

Learning Deep Structured Models <=SEP=> Many problems in real-world applications involve predicting several random variables that are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such dependencies. The goal of this paper is to combine MRFs with deep learning to estimate complex representations while taking into account the dependencies between the output random variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials. Our approach is efficient as it blends learning and inference and makes use of GPU acceleration. We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as tagging of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains.

A codebook-free and annotation-free approach for fine-grained image categorization <=SEP=> Fine-grained categorization refers to the task of classifying objects that belong to the same basic-level class (e.g. different bird species) and share similar shape or visual appearances. Most of the state-of-the-art basic-level object classification algorithms have difficulties in this challenging problem. One reason for this can be attributed to the popular codebook-based image representation, often resulting in loss of subtle image information that are critical for fine-grained classification. Another way to address this problem is to introduce human annotations of object attributes or key points, a tedious process that is also difficult to generalize to new tasks. In this work, we propose a codebook-free and annotation-free approach for fine-grained image categorization. Instead of using vector-quantized codewords, we obtain an image representation by running a high throughput template matching process using a large number of randomly generated image templates. We then propose a novel bagging-based algorithm to build a final classifier by aggregating a set of discriminative yet largely uncorrelated classifiers. Experimental results show that our method outperforms state-of-the-art classification approaches on the Caltech-UCSD Birds dataset.

Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. <=SEP=> Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.

Pachyonychia congenita type 2 (Jackson-Lawler syndrome) or PC-17: case report. <=SEP=> Pachyonychia congenita (PC) is a rare genodermatosis caused by mutations in any of the four genes KRT6A, KRT6B, KRT16, or KRT17, which can lead to dystrophic, thickened nails and focal palmoplantar keratoderma, among other manifestations. Although classically subdivided into two major variants, PC-1 (Jadassohn-Lewandowski syndrome) and PC-2 (Jackson-Lawler syndrome), according to the localization of the mutations in the KRT6A/KRT16 or KRT6B/KRT17 genes, respectively, a classification system based on the mutant gene (PC-6a, PC-6b, PC-16 and PC-17) has been recently proposed. We report a 2-year-old female patient with a history of thickened and discolored nails, small cystic papulonodules on the central face, dry, unruly and curly hair, slight palmoplantar hyperkeratosis, and natal teeth. Both her father and paternal grandfather presented onychodystrophy, palmoplantar keratoderma, and previous excision of "sebaceous" cysts. Molecular genetic analysis of the patient revealed a missense mutation (c.1163T>C) in heterozygosity in exon 6 of the KRT17 gene, confirming the diagnosis of PC-2 (Jackson-Lawler type), or PC-17. We conclude that PC is a relatively easy and consistent clinical diagnosis, but a high index of suspicion is required if the diagnosis is to be made correctly. With this case, the authors intend to draw attention to this condition and the role of the dermatologist in the diagnosis.

Kinematics and Dynamics of a New 16 DOF Humanoid Biped Robot with Active Toe Joint <=SEP=> Humanoid biped  robots are  typically complex in design, having numerous Degrees‐of‐Freedom (DOF) due to the ambitious goal of mimicking the human gait. The paper proposes a new architecture for a biped robot with  seven  DOF  per  each  leg  and  one  DOF corresponding  to  the  toe  joint.  Furthermore,  this work presents  close  equations  for  the  forward  and  inverse kinematics by dividing the walking gait into the Sagittal and  Frontal  planes.  This  paper  explains  the mathematical model of  the dynamics  equations  for  the legs  into  the  Sagittal  and  Frontal  planes  by  further applying the principle of Lagrangian dynamics. Finally, a control approach using a PD control law with gravity compensation  was  recurred  in  order  to  control  the desired  trajectories and  finding  the  required  torque by the  joints.  The  paper  contains  several  simulations  and numerical  examples  to  prove  the  analytical  results, using  SimMechanics  of  MATLAB  toolbox  and SolidWorks to verify the analytical results.

Residual Highway Convolutional Neural Networks for in-loop Filtering in HEVC <=SEP=> High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers. In the highway units, there are some paths that could allow unimpeded information across several layers. Moreover, there also exists one identity skip connection (shortcut) from the beginning to the end, which is followed by one small convolutional layer. Without conflicting with deblocking filter (DF) and sample adaptive offset (SAO) filter in HEVC, RHCNN is employed as a high-dimension filter following DF and SAO to enhance the quality of reconstructed frames. To facilitate the real application, we apply the proposed method to I frame, P frame, and B frame, respectively. For obtaining better performance, the entire quantization parameter (QP) range is divided into several QP bands, where a dedicated RHCNN is trained for each QP band. Furthermore, we adopt a progressive training scheme for the RHCNN where the QP band with lower value is used for early training and their weights are used as initial weights for QP band of higher values in a progressive manner. Experimental results demonstrate that the proposed method is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.

Understanding heart rate sharing: towards unpacking physiosocial space <=SEP=> Advances in biosensing make it possible to include heart rate monitoring in applications and several studies have suggested that heart rate communication has potential for improving social connectedness. However, it is not known how people understand heart rate feedback, or what issues need to be taken into account when designing technologies including heart rate feedback. To explore this, we created a heart rate communication probe that was used in two qualitative in-lab studies and a two-week field trial in participants' homes. Results show that heart rate feedback is a strong connectedness cue that affects the interaction in various ways, depending on a number of interrelated factors. In particular, we found two distinct categories of effects: heart rate as information and heart rate as connection. We propose two mechanisms that could explain these observations and draw out the implications they have for future use of heartbeat communication to support social connectedness or other aspects of social interaction.

Designing Twister 2 : Efficient Programming Environment Toolkit for Big Data <=SEP=> Data-driven applications are required to adapt to the everincreasing volume, velocity and veracity of data generated by a variety of sources including the Web and Internet of Things devices. At the same time, an event-driven computational paradigm is emerging as the core of modern systems designed for both database queries, data analytics and on-demand applications. MapReduce has been generalized to Map Collective and shown to be very effective in machine learning. However one often uses a dataflow computing model, which has been adopted by most major big data processing runtimes. The HPC community has also developed several asynchronous many tasks (AMT) systems according to the dataflow model. From a different point of view, the services community is moving to an increasingly event-driven model where (micro)services are composed of small functions driven by events in the form of Function as a Service(Faas) and serverless computing. Such designs allow the applications to scale quickly as well as be cost effective in cloud environments. An event-driven runtime designed for data processing consists of well-understood components such as communication, scheduling, and fault tolerance. One can make different design decisions for these components that will determine the type of applications a system can support efficiently. We find that modern systems are designed in a monolithic approach with a fixed set of choices that cannot be changed easily afterwards. Because of these design choices their functionality is limited to specific sets of applications. In this paper we study existing systems (candidate event-driven runtimes), the design choices they have made for each component, and how this affects the type of applications they can support. Further we propose a loosely coupled component-based approach for designing a big data toolkit where each component can have different implementations to support various applications. We believe such a polymorphic design would allow services and data analytics to be integrated seamlessly and expand from edge to cloud to high performance computing environments.

A comparative study between honey and povidone iodine as dressing solution for Wagner type II diabetic foot ulcers. <=SEP=> Honey dressing has been used to promote wound healing for years but scanty scientific studies did not provide enough evidences to justify it benefits in the treatment of diabetic foot ulcers. We conducted a prospective study to compare the effect of honey dressing for Wagner's grade-II diabetic foot ulcers with controlled dressing group (povidone iodine followed by normal saline). Surgical debridement and appropriate antibiotics were prescribed in all patients. There were 30 patients age between 31 to 65-years-old (mean of 52.1 years). The mean healing time in the standard dressing group was 15.4 days (range 9-36 days) compared to 14.4 days (range 7-26 days) in the honey group (p < 0.005). In conclusion, ulcer healing was not significantly different in both study groups. Honey dressing is a safe alternative dressing for Wagner grade-II diabetic foot ulcers.

A Tutorial on Support Vector Machines for Pattern Recognition <=SEP=> The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

Characterization of the Impact of Hardware Islands on OLTP <=SEP=> Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database systems mostly assume that all processing cores are the same and that microarchitecture differences are not significant enough to appear in critical database execution paths. As we demonstrate in this paper, however, non-uniform core topology does appear in the critical path and conventional database architectures achieve suboptimal and even worse, unpredictable performance. We perform a detailed performance analysis of OLTP deployments in servers with multiple cores per CPU (multicore) and multiple CPUs per server (multisocket). We compare different database deployment strategies where we vary the number and size of independent database instances running on a single server, from a single shared-everything instance to fine-grained shared-nothing configurations. We quantify the impact of non-uniform hardware on various deployments by (a) examining how efficiently each deployment uses the available hardware resources and (b) measuring the impact of distributed transactions and skewed requests on different workloads. We show that no strategy is optimal for all cases and that the best choice depends on the combination of hardware topology and workload characteristics. Finally, we argue that transaction processing systems must be aware of the hardware topology in order to achieve predictably high performance.

Resolving constraint conflicts <=SEP=> In this paper, we define constraint conflicts and examine properties that may aid in guiding their resolution. A constraint conflict is an inconsistency between the access control policy and the constraints specified to limit that policy. For example, a policy that permits a high integrity subject to access low integrity data is in conflict with a Biba integrity constraint. Constraint conflicts differ from typical policy conflicts in that constraints are never supposed to be violated. That is, a conflict with a constraint results in a policy compilation error, whereas policy conflicts are resolved at runtime. As we have found in the past, when constraint conflicts occur in a specification a variety of resolutions are both possible and practical. In this paper, we detail some key formal properties of constraint conflicts and show how these are useful in guiding conflict resolution. We use the SELinux example policy for Linux 2.4.19 as the source of our constraint conflicts and resolution examples. The formal properties are used to guide the selection of resolutions and provide a basis for a resolution language that we apply to resolve conflicts in the SELinux example policy.

An evaluation study of BigData frameworks for graph processing <=SEP=> When Google first introduced the Map/Reduce paradigm in 2004, no comparable system had been available to the general public. The situation has changed since then. The Map/Reduce paradigm has become increasingly popular and there is no shortage of Map/Reduce implementations in today's computing world. The predominant solution is currently Apache Hadoop, started by Yahoo. Besides employing custom Map/Reduce installations, customers of cloud services can now exploit ready-made made installations (e.g. the Elastic Map/Reduce System). In the mean time, other, second generation frameworks have started to appear. They either fine tune the Map/Reduce model for specific scenarios, or change the paradigm altogether, such as Google's Pregel. In this paper, we present a comparison between these second generation frameworks and the current de-facto standard Hadoop, by focusing on a specific scenario: large-scale graph analysis. We analyze the different means of fine-tuning those systems by exploiting their unique features. We base our analysis on the k-core decomposition problem, whose goal is to compute the centrality of each node in a given graph; we tested our implementation in a cluster of Amazon EC2 nodes with realistic datasets made publicly available by the SNAP project.

Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness <=SEP=> We consider a global optimization problem of a deterministic function f in a semimetric space, given a finite budget of n evaluations. The function f is assumed to be locally smooth (around one of its global maxima) with respect to a semi-metric l. We describe two algorithms based on optimistic exploration that use a hierarchical partitioning of the space at all scales. A first contribution is an algorithm, DOO, that requires the knowledge of l. We report a finite-sample performance bound in terms of a measure of the quantity of near-optimal states. We then define a second algorithm, SOO, which does not require the knowledge of the semimetric l under which f is smooth, and whose performance is almost as good as DOO optimally-fitted.

Classification ability of single hidden layer feedforward neural networks <=SEP=> Multilayer perceptrons with hard-limiting (signum) activation functions can form complex decision regions. It is well known that a three-layer perceptron (two hidden layers) can form arbitrary disjoint decision regions and a two-layer perceptron (one hidden layer) can form single convex decision regions. This paper further proves that single hidden layer feedforward neural networks (SLFN's) with any continuous bounded nonconstant activation function or any arbitrary bounded (continuous or not continuous) activation function which has unequal limits at infinities (not just perceptrons) can form disjoint decision regions with arbitrary shapes in multidimensional cases. SLFN's with some unbounded activation function can also form disjoint decision regions with arbitrary shapes.

Reactions to discrimination, stigmatization, ostracism, and other forms of interpersonal rejection: a multimotive model. <=SEP=> This article describes a new model that provides a framework for understanding people's reactions to threats to social acceptance and belonging as they occur in the context of diverse phenomena such as rejection, discrimination, ostracism, betrayal, and stigmatization. People's immediate reactions are quite similar across different forms of rejection in terms of negative affect and lowered self-esteem. However, following these immediate responses, people's reactions are influenced by construals of the rejection experience that predict 3 distinct motives for prosocial, antisocial, and socially avoidant behavioral responses. The authors describe the relational, contextual, and dispositional factors that affect which motives determine people's reactions to a rejection experience and the ways in which these 3 motives may work at cross-purposes. The multimotive model accounts for the myriad ways in which responses to rejection unfold over time and offers a basis for the next generation of research on interpersonal rejection.

An Energy-Efficient , Wireless Top-of-Rack to Top-of-Rack Datacenter Network using 60 GHz Links <=SEP=> Datacenters have become the digital backbone of the modern society and consume enormous amounts of power. Significant portion of the power consumption is due to the power hungry switching fabric necessary for communication in the datacenter. Additionally, the complex cabling in traditional datacenters pose design and maintenance challenges and increase the energy cost of the cooling infrastructure by obstructing the flow of chilled air. Recent research on wireless datacenters have proposed interconnecting rows of racks at the top-of-rack (ToR) level via wireless links to eliminate the need for complex network of power hungry routers and cables. Links are established using either highly directional phased array antennas or narrow-beam horned antennas between those ToR entities. ToR-to-ToR wireless links have also been used to augment existing wired networks, improving overall performance characteristics. All these wireless approaches advocate the use of 60GHz line-of-sight (LoS) communication paths between antennas for the establishment of reliable wireless channels. In this work, we explore the feasibility of a ToR-to-ToR wireless network for a small to medium-scale datacenter from the perspective of system-level performance. We evaluate a ToR-to-ToR wireless datacenter network (DCN) for network-level data rate and overall power consumption and compare it to a traditional fat-tree based DCN. We find that the ToR-to-ToR wireless DCN can sustain similar data rates for typical query based applications and consume less power compared to that of traditional datacenters. KeywordsWireless datacenter; Energy Efficiency; 60GHz; IEEE 802.11ad; Top-of-Rack;

2D diffuse optical imaging using clustered sparsity <=SEP=> Diffuse optical imaging (DOI) is a non-invasive technique which measures hemodynamic changes in the tissue with near infrared light, and has been increasingly used to study brain functions. Due to the nature of light propagation in the tissue, the reconstruction problem is severely ill-posed. Sparsity-regularization has achieved promising results in recent works for linearized DOI problem. In this paper, we exploit more prior information to improve DOI besides sparsity. Based on the functional specialization of the brain, the in vivo absorption changes caused by specific brain function can be clustered in certain region(s) and not randomly distributed. Thus, a new algorithm is proposed to utilize this prior in reconstruction. Results of numerical simulations and phantom experiments have demonstrated the superiority of the proposed method over the state-of-the-art.

The ethics of representation and action in virtual reality <=SEP=> This essay addresses ethical aspects of the design and use of virtual reality (VR) systems, focusing on the behavioral options made available in such systems and the manner in which reality is represented or simulated in them. An assessment is made of the morality of ‘immoral’ behavior in virtual reality, and of the virtual modeling of such behavior. Thereafter, the ethical aspects of misrepresentation and biased representation in VR applications are discussed.

Caricature synthesis with feature deviation matching under example-based framework <=SEP=> Example-based caricature synthesis techniques have been attracting large attentions for being able to generate attractive caricatures of various styles. This paper proposes a new example-based caricature synthesis system using a feature deviation matching method as a cross-modal distance metric. It employs the deviation values from average features across different feature spaces rather than the values of features themselves to search for similar components from caricature examples directly. Compared with traditional example-based systems, the proposed system can generate various styles of caricatures without requiring paired photograph–caricature example databases. The newly designed features can effectively capture visual characteristics of the hairstyles and facial components in input portrait images. In addition, this system can control the exaggeration of individual facial components and provide several similarity-based candidates to satisfy users’ different preferences. Experiments are conducted to prove the above ideas.