Food insufficiency and American school-aged children's cognitive, academic, and psychosocial development.

OBJECTIVE
This study investigates associations between food insufficiency and cognitive, academic, and psychosocial outcomes for US children and teenagers ages 6 to 11 and 12 to 16 years.

METHODS
Data from the Third National Health and Nutrition Examination Survey (NHANES III) were analyzed. Children were classified as food-insufficient if the family respondent reported that his or her family sometimes or often did not get enough food to eat. Regression analyses were conducted to test for associations between food insufficiency and cognitive, academic, and psychosocial measures in general and then within lower-risk and higher-risk groups. Regression coefficients and odds ratios for food insufficiency are reported, adjusted for poverty status and other potential confounding factors.

RESULTS
After adjusting for confounding variables, 6- to 11-year-old food-insufficient children had significantly lower arithmetic scores and were more likely to have repeated a grade, have seen a psychologist, and have had difficulty getting along with other children. Food-insufficient teenagers were more likely to have seen a psychologist, have been suspended from school, and have had difficulty getting along with other children. Further analyses divided children into lower-risk and higher-risk groups. The associations between food insufficiency and children's outcomes varied by level of risk.

CONCLUSIONS
The results demonstrate that negative academic and psychosocial outcomes are associated with family-level food insufficiency and provide support for public health efforts to increase the food security of American families.


Exploring the Influence of Online Consumers ’ Perception on Purchase Intention as Exemplified with an Online Bookstore

The purpose of this study is to use structural equation modeling (SEM) to explore the influence of online bookstore consumers’ perception on their purchase intention. Through literature review, four constructs were used to establish a causal relationship between perception of online shopping and consumers’ purchase intention. Questionnaires based on these four constructs were designed and distributed to the customers of an online bookstore; AMOS software as analytical tool was used to build and confirm a SEM model. Results of this study show that product perception, shopping experience, and service quality have positive and significant influence on consumers’ purchase intention, but perceived risk has negative influence on consumers’ purchase intention, and shopping experience is most important.


Application of a Lightweight Encryption Algorithm to a Quantized Speech Image for Secure IoT

The IoT Internet of Things being a promising technology of the future. It is expected to connect billions of devices. The increased communication number is expected to generate data mountain and the data security can be a threat. The devices in the architecture are fundamentally smaller in size and low powered. In general, classical encryption algorithms are computationally expensive and this due to their complexity and needs numerous rounds for encrypting, basically wasting the constrained energy of the gadgets. Less complex algorithm, though, may compromise the desired integrity. In this paper we apply a lightweight encryption algorithm named as Secure IoT (SIT) to a quantized speech image for Secure IoT. It is a 64-bit block cipher and requires 64-bit key to encrypt the data. This quantized speech image is constructed by first quantizing a speech signal and then splitting the quantized signal into frames. Then each of these frames is transposed for obtaining the different columns of this quantized speech image. Simulations result shows the algorithm provides substantial security in just five encryption rounds. Keywords—IoT, Security, Encryption, quantized speech image, SNR, PESQ, Histogram, Entropy, Correlation.


Conceptual data structures for personal knowledge management

Structured Abstract Purpose – Designing a model and tools that are (a) capable of representing and handling personal knowledge in different degrees of structuredness and formalisation and (b) usable and extensible by end-users. Design/methodology/approach – This paper presents the result of analysing literature and various data-models and formalism used to structure information on the desktop. Findings – The unified data model (CDS) is capable of representing structures from various information tools, e. g. documents, file system, hypertext, tagging, and mind maps. The five knowledge axes of CDS are: identity, order, hierarchy, annotation and linking. Research limitations / implications – The CDS model is based on text. Extensions for multimedia annotations have not been investigated. Practical implications – Future PKM tools should take the mentioned shortcoming of existing PKM tools into account. Implementing the CDS model can be a way to make PKM tools interoperable. Originality/value – This paper presents research combining cognitive psychology, personal knowledge management and semantic web technologies. The CDS model shows a way to let end-users work on different levels of granularity and different levels of formality in one environment. Introduction " The most important contribution of management in the 20th century was to increase manual worker productivity fifty-fold. The most important contribution of management in the 21st century will be to increase knowledge worker productivity – hopefully by the same percentage. [...] The methods, however, are totally different from those that increased the productivity of manual workers. " Drucker (1999a, p. 79) What might these methods be? The field of knowledge management investigates since 1995 (Stankosky, 2005) how people and knowledge work together. North (2007) defines knowledge work as work based on knowledge with an immaterial result; value creation is based on processing, generating and communicating knowledge. Polanyi (1966) makes a distinction between explicit knowledge encoded in artefacts such as books or web pages, and tacit knowledge which resides in the individual. The SECI-model of Nonaka (1994) describes knowledge transfers between humans and artefacts. The field of knowledge management has focused on knowledge transfer between people, either via socialisation or via externalised artefacts. The high expectations of central enterprise knowledge repositories have often not been met (Braganza and Mollenkramer, 2002). The following wave of expert finders and corporate white pages focused mostly on connecting the right people and let them communicate. Today, knowledge workers are flooded with information (Alvarado et al., 2003). The field of Personal …


Age Progression/Regression by Conditional Adversarial Autoencoder

If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.


Social Recommendation in Location Based Social Network Using Text Mining

The rapid growth of location-based social networks (LBSNs) has greatlyenriched people’s urban experience through social media and attracted increasingnumber of users in recent years. Typical location-based social networking sites allowusers to “check in” at a physical place and share the location with their onlinefriends, and therefore bridge the gap between the real world and online social networks.The purpose is that on the finalextraction of information, the results are worked for SocialRecommendation. However, the recommender systems presentsome failures in the filtering of the results and the way theyaresuggested to users.The method used here intendsto create an application thatperforms the Data Mining with textual information linked togeo-location data. The availability of large amounts of geographical and social data on LBSNsprovides an unprecedented opportunity to study human mobile behaviour throughdata analysis in a spatial-temporal-social context, enabling a variety of locationbasedservices, from mobile marketing to disaster relief.Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information information that can be used to increase revenue, cuts costs, or both.


A deep neuro-fuzzy method for multi-label malware classification and fuzzy rules extraction

Modern malware represents an emerging threat not only to individual privacy, but also to organizational security. Over the last few decades, a number of malware families have been codified, differentiated by their behaviour and functionality. The majority of machine learning methods work well with the general benign-malicious classification yet fail to distinguish among many classes. Deep learning is known to be successful for such applications, Deep Neural Networks in particular. There is no way however to extract an understandable model in order to characterize each malware family separately. Thus, the goal of this paper is many-fold: we investigated multinomial malware categorization using static and dynamic behavioural features extracted from Windows Portable Executables (PE32) malware collection as of the end of 2015. Furthermore, we proposed a novel Deep Neuro-Fuzzy architecture, a Hybrid Intelligence approach capable of boosting the performance of conventional Neuro-Fuzzy method targeting rules extraction. It proved capable of achieving a 49.335% better result than classical Neuro-Fuzzy and 7.347% improvement over the same complexity Deep Neural Network (DNN). Moreover, we studied the Deep Neural Network behaviour when handling such non-trivial problems as multinomial malware categorization. We believe that this model contributes to Windows PE32 malware categorization by improving the similarity-based detection of zero-days malware. It can serve as a stepping stone for malware analysts to speed up the detection and dissemination.


Realization by Biped Leg-wheeled Robot of Biped Walking and Wheel-driven Locomotion

Biped walking is easily adaptable to rough terrain such as stairs and stony paths, but the walk speed and energy efficiency on the flat ground is not so effective compared with wheeled locomotion. In this paper, we propose a biped robot to be able to walk or wheel according to the ground conditions. For wheeled locomotion, WS-2 (Waseda Shoes -No. 2) is developed which is composed of a DC motor, a spherical caster and two rubber pads on each foot. WS-2 is attached to the feet of WL-16 (Waseda Leg -No. 16) that is the world's first biped-walking robot capable of carrying a human. Also, a path planning for wheeled locomotion is presented. Through hardware experiments, the effectiveness of this foot module is confirmed.


Usability, quality, value and e-learning continuance decisions

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

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


Broadcasting Intermediate Blocks as a Defense Mechanism Against Selfish-Mine in Bitcoin

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

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

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.


Working Memory: A View from Neuroimaging

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

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

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.

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.

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

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

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

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

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.

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

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

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.


An evaluation study of BigData frameworks for graph processing

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

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

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.

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

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

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

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

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.


Lessons from applying the systematic literature review process within the software engineering domain

A consequence of the growing number of empirical studies in software engineering is the need to adopt systematic approaches to assessing and aggregating research outcomes in order to provide a balanced and objective summary of research evidence for a particular topic. The paper reports experiences with applying one such approach, the practice of systematic literature review, to the published studies relevant to topics within the software engineering domain. The systematic literature review process is summarised, a number of reviews being undertaken by the authors and others are described and some lessons about the applicability of this practice to software engineering are extracted. The basic systematic literature review process seems appropriate to software engineering and the preparation and validation of a review protocol in advance of a review activity is especially valuable. The paper highlights areas where some adaptation of the process to accommodate the domain-specific characteristics of software engineering is needed as well as areas where improvements to current software engineering infrastructure and practices would enhance its applicability. In particular, infrastructure support provided by software engineering indexing databases is inadequate. Also, the quality of abstracts is poor; it is usually not possible to judge the relevance of a study from a review of the abstract alone. 2006 Elsevier Inc. All rights reserved.


SLR-Tool - A Tool for Performing Systematic Literature Reviews

Systematic literature reviews (SLRs) have been gaining a significant amount of attention from Software Engineering researchers since 2004. SLRs are considered to be a new research methodology in Software Engineering, which allow evidence to be gathered with regard to the usefulness or effectiveness of the technology proposed in Software Engineering for the development and maintenance of software products. This is demonstrated by the growing number of publications related to SLRs that have appeared in recent years. While some tools exist that can support some or all of the activities of the SLR processes defined in (Kitchenham & Charters, 2007), these are not free. The objective of this paper is to present the SLR-Tool, which is a free tool and is available on the following website: http://alarcosj.esi.uclm.es/SLRTool/, to be used by researchers from any discipline, and not only Software Engineering. SLR-Tool not only supports the process of performing SLRs proposed in (Kitchenham & Charters, 2007), but also provides additional functionalities such as: refining searches within the documents by applying text mining techniques; defining a classification schema in order to facilitate data synthesis; exporting the results obtained to the format of tables and charts; and exporting the references from the primary studies to the formats used in bibliographic packages such as EndNote, BibTeX or Ris. This tool has, to date, been used by members of the Alarcos Research Group and PhD students, and their perception of it is that it is both highly necessary and useful. Our purpose now is to circulate the use of SLR-Tool throughout the entire research community in order to obtain feedback from other users.


Evidence-based software engineering

Objective: Our objective is to describe how softwareengineering might benefit from an evidence-basedapproach and to identify the potential difficultiesassociated with the approach.Method: We compared the organisation and technicalinfrastructure supporting evidence-based medicine (EBM)with the situation in software engineering. We consideredthe impact that factors peculiar to software engineering(i.e. the skill factor and the lifecycle factor) would haveon our ability to practice evidence-based softwareengineering (EBSE).Results: EBSE promises a number of benefits byencouraging integration of research results with a view tosupporting the needs of many different stakeholdergroups. However, we do not currently have theinfrastructure needed for widespread adoption of EBSE.The skill factor means software engineering experimentsare vulnerable to subject and experimenter bias. Thelifecycle factor means it is difficult to determine howtechnologies will behave once deployed.Conclusions: Software engineering would benefit fromadopting what it can of the evidence approach providedthat it deals with the specific problems that arise from thenature of software engineering.


In this paper, we empirically investigate an extension of the Technology Acceptance Model (TAM, Davis, 1989) to explain the individual acceptance and usage of websites. Conceptually, we examine perceived ease-of-use, usefulness, enjoyment, and their impact on attitude towards using, intention to use and actual use. The paper also introduces a new construct, “perceived visual attractiveness” of the website and suggest that it influences usefulness, enjoyment, and ease-of-use. For our empirical research we partnered with a Dutch generic portal site with over 300 000 subscribers at the time the research was conducted. The websurvey resulted in sample size of 825 respondents. The results confirmed all of the 12 hypotheses formulated. Three findings are worth mentioning in particular: (1) intention is most dominantly influenced by attitude (β = 0.51), (2) ease-of-use, enjoyment, and usefulness contribute equally to attitude towards using (β = 0.23, 0.23, and 0.17 respectively) and (3) visual attractiveness contributes remarkably well to both ease-of-use, enjoyment, and usefulness (β = 0.41, 0.35, and 0.21). Although this is not the first research to apply TAM to an internet context, we claim three major contributions: (1) a single website as the unit of analysis, (2) the introduction of visual attractiveness, and (3) the use of “real” website visitors rather than student samples. Promising future research lies in the conceptual connection between actual website features and website use, a connection for which the TAM framework provides a meaningful bridge. Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands


A Comparative Study of Data Clustering Algorithms

Data clustering is a process of partitioning data points into meaningful clusters such that a cluster holds similar data and different clusters hold dissimilar data. It is an unsupervised approach to classify data into different patterns. In general, the clustering algorithms can be classified into the following two categories: firstly, hard clustering, where a data object can belong to a single and distinct cluster and secondly, soft clustering, where a data object can belong to different clusters. In this report we have made a comparative study of three major data clustering algorithms highlighting their merits and demerits. These algorithms are: k-means, fuzzy c-means and K-NN clustering algorithm. Choosing an appropriate clustering algorithm for grouping the data takes various factors into account for illustration one is the size of data to be partitioned.


A low-Cost, Open-Source, BCI- VR Game Control Development Environment Prototype for Game Based Neurorehabilitation

In this paper we present a low-cost and open-source brain-computer interface (BCI) virtual-reality (VR) Game Control Development Environment prototype, which we demonstrate using real-time signal processing of Electroencephalography (EEG) event-related desynchronization and synchronization changes (ERD/ERS) within the Precentral Gyrus (Motor Cortex), allowing a user to control a 3D object within a Virtual Reality Environment. This BCI-VR system prototype was functionally tested on multiple participants and demonstrated live before an audience during the 2017 ‘Hack the Brain’ at the Dublin Science Gallery. The availability of such an open-source, effective, BCI-VR Game Control Development Environment, at a level acceptable for industry experimentation, has the potential to open up this field to a much wider range of researchers and games developers and to assist the investigation of gaming experiences which both incorporate the specific control features available through BCI as a core element of the game play and the potential for its use in neurorehabilitation.


Self-esteem and perceived regard: how I see myself affects my relationship satisfaction.

The authors explored the relations among self-esteem, perceived regard, and satisfaction in dating relationships. On the basis of the dependency regulation model (T. DeHart, B. Pelham, & S. Murray, 2004), the authors hypothesized that self-esteem would influence individuals' self-perceptions and views of how their partners perceive them (metaperception). They also hypothesized that perceived regard (self-perception minus metaperception) would predict relationship satisfaction. Regression analyses indicated that for moderate relationship-relevant traits (i.e., caring, loving), high self-esteem was associated with self-enhancement (idealization), whereas low self-esteem was associated with self-deprecation. For low relationship-relevant traits (i.e., quiet, reserved), both low and high self-esteem individuals self-enhanced. Hierarchical regression analyses indicated that self-esteem and perceived regard for moderate relationship-relevant traits predicted satisfaction. The authors discuss the implications of idealization, self-verification, and self-deprecation for the perceivers and their relationships.


A systematic survey on automated concurrency bug detection, exposing, avoidance, and fixing techniques

Currently, concurrent programs are becoming increasingly widespread to meet the demands of the rapid development of multi-core hardware. However, it could be quite expensive and challenging to guarantee the correctness and efficiency of concurrent programs. In this paper, we provide a systematic review of the existing research on fighting against concurrency bugs, including automated concurrency bug exposing, detection, avoidance, and fixing. These four categories cover the different aspects of concurrency bug problems and are complementary to each other. For each category, we survey the motivation, key issues, solutions, and the current state of the art. In addition, we summarize the classical benchmarks widely used in previous empirical studies and the contribution of active research groups. Finally, some future research directions on concurrency bugs are recommended. We believe this survey would be useful for concurrency programmers and researchers.


[Skin signs in child abuse].

Child abuse is far more prevalent today than is generally recognized. Up to 90% of victims suffer physical abuse that can be observed in signs on the skin. Dermatologists are particularly qualified to identify these signs and distinguish them from other conditions that can mimic abuse. This review covers the signs of child abuse that can be observed on the skin. We discuss clues that can help differentiate between lesions caused by abuse and those that are accidental, and we describe the skin conditions that mimic physical abuse.


Entity Resolution: Theory, Practice & Open Challenges

This tutorial brings together perspectives on ER from a variety of fields, including databases, machine learning, natural language processing and information retrieval, to provide, in one setting, a survey of a large body of work. We discuss both the practical aspects and theoretical underpinnings of ER. We describe existing solutions, current challenges, and open research problems.


Survey on 3D Hand Gesture Recognition

Three-dimensional hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 1) 3D hand modeling; 2) static hand gesture recognition; 3) hand trajectory gesture recognition; and 4) continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.


Model-Driven ERP Implementation

Enterprise Resource Planning (ERP) implementations are very complex. To obtain a fair level of understanding of the system, it is then necessary to model the supported business processes. However, the problem is the accuracy of the mapping between this model and the actual technical implementation. A solution is to make use of the OMG’s Model-Driven Architecture (MDA) framework. In fact, this framework lets the developer model his system at a high abstraction level and allows the MDA tool to generate the implementation details. This paper presents our results in applying the MDA framework to ERP implementation based on a high level model of the business processes. Then, we show how our prototype is structured and implemented in the IBM/Rational XDE environment


A General Divide and Conquer Approach for Process Mining

Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESEor passage-based decomposition). Moreover, the generic divide-andconquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.


Survey of Consistent Network Updates

Computer networks have become a critical infrastructure. As such, networks should not only meet strict requirements in terms of correctness, availability, and performance, but they should also be very flexible and support fast updates, e.g., due to a change in the security policy, increasing traffic, or failures. In this paper, we present a structured survey of mechanisms and protocols to update computer networks in a fast and consistent manner. In particular, we identify and discuss the different desirable consistency properties that should be provided throughout a network update, the algorithmic techniques which are needed to meet these consistency properties, and the implications on the speed and costs at which updates can be performed. We also explain the relationship between consistent network update problems and classic algorithmic optimization ones. While our survey is mainly motivated by the advent of Software-Defined Networks (SDNs) and their primary need for correct and efficient update techniques, the fundamental underlying problems are not new, and we provide a historical perspective of the subject as well. CCS Concepts: rGeneral and reference → Surveys and overviews; rNetworks → Network algorithms; rComputer systems organization→ Distributed architectures;


Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals

Prior research into learning translations from source and target language monolingual texts has treated the task as an unsupervised learning problem. Although many techniques take advantage of a seed bilingual lexicon, this work is the first to use that data for supervised learning to combine a diverse set of signals derived from a pair of monolingual corpora into a single discriminative model. Even in a low resource machine translation setting, where induced translations have the potential to improve performance substantially, it is reasonable to assume access to some amount of data to perform this kind of optimization. Our work shows that only a few hundred translation pairs are needed to achieve strong performance on the bilingual lexicon induction task, and our approach yields an average relative gain in accuracy of nearly 50% over an unsupervised baseline. Large gains in accuracy hold for all 22 languages (low and high resource) that we investigate.


HEVC/H.265 vs. VP9 state-of-the-art video coding comparison for HD and UHD applications

High Efficiency Video Coding (HEVC) and VP9 are the latest state-of-the-art high-efficiency video coding standards. Both standards aim to decrease the bit-rate while improving the compression efficiency. HEVC/H.265 video standard was collaboratively developed by ITU-T and ISO/IEC with up to 50% bit-rate reduction as compared to its predecessor H.264/AVC. On the other hand, VP9 is an open source video standard developed by Google. While maintaining the same video quality, VP9 has achieved 50% bit-rate reduction as compared to its predecessor VP8. This research paper focuses on the comparison of HEVC and VP9 based on both subjective as well as objective assessments. First, detailed differences in design and specification of the two standards are discussed. Later experiment results are provided, which compare the coding efficiency of both standards on various high-definition 720p, 1090p, and 2160p sample test bench videos. According to our experimental results, it has been empirically proved that HEVC/H.265 outperforms VP9 in bit-rate savings.


The Narrative Advantage : Gender and the Language of Crowdfunding

In this study, we set out to examine the role of language in the success of online fundraising—a new form of entrepreneurial project financing. In particular, we evaluate the influence of linguistic content on fundraising outcomes, above and beyond type of product or service offered. Online fundraising settings pose an interesting empirical puzzle: women are systematically more successful than men, an outcome contrary to offline gender inequality. We propose that this outcome is partially explained by linguistic differences between men and women in terms of language they use, and we test this mechanism using data from the online crowdfunding platform Indiegogo. The results support our theory, suggesting a link between micro-level linguistic choices and macro level outcomes: the institution of crowdfunding may reduce gender inequalities in the fundraising arena by benefitting the communication style of women.


Low voltage sub-nanosecond pulsed current driver IC for high-resolution LIDAR applications

This paper introduces a new low voltage sub-nanosecond monolithic pulsed current driver for light detection and ranging (LIDAR) applications. Unique architecture based on a controlled current source and Vernier activation sequence, combined with a monolithic implementation that allows operation with low input voltage levels, high-resolution pulse width and sub-nanosecond rise and fall times. An on-chip low voltage pulsed driver sub-nanosecond prototype has been implemented in a TS 0.18-μm 5V-gated power management process. It incorporates an integrated wide range sesnseFET based current sensor and a rail-to-rail comparator for current regulation. To characterize the avalanche capabilities of the integrated lateral MOSFET power devices required for the driver IC, a separate line of investigation has been carried out. Several lateral diffused MOS (LDMOS) power devices have been custom designed and experimentally evaluated for a life-cycle performance characterization. Post-layout analysis of the power driver IC is in a good agreement with the theoretical predictions. For a 5V input voltage, rise and fall times of the laser pulse light output are on the order of hundreds of picoseconds, with currents up to 5A. To validate the concept of high-resolution pulse width generation and short fall time, a discrete prototype has been constructed and experimentally tested.


Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval

Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large-scale cross-view retrieval tasks. Most cross-view hashing methods are developed by assuming that data from different views are well paired, e.g., text-image pairs. In real-world applications, however, this fully-paired multiview setting may not be practical. The more practical yet challenging semi-paired cross-view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi-paired cross-view retrieval, dubbed semi-paired discrete hashing (SPDH). In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi-paired data in the latent subspace, we construct the cross-view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization-based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit-by-bit manner with each bit generated with a closed-form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully-paired and semi-paired settings and the results demonstrate the superiority of SPDH over several other state-of-the-art methods in term of both accuracy and scalability.


Exploration and apprenticeship learning in reinforcement learning

We consider reinforcement learning in systems with unknown dynamics. Algorithms such as E3 (Kearns and Singh, 2002) learn near-optimal policies by using "exploration policies" to drive the system towards poorly modeled states, so as to encourage exploration. But this makes these algorithms impractical for many systems; for example, on an autonomous helicopter, overly aggressive exploration may well result in a crash. In this paper, we consider the apprenticeship learning setting in which a teacher demonstration of the task is available. We show that, given the initial demonstration, no explicit exploration is necessary, and we can attain near-optimal performance (compared to the teacher) simply by repeatedly executing "exploitation policies" that try to maximize rewards. In finite-state MDPs, our algorithm scales polynomially in the number of states; in continuous-state linear dynamical systems, it scales polynomially in the dimension of the state. These results are proved using a martingale construction over relative losses.


Simple, Accurate, and Robust Nonparametric Blind Super-Resolution

This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality.


The Role of the Gut Microbiota in Childhood Obesity.

BACKGROUND
Childhood and adolescent obesity has reached epidemic proportions worldwide. The pathogenesis of obesity is complex and multifactorial, in which genetic and environmental contributions seem important. The gut microbiota is increasingly documented to be involved in the dysmetabolism associated with obesity.

METHODS
We conducted a systematic search for literature available before October 2015 in the PubMed and Scopus databases, focusing on the interplay between the gut microbiota, childhood obesity, and metabolism.

RESULTS
The review discusses the potential role of the bacterial component of the human gut microbiota in childhood and adolescent-onset obesity, with a special focus on the factors involved in the early development of the gut bacterial ecosystem, and how modulation of this microbial community might serve as a basis for new therapeutic strategies in combating childhood obesity. A vast number of variables are influencing the gut microbial ecology (e.g., the host genetics, delivery method, diet, age, environment, and the use of pre-, pro-, and antibiotics); but the exact physiological processes behind these relationships need to be clarified.

CONCLUSIONS
Exploring the role of the gut microbiota in the development of childhood obesity may potentially reveal new strategies for obesity prevention and treatment.


Determination of the plan of the A Famosa Fortress , Malaysia

The “A Famosa Fortress” is one of the oldest partially extant European buildings in Malaysia. It was built in 1511 by the Portuguese and went through several architectural developments and changes before being largely destroyed during the British occupation in 1824. With the subsequent overbuilding of the site by Melaka city today, it is impossible to fully reconstruct this fortress in its physical context. In this paper, we focus on determining the fortress layout based on various textual descriptions and old drawings and plans in preparation to building a detailed 3-D digital model. We have identified several important issues arising from the lack of any authoritative documentation. Such plans as exist not only differ in their depiction of the fort, but also use various ancient measurement systems. The paper gives examples of these problems and shows how a verifiable conjectural layout has been constructed. This is then compared against such archaeological evidence as is currently available. We are not aware of any previously published attempt to verify the consistency, similarity and integrity of the documentary data.


Learning effective Gait features using LSTM

Human gait is an important biometric feature for person identification in surveillance videos because it can be collected at a distance without subject cooperation. Most existing gait recognition methods are based on Gait Energy Image (GEI). Although the spatial information in one gait sequence can be well represented by GEI, the temporal information is lost. To solve this problem, we propose a new feature learning method for gait recognition. Not only can the learned feature preserve temporal information in a gait sequence, but it can also be applied to cross-view gait recognition. Heatmaps extracted by a convolutional neutral network (CNN) based pose estimate method are used to describe the gait information in one frame. To model a gait sequence, the LSTM recurrent neural network is naturally adopted. Our LSTM model can be trained with unlabeled data, where the identity of the subject in a gait sequence is unknown. When labeled data are available, our LSTM works as a frame to frame view transformation model (VTM). Experiments on a gait benchmark demonstrate the efficacy of our method.


Intervention Studies on Forgiveness : A Meta-Analysis

79 A promising area of counseling research that emerged in the 1990s is the scientific investigation of forgiveness interventions. Although the notion of forgiving is ancient (Enright & the Human Development Study Group, 1991), it has not been systematically studied until relatively recently (Enright, Santos, & Al-Mabuk, 1989). Significant to counseling because of its interpersonal nature, forgiveness issues are relevant to the contexts of marriage and dating relationships, parent–child relationships, friendships, professional relationships, and others. In addition, forgiveness is integral to emotional constructs such as anger. As forgiveness therapies (Ferch, 1998; Fitzgibbons, 1986) and the empirical study of these therapies (Freedman & Enright, 1996) begin to unfold, it is important to ask if these interventions can consistently demonstrate salient positive effects on levels of forgiveness and on the mental health of targeted clients. The purpose of this article is to analyze via meta-analysis the existing published interventions on forgiveness. Meta-analysis is a popular vehicle of synthesizing results across multiple studies. Recent successful uses of this method include the study by McCullough (1999), who analyzed five studies that compared the efficacy for depression of standard approaches with counseling with religion-accommo-dative approaches. Furthermore, in order to reach conclusions about the influence of hypnotherapy on treatment for clients with obesity, Allison and Faith (1996) used meta-analysis to examine six studies that compared the efficacy of using cognitive-behavioral therapy (CBT) alone with the use of CBT combined with hypnotherapy. Finally, Morris, Audet, Angelillo, Chalmers, and Mosteller (1992) used meta-analysis to combine the results of 10 studies with contradictory findings to show that the benefits of chlorinating drinking water far outweighed the risks. Although there may be some concern that using forgiveness as an intervention in counseling is in too early a stage of development and that too few studies exist for a proper meta-analysis, the effectiveness of these recent meta-analyses supports this meta-analytic investigation. Certainly any findings must be tempered with due caution. However, this analysis may serve as important guidance for the structure and development of future counseling studies of forgiveness. We first examine the early work in forgiveness interventions by examining the early case studies. From there, we define forgiveness, discuss the models of forgiveness in counseling and the empirically based interventions, and then turn to the meta-analysis. The early clinical case studies suggested that forgiveness might be helpful for people who have experienced deep emotional pain because of unjust treatment. For …


Supervised Representation Learning for Audio Scene Classification

This paper investigates the use of supervised feature learning approaches for extracting relevant and discriminative features from acoustic scene recordings. Owing to the recent release of open datasets for acoustic scene classification problems, representation learning techniques can now be envisioned for solving the problem of feature extraction. This paper makes a step toward this goal by first introducing a supervised nonnegative matrix factorization SNMF. Our goal through this SNMF is to induce the matrix decomposition to carry out discriminative information in addition to the usual generative ones. We achieve this objective by augmenting the nonnegative matrix factorization optimization problem with a novel loss function related to class labels of each column of the matrix to decompose. While the scale of the datasets available is still small compared to those available in computer vision, we have studied models based on convolutional neural networks. We have analyzed the performances of these models on the DCASE-16 dataset and a corrected version of the LITIS Rouen one. Our experiments show that despite the small-scale setting, supervised feature learning is favorably competitive compared to the current state-of-the-art features. We also point out that for smaller scale dataset, SNMF is indeed slightly less prone to overfitting than convolutional neural networks. While the performances of these learned features are interesting per se, a deeper analysis of their behavior in the acoustic scene problem context raises open and difficult questions that we believe, need to be addressed for further performance breakthroughs.


Cross-domain video concept detection using adaptive svms

Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.


Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

This paper demonstrates theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results, which require O(m2) measurements. The new results for OMP are comparable with recent results for another approach called basis pursuit (BP). In some settings, the OMP algorithm is faster and easier to implement, so it is an attractive alternative to BP for signal recovery problems.


DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.


Fall detection using ceiling-mounted 3D depth camera

This paper proposes an algorithm for fall detection using a ceiling-mounted 3D depth camera. The lying pose is separated from common daily activities by a k-NN classifier, which was trained on features expressing head-floor distance, person area and shape's major length to width. In order to distinguish between intentional lying postures and accidental falls the algorithm also employs motion between static postures. The experimental validation of the algorithm was conducted on realistic depth image sequences of daily activities and simulated falls. It was evaluated on more than 45000 depth images and gave 0% error. To reduce the processing overload an accelerometer was used to indicate the potential impact of the person and to start an analysis of depth images.


Improving Dermoscopic Image Segmentation With Enhanced Convolutional-Deconvolutional Networks

Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We participated and extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index of 0.765 on the 600 challenge testing images, which ranked itself in the first place among 21 final submissions in the challenge.


Retinal vessel segmentation via deep learning network and fully-connected conditional random fields

Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.


Learning a non-linear knowledge transfer model for cross-view action recognition

This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that transfers knowledge from multiple views to a canonical view. The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view. The strength of our technique is that we learn a single NKTM for all actions and all camera viewing directions. Thus, NKTM does not require action labels during learning and knowledge of the camera viewpoints during training or testing. NKTM is learned once only from dense trajectories of synthetic points fitted to mocap data and then applied to real video data. Trajectories are coded with a general codebook learned from the same mocap data. NKTM is scalable to new action classes and training data as it does not require re-learning. Experiments on the IXMAS and N-UCLA datasets show that NKTM outperforms existing state-of-the-art methods for cross-view action recognition.


Realtime ray tracing and interactive global illumination

Interaktive 3D-Computergraphik basiert heutzutage fast ausschliesslich auf dem Rasterisierungsverfahren. Dieses ist heute sehr effizient und günstig realisierbar, stösst aber zunehmend an seine Grenzen hinsichtlich unterstützter Szenenkomplexität und erreichbarer Darstellungsqualität. Eine Alternative zum Rasterisierungsverfahren ist das Ray Tracing Verfahren, welches zwar allgemein für bessere Bildqualität bekannt ist, aber aufgrund hoher Rechenanforderungen bisher als unvereinbar mit interaktiver Performanz galt. In dieser Arbeit geht es um die Entwicklung der Echtzeit Ray Tracing Technologie, mit der das Ray Tracing Verfahren auch für interaktive Anwendungen ermöglicht wird. Die im Rahmen dieser Dissertation entwickelten Techniken ermöglichen nun erstmalig interaktive Ray Tracing Performanz selbst auf Standard PCs. Die dazu entwickelten Methoden beinhalten unter anderem einen extrem schnellen Ray Tracing Kern, ein effizientes Parallelisierungsframework, die Unterstützung dynamischer und extrem komplexer Modelle, sowie eine geeignete Programmierschnittstelle. Darauf aufbauend wurden dann weiterhin Verfahren entwickelt, die es erstmalig ermöglichen, auch die globale Lichtausbreitung interaktiv zu simulieren. Die im Rahmen der Dissertation entwickelten Techniken formen ein komplettes Framework für Echtzeit-Ray Tracing und interaktive Beleuchtungssimulation, welches interaktive Performanz selbst für massiv komplexe Szenen liefert, sowie erstmalig physikalisch basierte Computergraphik unter Echtzeitbedingungen ermöglicht.


Supervised clustering of streaming data for email batch detection

We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectively by exploiting collective information about entire batches of jointly generated messages. The application matches the problem setting of supervised clustering, because examples of correct clusterings can be collected. Known decoding procedures for supervised clustering are cubic in the number of instances. When decisions cannot be reconsidered once they have been made --- owing to the streaming nature of the data --- then the decoding problem can be solved in linear time. We devise a sequential decoding procedure and derive the corresponding optimization problem of supervised clustering. We study the impact of collective attributes of email batches on the effectiveness of recognizing spam emails.


VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals

Enabling touch gesture sensing on all surfaces of the mobile device, not limited to the touchscreen area, leads to new user interaction experiences. In this paper, we propose VSkin, a system that supports fine-grained gesture-sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense finger tapping and movements. By measuring both the amplitude and the phase of each path of sound signals, VSkin detects tapping events with an accuracy of 99.65% and captures finger movements with an accuracy of 3.59mm.


An Acoustic ECHO Suppressor Based on a Frequency-Domain Model of Highly Nonlinear Residual ECHO

This paper proposes a new acoustic echo suppressor based on a frequency-domain model of highly nonlinear residual echo. The proposed echo suppressor controls the gain in each frequency bin based on the new model where the highly nonlinear residual echo is approximated as a product of a regression coefficient and the echo replica in the frequency domain. To reduce annoying modulation by the error of the model, a flooring operation of estimated near-end signal level is introduced to the gain control. Simulation results with speech data recorded by a hands-free cellphone show that the proposed echo suppressor reduces the highly nonlinear residual echo to an almost inaudible level


W ORDS OR C HARACTERS ? F INE-GRAINED

Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children’s Book Test and Who Did What datasets. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.1


Are Metamorphic Viruses Really Invincible?

Metamorphic viruses enjoy the apparent invincibility because a virus writer has the advantage of knowing the weak spots of AV technologies. We could turn the tables if we can identify similar weak spots in a metamorphic virus. Indeed, Lakhotia and Singh close their otherwise gloomy article with one bright spot: " The good news is that a virus writer is confronted with the same theoretical limit as anti-virus technologies… It may be worth contemplating how this could be used to the advantage of anti-virus technologies. " SUMMARY In the game of " hide and seek, " where a virus tries to hide and the AV scanners tries to seek, the winner is the one that can take advantage of the other's weak spot. So far a virus writer has enjoyed the upper hand for she could exploit the limitations of AV technologies. Metamorphic viruses are particularly insidious in taking such advantage. A metamorphic virus thwarts detection by signature-based (static) AV technologies by morphing its code as it propagates. A virus can also thwart detection by emulation-based (dynamic) technologies. To do so it needs to detect whether it is running in an emulator and change its behavior. So are metamorphic viruses invincible? This paper investigates the above remark and identifies what promises to be the Achilles' heel of a metamorphic virus. The key observation is that in order to mutate its code, generations after generations, a metamorphic virus must analyze its own code. Thus, it too must face the limits of static and dynamic analyses. Beyond that a metamorphic virus has another constraint: it must be able to re-analyze the mutated code that it generates. Thus, the analysis within the virus, of how to transform the code in current generation, depends upon the complexity of transformations in the previous generation. To overcome the challenges of static and dynamic analyses, the virus has the following options: do not obfuscate the transformed code in any generation; use some coding conventions that can aid it in detecting its own obfuscations; or develop smart algorithms to detect its specific obfuscations. This paper uncovers the Achilles' heel of a metamorphic virus.


MapReduce network enabled algorithms for classification based on association rules


Optimizing memory transactions

Atomic blocks allow programmers to delimit sections of code as 'atomic', leaving the language's implementation to enforce atomicity. Existing work has shown how to implement atomic blocks over word-based transactional memory that provides scalable multi-processor performance without requiring changes to the basic structure of objects in the heap. However, these implementations perform poorly because they interpose on all accesses to shared memory in the atomic block, redirecting updates to a thread-private log which must be searched by reads in the block and later reconciled with the heap when leaving the block.This paper takes a four-pronged approach to improving performance: (1) we introduce a new 'direct access' implementation that avoids searching thread-private logs, (2) we develop compiler optimizations to reduce the amount of logging (e.g. when a thread accesses the same data repeatedly in an atomic block), (3) we use runtime filtering to detect duplicate log entries that are missed statically, and (4) we present a series of GC-time techniques to compact the logs generated by long-running atomic blocks.Our implementation supports short-running scalable concurrent benchmarks with less than 50\% overhead over a non-thread-safe baseline. We support long atomic blocks containing millions of shared memory accesses with a 2.5-4.5x slowdown.


Contextual gaps: privacy issues on Facebook

Social networking sites like Facebook are rapidly gaining in popularity. At the same time, they seem to present significant privacy issues for their users. We analyze two of Facebooks’s more recent features, Applications and News Feed, from the perspective enabled by Helen Nissenbaum’s treatment of privacy as “contextual integrity.” Offline, privacy is mediated by highly granular social contexts. Online contexts, including social networking sites, lack much of this granularity. These contextual gaps are at the root of many of the sites’ privacy issues. Applications, which nearly invisibly shares not just a users’, but a user’s friends’ information with third parties, clearly violates standard norms of information flow. News Feed is a more complex case, because it involves not just questions of privacy, but also of program interface and of the meaning of “friendship” online. In both cases, many of the privacy issues on Facebook are primarily design issues, which could be ameliorated by an interface that made the flows of information more transparent to users.


Map-based navigation in mobile robots:: II. A review of map-learning and path-planning strategies

This article reviews map-learning and path-planning strategies within the context of mapbased navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the coherency of these maps. Concerning path-planning, it distinguishes continuous from discretized spaces and describes procedures applicable when the execution of a plan fails. It insists on the need for an integrated conception of such procedures, that must be tightly tailored to the specific robot that is used notably to the capacities and limitations of its sensory-motor equipment and to the specific environment that is experienced. A hierarchy of navigation strategies is outlined in the discussion, together with the sort of adaptive capacities each affords to cope with unexpected obstacles or dangers encountered on an animat or robot’s way to its goal.


Effective Defense Schemes for Phishing Attacks on Mobile Computing Platforms

Recent years have witnessed the increasing threat of phishing attacks on mobile computing platforms. In fact, mobile phishing is particularly dangerous due to the hardware limitations of mobile devices and the habits of mobile users. In this paper, we did a comprehensive study on the security vulnerabilities caused by mobile phishing attacks, including web page phishing attacks, application phishing attacks, and account registry phishing attacks. Existing schemes designed for web phishing attacks on personal computers (PCs) cannot effectively address the various phishing attacks on mobile devices. Hence, we propose MobiFish, which is a novel automated lightweight antiphishing scheme for mobile platforms. MobiFish verifies the validity of web pages, applications, and persistent accounts by comparing the actual identity to the claimed identity. MobiFish has been implemented on a Nexus 4 smartphone running the Android 4.2 operating system. We experimentally evaluate the performance of MobiFish with 100 phishing URLs and corresponding legitimate URLs, as well as phishing apps. The results show that MobiFish is very effective in detecting phishing attacks on mobile phones.


Knowledge actionability: satisfying technical and business interestingness

Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.


Results of syndactyly release using a modification of the Flatt technique.

The results of 144 congenital syndactyly releases over a 12-year period by a single surgeon using a modified Flatt technique (dorsal hourglass flap, interdigitating zigzag flaps, and full-thickness skin grafts) are analyzed considering the association of skin grafts and web creep. The mean follow-up was 5 years. There were seven cases of graft failure, only one of which developed web creep. Web creep occurred in 4.2% of web releases. The results suggest that avoiding longitudinal straight-line scars across the web space may be an important factor in avoiding web creep when performing the modified Flatt technique described.