Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems

In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the HHH matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes HTHx, where x is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the HTH matrix. We also propose a simple estimation scheme which directly obtains an estimate of HTH (instead of an estimate of H), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of H. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.


A unified distance measurement for orientation coding in palmprint verification

Orientation coding based palmprint verification methods, such as competitive code, palmprint orientation code and robust line orientation code, are state-of-the-art verification algorithms with fast matching speeds. Orientation code makes use of two types of distance measure, SUM_XOR (angular distance) and OR_XOR (Hamming distance), yet little is known about the similarities and differences both SUM_XOR and OR_XOR can be regarded as special cases, and provide some principles for determining the parameters of the unified distance. Experimental results show that, using the same feature extraction and coding methods, the unified distance measure gets lower equal error rates than the original distance measures. & 2009 Elsevier B.V. All rights reserved.


Group recommendations with rank aggregation and collaborative filtering

The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendations for a group the system must satisfy, as much as possible, the individual preferences of the group's members.
 This paper analyzes the effectiveness of group recommendations obtained aggregating the individual lists of recommendations produced by a collaborative filtering system. We compare the effectiveness of individual and group recommendation lists using normalized discounted cumulative gain. It is observed that the effectiveness of a group recommendation does not necessarily decrease when the group size grows. Moreover, when individual recommendations are not effective a user could obtain better suggestions looking at the group recommendations. Finally, it is shown that the more alike the users in the group are, the more effective the group recommendations are.


Lightweight Monitoring of Distributed Streams

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds.
 Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated.
 Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB’s superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.


Comparison of vision-based and sensor-based systems for joint angle gait analysis

Gait analysis has become recently a popular research field and been widely applied to clinical diagnosis of neurodegenerative diseases. Various low-cost sensor-based and vision-based systems are developed for capturing the hip and knee joint angles. However, the performances of these systems have not been validated and compared between each other. The purpose of this study is to set up an experiment and compare the performances of a sensor-based system with multiple inertial measurement units (IMUs), a vision-based gait analysis system with marker detection, and a markerless vision-based system on capturing the hip and knee joint angles during normal walking. The obtained measurements were validated with the data acquired from goniometers as ground truth measurement. The results indicate that the IMUs-based sensor system gives excellent performance with small errors, while vision systems produce acceptable results with slightly larger errors.


Speed, Data, and Ecosystems: The Future of Software Engineering

An evaluation of recent industrial and societal trends revealed three key factors driving software engineering's future: speed, data, and ecosystems. These factors' implications have led to guidelines for companies to evolve their software engineering practices. This article is part of a special issue on the Future of Software Engineering.


Convolutional Neural Network for Brain MR Imaging Extraction Using Silver-Standards Masks

Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required for the training stage. Usually, manual annotation is considered to be the “gold-standard’. However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. Moreover, the single-rater manual annotation is often used in data-driven approaches making the algorithm optimal for one guideline only. In this work, we propose a convolutional neural network (CNN) for brain magnetic resonance (MR) imaging extraction, fully trained with what we refer to as silver-standard masks. To the best of our knowledge, our approach is the first deep learning approach that is fully trained using silver-standards masks, having an optimal generalization. Furthermore, regarding the Dice coefficient, we outperform the current skull-stripping (SS) state-of-the-art method in three datasets. Our method consists of 1) a dataset with silver-standard masks as input, 2) a tri-planar method using parallel 2D U-Net-based CNNs, which we refer to as CONSNet, and 3) an auto-context implementation of CONSNet. CONSNet refers to our integrated approach, i.e., training with silver-standard masks and us∗Corresponding author Email address: oeslle@dca.fee.unicamp.br (Oeslle Lucena) URL: miclab.fee.unicamp.br (Oeslle Lucena) Preprint submitted to Medical Image Analysis April 13, 2018 ar X iv :1 80 4. 04 98 8v 1 [ cs .C V ] 1 3 A pr 2 01 8 ing a CNN architecture. Masks are generated by forming a consensus from a set of publicly available automatic segmentation methods using the simultaneous truth and performance level estimation (STAPLE) algorithm. The CNN architecture is robust, with dropout and batch normalization as regularizers. It was inspired by the 2D U-Net model published by the RECOD group. We conducted our analysis using three publicly available datasets: the Calgary-Campinas-359 (CC-359 ), the LONI Probabilistic Brain Atlas (LPBA40), and the Open Access Series of Imaging Studies (OASIS). Five performance metrics were used in our experiments: Dice coefficient, sensitivity, specificity, Hausdorff distance, and symmetric surface-to-surface mean distance. Our results also demonstrate that we have comparable overall performance versus current methods, including recently proposed deep learningbased strategies that were trained using manually annotated masks. Our usage of silver-standard masks, however, reduced the cost of manual annotation, diminished inter-/intra-rater variability, and avoided CNN segmentation super-specialization towards one specific SS method found when mask generation is done through an agreement among many automatic methods. Moreover, usage of silver-standard masks enlarges the volume of input annotated data since we can generate annotation from unlabelled data. Also, our method has the advantage that, once trained, it takes only a few seconds to process a typical brain image volume using a modern graphics processing unit (GPU). In contrast, many of the other competitive consensus-building methods have processing times on the order of minutes.


Evidence for sensory prediction deficits in schizophrenia.

OBJECTIVE
Patients with schizophrenia experiencing delusions and hallucinations can misattribute their own actions to an external source. The authors test the hypothesis that patients with schizophrenia have defects in their ability to predict the sensory consequences of their actions.

METHOD
The authors measured sensory attenuation of self-produced stimuli by patients with schizophrenia and by healthy subjects.

RESULTS
Patients with schizophrenia demonstrated significantly less sensory attenuation than healthy subjects.

CONCLUSIONS
Patients with a diagnosis of schizophrenia have a dysfunction in their predictive mechanisms.


Implementing an Effective Test Automation Framework

Testing automation tools enable developers and/or testers to easily automate the entire process of testing in software development. Nevertheless, adopting automated testing is not easy and unsuccessful due to a lack of key information and skills. In order to help solve such problems, we have designed a new framework to support a clear overview of the test design and/or plan for automating tests in distributed environments. Those that are new to testing do not need to delve into complex automation tools or test scripts. This framework allows a programmer or tester to graphically specify the granularity of execution control and aids communication between various stakeholders and software developers. It also enables them to perform the automated Continuous Integration environment. In this paper, we describe details on experiences and usage of the proposed framework.


Synonym set extraction from the biomedical literature by lexical pattern discovery

Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst [1], but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.


Age differences in the neural systems supporting human allocentric spatial navigation

Age-related declines in spatial navigation are well-known in human and non-human species. Studies in non-human species suggest that alteration in hippocampal and other neural circuitry may underlie behavioral deficits associated with aging but little is known about the neural mechanisms of human age-related decline in spatial navigation. The purpose of the present study was to examine age differences in functional brain activation during virtual environment navigation. Voxel-based analysis of activation patterns in young subjects identified activation in the hippocampus and parahippocampal gyrus, retrosplenial cortex, right and left lateral parietal cortex, medial parietal lobe and cerebellum. In comparison to younger subjects, elderly participants showed reduced activation in the hippocampus and parahippocampal gyrus, medial parietal lobe and retrosplenial cortex. Relative to younger participants elderly subjects showed increased activation in anterior cingulate gyrus and medial frontal lobe. These results provide evidence of age specific neural networks supporting spatial navigation and identify a putative neural substrate for age-related differences in spatial memory and navigational skill.


Advanced LSTM: A Study About Better Time Dependency Modeling in Emotion Recognition

Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However, conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM.


Carpenter: finding closed patterns in long biological datasets

The growth of bioinformatics has resulted in datasets with new characteristics. These datasets typically contain a large number of columns and a small number of rows. For example, many gene expression datasets may contain 10,000-100,000 columns but only 100-1000 rows.Such datasets pose a great challenge for existing (closed) frequent pattern discovery algorithms, since they have an exponential dependence on the average row length. In this paper, we describe a new algorithm called CARPENTER that is specially designed to handle datasets having a large number of attributes and relatively small number of rows. Several experiments on real bioinformatics datasets show that CARPENTER is orders of magnitude better than previous closed pattern mining algorithms like CLOSET and CHARM.


Incorporating intra-class variance to fine-grained visual recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.


Social-information-processing factors in reactive and proactive aggression in children's peer groups.

We examined social-information-processing mechanisms (e.g., hostile attributional biases and intention-cue detection deficits) in chronic reactive and proactive aggressive behavior in children's peer groups. In Study 1, a teacher-rating instrument was developed to assess these behaviors in elementary school children (N = 259). Reactive and proactive scales were found to be internally consistent, and factor analyses partially supported convergent and discriminant validities. In Study 2, behavioral correlates of these forms of aggression were examined through assessments by peers (N = 339). Both types of aggression related to social rejection, but only proactively aggressive boys were also viewed as leaders and as having a sense of humor. In Study 3, we hypothesized that reactive aggression (but not proactive aggression) would occur as a function of hostile attributional biases and intention-cue detection deficits. Four groups of socially rejected boys (reactive aggressive, proactive aggressive, reactive-proactive aggressive, and nonaggressive) and a group of average boys were presented with a series of hypothetical videorecorded vignettes depicting provocations by peers and were asked to interpret the intentions of the provocateur (N = 117). Only the two reactive-aggressive groups displayed biases and deficits in interpretations. In Study 4, attributional biases and deficits were found to be positively correlated with the rate of reactive aggression (but not proactive aggression) displayed in free play with peers (N = 127). These studies supported the hypothesis that attributional biases and deficits are related to reactive aggression but not to proactive aggression.


n ensemble learning framework for anomaly detection in building nergy consumption

During building operation, a significant amount of energy is wasted due to equipment and humanrelated faults. To reduce waste, today’s smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energysaving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers


The Other Side of the Coin: A Framework for Detecting and Analyzing Web-based Cryptocurrency Mining Campaigns

Mining for crypto currencies is usually performed on high-performance single purpose hardware or GPUs. However, mining can be easily parallelized and distributed over many less powerful systems. Cryptojacking is a new threat on the Internet and describes code included in websites that uses a visitor's CPU to mine for crypto currencies without the their consent. This paper introduces MiningHunter, a novel web crawling framework which is able to detect mining scripts even if they obfuscate their malicious activities. We scanned the Alexa Top 1 million websites for cryptojacking, collected more than 13,400,000 unique JavaScript files with a total size of 246 GB and found that 3,178 websites perform cryptocurrency mining without their visitors' consent. Furthermore, MiningHunter can be used to provide an in-depth analysis of cryptojacking campaigns. To show the feasibility of the proposed framework, three of such campaigns are examined in detail. Our results provide the most comprehensive analysis to date of the spread of cryptojacking on the Internet.


Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems. This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. The algorithm often outperforms the well-known ReliefF attribute estimator when used as a preprocessing step for naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees. It performs more feature selection than ReliefF does—reducing the data dimensionality by fifty percent in most cases. Also, decision and model trees built from the preprocessed data are often significantly smaller.


Regression Shrinkage and Selection via the Lasso

We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients hat are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.


Algorithms for Infinitely Many-Armed Bandits

We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new selected arm which characterizes its probability of being a near-optimal arm. Our assumption is weaker than in previous works. We describe algorithms based on upper-confidence-bounds applied to a restricted set of randomly selected arms and provide upper-bounds on the resulting expected regret. We also derive a lower-bound which matches (up to a logarithmic factor) the upper-bound in some cases.


Wrappers for Feature Subset Selection

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach and show a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes. @ 1997 Elsevier Science B.V.


Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.


Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.


Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach

Weather forecasting is the process of recording the parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. Back Propagation Algorithm can be applied on these parameters in order to predict the future weather. In “Classification and Prediction of Future Weather by using Back Propagation Algorithm” technique, one parameter say temperature varies by some unit and the variation on other parameters say humidity, temperature and rainfall will be predicted with respect to temperature. In this paper, different models which were used in the past for weather forecasting are discussed. This paper is focusing on three parts, first is different models which were used in weather forecasting, second part is introducing a new wireless kit use for weather forecasting and third part includes the Back Propagation Algorithm can be applied on different parameters of weather forecast. Keywords--Back Propagation Algorithm, Artificial Neural Network, Wireless kit, Classification and Prediction


Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement

Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of significance. The main goal of this paper was to summarize current video and image defogging algorithms. We first presented a review of the detection and classification method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied. The code of all algorithms will be available at <;uri xlink:href="http://www.yongxu.org/lunwen.html" xlink:type="simple">http://www.yongxu.org/lunwen.html<;/uri>.


Hardware enclaves such as Intel SGX are a promising technology to increase the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hypervisor/OS, and encryption of data in memory. However, for applications that use large amounts of memory—including most realistic databases—enclaves do not protect against access pattern leaks, where an attacker observes which locations in memory are accessed. The näıve way to address this issue, using Oblivious RAM (ORAM) primitives, adds prohibitive overhead. In this paper, we propose ObliDB, a database that co-designs both its data structures (e.g., oblivious B+ trees) and physical operators to accelerate oblivious relational queries, giving up to 329× speedup over näıve ORAM. On analytics workloads, ObliDB ranges from competitive to 19× faster than previous oblivious systems designed only for analytics, such as Opaque, and comes within 2.6× of Spark SQL. Moreover, ObliDB also supports point queries, insertions, and deletions with latencies of 3–10ms, which is 7–22× faster than previously published oblivious data structures, and makes ObliDB suitable for transactional workloads too. To our knowledge, ObliDB is the first general-purpose oblivious database to approach practical performance. PVLDB Reference Format: Saba Eskandarian and Matei Zaharia. An Efficient Oblivious Database for the Public Cloud. PVLDB, 11 (4): xxxx-yyyy, 2017. DOI: https://doi.org/TBD


Colors and emotions: preferences and combinations.

Within three age groups (7-year-old children, 11-year-old children, and adults), preferences for colors and emotions were established by means of two distinct paired-comparison tasks. In a subsequent task, participants were asked to link colors to emotions by selecting an appropriate color. It was hypothesized that the number of times that each color was tied to a specific emotion would be predictable from the separate preferences for colors and emotions. Within age groups, participants had consistent preferences for colors and emotions, but preferences differed from one age group to another. Especially in the youngest group, the pattern of combinations between colors and emotions appeared to be meaningfully related to the preference order for colors and emotions.


Spark: A Big Data Processing Platform Based on Memory Computing

Spark is a memory-based computing framework which has a better ability of computing and fault tolerance, supports batch, interactive, iterative and flow calculations. In this paper, we analyze the Spark's primary framework, core technologies, and point out the advantages and disadvantages of the Spark. In the end, we make a discussion for the future trends of the Spark technologies.


Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.


An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.


Music Cognition and the Cognitive Sciences

Why should music be of interest to cognitive scientists, and what role does it play in human cognition? We review three factors that make music an important topic for cognitive scientific research. First, music is a universal human trait fulfilling crucial roles in everyday life. Second, music has an important part to play in ontogenetic development and human evolution. Third, appreciating and producing music simultaneously engage many complex perceptual, cognitive, and emotional processes, rendering music an ideal object for studying the mind. We propose an integrated status for music cognition in the Cognitive Sciences and conclude by reviewing challenges and big questions in the field and the way in which these reflect recent developments.


Competency based management: a review of systems and approaches

Purpose – Aims to review the key concepts of competency management (CM) and to propose method for developing competency method. Design/methodology/approach – Examines the CM features of 22 CM systems and 18 learning management systems. Findings – Finds that the areas of open standard (XML, web services, RDF), semantic technologies (ontologies and the semantic web) and portals with self-service technologies are going to play a significant part in the evolution of CM systems. Originality/value – Emphasizes the beneficial attributes of CM for private and public organizations.


Nonoutsourceable Scratch-Off Puzzles to Discourage Bitcoin Mining Coalitions

An implicit goal of Bitcoin's reward structure is to diffuse network influence over a diverse, decentralized population of individual participants. Indeed, Bitcoin's security claims rely on no single entity wielding a sufficiently large portion of the network's overall computational power. Unfortunately, rather than participating independently, most Bitcoin miners join coalitions called mining pools in which a central pool administrator largely directs the pool's activity, leading to a consolidation of power. Recently, the largest mining pool has accounted for more than half of network's total mining capacity. Relatedly, "hosted mining" service providers offer their clients the benefit of economies-of-scale, tempting them away from independent participation. We argue that the prevalence of mining coalitions is due to a limitation of the Bitcoin proof-of-work puzzle -- specifically, that it affords an effective mechanism for enforcing cooperation in a coalition. We present several definitions and constructions for "nonoutsourceable" puzzles that thwart such enforcement mechanisms, thereby deterring coalitions. We also provide an implementation and benchmark results for our schemes to show they are practical.


LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications

This article provides an overview of the long-term evolution-vehicle (LTE-V) standard supporting sidelink or vehicle-to-vehicle (V2V) communications using LTE's direct interface named PC5 in LTE. We review the physical layer changes introduced under Release 14 for LTE-V, its communication modes 3 and 4, and the LTE-V evolutions under discussion in Release 15 to support fifth-generation (5G) vehicle-to-everything (V2X) communications and autonomous vehicles' applications. Modes 3 and 4 support direct V2V communications but differ on how they allocate the radio resources. Resources are allocated by the cellular network under mode 3. Mode 4 does not require cellular coverage, and vehicles autonomously select their radio resources using a distributed scheduling scheme supported by congestion control mechanisms. Mode 4 is considered the baseline mode and represents an alternative to 802.11p or dedicated shortrange communications (DSRC). In this context, this article also presents a detailed analysis of the performance of LTE-V sidelink mode 4, and proposes a modification to its distributed scheduling.


MatrixWave: Visual Comparison of Event Sequence Data

Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparative analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Our study participants made fewer errors with MatrixWave and preferred it over the more familiar Sankey diagram.


Wireless Power Transfer for Vehicular Applications: Overview and Challenges

More than a century-old gasoline internal combustion engine is a major contributor to greenhouse gases. Electric vehicles (EVs) have the potential to achieve eco-friendly transportation. However, the major limitation in achieving this vision is the battery technology. It suffers from drawbacks such as high cost, rare material, low energy density, and large weight. The problems related to battery technology can be addressed by dynamically charging the EV while on the move. In-motion charging can reduce the battery storage requirement, which could significantly extend the driving range of an EV. This paper reviews recent advances in stationary and dynamic wireless charging of EVs. A comprehensive review of charging pad, power electronics configurations, compensation networks, controls, and standards is presented.


Using NLP techniques for file fragment classification

The classification of file fragments is an important problem in digital forensics. The literature does not include comprehensive work on applying machine learning techniques to this problem. In this work, we explore the use of techniques from natural language processing to classify file fragments. We take a supervised learning approach, based on the use of support vector machines combined with the bag-of-words model, where text documents are represented as unordered bags of words. This technique has been repeatedly shown to be effective and robust in classifying text documents (e.g., in distinguishing positive movie reviews from negative ones). In our approach, we represent file fragments as “bags of bytes” with feature vectors consisting of unigram and bigram counts, as well as other statistical measurements (including entropy and others). We made use of the publicly available Garfinkel data corpus to generate file fragments for training and testing. We ran a series of experiments, and found that this approach is effective in this domain as well. a 2012 O. Zhulyn, S. Fitzgerald & G. Mathews. Published by Elsevier Ltd. All rights reserved.


Perceptual Generative Adversarial Networks for Small Object Detection

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K [45] and the Caltech [9] benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.


Techniques for Machine Learning based Spatial Data Analysis: Research Directions

Today, machine learning techniques play a significant role in data analysis, predictive modeling and visualization. The main aim of the machine learning algorithms is that they first learn from the empirical data and can be utilized in cases for which the modeled phenomenon is hidden or not yet described. Several significant techniques like Artificial Neural Network, Support Vector Regression, k-Nearest Neighbour, Bayesian Classifiers and Decision Trees are developed in past years to acheive this task. The first and most complicated problem concerns is the amount of available data. This paper reviews state-of-the-art in the domain of spatial data analysis by employing machine learning approaches. First various methods have been summarized, which exist in the literature. Further, the current research scenarios which are going on in this area are described. Based on the research done in past years, identification of the problem in the existing system is also presented in this paper and have given future research directions.


DRCD: a Chinese Machine Reading Comprehension Dataset

重視硫酸的種類以及它們在醫學上的價值...... 在 1746年,John Roebuck則運用這個原則,開 創鉛室法,以更低成本有效地大量生產硫 酸。...... ... Others, such as Ibn Sīnā, are more concerned about the type of sulfuric acid and their medical value. In 1746, John Roebuck applied this method to create lead chamber process to efficiently produce large quantities of sulfuric acid at a lower cost. Question: 重視硫酸的種類以及它們在醫學上的 價值地為哪位醫師? Who value the type of sulfuric acid and their medical value? Answer: 伊本·西那/ Ibn Sīnā Question: 鉛室法於西元幾年開創? / When did lead chamber process being invented? Answer: 1746年 / 1746


Parental delay or refusal of vaccine doses, childhood vaccination coverage at 24 months of age, and the Health Belief Model.

OBJECTIVE
We evaluated the association between parents' beliefs about vaccines, their decision to delay or refuse vaccines for their children, and vaccination coverage of children at aged 24 months.

METHODS
We used data from 11,206 parents of children aged 24-35 months at the time of the 2009 National Immunization Survey interview and determined their vaccination status at aged 24 months. Data included parents' reports of delay and/or refusal of vaccine doses, psychosocial factors suggested by the Health Belief Model, and provider-reported up-to-date vaccination status.

RESULTS
In 2009, approximately 60.2% of parents with children aged 24-35 months neither delayed nor refused vaccines, 25.8% only delayed, 8.2% only refused, and 5.8% both delayed and refused vaccines. Compared with parents who neither delayed nor refused vaccines, parents who delayed and refused vaccines were significantly less likely to believe that vaccines are necessary to protect the health of children (70.1% vs. 96.2%), that their child might get a disease if they aren't vaccinated (71.0% vs. 90.0%), and that vaccines are safe (50.4% vs. 84.9%). Children of parents who delayed and refused also had significantly lower vaccination coverage for nine of the 10 recommended childhood vaccines including diphtheria-tetanus-acellular pertussis (65.3% vs. 85.2%), polio (76.9% vs. 93.8%), and measles-mumps-rubella (68.4% vs. 92.5%). After adjusting for sociodemographic differences, we found that parents who were less likely to agree that vaccines are necessary to protect the health of children, to believe that their child might get a disease if they aren't vaccinated, or to believe that vaccines are safe had significantly lower coverage for all 10 childhood vaccines.

CONCLUSIONS
Parents who delayed and refused vaccine doses were more likely to have vaccine safety concerns and perceive fewer benefits associated with vaccines. Guidelines published by the American Academy of Pediatrics may assist providers in responding to parents who may delay or refuse vaccines.


An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach

Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.


Political Issue Extraction Model: A Novel Hierarchical Topic Model That Uses Tweets By Political And Non-Political Authors

People often use social media to discuss opinions, including political ones. We refer to relevant topics in these discussions as political issues, and the alternate stands towards these topics as political positions. We present a Political Issue Extraction (PIE) model that is capable of discovering political issues and positions from an unlabeled dataset of tweets. A strength of this model is that it uses twitter timelines of political and non-political authors, and affiliation information of only political authors. The model estimates word-specific distributions (that denote political issues and positions) and hierarchical author/group-specific distributions (that show how these issues divide people). Our experiments using a dataset of 2.4 million tweets from the US show that this model effectively captures the desired properties (with respect to words and groups) of political discussions. We also evaluate the two components of the model by experimenting with: (a) Use to alternate strategies to classify words, and (b) Value addition due to incorporation of group membership information. Estimated distributions are then used to predict political affiliation with 68% accuracy.


Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample

A mailed questionnaire was used to obtain comparative risk judgments for 32 different hazards from a random sample of 296 individuals living in central New Jersey. The results demonstrate that an optimistic bias about susceptibility to harm-a tendency to claim that one is less at risk than one's peers—is not limited to any particular age, sex, educational, or occupational group. It was found that an optimistic bias is often introduced when people extrapolate from their past experience to estimate their future vulnerability. Thus, the hazards most likely to elicit unrealistic optimism are those associated with the belief (often incorrect) that if the problem has not yet appeared, it is unlikely to occur in the future. Optimistic biases also increase with the perceived preventability of a hazard and decrease with perceived frequency and personal experience. Other data presented illustrate the inconsistent relationships between personal risk judgments and objective risk factors.


The cultural construction of self-enhancement: an examination of group-serving biases.

Self-serving biases, found routinely in Western samples, have not been observed in Asian samples. Yet given the orientation toward individualism and collectivism in these 2 cultures, respectively, it is imperative to examine whether parallel differences emerge when the target of evaluation is the group. It may be that Asians show a group-serving bias parallel to the Western self-serving bias. In 2 studies, group-serving biases were compared across European Canadian, Asian Canadian, and Japanese students. Study 1 revealed that Japanese students evaluated a family member less positively than did both groups of Canadian students. Study 2 replicated this pattern with students' evaluations of their universities. The data suggest that cultural differences in enhancement biases are robust, generalizing to individuals' evaluations of their groups.


Unrealistic optimism about susceptibility to health problems

In this study, 100 college students compared their own chances of experiencing 45 different health- and life-threatening problems with the chances of their peers. They showed a significant optimistic bias for 34 of these hazards, consistently considering their own chances to be below average. Attempts to account for the amount of bias evoked by different hazards identified perceived controllability, lack of previous experience, and the belief that the problem appears during childhood as factors that tend to increase unrealistic optimism. The investigation also examined the importance of beliefs and emotions as determinants of self-reported interest in adopting precautions to reduce one's risk. It found that: (a) beliefs about risk likelihood, beliefs about risk severity, and worry about the risk all made independent contributions to interest in risk reduction; (b) unrealistic optimism undermined interest in risk reduction indirectly by decreasing worry; and (c) beliefs about risk likelihood and severity were not sufficient to explain the amount of worry expressed about different hazards.


Flawed Self-Assessment: Implications for Health, Education, and the Workplace.

Research from numerous corners of psychological inquiry suggests that self-assessments of skill and character are often flawed in substantive and systematic ways. We review empirical findings on the imperfect nature of self-assessment and discuss implications for three real-world domains: health, education, and the workplace. In general, people's self-views hold only a tenuous to modest relationship with their actual behavior and performance. The correlation between self-ratings of skill and actual performance in many domains is moderate to meager-indeed, at times, other people's predictions of a person's outcomes prove more accurate than that person's self-predictions. In addition, people overrate themselves. On average, people say that they are "above average" in skill (a conclusion that defies statistical possibility), overestimate the likelihood that they will engage in desirable behaviors and achieve favorable outcomes, furnish overly optimistic estimates of when they will complete future projects, and reach judgments with too much confidence. Several psychological processes conspire to produce flawed self-assessments. Research focusing on health echoes these findings. People are unrealistically optimistic about their own health risks compared with those of other people. They also overestimate how distinctive their opinions and preferences (e.g., discomfort with alcohol) are among their peers-a misperception that can have a deleterious impact on their health. Unable to anticipate how they would respond to emotion-laden situations, they mispredict the preferences of patients when asked to step in and make treatment decisions for them. Guided by mistaken but seemingly plausible theories of health and disease, people misdiagnose themselves-a phenomenon that can have severe consequences for their health and longevity. Similarly, research in education finds that students' assessments of their performance tend to agree only moderately with those of their teachers and mentors. Students seem largely unable to assess how well or poorly they have comprehended material they have just read. They also tend to be overconfident in newly learned skills, at times because the common educational practice of massed training appears to promote rapid acquisition of skill-as well as self-confidence-but not necessarily the retention of skill. Several interventions, however, can be introduced to prompt students to evaluate their skill and learning more accurately. In the workplace, flawed self-assessments arise all the way up the corporate ladder. Employees tend to overestimate their skill, making it difficult to give meaningful feedback. CEOs also display overconfidence in their judgments, particularly when stepping into new markets or novel projects-for example, proposing acquisitions that hurt, rather then help, the price of their company's stock. We discuss several interventions aimed at circumventing the consequences of such flawed assessments; these include training people to routinely make cognitive repairs correcting for biased self-assessments and requiring people to justify their decisions in front of their peers. The act of self-assessment is an intrinsically difficult task, and we enumerate several obstacles that prevent people from reaching truthful self-impressions. We also propose that researchers and practitioners should recognize self-assessment as a coherent and unified area of study spanning many subdisciplines of psychology and beyond. Finally, we suggest that policymakers and other people who makes real-world assessments should be wary of self-assessments of skill, expertise, and knowledge, and should consider ways of repairing self-assessments that may be flawed.


Judgment under Uncertainty: Heuristics and Biases.

This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.


Fault tolerant parallel data-intensive algorithms

Fault-tolerance is rapidly becoming a crucial issue in high-end and distributed computing, as increasing number of cores are decreasing the mean-time to failure of the systems. In this work, we present an algorithm-based fault tolerance solution that handles fail-stop failures for a class of iterative data intensive algorithms. We intelligently replicate the data to minimize data loss in multiple failures and decrease re-execution in recovery by little modifications in the algorithms. We evaluate our approach by using two data mining algorithms, kmeans and Apriori. We show that our approach has negligible overhead and allows us to gracefully handle different number of failures. In addition, our approach outperforms Hadoop both in absence and presence of failures.


Confidence sets for persistence diagrams

Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features as one varies a tuning parameter. Features with short lifetimes are informally considered to be “topological noise,” and those with a long lifetime are considered to be “topological signal.” In this paper, we bring some statistical ideas to persistent homology. In particular, we derive confidence sets that allow us to separate topological signal from topological noise.


Word Semantic Representations using Bayesian Probabilistic Tensor Factorization

Many forms of word relatedness have been developed, providing different perspectives on word similarity. We introduce a Bayesian probabilistic tensor factorization model for synthesizing a single word vector representation and per-perspective linear transformations from any number of word similarity matrices. The resulting word vectors, when combined with the per-perspective linear transformation, approximately recreate while also regularizing and generalizing, each word similarity perspective. Our method can combine manually created semantic resources with neural word embeddings to separate synonyms and antonyms, and is capable of generalizing to words outside the vocabulary of any particular perspective. We evaluated the word embeddings with GRE antonym questions, the result achieves the state-ofthe-art performance.


Facial performance enhancement using dynamic shape space analysis

The facial performance of an individual is inherently rich in subtle deformation and timing details. Although these subtleties make the performance realistic and compelling, they often elude both motion capture and hand animation. We present a technique for adding fine-scale details and expressiveness to low-resolution art-directed facial performances, such as those created manually using a rig, via marker-based capture, by fitting a morphable model to a video, or through Kinect reconstruction using recent faceshift technology. We employ a high-resolution facial performance capture system to acquire a representative performance of an individual in which he or she explores the full range of facial expressiveness. From the captured data, our system extracts an expressiveness model that encodes subtle spatial and temporal deformation details specific to that particular individual. Once this model has been built, these details can be transferred to low-resolution art-directed performances. We demonstrate results on various forms of input; after our enhancement, the resulting animations exhibit the same nuances and fine spatial details as the captured performance, with optional temporal enhancement to match the dynamics of the actor. Finally, we show that our technique outperforms the current state-of-the-art in example-based facial animation.


A fast image dehazing algorithm based on negative correction


Improving Distant Supervision with Maxpooled Attention and Sentence-Level Supervision

We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FBNYT dataset.


Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network

Transferring a huge amount of data between different network locations over the network links depends on the network's traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model.


A FRAMEWORK FOR DESIGNING MOBILE QURANIC MEMORIZATION TOOL USING MULTIMEDIA INTERACTIVE LEARNING METHOD FOR CHILDREN

Quran is the fundamental holy book of Islam. Among the most important concerns is to learn it by heart. However, current method in Quranic schools is becoming less effective towards young generation. Thus, there is a need to find alternative solutions to memorize Quran for young Muslims. Mobile learning is an alternative to conventional learning that will support the existing method. Mobile devices have made an immediate impact on teaching and learning practices. However, for mobile learning to be effective for children in memorizing Quran, it is necessary to find specific design guidelines and pedagogy to this learning method. This paper aims at providing a unifying framework for developing Quran memorizer application using multimedia interactive method and learning theories for mobile learning.


The WHO public health approach to HIV treatment and care: looking back and looking ahead.

In 2006, WHO set forth its vision for a public health approach to delivering antiretroviral therapy. This approach has been broadly adopted in resource-poor settings and has provided the foundation for scaling up treatment to over 19·5 million people. There is a global commitment to end the AIDS epidemic as a public health threat by 2030 and, to support this goal, there are opportunities to adapt the public health approach to meet the ensuing challenges. These challenges include the need to improve identification of people with HIV infection through expanded approaches to testing; further simplify and improve treatment and laboratory monitoring; adapt the public health approach to concentrated epidemics; and link HIV testing, treatment, and care to HIV prevention. Implementation of these key public health principles will bring countries closer to the goals of controlling the HIV epidemic and providing universal health coverage.


The effects of students' motivation, cognitive load and learning anxiety in gamification software engineering education: a structural equation modeling study

Past research has proven the significant effects of game-based learning on learning motivation and academic performance, and described the key factors in game-based design. Nonetheless, research on the correlations among learning motivation, cognitive load, learning anxiety and academic performance in gamified learning environments has been minimal. This study, therefore, aims to develop a Gamification Software Engineering Education Learning System (GSEELS) and evaluate the effects of gamification, learning motivation, cognitive load and learning anxiety on academic performance. By applying Structural Equation Modeling (SEM) to the empirical research, the questionnaire contains: 1. a Gamification Learning Scale; 2. a Learning Motivation Scale; 3. a Cognitive Load Scale; 4. a Learning Anxiety Scale; and 5. an Academic Performance Scale. A total of 107 undergraduates in two classes participated in this study. The Structural Equation Modeling (SEM) analysis includes the path directions and relationship between descriptive statistics, measurement model, structural model evaluation and five variables. The research results support all nine hypotheses, and the research findings also show the effects of cognitive load on learning anxiety, with strong learning motivation resulting from a low learning anxiety. As a result, it is further proven in this study that a well-designed GSEELS would affect student learning motivation and academic performance. Finally, the relationship model between gamification learning, learning motivation, cognitive load, learning anxiety and academic performance is elucidated, and four suggestions are proffered for instructors of software engineering education courses and for further research, so as to assist instructors in the application of favorable gamification teaching strategies.


Towards a global participatory platform : democratising open data , complexity science and collective intelligence

The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted 110 The European Physical Journal Special Topics government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project’s own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of selforganization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed.


Comparison of supervised learning methods for spike time coding in spiking neural networks

In this review we focus our attention on the supervised learning methods for spike time coding in Spiking Neural Networks (SNN). This study is motivated by the recent experimental results on information coding in the biological neural systems which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We pose a fundamental question, what paradigms of neural temporal coding can be implemented with the recent learning methods. In order to answer this question we discuss various approaches to the considered learning task. We shortly describe the particular learning algorithms and report the results of experiments. Finally we discuss properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.


Enhanced parallel cat swarm optimization based on the Taguchi method

In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the Liu, Chen, and Chou method (2009). The optimal solutions can be found by the proposed EPCSO method in a very short time. 2011 Elsevier Ltd. All rights reserved.


Conducting a meta-ethnography of qualitative literature: Lessons learnt

BACKGROUND
Qualitative synthesis has become more commonplace in recent years. Meta-ethnography is one of several methods for synthesising qualitative research and is being used increasingly within health care research. However, many aspects of the steps in the process remain ill-defined.

DISCUSSION
We utilized the seven stages of the synthesis process to synthesise qualitative research on adherence to tuberculosis treatment. In this paper we discuss the methodological and practical challenges faced; of particular note are the methods used in our synthesis, the additional steps that we found useful in clarifying the process, and the key methodological challenges encountered in implementing the meta-ethnographic approach. The challenges included shaping an appropriate question for the synthesis; identifying relevant studies; assessing the quality of the studies; and synthesising findings across a very large number of primary studies from different contexts and research traditions. We offer suggestions that may assist in undertaking meta-ethnographies in the future.

SUMMARY
Meta-ethnography is a useful method for synthesising qualitative research and for developing models that interpret findings across multiple studies. Despite its growing use in health research, further research is needed to address the wide range of methodological and epistemological questions raised by the approach.


Linking CRM Strategy , Customer Performance Measures and Performance in the Hotel Industry

Customer relationship management (CRM) has been increasingly adopted because of its benefits of greater customer satisfaction and loyalty, which in turn, leads to enhanced financial and competitive performance. This paper reports on a study that examines the relationship between CRM strategy and performance and determines whether the use of customer performance measures plays a mediating role in the relationship between CRM strategy and performance. This study contributes to the limited literature on CRM strategy since little is known about the use of CRM strategy and customer performance measures and their relation with performance in the hotel industry in Malaysia. Data were collected through a questionnaire survey of hotels in Malaysia. Hierarchical regression analyses on a sample of 95 hotels revealed that only the information technology dimension of CRM strategy has a significant and positive effect on performance. In addition, the hypothesis concerning the role of customer performance measures as a mediator was supported.


Predicting NDUM Student's Academic Performance Using Data Mining Techniques

The ability to predict the students’ academic performance is very important in institution educational system. Recently some researchers have been proposed data mining techniques for higher education. In this paper, we compare two data mining techniques which are: Artificial Neural Network (ANN) and the combination of clustering and decision tree classification techniques for predicting and classifying students’ academic performance. The data set used in this research is the student data of Computer Science Department, Faculty of Science and Defence Technology, National Defence University of Malaysia (NDUM).


Speeding up k-means Clustering by Bootstrap Averaging

K-means clustering is one of the most popular clustering algorithms used in data mining. However, clustering is a time consuming task, particularly w ith the large data sets found in data mining. In this p aper we show how bootstrap averaging with k-means can produce results comparable to clustering all of the data but in much less time. The approach of bootstr ap (sampling with replacement) averaging consists of running k-means clustering to convergence on small bootstrap samples of the training data and averagin g similar cluster centroids to obtain a single model. We show why our approach should take less computation time and empirically illustrate its benefits. We sh ow that the performance of our approach is a monotonic function of the size of the bootstrap sample. Howev er, knowing the size of the bootstrap sample that yield s as good results as clustering the entire data set rema ins an open and important question.


An Inoperability Input-Output Model (IIM) for disruption propagation analysis

Today's Supply Chains (SCs) are global and getting more complex, due to the exigencies of the world economy, making them hard to manage. This is exacerbated by the increasingly frequent occurrence of natural disasters and other high impact disruptions. To stay competitive, companies are therefore seeking ways to better understand the impact of such disruptions to their SCs. In addressing this need, this paper proposes an approach for disruption propagation analysis. We develop a method based on a variation of an Inoperability Input-Output Model (adapted from a Leontief I-O model) to quantify the impact of disruptions across the entire SC. We then analyse the factors that have the most influential impacts on the SCs during disruptions. Initial results show that trading volume handled by a company/ node is an important factor in determining the disruption impact to SCs, besides the number of SC partners (connections) as implied from previous work.


CryptVMI: Encrypted Virtual Machine Introspection in the Cloud

Virtualization techniques are the key in both public and private cloud computing environments. In such environments, multiple virtual instances are running on the same physical machine. The logical isolation between systems makes security assurance weaker than physically isolated systems. Thus, Virtual Machine Introspection techniques become essential to prevent the virtual system from being vulnerable to attacks. However, this technique breaks down the borders of the segregation between multiple tenants, which should be avoided in a public cloud computing environment. In this paper, we focus on building an encrypted Virtual Machine Introspection system, CryptVMI, to address the above concern, especially in a public cloud system. Our approach maintains a query handler on the management node to handle encrypted queries from user clients. We pass the query to the corresponding compute node that holds the virtual instance queried. The introspection application deployed on the compute node processes the query and acquires the encrypted results from the virtual instance for the user. This work shows our design and preliminary implementation of this system.


Acute Ischemic Stroke Therapy Overview.

The treatment of acute ischemic stroke has undergone dramatic changes recently subsequent to the demonstrated efficacy of intra-arterial (IA) device-based therapy in multiple trials. The selection of patients for both intravenous and IA therapy is based on timely imaging with either computed tomography or magnetic resonance imaging, and if IA therapy is considered noninvasive, angiography with one of these modalities is necessary to document a large-vessel occlusion amenable for intervention. More advanced computed tomography and magnetic resonance imaging studies are available that can be used to identify a small ischemic core and ischemic penumbra, and this information will contribute increasingly in treatment decisions as the therapeutic time window is lengthened. Intravenous thrombolysis with tissue-type plasminogen activator remains the mainstay of acute stroke therapy within the initial 4.5 hours after stroke onset, despite the lack of Food and Drug Administration approval in the 3- to 4.5-hour time window. In patients with proximal, large-vessel occlusions, IA device-based treatment should be initiated in patients with small/moderate-sized ischemic cores who can be treated within 6 hours of stroke onset. The organization and implementation of regional stroke care systems will be needed to treat as many eligible patients as expeditiously as possible. Novel treatment paradigms can be envisioned combining neuroprotection with IA device treatment to potentially increase the number of patients who can be treated despite long transport times and to ameliorate the consequences of reperfusion injury. Acute stroke treatment has entered a golden age, and many additional advances can be anticipated.


Automated Non-Gaussian Clustering of Polarimetric Synthetic Aperture Radar Images

This paper presents an automatic image segmentation method for polarimetric synthetic aperture radar data. It utilizes the full polarimetric information and incorporates texture by modeling with a non-Gaussian distribution for the complex scattering coefficients. The modeling is based upon the well-known product model, with a Gamma-distributed texture parameter leading to the K-Wishart model for the covariance matrix. The automatic clustering is achieved through a finite mixture model estimated with a modified expectation maximization algorithm. We include an additional goodness-of-fit test stage that allows for splitting and merging of clusters. This not only improves the model fit of the clusters, but also dynamically selects the appropriate number of clusters. The resulting image segmentation depicts the statistically significant clusters within the image. A key feature is that the degree of sub-sampling of the input image will affect the detail level of the clustering, revealing only the major classes or a variable level of detail. Real-world examples are shown to demonstrate the technique.


Mining Vessel Tracking Data for Maritime Domain Applications

The growing number of remote sensing systems and ship reporting technologies (e.g. Automatic Identification System, Long Range Identification and Tracking, radar tracking, Earth Observation) are generating an overwhelming amount of spatio-temporal and geographically distributed data related to vessels and their movements. Research on reliable data mining techniques has proven essential to the discovery of knowledge from such increasingly available information on ship traffic at sea. Data driven knowledge discovery has very recently demonstrated its value in fields that go beyond the original maritime safety and security remits of such data. They include, but are not limited to, fisheries management, maritime spatial planning, gridding ship emissions, mapping activities at sea, risk assessment of offshore platforms, and trade indicators. The extraction of useful information from maritime Big Data is thus a key element in providing operational authorities, policy-makers and scientists with supporting tools to understand what is happening at sea and improve maritime knowledge. This work will provide a survey of the recent JRC research activities relevant to automatic anomaly detection and knowledge discovery in the maritime domain. Data mining, data analytics and predictive analysis examples are introduced using real data. In addition, this paper presents approaches to detect anomalies in reporting messages and unexpected behaviours at sea.


Requirements Engineering with Use Cases - a Basis for Software Development

Successful development of software systems depends on the quality of the requirements engineering process. Use cases and scenarios are promising vehicles for eliciting, specifying and validating requirements. This thesis investigates the role of use case modelling in requirements engineering and its relation to system verification and validation. The thesis includes studies of concepts and representations in use case modelling. Semantic issues are discussed and notations based on natural and graphical languages are provided, which allow a hierarchical structure and enable representation at different abstraction levels. Two different strategies for integrating use case modelling with system testing are presented and evaluated, showing how use cases can be a basis for test case generation and reliability assessment. An experiment on requirements validation using inspections with perspective-based reading is also reported, where one of the perspectives applies use case modelling. The results of the experiment indicate that a combination of multiple perspectives may not give higher defect detection rate compared to single perspective reading. Pilot studies of the transition from use case based requirements to high-level design are described, where use cases are successfully applied for documenting how functional requirements are distributed on architectural elements. The investigation of an industrial requirements engineering process improvement programme is also reported, where the introduction of a release-driven prioritisation method contributed to a measurable improvement in delivery precision and product quality. The results presented in the thesis provide further support for how to successfully apply requirements engineering with use cases as an important basis for software development.


Skyline: an open source document editor for creating and analyzing targeted proteomics experiments

SUMMARY
Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools.

AVAILABILITY
Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.


Real time action recognition using histograms of depth gradients and random decision forests

We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames/second.


LIBLINEAR: A Library for Large Linear Classification

LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets.


Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.


Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction

This paper presents a novel visual representation, called orderlets, for real-time human action recognition with depth sensors. An orderlet is a middle level feature that captures the ordinal pattern among a group of low level features. For skeletons, an orderlet captures specific spatial relationship among a group of joints. For a depth map, an orderlet characterizes a comparative relationship of the shape information among a group of subregions. The orderlet representation has two nice properties. First, it is insensitive to small noise since an orderlet only depends on the comparative relationship among individual features. Second, it is a frame-level representation thus suitable for real-time online action recognition. Experimental results demonstrate its superior performance on online action recognition and cross-environment action recognition.


Pedestrian Motion Tracking and Crowd Abnormal Behavior Detection Based on Intelligent Video Surveillance

Pedestrian tracking and detection of crowd abnormal activity under dynamic and complex background using Intelligent Video Surveillance (IVS) system are beneficial for security in public places. This paper presents a pedestrian tracking method combing Histogram of Oriented Gradients (HOG) detection and particle filter. This method regards the particle filter as the tracking framework, identifies the target area according to the result of HOG detection and modifies particle sampling constantly. Our method can track pedestrians in dynamic backgrounds more accurately compared with the traditional particle filter algorithms. Meanwhile, a method to detect crowd abnormal activity is also proposed based on a model of crowd features using Mixture of Gaussian (MOG). This method calculates features of crowd-interest points, then establishes the crowd features model using MOG, conducts self-adaptive updating and detects abnormal activity by matching the input feature with model distribution. Experiments show our algorithm can efficiently detect abnormal velocity and escape panic in crowds with a high detection rate and a relatively low false alarm rate.


Harnessing Context Sensing to Develop a Mobile Intervention for Depression

BACKGROUND
Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder.

OBJECTIVE
The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing.

METHODS
We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients' mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients' self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks.

RESULTS
Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta(week) = -.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta(week) = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b(week) = -.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta(week) = -.71, P < .001, per-protocol Cohen d = 2.58).

CONCLUSIONS
Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed.

TRIAL REGISTRATION
Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n).


Color and Texture Based Identification and Classification of food Grains using different Color Models and Haralick features

This paper presents the study on identification and classification of food grains using different color models such as L*a*b, HSV, HSI and YCbCr by combining color and texture features without performing preprocessing. The K-NN and minimum distance classifier are used to identify and classify the different types of food grains using local and global features. Texture and color features are the important features used in the classification of different objects. The local features like Haralick features are computed from co-occurrence matrix as texture features and global features from cumulative histogram are computed along with color features. The experiment was carried out on different food grains classes. The non-uniformity of RGB color space is eliminated by L*a*b, HSV, HSI and YCbCr color space. The correct classification result achieved for different color models is quite good. KeywordsFeature Extraction; co-occurrence matrix; texture information; Global Features; cumulative histogram; RGB, L*a*b, HSV, HSI and YCbCr color model.


ZIGZAG: An Efficient Peer-to-Peer Scheme for Media Streaming

We design a peer-to-peer technique called ZIGZAG for single-source media streaming. ZIGZAG allows the media server to distribute content to many clients by organizing them into an appropriate tree rooted at the server. This applicationlayer multicast tree has a height logarithmic with the number of clients and a node degree bounded by a constant. This helps reduce the number of processing hops on the delivery path to a client while avoiding network bottleneck. Consequently, the end-to-end delay is kept small. Although one could build a tree satisfying such properties easily, an efficient control protocol between the nodes must be in place to maintain the tree under the effects of network dynamics and unpredictable client behaviors. ZIGZAG handles such situations gracefully requiring a constant amortized control overhead. Especially, failure recovery can be done regionally with little impact on the existing clients and mostly no burden on the server.


GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we proposeGPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering2.


A dexterous gripper for in-hand manipulation

During the last few decades, robotic grippers are developed by research community to solve grasping complexities of several objects as their primary objective. Due to the increasing demands of industries, many issues are rising and remain unsolved such as in-hand manipulation, placing object with appropriate posture. Operations like twisting, altering orientation of object, in a hand, requires significant dexterity of the gripper that must be achieved from a compact mechanical design at the first place. In this paper, a newly designed gripper is proposed whose primary goal is to solve in-hand manipulation. The gripper is derived from four identical finger modules; each module has four DOFs, consists of a five bar linkage to grasp and additional two DOFs mechanism similar to ball spline is conceived to manipulate object. Hence the easily constructible gripper is capable to grasp and manipulate plurality of object. As a preliminary inspection, an optimized platform that represents the central concept of the proposed gripper is developed, to the aim of evaluating kinematic feasibilities of manipulation of different objects.


Privacy-preserving photo sharing based on a secure JPEG

Sharing photos online is a common activity on social networks and photo hosting platforms, such as Facebook, Pinterest, Instagram, or Flickr. However, after reports of citizens surveillance by governmental agencies and the scandalous leakage of celebrities private photos online, people have become concerned about their online privacy and are looking for ways to protect it. Popular social networks typically offer privacy protection solutions only in response to the public demand and therefore are often rudimental, complex to use, and provide limited degree of control and protection. Most solutions either allow users to control who can access the shared photos or for how long they can be accessed. In contrast, in this paper, we take a structured privacy by design approach to the problem of online photo privacy protection. We propose a privacy-preserving photo sharing architecture that takes into account content and context of a photo with privacy protection integrated inside the JPEG file itself in a secure way. We demonstrate the proposed architecture with a prototype mobile iOS application called ProShare that offers scrambling as the privacy protection tool for a selected region in a photo, secure access to the protected images, and secure photo sharing on Facebook.