title: Mindfulness-based treatments for posttraumatic stress disorder: a review of the treatment literature and neurobiological evidence.

abstract: Mindfulness-based treatments for posttraumatic stress disorder (PTSD) have emerged as promising adjunctive or alternative intervention approaches. A scoping review of the literature on PTSD treatment studies, including approaches such as mindfulness-based stress reduction, mindfulness-based cognitive therapy and metta mindfulness, reveals low attrition with medium to large effect sizes. We review the convergence between neurobiological models of PTSD and neuroimaging findings in the mindfulness literature, where mindfulness interventions may target emotional under- and overmodulation, both of which are critical features of PTSD symptomatology. Recent emerging work indicates that mindfulness-based treatments may also be effective in restoring connectivity between large-scale brain networks among individuals with PTSD, including connectivity between the default mode network and the central executive and salience networks. Future directions, including further identification of the neurobiological mechanisms of mindfulness interventions in patients with PTSD and direct comparison of these interventions to first-line treatments for PTSD are discussed.


title: Extraction and classification of facemarks

abstract: We propose methods for extracting facemarks (emoticons) in text and classifying them into some emotional categories. In text-based communication, facemarks have gained popularity, since they help us understand what writers imply. However, there are two problems in text-based communication using facemarks; the first is the variety of facemarks and the second is lack of good comprehension in using facemarks. These problems are more serious in the areas where 2-byte characters are used, because the 2-byte characters can generate a quite large number of different facemarks. Therefore, we are going to propose methods for extraction and classification of facemarks. Regarding the extraction of facemarks as a chunking task, we automatically annotate a tag to each character in text. In the classification of the extracted facemarks, we apply the dynamic time alignment kernel (DTAK) and the string subsequence kernel (SSK) for scoring in the k-nearest neighbor (k-NN) method and for expanding usual Support Vector Machines (SVMs) to accept sequential data such as facemarks. We empirically show that our methods work well in classification and extraction of facemarks, with appropriate settings of parameters.


Enhanced Sphere Tracing


title: New Comprehensive Taxonomies on Mobile Security and Malware Analysis

abstract: Taxonomies are known to provide a systematic and theoretical classification of elements in a particular domain and could be efficiently used to express concepts in a structural manner. Unfortunately, security literature witnesses a few taxonomies having about 40 nodes on average in mostly a narrowed scope and maximum of 25 nodes on mobile scope only. This study surveyed security related taxonomies with quality criteria and proposes new comprehensive mobile security taxonomy and mobile malware analysis subtaxonomy from not only defensive but also offensive point of view. We have developed a levelling scheme and notation for security taxonomies in general and proposed a new definite method to build the taxonomies having over 1,300 nodes. We have also visualized our taxonomies for researchers, security professionals and even common end users to provide comprehensible, well structured, and handy maps. As security threats and vulnerabilities dynamically increase and diversify, these new taxonomies would help to see the entire perspective of mobile security without losing any details and present new perspective to bring mobile computing and cyber security disciplines closer.


title: Developing a crack inspection robot for bridge maintenance

abstract: One of the important tasks for bridge maintenance is bridge deck crack inspection. Traditionally, a human inspector detects cracks using his/her eyes and finds the location of cracks manually. Thus the accuracy of the inspection result is low due to the subjective nature of human judgement. We propose a system that uses a mobile robot to conduct the inspection, where the robot collects bridge deck images with a high resolution camera. In this method, the Laplacian of Gaussian algorithm is used to detect cracks and the global crack map is obtained through camera calibration and robot localization. To ensure that the robot collects all the images on the bridge deck, we develop a complete coverage path planning algorithm for the mobile robot. We compare it with other path planning strategies. Finally, we validate our proposed system through experiments and simulation.


title: A Review of Research on Child Abuse in Malaysia.

abstract: The aim of this review was to summarise published literature on child abuse and neglect and its consequences in Malaysia, to discuss the implications of the research findings and to identify gaps in the local literature on child abuse and neglect. Medical and social literature in the English language published between the year 2000 to 2015 were searched for, resulting in forty four papers to be reviewed inclusive of a few key papers in the earlier years to provide some background information. The literature shows that child abuse and neglect is an important impact factor on mental health outcomes, involvement in substance abuse and delinquency due to the slant of the research interest from social studies. At least 70% of perpetrators are known to the affected children according to school-based prevalence studies. Safety programs and rehabilitation outcome studies involve small cohort groups. Studies on childhood mortality from child abuse or neglect are very limited. Overall, there are a few comprehensive studies involving school children but overall available studies are too patchy in to advocate for resource allocation, change in statutory procedures or training requirements. More extensive studies looking at the complex interaction of social environment, parenting skills, societal attitudes and responses, resilience factors and child safety nets and statutory response and their impact on different types of abuse or neglect are required.


title: Glabella: Continuously Sensing Blood Pressure Behavior using an Unobtrusive Wearable Device

abstract: We propose Glabella, a wearable device that continuously and unobtrusively monitors heart rates at three sites on the wearer’s head. Our glasses prototype incorporates optical sensors, processing, storage, and communication components, all integrated into the frame to passively collect physiological data about the user without the need for any interaction. Glabella continuously records the stream of reflected light intensities from blood flow as well as inertial measurements of the user’s head. From the temporal differences in pulse events across the sensors, our prototype derives the wearer’s pulse transit time on a beat-to-beat basis. Numerous efforts have found a significant correlation between a person’s pulse transit time and their systolic blood pressure. In this paper, we leverage this insight to continuously observe pulse transit time as a proxy for the behavior of systolic blood pressure levels—at a substantially higher level of convenience and higher rate than traditional blood pressure monitors, such as cuff-based oscillometric devices. This enables our prototype to model the beat-to-beat fluctuations in the user’s blood pressure over the course of the day and record its short-term responses to events, such as postural changes, exercise, eating and drinking, resting, medication intake, location changes, or time of day. During our in-the-wild evaluation, four participants wore a custom-fit Glabella prototype device over the course of five days throughout their daytime job and regular activities. Participants additionally measured their radial blood pressure three times an hour using a commercial oscillometric cuff. Our analysis shows a high correlation between the pulse transit times computed on our devices with participants’ heart rates (mean r = 0.92, SE = 0.03, angular artery) and systolic blood pressure values measured using the oscillometric cuffs (mean r = 0.79, SE = 0.15, angular-superficial temporal artery, considering participants’ self-administered cuff-based measurements as ground truth). Our results indicate that Glabella has the potential to serve as a socially-acceptable capture device, requiring no user input or behavior changes during regular activities, and whose continuous measurements may prove informative to physicians as well as users’ self-tracking activities.


On the water produced when CO2 reacts with soda lime.


title: Umbrella wheel — A stair-climbing and obstacle-handling wheel design concept

abstract: This paper proposes a new design for stair-climbing using a wheel that can split into segments and walk up stairs or surmount other obstacles often found where humans traverse, while still being able to retain a perfectly round shape for traveling on smooth ground. Using this change of configuration, staircases with a wide range of dimensions can be covered efficiently and safely. The design, named Umbrella Wheel, can consist of as many wheel segments as desired, and as few as two. A smaller or higher number of wheel segments has advantages and disadvantages depending on the specific situation. Modeling the trajectory of the wheel when as it ascends or descends stairs is given and the results are analyzed.


title: Core techniques of question answering systems over knowledge bases: a survey

abstract: The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data. Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.


title: FACTOR ANALYSIS OF THE CHILDREN'S DEPRESSION INVENTORY1

abstract: Summary.-Factor analyses of responses to the Children's Depression Inventory were compared for two groups: 123 children at a psychiatric outpatient clinic and 175 elementary school children who attended Gndes 4, 5, and 6. Varimsx rorztions of principal-axis factors yielded four factors for the clinic children and two factors for the non-clinic student group. The items constituting the factors for the clinic group appeared to be theoretically related. These four fsctors were considered to reflect cognitive, motivational, social integration, and somatic components. In contrast, for the students there were a general factor with no predominant theme and a second factor charactcrized by non-compliant behavior. The implications for the relationship between adult and childhood depressive symptomatology are discussed as well as suggestions for studying the inventory as a screening instrument.


title: Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network

abstract: Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeer$$^{\mathrm{X}}$$X. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.


title: Transaction Healing: Scaling Optimistic Concurrency Control on Multicores

abstract: Today's main-memory databases can support very high transaction rate for OLTP applications. However, when a large number of concurrent transactions contend on the same data records, the system performance can deteriorate significantly. This is especially the case when scaling transaction processing with optimistic concurrency control (OCC) on multicore machines. In this paper, we propose a new concurrency-control mechanism, called transaction healing, that exploits program semantics to scale the conventional OCC towards dozens of cores even under highly contended workloads. Transaction healing captures the dependencies across operations within a transaction prior to its execution. Instead of blindly rejecting a transaction once its validation fails, the proposed mechanism judiciously restores any non-serializable operation and heals inconsistent transaction states as well as query results according to the extracted dependencies. Transaction healing can partially update the membership of read/write sets when processing dependent transactions. Such overhead, however, is largely reduced by carefully avoiding false aborts and rearranging validation orders. We implemented the idea of transaction healing in TheDB, a main-memory database prototype that provides full ACID guarantee with a scalable commit protocol. By evaluating TheDB on a 48-core machine with two widely-used benchmarks, we confirm that transaction healing can scale near-linearly, yielding significantly higher transaction rate than the state-of-the-art OCC implementations.


title: Essential immunologic orchestrators of intestinal homeostasis

abstract: Over the past 25 years, substantial advances have been made in our understanding of the cellular and molecular pathways that are essential to maintain a state of health in the mammalian gastrointestinal tract, an organ that is densely colonized by both immune cells and trillions of microbes. Seminal studies in the 1990s identified that several cytokines, antigen-presentation molecules, and components of the T cell receptor were necessary to prevent the development of spontaneous intestinal inflammation in mice. Subsequent research revealed that these pathways orchestrate beneficial interactions with intestinal microbes, involve complex communication between innate and adaptive immune cells, and can be dysregulated in human inflammatory bowel disease. Here, we discuss how these early findings set the stage for numerous other advances and shaped our current knowledge of host-microbiota interactions and intestinal homeostasis in mammals. It is expected that continued investigation of these areas will define previously unknown immunologic mechanisms of tolerance and inflammation in the intestine that can be exploited to benefit human health.


title: Early detection of mild cognitive impairment in elderly through IoT: Preliminary findings

abstract: Mild Cognitive Impairment (MCI) results in the gradual decline in a person's cognitive abilities, and subsequently an increased risk of developing dementia. Although there is no cure for dementia, timely medical and clinical interventions can be administered to elderly who have been diagnosed with MCI, to decelerate the process of further cognitive decline and prolong the duration that they enjoy quality of life. In this paper, we present our preliminary findings of early detection of MCI in elderly who are living in the community, through the use of Internet of Things (IoT) devices for continuous, unobtrusive sensing. Multi-modal sensors are placed in the residences of elderly, to monitor their Activities of Daily Living (ADL), as well as to detect signs of forgetfulness, which are considered symptoms of MCI. Early results indicate that IoT is a promising technology that can potentially pick up signs of early cognitive decline in the elderly.


title: Wisdom and Life Satisfaction in Old Age

abstract: According to previous research findings, objective life conditions such as physical health, socioeconomic status, financial situation, the physical environment, and social involvement cannot fully explain the well-being of older persons. Instead, personality characteristics and developmental influences appear to have a stronger impact. This study combines personality and individual development by introducing the ancient but neglected concept of wisdom as a predictor of life satisfaction. Using a sample of 120 elderly women and men from the 1968169 Berkeley Guidance Study, structural equation models with latent variables show that wisdom (defined as a composite of cognitive, reflective, and affective qualities) has a profoundly positive influence on life satisfaction independent of objective circumstances. The inclusion of wisdom as an additional predictor of subjective well-being increases the explanatory power of the model considerably. Gender differences in predictors of life satisfaction are discussed.


title: Comparative Evaluation of Advanced Three-Phase Three-Level Inverter/Converter Topologies Against Two-Level Systems

abstract: Efficient energy conversion in low-voltage applications has gained more attention due to increasing energy costs and environmental issues. Accordingly, three-level converters have been discussed as an alternative to the standard two-level voltage-source converter because they offer an increased efficiency at higher switching frequencies. From a system perspective, the benefits of using three-level converters are not only limited to the converter itself, but there are additional positive impacts on the surrounding such as on the load machine losses or on the electromagnetic interference input filter volume. In this paper, a holistic comparison of advanced three-level topologies against the two-level topology is given. Simple analytical calculations and measurements show the benefits and the optimization potential concerning several aspects, such as the necessary semiconductor chip area, the harmonic losses in the load machine and in filter components, and the volume of passive components.


title: A behavioral similarity measure between labeled Petri nets based on principal transition sequences

abstract: Being able to determine the degree of similarity between process models is important for management, reuse, and analysis of business process models. In this paper we propose a novel method to determine the degree of similarity between process models, which exploits their semantics. Our approach is designed for labeled Petri nets as these can be seen as a foundational theory for process modeling. As the set of traces of a labeled Petri net may be infinite, the challenge is to find a way to represent behavioral characteristics of a net in a finite manner. Therefore, the proposed similarity measure is based on the notion of so-called "principal transition sequences", which aim to provide an approximation of the essence of a process model. This paper defines a novel similarity measure, proposes a method to compute it, and demonstrates that it offers certain benefits with respect to the state-of-the-art in this field.


Design of Millimeter-wave Slotted-Waveguide Planar Antenna for Sub-array of Beam-scanning Antenna


title: Automatic Discovery of Attribute Synonyms Using Query Logs and Table Corpora

abstract: Attribute synonyms are important ingredients for keyword-based search systems. For instance, web search engines recognize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct answers for them using a combination of knowledge bases, web tables and documents. However, users often refer to an attribute in their e+a query differently from how it is referred in the web table or text passage. In such cases, search engines may fail to return relevant answers. To address that problem, we propose to automatically discover all the alternate ways of referring to the attributes of a given class of entities (referred to as attribute synonyms) in order to improve search quality. The state-of-the-art approach that relies on attribute name co-occurrence in web tables suffers from low precision. Our main insight is to combine positive evidence of attribute synonymity from query click logs, with negative evidence from web table attribute name co-occurrences. We formalize the problem as an optimization problem on a graph, with the attribute names being the vertices and the positive and negative evidences from query logs and web table schemas as weighted edges. We develop a linear programming based algorithm to solve the problem that has bi-criteria approximation guarantees. Our experiments on real-life datasets show that our approach has significantly higher precision and recall compared with the state-of-the-art.


title: Support vector regression fusion scheme in phone duration modeling

abstract: A fusion scheme of phone duration models (PDMs) is presented in this work. Specifically, a support vector regression (SVR)-fusion model is fed with the predictions of a group of independent PDMs operating in parallel. The American-English KED TIMIT and the Greek WCL-1 databases are used for evaluating the PDMs and the fusion scheme. The fusion scheme contributes to the accuracy improvement over the best individual model, achieving a relative reduction of the mean absolute error (MAE) and the root mean square error (RMSE), by 1.9% and 2.0% on KED TIMIT, and 2.6% and 1.8% respectively on WCL-1. Moreover, for evaluating the impact the accuracy improvement will have on synthetic speech, perceptual evaluation test was performed. This test showed that the accuracy improvement achieved by the SVR-fusion would contribute to the improvement of the naturalness of synthetic speech.


title: Interleukin-6-stimulated progranulin expression contributes to the malignancy of hepatocellular carcinoma cells by activating mTOR signaling

abstract: This study aimed to determine the expression of progranulin (PGRN) in hepatocellular carcinoma (HCC) cells in response to interleukin 6 (IL-6), a non-cellular component of the tumor microenvironment, and the molecular mechanism of PGRN oncogenic activity in hepatocarcinogenesis. Levels of IL-6 and PGRN were increased and positively correlated in HCC tissues. IL-6 dose- and time-dependently increased PGRN level in HCC cells. IL-6-driven PGRN expression was at least in part mediated by Erk/C/EBPβ signaling, and reduced expression of PGRN impaired IL-6-stimulated proliferation, migration and invasion of HepG2 cells. PGRN activated mammalian target of rapamycin (mTOR) signaling, as evidenced by increased phosphorylation of p70S6K, 4E-BP1, and Akt-Ser473/FoxO1. Inhibition of mTOR signaling with rapamycin, an mTOR signaling inhibitor, disturbed PGRN- or IL-6-mediated proliferation, migration and invasion of HCC cells in vitro. Persistent activation of mTOR signaling by knockdown of TSC2 restored PGRN-knockdown-attenuated pro-proliferation effects of IL-6 in HepG2 cells. In addition, rapamycin treatment in vivo in mice slowed tumor growth stimulated by recombinant human PGRN. Our findings provide a better understanding of the biological activities of the IL-6/PGRN/mTOR cascade in the carcinogenesis of HCC, which may suggest a novel target in the treatment of HCC.


title: agriGO: a GO analysis toolkit for the agricultural community

abstract: Gene Ontology (GO), the de facto standard in gene functionality description, is used widely in functional annotation and enrichment analysis. Here, we introduce agriGO, an integrated web-based GO analysis toolkit for the agricultural community, using the advantages of our previous GO enrichment tool (EasyGO), to meet analysis demands from new technologies and research objectives. EasyGO is valuable for its proficiency, and has proved useful in uncovering biological knowledge in massive data sets from high-throughput experiments. For agriGO, the system architecture and website interface were redesigned to improve performance and accessibility. The supported organisms and gene identifiers were substantially expanded (including 38 agricultural species composed of 274 data types). The requirement on user input is more flexible, in that user-defined reference and annotation are accepted. Moreover, a new analysis approach using Gene Set Enrichment Analysis strategy and customizable features is provided. Four tools, SEA (Singular enrichment analysis), PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA), are integrated as a toolkit to meet different demands. We also provide a cross-comparison service so that different data sets can be compared and explored in a visualized way. Lastly, agriGO functions as a GO data repository with search and download functions; agriGO is publicly accessible at http://bioinfo.cau.edu.cn/agriGO/.


title: Cross-Entropy Clustering

abstract: We build a general and easily applicable clustering theory, which we call crossentropy clustering (shortly CEC), which joins the advantages of classical kmeans (easy implementation and speed) with those of EM (ane invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC nds the optimal number of clusters by automatically removing groups which have negative information cost.


title: Performance analysis of MapReduce programs on Hadoop cluster

abstract: This paper discusses various MapReduce applications like pi, wordcount, grep, Terasort. We have shown experimental results of these applications on a Hadoop cluster. In this paper, performance of above application has been shown with respect to execution time and number of nodes. We find that as the number of nodes increases the execution time decreases. This paper is basically a research study of above MapReduce applications.


title: A space-sweep approach to true multi-image matching

abstract: The problem of determining feature correspondences across multiple views is considered. The term "true multi-image" matching is introduced to describe techniques that make full and efficient use of the geometric relationships between multiple images and the scene. A true multi-image technique must generalize to any number of images, be of linear algorithmic complexity in the number of images, and use all the images in an equal manner. A new space-sweep approach to true multi-image matching is presented that simultaneously determines 2D feature correspondences and the 3D positions of feature points in the scene. The method is illustrated on a seven-image matching example from the aerial image domain.


title: SUSTAINABLE TOURISM INFRASTRUCTURE PLANNING: A GIS BASED APPROACH

abstract: This paper presents a conceptual GIS-supported sustainable tourism infrastructure planning approach (STIP). This approach aims at integrating a comprehensive set of sustainability criteria (i.e., dealing with development objectives, preferred visitor experiences, and carrying capacity standards) in tourism planning, using GIS. STIP involves three phases: a visitor segmentation phase, a zoning phase, and a transportation network planning phase. To demonstrate the integration of these phases, STIP was applied to the Sinharaja Forest Reserve, a tropical rainforest in Sri Lanka’s southwestern wet-zone. The area is experiencing increasing visitor use and requires additional trail development, as to mitigate resource stress. The resulting trail networks, which were mapped conform the sustainability criteria, provide directives for the area’s sustainable development.


title: Empirical evidence in global software engineering: a systematic review

abstract: Recognized as one of the trends of the 21st century, globalization of the world economies brought significant changes to nearly all industries, and in particular it includes software development. Many companies started global software engineering (GSE) to benefit from cheaper, faster and better development of software systems, products and services. However, empirical studies indicate that achieving these benefits is not an easy task. Here, we report our findings from investigating empirical evidence in GSE-related research literature. By conducting a systematic review we observe that the GSE field is still immature. The amount of empirical studies is relatively small. The majority of the studies represent problem-oriented reports focusing on different aspects of GSE management rather than in-depth analysis of solutions for example in terms of useful practices or techniques. Companies are still driven by cost reduction strategies, and at the same time, the most frequently discussed recommendations indicate a necessity of investments in travelling and socialization. Thus, at the same time as development goes global there is an ambition to minimize geographical, temporal and cultural separation. These are normally integral parts of cross-border collaboration. In summary, the systematic review results in several descriptive classifications of the papers on empirical studies in GSE and also reports on some best practices identified from literature.


title: Similarity Group-By

abstract: Group-by is a core database operation that is used extensively in OLTP, OLAP, and decision support systems. In many application scenarios, it is required to group similar but not necessarily equal values. In this paper we propose a new SQL construct that supports similarity-based Group-by (SGB). SGB is not a new clustering algorithm, but rather is a practical and fast similarity grouping query operator that is compatible with other SQL operators and can be combined with them to answer similarity-based queries efficiently. In contrast to expensive clustering algorithms, the proposed similarity group-by operator maintains low execution times while still generating meaningful groupings that address many application needs. The paper presents a general definition of the similarity group-by operation and gives three instances of this definition. The paper also discusses how optimization techniques for the regular group-by can be extended to the case of SGB. The proposed operators are implemented inside PostgreSQL. The performance study shows that the proposed similarity-based group-by operators have good scalability properties with at most only 25% increase in execution time over the regular group-by.


title: Segmentation of liver using marker watershed transform algorithm for CT scan images

abstract: The Liver is a largest gland in the body. Distinct diseases affected on the liver. Liver diseases is one of the most serious health problem worldwide. For detecting the liver diseases the Segmentation Technique is essential. Segmentation is used for the classification of liver diseases. The liver diseases are focal or diffused is easily understood by the physician using segmentation. We use CT scan image for segmentation but the noise is present in the image. Therefore preprocessing is applied on the image for the removal of noise. In this paper, Watershed Transform segmentation Algorithm is used because it produce complete division of images in separate region even if contrast is poor. Therefore this method could be achieved 92.1% accuracy.


title: Event detection and tracking in social streams

abstract: Events and stories can be characterized by a set of de- scriptive, collocated keywords. Intuitively, documents describing the same event will contain similar sets of keywords, and the graph of keywords for a document collection will contain clusters individual events. In this paper we build a network of keywords based on their co-occurrence in documents. We propose and develop a new event detection algorithm which creates a keyword graph and uses community detection methods analo- gous to those used for social network analysis to dis- cover and describe events. Constellations of keywords describing an event may be used to find related articles. We also use the proposed algorithm to analyze events and track stories in social streams.


title: Performance of NB and SVM classifiers in Islamic Arabic data

abstract: Text categorization is one of the well studied problems in data mining and information retrieval. Given a large quantity of documents in a data set where each document is associated with its corresponding category. Categorization involves building a model from classified documents, in order to classify previously unseen documents as accurately as possible. This paper investigates Naive Bayesian method (NB) and Support Vector Machine (SVM) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorisation data sets reveal that SVM algorithm outperforms the NB with regards to all measures.


title: Efficiency improvement in adjustable deadtime of LLC resonant converters

abstract: This paper presents an analysis of high efficiency feasibility at wide load in LLC-SRC Resonant Converter. LLC-SRC Resonant Converter could be achieved zero voltage switching turn on by resonant scheme. A fixed deadtime must be larger than the discharge time for the effective drain-source capacitance of Mosfet during switching off. Ensure that parasitic capacitance of the half bridge including Mosfet's capacitance has been discharged before switching turn on. But conduction loss is appeared Transformer and Power Mosfet at heavy load. Because resonant current is higher than enough to obtain discharge capability by proper magnetic inductance. Especially, the resonant tank current flows through into the body diode of Mosfet at heavy load. In order to achieve high efficiency of LLC-SRC resonant converters. An adjustable deadtime could be feasibly to improve efficiency from light load to heavy load.


title: Magnetic resonance fingerprinting

abstract: A new approach to magnetic resonance, ‘magnetic resonance fingerprinting', is reported, which combines a data acquisition scheme with a pattern-recognition algorithm that looks for the ‘fingerprints’ of interest within the data.


title: Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart

abstract: Recent advances in deep generative models have shown promising potential in image inpanting, which refers to the task of predicting missing pixel values of an incomplete image using the known context. However, existing methods can be slow or generate unsatisfying results with easily detectable flaws. In addition, there is often perceivable discontinuity near the holes and require further post-processing to blend the results. We present a new approach to address the difficulty of training a very deep generative model to synthesize high-quality photo-realistic inpainting. Our model uses conditional generative adversarial networks (conditional GANs) as the backbone, and we introduce a novel block-wise procedural training scheme to stabilize the training while we increase the network depth. We also propose a new strategy called adversarial loss annealing to reduce the artifacts. We further describe several losses specifically designed for inpainting and show their effectiveness. Extensive experiments and user-study show that our approach outperforms existing methods in several tasks such as inpainting, face completion and image harmonization. Finally, we show our framework can be easily used as a tool for interactive guided inpainting, demonstrating its practical value to solve common real-world challenges.


title: De novo synthesis of pyrimidine nucleotides; emerging interfaces with signal transduction pathways

abstract: The de novo biosynthesis of pyrimidine nucleotides provides essential precursors for multiple growth-related events in higher eukaryotes. Assembled from ATP, bicarbonate and glutamine, the uracil and cytosine nucleotides are fuel for the synthesis of RNA, DNA, phospholipids, UDP sugars and glycogen. Over the past 2 decades considerable progress has been made in elucidating the mechanisms by which cellular pyrimidines are modulated to meet the needs of the cell. Recent studies demonstrate that CAD, a rate-limiting enzyme in the de novo synthesis of pyrimidines, is regulated through reversible phosphorylation, Myc-dependent transcriptional changes and caspase-mediated degradation. These studies point to increasing evidence for cooperation between key cell signaling pathways and basic elements of cellular metabolism, and suggest that these events have the potential to determine distinct cellular fates, including growth, differentiation and death. This review highlights some of the recent advances in the regulation of pyrimidine synthesis by growth-factor-stimulated signaling pathways.


title: Community Structure of the Physical Review Citation Network

abstract: We investigate the community structure of physics subfields in the citation network of all Physical Review publications between 1893 and August 2007. We focus on well-cited publications (those receiving more than 100 citations), and apply modularity maximization to uncover major communities that correspond to clearly identifiable subfields of physics. While most of the links between communities connect those with obvious intellectual overlap, there sometimes exist unexpected connections between disparate fields due to the development of a widely applicable theoretical technique or by cross fertilization between theory and experiment. We also examine communities decade by decade and also uncover a small number of significant links between communities that are widely separated in time.


title: Predicting movie ratings from audience behaviors

abstract: We propose a method of representing audience behavior through facial and body motions from a single video stream, and use these features to predict the rating for feature-length movies. This is a very challenging problem as: i) the movie viewing environment is dark and contains views of people at different scales and viewpoints; ii) the duration of feature-length movies is long (80–120 mins) so tracking people uninterrupted for this length of time is still an unsolved problem; and iii) expressions and motions of audience members are subtle, short and sparse making labeling of activities unreliable. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input. We then utilize motion-history features which capture the subtle movements of a person within a pre-defined volume, and then form a group representation of the audience by a histogram of pair-wise correlations over a small-window of time. Using this group representation, we learn our movie rating classifier from crowd-sourced ratings collected by rottentomatoes.com and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).


title: End-to-end training of object class detectors for mean average precision

abstract: We present a method for training CNN-based object class detectors directly using mean average precision (mAP) as the training loss, in a truly end-to-end fashion that includes non-maximum suppresion (NMS) at training time. This contrasts with the traditional approach of training a CNN for a window classification loss, then applying NMS only at test time, when mAP is used as the evaluation metric in place of classification accuracy. However, mAP following NMS forms a piecewise-constant structured loss over thousands of windows, with gradients that do not convey useful information for gradient descent. Hence, we define new, general gradient-like quantities for piecewise constant functions, which have wide applicability. We describe how to calculate these efficiently for mAP following NMS, enabling to train a detector based on Fast R-CNN [1] directly for mAP. This model achieves equivalent performance to the standard Fast R-CNN on the PASCAL VOC 2007 and 2012 datasets, while being conceptually more appealing as the very same model and loss are used at both training and test time.


title: Early diagnosis of autism and impact on prognosis: a narrative review

abstract: Autism spectrum disorders involve a set of clinical phenotypes that mirror an early onset of neurodevelopmental deviations, with core symptoms that can probably be related to a deficiency in the social instinct. Underlying the cognitive impairments there are physiological brain problems, caused by a large number of medical factors. This narrative review of systematic reviews and meta-analyses from the last 5 years (2008–2012) presents aspects from many areas in autism spectrum disorder research, with a particular focus on early intervention and the subsequent impact on prognosis. Other major areas discussed are epidemiology, early symptoms and screening, early diagnosis, neuropsychology, medical factors, and the existence of comorbidities. There is limited evidence that any of the broadband “early intervention” programs are effective in changing the natural long-term outcome for many individuals with an early diagnosis of autism. However, there is some evidence that Early Intensive Behavioral Intervention (EIBI) is an effective treatment for some children with ASD. Nevertheless, there is emerging consensus that early diagnosis and information are needed in order that an autism-friendly environment be “created” around affected individuals.


title: Design of Class E Resonant Rectifiers and Diode Evaluation for VHF Power Conversion

abstract: Resonant rectifiers have important applications in very-high-frequency (VHF) power conversion systems, including dc–dc converters, wireless power transfer systems, and energy recovery circuits for radio-frequency systems. In many of these applications, it is desirable for the rectifier to appear as a resistor at its ac input port. However, for a given dc output voltage, the input impedance of a resonant rectifier varies in magnitude and phase as output power changes. This paper presents a design methodology for class E rectifiers that maintain near-resistive input impedance along with the experimental demonstration of this approach. Resonant rectifiers operating at 30 MHz over 10:1 and 2:1 power ranges are used to validate the design methodology and identify its limits. Furthermore, a number of Si Schottky diodes are experimentally evaluated for VHF rectification and categorized based on performance.


title: Closed Multidimensional Sequential Pattern Mining

abstract: We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed itemset pattern mining. Based on this method, we show that (1) the number of complete closed multidimensional sequential patterns is not larger than the number of complete multidimensional sequential patterns (2) the set of complete closed multidimensional sequential patterns covers the complete resulting set of multidimensional sequential patterns. In addition, mining using closed itemset pattern mining on multidimensional information would mine only multidimensional information associated with mined closed sequential patterns, and mining using closed sequential pattern mining on sequences would mine only sequences associated with mined closed itemset patterns.


title: Transformerless micro-inverter for grid-connected photovoltaic systems

abstract: The leakage currents caused by high-frequency common-mode (CM) voltage have become a major concern in transformerless photovoltaic (PV) inverters. This paper addresses to a review on dc-ac converters applied to PV systems that can avoid the circulation of leakage currents. Looking for a lower cost and higher reliability solution, a 250 W PV transformerless micro-inverter prototype based on the bipolar full-bridge topology was built and tested. As it is confirmed by experimental results, this topology is able to maintain the CM voltage constant and prevent the circulation of CM currents through the circuit.


title: “Sustainable Packaging Logistics”. The link between Sustainability and Competitiveness in Supply Chains

abstract: Packaging is one element that can support and promote improvements and innovations in sustainable management of supply chains. This multifunctional vision has brought about the approach “sustainable packaging logistics” (SPL), which refers to the integration of packaging design, logistics management and new product development. The main aim of this paper is twofold. Firstly, to characterise the main aspects associated with SPL as a preliminary step towards constructing a conceptual model that can be validated in a quantitative way. Secondly, to carry out an exploratory study in the Spanish toy manufacturing sector in order to identify whether SPL deployment promotes changes and innovations in packaging that can lead to improved sustainability. The literature review was conducted via content analysis, and the methodology used to carry out empirical analysis was an electronic questionnaire dealing with aspects of SPL. The number of toy manufacturers participating in the study was 70. The results analysis indicates that further development of the main aspects of SPL actively contributes to better sustainable performance in almost all items considered in the study. The SPL approach is considered particularly innovative, because there is a scarcity of literature that deals jointly with packaging, logistics and sustainability from an empirical standpoint, rather than just the analysis of case studies.


title: Communication of Emotion in Social Robots through Simple Head and Arm Movements

abstract: Understanding how people perceive robot gestures will aid the design of robots capable of social interaction with humans. We examined the generation and perception of a restricted form of gesture in a robot capable of simple head and arm movement, referring to point-light animation and video experiments in human motion to derive our hypotheses. Four studies were conducted to look at the effects of situational context, gesture complexity, emotional valence and author expertise. In Study 1, four participants created gestures with corresponding emotions based on 12 scenarios provided. The resulting gestures were judged by 12 participants in a second study. Participants’ recognition of emotion was better than chance and improved when situational context was provided. Ratings of lifelikeness were found to be related to the number of arm movements (but not head movements) in a gesture. In Study 3, five novices and five puppeteers created gestures conveying Ekman’s six basic emotions which were shown to 12 Study 4 participants. Puppetry experience improved identification rates only for the emotions of fear and disgust, possibly because of limitations with the robot’s movement. The results demonstrate the communication of emotion by a social robot capable of only simple head and arm movement.


title: A framework based on RSA and AES encryption algorithms for cloud computing services

abstract: Cloud computing is an emerging computing model in which resources of the computing communications are provided as services over the Internet. Privacy and security of cloud storage services are very important and become a challenge in cloud computing due to loss of control over data and its dependence on the cloud computing provider. While there is a huge amount of transferring data in cloud system, the risk of accessing data by attackers raises. Considering the problem of building a secure cloud storage service, current scheme is proposed which is based on combination of RSA and AES encryption methods to share the data among users in a secure cloud system. The proposed method allows providing difficulty for attackers as well as reducing the time of information transmission between user and cloud data storage.


title: Using RDF Summary Graph For Keyword-based Semantic Searches

abstract: The Semantic Web began to emerge as its standards and technologies developed rapidly in the recent years. The continuing development of Semantic Web technologies has facilitated publishing explicit semantics with data on the Web in RDF data model. This study proposes a semantic search framework to support efficient keyword-based semantic search on RDF data utilizing near neighbor explorations. The framework augments the search results with the resources in close proximity by utilizing the entity type semantics. Along with the search results, the system generates a relevance confidence score measuring the inferred semantic relatedness of returned entities based on the degree of similarity. Furthermore, the evaluations assessing the effectiveness of the framework and the accuracy of the results are presented.


Simultaneous recovery of ampicillin-sensitive and ampicillin-resistant H. influenzae from blood.


title: A Model of Social Media Engagement: User Profiles, Gratifications, and Experiences

abstract: This chapter presents a model of social media engagement. The model’s components–presentation of self, action and participation, uses and gratifications, positive experiences, usage and activity counts, and social context–are discussed in depth with relevant evidence and examples. The model supports the main thrust of the chapter to combine tangible, e.g., usage and activity counts, and more abstract, e.g., positive user experiences, indicators of engagement in order to better understand why people engage with social media, the extent to which they engage, what social media platforms they interact with, and the outcomes of their engagement. The chapter includes illustrative case studies and concludes with some thought-provoking questions to guide future research, including the ethical and social implications of social media engagement


title: Image enhancement method for laser infrared thermography defect detection in aviation composites

abstract: A new infrared preprocessing modality called sequence differential processing is proposed. Different from the cold image subtraction which only decreases the background noise, the proposed modality takes the parallel test into account to preprocess the raw thermal data sequence prior to applying the advanced postprocessing techniques such as principal component thermography and pulsed phase thermography. The results show that the preprocessing method not only effectively reduces the influence of uneven laser energy distribution on detection efficiency but also enhances internal defect information. Moreover, the combination of the proposed preprocessing method with the pulsed phase thermography and principal component analysis algorithms improves the ability of laser infrared thermography to detect defects inside aviation carbon-fiber-reinforced plastics.


title: Psychosomatic Aspects in Myocardial Infarction in Comparison with Angina Pectoris

abstract: Although there are opinions to the contrary, the literature provides fairly convincing evidence that some psychodynamic features are more frequent than others in subjects prone to coronary heart disease. As personality changes often take place following myocardial infarction, retrospective studies are less convincing than prospective ones. Most of the latter are rather superficial in nature. However, they point in the direction of predominance of obsessive-compulsive character defences, more in myocardial infarction than in angina pectoris without myocardial infarction. Poverty of fantasy life, operational mode of mental functioning (‘pensee operatoire’ of the French school), repression and control of emotional reactions appear predominantly in the former.


title: Using model checking to control the structural errors in BPMN models

abstract: The emergence of BPMN as a standard notation to express the business processes is based on its simplicity of notations and its exhaustive expressiveness. Nevertheless the lack of formal semantics in the BPMN can cause syntactic and structural errors. The former requires less effort to be checked, while the later usually requires attention to prove some properties, like deadlock-freedom and livelock-freedom. In this paper, we address the issue of detecting the structural errors with an approach based on model checking. It verifies the soundness of business process model and helps the business modelers to avoid the deadlocks, livelocks, and multiple terminations errors.


title: Treatment Fit: A Description and Demonstration via Video of a Brief and Functional Treatment Fit Model

abstract: Treatment fit is the degree to which the counselor and the client agree upon the presenting issues, counseling goals and the initial treatment plan. Research indicates that treatment At is one of the strongest predictors of client outcome. As such, a brief functional treatment At model (TFM) is presented to assist counselors in conducting multidimensional needs assessments and developing co-created treatment plans. Application of this model is demonstrated with a case study. In addition, a Ank to a video demonstration of this model is included, along with a discussion as to the need for including links to videos in counseling journals.Keywords: treatment fit, multidimensional needs assessment, treatment plan, counseling goals, video demonstrationThis is an exciting time in the mental health profession as health care specialists and policy makers are recognizing the value of counseling in increasing the quality of patient care while decreasing overall health care costs (Burtnett, 2012; Curtis & Christian, 2012; Lee et al., 2012; Wos, 2013). As such, counselors are increasingly being recognized not just as mental health providers, but as viable health care professionals who may well be the crucial link in improving a flawed and exorbitantly expensive health care system (American Psychological Association [APA], 2012; Brill, 2013; Paquette et al., 2003). With this positive momentum, however, it becomes even more imperative for counselors to consistently utilize evidence-based practices (i.e., empirically supported counseling strategies), which include practice-based evidence (i.e., continuous feedback between counselor and client) to ensure optimal treatment. Unfortunately, the utilization rates of evidence-based practices by counselors are minimal and inconsistent (Beutler, 2009; Olmstead, Abraham, Martino, & Roman, 2012), which may in part be the reason why the general public, when surveyed, have indicated that the primary reason for not seeking counseling was their lack of confidence in positive outcomes (Harris Interactive, 2004). One could argue that while it is critically important for counselors to continue conducting innovative treatment research, it is equally imperative that counselors increase efforts in implementing well-established evidencebased counseling strategies (Olmstead et al., 2012).One of the best predictors of client outcome is treatment fit (APA, 2012; Budd & Hughes, 2009; Kim, Ng, & Ahn, 2009), the process by which the client and counselor collaboratively assess mental health issues, set goals, and create an initial treatment plan. At the heart of treatment fit is the collaboration between client and counselor including continuous feedback about issues, goals and treatment to ensure the optimal provision of care. A parallel to treatment fit in the medical community is the increasing use of checklists to ensure that evidencebased procedures are being properly implemented. In surgical safety research, for instance, Haynes et al. (2009) found in a large multi-site international study that when medical professionals followed a brief surgical checklist (e.g., the patient has verified his or her identity, the surgical site, and procedure and consent), patient deaths decreased from 1.5% to 0.8% and inpatient complications following surgery decreased from 11.0% to 7.0% (Haynes et al., 2009). As demonstrated in this surgical safety study, the consistent application of evidencebased procedures had a significant impact on patients' lives. Similarly, the purpose of this article is to show how evidence-based counseling strategies that comprise treatment fit can help counselors more consistently and frequently implement treatments that work in order to increase the likelihood of positive client outcomes.This article is comprised of two primary objectives. The first objective is to describe a brief, functional, firstsession counseling protocol for ensuring treatment fit with clients. …


title: Tap-Kick-Click: Foot Interaction for a Standing Desk

abstract: Foot interaction techniques for controlling conventional desktop applications at a standing desk are described. Indirect, discrete two-foot input using combinations of spatial kicks, taps, jumps, and standing postures are tracked using a depth camera and instrumented shoes. An implemented system shows how visual feedback and interface augmentation can make foot input compatible with existing desktop applications. Application scenarios using the system demonstrate productive pure foot input breaks with real application tasks like web browsing and code debugging, as well as using feet as a secondary input channel with mouse and keyboard. An evaluation validates the usability of the approach.


title: Ranking of text documents using TF-IDF weighting and association rules mining

abstract: The amount of information has increased exponentially over the past few years. Most of the text documents are unstructured and not organized and hence user facing more difficult to find his information needs. It creates a challenge for information retrieval for ranking documents according to the user relevance. One of more useful and efficient retrieval model is the vector space model. It uses term weighting tf-idf for assigning a score to a query/document pair, in order to rank the documents. This method is based on the bag-of-words model and does not capture position of terms in document and semantics. Keeping this approach, here we propose a new ranking measure that combines the vector space measure denoted tf-idf and new factor deducted from association rules technique based on dominance relations. This approach will help the user to get the most relevant documents at the beginning. The experiments on TREC collection show that the proposed method is giving better results compared to the baselines and the semantic ranking ESA.


title: Real-time sensor data streams

abstract: Today's observation streams of large numbers of geographically distributed sensors in geosensor networks arrive continuously at powerful servers, and users are interested in analyzing the sensor data streams in real-time. This article investigates challenges in data management that arise when massive real-time data streams become available and discusses different data management technologies for managing and analyzing real-time sensor data streams.


title: A Real-time Facial Expression Recognizer using Deep Neural Network

abstract: With increasing boundaries of deep learning based recognition model, particular demands for real-time user state recognizer targeting smart home devices have emerged. This paper suggests real-time facial expression recognizer to satisfy them. We use HOG feature descriptor to detect a human face, correlation tracker to track detected face and deep Convolutional Neural Network (CNN) based recognizer on our model. Our CNN model is trained and tested with Kaggle facial expression recognition challenge dataset. The experimental result shows that high test accuracy and low computation time are achieved by our recognizer enabling real-time high-performance human expression recognition for mobile use.


title: Anti-atherogenic properties of date vs. pomegranate polyphenols: the benefits of the combination

abstract: Hydrolysable tannin polyphenols in pomegranate and phenolic acids in date fruit and seeds are potent antioxidants and anti-atherogenic agents, and thus, in the present study we investigated the possible benefits of combining them in vivo in atherosclerotic apolipoprotein E KO (E(0)) mice, compared with the individual fruit. In vitro studies revealed that the date seed extract contains more polyphenols than Amari or Hallawi date extracts, and possesses a most impressive free radical scavenging capacity. Similarly, pomegranate juice (PJ), punicalagin, punicalain, gallic acid, and urolithins A and B are very potent antioxidants. E(0) mice consumed 0.5 μmol gallic acid equivalents (GAE) per mouse per day of PJ, Hallawi extract, date seed extract, or a combination for 3 weeks. Consumption of the combination was the most potent treatment, as it decreased serum cholesterol and triglyceride levels, and increased serum paraoxonase 1 (PON1) activity. Consumption of the combination also significantly reduced mouse peritoneal macrophage (MPM) oxidative stress, MPM cholesterol content, and MPM LDL uptake. Finally, the lipid peroxide content in the aortas of the mice significantly decreased, and the PON lactonase activity of the aortas increased after treatment with the combination. We thus conclude that consumption of pomegranate, together with date fruit and date seeds, has the most beneficial anti-atherogenic effects on E(0) mice serum, macrophages, and aortas, probably due to their unique and varied structures.


title: ALISA: An automatic lightly supervised speech segmentation and alignment tool

abstract: Abstract This paper describes the ALISA tool, which implements a lightly supervised method for sentence-level alignment of speech with imperfect transcripts. Its intended use is to enable the creation of new speech corpora from a multitude of resources in a language-independent fashion, thus avoiding the need to record or transcribe speech data. The method is designed so that it requires minimum user intervention and expert knowledge, and it is able to align data in languages which employ alphabetic scripts. It comprises a GMM-based voice activity detector and a highly constrained grapheme-based speech aligner. The method is evaluated objectively against a gold standard segmentation and transcription, as well as subjectively through building and testing speech synthesis systems from the retrieved data. Results show that on average, 70% of the original data is correctly aligned, with a word error rate of less than 0.5%. In one case, subjective listening tests show a statistically significant preference for voices built on the gold transcript, but this is small and in other tests, no statistically significant differences between the systems built from the fully supervised training data and the one which uses the proposed method are found.


Lexical and Learning-based Emotion Mining from Text


title: Statistical Language Models for Information Retrieval

abstract: Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical language models not only achieve superior empirical performance, but also facilitate parameter tuning and provide a more principled way, in general, for modeling various kinds of complex and non-traditional retrieval problems.


title: Goal striving, need satisfaction, and longitudinal well-being: The self-concordance model.

abstract: An integrative model of the conative process, which has important ramifications for psychological need satisfaction and hence for individuals' well-being, is presented. The self-concordance of goals (i.e., their consistency with the person's developing interests and core values) plays a dual role in the model. First, those pursuing self-concordant goals put more sustained effort into achieving those goals and thus are more likely to attain them. Second, those who attain self-concordant goals reap greater well-being benefit from their attainment. Attainment-to-well-being effects are mediated by need satisfaction, i.e., daily activity-based experiences of autonomy, competence, and relatedness that accumulate during the period of striving. The model is shown to provide a satisfactory fit to 3 longitudinal data sets and to be independent of the effects of self-efficacy, implementation intentions, avoidance framing, and life skills.


title: Quantum Cryptography

abstract: Quantum cryptography could well be the first application of quantum mechanics at the individual quanta level. The very fast progress in both theory and experiments over the recent years are reviewed, with emphasis on open questions and technological issues.


title: Practical Web Data Extraction: Are We There Yet? - A Short Survey

abstract: The number of web documents as well as the inherent data and information is growing at a rapid pace. The interest in extracting and utilizing this data is rising likewise. The prospects that are unlocked by Web Data Extraction to its users are as broad as the extensiveness of topics and fields on the Web. The major obstacle is to utilize the available data, contents and processes. Several, mostly older survey papers have already shown developments and approaches to solve Web Data Extraction tasks, but there is a need for a more up-to-date review, showing the latest developments. Additionally when looking from the user perspective, there is still a gap between research results and practical applicability. Available solutions, including research results, commercial products and open source solutions lack certain capabilities or suffer from severe usability issues. This paper therefore gives a short review of the state of the art in Web Data Extraction and relates this to the practical application of these technologies.


title: Exploring the factors associated with Web site success in the context of electronic commerce

abstract: Web sites are being widely deployed commercially. As the widespread use and dependency on Web technology increases, so does the need to assess factors associated with Web site success. The objective is to explore these factors in the context of electronic commerce (EC). The research framework was derived from information systems and marketing literature. Webmasters from Fortune 1000 companies were used as the target group for a survey. Four factors that are critical to Web site success in EC were identified: (1) information and service quality, (2) system use, (3) playfulness, and (4) system design quality. An analysis of the data provides valuable managerial implications for Web site success in the context of electronic commerce.


title: Novel Modified Pythagorean Tree Fractal Monopole Antennas for UWB Applications

abstract: A novel modified microstrip-fed ultrawide-band (UWB) printed Pythagorean tree fractal monopole antenna is presented. In this letter, by inserting a modified Pythagorean tree fractal in the conventional T-patch, much wider impedance bandwidth and new resonances will be produced. By only increasing the tree fractal iterations, new resonances are obtained. The designed antenna has a compact size of 25 × 25 × 1 mm3 and operates over the frequency band between 2.6 and 11.12 GHz for VSWR <; 2. Using multifractal concept in modified Pythagorean tree fractal antenna design makes monopole antennas flexible in terms of controlling resonances and bandwidth. In this letter, the improvement process of the impedance bandwidth has been presented and discussed.


title: Exercise treatment for depression: efficacy and dose response.

abstract: BACKGROUND ::: This study, conducted between 1998 and 2001 and analyzed in 2002 and 2003, was designed to test (1) whether exercise is an efficacious treatment for mild to moderate major depressive disorder (MDD), and (2) the dose-response relation of exercise and reduction in depressive symptoms. ::: ::: ::: DESIGN ::: The study was a randomized 2x2 factorial design, plus placebo control. ::: ::: ::: SETTING/PARTICIPANTS ::: All exercise was performed in a supervised laboratory setting with adults (n =80) aged 20 to 45 years diagnosed with mild to moderate MDD. ::: ::: ::: INTERVENTION ::: Participants were randomized to one of four aerobic exercise treatment groups that varied total energy expenditure (7.0 kcal/kg/week or 17.5 kcal/kg/week) and frequency (3 days/week or 5 days/week) or to exercise placebo control (3 days/week flexibility exercise). The 17.5-kcal/kg/week dose is consistent with public health recommendations for physical activity and was termed "public health dose" (PHD). The 7.0-kcal/kg/week dose was termed "low dose" (LD). ::: ::: ::: MAIN OUTCOME MEASURES ::: The primary outcome was the score on the 17-item Hamilton Rating Scale for Depression (HRSD(17)). ::: ::: ::: RESULTS ::: The main effect of energy expenditure in reducing HRSD(17) scores at 12 weeks was significant. Adjusted mean HRSD(17) scores at 12 weeks were reduced 47% from baseline for PHD, compared with 30% for LD and 29% for control. There was no main effect of exercise frequency at 12 weeks. ::: ::: ::: CONCLUSIONS ::: Aerobic exercise at a dose consistent with public health recommendations is an effective treatment for MDD of mild to moderate severity. A lower dose is comparable to placebo effect.


Surface Tension Temperature Relation


title: FeUdal Networks for Hierarchical Reinforcement Learning

abstract: We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels - allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the Worker. The Worker generates primitive actions at every tick of the environment. The decoupled structure of FuN conveys several benefits - in addition to facilitating very long timescale credit assignment it also encourages the emergence of sub-policies associated with different goals set by the Manager. These properties allow FuN to dramatically outperform a strong baseline agent on tasks that involve long-term credit assignment or memorisation.


title: CiDRA: A cache-inspired DRAM resilience architecture

abstract: Although aggressive technology scaling has allowed manufacturers to integrate Giga bits of cells into a cost-sensitive main memory DRAM device, these cells have become more defect-prone. With increased cell failure rates, conventional solutions such as populating spare DRAM rows and relying on error-correcting codes (ECCs) have shown limited success due to high area overhead, the latency penalties of data coding, and interference between ECC within a device (in-DRAM ECC) and other ECC across devices (rank-level ECC). In this paper, we propose CiDRA, a cache-inspired DRAM resilience architecture, which substantially reduces the area and latency overheads of correcting bit errors on random locations due to these faulty cells. We put a small SRAM cache within a DRAM device to replace accesses to the addresses including the faulty cells with ones that correspond to the cache data array. This CiDRA cache is paired with a Bloom filter to minimize the energy overhead of accessing the cache tags for every DRAM access and is also partitioned into small pieces, each being associated with the I/O pads for better area efficiency. Both the cache and DRAM banks are accessed in parallel while the banks are much slower. Consequently, the cache and filter are not in the critical path for normal DRAM accesses and incur no latency overhead. We also enhance the traditional in-DRAM ECC with error position bits and the appropriate error detecting capability while preventing interference with the traditional rank-level ECC scheme. By combining this enhanced in-DRAM ECC with the cache and Bloom filter, CiDRA becomes more area efficient because the in-DRAM ECC corrects most bit errors that are sporadic while the cache deals with the remaining relatively few pathological cases.


title: Opinion dynamics in social networks with stubborn agents: an issue-based perspective

abstract: Classic models on opinion dynamics usually focus on a group of agents forming their opinions interactively over single issue. Yet generally consensus can not be achieved over single issue when agents are not completely open to interpersonal influence. In this paper, opinion consensus in social networks with stubborn agents is considered over issue sequences. The social network with stubborn agents is described by the Friedkin-Johnsen (F-J) model where agents are stubborn to their initial opinions. Firstly, we propose some sufficient and necessary conditions both in terms of network topology and system matrix for convergence of the F-J model over single issue. Secondly, opinion consensus of the F-J model is investigated over issue sequences. Our analysis establishes connections between the interpersonal influence network and the network describing the relationship of agents' initial opinions for successive issues. Taking advantage of these connections, we derive the sufficient and necessary condition for the F-J model to achieve opinion consensus over issue sequences. Finally, we consider a more general scenario where each agent has bounded confidence in forming its initial opinion for every issue. By analyzing the evolution of agents' ultimate opinions for each issue over issue sequences, we prove that the connectivity of the state-dependent network is preserved in this setting. Then the conditions for agents to achieve opinion consensus over issue sequences are proposed. Simulation examples are provided to illustrate the effectiveness of our theoretical results.


title: CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods

abstract: Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( ::: http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ::: ).


title: A co-citation bibliometric analysis of strategic management research

abstract: Strategic management remains a recent field of research that is dynamic and changing with the global business economy. Given the sheer importance of research on this field of business management, this paper aims to conduct a co-citation bibliometric analysis of strategic management research. We map the authors and the most relevant approaches as well as detailing the new theoretical perspectives to strategic management theory. The analysis conducted uses three multivariate statistical analysis techniques in addition to the co-citation matrix to shed light on these issues. By incorporating all the citations that are included in the Science Citation Index and the Social Science Citation Index, we analyze co-citation patterns of the strategic management field during the period 1971---2014 and identify six subfields (clusters) that constitute the intellectual structure and investigate their mutual relationships. The main findings of the factor analysis suggest that there is a clear division between strategic entrepreneurship and corporate entrepreneurship. In addition, the concept of strategic behavior affects most strategic management research as evidenced by the co-citation analysis. A debate of future directions on the strategic management literature is discussed, which highlights the importance of combining more of a strategic entrepreneurship perspective based on behavioral intentions to the emerging research.


title: GPS-Inspired Stretchable Self-Powered Electronic Skin

abstract: Electronic skin has attracted much attention for their profound implications for human/machine interaction and medicine recently. To imitate the unique characteristics of skin, the electronic skin is moving towards stretchable, multifunctional, biodegradable, more sensitive and accurate. Here, we present a novel stretchable self-powered electronic skin, to detect the touch location in an analog method. Thanks to triboelectrification and planar electrostatic induction, the generation of location signals does not need an extra power supply. Inspired by Global Positioning System, the electronic skin has simple structure of a single layer poly(dimethylsiloxane) (PDMS) substrate and three carbon nanotube-PDMS (CNT-PDMS) composite electrodes, which not only reduce the electrode amount but also make the device stretchable. The less electrode number means the less fabrication cost, extra difficulties on electrode extraction, and in particular, less signal interference and data processing. Stretchability extends the application scenarios of the electronic skin to the curvilinear surfaces, such as cylindrical surfaces and spherical surfaces. Through special location method, the touch position can be read out easily and directly with averaged error sums of ∼1 mm. This new electronic skin takes a significant step forward in practical application.


title: Fast heuristic search for RTS game combat scenarios

abstract: Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method — Alpha-Beta search for durative moves — that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable — opening the door for a promising line of research on opponent combat modelling.


title: Patient health management system using e-health monitoring architecture

abstract: This paper illustrates the design and implementation of an e-health monitoring networked system. The architecture for this system is based on smart devices and wireless sensor networks for real time analysis of various parameters of patients. This system is aimed at developing a set of modules which can facilitate the diagnosis for the doctors through tele-monitoring of patients. It also facilitates continuous investigation of the patient for emergencies looked over by attendees and caregivers. A set of medical and environmental sensors are used to monitor the health as well as the surrounding of the patient. This sensor data is then relayed to the server using a smart device or a base station in close proximity. The doctors and caregivers monitor the patient in real time through the data received through the server. The medical history of each patient including medications and medical reports are stored on cloud for easy access and processing for logistics and prognosis of future complications. The architecture is so designed for monitoring a unitary patient privately at home as well as multiple patients in hospitals and public health care units. Use of smartphones to relay data over internet reduces the total cost of the system. We have also considered the privacy and security aspects of the system keeping the provision for selective authority for patients and their relatives to access the cloud storage as well as the possible threats to the system. We have also introduced a novel set of value added services through this paper which include Real Time Health Advice and Action (ReTiHA) and Parent monitoring for people with their family living abroad.


abstract: Auditory Saliency Using Natural Statistics Tomoki Tsuchida (ttsuchida@ucsd.edu) Garrison W. Cottrell (gary@ucsd.edu) Department of Computer Science and Engineering 9500 Gilman Drive, Mail Code 0404 La Jolla CA 92093-0404 USA Abstract In contrast to the wealth of saliency models in the vision lit- erature, there is a relative paucity of models exploring audi- tory saliency. In this work, we integrate the approaches of (Kayser, Petkov, Lippert, & Logothetis, 2005) and (Zhang, Tong, Marks, Shan, & Cottrell, 2008) and propose a model of auditory saliency. The model combines the statistics of natural soundscapes and the recent past of the input signal to predict the saliency of an auditory stimulus in the frequency domain. To evaluate the model output, a simple behavioral experiment was performed. Results show the auditory saliency maps cal- culated by the model to be in excellent accord with human judgments of saliency. Keywords: attention; saliency map; audition; auditory percep- tion; environmental statistics Introduction In general, attention plays a very important role in the survival of an organism, by separating behaviorally relevant signals from irrelevant ones. One approach to understanding how attention functions in the brain is to consider the “saliency map” over the sensory input space, which may determine sub- sequent motor control targets or selectively modulate percep- tual contrast thresholds. The brain’s putative saliency maps can be thought of as interest operators that organisms use to enhance or filter sensory signals. Many visual saliency models have been investigated, but relatively little attention has been paid to modeling auditory saliency. However, since the fundamental necessity for per- ceptual modulation remains the same regardless of modal- ity, the principles of visual saliency models should apply equally well to auditory saliency with appropriate sensory input features. Two representative visual saliency models are the center-surround contrast model (Itti, Koch, & Niebur, 2002) and the SUN (Salience Using Natural Statistics) model (Zhang et al., 2008). Itti et al.’s model is neurally-inspired, with the response of many feature maps (e.g., orientation, motion, color) combined to create a salience map. The SUN model uses a single feature map learned using Indepen- dent Components Analysis (ICA) of natural images, and the salience at any point is based on the rarity of the feature re- sponses at that point - novelty attracts attention. Here, rarity is based on statistics taken from natural images, so the model assumes experience is necessary to represent novelty. Previous works that apply the visual saliency paradigm to the auditory domain include the models of (Kayser et al., 2005) and (Kalinli & Narayanan, 2007). Both adapt the visual saliency model of (Itti et al., 2002) to the auditory domain by using spectrographic images as inputs to the model. Al- though this is a reasonable approach, these models fail to cap- ture several important aspects of the auditory modality. First, this approach treats time as simply another dimension within the spectrographic representation of the sound. Even though these models utilize asymmetric temporal filters, the resulting saliency map at each time point is contaminated by informa- tion from the future. Second, spectrographic features are not the most realistic representations of human auditory sensa- tions, since the cochlea exhibits complex nonlinear responses to sound signals (Lyon, Katsiamis, & Drakakis, 2010). Fi- nally, Itti et al.’s model determines the saliency values from the current input signal, with no contribution from the life- time experience of the organism. This makes it impossible for the model to account for potential perceptual differences induced by differences in individual experience. The Auditory Saliency Model In this work, we propose the Auditory Salience Using Natural statistics model (ASUN) as an extension of the SUN model. The extension involves (1) using realistic auditory features instead of visual ones, and (2) combining long-term statis- tics (as in SUN) with short-term, temporally local statistics. Although the SUN model has both a top-down, task-based component and a bottom-up, environmentally driven compo- nent, here we restrict ourselves to just the bottom-up portion of SUN. SUN defines the bottom-up saliency of point x in the image at time t as: s x (t) ∝ − log P(F x = f x ) Here, f is a vector of feature values, whose probability is computed based on prior experience. This is also known as the “self-information” of the features, and conveys that rare feature values will attract attention. In the SUN model, this probability is based on the lifetime experience of the organ- ism, meaning that the organism already knows when feature values are common and when they are rare. Assuming the primary purpose of attention is to separate remarkable events from the humdrum, it is logical to equate the rarity of the event with the saliency of it. For example, a loud bang may be salient not only because of its physical energy content, but also because of its relative rarity in the soundscape. An or- ganism living under constant noise may not find an explosion to be as salient as another organism acclimated to a quieter environment.


title: Asymmetric Duty Control of a Dual-Half-Bridge DC/DC Converter for Single-Phase Distributed Generators

abstract: A dual-half-bridge (DHB) converter is integrated with a half-bridge 60-Hz inverter as a converter/inverter system for a small distributed generator. This topology provides an isolation between the power source and the load with a 100-kHz transformer, and therefore, the system volume is small. On the other hand, it reduces the number of switching devices greatly. However, the half-bridge inverter causes severe capacitor-voltage fluctuations. The unbalanced voltage problem can be solved by controlling the converter switching: In addition to the conventional phase-shift control method, an asymmetric charging method is used that charges the upper and lower secondary capacitors differently. This means that the voltage imbalance is corrected by adjusting the switching time of the secondary switches. Furthermore, a decoupling control algorithm can be derived from this approach. The usefulness of this method is then validated by simulation and experimental results.


title: The relationship between nursing leadership and patient outcomes: a systematic review update

abstract: AIM ::: Our aim was to describe the findings of a systematic review of studies that examine the relationship between nursing leadership practices and patient outcomes. ::: ::: ::: BACKGROUND ::: As healthcare faces an economic downturn, stressful work environments, upcoming retirements of leaders and projected workforce shortages, implementing strategies to ensure effective leadership and optimal patient outcomes are paramount. However, a gap still exists in what is known about the association between nursing leadership and patient outcomes. ::: ::: ::: METHODS ::: Published English-only research articles that examined leadership practices of nurses in formal leadership positions and patient outcomes were selected from eight online bibliographic databases. Quality assessments, data extraction and analysis were completed on all included studies. ::: ::: ::: RESULTS ::: A total of 20 studies satisfied our inclusion criteria and were retained. Current evidence suggests relationships between positive relational leadership styles and higher patient satisfaction and lower patient mortality, medication errors, restraint use and hospital-acquired infections. ::: ::: ::: CONCLUSIONS ::: The findings document evidence of a positive relationship between relational leadership and a variety of patient outcomes, although future testing of leadership models that examine the mechanisms of influence on outcomes is warranted. ::: ::: ::: IMPLICATIONS FOR NURSING MANAGEMENT ::: Efforts by organisations and individuals to develop transformational and relational leadership reinforces organisational strategies to improve patient outcomes.


title: Complexity and Performance Comparison of Filter Bank Multicarrier and OFDM in Uplink of Multicarrier Multiple Access Networks

abstract: We compare filter bank multicarrier (FBMC) and orthogonal frequency-division multiplexing (OFDM) in the uplink of a multiple access network. Our study reveals that the high sensitivity of OFDM to carrier frequency offset (CFO) among different users and the need for interference cancellation methods to reduce this sensitivity leads to very complex and yet not very high performance systems. In FBMC-based networks, on the other hand, near-perfect performance is achieved without any need for interference cancellation, thanks to the excellent frequency localized filters used in the realization of FBMC systems.


title: Toward a System Building Agenda for Data Integration

abstract: In this paper we argue that the data management community should devote far more effort to building data integration (DI) systems, in order to truly advance the field. Toward this goal, we make three contributions. First, we draw on our recent industrial experience to discuss the limitations of current DI systems. Second, we propose an agenda to build a new kind of DI systems to address these limitations. These systems guide users through the DI workflow, step by step. They provide tools to address the "pain points" of the steps, and tools are built on top of the Python data science and Big Data ecosystem (PyData). We discuss how to foster an ecosystem of such tools within PyData, then use it to build DI systems for collaborative/cloud/crowd/lay user settings. Finally, we discuss ongoing work at Wisconsin, which suggests that these DI systems are highly promising and building them raises many interesting research challenges.


title: New Methodology for the Design of Efficient Binary Addition Circuits in QCA

abstract: The quantum-dot cellular automata (QCA) approach is an attractive emerging technology suitable for the development of ultradense low-power high-performance digital circuits. Even though several solutions have been proposed recently for binary addition circuits, the design of efficient adders in QCA still poses several challenges since, most often, designers tend to implement strategies and methodologies close to those consolidated for the CMOS logic design. In this paper, we propose a new design method that exploits in original ways the properties of auxiliary propagate and generates signals to reduce the number of majority gates required to implement adders in QCA and/or the addition time. Three new formulations of basic logic equations frequently used in the designs of fast binary adders are proposed. To evaluate the potential advantage of the new strategy, two examples of application of the aforementioned method are discussed in this paper.


title: Zero-Shot Recognition Using Dual Visual-Semantic Mapping Paths

abstract: Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.


title: Kaizen in Japan: an empirical study

abstract: This paper reports the study of kaizen as practised in a selection of Japanese companies. After discussing the general understanding of kaizen and proposing a clear definition, the paper describes the methodology of the study, and presents findings from the research, taking Nippon Steel Corporation (NSC) as a base model and comparing this with the data from other companies. The development of kaizen activity in NSC is presented together with a description of the current nature of kaizen, which is compared with other firms in the steel and automotive industries to assess uniformity. The paper concludes that kaizen evolves uniquely within each organisation, following changes to the organisation's business environment. Detailed implementations vary considerably between organisations, but all rely on kaizen to achieve targets as an integral element in the operations management system. This yields insights into kaizen's sustainability, and points to its vulnerability to external economic conditions.


title: Crowdsourcing geographic information for disaster response: a research frontier

abstract: Abstract Geographic data and tools are essential in all aspects of emergency management: preparedness, response, recovery, and mitigation. Geographic information created by amateur citizens, often known as volunteered geographic information, has recently provided an interesting alternative to traditional authoritative information from mapping agencies and corporations, and several recent papers have provided the beginnings of a literature on the more fundamental issues raised by this new source. Data quality is a major concern, since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data. During emergencies time is the essence, and the risks associated with volunteered information are often outweighed by the benefits of its use. An example is discussed using the four wildfires that impacted the Santa Barbara area in 2007–2009, and lessons are drawn.


title: An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems

abstract: We propose a new fast algorithm for solving one of the standard approaches to ill-posed linear inverse problems (IPLIP), where a (possibly nonsmooth) regularizer is minimized under the constraint that the solution explains the observations sufficiently well. Although the regularizer and constraint are usually convex, several particular features of these problems (huge dimensionality, nonsmoothness) preclude the use of off-the-shelf optimization tools and have stimulated a considerable amount of research. In this paper, we propose a new efficient algorithm to handle one class of constrained problems (often known as basis pursuit denoising) tailored to image recovery applications. The proposed algorithm, which belongs to the family of augmented Lagrangian methods, can be used to deal with a variety of imaging IPLIP, including deconvolution and reconstruction from compressive observations (such as MRI), using either total-variation or wavelet-based (or, more generally, frame-based) regularization. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence sufficient conditions are known; we show that these conditions are satisfied by the proposed algorithm. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is a strong contender for the state-of-the-art.


title: Land-use scene classification using multi-scale completed local binary patterns

abstract: In this paper, we introduce the completed local binary patterns (CLBP) operator for the first time on remote sensing land-use scene classification. To further improve the representation power of CLBP, we propose a multi-scale CLBP (MS-CLBP) descriptor to characterize the dominant texture features in multiple resolutions. Two different kinds of implementations of MS-CLBP equipped with the kernel-based extreme learning machine are investigated and compared in terms of classification accuracy and computational complexity. The proposed approach is extensively tested on the 21-class land-use dataset and the 19-class satellite scene dataset showing a consistent increase on performance when compared to the state of the arts.