title: Classification with Streaming Features: An Emerging-Pattern Mining Approach

abstract: Many datasets from real-world applications have very high-dimensional or increasing feature space. It is a new research problem to learn and maintain a classifier to deal with very high dimensionality or streaming features. In this article, we adapt the well-known emerging-pattern--based classification models and propose a semi-streaming approach. For streaming features, it is computationally expensive or even prohibitive to mine long-emerging patterns, and it is nontrivial to integrate emerging-pattern mining with feature selection. We present an online feature selection step, which is capable of selecting and maintaining a pool of effective features from a feature stream. Then, in our offline step, separated from the online step, we periodically compute and update emerging patterns from the pool of selected features from the online step. We evaluate the effectiveness and efficiency of the proposed method using a series of benchmark datasets and a real-world case study on Mars crater detection. Our proposed method yields classification performance comparable to the state-of-art static classification methods. Most important, the proposed method is significantly faster and can efficiently handle datasets with streaming features.


title: Creative Musical Talent, Cognitive Functioning, and Gender: Psychobiological Aspects

abstract: Compositions by male and female composers were evaluated. Rating scores were correlated with scores from musical, spatial, and verbal psychometric tests, with dichotic and dichaptic measures, and with salivary testosterone output. Results were discussed in connection with new speculations on the biological substrates of special talents proposed by Geschwind and Galaburda (1985) and by Waterhouse (1988).


title: Dissipation rates of fenitrothion in greenhouse grown lettuce and under cold storage conditions

abstract: Summary ::: ::: The dissipation of fenitrothion (O,O-dimethyl O-p-nitro-m-tolyl phosphothioate) was evaluated by gas chromatography (GC) with nitrogen-phosphorus detection (NPD) in lettuce samples grown in an experimental greenhouse and harvested over a 4 weeks period following treatment with this insecticide. Dissipation followed a pseudo-first-order kinetics (r was 0.996). The recommended preharvest time (PT) of application is 15 days, and when the residual value of fenitrothion was evaluated at harvest, it was found to be above the maximum residue limit (MRL) established by Spanish law in the field experiment for this compound. During the experiment, the evolution of the two main fenitrothion metabolites (3-methyl-4-nitrophenol and fenitrothion-oxon) was also evaluated. When the dissipation of fenitrothion was evaluated in cold-stored lettuce the half-life was 9–10 times greater than in greenhouse conditions.


title: The effects of temperature, body mass and feeding on metabolic rate in the tsetse fly Glossina morsitans centralis

abstract: Metabolic rate variation with temperature, body mass, gender and feed- ing status is documented for Glossina morsitans centralis . Metabolic rate (mean ± SE; VCO 2 = 19.78 ± 3.11 L CO 2 h �1 in males (mean mass = 22.72 ± 1.41 mg) and 27.34 ± 3.86 L CO 2 h �1 in females (mean mass = 29.28 ± 1.96 mg) at 24 °C in fasted individuals) is strongly influenced by temperature, body mass and feeding status, but not by gender once the effects of body mass have been accounted for. A significant interaction between gender and feeding status is seen, similar to patterns of metabolic rate variation documented in Glossina morsitans morsitans . Synthesis of metabolic rate-temperature relationships in G. m. centralis , G. m. morsitans and Glossina pallidipes indicate that biting frequency as well as mortality risks associ- ated with foraging will probably increase with temperature as a consequence of in- creasing metabolic demands, although there is little evidence for variation among species at present. Furthermore, metabolic rate - body mass relationships appear to be similarly invariant among these species. These data provide important physiolog- ical information for bottom-up modelling of tsetse fly population dynamics.


title: Biomechanical strategies for mitigating collision damage in insect wings: structural design versus embedded elastic materials.

abstract: The wings of many insects accumulate considerable wear and tear during their lifespan, and this irreversible structural damage can impose significant costs on insect flight performance and survivability. Wing wear in foraging bumblebees (and likely many other species) is caused by inadvertent, repeated collisions with vegetation during flight, suggesting the possibility that insect wings may display biomechanical adaptations to mitigate the damage associated with collisions. We used a novel experimental technique to artificially induce wing wear in bumblebees and yellowjacket wasps, closely related species with similar life histories but distinct wing morphologies. Wasps have a flexible resilin joint (the costal break) positioned distally along the leading edge of the wing, which allows the wing tip to crumple reversibly when it hits an obstacle, whereas bumblebees lack an analogous joint. Through experimental manipulation of its stiffness, we found that the costal break plays a critical role in mitigating collision damage in yellowjacket wings. However, bumblebee wings do not experience as much damage as would be expected based on their lack of a costal break, possibly due to differences in the spatial arrangement of supporting wing veins. Our results indicate that these two species utilize different wing design strategies for mitigating damage resulting from collisions. A simple inertial model of a flapping wing reveals the biomechanical constraints acting on the costal break, which may help explain its absence in bumblebee wings.


title: Odor Cues During Slow-Wave Sleep Prompt Declarative Memory Consolidation

abstract: Sleep facilitates memory consolidation. A widely held model assumes that this is because newly encoded memories undergo covert reactivation during sleep. We cued new memories in humans during sleep by presenting an odor that had been presented as context during prior learning, and so showed that reactivation indeed causes memory consolidation during sleep. Re-exposure to the odor during slow-wave sleep (SWS) improved the retention of hippocampus-dependent declarative memories but not of hippocampus-independent procedural memories. Odor re-exposure was ineffective during rapid eye movement sleep or wakefulness or when the odor had been omitted during prior learning. Concurring with these findings, functional magnetic resonance imaging revealed significant hippocampal activation in response to odor re-exposure during SWS.


title: HRL: Efficient and flexible reconfigurable logic for near-data processing

abstract: The energy constraints due to the end of Dennard scaling, the popularity of in-memory analytics, and the advances in 3D integration technology have led to renewed interest in near-data processing (NDP) architectures that move processing closer to main memory. Due to the limited power and area budgets of the logic layer, the NDP compute units should be area and energy efficient while providing sufficient compute capability to match the high bandwidth of vertical memory channels. They should also be flexible to accommodate a wide range of applications. Towards this goal, NDP units based on fine-grained (FPGA) and coarse-grained (CGRA) reconfigurable logic have been proposed as a compromise between the efficiency of custom engines and the flexibility of programmable cores. Unfortunately, FPGAs incur significant area overheads for bit-level reconfiguration, while CGRAs consume significant power in the interconnect and are inefficient for irregular data layouts and control flows. This paper presents Heterogeneous Reconfigurable Logic (HRL), a reconfigurable array for NDP systems that improves on both FPGA and CGRA arrays. HRL combines both coarse-grained and fine-grained logic blocks, separates routing networks for data and control signals, and uses specialized units to effectively support branch operations and irregular data layouts in analytics workloads. HRL has the power efficiency of FPGA and the area efficiency of CGRA. It improves performance per Watt by 2.2x over FPGA and 1.7x over CGRA. For NDP systems running MapReduce, graph processing, and deep neural networks, HRL achieves 92% of the peak performance of an NDP system based on custom accelerators for each application.


title: A Novel Deep Learning Based Multi-class Classification Method for Alzheimer’s Disease Detection Using Brain MRI Data

abstract: Alzheimer’s Disease is a severe neurological brain disorder. It destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. Alzheimer’s Disease is not curable, but earlier detection can help improve symptoms in a great deal. Machine learning techniques can vastly improve the process for accurate diagnosis of Alzheimer’s Disease. In recent days deep learning techniques have achieved major success in medical image analysis. But relatively little investigation has been done to applying deep learning techniques for Alzheimer’s Disease detection and classification. This paper presents a novel deep learning model for multi-Class Alzheimer’s Disease detection and classification using Brain MRI Data. We design a very deep convolutional network and demonstrate the performance on the Open Access Series of Imaging Studies (OASIS) database.


title: Pedestrian detection combining RGB and dense LIDAR data

abstract: Why is pedestrian detection still very challenging in realistic scenes? How much would a successful solution to monocular depth inference aid pedestrian detection? In order to answer these questions we trained a state-of-theart deformable parts detector using different configurations of optical images and their associated 3D point clouds, in conjunction and independently, leveraging upon the recently released KITTI dataset. We propose novel strategies for depth upsampling and contextual fusion that together lead to detection performance which exceeds that of the RGB-only systems. Our results suggest depth cues as a very promising mid-level target for future pedestrian detection approaches.


title: Advanced Network Technologies Virtual Lab: A Human-Computer Interface for performing experiments on Wireless Sensor Networks

abstract: Research analysis manifest clear trends of growing interest of the field of Human Computer Interaction (HCI) towards virtualized learning. To keep up this trend, as well as the pace with the rapid advancement of software technologies, the Advanced Network Technologies Virtual Lab (ANT VLab) has been developed by us at Indian Institute of Technology, Kharagpur, India. This lab exposes students to various advanced topics including Wi-Fi, WiMAX, Satellite Networks, Mobile Adhoc Networks (MANETs), Wireless Sensor Networks (WSNs), Bluetooth, Zigbee, and encourages them to work on related problems in a partially constrained environment. Apart from providing detailed theory on each topic, each virtual experiment in the lab has a set of exercises to be solved using remote network simulations. Network Simulator-2 (NS-2) / Mannasim framework and Network Simulator-3 (NS-3) have been used for this purpose. The ANT VLab also offers benefits such as reduced software and hardware costs as against setting up an actual advanced network technology lab, improved learning by interactive visualization, and self-learning advanced network technologies in the absence of a real-life instructor. Additionally, the students can interface and learn at their own pace any time and from any place having a PC with high speed Internet connectivity. WSNs is an emerging field in computer networking. In this paper, we discuss the development of a module for WSNs, that can be used for performing different WSNs experiments in the virtual mode.


title: An Effective Requirement Engineering Process Model for Software Development and Requirements Management

abstract: Requirement engineering is the most effective phase of software development process. It aims to collect good requirements from stakeholders in the right way. It is important for every organization to develop quality software products that can satisfy user’s needs. Requirements engineering for software development process is a complex exercise that considers product demands from a vast number of viewpoints, roles, responsibilities, and objectives. Therefore, it becomes necessary to apply requirement engineering practices in every phase of software development process. In this paper, we propose an effective requirements engineering process model to produce quality requirements for software development. Requirement management and planning phase is executed independently for an effective management of requirements. It is iterative in nature for better requirement engineering and later maintenance. The successful implementation of proposed requirement engineering process can have a good impact on the production of quality software product.


title: Exploiting Ontology Lexica for Generating Natural Language Texts from RDF Data

abstract: The increasing amount of machinereadable data available in the context of the Semantic Web creates a need for methods that transform such data into human-comprehensible text. In this paper we develop and evaluate a Natural Language Generation (NLG) system that converts RDF data into natural language text based on an ontology and an associated ontology lexicon. While it follows a classical NLG pipeline, it diverges from most current NLG systems in that it exploits an ontology lexicon in order to capture context-specific lexicalisations of ontology concepts, and combines the use of such a lexicon with the choice of lexical items and syntactic structures based on statistical information extracted from a domain-specific corpus. We apply the developed approach to the cooking domain, providing both an ontology and an ontology lexicon in lemon format. Finally, we evaluate fluency and adequacy of the generated recipes with respect to two target audiences: cooking novices and advanced cooks.


title: QAM-FBMC: A New Multi-Carrier System for Post-OFDM Wireless Communications

abstract: Recently, as asynchronous heterogeneous network scenario becoming one of the key features for next generation wireless communications, superior spectrum confinement as well as higher spectral efficiency compared to cyclic prefixed orthogonal frequency division multiplexing (CP-OFDM) has been taken into consideration for future radio access technologies. In this paper, we propose a new quadrature amplitude modulation based filter-bank multi-carrier (QAM-FBMC) system that provides with an inherent spectral efficiency gain against CP-OFDM, which comes from reduction of the redundancies such as cyclic prefix (CP) and guard-band, while keeping the symbol rate same as in CP-less OFDM. We propose a new transceiver structure consisting of at least two different filter-bank bases at both transmitter and receiver sides. Practical algorithms including channel estimation and equalization to mitigate multi-path fading channel without CP are proposed. Various evaluation results show that the proposed system performs comparable to the CP-OFDM system even without CP and guard-band reduction is also available from the well-confined spectrum.


title: Object Detection with Multi-RCNN Detectors

abstract: Object detection and tracking are important in real time surveillance systems, such as traffic control, counting and public security field. Deep neural networks have been applied to solve many hard problems, computer vision problems for particular. Convolutional neural networks have been widely used in object detection and tracking, such as RCNN, Fast RCNN and Faster RCNN. However, accuracy and speed are two main challenges in object detection and tracking. Before object detection, we use Gaussian mixture model to subtract background, because background subtraction has a great impact on object detection. Then a multi-detector model based on Fast RCNN is proposed to detect objects in images captured. The experiment shows that the model we propose can improve the performance of detection, speed in particular.


title: Treevis.net: A Tree Visualization Reference

abstract: Tree visualization is one of the best-studied areas of information visualization; researchers have developed more than 200 visualization and layout techniques for trees. The treevis.net project aims to provide a hand-curated bibliographical reference to this ever-growing wealth of techniques. It offers a visual overview that users can filter to a desired subset along the design criteria of dimensionality, edge representation, and node alignment. Details, including links to the original publications, can be brought up on demand. Treevis.net has become a community effort, with researchers sending in preprints of their tree visualization techniques to be published or pointing out additional information.


Human Activity Recognition using Smartphone Sensors


title: Remembering the Earthquake: Direct Experience vs. Hearing the News

abstract: Three groups of informants—two in California, one in Atlanta—recalled their experiences of the 1989 Loma Prieta earthquake shortly after the event and again 1 1/2 years later. The Californians' recalls of their own earthquake experiences were virtually perfect. Even their recalls of hearing the news of an earthquake-related event were very good: much higher than Atlantan recalls of hearing about the quake itself. Atlantans who had relatives in the affected area remembered significantly more than those who did not. These data show that personal involvement in the quake led to greatly improved recall, but do not show why. Many Californian informants reported low levels of stress/arousal during the event; arousal ratings were not significantly correlated with recall. The authors suggest that repeated narrative rehearsals may have played an important role.


title: Automatic extrinsic calibration for lidar-stereo vehicle sensor setups

abstract: Sensor setups consisting of a combination of 3D range scanner lasers and stereo vision systems are becoming a popular choice for on-board perception systems in vehicles; however, the combined use of both sources of information implies a tedious calibration process. We present a method for extrinsic calibration of lidar-stereo camera pairs without user intervention. Our calibration approach is aimed to cope with the constraints commonly found in automotive setups, such as low-resolution and specific sensor poses. To demonstrate the performance of our method, we also introduce a novel approach for the quantitative assessment of the calibration results, based on a simulation environment. Tests using real devices have been conducted as well, proving the usability of the system and the improvement over the existing approaches. Code is available at this http URL


title: Investigation and Experimental Verification of a Novel Spoke-Type Ferrite-Magnet Motor for Electric-Vehicle Traction Drive Applications

abstract: In order to exhibit high performance, traction drive motors that use rare-earth magnets have been mainly applied to hybrid and electric vehicles (EVs). However, the raw materials of the magnets have critical issues such as unstable supply and high cost. Thus, a variety of motors not using rare-earth materials have been intensively developed for the traction motor. Nevertheless, it has limitation in increasing the performance of such motors without the use of rare-earth magnets. Accordingly, this paper investigates the availability of a spoke-type ferrite-magnet motor (FMM) with a novel rotor configuration for EV traction motors. In particular, the proposed motor has two rotor designs, and its characteristics based on finite-element analysis are compared according to each rotor design. Furthermore, various mechanical characteristics such as thermal stability, structural safety, and acoustic noise are checked in the design stage of the spoke-type FMM. Finally, the spoke-type FMM is tested in order to verify its performance and feasibility, including vehicle installation and on-road test in LA4 city driving mode.


title: The design of SREE: a prototype potential ambiguity finder for requirements specifications and lessons learned

abstract: [Context and Motivation] Many a tool for finding ambiguities in natural language (NL) requirements specifications (RSs) is based on a parser and a parts-of-speech identifier, which are inherently imperfect on real NL text. Therefore, any such tool inherently has less than 100% recall. Consequently, running such a tool on a NL RS for a highly critical system does not eliminate the need for a complete manual search for ambiguity in the RS. [Question/Problem] Can an ambiguity-finding tool (AFT) be built that has 100% recall on the types of ambiguities that are in the AFT's scope such that a manual search in an RS for ambiguities outside the AFT's scope is significantly easier than a manual search of the RS for all ambiguities? [Principal Ideas/Results] This paper presents the design of a prototype AFT, SREE (Systemized Requirements Engineering Environment), whose goal is achieving a 100% recall rate for the ambiguities in its scope, even at the cost of a precision rate of less than 100%. The ambiguities that SREE searches for by lexical analysis are the ones whose keyword indicators are found in SREE's ambiguity-indicator corpus that was constructed based on studies of several industrial strength RSs. SREE was run on two of these industrial strength RSs, and the time to do a completely manual search of these RSs is compared to the time to reject the false positives in SREE's output plus the time to do a manual search of these RSs for only ambiguities not in SREE's scope. [Contribution] SREE does not achieve its goals. However, the time comparison shows that the approach to divide ambiguity finding between an AFT with 100% recall for some types of ambiguity and a manual search for only the other types of ambiguity is promising enough to justify more work to improve the implementation of the approach. Some specific improvement suggestions are offered.


title: User-centered development and testing of a monitoring system that provides feedback regarding physical functioning to elderly people

abstract: Purpose: To involve elderly people during the development of a mobile interface of a monitoring system that provides feedback to them regarding changes in physical functioning and to test the system in a pilot study. ::: Methods and participants: The iterative user-centered development process consisted of the following phases: (1) selection of user representatives; (2) analysis of users and their context; (3) identification of user requirements; (4) development of the interface; and (5) evaluation of the interface in the lab. Subsequently, the monitoring and feedback system was tested in a pilot study by five patients who were recruited via a geriatric outpatient clinic. Participants used a bathroom scale to monitor weight and balance, and a mobile phone to monitor physical activity on a daily basis for six weeks. Personalized feedback was provided via the interface of the mobile phone. Usability was evaluated on a scale from 1 to 7 using a modified version of the Post-Study System Usability Questionnaire (PSSUQ); higher scores indicated better usability. Interviews were conducted to gain insight into the experiences of the participants with the system. ::: Results: The developed interface uses colors, emoticons, and written and/or spoken text messages to provide daily feedback regarding (changes in) weight, balance, and physical activity. The participants rated the usability of the monitoring and feedback system with a mean score of 5.2 (standard deviation 0.90) on the modified PSSUQ. The interviews revealed that most participants liked using the system and appreciated that it signaled changes in their physical functioning. However, usability was negatively influenced by a few technical errors. ::: Conclusion: Involvement of elderly users during the development process resulted in an interface with good usability. However, the technical functioning of the monitoring system needs to be optimized before it can be used to support elderly people in their self-management.


title: Research Designs for Intervention Research with Small Samples II: Stepped Wedge and Interrupted Time-Series Designs

abstract: The stepped wedge design (SWD) and the interrupted time-series design (ITSD) are two alternative research designs that maximize efficiency and statistical power with small samples when contrasted to the operating characteristics of conventional randomized controlled trials (RCT). This paper provides an overview and introduction to previous work with these designs and compares and contrasts them with the dynamic wait-list design (DWLD) and the regression point displacement design (RPDD), which were presented in a previous article (Wyman, Henry, Knoblauch, and Brown, Prevention Science. 2015) in this special section. The SWD and the DWLD are similar in that both are intervention implementation roll-out designs. We discuss similarities and differences between the SWD and DWLD in their historical origin and application, along with differences in the statistical modeling of each design. Next, we describe the main design characteristics of the ITSD, along with some of its strengths and limitations. We provide a critical comparative review of strengths and weaknesses in application of the ITSD, SWD, DWLD, and RPDD as small sample alternatives to application of the RCT, concluding with a discussion of the types of contextual factors that influence selection of an optimal research design by prevention researchers working with small samples.


title: Steroidal glycosides from the bulbs of Fritillaria meleagris and their cytotoxic activities

abstract: Steroidal glycosides (1-18), including 10 new compounds (1-10), were isolated from the bulbs of Fritillaria meleagris (Liliaceae). The structures of the new compounds were determined by two-dimensional (2D) NMR analysis, and by hydrolytic cleavage followed by spectroscopic and chromatographic analysis. The isolated compounds and their aglycones were evaluated for cytotoxic activity against HL-60 human promyelocytic leukemia cells and A549 human lung adenocarcinoma cells. Morphological observation and flow cytometry analysis showed that 5β-spirostanol glycoside (2) and a cholestane derivative (17a) induced apoptotic cell death in HL-60 cells through different mechanisms of action. Furthermore, the (22R)-spirosolanol glycoside (11) selectively induced apoptosis in A549 cells without affecting the caspase-3 activity level.


title: Smooth immersion: the benefits of making the transition to virtual environments a continuous process

abstract: In this paper we discuss the benefits and the limitations, as well as different implementation options for smooth immersion into a HMD-based IVE. We evaluated our concept in a preliminary user study, in which we have tested users' awareness, reality judgment and experience in the IVE, when using different transition techniques to enter it. Our results show that a smooth transition to the IVE improves the awareness of the user and may increase the perceived interactivity of the system.


title: A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise

abstract: The known median-based denoising methods tend to work well for restoring the images corrupted by random-valued impulse noise with low noise level but poorly for highly corrupted images. This letter proposes a new impulse detector, which is based on the differences between the current pixel and its neighbors aligned with four main directions. Then, we combine it with the weighted median filter to get a new directional weighted median (DWM) filter. Extensive simulations show that the proposed filter not only can provide better performance of suppressing impulse with high noise level but can preserve more detail features, even thin lines. As extended to restoring corrupted color images, this filter also performs very well


title: 3D active surfaces for liver segmentation in multisequence MRI images

abstract: Fast automatic and accurate method for multichannel MRI 3D liver segmentation.The method proposes a model based 3D active surface for multichannel MRI.New approach to handle multichannel information by means of a compact descriptor.The compact descriptor replaces the input images by a model based probability map.Validation of the performance of the method with well established quality metrics. Biopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.


title: Textural Features Corresponding to Visual Perception

abstract: Textural features corresponding to human visual perception are very useful for optimum feature selection and texture analyzer design. We approximated in computational form six basic textural features, namely, coarseness, contrast, directionality, line-likeness, regularity, and roughness. In comparison with psychological measurements for human subjects, the computational measures gave good correspondences in rank correlation of 16 typical texture patterns. Similarity measurements using these features were attempted. The discrepancies between human vision and computerized techniques that we encountered in this study indicate fundamental problems in digital analysis of textures. Some of them could be overcome by analyzing their causes and using more sophisticated techniques.


title: Learning to complete sentences

abstract: We consider the problem of predicting how a user will continue a given initial text fragment. Intuitively, our goal is to develop a “tab-complete” function for natural language, based on a model that is learned from text data. We consider two learning mechanisms that generate predictive models from collections of application-specific document collections: we develop an N-gram based completion method and discuss the application of instance-based learning. After developing evaluation metrics for this task, we empirically compare the model-based to the instance-based method and assess the predictability of call-center emails, personal emails, and weather reports.


title: Brain storm optimization algorithm

abstract: Human being is the most intelligent animal in this world. Intuitively, optimization algorithm inspired by human being creative problem solving process should be superior to the optimization algorithms inspired by collective behavior of insects like ants, bee, etc. In this paper, we introduce a novel brain storm optimization algorithm, which was inspired by the human brainstorming process. Two benchmark functions were tested to validate the effectiveness and usefulness of the proposed algorithm.


title: Relational vs. NoSQL Databases: A Survey

abstract: The huge growth in the Internet market and the emerging of the new web technologies and the trend toward what is called web 2.0 and recently web 3.0 come with a new challenges, new applications and new concepts such as NoSQL databases which is recently becomes a very popular as an alternative to the relational databases specially in dealing with large data which is one of the most common features of web today, providing high availability and scalability to the distributed systems which need fast access time and can't tolerate any down time during failures and have been used heavily by the big enterprises and web companies such as Facebook, amazon and google. Every new technology faced many challenges like Security vulnerabilities. This paper addresses the concepts of NoSQL, the movement, motivations and needs behind it, and reviews the types of NoSQL databases and the issues concerning to these databases mainly areas of application and the security issues compared with traditional relational databases.


title: Beyond Repeated measures ANOVA: advanced statistical methods for the analysis of longitudinal data in anesthesia research

abstract: Background and Objectives—Research in the field of anesthesiology relies heavily on longitudinal designs for answering questions about long-term efficacy and safety of various anesthetic and pain regimens. Yet, anesthesiology research is lagging in the use of advanced statistical methods for analyzing longitudinal data. The goal of this paper is to increase awareness of the advantages of modern statistical methods and promote their use in anesthesia research. Methods—Here we introduce 2 modern and advanced statistical methods for analyzing longitudinal data: the generalized estimating equations (GEE) and mixed effects models (MEM). These methods are compared to the conventional repeated measures ANOVA (RM-ANOVA) through a clinical example with 2 types of endpoints (continuous and binary). In addition, we compare GEE and MEM to RM-ANOVA through a simulation study with varying sample sizes, varying number of repeated measures, and scenarios with and without missing data. Results—In the clinical study, the 3 methods are found to be similar in terms of statistical estimation, while the parameter interpretations are somewhat different. The simulation study shows that the methods of GEE and MEM are more efficient in that they are able to achieve higher power with smaller sample size or lower number of repeated measurements in both complete and missing data scenarios. Conclusions—Based on their advantages over RM-ANOVA, GEE and MEM should be strongly considered for the analysis of longitudinal data. In particular, GEE should be utilized to explore overall average effects, and MEM should be employed when subject-specific effects (in addition to overall average effects) are of primary interest.


title: Failure to Demonstrate That Playing Violent Video Games Diminishes Prosocial Behavior

abstract: BACKGROUND: Past research has found that playing a classic prosocial video game resulted in heightened prosocial behavior when compared to a control group, whereas playing a classic violent video game had no effect. Given purported links between violent video games and poor social behavior, this result is surprising. Here our aim was to assess whether this finding may be due to the specific games used. That is, modern games are experienced differently from classic games (more immersion in virtual environments, more connection with characters, etc.) and it may be that playing violent video games impacts prosocial behavior only when contemporary versions are used. METHODS AND FINDINGS: Experiments 1 and 2 explored the effects of playing contemporary violent, non-violent, and prosocial video games on prosocial behavior, as measured by the pen-drop task. We found that slight contextual changes in the delivery of the pen-drop task led to different rates of helping but that the type of game played had little effect. Experiment 3 explored this further by using classic games. Again, we found no effect. CONCLUSIONS: We failed to find evidence that playing video games affects prosocial behavior. Research on the effects of video game play is of significant public interest. It is therefore important that speculation be rigorously tested and findings replicated. Here we fail to substantiate conjecture that playing contemporary violent video games will lead to diminished prosocial behavior. Language: en


title: An experiential account of phantom vibration syndrome

abstract: Previous data show a high prevalence for the experience phantom phone vibrations.An original habit-based account of phantom vibration syndrome is developed.Reflections on implications for our modern technological situation are offered. A small handful of recent studies have addressed a phenomenon known as "phantom vibration syndrome". This refers to when phone users perceive their device to vibrate, indicating that a call or text is incoming, when in fact the phone did not vibrate at all. Though these studies show that most users do not find phantom vibration hallucinations to be very bothersome, they also find that a large majority of users experience this phenomenon. This paper explores what the striking prevalence of phantom vibration syndrome means for our contemporary relationships with technology. I begin with a review of the theories purporting to explain these data, which largely rely on particular understandings of the brain. Next I develop an alternative theory of phantom vibration syndrome based on insights from the philosophical tradition of phenomenology. This account considers the ways users develop bodily and perceptual habits regarding how the phone is understood and used. By critically contrasting these different theories, we can refine our questions about what the high prevalence of phantom vibration syndrome implies about our contemporary technological situation.


title: The value of provocative tests in diagnosis of cervical radiculopathy.

abstract: BACKGROUND ::: This study was aimed at assessing the accuracy of provocative tests in diagnosis of acute or chronic Cervical Radiculopathy (CR) based on an electrodiagnostic reference criterion. ::: ::: ::: MATERIALS AND METHODS ::: Shoulder Abduction Test (SAT), Spurling Test (ST), Upper Limb Tension Test (ULTT), and electromyography were done on 97 patients who referred to Electrodiagnostic center in the university hospital from January 2010 to March 2011. All of the participants had neck and radicular pain for at least 3 weeks. They were classified according to electrodiagnostic findings. Then diagnostic values of provocative tests were assessed in diagnosis of acute or chronic CR on the basis of reference criterion. ::: ::: ::: RESULTS ::: SAT and ST were more specific (85%) compared to ULTT, while ULTT was more sensitive (60.46% in acute and 35.29% in chronic) than the other two. SAT and ST had a significant accuracy for comparison between acute and chronic CR (P < 0.05). ::: ::: ::: CONCLUSION ::: ULTT is suitable for screening of CR, while SAT and ST can support diagnosis. SAT and ST are good diagnostic tests for comparison between acute and chronic CR.


title: A Low-power wireless multi-channel surface EMG sensor with simplified ADPCM data compression

abstract: The ubiquitous real-time monitoring and recording of surface electromyography (sEMG) signal is essential to several rehabilitation applications, such as muscle recovery analysis. We present an inexpensive wireless sEMG sensor using a commercial off-the-shelf wireless microcontroller unit (MCU) incorporating a simplified adaptive differential pulse code modulation (ADPCM) routine for real-time data compression. In single-channel configuration, the presented approach reduces power consumption of a transmission subsystem by up to 69%, leading to longer operation life expectancy. Due to the excessive amount of data as well as the limited processing power of embedded MCUs, multi-channel configurations would normally not be feasible. However, the proposed compression method makes a multi-channel EMG sensor possible. The distortion induced by this approach on EMG signal is on the order of 1%. Test results from in vivo trials with humans are presented.


title: Learning Multimodal Attention LSTM Networks for Video Captioning

abstract: Automatic generation of video caption is a challenging task as video is an information-intensive media with complex variations. Most existing methods, either based on language templates or sequence learning, have treated video as a flat data sequence while ignoring intrinsic multimodality nature. Observing that different modalities (e.g., frame, motion, and audio streams), as well as the elements within each modality, contribute differently to the sentence generation, we present a novel deep framework to boost video captioning by learning Multimodal Attention Long-Short Term Memory networks (MA-LSTM). Our proposed MA-LSTM fully exploits both multimodal streams and temporal attention to selectively focus on specific elements during the sentence generation. Moreover, we design a novel child-sum fusion unit in the MA-LSTM to effectively combine different encoded modalities to the initial decoding states. Different from existing approaches that employ the same LSTM structure for different modalities, we train modality-specific LSTM to capture the intrinsic representations of individual modalities. The experiments on two benchmark datasets (MSVD and MSR-VTT) show that our MA-LSTM significantly outperforms the state-of-the-art methods with 52.3 BLEU@4 and 70.4 CIDER-D metrics on MSVD dataset, respectively.


title: Detecting professional versus personal closeness using an enterprise social network site

abstract: In this work we analyze the behavior on a company-internal social network site to determine which interaction patterns signal closeness between colleagues. Regression analysis suggests that employee behavior on social network sites (SNSs) reveals information about both professional and personal closeness. While some factors are predictive of general closeness (e.g. content recommendations), other factors signal that employees feel personal closeness towards their colleagues, but not professional closeness (e.g. mutual profile commenting). This analysis contributes to our understanding of how SNS behavior reflects relationship multiplexity: the multiple facets of our relationships with SNS connections.


title: The effect of type and timing of feedback on learning from multiple-choice tests

abstract: Two experiments investigated how the type and timing of feedback influence learning from a multiplechoice test. First, participants read 12 prose passages, which covered various general knowledge topics (e.g., The Sun) and ranged between 280 and 300 words in length. Next, they took an initial six-alternative, multiple-choice test on information contained in the passages. Feedback was given immediately for some of the multiple-choice items or after delay for other items. Participants were either shown the correct answer as feedback (standard feedback) or were allowed to keep answering until the correct answer was discovered (answer-until-correct feedback). Learning from the test was assessed on a delayed cued-recall test. The results indicated that delayed feedback led to superior final test performance relative to immediate feedback. However, type of feedback did not matter: discovering the correct answer through answer-until-correct feedback produced equivalent performance relative to standard feedback. This research suggests that delaying the presentation of feedback after a test is beneficial to learning because of the spaced presentation of information.


title: Case records of the Massachusetts General Hospital. Case 37-2014. A 35-year-old woman with suspected mite infestation.

abstract: A 35-year-old woman with a history of drug dependency was seen in the emergency department because of a rash that was thought to be due to mite infestation. She reported seeing bugs on her skin. Examination revealed excoriations on her skin, but the midback was spared.


title: Narrowband Internet of Things: Evolutions, Technologies, and Open Issues

abstract: We are on the threshold of the explosive growth in the global Internet-of-Things (IoT) market. Comparing with the legacy human-centric applications, machine type communication scenarios exhibit totally different characteristics, such as low throughput, delay insensitivity, occasional transmission, and deep coverage. Meanwhile, it also requires the terminal devices to be cheap enough and sustain long battery life. These demands hasten the prosperity of low power wide area (LPWA) technologies. Narrowband IoT (NB-IoT) is the newest Long Term Evolution (LTE) specification ratified by the third generation partner project as one of the LPWA solutions to achieve the objectives of super coverage, low power, low cost, and massive connection. Working in the licensed frequency band, it is designed to reuse and coexist with the existing LTE cellular networks, which endows it with outstanding advantages in decreasing network installation cost and minimizing product time-to-market. In this backdrop, it has been extensively regarded as one of the most promising technologies toward the IoT landscape. However, as a new LTE standard, there are still a lot of challenges that need to overcome. This paper surveys its evolutions, technologies, and issues, spanning from performance analysis, design optimization, combination with other leading technologies, to implementation and application. The goal is to deliver a holistic understanding for the emerging wireless communication system, in helping to spur further research in accelerating the broad use of NB-IoT.


title: Analysis of FSS Radomes Based on Physical Optics Method and Ray Tracing Technique

abstract: In this letter, we analyze the electromagnetic characteristic of a frequency selective surface (FSS) radome using the physical optics (PO) method and ray tracing technique. We consider the cross-loop slot FSS and the tangent-ogive radome. Radiation pattern of the FSS radome is computed to illustrate the electromagnetic transmission characteristic.


title: ENVIRONMENT DESIGN FOR HUMAN-ROBOT SYMBIOSIS-INTRODUCTION OF WABOT-HOUSE PROJECT -

abstract: In this article, we would like to introduce the WABOT-HOUSE project. The attraction of the Waseda University, the School of Science and Engineering, through the Gifu prefecture was the cue of the project. In consequence, the WABOT-HOUSE is the co-operation project with industrial and administrative sectors. Its goal is to encourage the regional industry and to develop region itself due to the new technology robotics in the Gifu prefecture. In order to prepare the lifestyle with robots and to launch the robotic industry, we need to create a demonstrative space and social system for human-robots symbiosis. Kagamigahara-City, where WABOT-HOUSE laboratory locates, has been already designated for the special zone for robotics by the Japanese government. We are also ready for conducting the pilot programs for robots. Therefore we research and develop not only robotics itself but also planning theories of space, construction methods and social systems for the human-robot symbiosis. 1. INTRODUTION 1.1 Background A wide range of new technologies relating to robots has recorded a dynamic development above all in the entertainment field. However, the robot technology relating to human-robot symbiosis has rather been underestimated. The advanced adaptability of robots to humanity and environment is highly required in the aging society and in the symbiotic future with natural environment. That is because existing robotic researches have focused mainly on the more advanced function of the robots themselves. Therefore, they didn't care much about the environment where the robots were to live. Thus researches on a space where men could share their life with robots in comfort should be done. It goes without saying that the social systems should support the practical use of the new technology robotics in the future. Accordingly, the WABOT-HOUSE is our way of solving these issues. We research and develop not only robotics itself but also planning theories of space, construction methods and social systems for the human-robot symbiosis.


title: An energy-efficient MAC protocol for wireless sensor networks

abstract: This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in almost every way: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses three novel techniques to reduce energy consumption and support self-configuration. To reduce energy consumption in listening to an idle channel, nodes periodically sleep. Neighboring nodes form virtual clusters to auto-synchronize on sleep schedules. Inspired by PAMAS, S-MAC also sets the radio to sleep during transmissions of other nodes. Unlike PAMAS, it only uses in-channel signaling. Finally, S-MAC applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network. We evaluate our implementation of S-MAC over a sample sensor node, the Mote, developed at University of California, Berkeley. The experiment results show that, on a source node, an 802.11-like MAC consumes 2-6 times more energy than S-MAC for traffic load with messages sent every 1-10 s.


title: x-RDF-3X: Fast Querying, High Update Rates, and Consistency for RDF Databases

abstract: The RDF data model is gaining importance for applications in computational biology, knowledge sharing, and social communities. Recent work on RDF engines has focused on scalable performance for querying, and has largely disregarded updates. In addition to incremental bulk loading, applications also require online updates with flexible control over multi-user isolation levels and data consistency. The challenge lies in meeting these requirements while retaining the capability for fast querying. ::: ::: This paper presents a comprehensive solution that is based on an extended deferred-indexing method with integrated versioning. The version store enables time-travel queries that are efficiently processed without adversely affecting queries on the current data. For flexible consistency, transactional concurrency control is provided with options for either snapshot isolation or full serializability. All methods are integrated in an extension of the RDF-3X system, and their very good performance for both queries and updates is demonstrated by measurements of multi-user workloads with real-life data as well as stress-test synthetic loads.


title: User Simulation in Dialogue Systems using Inverse Reinforcement Learning

abstract: Spoken Dialogue Systems (SDS) are man-machine interfaces which use natural language as the medium of interaction. Dialogue corpora collection for the purpose of training and evaluating dialogue systems is an expensive process. User simulators aim at simulating human users in order to generate synthetic data. Existing methods for user simulation mainly focus on generating data with the same statistical consistency as in some reference dialogue corpus. This paper outlines a novel approach for user simulation based on Inverse Reinforcement Learning (IRL). The task of building the user simulator is perceived as a task of imitation learning.


title: The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

abstract: One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine [10]. The core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.


title: A prototype architecture for cyber-physical systems

abstract: Cyber-Physical Systems (CPS) is an exciting emerging research area that has drawn the attention of many researchers. Although the question of "What is a CPS?" remains open, widely recognized and accepted attributes of a CPS include timeliness, distributed, reliability, fault-tolerance, security, scalability and autonomous. In this paper, a CPS definition is given and a prototype architecture is proposed. It is argued that this architecture captures the essential attributes of a CPS and lead to identification of many research challenges.


title: Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model

abstract: The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder.


title: A working guide to boosted regression trees

abstract: Summary 1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel ( Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.


title: Evaluating approaches to resource demand estimation

abstract: Abstract Resource demands are a key parameter of stochastic performance models that needs to be determined when performing a quantitative performance analysis of a system. However, the direct measurement of resource demands is not feasible in most realistic systems. Therefore, statistical approaches that estimate resource demands based on coarse-grained monitoring data (e.g., CPU utilization, and response times) have been proposed in the literature. These approaches have different assumptions and characteristics that need to be considered when estimating resource demands. This paper surveys the state-of-the-art in resource demand estimation and proposes a classification scheme for estimation approaches. Furthermore, it contains an experimental evaluation comparing the impact of different factors (monitoring window size, number of workload classes, load level, collinearity, and model mismatch) on the estimation accuracy of seven different approaches. The classification scheme and the experimental comparison helps performance engineers to select an approach to resource demand estimation that fulfills the requirements of a given analysis scenario.


title: Overview of camera calibration for computer vision

abstract: Camera calibration is a significant process not only for photography but also for computer vision. The accuracy and stability of the camera calibration methods directly affect the whole vision system. In the past few years, a series of methods for camera calibration have been researched. In this paper, these methods are reviewed, analyzed and compared for the practical application and future research.


The Effect of Family Income on Risk of Child Maltreatment


title: EmotioNet Challenge: Recognition of facial expressions of emotion in the wild

abstract: This paper details the methodology and results of the EmotioNet challenge. This challenge is the first to test the ability of computer vision algorithms in the automatic analysis of a large number of images of facial expressions of emotion in the wild. The challenge was divided into two tracks. The first track tested the ability of current computer vision algorithms in the automatic detection of action units (AUs). Specifically, we tested the detection of 11 AUs. The second track tested the algorithms' ability to recognize emotion categories in images of facial expressions. Specifically, we tested the recognition of 16 basic and compound emotion categories. The results of the challenge suggest that current computer vision and machine learning algorithms are unable to reliably solve these two tasks. The limitations of current algorithms are more apparent when trying to recognize emotion. We also show that current algorithms are not affected by mild resolution changes, small occluders, gender or age, but that 3D pose is a major limiting factor on performance. We provide an in-depth discussion of the points that need special attention moving forward.


title: Benchmarking Static Analysis Tools for Web Security

abstract: Static analysis tools are recurrently used by developers to search for vulnerabilities in the source code of web applications. However, distinct tools provide different results depending on factors such as the complexity of the code under analysis and the application scenario; thus, missing some of the vulnerabilities while reporting false problems. Benchmarks can be used to assess and compare different systems or components, however, existing benchmarks have strong representativeness limitations, disregarding the specificities of the environment, where the tools under benchmarking will be used. In this paper, we propose a benchmark for assessing and comparing static analysis tools in terms of their capability to detect security vulnerabilities. The benchmark considers four real-world development scenarios, including workloads composed of real web applications with different goals and constraints, ranging from low budget to high-end applications. Our benchmark was implemented and assessed experimentally using a set of 134 WordPress plugins, which served as the basis for the evaluation of five free PHP static analysis tools. Results clearly show that the best solution depends on the deployment scenario and class of vulnerability being detected; therefore, highlighting the importance of these aspects in the design of the benchmark and of future static analysis tools.


title: Multiwinner Approval Rules as Apportionment Methods

abstract: We establish a link between multiwinner elections and apportionment problems by showing how approval-based multiwinner election rules can be interpreted as methods of apportionment. We consider several multiwinner rules and observe that they induce apportionment methods that are well-established in the literature on proportional representation. For instance, we show that Proportional Approval Voting induces the D'Hondt method and that Monroe's rule induces the largest reminder method. We also consider properties of apportionment methods and exhibit multiwinner rules that induce apportionment methods satisfying these properties.


title: A Series–Parallel Current-Driven Full-Bridge DC/DC Converter

abstract: This paper presents a novel series–parallel current-driven (SPCD) full-bridge dc/dc converter, which is able to process and deliver power efficiently over a wide range of load variations. In order to guarantee reliable operation of high-frequency dc/dc converters, the converter should be able to sustain soft switching for a wide range of operating conditions. The SPCD full-bridge converter, proposed in this paper, is able to offer soft switching for the input power semiconductors and smooth commutations for the output diodes. Also, the particular structure of the proposed converter eliminates the need for extra auxiliary circuits to provide reactive current for soft switching at light loads. The proposed topology can fully eliminate voltage spikes across the output diodes by providing smooth and lossless commutations for the output diodes. Thus, the proposed converter can be an efficient and reliable solution for variety of applications with a high switching frequency and a high output voltage. The SPCD full-bridge converter has the ability to integrate all magnetic components into an integrated transformer in order to achieve a high power density. The integrated transformer is thoroughly analyzed using ANSYS high-frequency structure simulator. Simulation and experimental results confirm the superior performance of the proposed SPCD full-bridge dc/dc converter.


title: Efficient QR Code Beautification With High Quality Visual Content

abstract: Quick response (QR) code is generally used for embedding messages such that people can conveniently use mobile devices to capture the QR code and acquire information through a QR code reader. In the past, the design of QR code generators only aimed to achieve high decodability and the produced QR codes usually look like random black-and-white patterns without visual semantics. In recent years, researchers have been tried to endow the QR code with aesthetic elements and QR code beautification has been formulated as an optimization problem that minimizes the visual perception distortion subject to acceptable decoding rate. However, the visual quality of the QR code generated by existing methods still leaves much to be desired. In this work, we propose a two-stage approach to generate QR code with high quality visual content. In the first stage, a baseline QR code with reliable decodability but poor visual quality is first synthesized based on the Gauss-Jordan elimination procedure. In the second stage, a rendering mechanism is designed to improve the visual quality while avoiding affecting the decodability of the QR code. The experimental results show that the proposed method substantially enhances the appearance of the QR code and the processing complexity is near real-time.


title: Leader-member exchange and citizenship behaviors: a meta-analysis.

abstract: This article provides a meta-analytic review of the relationship between the quality of leader–member exchanges (LMX) and citizenship behaviors performed by employees. Results based on 50 independent samples (N 9,324) indicate a moderately strong, positive relationship between LMX and citizenship behaviors ( .37). The results also support the moderating role of the target of the citizenship behaviors on the magnitude of the LMX–citizenship behavior relationship. As expected, LMX predicted individual-targeted behaviors more strongly than it predicted organizational targeted behaviors ( .38 vs. .31), and the difference was statistically significant. Whether the LMX and the citizenship behavior ratings were provided by the same source or not also influenced the magnitude of the correlation between the 2 constructs.


title: A real-time monocular vision-based frontal obstacle detection and avoidance for low cost UAVs in GPS denied environment

abstract: This paper presents a novel monocular vision-based realtime obstacle detection and avoidance for a low cost unmanned aerial vehicle (UAV) in an unstructured, GPS denied environment. We propose a mathematical model to estimate the relative distance from the UAV's camera to an obstacle which will subsequently be used in a collision avoidance algorithm. We validate our model with some real time experiments under both stationary and dynamic motion of the UAV during its flight. The results show good agreement with the ground truth values with an acceptable percentage of error in estimation under 3% thus proving it can facilitate obstacle detection and avoidance for low cost and lightweight UAVs.


title: Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction

abstract: One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.


title: Game AI as Storytelling

abstract: Much research on artificial intelligence in games has been devoted to creating opponents that play competently against human players. We argue that the traditional goal of AI in games-to win the game-is but one of several interesting goals to pursue. We promote the alternative goal of making the human player’s play experience “better,” i.e., AI systems in games should reason about how to deliver the best possible experience within the context of the game. The key insight we offer is that approaching AI reasoning for games as “storytelling reasoning” makes this goal much more attainable. We present a framework for creating interactive narratives for entertainment purposes based on a type of agent called an experience manager. An experience manager is an intelligent computer agent that manipulates a virtual world to dynamically adapt the narrative content the player experiences, based on his or her actions and inferences about his or her preferred style of play. Following a theoretical perspective on game AI as a form of storytelling, we discuss the implications of such a perspective in the context of several AI technological approaches.


title: Effects of Various Prāṇāyāma on Cardiovascular and Autonomic Variables

abstract: Cardiovascular functions are controlled by neural factors, temperature, hormones, etc., Of these, neural factors primarily concern the autonomic nervous system, which plays a major role in maintaining and regulating cardiac functions, e.g., blood pressure and heart rate. Prāṇāyāma is one of the most important yogic practices. There are various review articles on Yoga and its effects but, though Prāṇāyāma is a part of yoga, there is lack of review articles. To the best of our knowledge there is no known review article on effect of various Prāṇāyāma on cardiovascular and autonomic variables. To provide a general overview about the effect of various prāṇāyāma (breathing techniques) on cardiovascular and autonomic variables. A narrative review was performed based on the available scientific literature. An electronic data search was performed in Medline/PubMed database to review relevant articles, using keywords such as "Prāṇāyāma, Yogic breathing techniques, Unilateral nostril breathing, Alternate nostril breathing, Kapalbhati, Bhastrika and Bhramari Pranayama". All the relevant articles published from 1988 to 06-04-2016 were included in this review. Slow type of yogic breathing technique was reported to produce beneficial effect on cardiovascular and autonomic variables while fast breathing techniques do not produce such effects. There is lack of consistency in the results of specific nostril yogic breathing techniques and the mechanisms behind the effects of various prāṇāyāma. This review suggests that different types of Prāṇāyāma techniques produce different effects and the mechanisms behind these effects are not fully understood.


title: Chronic kidney disease: global dimension and perspectives

abstract: Chronic kidney disease is defined as a reduced glomerular filtration rate, increased urinary albumin excretion, or both, and is an increasing public health issue. Prevalence is estimated to be 8-16% worldwide. Complications include increased all-cause and cardiovascular mortality, kidney-disease progression, acute kidney injury, cognitive decline, anaemia, mineral and bone disorders, and fractures. Worldwide, diabetes mellitus is the most common cause of chronic kidney disease, but in some regions other causes, such as herbal and environmental toxins, are more common. The poorest populations are at the highest risk. Screening and intervention can prevent chronic kidney disease, and where management strategies have been implemented the incidence of end-stage kidney disease has been reduced. Awareness of the disorder, however, remains low in many communities and among many physicians. Strategies to reduce burden and costs related to chronic kidney disease need to be included in national programmes for non-communicable diseases.


title: A practical microcylinder appearance model for cloth rendering

abstract: This article introduces a practical shading model for cloth that can simulate both anisotropic highlights as well as the complex color shifts seen in cloth made of different colored threads. Our model is based on extensive Bidirectional Reflectance Distribution Function (BRDF) measurements of several cloth samples. We have also measured the scattering profile of several different individual cloth threads. Based on these measurements, we derived an empirical shading model capable of predicting the light scattering profile of a variety of threads. From individual threads, we synthesized a woven cloth model, which provides an intuitive description of the layout of the constituent threads as well as their tangent directions. Our model is physically plausible, accounting for shadowing and masking by the threads. We validate our model by comparing predicted and measured light scattering values and show how it can reproduce the appearance of many cloth and thread types, including silk, velvet, linen, and polyester. The model is robust, easy to use, and can simulate the appearance of complex highlights and color shifts that cannot be fully handled by existing models.


Revising Perceptual Linear Prediction (PLP).


title: Modeling intergovernmental collaboration: a system dynamics approach

abstract: Describes the development and testing of a system-dynamics model of collaboration, trust-building and knowledge sharing in a complex, inter-governmental information system project. The model-building and testing activity was an exploration of the feasibility of applying these modeling methods to a complex inter-organizational process about which only qualitative data were available. The process to be modeled was the subject of qualitative field research studying knowledge and information sharing in inter-organizational networks. This research had produced a large volume of observational and interview data and analyses about the technology project. In the course of collecting and analyzing data from this project, the researchers noted evidence of what appeared to be important feedback effects. The feedback loops appeared to influence the collaboration and knowledge sharing, critical parts of how the information system design and construction progressed. These observations led to conversations with colleagues who had extensive experience in dynamic modeling. All agreed that applying dynamic modeling methods to this process had considerable potential to yield valuable insights into collaboration. As a novel application of the methods, it could yield new modeling insights as well. The modeling experience supported both propositions and was judged a success that will lead to continued exploration of these questions.


title: Micro structuring of inorganic glass by hot embossing of coated glass wafers

abstract: Hot embossing is an economical manufacturing process for structuring of glass. However, this process is limited by sticking of glass melts on the moulding tools. In order to prevent this problem, the glass substrate was coated with thin metal, carbon and oxide layers. In the current work, the behaviour and suitability of the coatings for the embossing process were investigated. As a result, it was found that all coatings showed a clear decrease of adhesive forces and an increase of sticking temperature out of the range relevant for hot embossing. The examined coatings behave ductile, however, under larger elongations they tend to crack. Examples of fluidic structures with lateral geometries in millimetre and micrometre range were realized. The coatings could be successfully applied in moulding experiments without reduction of quality of embossed structures by sticking of the glass melts. The largest aspect ratio of embossing depth to structural width amounted to 3.


title: Image-Based Food Calorie Estimation Using Knowledge on Food Categories, Ingredients and Cooking Directions

abstract: Image-based food calorie estimation is crucial to diverse mobile applications for recording everyday meal. However, some of them need human help for calorie estimation, and even if it is automatic, food categories are often limited or images from multiple viewpoints are required. Then, it is not yet achieved to estimate food calorie with practical accuracy and estimating food calories from a food photo is an unsolved problem. Therefore, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN [1]. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In this experiment, we trained multi-task and single-task CNNs. As a result, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coefficient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN.


title: New Bridgeless DCM Sepic and Cuk PFC Rectifiers With Low Conduction and Switching Losses

abstract: New bridgeless single-phase ac-dc power factor correction (PFC) rectifiers based on Sepic and Cuk topologies are proposed. The absence of an input diode bridge and the presence of only two semiconductor switches in the current flowing path during each switching cycle result in less conduction losses and improved thermal management compared to the conventional Sepic and Cuk PFC converters. The proposed topologies are designed to work in discontinuous conduction mode (DCM) to achieve almost unity power factor in a simple and effective manner. The DCM operation gives additional advantages such as zero-current turn-on in the power switches, zero-current turn-off in the output diode and reduces the complexity of the control circuitry. The proposed rectifiers are theoretically investigated. Performance comparisons between the proposed and conventional Sepic PFC rectifiers are performed. Simulation and experimental results are provided for a design example of a 65-W/48-V at 100- Vrms line voltage to evaluate the performance of the proposed PFC rectifier.


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title: DEX: A high-performance graph database management system

abstract: The amount of applications calling for efficient large graph management is dramatically increasing. Social network analysis, Internet or biocomputation are just three examples of such applications. In these cases, the interest focuses on the structural analysis of the relationships between different entities organized in huge networks or graph-like structures. Being able to efficiently handle such graphs becomes essential, placing graph database management systems in the eye of the storm. Among the different challenges posed by graph databases, finding an efficient way to represent and manipulate huge graphs that do not entirely fit in memory is still an unresolved problem. In this work, we present DEX, a high performance graph database management system based on bitmaps and other secondary structures. We show that by using bitmap structures for graph representation it is possible to improve the performance of a graph database system, allowing for the efficient manipulation of very large graphs containing thousands of millions of nodes and edges.


title: Review of polymer MEMS micromachining

abstract: The development of polymer micromachining technologies that complement traditional silicon approaches has enabled the broadening of microelectromechanical systems (MEMS) applications. Polymeric materials feature a diverse set of properties not present in traditional microfabrication materials. The investigation and development of these materials have opened the door to alternative and potentially more cost effective manufacturing options to produce highly flexible structures and substrates with tailorable bulk and surface properties. As a broad review of the progress of polymers within MEMS, major and recent developments in polymer micromachining are presented here, including deposition, removal, and release techniques for three widely used MEMS polymer materials, namely SU-8, polyimide, and Parylene C. The application of these techniques to create devices having flexible substrates and novel polymer structural elements for biomedical MEMS (bioMEMS) is also reviewed.


title: Face liveness detection based on enhanced local binary patterns

abstract: For face recognition systems, impostors can obtain legal identity authentication by presenting the printed images, the downloaded images or candid videos to the sensor. In this paper, an enhanced face local binary feature (ELBP) of a face map is extracted as a classification feature to identify whether the face map is a real face or a fake face. Compared with the dynamic or static methods proposed in the previous literature, the proposed method is convenient and effective. We achieved over 95% correct recognition rate on NUAA datasets. Through comprehensive analysis and comparison with other static-based algorithms such as M-LBP or LTP, the proposed algorithm can achieve better performances.


title: Tagging Personal Photos with Transfer Deep Learning

abstract: The advent of mobile devices and media cloud services has led to the unprecedented growing of personal photo collections. One of the fundamental problems in managing the increasing number of photos is automatic image tagging. Existing research has predominantly focused on tagging general Web images with a well-labelled image database, e.g., ImageNet. However, they can only achieve limited success on personal photos due to the domain gaps between personal photos and Web images. These gaps originate from the differences in semantic distribution and visual appearance. To deal with these challenges, in this paper, we present a novel transfer deep learning approach to tag personal photos. Specifically, to solve the semantic distribution gap, we have designed an ontology consisting of a hierarchical vocabulary tailored for personal photos. This ontology is mined from $10,000$ active users in Flickr with 20 million photos and 2.7 million unique tags. To deal with the visual appearance gap, we discover the intermediate image representations and ontology priors by deep learning with bottom-up and top-down transfers across two domains, where Web images are the source domain and personal photos are the target. Moreover, we present two modes (single and batch-modes) in tagging and find that the batch-mode is highly effective to tag photo collections. We conducted personal photo tagging on 7,000 real personal photos and personal photo search on the MIT-Adobe FiveK photo dataset. The proposed tagging approach is able to achieve a performance gain of $12.8\%$ and $4.5\%$ in terms of NDCG@5, against the state-of-the-art hand-crafted feature-based and deep learning-based methods, respectively.


Multi-agent Deep Reinforcement Learning for Task Allocation in Dynamic Environment


title: UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss

abstract: In the era of end-to-end deep learning, many advances in computer vision are driven by large amounts of labeled data. In the optical flow setting, however, obtaining dense per-pixel ground truth for real scenes is difficult and thus such data is rare. Therefore, recent end-to-end convolutional networks for optical flow rely on synthetic datasets for supervision, but the domain mismatch between training and test scenarios continues to be a challenge. Inspired by classical energy-based optical flow methods, we design an unsupervised loss based on occlusion-aware bidirectional flow estimation and the robust census transform to circumvent the need for ground truth flow. On the KITTI benchmarks, our unsupervised approach outperforms previous unsupervised deep networks by a large margin, and is even more accurate than similar supervised methods trained on synthetic datasets alone. By optionally fine-tuning on the KITTI training data, our method achieves competitive optical flow accuracy on the KITTI 2012 and 2015 benchmarks, thus in addition enabling generic pre-training of supervised networks for datasets with limited amounts of ground truth.


title: Topology Optimization of Conical-Beam Antennas Exploiting Rotational Symmetry

abstract: Microstrip conical-beam antennas are designed by the topology optimization of metallic patches combining the discrete optimization of vias. The antennas are designed to be angular-periodic structures with eight identical sectors, which makes the rotational symmetric radiation pattern more easily to be achieved and the optimization problem much easier to be solved. A restarting technique is introduced for the circular polarization design case, where the high cross-polarization discrimination (XPD) is hard to be realized. By increasing the weight of cross-polarization penalization function after the optimization restarts, much higher XPD is achieved compared to that of the antennas optimized with fixed subobjective weighting factors. By this method, two conical-beam antennas, which are linearly polarized and left-handed circularly polarized antennas, respectively, are designed. Simulated and measured results show that the antennas have expected conical shape radiation patterns, good matching, and high polarization purities.


title: Citation graph, weighted impact factors and performance indices

abstract: A scheme of evaluating an impact of a given scientific paper based on importance of papers quoting it is investigated. Introducing a weight of a given citation, dependent on the previous scientific achievements of the author of the citing paper, we define the weighting factor of a given scientist. Technically the weighting factors are defined by the components of the normalized leading eigenvector of the matrix describing the citation graph. The weighting factor of a given scientist, reflecting the scientific output of other researchers quoting his work, allows us to define weighted number of citation of a given paper, weighted impact factor of a journal and weighted Hirsch index of an individual scientist or of an entire scientific institution.


title: Jointly modeling deep video and compositional text to bridge vision and language in a unified framework

abstract: Recently, joint video-language modeling has been attracting more and more attention. However, most existing approaches focus on exploring the language model upon on a fixed visual model. In this paper, we propose a unified framework that jointly models video and the corresponding text sentences. The framework consists of three parts: a compositional semantics language model, a deep video model and a joint embedding model. In our language model, we propose a dependency-tree structure model that embeds sentence into a continuous vector space, which preserves visually grounded meanings and word order. In the visual model, we leverage deep neural networks to capture essential semantic information from videos. In the joint embedding model, we minimize the distance of the outputs of the deep video model and compositional language model in the joint space, and update these two models jointly. Based on these three parts, our system is able to accomplish three tasks: 1) natural language generation, and 2) video retrieval and 3) language retrieval. In the experiments, the results show our approach outperforms SVM, CRF and CCA baselines in predicting Subject-Verb-Object triplet and natural sentence generation, and is better than CCA in video retrieval and language retrieval tasks.


title: Eight-Way Substrate Integrated Waveguide Power Divider With Low Insertion Loss

abstract: We describe a compact radial cavity power divider based on the substrate integrated waveguide (SIW) technology in this paper. The equivalent-circuit model is used to analyze the multiport structure, and a design procedure is also established for the structure. An eight-way C-band SIW power divider with low insertion loss is designed, fabricated, and measured. Good agreement between simulated and measured results is found for the pro posed power divider. The measured minimum insertion loss of the eight-way power divider is approximately 0.2 dB and return loss is approximately 30 dB at 5.25 GHz. The measured 15-dB return-loss bandwidth is found to be approximately 500 MHz, and its 1-dB insertion-loss bandwidth is approximately 1.2 GHz. Furthermore, the isolations between the output ports of the eight-way power divider are also discussed.


title: JeCache: Just-Enough Data Caching with Just-in-Time Prefetching for Big Data Applications

abstract: Big data clusters introduce an intermediate cache layer between the computing frameworks and the underlying distributed file systems, to enable upper-level applications or end users to efficiently access big datasets in cache and effectively share them among different computing frameworks. As caches are shared by multiple applications or end users, directly applying existing on-demand caching strategies will result in intense conflicts, when big datasets are cached as a whole. Meanwhile, big data applications usually involve massive numbers of file scans, cached-in data blocks may have little chance of being accessed before they are cached out to make way for other on-demand data blocks. Thus, it is unwise to cache data blocks long before they are actually accessed. In this paper, we propose a novel just-enough big data caching scheme for just-in-time block prefetching to improve the cache effectiveness of big data clusters. With just-in-time block prefetching, a block is cached in just before the task begins to process the block, rather than being cached in along with other blocks of the same dataset being processed. We monitor block accesses to measure the average processing time of data blocks, and then estimate the minimal number of blocks that should be kept in cache for a big dataset, so that the speed of data processing matches with that of data prefetching, and each upper-level task can obtain its input blocks from cache just in time. Our experimental results show that the proposed cache method can restrain over-requirement of cache resources in big data applications, and provides the same performance improvement as when all data blocks are cached.


title: Understanding Human Motion and Gestures for Underwater Human-Robot Collaboration

abstract: In this paper, we present a number of robust methodologies for an underwater robot to visually detect, follow, and interact with a diver for collaborative task execution. We design and develop two autonomous diver-following algorithms, the first of which utilizes both spatial- and frequency-domain features pertaining to human swimming patterns in order to visually track a diver. The second algorithm uses a convolutional neural network-based model for robust tracking-by-detection. In addition, we propose a hand gesture-based human-robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar-based frameworks. In the proposed interaction framework, deep visual detectors are used to provide accurate hand gesture recognition; subsequently, a finite-state machine performs robust and efficient gesture-to-instruction mapping. The distinguishing feature of this framework is that it can be easily adopted by divers for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules. Furthermore, we validate the performance and effectiveness of the proposed methodologies through extensive field experiments in closed- and open-water environments. Finally, we perform a user interaction study to demonstrate the usability benefits of our proposed interaction framework compared to existing methods.


title: Observational emergence of a fuzzy controller evolved by genetic algorithm

abstract: Explaining emergence is a difficult work, such that there are many arguments on what it is or how it can be explained. Nonetheless, it is frequently referred to in many fields, such as behavior-based robotics, artificial life and complex systems, without any formal definition. In this paper, we develop a fuzzy logic controller for a simulated mobile robot with a genetic algorithm and analyze the behavior of the controller from the perspective of observational emergence. The analysis shows that the fuzzy logic controller has acquired emergent behavior through the interactions of the underlying fuzzy rules.


title: Control of a doubly fed induction generator in a wind turbine during grid fault ride-through

abstract: Summary form only given. This paper analyzes the ability of a doubly fed induction generator (DFIG) in a wind turbine to ride through a grid fault and the limitations to its performance. The fundamental difficulty for the DFIG in ride-through is the e.m.f. induced in the machine rotor during the fault, which depends upon the d.c. and negative sequence components in the stator flux linkage and the rotor speed. The investigation develops a control method to increase the probability of successful grid fault ride-through, given the current and voltage capabilities of the rotor side converter. A time domain computer simulation model is developed and laboratory experiments are made to verify the model and the control method proposed. Case studies are then performed on a representatively sized system to define the feasibility regions of successful ride-through for different types of grid fault.


title: Torque Ripple Minimization Using Online Estimation of the Stator Resistances With Consideration of Magnetic Saturation

abstract: The tolerances and limitations in design and manufacturing of both electrical motors and inverters lead to parasitic resistances in the phases. Therefore, the phase resistances are asymmetric, which causes an asymmetry among the phase currents and a resulting increase in torque ripple. This paper proposes a novel scheme for torque ripple minimization using online estimation of unbalanced stator resistances of a permanent-magnet synchronous motor. The identifiability of the stator resistances in a nonlinear model is investigated by employing a sensitivity analysis. The phase resistances are estimated by recursive least squares in the rotating reference frame in real time. Using the estimated resistances, torque ripple reduction is achieved by a compensation scheme in the stationary reference frame. The effectiveness of the proposed scheme is verified in both simulation and test-bench experiments. Results with and without consideration of magnetic saturation are compared.


title: A journey to highly dynamic, self-adaptive service-based applications

abstract: Future software systems will operate in a highly dynamic world. Systems will need to operate correctly despite of unespected changes in factors such as environmental conditions, user requirements, technology, legal regulations, and market opportunities. They will have to operate in a constantly evolving environment that includes people, content, electronic devices, and legacy systems. They will thus need the ability to continuously adapt themselves in an automated manner to react to those changes. To realize dynamic, self-adaptive systems, the service concept has emerged as a suitable abstraction mechanism. Together with the concept of the service-oriented architecture (SOA), this led to the development of technologies, standards, and methods to build service-based applications by flexibly aggregating individual services. This article discusses how those concepts came to be by taking two complementary viewpoints. On the one hand, it evaluates the progress in software technologies and methodologies that led to the service concept and SOA. On the other hand, it discusses how the evolution of the requirements, and in particular business goals, influenced the progress towards highly dynamic self-adaptive systems. Finally, based on a discussion of the current state of the art, this article points out the possible future evolution of the field.


title: Reinforcement Learning of Question-Answering Dialogue Policies for Virtual Museum Guides

abstract: We use Reinforcement Learning (RL) to learn question-answering dialogue policies for a real-world application. We analyze a corpus of interactions of museum visitors with two virtual characters that serve as guides at the Museum of Science in Boston, in order to build a realistic model of user behavior when interacting with these characters. A simulated user is built based on this model and used for learning the dialogue policy of the virtual characters using RL. Our learned policy outperforms two baselines (including the original dialogue policy that was used for collecting the corpus) in a simulation setting.


title: URL Based Gateway Side Phishing Detection Method

abstract: Phishing attack has become the most dangerous form of fraud to hit online and mobile businesses. In this paper, we reveal some new aspects of the common features that appear in the phishing URLs, and introduce a statistical machine learning classifier to detect the phishing sites which relies on these selected features. Unlike previous studies, we do not utilize a single model for different regions since the result of our analysis shows that the features in different phishing domains have mismatched distributions. As it is impossible for us to recollect enough data and rebuild the models, we adjust the existing model by the transfer learning algorithm to solve these problems. A number of comprehensive experiments show that our proposed method achieves more than 93% accuracy over a balanced dataset and less than 1% error rates in the simulated real phishing scene. Moreover, the well performance in the target domain demonstrates the use of transfer learning algorithm in the anti-phishing scenario is feasible.


B 2 B Supply Chain Performance Enhancement Road Map Using Data Mining Techniques