title: Driver assistance system for lane detection and vehicle recognition with night vision

abstract: The objective of this research is to develop a vision-based driver assistance system to enhance the driver's safety in the nighttime. The proposed system performs both lane detection and vehicle recognition. In lane detection, three features including lane markers, brightness, slenderness and proximity are applied to detect the positions of lane markers in the image. On the other hand, vehicle recognition is achieved by using an evident feature which is extracted through three four steps: taillight standing-out process, adaptive thresholding, centroid detection, and taillight pairing algorithm. Besides, an automatic method is also provided to calculate the tilt and the pan of the camera by using the position of vanishing point which is detected in the image by applying Canny edge detection, Hough transform, major straight line extraction and vanishing point estimation. Experimental results for thousands of images are provided to demonstrate the effectiveness of the proposed approach in the nighttime. The lane detection rate is nearly 99%, and the vehicle recognition rate is about 91%. Furthermore, our system can process the image in almost real time.


title: Quantum-Chemical Insights from Deep Tensor Neural Networks

abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol-1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.


title: Multi-sphere Support Vector Data Description for Outliers Detection on Multi-distribution Data

abstract: SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usually not good enough. This paper proposes an multi-sphere SVDD approach, named MS-SVDD, for outlier detection on multi-distribution data. First, an adaptive sphere detection method is proposed to detect data distributions in the dataset. The data is partitioned in terms of the identified data distributions, and the corresponding SVDD classifiers are constructed separately. Substantial experiments on both artificial and real-world datasets have demonstrated that the proposed approach outperforms original SVDD.


title: Trust and tacit knowledge sharing and use

abstract: Purpose – This study aims to explore the impact of affect‐based and cognition‐based trust of co‐workers on the willingness of professionals to share and use tacit knowledge.Design/methodology/approach – The relationships were examined through data provided by a sample of 202 professionals and managers in world headquarters of an international organization.Findings – The levels of both types of trust influence the extent to which staff members are willing to share and use tacit knowledge. Affect‐based trust has a significantly greater effect on the willingness to share tacit knowledge, while cognition‐based trust plays a greater role in willingness to use tacit knowledge.Research limitations/implications – The data are cross‐sectional and were also collected in one organization. Future studies should consider longitudinal designs across multiple organizations. Alternatively, archival information could be used to measure actual tacit knowledge sharing and use among co‐workers.Practical implications – The re...


title: Integrating direct load control with interruptible load management to provide instantaneous reserves for ancillary services

abstract: This paper presents a novel adaptive control strategy for integrating direct load control (DLC) with interruptible load management (ILM) to provide instantaneous reserves for ancillary services in deregulated power systems. Fuzzy dynamic programming is used to satisfy customers' requirements and yields a near optimal pre-scheduling of the DLC. Then, the energy payback associated with the DLC is further eliminated by the adaptive control strategy, which exploits interruptible loads to modify the DLC schedule in real-time. Through the developed adaptive control strategy, the influences of the load uncertainties and forecasting errors on the pre-scheduling of the DLC can also be excluded. The proposed algorithm was practically tested on the Taiwan power (Taipower) 38-unit system with 20 air-conditioner loads and 15 interruptible loads. The outcomes reveal that an exact amount of instantaneous reserves can be successfully acquired, and the results are robust against dynamic disturbances of the power system.


title: A Relay Scheme for Distribution Networks Considering the Control Mode of Inverter-Based Distributed Generator

abstract: In the distribution system including distributed generators, it is difficult to meet the requirements of selectivity and sensitivity for traditional current protection. Currently, inverter-based distributed generators (IBDG) are widely used in distribution systems. The transient process of IBDG is rapid, and its transient characteristics are influenced by the control strategy. The controlled current source equivalent model of IBDG with positive-sequence P-Q control is put forward. After analyzing the fault characteristics, the protection scheme using negative sequence current is proposed, which can be used in the distribution systems including IBDG under positive-sequence P-Q control. The simulation results on a 10kV distribution system have verified the correctness of faults characteristics and the effectiveness of protection scheme.


title: CPW-Fed Circular Fractal Slot Antenna Design for Dual-Band Applications

abstract: Using configuration synthesis and design map, the CPW-fed circular fractal slot antennas are proposed for dual-band applications. In practice, the experimental results with broadband and dual-band responses (47.4% and 13.5% bandwidth) and available radiation gains (peak gain 3.58 and 7.28 dBi) at 0.98 and 1.84 GHz respectively for half-wavelength design are achieved firstly. Then, the other broadband and dual-band responses (75.9% and 16.1% bandwidth) and available radiation gains (peak gain 3.16 and 6.62 dBi) at 2.38 and 5.35 GHz for quarter-wavelength design are described herein. Contour distribution patterns are applied to figure out the omni-directional patterns. The demonstration among the design map and the EM characteristics of the antenna is presented by current distributions.


title: The Mediating Role of Customer Trust on Customer Loyalty

abstract: The review of the literature reveals various relationships between the constructs of social identity, customer trust, and customer loyalty. Such results lead to questioning the nature of the conceptual structure that would include these constructs as well as their interrelationships in a structural model. From this perspective, the purpose of this study is to evaluate the mediating role of customer trust on customer loyalty in presence of corporate social identity in the context of financial institutions. Using data collected from 1296 customers-members of credit unions, the results of this study help to legitimize the idea that customer trust intervenes as a mediating variable that enhances the impact of corporate identity, corporate image and the reputation of the firm on customer loyalty. The implications of the study are discussed from both the research and managerial perspectives.


title: Metasurface holograms reaching 80% efficiency

abstract: Surfaces covered by ultrathin plasmonic structures--so-called metasurfaces--have recently been shown to be capable of completely controlling the phase of light, representing a new paradigm for the design of innovative optical elements such as ultrathin flat lenses, directional couplers for surface plasmon polaritons and wave plate vortex beam generation. Among the various types of metasurfaces, geometric metasurfaces, which consist of an array of plasmonic nanorods with spatially varying orientations, have shown superior phase control due to the geometric nature of their phase profile. Metasurfaces have recently been used to make computer-generated holograms, but the hologram efficiency remained too low at visible wavelengths for practical purposes. Here, we report the design and realization of a geometric metasurface hologram reaching diffraction efficiencies of 80% at 825 nm and a broad bandwidth between 630 nm and 1,050 nm. The 16-level-phase computer-generated hologram demonstrated here combines the advantages of a geometric metasurface for the superior control of the phase profile and of reflectarrays for achieving high polarization conversion efficiency. Specifically, the design of the hologram integrates a ground metal plane with a geometric metasurface that enhances the conversion efficiency between the two circular polarization states, leading to high diffraction efficiency without complicating the fabrication process. Because of these advantages, our strategy could be viable for various practical holographic applications.


title: Achieving ICS Resilience and Security through Granular Data Flow Management

abstract: Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.


title: Text Line Segmentation using AHTC and Watershed Algorithm for Handwritten Document Images

abstract: Text line segmentation is a critical task in handwritten document recognition. In this paper, we propose a novel text-line segmentation method using baseline estimation and watershed. The baseline-detection algorithm estimates the baseline using Adaptive Head-Tail Connection(AHTC) on the document. Then, the watershed method segments the line region using the baseline-detection result. Finally, the text lines are separated by watershed result and a post-processing algorithm defines the lines more correctly. The scheme successfully segments text lines with 97% accuracy from the handwritten document images in the ICDAR database.


title: Towards Large-Scale 3D Face Recognition

abstract: 3D face recognition holds great promise in achieving robustness to pose, expressions and occlusions. However, 3D face recognition algorithms are still far behind their 2D counterparts due to the lack of large-scale datasets. We present a model based algorithm for 3D face recognition and test its performance by combining two large public datasets of 3D faces. We propose a Fully Convolutional Deep Network (FCDN) to initialize our algorithm. Reliable seed points are then extracted from each 3D face by evolving level set curves with a single curvature dependent adaptive speed function. We then establish dense correspondence between the faces in the training set by matching the surface around the seed points on a template face to the ones on the target faces. A morphable model is then fitted to probe faces and face recognition is performed by matching the parameters of the probe and gallery faces. Our algorithm achieves state of the art landmark localization results. Face recognition results on the combined FRGCv2 and Bosphorus datasets show that our method is effective in recognizing query faces with real world variations in pose and expression, and with occlusion and missing data despite a huge gallery. Comparing results of individual and combined datasets show that the recognition accuracy drops when the size of the gallery increases.


title: Using mobile communication technology in high school education: Motivation, pressure, and learning performance

abstract: Motivation and pressure are considered two factors impacting vocational senior high school student learning. New communication technology, especially mobile communication technology, is supposed to be effective in encouraging interaction between the student and the instructor and improving learning efficiency. Social presence and information richness theory was applied to analyze the media and their impacts on the instruction process firstly. Then the author observed firstly the impact of using individual communication technologies - namely Short Message Service (SMS), email, and online forum - on student learning motivation, pressure, and performance, based on a comparative investigation of 176 students. Then the impacts of using combination of mobile and Internet communication technology - SMS+email, SMS+online forum - were examined with another experiment of 45 students. The result showed that instant messaging helps bonding the two roles - student and instructor - in the instruction process effectively. When combined with Internet communication media, it can significantly increase student extrinsic motivation without causing higher pressure. Additionally, communication media demanding public expression rather than private dialogue should be adopted with careful consideration, since they may raise student pressure.


title: The Evolved Transformer

abstract: Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments -- the Evolved Transformer -- demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT'14 English-German; at smaller sizes, it achieves the same quality as the original "big" Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters.


title: The Golden Age of Software Architecture: A Comprehensive Survey

abstract: Abstract : This retrospective on nearly two decades of software architecture research examines the maturation of the software architecture research area by tracing the evolution of research questions and results through their maturation cycle. We show how early qualitative results set the stage for later precision, formality, and automation, how results have built up over time, and how the research results have moved into practice.


title: Differential evolution embedded Otsu's method for optimized image thresholding

abstract: Image segmentation is a challenging task in digital image processing. Thresholding, an important technique for image segmentation, has gained considerable attention from the researchers for selecting reasonable thresholds. Since gray levels characterize the objects in a gray image, many thresholding methods extract objects from their background based on the statistics of one-dimensional (1D) histogram of gray levels and two-dimensional (2D) histogram of gray levels. A frequently used method is Otsu's method, which selects the global optimal threshold by maximizing the between-class variance. In this paper Differential Evolution (DE) has been embedded in Otsu's method for selecting an optimized threshold value. The proposed method is tested on a set of four images and the results validate the effectiveness of the proposed technique.


title: Genetic diversity of free-living Symbiodinium in surface water and sediment of Hawai‘i and Florida

abstract: Marine dinoflagellates in the genus Symbiodinium are primarily known for their symbiotic associations with invertebrates and protists, although they are also found free-living in nanoplankton and microphytobenthic communities. Free-living Symbiodinium are necessary for hosts that must acquire their symbionts anew each generation and for the possible reestablishment of endosymbiosis in bleached adults. The diversity and ecology of free-living Symbiodinium are not well studied by comparison with their endosymbiotic counterparts, and as a result, our understanding of the linkages between free-living and endosymbiotic Symbiodinium is poor. Here, we begin to address this knowledge gap by describing the genetic diversity of Symbiodinium in the surface water and reef sediments of Hawai‘i and Florida using Symbiodinium-specific primers for the hypervariable region of the chloroplast 23S domain V (cp23S-HVR). In total, 29 Symbiodinium sequence types were detected, 16 of which were novel. The majority of Symbiodinium sequence types in free-living environments belonged to clades A and B, but smaller numbers of sequence types belonging to clades C, D, and G were also detected. The majority of sequences recovered from Hawai‘i belonged to clades A and C and those from Florida to clade B. Such distribution patterns are consistent with the endosymbiotic diversity previously reported for these two regions. The ancestral sequence types in each clade were typically recovered from surface water and sediments both in Hawai‘i and Florida and have been previously reported as endosymbionts of a range of invertebrates, suggesting that these types have the capacity to exploit a range of very different habitats. More derived sequence types in clades A, B, C, and G were not recovered here, suggesting they are potentially restricted to endosymbiotic environments.


title: A Context-Aware User-Item Representation Learning for Item Recommendation

abstract: Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode her preference without considering the particular characteristics of each candidate item. We argue that this static encoding scheme is difficult to fully capture the users' preference. In this paper, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL. Namely, CARL derives a joint representation for a given user-item pair based on their individual latent features and latent feature interactions. Then, CARL adopts Factorization Machines to further model higher-order feature interactions on the basis of the user-item pair for rating prediction. Specifically, two separate learning components are devised in CARL to exploit review data and interaction data respectively: review-based feature learning and interaction-based feature learning. In review-based learning component, with convolution operations and attention mechanism, the relevant features for a user-item pair are extracted by jointly considering their corresponding reviews. However, these features are only review-driven and may not be comprehensive. Hence, interaction-based learning component further extracts complementary features from interaction data alone, also on the basis of user-item pairs. The final rating score is then derived with a dynamic linear fusion mechanism. Experiments on five real-world datasets show that CARL achieves significantly better rating prediction accuracy than existing state-of-the-art alternatives. Also, with attention mechanism, we show that the relevant information in reviews can be highlighted to interpret the rating prediction.


Disintermediation, Reintermediation, or Cybermediation? The Future of Intermediaries in Electronic Marketplaces


title: CEPR: A Collaborative Exploration and Periodically Returning Model for Location Prediction

abstract: With the growing popularity of location-based social networks, numerous location visiting records (e.g., check-ins) continue to accumulate over time. The more these records are collected, the better we can understand users’ mobility patterns and the more accurately we can predict their future locations. However, due to the personality trait of neophilia, people also show propensities of novelty seeking in human mobility, that is, exploring unvisited but tailored locations for them to visit. As such, the existing prediction algorithms, mainly relying on regular mobility patterns, face severe challenges because such behavior is beyond the reach of regularity. As a matter of fact, the prediction of this behavior not only relies on the forecast of novelty-seeking tendency but also depends on how to determine unvisited candidate locations. To this end, we put forward a Collaborative Exploration and Periodically Returning model (CEPR), based on a novel problem, Exploration Prediction (EP), which forecasts whether people will seek unvisited locations to visit, in the following. When people are predicted to do exploration, a state-of-the-art recommendation algorithm, armed with collaborative social knowledge and assisted by geographical influence, will be applied for seeking the suitable candidates; otherwise, a traditional prediction algorithm, incorporating both regularity and the Markov model, will be put into use for figuring out the most possible locations to visit. We then perform case studies on check-ins and evaluate them on two large-scale check-in datasets with 6M and 36M records, respectively. The evaluation results show that EP achieves a roughly 20p classification error rate on both datasets, greatly outperforming the baselines, and that CEPR improves performances by as much as 30p compared to the traditional location prediction algorithms.


title: Detecting mischoice of public transportation route based on smartphone and GIS

abstract: We investigate a method to detect mischoice of route taking in a public transportation trip. A station toward which a user is heading is identified to judge if he/she is taking a correct route, which is realized by comparing the angle between the vector from the previous station to the user's current position and other vectors to branch stations. An experiment showed that five of six routes had above 85% accuracy of "next station identification".


title: What Does the Acceptance and Action Questionnaire (AAQ-II) Really Measure?

abstract: The present study seeks to investigate the extent to which the Acceptance and Action Questionnaire (AAQ-II) is successful in discriminating between experiential avoidance/psychological flexibility on the one hand and the supposed outcomes in terms of psychological well-being of having this trait on the other. This was done using exploratory factor analysis on an item pool containing the AAQ-II items, and items designed for the present study to measure distress and acceptance/non-acceptance, to see what factors are identified and on which factor(s) the AAQ-II items had the highest factor loadings. Interestingly, the analysis found the items of the AAQ-II to be more strongly related to items designed to measure distress than items designed to measure acceptance/nonacceptance with minimal references to functional outcomes. The results of the study are interpreted and discussed in relation to the widespread use of the AAQ in both clinical and scientific contexts and given the centrality of the measure in empirically validating the ACT model of psychopathology and treatment.


title: A Boosted Co-Training Algorithm for Human Action Recognition

abstract: This paper proposes a boosted co-training algorithm for human action recognition. To address the view-sufficiency and view-dependency issues in co-training, two new confidence measures, namely, inter-view confidence and intra-view confidence, are proposed. They are dynamically fused into a semi-supervised learning process. Mutual information is employed to quantify the inter-view uncertainty and measure the independence among respective views. Intra-view confidence is estimated from boosted hypotheses to measure the total data inconsistency of labeled data and unlabeled data. Given a small set of labeled videos and a large set of unlabeled videos, the proposed semi-supervised learning algorithm trains a classifier by maximizing the inter-view confidence and intra-view confidence, and dynamically incorporating unlabeled data into the labeled data set. To evaluate the proposed boosted co-training algorithm, eigen-action and information saliency feature vectors are employed as two input views. The KTH and Weizmann human action databases are used for experiments, average recognition accuracy of 93.2% and 99.6% are obtained, respectively.


Social Networks : Perspective of Social Influence Maximization


title: A REVIEW ON RECONFIGURABLE INTEGRATED FILTER AND ANTENNA

abstract: In the past few years, several satisfying various objectives and designs of reconfigurable integrated microwave filter and antenna have been proposed for wireless communication systems. Several designs are new concepts and techniques, whereas others are inspired from previous works. The improvement concepts of these designs can be reviewed from this compilation of studies. This paper begins with an explanation of the reconfigurable filter, reconfigurable antenna and reconfigurable integrated microwave filter and antenna, followed by discussion on several designs in terms of size, measurement, performance and technology used. Among various designs, reconfigurable on planar structures are extensively used because of their simple design procedures and easy to tune on the desired frequency, bandwidth and attenuation. Most of the existing studies are focusing tunable on single element, i.e. either on filter or antenna side; however, it limits the tunable range and flexibility of the filtering antenna response. An alternative design of filtering antenna can be suggested to produce reconfigurable tuning capabilities on both microwave filter and antenna to produce overall good performance for multifunction operation in RF/microwave applications.


title: UVM-based verification of ECC module for flash memories

abstract: In this contribution, we present a coverage driven functional verification environment based on the UVM framework and the System Verilog language to certify the operational correctness of the ECC error management logic used in volatile and nonvolatile memories. We apply this methodology to floatinggate nonvolatile memories for the embedded market, which requires a read error rate of 10−14. The proposed environment achieves the complete validation of ECC features by 80 coverage main points. Additionally, we cover also the error distribution in the cache buffer, on board the memory, which requires additional 240 coverage points. The advantage of code reusability of this kind of verification environment and the wished coverage plan, achieved in few minutes with respect to the exhaustive approach, make easier the application of the proposed solution to any kind of volatile and nonvolatile memories.


title: Real-time Semantic Image Segmentation via Spatial Sparsity

abstract: We propose an approach to semantic (image) segmentation that reduces the computational costs by a factor of 25 with limited impact on the quality of results. Semantic segmentation has a number of practical applications, and for most such applications the computational costs are critical. The method follows a typical two-column network structure, where one column accepts an input image, while the other accepts a half-resolution version of that image. By identifying specific regions in the full-resolution image that can be safely ignored, as well as carefully tailoring the network structure, we can process approximately 15 highresolution Cityscapes images (1024x2048) per second using a single GTX 980 video card, while achieving a mean intersection-over-union score of 72.9% on the Cityscapes test set.


title: Intrinsically Motivated Learning Systems: An Overview

abstract: This chapter introduces the field of intrinsically motivated learning systems and illustrates the content, objectives, and organisation of the book. The chapter first expands the concept of intrinsic motivations, then introduces a taxonomy of three classes of intrinsic-motivation mechanisms (based on predictors, on novelty detection, and on competence), and finally introduces and reviews the various contributions of the book. The contributions are organised in six parts. The contributions of the first part provide general overviews on the concept of intrinsic motivations, the possible mechanisms that may implement them, and the functions that they can play. The contributions of the second, third, and fourth part focus on the three classes of the aforementioned intrinsic-motivation mechanisms. The contributions of the fifth part discuss mechanisms that are complementary to intrinsic motivations. The contributions of the sixth part introduce tools and experimental paradigms that can be used to investigate intrinsic motivations.


title: Molecular phylogenetic evidence for the reorganization of the Hyperiid amphipods, a diverse group of pelagic crustaceans

abstract: Within the crustaceans, the Amphipoda rank as one of the most speciose extant orders. Amphipods have successfully invaded and become major constituents of a variety of ecosystems. The hyperiid amphipods are classically defined as an exclusively pelagic group broadly inhabiting oceanic midwater environments and often having close associations with gelatinous zooplankton. As with other amphipod groups they have largely been classified based on appendage structures, however evidence suggests that at least some of these characters are the product of convergent evolution. Here we present the first multi-locus molecular phylogenetic assessment of relationships among the hyperiid amphipods. We sampled 51 species belonging to 16 of the 23 recognized hyperiidian families for three nuclear loci (18S, 28S, and H3) and mitochondrial COI. We performed both Bayesian Inference and Maximum Likelihood analyses of concatenated sequences. In addition, we also explored the utility of species-tree methods for reconstructing deep evolutionary histories using the Minimize Deep Coalescence (MDC) approach. Our results are compared with previous molecular analyses and traditional systematic groupings. We discuss these results within the context of adaptations correlated with the pelagic life history of hyperiid amphipods. Within the infraorder Physocephalata (Bowman and Gruner, 1973) we inferred support for three reciprocally monophyletic clades; the Platysceloidea, Vibilioidea, and Phronimoidea. Our results also place the enigmatic Cystisomatidae and Paraphronimidae at the base of the infraorder Physosomata (Bowman and Gruner, 1973) suggesting that Physosomata as traditionally recognized is paraphyletic. Based on our multilocus phylogeny, major rearrangements to existing taxonomic groupings of hyperiid amphipods are warranted.


title: A transmission control scheme for media access in sensor networks

abstract: We study the problem of media access control in the novel regime of sensor networks, where unique application behavior and tight constraints in computation power, storage, energy resources, and radio technology have shaped this design space to be very different from that found in traditional mobile computing regime. Media access control in sensor networks must not only be energy efficient but should also allow fair bandwidth allocation to the infrastructure for all nodes in a multihop network. We propose an adaptive rate control mechanism aiming to support these two goals and find that such a scheme is most effective in achieving our fairness goal while being energy efficient for both low and high duty cycle of network traffic.


title: The Shaping of Information by Visual Metaphors

abstract: The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization.


title: Overview of Power Integrity Solutions on Package and PCB: Decoupling and EBG Isolation

abstract: Mitigating power distribution network (PDN) noise is one of the main efforts for power integrity (PI) design in high-speed or mixed-signal circuits. Possible solutions, which are based on decoupling or isolation concept, for suppressing PDN noise on package or printed circuit board (PCB) levels are reviewed in this paper. Keeping the PDN impedance very low in a wide frequency range, except at dc, by employing a shunt capacitors, which can be in-chip, package, or PCB levels, is the first priority way for PI design. The decoupling techniques including the planes structure, surface-mounted technology decoupling capacitors, and embedded capacitors will be discussed. The isolation approach that keeps part of the PDN at high impedance is another way to reduce the PDN noise propagation. Besides the typical isolation approaches such as the etched slots and filter, the new isolation concept using electromagnetic bandgap structures will also be discussed.


title: What's in a Name? Using First Names as Features for Gender Inference in Twitter

abstract: Despite significant work on the problem of inferring a Twitter user’s gender from her online content, no systematic investigation has been made into leveraging the most obvious signal of a user’s gender: first name. In this paper, we perform a thorough investigation of the link between gender and first name in English tweets. Our work makes several important contributions. The first and most central contribution is two different strategies for incorporating the user’s self-reported name into a gender classifier. We find that this yields a 20% increase in accuracy over a standard baseline classifier. These classifiers are the most accurate gender inference methods for Twitter data developed to date. In order to evaluate our classifiers, we developed a novel way of obtaining gender-labels for Twitter users that does not require analysis of the user’s profile or textual content. This is our second contribution. Our approach eliminates the troubling issue of a label being somehow derived from the same text that a classifier will use to infer the label. Finally, we built a large dataset of gender-labeled Twitter users and, crucially, have published this dataset for community use. To our knowledge, this is the first gender-labeled Twitter dataset available for researchers. Our hope is that this will provide a basis for comparison of gender inference methods.


title: Text-Line Detection in Camera-Captured Document Images Using the State Estimation of Connected Components

abstract: Camera-based text processing has attracted considerable attention and numerous methods have been proposed. However, most of these methods have focused on the scene text detection problem and relatively little work has been performed on camera-captured document images. In this paper, we present a text-line detection algorithm for camera-captured document images, which is an essential step toward document understanding. In particular, our method is developed by incorporating state estimation (an extension of scale selection) into a connected component (CC)-based framework. To be precise, we extract CCs with the maximally stable extremal region algorithm and estimate the scales and orientations of CCs from their projection profiles. Since this state estimation facilitates a merging process (bottom–up clustering) and provides a stopping criterion, our method is able to handle arbitrarily oriented text-lines and works robustly for a range of scales. Finally, a text-line/non-text-line classifier is trained and non-text candidates (e.g., background clutters) are filtered out with the classifier. Experimental results show that the proposed method outperforms conventional methods on a standard dataset and works well for a new challenging dataset.


title: Named Entity Recognition from Biomedical Text Using SVM

abstract: Nowadays biomedical research is developing rapidly. A large number of biomedical knowledge exists in the form of unstructured text documents in various files. Named Entity Recognition (NER) from biomedical text is one of the basic task s of biomedical text mining, of which purpose is to recognize the name of the specified type from the collection of biomedical text. NER result is usually the processing object of other text mining. NER from biological text is the foundation of bioinformatics research. At present, the best f-measure of biological named entity recognition system has reached more than 80%, but is lower than general NER system which can reach about 90%. Here we use support vector machine (SVM), which is an effective and efficient tool to analyze data and recognize patterns, to recognize biomedical named entity. We get data set from GENIA corpus which is a collection of Medline abstracts. In the experiment, we get precision rate= 84.24% and recall rate=80.76% finally.


title: A wireless micro inertial measurement unit (IMU)

abstract: In this paper, we present a wireless micro inertial measurement unit (IMU) with the smallest volume and weight requirements available at the moment. With a size of 18mm × 16mm × 4 mm, this IMU provides full control over the data of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. It meets the design prerequisites of a space-saving design and eliminates the need of a hard-wired data communication while still being comparable to state of the art commercially available MEMS IMUs. A CC430 microcontroller sends the collected raw data to a base station wirelessly with a maximum sensor sample rate of 640 samples per second. Thereby, the IMU performance is optimized by moving data post processing to the base station. This development offers important features in embedded microsystem applications with their significant size and weight requirements.


Boosting and naive Bayesian learning


Using Machine Learning Algorithms for Author Profiling In Social Media


title: AdaBoost learning for human detection based on histograms of oriented gradients

abstract: We developed a novel learning-based human detection system, which can detect people having different sizes and orientations, under a wide variety of backgrounds or even with crowds. To overcome the affects of geometric and rotational variations, the system automatically assigns the dominant orientations of each block-based feature encoding by using the rectangular- and circular-type histograms of orientated gradients (HOG), which are insensitive to various lightings and noises at the outdoor environment. Moreover, this work demonstrated that Gaussian weight and tri-linear interpolation for HOG feature construction can increase detection performance. Particularly, a powerful feature selection algorithm, AdaBoost, is performed to automatically select a small set of discriminative HOG features with orientation information in order to achieve robust detection results. The overall computational time is further reduced significantly without any performance loss by using the cascade-ofrejecter structure, whose hyperplanes and weights of each stage are estimated by using the AdaBoost approach.


title: High performance CAN/FlexRay gateway design for in-vehicle network

abstract: A gateway in in-vehicle network (IVN) is an indispensable component as a conversion device between different communication protocols. Due to meet more complex network for IVN, how to improving the performance of gateway is more important. In this paper, a high efficient of CAN/FlexRay gateway using hardware/software (HW/SW) co-design method is proposed which is implemented with Xilinx FPGA system. The CAN controller and FlexRay controller are designed by SW and HW, respectively. The interface of HW and SW are used AXI-bus. The convert module is evaluated by a cost function. In addition, a transceiver PCB for connecting between the proposed gateway and external CAN/FlexRay-based ECUs is implemented. Experimental results show that the proposed gateway system can reduce at most 94.7% on execution time with related works which is more suited for IVN.


title: CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Cognitive and Communicative Abilities of Grey Parrots

abstract: Abstract This paper presents results of almost 30 years of study of the cognitive and communicative activities of Grey parrots ( Psittacus erithacus ), conventionally regarded as mindless mimics. These studies have demonstrated that Grey parrots can solve various cognitive tasks and acquire and use English speech in ways that often resemble those of very young children. Examples include the concepts of same/different, colour, size and shape. The parrot Alex can also recognize and distinguish numbers up to six, and spontaneously demonstrated his ability to grasp the concept of "none". Given the evolutionary distance between birds and mammals, these results have intriguing implications for the evolution of intelligence, the study of comparative intelligence, and the care and maintenance of birds held in captivity in zoos and as companion animals.


title: Intelligent parking technology adoption

abstract: Inefficient parking practices have costs associated with them that extend well beyond lost profits and frustrated customers. This dissertation focuses on why parking providers appear hesitant in adopting Intelligent Parking ::: Technology (IPT) that could help benefit all stakeholders. More specifically, this dissertation analyses the following question: ::: ::: Why do parking providers appear reluctant to adopt intelligent parking technologies? ::: ::: Intelligent Parking Technology (IPT) potentially offers drivers many benefits including faster parking times, more convenient payment options and safer parking lots. For parking providers, benefits of IPT implementation might include higher profits realized through increased ::: business process efficiencies such as automated payment collection. Society could potentially benefit from IPT through primary effects such as reduced traffic congestion, greenhouse gas emissions and driver frustration levels. It may also benefit through secondary effects where, for example, a government uses profits realized through increased efficiencies and spends it on things that will benefit the community such as better roads and more ::: green spaces. ::: ::: We define Intelligent Parking Technology (IPT) as technologies that are managed by a parking service provider and add value by adapting themselves to whatever a parking situation may be. Intelligent Parking Technology (IPT) includes those technologies that offer a unique ::: functionality such as giving customers the ability to pay for parking using their cellular telephone (m-commerce), automatically directs them to empty parking spaces or automates payment via smart cards. ::: ::: The parking providers investigated include businesses, schools, and governments. We consider technologies currently available to them as well market willingness to adopt them. ::: ::: The Delphi technique is used and consists of interviewing independent content experts such as parking services managers within various organizations. We also interview one senior level manager working for a company that licenses or sells Intelligent Parking Technology (IPT). ::: Questions formulated from these interviews are then brought forward and used in surveying drivers. Data is collected from the surveys and then analysed and interpreted through discussion with the independent content experts initially interviewed so that they may corroborate or disapprove the ::: findings. Their interpretations of the data are also considered in the study, as this research is primarily qualitative in nature. ::: ::: The driver survey included questions that explored the perceived value drivers might get from different parking technologies currently available to parking providers. Findings from the driver surveys clearly show that ::: drivers are more than willing to adopt Intelligent Parking Technology (IPT). ::: ::: In every case, the majority of drivers indicated that they would get at least some value from the specific parking technologies presented. This varied from a low of 60.9 percent of respondents indicating they would get value ::: from a robotic parking system to a high of 94.7 percent indicating they would receive value from a system that would direct them to empty parking spaces. ::: ::: Over 66 percent of drivers also stated that they would be willing to pay more for a parking space if Intelligent Parking Technology (IPT) added value for them. ::: ::: The senior manager from the parking technology company was not at all surprised by the driver survey results. Parking providers, on the other hand, seemed somewhat surprised by the survey findings and the follow-up interviews where these findings were discussed seemed to initiate a state of ::: reflection for them.


Multiword Expressions Dataset for Indian Languages.


title: Attack Tree Based Android Malware Detection with Hybrid Analysis

abstract: This paper proposes an Android malware detection approach based on attack tree. Attack tree model is extended to provide a novel way to organize and exploit behavior rules. Connections between attack goals and application capability are represented by an attack tree structure and behavior rules are assigned to every attack path in the attack tree. In this way, fine-grained and comprehensive static capability estimation and dynamic behavior detection can be achieved. This approach employs a hybrid static-dynamic analysis method. Static analysis tags attack tree nodes based on application capability. It filters the obviously benign applications and highlights the potential attacks in suspicious ones. Dynamic analysis selects rules corresponding to the capability and conducts detection according to runtime behaviors. In dynamic analysis, events are simulated to trigger behaviors based on application components, and hence it achieves high code coverage. Finally, in this way, we implement an automatic malware detection prototype system called AM Detector. The experiment result shows that the true positive rate is 88.14% and the false positive rate is as low as 1.80%.


title: Content-Centric Networking Management Based on Software Defined Networks: Survey

abstract: Content-centric networks (CCNs) have received a lot of interest as one of the major innovative future Internet paradigms. The CCN key feature is built around the named content which can be requested, replayed, routed by name, and stored through in-network caching. Software defined networking (SDN) represents a new concept that can leverage the research and innovation enabling migration to future Internet. This stream of thought can be explored through certain research projects. In this survey, we present the basic concept of each paradigm. We introduce a thorough description of CCN mechanisms as well as their related studies. Therefore, we highlight the benefit of combining CCN networks with SDN and OpenFlow. Then, we analyze some of the most prominent approaches combining the CCN and the OpenFlow.


title: Solar powered ZCS bidirectional buck-boost converter used in battery energy storage systems

abstract: The ZCS bidirectional buck boost converter for energy storage applications is a soft switched bidirectional converter with main switches, resonant capacitors and resonant inductors. ZCS or Zero Current Switching is a soft switching technique used to reduce the switching stresses. Switching stresses causes loss and to reduce this switches are turned on and off at zero current or zero voltage instants. In bidirectional buck boost converter the buck mode as well as the boost mode can be operated with bidirectional current and power flow capability. Renewable energy can be effectively utilized by having a Battery Energy Storage System (BESS) as the energy generation is not constant. The system can be mainly used for battery banks, electric vehicles etc. and also where there will be an application of both renewable and non renewable resources. Photo Voltaic panels are to be used as one of the input. The generated energy is stored in a BESS. This creates an energy management system. The bidirectional converter creates an interface between these exchanges. With the converter, the use of the stored energy can be done easily and when needed. To control the working of the bidirectional converter a control system has to be implemented.


title: Priming of semantic autobiographical knowledge: A case study of retrograde amnesia

abstract: Abstract The case of a 36-year-old man who suffers dense retrograde and anterograde amnesia as a result of closed-head injury that caused extensive damage to his left frontal-parietal and right parieto-occipital lobes is described. Patient K.C. has normal intelligence and relatively well-preserved perceptual, linguistic, short-term memory, and reasoning abilities. He possesses some fragmentary general knowledge about his autobiographical past, but he does not remember a single personal event or happening from any time of his life. He has some preserved expert knowledge related to the work he did for 3 years before the onset of amnesia, although he has no personal recollections from that period. Some features of K.C.'s retrograde amnesia can be interpreted in terms of the distinction between episodic and semantic memory, and in terms of the distinction between episodic and semantic autobiographical knowledge. K.C.'s semantic knowledge, but not his episodic knowledge, showed progressive improvement, or priming, in the course of the investigation.


title: Query-adaptive Video Summarization via Quality-aware Relevance Estimation

abstract: Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied. We address this problem by posing query-relevant summarization as a video frame subset selection problem, which lets us optimise for summaries which are simultaneously diverse, representative of the entire video, and relevant to a text query. We quantify relevance by measuring the distance between frames and queries in a common textual-visual semantic embedding space induced by a neural network. In addition, we extend the model to capture query-independent properties, such as frame quality. We compare our method against previous state of the art on textual-visual embeddings for thumbnail selection and show that our model outperforms them on relevance prediction. Furthermore, we introduce a new dataset, annotated with diversity and query-specific relevance labels. On this dataset, we train and test our complete model for video summarization and show that it outperforms standard baselines such as Maximal Marginal Relevance.


title: Approach to the Management of Patients With Hidradenitis Suppurativa: A Consensus Document

abstract: BACKGROUND ::: Hidradenitis suppurativa (HS) is a painful, debilitating, and poorly understood condition, which is suboptimally diagnosed, managed, and treated. Evidence supporting various treatment modalities is sparse. ::: ::: ::: OBJECTIVES ::: To incorporate scientific evidence and expert opinions to develop useful guidance for the evaluation and management of patients with HS. ::: ::: ::: METHODS ::: An expert panel of Canadian dermatologists and surgeons developed statements and recommendations based on available evidence and clinical experience. The statements and recommendations were subjected to analysis and refinement by the panel, and voting was conducted using a modified Delphi technique with a prespecified cutoff agreement of 75%. ::: ::: ::: RESULTS ::: Ten specific statements and recommendations were accepted by the expert panel. These were grouped into 4 domains: diagnosis and assessment, treatment and management, comorbidities and a multidisciplinary approach, and education. ::: ::: ::: CONCLUSIONS ::: These statements and recommendations will serve to increase awareness of HS and provide a framework for decisions involving diagnosis and management. Evidence suggests that antibacterial and anti-tumour necrosis factor therapies are effective in the treatment of HS. This is supported by the clinical experience of the authors. Further clinical research and the establishment of multidisciplinary management teams will continue to advance management of HS in Canada.


title: Explaining prediction models and individual predictions with feature contributions

abstract: We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method's usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method's explanations improved the participants' understanding of the model.


title: Love and the commitment problem in romantic relations and friendship

abstract: On the basis of the proposition that love promotes commitment, the authors predicted that love would motivate approach, have a distinct signal, and correlate with commitment-enhancing processes when relationships are threatened. The authors studied romantic partners and adolescent opposite-sex friends during interactions that elicited love and threatened the bond. As expected, the experience of love correlated with approach-related states (desire, sympathy). Providing evidence for a nonverbal display of love, four affiliation cues (head nods, Duchenne smiles, gesticulation, forward leans) correlated with self-reports and partner estimates of love. Finally, the experience and display of love correlated with commitment-enhancing processes (e.g., constructive conflict resolution, perceived trust) when the relationship was threatened. Discussion focused on love, positive emotion, and relationships. Things base and vile, holding no quantity, Love can transform to form and dignity, Love looks not with the eyes, but with the mind, And therefore is winged Cupid painted blind. —William Shakespeare, A Midsummer Night's Dream In Shakespeare's reflection on love, one finds a poignant truth that resonates with experience: In transforming the mundane into the sublime, love can seem blind, irrational, and disconnected from what seems to be true and real. This observation dovetails with our ensuing theoretical analysis of the momentary experience of love. Yet we also take exception with what the great bard has to say about this emotion. Unlike Shakespeare, we contend that people also see love with their eyes; that love has a physical side that is evident in movements of the face and the body that prompt the mind's more sublime operations. More specifically, we propose that the momentary experience of love helps intimate partners remain committed to one another (e.g., Frank, 1988; Steinberg, 1986). This treatment of love as a commitment device leads to the following hypotheses. In terms of the experience of love, we posited that love would correlate with approach-related states. In terms of the display of love, we expected that the experience of love would be encoded in a distinct


title: Feature selection for high-dimensional classification using a competitive swarm optimizer

abstract: When solving many machine learning problems such as classification, there exists a large number of input features. However, not all features are relevant for solving the problem, and sometimes, including irrelevant features may deteriorate the learning performance.Please check the edit made in the article title Therefore, it is essential to select the most relevant features, which is known as feature selection. Many feature selection algorithms have been developed, including evolutionary algorithms or particle swarm optimization (PSO) algorithms, to find a subset of the most important features for accomplishing a particular machine learning task. However, the traditional PSO does not perform well for large-scale optimization problems, which degrades the effectiveness of PSO for feature selection when the number of features dramatically increases. In this paper, we propose to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems. In addition, the CSO, which was originally developed for continuous optimization, is adapted to perform feature selection that can be considered as a combinatorial optimization problem. An archive technique is also introduced to reduce computational cost. Experiments on six benchmark datasets demonstrate that compared to the canonical PSO-based and a state-of-the-art PSO variant for feature selection, the proposed CSO-based feature selection algorithm not only selects a much smaller number of features, but result in better classification performance as well.


title: Turbocharging ambient backscatter communication

abstract: Communication primitives such as coding and multiple antenna processing have provided significant benefits for traditional wireless systems. Existing designs, however, consume significant power and computational resources, and hence cannot be run on low complexity, power constrained backscatter devices. This paper makes two main contributions: (1) we introduce the first multi-antenna cancellation design that operates on backscatter devices while retaining a small form factor and power footprint, (2) we introduce a novel coding mechanism that enables long range communication as well as concurrent transmissions and can be decoded on backscatter devices. We build hardware prototypes of the above designs that can be powered solely using harvested energy from TV and solar sources. The results show that our designs provide benefits for both RFID and ambient backscatter systems: they enable RFID tags to communicate directly with each other at distances of tens of meters and through multiple walls. They also increase the communication rate and range achieved by ambient backscatter systems by 100X and 40X respectively. We believe that this paper represents a substantial leap in the capabilities of backscatter communication.


title: Deep Learning for Extreme Multi-label Text Classification

abstract: Extreme multi-label text classification (XMTC) refers to the problem of assigning to each document its most relevant subset of class labels from an extremely large label collection, where the number of labels could reach hundreds of thousands or millions. The huge label space raises research challenges such as data sparsity and scalability. Significant progress has been made in recent years by the development of new machine learning methods, such as tree induction with large-margin partitions of the instance spaces and label-vector embedding in the target space. However, deep learning has not been explored for XMTC, despite its big successes in other related areas. This paper presents the first attempt at applying deep learning to XMTC, with a family of new Convolutional Neural Network (CNN) models which are tailored for multi-label classification in particular. With a comparative evaluation of 7 state-of-the-art methods on 6 benchmark datasets where the number of labels is up to 670,000, we show that the proposed CNN approach successfully scaled to the largest datasets, and consistently produced the best or the second best results on all the datasets. On the Wikipedia dataset with over 2 million documents and 500,000 labels in particular, it outperformed the second best method by 11.7%~15.3% in precision@K and by 11.5%~11.7% in NDCG@K for K = 1,3,5.


title: Predict Information Diffusion using a Latent Representation Space

abstract: We introduce a model for predicting the diffusion of content information on social media. When propagation is usually modeled on discrete graph structures, we introduce here a continuous diffusion model, where nodes in a diffusion cascade are projected onto a latent space with the property that their proximity in this space reflects the temporal diffusion process. We focus on the task of predicting contaminated users for an initial initial information source and provide preliminary results on differents datasets.


title: Adaptive 3-D object classification with reinforcement learning

abstract: We propose an adaptive approach to 3-D object classification. In this approach appropriate 3-D feature descriptor ::: ::: algorithms for 3-D point clouds are selected via reinforcement learning depending on properties of the ::: ::: objects to be classified. This approach is supposed to be able to learn strategies for an advantageous selection ::: ::: of 3-D point cloud descriptor algorithms in an autonomous and adaptive way, and thus is supposed to yield ::: ::: higher object classification rates in unfamiliar environments than any of the single algorithms alone. In addition, ::: ::: we expect our approach to be able to adapt to subsequently added 3-D feature descriptor algorithms as ::: ::: well as to autonomously learn new object categories when encountered in the environment without further user ::: ::: assistance. We describe the 3-D object classification pipeline based on local 3-D features and its integration ::: ::: into the reinforcement learning environment.


title: Towards work-integrated learning communities

abstract: In this paper we raise the concept of Work-Integrated Learning (WIL) as a means of supporting activities of competence development and learning in organisations. In order to illustrate our conceptual understanding of WIL, the paper includes empirical results from a case study in a consultant firm, Ypsilon, which operates in the education sector and delivers courses for IT-professionals. Ypsilon is at present undergoing a transition from having one big customer to being forced into a more flexible way of meeting the different demands from many new clients. The results highlights three main considerations believed to be critical for WIL activities: (i) A shift from an individual teaching culture to a social and collaborative culture. (ii ) Extending the role of technology from codifying competencies to supporting negotiation of practice. (iii) Adoption of holistic and integrated strategy that can cope with a dynamic business context. We conclude the paper by pointing at several implications for how the understanding and advancement of work-integrated learning can be approached in the future.


title: A Novel Approach to Trust Management in Unattended Wireless Sensor Networks

abstract: Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between sink visits. The absence of an online trusted third party implies that existing WSN trust management schemes are not applicable to UWSNs. In this paper, we propose a trust management scheme for UWSNs to provide efficient and robust trust data storage and trust generation. For trust data storage, we employ a geographic hash table to identify storage nodes and to significantly decrease storage cost. We use subjective logic based consensus techniques to mitigate trust fluctuations caused by environmental factors. We exploit a set of trust similarity functions to detect trust outliers and to sustain trust pollution attacks. We demonstrate, through extensive analyses and simulations, that the proposed scheme is efficient, robust and scalable.


title: Design issues in cross-coupled inverter sense amplifier

abstract: This paper presents an analytical approach to the design of CMOS cross-coupled inverter sense amplifiers. The effects of the equilibrating transistors and the tail current source on the speed of the sense amplifier are analyzed. An analysis of the offset due to mismatch in various parameters is performed, showing that a complete offset analysis has to account for the cell and bitline structure. A new figure of merit for the offset in the sense amplifier and several new design insights are introduced.


title: Joint entity and relation extraction based on a hybrid neural network

abstract: Entity and relation extraction is a task that combines detecting entity mentions and recognizing entities' semantic relationships from unstructured text. We propose a hybrid neural network model to extract entities and their relationships without any handcrafted features. The hybrid neural network contains a novel bidirectional encoder-decoder LSTM module (BiLSTM-ED) for entity extraction and a CNN module for relation classification. The contextual information of entities obtained in BiLSTM-ED further pass though to CNN module to improve the relation classification. We conduct experiments on the public dataset ACE05 (Automatic Content Extraction program) to verify the effectiveness of our method. The method we proposed achieves the state-of-the-art results on entity and relation extraction task. (C) 2017 Elsevier B.V. All rights reserved.


Kinematics and Statics Including Cable Sag for Large Cable-Suspended Robots


title: A cluster validity index for fuzzy clustering

abstract: Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity index for fuzzy clustering called a partition coefficient and exponential separation (PCAES) index. It uses the factors from a normalized partition coefficient and an exponential separation measure for each cluster and then pools these two factors to create the PCAES validity index. Considerations involving the compactness and separation measures for each cluster provide different cluster validity merits. In this paper, we also discuss the problem that the validity indexes face in a noisy environment. The efficiency of the proposed PCAES index is compared with several popular validity indexes. More information about these indexes is acquired in series of numerical comparisons and also three real data sets of Iris, Glass and Vowel. The results of comparative study show that the proposed PCAES index has high ability in producing a good cluster number estimate and in addition, it provides a new point of view for cluster validity in a noisy environment.


title: Semi-Calibrated Photometric Stereo

abstract: While conventional calibrated photometric stereo methods assume that light intensities and sensor exposures are known or unknown but identical across observed images, this assumption easily breaks down in practical settings due to individual light bulb's characteristics and limited control over sensors. This paper studies the effect of unknown and possibly non-uniform light intensities and sensor exposures among observed images on the shape recovery based on photometric stereo. This leads to the development of a “semi-calibrated” photometric stereo method, where the light directions are known but light intensities (and sensor exposures) are unknown. We show that the semi-calibrated photometric stereo becomes a bilinear problem, whose general form is difficult to solve, but in the photometric stereo context, there exists a unique solution for the surface normal and light intensities (or sensor exposures). We further show that there exists a linear solution method for the problem, and develop efficient and stable solution methods. The semi-calibrated photometric stereo is advantageous over conventional calibrated photometric stereo in accurate determination of surface normal, because it relaxes the assumption of known light intensity ratios/sensor exposures. The experimental results show superior accuracy of the semi-calibrated photometric stereo in comparison to conventional methods in practical settings.


title: An enhanced adaptive frequency locked loop for variable frequency controls

abstract: These days, variable frequency current mode controls are widely used in voltage regulators (VRs) for their higher light load efficiency, better small signal property and better transient response than fixed frequency current mode controls. One major issue for variable frequency control is its large switching frequency (fsw) variation with duty cycle. This variable fSW creates unpredictable noise in the system which is difficult to filter out, and hence not preferred by many frequency sensitive applications like mobile applications. While using variable frequency control e.g. constant on-time control (COT) in these applications, people use ‘Adaptive COT’ control which slowly changes the Ton with duty cycle at steady state operation to keep the steady state frequency constant. V in /V ref Feed-forward method and ‘Phase Locked Loop (PLL)’ method are very widely used to realize Adaptive COT control in the industry. Normally, V in /V ref feed-forward method is very simple and fast but naturally suffers inaccuracy issue for its feed-forward approach; while PLL method is very accurate but suffers from complicated additional loop adjustment. Therefore, this paper proposes a new accurate but simple frequency control loop for variable frequency controls. This paper also proposes two additional techniques for performance improvements in proposed method, namely, faster loop settling time by using feed-forward signal and better loop stability with duty cycle variation by adding adaptive loop gain control.


title: Gamma Processes, Stick-Breaking, and Variational Inference

abstract: While most Bayesian nonparametric models in machine learning have focused on the Dirichlet process, the beta process, or their variants, the gamma process has recently emerged as a useful nonparametric prior in its own right. Current inference schemes for models involving the gamma process are restricted to MCMC-based methods, which limits their scalability. In this paper, we present a variational inference framework for models involving gamma process priors. Our approach is based on a novel stick-breaking constructive definition of the gamma process. We prove correctness of this stick-breaking process by using the characterization of the gamma process as a completely random measure (CRM), and we explicitly derive the rate measure of our construction using Poisson process machinery. We also derive error bounds on the truncation of the infinite process required for variational inference, similar to the truncation analyses for other nonparametric models based on the Dirichlet and beta processes. Our representation is then used to derive a variational inference algorithm for a particular Bayesian nonparametric latent structure formulation known as the infinite Gamma-Poisson model, where the latent variables are drawn from a gamma process prior with Poisson likelihoods. Finally, we present results for our algorithms on nonnegative matrix factorization tasks on document corpora, and show that we compare favorably to both sampling-based techniques and variational approaches based on beta-Bernoulli priors.


The Structure and Performance of Efficient Interpreters


title: ‘Nordic’ Hamstrings Exercise – Engagement Characteristics and Training Responses

abstract: The present study examined the neuromuscular activation characteristics of the hamstrings during the 'Nordic' hamstrings exercise (NHE) and changes in the eccentric strength of the knee flexors with NHE training. Initially, the normalised root mean square electromyographic (EMG) activity of the hamstrings of both limbs during various phases (90-61°, 60-31° and 30-0° of knee extension) of the NHE were determined in 18 soccer players. Subsequently participants were randomly allocated to either a training (n=10) or control group. The isokinetic eccentric peak torques of the dominant and non-dominant limbs were recorded at 60, 120 and 240°/s pre- and post-training. The EMG values of both limbs were comparable (P=0.184) and greater EMG activity was recorded at more extended knee positions of the NHE (P=0.001). 4 weeks of NHE training significantly improved peak torque by up to 21% in all assessment conditions. Data indicate the hamstrings of both limbs are engaged identically during the NHE and training results in gains in the eccentric peak torque of the hamstrings of both limbs; these gains may augment the force that the hamstrings can withstand when forcefully stretched, attenuating injury risk.


title: Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective

abstract: Accurate and infrastructure-free indoor positioning can be very useful in a variety of applications. However, most existing approaches (e.g., WiFi and infrared-based methods) for indoor localization heavily rely on infrastructure, which is neither scalable nor pervasively available. In this paper, we propose a novel indoor localization and tracking approach, termed VMag, that does not require any infrastructure assistance. The user can be localized while simply holding a smartphone. To the best of our knowledge, the proposed method is the first exploration of fusing geomagnetic and visual sensing for indoor localization. More specifically, we conduct an in-depth study on both the advantageous properties and the challenges in leveraging the geomagnetic field and visual images for indoor localization. Based on these studies, we design a context-aware particle filtering framework to track the user with the goal of maximizing the positioning accuracy. We also introduce a neural-network-based method to extract deep features for the purpose of indoor positioning. We have conducted extensive experiments on four different indoor settings including a laboratory, a garage, a canteen, and an office building. Experimental results demonstrate the superior performance of VMag over the state of the art with these four indoor settings.


title: Steganography -- The Art of Hiding Data

abstract: Given the shear volume of data stored and transmitted electronically in the world today, it is no surprise that countless methods of protecting such data have evolved. One lesser-known but rapidly growing method is steganography, the art and science of hiding information so that it does not even appear to exist. Moreover, in an ideal world we would all be able to openly send encrypted email or files to each other with no fear of reprisals. However, there are often cases when this is not possible, either because you are working for a company that does not allow encrypted emails or perhaps the local government does not approve of encrypted communication. This is one of the cases where Steganography can help hide the encrypted messages, images, keys, secret data, etc. This paper discusses the purpose of steganography. Explains how steganography is related to cryptography as well as what it can and cannot be used for. It also discusses a brief history of steganography. In addition, some of the tools and software used in steganography are demonstrated and including some discussion of the most popular algorithms involved in these tools. This paper further explains the advantages and disadvantages, as well as, strengths and weaknesses in the use of steganography.


Security Attacks and Solutions in Clouds


title: Parallel bargrams for consumer-based information exploration and choice

abstract: In this paper we introduce multidimensional visualization and interaction techniques that are an extension to related work in parallel histograms and dynamic querying. Bargrams are, in effect, histograms whose bars have been tipped over and lined up end-to-end. We discuss affordances of parallel bargrams in the context of systems that support consumer-based information exploration and choice based on the attributes of the items in the choice set. Our tool called EZChooser has enabled a number of prototypes in such domains as Internet shopping, investment decisions, college choice, and so on, and a limited version has been deployed for car shopping. Evaluations of the techniques include an experiment indicating that trained users prefer EZChooser over static tables for choice tasks among sets of 50 items with 7-9 attributes.


title: Multi-level hermite variational interpolation and quasi-interpolation

abstract: Based on the Hermite variational implicit surface reconstruction presented in Pan et al. (Science in China Series F: Information Sciences 52(2):308---315, 2009), we propose a multi-level interpolation method to overcome the problems resulted from using compactly supported radial basis functions (CSRBFs). In addition, we present a multi-level quasi-interpolation method which directly uses normal vectors to construct non-zero constraints and avoids solving any linear system, a common step of variational surface reconstruction, and leads to a fast and stable surface reconstruction from scattered points. With adaptive support size, our approach is robust and can successfully reconstruct surfaces on non-uniform and noisy point sets. Moreover, as the computation of quasi-interpolation is independent for each point, it can be easily parallelized on multi-core CPUs.


A review on task planning in Human-Robot Teams


title: Granny and the robots: ethical issues in robot care for the elderly

abstract: The growing proportion of elderly people in society, together with recent advances in robotics, makes the use of robots in elder care increasingly likely. We outline developments in the areas of robot applications for assisting the elderly and their carers, for monitoring their health and safety, and for providing them with companionship. Despite the possible benefits, we raise and discuss six main ethical concerns associated with: (1) the potential reduction in the amount of human contact; (2) an increase in the feelings of objectification and loss of control; (3) a loss of privacy; (4) a loss of personal liberty; (5) deception and infantilisation; (6) the circumstances in which elderly people should be allowed to control robots. We conclude by balancing the care benefits against the ethical costs. If introduced with foresight and careful guidelines, robots and robotic technology could improve the lives of the elderly, reducing their dependence, and creating more opportunities for social interaction


title: Tourism Marketing through Virtual Environment Experience

abstract: This article proposes the develop of a dynamic virtual environment that with the consumption of real time data about the state of a place, offer an immersion to the tourist like be at the desired location. The development implements a communication and loader structure from many information sources, manual information data loaded from mobile devices and data loader from collecting equipment that get environmental and atmospheric data. The virtual reality application use Google Maps, and worldwide heightmap to get 3D geographic map models; HTC VIVE and Oculus SDK for support virtual reality experience; and weather API to show the weather information from the desired location in real time. In addition, the proposed virtual reality application emphasizes user interaction on the virtual environment by displaying dynamic and up-to-date information about tourism services.


title: Towards a personalized, scalable, and exploratory academic recommendation service

abstract: Literature search is an integral part of the academic research. Academic recommendation services have been developed to help researchers with their literature search, many of which only provide a text-based search functionality. Such services are suitable for a first-level bibliographic search; however, they lack the benefits of today's recommendation engines. In this paper, we identify three important properties that an academic recommendation service could provide for better literature search: personalization, scalability, and exploratory search. With these objectives in mind, we present a web service called theadvisor which helps the users build a strong bibliography by extending the document set obtained after a first-level search. Along with an efficient and personalized recommendation algorithm, the service also features result diversification, relevance feedback, visualization for exploratory search. We explain the design criteria and rationale we employed to make the theadvisor a useful and scalable web service with a thorough evaluation.


title: USSD: The Third Universal App

abstract: In this paper we argue for the use of Unstructured Supplementary Service Data (USSD) as a platform for universal cell phone applications. We examine over a decade of ICT4D research, analyzing how USSD can extend and complement current uses of IVR and SMS for data collection, messaging, information access, social networking and complex user initiated transactions. Based on these findings we identify situations when a mobile based project should consider using USSD with increasingly common third party gateways over other mediums. This analysis also motivates the design and implementation of an open source library for rapid development of USSD applications. Finally, we explore three USSD use cases, demonstrating how USSD opens up a design space not available with IVR or SMS.


title: Why information security is hard - an economic perspective

abstract: According to one common view, information security comes down to technical measures. Given better access control policy models, formal proofs of cryptographic protocols, approved firewalls, better ways of detecting intrusions and malicious code, and better tools for system evaluation and assurance, the problems can be solved. The author puts forward a contrary view: information insecurity is at least as much due to perverse incentives. Many of the problems can be explained more clearly and convincingly using the language of microeconomics: network externalities, asymmetric information, moral hazard, adverse selection, liability dumping and the tragedy of the commons.


title: Lie experts' beliefs about nonverbal indicators of deception

abstract: Beliefs about behavioral clues to deception were investigated in 212 people, consisting of prisoners, police detectives, patrol police officers, prison guards, customs officers, and college students. Previous studies, mainly conducted with college students as subjects, showed that people have some incorrect beliefs about behavioral clues to deception. It was hypothesized that prisoners would have the best notion about clues of deception, due to the fact that they receive the most adequate feedback about successful deception strategies. The results supported this hypothesis.


title: Machine learning and systems genomics approaches for multi-omics data

abstract: In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.


title: Hybrid CDN-P2P architectures for live video streaming: Comparative study of connected and unconnected meshes

abstract: There are two main scalable methods for streaming live video over the Internet: Content Delivery Networks (CDNs) and Peer-to-Peer (P2P) networks. Though both have their own problems, P2P streaming systems challenge delivering video with constant quality and CDNs approaches require deployment of large number of servers throughout the Internet that is costly. Recently, using hybrid architectures based on both CDN and P2P networks has shown to be an efficient approach for large-scale video distribution over the Internet. This paper is compared the performance of two main hybrid CDN-P2P architectures includes: (i) CDN-P2P unconnected mesh in which independent P2P mesh networks are constructed under each CDN node, and (ii) CDN-P2P connected mesh in which CDN nodes and peers participate in construction of a single P2P mesh network. The comparison is preformed in addition, to the pure mesh-based P2P video streaming, using extensive simulation and based on different QoS metrics.


title: Occupational Therapy Effects on Visual-Motor Skills in Preschool Children

abstract: OBJECTIVE. The purpose of this study was to evaluate the assumption that preschool children who receive occupational therapy will demonstrate significant improvement in their visual-motor skills as measured on the Developmental Test of Visual-Motor Integration (VMI) and the two supplemental Visual Perception and Motor Coordination tests. METHOD. Preschool children with developmental delays (n = 12) received occupational therapy a minimum of one individual 30-minute session, and one group 30-minute session per week for 1 school year. Their performance was compared to that of two control groups; preschool students without disabilities who received occupational therapy (n = 16) for one 30-minute group session per week and students without disabilities (n = 15) who received no occupational therapy. The VMI and two supplemental tests were administered three times to each student, at the beginning, middle, and end of school year. RESULTS. Planned comparison tests showed that students with developmental delays demonstrated statistically significant improvement in visual-motor skills and developed skills at a rate faster than expected when compared to typically developing peers on the VMI. The effect size for preschool students without disabilities who received occupational therapy exceeded the effect size for the VMI and Visual Perception supplemental test for the preschool students without disabilities who received no therapy, although, the difference in the post-test performance of these two groups was not statistically significant. DISCUSSION. The results of this study demonstrate that intervention, including occupational therapy, can effectively improve visual-motor skills in preschool-aged children.


title: AAAI: an Argument Against Artificial Intelligence

abstract: The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI.


title: Electric contacts inspection using machine vision

abstract: Machine vision is an excellent tool for inspecting a variety of industrial items such as textiles, printed circuit boards, electric components, labels, integrated circuits (IC), machine tools and fruits. In this paper, we propose machine vision-based inspection system for electric contact (EC), which are popularly used in switches, breakers and relays, and are important components in the electrical industry. The proposed system consists of three sub-systems, which inspect the top, side, and bottom surfaces of electric contact for different types of defects respectively. The system acquires the digital image of three views and classifies the surface defects including cracks, breaks, and scratches. For each view, this study develops different image pre-processing and feature extraction methods to enhance and detect the surface defects. The proposed system was implemented and verified using 229 samples collected from the EC production lines. Experimental results show the proposed system is effective and efficient in identifying EC defects.


The Impact of Brand Awareness on Consumer Purchase Intention: The Mediating Effect of Perceived Quality and Brand Loyalty’’The


title: Robust Partitioning Policies of Multiprocessor Systems

abstract: Abstract Various techniques for multiprogramming parallel multiprocessor systems have been proposed recently as a way to improve performance. A natural approach is to divide the set of processing elements into independent partitions, and simultaneously execute a different parallel program in each partition. Several issues arise, including the determination of the optimal number of programs allowed to execute simultaneously (i.e., the number of partitions) and the corresponding partition sizes. This can be done statically, dynamically, or adaptively, depending on the system and workload characteristics. In this paper several adaptive partitioning policies are evaluated. Their behavior, as well as the behavior of static policies, is investigated using real parallel programs. The policy applicability to actual systems is addressed, and implementation results of the proposed policies on an iPSC/2 hypercube system are reported. The concept of robustness (i.e., the ability to perform well on a wide range of workload types over a wide range of arrival rates) is presented and quantified. Relative rankings of the policies are obtained, depending on the specific workload characteristics. A trade-off is shown between potential performance and the amount of knowledge of the workload characteristics required to select the best policy. A policy that performs best when such knowledge of workload parallelism and/or arrival rate is not available is proposed as the most robust of those analyzed.


ANALYSING ELECTRONIC WORD-OF-MOUTH ( eWOM ) IN SOCIAL MEDIA FOR CONSUMER INSIGHTS – A MULTIDISCIPLINARY APPROACH


title: A semi-supervised color image segmentation method

abstract: A new color image segmentation algorithm based on semi-supervised clustering is proposed, which integrates limited human assistance, a user indicates the relationship of some different regions in an image by mouse, to get the final accurate segmentation result which satisfies the prior segmentation constraints. The algorithm first has the image quantified and then clusters in the quantified color space with prior segmentation information. Experiment results show that the proposed algorithm is effective and has high value of utility.


Some Notes on Modeling the Semantic Flow of Narratives


title: An Improved Smartphone-Based Non-Participatory Crowd Monitoring System in Smart Environments

abstract: Mobile Crowd Sensing and Computing (MCSC) is a replacement of static sensing infrastructure by user's mobile sensor-enhanced devices. MCSC collects user's local knowledge such as local information, ambient, and traffic conditions using sensor-enabled devices. The collected information is further aggregated and transferred to the cloud for detailed analysis. In this paper, we propose a Smartphone-based non-participatory crowd monitoring system, named CrowdTrack, to monitor the movement patterns of one or more persons (non-participatory) using unmodified Smartphones in a densely crowded environment. CrowdTrack uses the Smartphone as a sensing unit without any hardware modification to extract the MAC ids from the wireless probe requests emitted from the users' devices. MAC ids are stored and processed locally for short-term analysis and then the filtered data is uploaded to the server for better analysis and visualization. We have also developed a real-time testbed to identify mobility patterns in the data collected from our Institute campus and it is deployed to find the visiting sequences of students. Real-time experiments on a proof-of-concept prototype testbed with our dataset show the usability of our proposed system.


title: Effectiveness of Virtual Reality Exposure Therapy for Active Duty Soldiers in a Military Mental Health Clinic

abstract: Exposure therapy is an evidence-based treatment for posttraumatic stress disorder (PTSD), but research evaluating its effectiveness with active duty service members is limited. This report examines the effectiveness of virtual reality exposure therapy (VRE) for active duty soldiers (N = 24) seeking treatment following a deployment to Iraq or Afghanistan. Relative to their pretreatment self-reported symptoms on the PTSD Checklist, Military Version (M = 60.92; SD = 11.03), patients reported a significant reduction at posttreatment (M = 47.08; SD = 12.70; p < .001). Sixty-two percent of patients (n = 15) reported a reliable change of 11 points or more. This study supports the effectiveness of exposure therapy for active duty soldiers and extends previous research on VRE to this population.


title: Shared understanding for collaborative control

abstract: This paper presents results from three experiments in which human operators were teamed with a mixed-initiative robot control system to accomplish various indoor search and exploration tasks. By assessing human workload and error together with overall performance, these experiments provide an objective means to contrast different modes of robot autonomy and to evaluate both the usability of the interface and the effectiveness of autonomous robot behavior. The first experiment compares the performance achieved when the robot takes initiative to support human driving with the opposite case when the human takes initiative to support autonomous robot driving. The utility of robot autonomy is shown through achievement of better performance when the robot is in the driver's seat. The second experiment introduces a virtual three-dimensional (3-D) map representation that supports collaborative understanding of the task and environment. When used in place of video, the 3-D map reduced operator workload and navigational error. By lowering bandwidth requirements, use of the virtual 3-D interface enables long-range, nonline-of-sight communication. Results from the third experiment extend the findings of experiment 1 by showing that collaborative control can increase performance and reduce error even when the complexity of the environment is increased and workload is distributed amongst multiple operators.