title: Encoding bird's trajectory using Recurrent Neural Networks

abstract: Recurrent Neural Networks (RNNs) are currently state of art tools for processing and classifying data sequences. This work aims to exploit these capabilities in Long-Short Term Memory (LSTM) networks which are a powerful variant of RNNs for encoding the birds' trajectory data into state vectors. These vectors should encapsulate the contextual information about the immediate trajectory coordinates. Therefore, they can generate new trajectory points based on their state and the latest output. However, probabilistic behavior of birds, effects of environment and noisy nature of measurements pose challenges for training and testing of the LSTM network models. This study solely focuses on the effects of spatial context and their significance in subsequent outputs to achieve compact representation of the traversed trajectory. Therefore, trajectory coordinates of birds were used as input to LSTM networks to learn spatial path features encoded in hidden vectors of the network. In the end, t-SNE method is used to visualize the state vectors in lower dimensional space embeddings and It was observed that these vectors contained contextual information about the traversed path.


title: Impedance Control of an Intrinsically Compliant Parallel Ankle Rehabilitation Robot

abstract: Robot-aided physical therapy should encourage subject’s voluntary participation to achieve rapid motor function recovery. In order to enhance subject’s cooperation during training sessions, the robot should allow deviation in the prescribed path depending on the subject’s modified limb motions subsequent to the disability. In the present work, an interactive training paradigm based on the impedance control was developed for a lightweight intrinsically compliant parallel ankle rehabilitation robot. The parallel ankle robot is powered by pneumatic muscle actuators (PMAs). The proposed training paradigm allows the patients to modify the robot imposed motions according to their own level of disability. The parallel robot was operated in four training modes namely position control, zero-impedance control, nonzero-impedance control with high compliance, and nonzero-impedance control with low compliance to evaluate the performance of proposed control scheme. The impedance control scheme was evaluated on 10 neurologically intact subjects. The experimental results show that an increase in robotic compliance encouraged subjects to participate more actively in the training process. This work advances the current state of the art in the compliant actuation of parallel ankle rehabilitation robots in the context of interactive training.


title: A wideband coplanar stripline to microstrip transition

abstract: A wideband coplanar stripline (CPS)-to-microstrip line was developed. The transition has a simple structure for the ease of fabrication with low cost. The measured performance of two back-to-back transitions exhibits an insertion loss of less than 3 dB and a return loss of better than 10 dB over a bandwidth from 1.3 GHz to 13.3 GHz (1:10.2). For narrower bandwidth, an insertion loss of less than 1 dB with a return loss of better than 10 dB was achieved from 1.4 GHz to 7.3 GHz (1:5.2).


title: Virtual organization for supply chain integration: Two cases in the textile and fashion retailing industry

abstract: A supply chain is an alternative form with competitive entity consisting of a group of companies in the business environment. Some supply chain projects are initiated by associated firms while some are started by the head office of multinational enterprises in order to integrate the diversified subordinates. This study focuses on the investigation on the supply chain structures within the boundaries of two multinational textile enterprises, respectively. One enterprise tries to integrate the market side by merging a brand owner. The other seeks an integration solution to compensate from its loss of control of sub-manufacturing sites which during corporate expansion were registered as independent firms. Both enterprises have initiated their global logistics management projects in order to balance the demand and supply. By participating in the two projects, our research indicates the different barriers of integrating toward the upstream and downstream supply chains and provides a mutual solution by building up the e-Fashion global supply chains. Similarities and dissimilarities of both cases towards the virtual organization approach are also addressed in this paper.


ARTEMIS: A Vision for Remote Triage and Emergency Management Information Integration


title: Deep Transfer Network: Unsupervised Domain Adaptation

abstract: Domain adaptation aims at training a classifier in one dataset and applying it to a related but not identical dataset. One successfully used framework of domain adaptation is to learn a transformation to match both the distribution of the features (marginal distribution), and the distribution of the labels given features (conditional distribution). In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process. ::: This is achieved by two types of layers in DTN: the shared feature extraction layers which learn a shared feature subspace in which the marginal distributions of the source and the target samples are drawn close, and the discrimination layers which match conditional distributions by classifier transduction. We also show that DTN has a computation complexity linear to the number of training samples, making it suitable to large-scale problems. By combining the best paradigms in both worlds (deep neural networks in recognition, and matching marginal and conditional distributions in domain adaptation), we demonstrate by extensive experiments that DTN improves significantly over former methods in both execution time and classification accuracy.


title: Cognitive control, cognitive reserve, and memory in the aging bilingual brain

abstract: In recent years bilingualism has been linked to both advantages in executive control and positive impacts on aging. Such positive cognitive effects of bilingualism have been attributed to the increased need for language control during bilingual processing and increased cognitive reserve, respectively. However, a mechanistic explanation of how bilingual experience contributes to cognitive reserve is still lacking. The current paper proposes a new focus on bilingual memory as an avenue to explore the relationship between executive control and cognitive reserve. We argue that this focus will enhance our understanding of the functional and structural neural mechanisms underlying bilingualism-induced cognitive effects. With this perspective we discuss and integrate recent cognitive and neuroimaging work on bilingual advantage, and suggest an account that links cognitive control, cognitive reserve, and brain reserve in bilingual aging and memory.


title: Automated parameter optimization of classification techniques for defect prediction models

abstract: Defect prediction models are classifiers that are trained to identify defect-prone software modules. Such classifiers have configurable parameters that control their characteristics (e.g., the number of trees in a random forest classifier). Recent studies show that these classifiers may underperform due to the use of suboptimal default parameter settings. However, it is impractical to assess all of the possible settings in the parameter spaces. In this paper, we investigate the performance of defect prediction models where Caret --- an automated parameter optimization technique --- has been applied. Through a case study of 18 datasets from systems that span both proprietary and open source domains, we find that (1) Caret improves the AUC performance of defect prediction models by as much as 40 percentage points; (2) Caret-optimized classifiers are at least as stable as (with 35% of them being more stable than) classifiers that are trained using the default settings; and (3) Caret increases the likelihood of producing a top-performing classifier by as much as 83%. Hence, we conclude that parameter settings can indeed have a large impact on the performance of defect prediction models, suggesting that researchers should experiment with the parameters of the classification techniques. Since automated parameter optimization techniques like Caret yield substantially benefits in terms of performance improvement and stability, while incurring a manageable additional computational cost, they should be included in future defect prediction studies.


title: A Yokeless Linear Permanent Magnet Machine

abstract: This paper presents a novel yokeless linear permanent magnet (PM) machine topology. The armature windings employ fractional slot concentric configuration and can be prewound on separate teeth consecutively or alternatively with a high slot filling factor. A number of advantages are obtained from the proposed yokeless topology. First, the volume of the iron core is reduced and more space is available for the windings, which leads to higher force density, lower copper loss, and higher efficiency. The absence of the yoke iron also reduces the iron loss and like-hood of saturation, and hence improves efficiency and linearity of thrust force characteristic. Without the yoke, the slot leakage flux and the magnetomotive force drop in the iron core are also reduced and consequently the utilization of PMs and the machine force capability are improved. These benefits are demonstrated through a design case study.


title: Recurrent Binary Embedding for GPU-Enabled Exhaustive Retrieval from Billion-Scale Semantic Vectors

abstract: Rapid advances in GPU hardware and multiple areas of Deep Learning open up a new opportunity for billion-scale information retrieval with exhaustive search. Building on top of the powerful concept of semantic learning, this paper proposes a Recurrent Binary Embedding (RBE) model that learns compact representations for real-time retrieval. The model has the unique ability to refine a base binary vector by progressively adding binary residual vectors to meet the desired accuracy. The refined vector enables efficient implementation of exhaustive similarity computation with bit-wise operations, followed by a near-lossless k-NN selection algorithm, also proposed in this paper. The proposed algorithms are integrated into an end-to-end multi-GPU system that retrieves thousands of top items from over a billion candidates in real-time. The RBE model and the retrieval system were evaluated with data from a major paid search engine. When measured against the state-of-the-art model for binary representation and the full precision model for semantic embedding, RBE significantly outperformed the former, and filled in over 80% of the AUC gap in-between. Experiments comparing with our production retrieval system also demonstrated superior performance. While the primary focus of this paper is to build RBE based on a particular class of semantic models, generalizing to other types is straightforward, as exemplified by two different models at the end of the paper.


title: Thermoelectric Cooling-Aided Bead Geometry Regulation in Wire and Arc-Based Additive Manufacturing of Thin-Walled Structures

abstract: Wire and arc-based additive manufacturing (WAAM) is a rapidly developing technology which employs a welding arc to melt metal wire for additive manufacturing purposes. During WAAM of thin-walled structures, as the wall height increases, the heat dissipation to the substrate is slowed down gradually and so is the solidification of the molten pool, leading to variation of the bead geometry. Though gradually reducing the heat input via adjusting the process parameters can alleviate this issue, as suggested by previous studies, it relies on experience to a large extent and inevitably sacrifices the deposition rate because the wire feed rate is directly coupled with the heat input. This study introduces for the first time an in-process active cooling system based on thermoelectric cooling technology into WAAM, which aims to eliminate the difference in heat dissipation between upper and lower layers. The case study shows that, with the aid of thermoelectric cooling, the bead width error is reduced by 56.8%, the total fabrication time is reduced by 60.9%, and the average grain size is refined by 25%. The proposed technique provides new insight into bead geometry regulation during WAAM with various benefits in terms of geometric accuracy, productivity, and microstructure.


title: An efficient design and implementation of LSM-tree based key-value store on open-channel SSD

abstract: Various key-value (KV) stores are widely employed for data management to support Internet services as they offer higher efficiency, scalability, and availability than relational database systems. The log-structured merge tree (LSM-tree) based KV stores have attracted growing attention because they can eliminate random writes and maintain acceptable read performance. Recently, as the price per unit capacity of NAND flash decreases, solid state disks (SSDs) have been extensively adopted in enterprise-scale data centers to provide high I/O bandwidth and low access latency. However, it is inefficient to naively combine LSM-tree-based KV stores with SSDs, as the high parallelism enabled within the SSD cannot be fully exploited. Current LSM-tree-based KV stores are designed without assuming SSD's multi-channel architecture. To address this inadequacy, we propose LOCS, a system equipped with a customized SSD design, which exposes its internal flash channels to applications, to work with the LSM-tree-based KV store, specifically LevelDB in this work. We extend LevelDB to explicitly leverage the multiple channels of an SSD to exploit its abundant parallelism. In addition, we optimize scheduling and dispatching polices for concurrent I/O requests to further improve the efficiency of data access. Compared with the scenario where a stock LevelDB runs on a conventional SSD, the throughput of storage system can be improved by more than 4X after applying all proposed optimization techniques.


title: Ergogenic effects of caffeine and sodium bicarbonate supplementation on intermittent exercise performance preceded by intense arm cranking exercise

abstract: Caffeine and sodium bicarbonate ingestion have been suggested to improve high-intensity intermittent exercise, but it is unclear if these ergogenic substances affect performance under provoked metabolic acidification. To study the effects of caffeine and sodium bicarbonate on intense intermittent exercise performance and metabolic markers under exercise-induced acidification, intense arm-cranking exercise was performed prior to intense intermittent running after intake of placebo, caffeine and sodium bicarbonate. Male team-sports athletes (n = 12) ingested sodium bicarbonate (NaHCO3; 0.4 g.kg−1 b.w.), caffeine (CAF; 6 mg.kg−1 b.w.) or placebo (PLA) on three different occasions. Thereafter, participants engaged in intense arm exercise prior to the Yo-Yo intermittent recovery test level-2 (Yo-Yo IR2). Heart rate, blood lactate and glucose as well as rating of perceived exertion (RPE) were determined during the protocol. CAF and NaHCO3 elicited a 14 and 23% improvement (P < 0.05), respectively, in Yo-Yo IR2 performance, post arm exercise compared to PLA. The NaHCO3 trial displayed higher [blood lactate] (P < 0.05) compared to CAF and PLA (10.5 ± 1.9 vs. 8.8 ± 1.7 and 7.7 ± 2.0 mmol.L−1, respectively) after the Yo-Yo IR2. At exhaustion CAF demonstrated higher (P < 0.05) [blood glucose] compared to PLA and NaHCO3 (5.5 ± 0.7 vs. 4.2 ± 0.9 vs. 4.1 ± 0.9 mmol.L−1, respectively). RPE was lower (P < 0.05) during the Yo-Yo IR2 test in the NaHCO3 trial in comparison to CAF and PLA, while no difference in heart rate was observed between trials. Caffeine and sodium bicarbonate administration improved Yo-Yo IR2 performance and lowered perceived exertion after intense arm cranking exercise, with greater overall effects of sodium bicarbonate intake.


title: HTTP-CoAP cross protocol proxy: an implementation viewpoint

abstract: In recent years there has been growing interest for the vision of a so-called Web of Things, which pursues the access to a wide variety of everyday objects through a regular web browser. We believe that this vision is right now at a key moment in its realization, analogous to the '90 for the World Wide Web. The Constrained Application Protocol (CoAP), a web protocol optimized for constrained networks and devices, will hopefully help wireless sensor nodes, a.k.a., smart objects, to become active citizens of the web. Indeed, CoAP has been designed to be easily mapped to HTTP. In this paper we discuss the HTTP mapping of CoAP, and we highlight its different facets and issues. We will describe what are the currently open issues, spanning the deployment of an HTTP-CoAP proxy to the security mapping issues. We also share the authors' experience gained in the design and implementation of two distinct HTTP-CoAP proxies.


title: Joint Object Segmentation and Depth Upsampling

abstract: With the advent of powerful ranging and visual sensors, nowadays, it is convenient to collect sparse 3-D point clouds and aligned high-resolution images. Benefitted from such convenience, this letter proposes a joint method to perform both depth assisted object-level image segmentation and image guided depth upsampling. To this end, we formulate these two tasks together as a bi-task labeling problem, defined in a Markov random field. An alternating direction method (ADM) is adopted for the joint inference, solving each sub-problem alternatively. More specifically, the sub-problem of image segmentation is solved by Graph Cuts, which attains discrete object labels efficiently. Depth upsampling is addressed via solving a linear system that recovers continuous depth values. By this joint scheme, robust object segmentation results and high-quality dense depth maps are achieved. The proposed method is applied to the challenging KITTI vision benchmark suite, as well as the Leuven dataset for validation. Comparative experiments show that our method outperforms stand-alone approaches.


title: Spatial Pyramid Pooling in Structured Sparse Representation for Flame Detection

abstract: Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored. This paper presents a novel solid solution based on sparse representation with spatial pyramid pooling. Traditional sparse representation, as one of prevalent feature learning methods, is successfully applied for object detection. But it has some intrinsic defects. Firstly, it requires fixed input image size. Secondly, the accuracy of detection heavily depends on discriminative dictionary learning and feature coding. At last, it is usually very time-consuming. In this paper, we have proposed a novel dictionary learning method with the structure sparsity constraint to train a discriminative dictionary. In feature coding stage, we compute sparse codes of each patch with dictionaries learned from data and pool them to form local histogram in spatial pyramid manner. At lat, the feature vector is pipelined into a linear SVM classifier to train the model. For improving the efficiency, we also adopt the selective search approach to generate the candidate region proposals in the preprocessing stage. In processing test images, our method achieved better or comparable accuracy to the state-of-the-art on FlameDetection2010 Dataset.


title: Sentence simplification, compression, and disaggregation for summarization of sophisticated documents

abstract: Sophisticated documents like legal cases and biomedical articles can contain unusually long sentences. Extractive summarizers can select such sentences-potentially adding hundreds of unnecessary words to the summary-or exclude them and lose important content. Sentence simplification or compression seems on the surface to be a promising solution. However, compression removes words before the selection algorithm can use them, and simplification generates sentences that may be ambiguous in an extractive summary. We therefore compare the performance of an extractive summarizer selecting from the sentences of the original document with that of the summarizer selecting from sentences shortened in three ways: simplification, compression, and disaggregation, which splits one sentence into several according to rules designed to keep all meaning. We find that on legal cases and biomedical articles, these shortening methods generate ungrammatical output. Human evaluators performed an extrinsic evaluation consisting of comprehension questions about the summaries. Evaluators given compressed, simplified, or disaggregated versions of the summaries answered fewer questions correctly than did those given summaries with unaltered sentences. Error analysis suggests 2 causes: Altered sentences sometimes interact with the sentence selection algorithm, and alterations to sentences sometimes obscure information in the summary. We discuss future work to alleviate these problems.


Adversarial Multitask Learning of Deep Neural Networks for Robust Speech Recognition


title: Robust Web Image Annotation via Exploring Multi-Facet and Structural Knowledge

abstract: Driven by the rapid development of Internet and digital technologies, we have witnessed the explosive growth of Web images in recent years. Seeing that labels can reflect the semantic contents of the images, automatic image annotation, which can further facilitate the procedure of image semantic indexing, retrieval, and other image management tasks, has become one of the most crucial research directions in multimedia. Most of the existing annotation methods, heavily rely on well-labeled training data (expensive to collect) and/or single view of visual features (insufficient representative power). In this paper, inspired by the promising advance of feature engineering (e.g., CNN feature and scale-invariant feature transform feature) and inexhaustible image data (associated with noisy and incomplete labels) on the Web, we propose an effective and robust scheme, termed robust multi-view semi-supervised learning (RMSL), for facilitating image annotation task. Specifically, we exploit both labeled images and unlabeled images to uncover the intrinsic data structural information. Meanwhile, to comprehensively describe an individual datum, we take advantage of the correlated and complemental information derived from multiple facets of image data (i.e., multiple views or features). We devise a robust pairwise constraint on outcomes of different views to achieve annotation consistency. Furthermore, we integrate a robust classifier learning component via $\ell _{2,p}$ loss, which can provide effective noise identification power during the learning process. Finally, we devise an efficient iterative algorithm to solve the optimization problem in RMSL. We conduct comprehensive experiments on three different data sets, and the results illustrate that our proposed approach is promising for automatic image annotation.


title: Theius: A Streaming Visualization Suite for Hadoop Clusters*

abstract: As cloud computing clusters continue to grow, maintaining the health of these clusters becomes increasingly challenging. Recent work has studied how we can efficiently monitor the status of machines in these clusters and how we can detect problems or predict them before they occur, yet little work has focused on addressing the bottleneck between when these failures occur and when they are fixed: system administrators. As monitoring and failure detection systems mature, we are able to extract tremendous amounts of information about the status of the system in real time. However, this amount of data is difficult to understand for human beings, especially those inexperienced with the particular cluster. In this paper, we introduce a web-based visualization suite called Theius to allow system administrators to quickly understand the state of the cloud system as a whole. We outline the key features of this visualization tool, and show that it is more intuitive and easy to use than Ganglia, a state-of-the art visualization tool for clusters. Likewise, we demonstrate that our tool can scale, presenting a use case with our visualization showing a 5000 node cluster. Although our tool is implemented for Hadoop clusters, our contribution is general to any cloud computing system.


title: Ultraviolet Radiation on the Skin: A Painful Experience?

abstract: Summary ::: ::: Excessive exposure of skin to ultraviolet radiation (UVR) has dramatic clinical effects in humans, and it is a significant public health concern. Discomfort and sensory changes caused by skin sunburn are the main common features experienced by many of us, a phenomena triggered by the combination of long and short wavelengths radiation (UVA and UVB, respectively). Although the biological processes underlying UVR exposure are not fully understood, in the last few years many studies have made significant progress in characterizing sunburn at the cellular and molecular levels, making use of both humans and laboratory animal models. Here we review and reason that UVR can be used as an excellent model of sensitization and inflammation for pain research. UVR, particularly UVB, produces a controllable and sterile inflammation that causes a robust dose-dependent hypersensitivity with minimal confounding effects. Importantly, we show that UVR animal models precisely recapitulate the sensory, cellular, and molecular changes observed in human skin, giving it great confidence as a translational model. Furthermore, in this article, we give an overview of the pharmacology underlying UVB inflammation, the latest advances in the field, and potential new targets for inflammatory pain.


title: Hashing Techniques: A Survey and Taxonomy

abstract: With the rapid development of information storage and networking technologies, quintillion bytes of data are generated every day from social networks, business transactions, sensors, and many other domains. The increasing data volumes impose significant challenges to traditional data analysis tools in storing, processing, and analyzing these extremely large-scale data. For decades, hashing has been one of the most effective tools commonly used to compress data for fast access and analysis, as well as information integrity verification. Hashing techniques have also evolved from simple randomization approaches to advanced adaptive methods considering locality, structure, label information, and data security, for effective hashing. This survey reviews and categorizes existing hashing techniques as a taxonomy, in order to provide a comprehensive view of mainstream hashing techniques for different types of data and applications. The taxonomy also studies the uniqueness of each method and therefore can serve as technique references in understanding the niche of different hashing mechanisms for future development.


title: A bibliometric analysis of industrial wastewater research: current trends and future prospects

abstract: Global industrialization is accelerated under the driving force of developing countries' rapid economic development. Water pollution is inevitably worsened due to lagging investment in basic treatment infrastructure. Wastewater treatment cannot rely on just one treatment technique, so research in this field has attracted much attention to satisfy stringent recovery and emissions standards increasingly imposed on industrial wastewater. This case study is a bibliometric analysis conducted to evaluate industrial wastewater treatment research from 1991 to 2014, based on the Science Citation Index Expanded (SCIE) database. Journal of Chemical Technology and Biotechnology is the leading journal in this field, publishing 3.8 % of articles over this period, followed by Journal of Hazardous Materials and Water Research, the latter of which has the highest impact factor and h-index of all journals in this field. India and the Chinese Academy of Sciences were the most productive country and institution, respectively, while the USA, was the most internationally collaborative and had the highest h-index (82) of all countries. A new method named "word cluster analysis" was successfully applied to trace the research hotspots. Innovation in treatment methods is thought to relate to the growth in volume and increase in complexity of industrial wastewater, as well as to policy decisions in developing countries that encourage effective industrial wastewater treatment.


title: Attachment typologies and posttraumatic stress disorder (PTSD), depression and anxiety: a latent profile analysis approach

abstract: Background: Bartholomew (1990) proposed a four category adult attachment model based on Bowlby’s (1973) proposal that attachment is underpinned by an individual’s view of the self and others. Previous cluster analytic techniques have identified four and two attachment styles based on the Revised Adult Attachment Scale (RAAS). In addition, attachment styles have been proposed to meditate the association between stressful life events and subsequent psychiatric status. Objective: The current study aimed to empirically test the attachment typology proposed by Collins and Read (1990). Specifically, LPA was used to determine if the proposed four styles can be derived from scores on the dimensions of closeness / dependency and anxiety. In addition, we aimed to test if the resultant attachment styles predicted the severity of psychopathology in response to a whiplash trauma. Method: A large sample of Danish trauma victims (N=1577) participated. A Latent Profile Analysis was conducted, using Mplus 5.1, on scores from the RAAS scale to ascertain if there were underlying homogeneous attachment classes / subgroups. Class membership was used in a series of one-way ANOVA tests to determine if classes were significantly different in terms of mean scores on measures of psychopathology. Results: The three class solution was considered optimal. Class one was termed Fearful (18.6%), Class two Preoccupied (34.5%), and Class three Secure (46.9%). The secure class evidenced significantly lower mean scores on PTSD, depression, and anxiety measures compared to other classes, whereas the fearful class evidenced significantly higher mean scores compared to other classes. Conclusions: The results demonstrated evidence of three discrete classes of attachment styles, which were labelled secure, preoccupied, and fearful. This is in contrast to previous cluster analytic techniques which have identified four and two attachment styles based on the RAAS. In addition, Securely attached individuals display lower levels of psychopathology post whiplash trauma. Keywords: Attachment typology; latent profile analysis; posttraumatic stress disorder; depression; anxiety (Published: 15 December 2011) Citation: European Journal of Psychotraumatology 2011, 2 : 6018 - DOI: 10.3402/ejpt.v2i0.6018 For the abstract or full text in other languages, please see Supplementary files in the column to the right (under Reading Tools).


Deep Hybrid Models : Bridging Discriminative and Generative Approaches


title: Robust and discriminative distance for Multi-Instance Learning

abstract: Multi-Instance Learning (MIL) is an emerging topic in machine learning, which has broad applications in computer vision. For example, by considering video classification as a MIL problem where we only need labeled video clips (such as tagged online videos) but not labeled video frames, one can lower down the labeling cost, which is typically very expensive. We propose a novel class specific distance Metrics enhanced Class-to-Bag distance (M-C2B) method to learn a robust and discriminative distance for multi-instance data, which employs the not-squared l 2 -norm distance to address the most difficult challenge in MIL, i.e., the outlier instances that abound in multi-instance data by nature. As a result, the formulated objective ends up to be a simultaneous l 2, 1 -norm minimization and maximization (minmax) problem, which is very hard to solve in general due to the non-smoothness of the l 2, 1 -norm. We thus present an efficient iterative algorithm to solve the general l 2, 1 -norm minmax problem with rigorously proved convergence. To the best of our knowledge, we are the first to solve a general l 2, 1 -norm minmax problem in literature. We have conducted extensive experiments to evaluate various aspects of the proposed method, in which promising results validate our new method in cost-effective video classification.


title: A reproducible olfactory display for exploring olfaction in immersive media experiences

abstract: Olfaction, the sense of smell, is an important perceptual function. It has been shown to enhance the quality of life and to facilitate memory recall. However, despite its importance and potential benefits, olfaction is often neglected when creating immersive media experiences (IMEs). The probable reason is that olfactory displays are not readily available. We have developed a reproducible olfactory display that is simple and affordable to make olfactory displays more available. More importantly, we have conducted three exploratory studies on the effects of olfaction. The results of these studies indicate that olfaction is a nuanced sense that can have both positive and negative effects on user experiences. Hence, further research is needed to better understand and design for olfaction in IMEs.


title: Constraint cascading style sheets for the Web

abstract: Cascading Style Sheets have been introduced by the W3C as a mechanism for controlling the appearance of HTML documents. In this paper, we demonstrate how constraints provide a powerful unifying formalism for declaratively understanding and specifying style sheets for web documents. With constraints we can naturally and declaratively specify complex behavior such as inheritance of properties and cascading of conflicting style rules. We give a detailed description of a constraint-based style sheet model, CCSS, which is compatible with virtually all of the CSS 2.0 specification. It allows more flexible specification of layout, and also allows the designer to provide multiple layouts that better meet the desires of the user and environmental restrictions. We also describe a prototype extension of the Amaya browser that demonstrates the feasibility of CCSS.


title: New Analysis and Results for the Frank-Wolfe Method

abstract: We present new results for the Frank-Wolfe method (also known as the conditional gradient method). We derive computational guarantees for arbitrary step-size sequences, which are then applied to various step-size rules, including simple averaging and constant step-sizes. We also develop step-size rules and computational guarantees that depend naturally on the warm-start quality of the initial (and subsequent) iterates. Our results include computational guarantees for both duality/bound gaps and the so-called FW gaps. Lastly, we present complexity bounds in the presence of approximate computation of gradients and/or linear optimization subproblem solutions.


title: GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media

abstract: Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a group-level mobility modeling method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs. Our extensive experiments on two real-life data sets demonstrate that GMove can effectively generate meaningful group-level mobility models. Moreover, with context-aware location prediction as an example application, we find that GMove significantly outperforms baseline mobility models in terms of prediction accuracy.


Moments of moments: Acoustic phonetic character and within-category variability of the Basque three-sibilant contrast


title: MAC Throughput Limit Analysis of Slotted CSMA / CA in IEEE 802 . 15 . 4 WPAN

abstract: IEEE 802.15.4 low-rate Wireless Personal Area Networks (WPAN) are expected to provide ubiquitous networking between small personal/home devices and sensors with low power consumption and low cost features. The technology employs special CSMA/CA to save power consumption for battery-powered small or portable WPAN devices. In this letter, we present a new model for the slotted CSMA/CA of IEEE 802.15.4 Medium Access Control (MAC) and evaluate its throughput limit in order to grasp the characteristics of IEEE 802.15.4 WPAN.


title: Shaping beliefs in experimental markets for expert services: Guilt aversion and the impact of promises and money-burning options

abstract: In a credence goods game with an expert and a consumer, we study experimentally the impact of two devices that are predicted to induce consumer-friendly behavior if the expert has a propensity to feel guilty when he believes that he violates the consumerʼs payoff expectations: (i) an opportunity for the expert to make a non-binding promise; and (ii) an opportunity for the consumer to burn money. In belief-based guilt aversion theory the first opportunity shapes an expertʼs behavior if an appropriate promise is made and if it is expected to be believed by the consumer; by contrast, the second opportunity might change behavior even though this option is never used along the predicted path. Experimental results confirm the behavioral relevance of (i) but fail to confirm (ii).


title: Electric ship power system - research at the University of Texas at Austin

abstract: Recognizing the importance of electric ships and the myriad of technical challenges they present, the University of Texas at Austin, with seed funding from the Office of Naval Research, established a virtual electrical ship research and development center. The reason for the virtual center is that the complex challenges of the future require multidisciplinary solutions. The virtual center contains staff members from electrical engineering, mechanical engineering and two research centers who work collaboratively with a number of other universities and with industry. This program has two anticipated significant outcomes. At the system level, the first anticipated outcome is to develop a comprehensive set of modeling approaches that guide the evolution of electric ship power systems. The second outcome is an approach to the replacement of the hydraulic system on current ships with electrical actuators on future ships.


title: A Brief Taxonomy of Tactile Illusions and Demonstrations That Can Be Done in a Hardware Store

abstract: This paper surveys more than twenty types of tactile illusions and discusses several of their aspects. These aspects include the ease with which they can be demonstrated and whether they have clear visual analogs. The paper also shows how to construct equipment made of simple supplies able to deliver well controlled tactile signals in order to conveniently demonstrate four dierent tactile illusions.


Investigating the effect of Internet Addiction on Social Skills and in High School Students ' Achievement


title: Psychometric Properties of the Beck Depression Inventory—II (BDI-II) Among Community-Dwelling Older Adults

abstract: The psychometric properties of the Beck Depression Inventory-II (BDI-II) as a self-administered screening tool for depressive symptoms were examined in a sample of community-dwelling older and younger adults. Participants completed the BDI-II, the Center for Epidemiologic Studies Depression Scale, the Coolidge Axis II Inventory, the Perceived Stress Scale, and the Short Psychological Well-Being Scale. Internal reliability of the BDI-II was found to be good among older and younger adults. The average BDI-II depression score did not differ between younger and older adults. Solid evidence for convergent and discriminant validity was demonstrated by correlations between the BDI-II with the other measures. The BDI-II appears to have strong psychometric support as a screening measure for depression among older adults in the general population. Implications for using the BDI-II as an assessment instrument in behaviorally based psychotherapy are discussed.


title: Visualizing Class Information

abstract: A class is used in object oriented programming to describe each object in the system. It is as a template contains the methods and attributes for each object. The volume of information within the class has a role in the time required for its implementation, testing and understanding the class. Developers are dealing with large projects that contain a large number of Lines Of Code (LOC). So that, extracting information about the classes requires time and big effort from the developers. To solve this problem, we present a visualization approach to display class information for developers. Our method assumed each class is a cone chart and each cone represents one of the class metrics that include number of methods, attributes and the number of Lines Of Code (LOC). The height of each cone indicates the total number of methods, attributes and number of Lines Of Code (LOC) which found in a class. Also, each cone has a different color from the other to facilitate the distinction among them


From Camera to Deathbed: Understanding Dangerous Selfies on Social Media.


title: A FVF LDO regulator with damping-factor-control frequency compensation for SOC application

abstract: An output-capacitorless low-dropout regulator (OCL-LDO), which is based on flipped-voltage-follower (FVF) using damping-factor-control (DFC) frequency compensation for SOC application, is presented in this paper. The proposed LDO with 1.2V supply, 100mA load current was designed by SMIC 0.13um standard CMOS process. Simulation results have shown that the LDO can be stable for a load capacitance ranging from 0 to 80pF. The line regulation and load regulation are 3.3mV/V and 62uV/mA, respectively, and the quiescent current consumption is only 27uA. Under maximum load current changes, the overshoots and undershoots are less than 90mV and 140mV, and the recovery time is about 2.5us.


title: A leg-wheel robot-based approach to the solution of flipper-track robot kinematics

abstract: This paper presents the method of solving kinematics of a flipper-track robot. A general kinematics framework based on the screw theory is introduced for the leg-wheel robots. Focusing on the contact point between a flipper and the ground surface, the flipper-track robot kinematics is presented with the equivalent hybrid leg-wheel robot kinematics. For a studied four flipper-track robot, several robot control modes and corresponding kinematics solutions are identified. The algorithm for robot posture and tracking control is presented respecting robot's mechanical constraints. The kinematics and robot posture control were tested by simulation on a virtual robot in the MATLAB-ODE simulation environment.


title: Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud

abstract: With the character of low maintenance, cloud computing provides an economical and efficient solution for sharing group resource among cloud users. Unfortunately, sharing data in a multi-owner manner while preserving data and identity privacy from an untrusted cloud is still a challenging issue, due to the frequent change of the membership. In this paper, we propose a secure multi-owner data sharing scheme, named Mona, for dynamic groups in the cloud. By leveraging group signature and dynamic broadcast encryption techniques, any cloud user can anonymously share data with others. Meanwhile, the storage overhead and encryption computation cost of our scheme are independent with the number of revoked users. In addition, we analyze the security of our scheme with rigorous proofs, and demonstrate the efficiency of our scheme in experiments.


title: Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

abstract: Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classification. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classifiers. We collect more than 50,000 bug reports from five development projects from two companies in different domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. We show that SG scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.


title: A Big Data Analytics Architecture for the Internet of Small Things

abstract: The SK Telecom Company of South Korea recently introduced the concept of IoST to its business model. The company deployed IoST, which constantly generates data via the LoRa wireless platform. The increase in data rates generated by IoST is escalating exponentially. After attempting to analyze and store the massive volume of IoST data using existing tools and technologies, the South Korean company realized the shortcomings immediately. The current article addresses some of the issues and presents a big data analytics architecture for its IoST. A system developed using the proposed architecture will be able to analyze and store IoST data efficiently while enabling better decisions. The proposed architecture is composed of four layers, namely the small things layer, infrastructure layer, platform layer, and application layer. Finally, a detailed analysis of a big data implementation of the IoST used to track humidity and temperature via Hadoop is presented as a proof of concept.


title: Speaker-independent Speech Separation with Deep Attractor Network

abstract: Despite the recent success of deep learning for many speech processing tasks, single-microphone, speaker-independent speech separation remains challenging for two main reasons. The first reason is the arbitrary order of the target and masker speakers in the mixture (permutation problem), and the second is the unknown number of speakers in the mixture (output dimension problem). We propose a novel deep learning framework for speech separation that addresses both of these issues. We use a neural network to project the time-frequency representation of the mixture signal into a high-dimensional embedding space. A reference point (attractor) is created in the embedding space to represent each speaker which is defined as the centroid of the speaker in the embedding space. The time-frequency embeddings of each speaker are then forced to cluster around the corresponding attractor point which is used to determine the time-frequency assignment of the speaker. We propose three methods for finding the attractors for each source in the embedding space and compare their advantages and limitations. The objective function for the network is standard signal reconstruction error which enables end-to-end operation during both training and test phases. We evaluated our system using the Wall Street Journal dataset (WSJ0) on two and three speaker mixtures and report comparable or better performance than other state-of-the-art deep learning methods for speech separation.


title: Design and Application of Surface Wave Sensors for Nonintrusive Power Line Fault Detection

abstract: This paper describes a novel conformal surface wave (CSW) launcher that can excite electromagnetic surface waves along unshielded power line cables nonintrusively. This CSW launcher can detect open circuit faults on power cables. Unlike conventional horn-type launchers, this CSW launcher is small, lightweight, and cost effective, and can be placed easily on a power cable. For a nonintrusive open fault detection, the error is <; 5% when the cable length is <; 10 m, which is comparable with other direct-connect fault-finding techniques. For a cable length of 15.14 m, 7.6% error is noted. Besides cable fault detection, the potential applications of the proposed launcher include broadband power line communication and high-frequency power transmission.


title: Unknown malware detection using network traffic classification

abstract: We present an end-to-end supervised based system for detecting malware by analyzing network traffic. The proposed method extracts 972 behavioral features across different protocols and network layers, and refers to different observation resolutions (transaction, session, flow and conversation windows). A feature selection method is then used to identify the most meaningful features and to reduce the data dimensionality to a tractable size. Finally, various supervised methods are evaluated to indicate whether traffic in the network is malicious, to attribute it to known malware “families” and to discover new threats. A comparative experimental study using real network traffic from various environments indicates that the proposed system outperforms existing state-of-the-art rule-based systems, such as Snort and Suricata. In particular, our chronological evaluation shows that many unknown malware incidents could have been detected at least a month before their static rules were introduced to either the Snort or Suricata systems.


title: Kinematic Analysis of the Wrist and Forearm during Baseball Pitching

abstract: Previous researchers studying baseball pitching have compared kinematic and kinetic parameters among different types of pitches, focusing on the trunk, shoulder, and elbow. The lack of data on the wrist and forearm limits the understanding of clinicians, coaches, and researchers regarding the mechanics of baseball pitching and the differences among types of pitches. The purpose of this study was to expand existing knowledge of baseball pitching by quantifying and comparing kinematic data of the wrist and forearm for the fastball (FA), curveball (CU) and change-up (CH) pitches. Kinematic and temporal parameters were determined from 8 collegiate pitchers recorded with a four-camera system (200 Hz). Although significant differences were observed for all pitch comparisons, the least number of differences occurred between the FA and CH. During arm cocking, peak wrist extension for the FA and CH pitches was greater than for the CU, while forearm supination was greater for the CU. In contrast to the current stud...


title: Desktop tools for offline plagiarism detection in computer programs

abstract: Plagiarism in universities has always been a difficult problem to overcome. Various tools have been developed over the past few years to help teachers detect plagiarism in students' work. By being able to categorize the multitude of plagiarism detection tools, it is possible to estimate their capabilities, advantages and disadvantages. In this article I consider modern plagiarism software solutions, paying attention mostly to desktop systems intended for plagiarism detection in program code. I also estimate the speed and reliability of different plagiarism detection systems that are currently available.


title: Named Entity Recognition With A Maximum Entropy Approach

abstract: The named entity recognition (NER) task involves identifying noun phrases that are names, and assigning a class to each name. This task has its origin from the Message Understanding Conferences (MUC) in the 1990s, a series of conferences aimed at evaluating systems that extract information from natural language texts. It became evident that in order to achieve good performance in information extraction, a system needs to be able to recognize names. A separate subtask on NER was created in MUC-6 and MUC-7 (Chinchor, 1998).


title: Self-aware Object Tracking in Multi-Camera Networks

abstract: This chapter discusses another example of self-aware and self-expressive systems: a multi-camera network for object tracking. It provides a detailed description of how the concepts of self-awareness and self-expression can be implemented in a real network of smart cameras. In contrast to traditional cameras, smart cameras are able to perform image analysis on-board and collaborate with other cameras in order to analyse the dynamic behaviour of objects in partly unknown environments. Self-aware and self-expressive smart cameras are even able to reason about their current state and to adapt their algorithms in response to changes in their environment and the network. Self-awareness and self-expression allow them to manage the trade-off among performance, flexibility, resources and reliability during runtime. Due to the uncertainties and dynamics in the network a fixed configuration of the cameras is infeasible. We adopt the concepts of self-awareness and self-expression for autonomous monitoring of the state and progress of each camera in the network and adapt its behaviour to changing conditions. In this chapter we focus on describing the building blocks for self-aware camera networks and demonstrate the key characteristics in a multi-camera object tracking application both in simulation and in a real camera network. The proposed application implements the goal sharing with time-awareness capability pattern, including meta-self-awareness capabilities as discussed in Chapter 5. Furthermore, the distributed camera network employs the middleware system described in Chapter 11 to facilitate distributed coordination of tracking responsibilities. Moreover, the application uses socially inspired techniques and mechanisms discussed in Chapter 7.


title: Optimized Wind Energy Harvesting System Using Resistance Emulator and Active Rectifier for Wireless Sensor Nodes

abstract: This paper presents an optimized wind energy harvesting (WEH) system that uses a specially designed ultra-low-power-management circuit for sustaining the operation of a wireless sensor node. The proposed power management circuit has two distinct features: 1) an active rectifier using MOSFETs for rectifying the low amplitude ac voltage generated by the wind turbine generator under low wind speed condition efficiently and 2) a dc-dc boost converter with resistor emulation algorithm to perform maximum power point tracking (MPPT) under varying wind-speed conditions. As compared to the conventional diode-bridge rectifier, it is shown that the efficiency of the active rectifier with a low input voltage of 1.2 V has been increased from 40% to 70% due to the significant reduction in the ON-state voltage drop (from 0.6 to 0.15 V) across each pair of MOSFETs used. The proposed robust low-power microcontroller-based resistance emulator is implemented with closed-loop resistance feedback control to ensure close impedance matching between the source and the load, resulting in an efficient power conversion. From the experimental test results obtained, an average electrical power of 7.86 mW is harvested by the optimized WEH system at an average wind speed of 3.62 m/s, which is almost four times higher than the conventional energy harvesting method without using the MPPT.


title: On locating malicious code in piggybacked Android apps

abstract: To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy@5 of 83.6% for such packages that are triggered through method invocations and an accuracy@5 of 82.2% for such packages that are triggered independently.


title: Tensor Embedding Methods Guang Dai

abstract: Over the past few years, some embedding methods have been proposed for feature extraction and dimensionality reduction in various machine learning and pattern classification tasks. Among the methods proposed are Neighborhood Preserving Embedding (NPE), Locality Preserving Projection (LPP) and Local Discriminant Embedding (LDE) which have been used in such applications as face recognition and image/video retrieval. However, although the data in these applications are more naturally represented as higher-order tensors, the embedding methods can only work with vectorized data representations which may not capture well some useful information in the original data. Moreover, high-dimensional vectorized representations also suffer from the curse of dimensionality and the high computational demand. In this paper, we propose some novel tensor embedding methods which, unlike previous methods, take data directly in the form of tensors of arbitrary order as input. These methods allow the relationships between dimensions of a tensor representation to be efficiently characterized. Moreover, they also allow the intrinsic local geometric and topological properties of the manifold embedded in a tensor space to be naturally estimated. Furthermore, they do not suffer from the curse of dimensionality and the high computational demand. We demonstrate the effectiveness of the proposed tensor embedding methods on a face recognition application and compare them with some previous methods. Extensive experiments show that our methods are not only more effective but also more efficient.


title: Productivity–diversity relationships for stream invertebrates differ geographically

abstract: More productive environments typically have more species, although the specific form of this relationship is unclear and can vary with spatial scale. This relationship has received little direct attention in lotic systems, and thus the nature of the relationship is unclear, as is any effect of spatial scale. We examined the link between stream primary productivity and macroinvertebrate diversity in Spain and New Zealand and hypothesized that macroinvertebrate diversity would increase log-linearly with increasing productivity in both regions. We sampled 24 streams in Cantabria, Spain, and 24 in the central North Island, New Zealand. Algal primary productivity was approximately three times higher in Spanish streams, but taxonomic richness of invertebrates did not differ between the regions. Richness and Shannon diversity only responded to productivity in the New Zealand streams, exhibiting the predicted log-linear increase. In the Spanish streams, only the total number of individuals increased with productivity. However, when plotted on the same axes, richness in the Spanish streams simply occurred on the linear portion of the graph to the right of the New Zealand streams. We speculate that productivity in the Spanish streams never became low enough to constrain diversity, but did in the New Zealand streams. Combining results from the two regions, there is no evidence of a decline in diversity with higher productivity.


title: MIMO Radar Ambiguity Properties and Optimization Using Frequency-Hopping Waveforms

abstract: The concept of multiple-input multiple-output (MIMO) radars has drawn considerable attention recently. Unlike the traditional single-input multiple-output (SIMO) radar which emits coherent waveforms to form a focused beam, the MIMO radar can transmit orthogonal (or incoherent) waveforms. These waveforms can be used to increase the system spatial resolution. The waveforms also affect the range and Doppler resolution. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. Recently, the idea of ambiguity function has been extended to the case of MIMO radar. In this paper, some mathematical properties of the MIMO radar ambiguity function are first derived. These properties provide some insights into the MIMO radar waveform design. Then a new algorithm for designing the orthogonal frequency-hopping waveforms is proposed. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions.


title: Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems

abstract: With the rapid development of the Internet of Things, cloud computing, and big data, more comprehensive and powerful applications become available. Meanwhile, people pay more attention to higher QoE and QoS in a “terminal- cloud” integrated system. Specifically, both advanced terminal technologies (e.g., smart clothing) and advanced cloud technologies (e.g., big data analytics and cognitive computing in clouds) are expected to provide people with more reliable and intelligent services. Therefore, in this article we propose a Wearable 2.0 healthcare system to improve QoE and QoS of the next generation healthcare system. In the proposed system, washable smart clothing, which consists of sensors, electrodes, and wires, is the critical component to collect users’ physiological data and receive the analysis results of users’ health and emotional status provided by cloud-based machine intelligence.


title: A Framework for Managing User-defined Security Policies to Support Network Security Functions

abstract: Network Functions Virtualization (NFV) and Software Defined Networking (SDN) make it easier for security administrators to manage security policies on a network system. However, it is still challenging to map high-level security policies defined by users into low-level security policies that can be applied to network security devices. To address this problem, we introduce a framework for effectively managing user-defined security policies for network security functions based on standard interfaces that are currently being standardized in an IETF working group. To show the feasibility of the proposed framework, we implemented a prototype based on the RESTCONF protocol and showed that the proposed framework can be applied in real-world scenarios for network separation, DDoS mitigation and ransomeware prevention.


title: ASGR1 - a new target for lowering non-HDL cholesterol

abstract: Non-HDL cholesterol (non-HDL-C) has been introduced as an alternative cardiovascular (CV) risk marker and a secondary therapeutic target in patients with combined hyperlipidemia, diabetes mellitus, metabolic syndrome, or chronic kidney disease. An important genetic study on the Icelandic population has recently identified a strong link between a new gene - ASGR1 (for asialoglycoprotein receptor) - mutation, plasma non-HDL-C levels, and coronary heart disease (CHD). Heterozygous carriers of a rare noncoding 12-base-pair (bp) deletion (del12 deletion) in intron 4 of ASGR1 had a 13.6 mg/dl lower level of non-HDL-C and a 34% lower risk of CHD than non carriers. The cardioprotective effect of ASGR1 loss-of-function is surprisingly larger than predicted by its effect on the levels of non-HDL-C, which suggests that the atheroprotective effects of del12 mutation go beyond the lowering of serum cholesterol levels. This has shed some light on a new path - the sialylation pathway - possibly leading to a novel therapy that neutralize ASGR1 for heart disease prevention and treatment.


title: An analytic simulation model for a supercapacitor-based energy storage system

abstract: Simulations of models containing a supercapacitor based energy storage system (ESS) are slow due to a small simulation step required by a switching-mode character of the ESS power converter. In the article, a SIMULINK model built on the analytic equations of the ESS power stage is proposed and verified by applying it in a model of a tram equipped with an onboard ESS. Performance of the proposed analytic model is compared with the switching and average ones. The model provides approximately 75-fold decrease in the simulation time in comparison to the switching model.


title: Five-phase permanent-magnet motor drives

abstract: A five-phase brushless permanent-magnet (PM) motor is introduced. The proposed motor has concentrated windings such that the produced back electromotive force is almost trapezoidal. The motor is supplied with the combined sinusoidal plus third harmonic of currents. This motor, while generating the same average torque as an equivalent PM brushless dc motor (BLDC), overcomes its disadvantages. The motor equations are obtained in the d/sub 1/q/sub 1/d/sub 3/q/sub 3/0 rotating reference frame. Therefore, the so-called vector control is easily applicable to this kind of motors and the motor has the same controllability as a PM synchronous motor (PMSM). For presenting the superior performance of the proposed five-phase motor, its three and five-phase PMSM and BLDC counterparts are also analyzed. Finite element method is used for studying the flux density and calculating the developed static torque. Also, the developed torque is obtained using the mathematical model in the d-q reference frame. The average torque and the torque ripple for all cases are calculated and compared. Experimental results are in good agreement with the simulation results.


title: Relationships of Three Components of Reading Fluency to Reading Comprehension

abstract: This study examined the relationships of 3 levels of reading fluency--the individual word, the syntactic unit, and the whole passage--to reading comprehension among 278 5th graders heterogeneous in reading ability. Hierarchical regression analyses revealed that reading fluency at each level related uniquely to performance on a standardized reading comprehension test in a model including inferencing skill and background knowledge. The study supports an automaticity effect for word recognition speed and an automaticity-like effect related to syntactic processing skill. In addition, hierarchical regressions using longitudinal data suggest that fluency and reading comprehension have a bidirectional relationship. The discussion emphasizes the theoretical expansion of reading fluency to 3 levels of cognitive processes and the relations of these processes to reading comprehension.


title: Brain imaging study of the acute effects of Δ9-tetrahydrocannabinol (THC) on attention and motor coordination in regular users of marijuana

abstract: Procedure ::: Twelve regular users of marijuana underwent two positron emission tomography (PET) scans using [18F] Fluorodeoxyglucose (FDG), one while subject to the effects of 17 mg THC, the other without THC. In both sessions, a virtual reality maze task was performed during the FDG uptake period.


title: An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking

abstract: We highlight a practical yet rarely discussed problem in dialogue state tracking (DST), namely handling unknown slot values. Previous approaches generally assume predefined candidate lists and thus are not designed to output unknown values, especially when the spoken language understanding (SLU) module is absent as in many end-to-end (E2E) systems. We describe in this paper an E2E architecture based on the pointer network (PtrNet) that can effectively extract unknown slot values while still obtains state-of-the-art accuracy on the standard DSTC2 benchmark. We also provide extensive empirical evidence to show that tracking unknown values can be challenging and our approach can bring significant improvement with the help of an effective feature dropout technique.


A SECURE ONLINE PAYMENT SYSTEM USING STEGANOGRAPHY AND VISUAL CRYPTOGRAPHY


title: Explanation of the Charge Trapping Properties of Silicon Nitride Storage Layers for NVMs—Part II: Atomistic and Electrical Modeling

abstract: Based on the material analysis of the SiN layers presented in part I of this paper, we develop accurate atomistic and electrical models for the silicon nitride (SiN)-based nonvolatile memory devices, taking into account the candidate SiN defects responsible for the memory effect. Our analysis points out the role of the hydrogen atoms and Si dangling bonds in the trapping properties of SiN films with different stoichiometries. The atomistic models provide a comprehensive picture describing the energy level and the occupation number of the different defects in the SiN. The electrical model coupled with the atomistic results, for the first time, demonstrates the ability to describe the program/erase curves of charge-trap memory cells with SiN storage layers with diversified composition. Good agreement between simulations and experimental results coming from the material analysis and the electrical characterization of thin (type-B device) and thick (type-A device) tunnel oxide memory cells is shown.


title: Exploiting partially overlapping channels in wireless networks: turning a peril into an advantage

abstract: Interference has always been considered as an unavoidable peril in wireless networks. A single data transmission is useful to some nodes and becomes interference to others. Based on channel of origin, interference can be categorized into co-channel (from transmissions on the same channel as the receiver) and adjacent-channel (transmissions on adjacent and overlapping channels). In this paper, we define specific mechanisms that can transform partially overlapped channels into an advantage, instead of a peril. We construct simple analytical and empirical models of such interference occurring in IEEE 802.11 networks, and illustrate two scenarios where such interference can be exploited. First, we apply partially overlapping channels to improve spatial channel re-use in Wireless LANs (WLANs). Second, we leverage such channels to enable nodes with a single radio interface to communicate more efficiently with their peers in 802.11 ad-hoc mode potentially using multi-hop paths. We evaluate both capabilities through testbed measurements.


Review of Generator Systems for Direct-Drive Wind Turbines


An evaluation of educational values of YouTube videos for academic writing


title: A Holistic Approach for Data-Driven Object Cutout

abstract: Object cutout is a fundamental operation for image editing and manipulation, yet it is extremely challenging to automate it in real-world images, which typically contain considerable background clutter. In contrast to existing cutout methods, which are based mainly on low-level image analysis, we propose a more holistic approach, which considers the entire shape of the object of interest by leveraging higher-level image analysis and learnt global shape priors. Specifically, we leverage a deep neural network (DNN) trained for objects of a particular class (chairs) for realizing this mechanism. Given a rectangular image region, the DNN outputs a probability map (P-map) that indicates for each pixel inside the rectangle how likely it is to be contained inside an object from the class of interest. We show that the resulting P-maps may be used to evaluate how likely a rectangle proposal is to contain an instance of the class, and further process good proposals to produce an accurate object cutout mask. This amounts to an automatic end-to-end pipeline for catergory-specific object cutout. We evaluate our approach on segmentation benchmark datasets, and show that it significantly outperforms the state-of-the-art on them.


title: Spreadsheet-based Business Process modeling

abstract: Business Process models help to visualize processes of an organization. In enterprises, these processes are often specified in internal regulations, resolutions or other law acts of a company. Such descriptions, like task lists, have mostly form of enumerated lists or spreadsheets. We present a method how to generate a BPMN process model from a spreadsheet-based representation. In contrast to the existing approaches, our method does not require explicit specification of gateways in the spreadsheet, but it takes advantage of nested list form.


title: The cortico-basal ganglia integrative network: The role of the thalamus

abstract: Abstract The thalamus is a critical component of the frontal cortical-basal ganglia–thalamic circuits that mediate motivation and emotional drive, planning and cognition for the development and expression of goal-directed behaviors. Each functional region of the frontal cortex is connected with specific areas of each basal ganglia (BG) structure and of the thalamus. In addition, the thalamus sends a massive, topographically organized projection directly to the striatum. Tract-tracing and physiological experiments have indicated a general topographic organization of the cortical-BG–thalamic loops and supported a model of BG function based on parallel and segregated pathways. However, the learning and execution of appropriate behavioral responses require integration of inputs related to emotional, cognitive, and motor cortical functions. Our recent data indicate that integration may occur via non-reciprocal connections between the striatum and substantia nigra and within “hot spots” of convergence between corticostriatal projections from different functional regions. Similarly, integration may exist in the thalamus. There are non-reciprocal connections between the thalamus and cortex via thalamocortical projections that terminate in the superficial and deep cortical layers. These terminals can influence different functional cortical areas that, in turn, project to the striatum and back to the thalamus. In addition, a non-reciprocal corticothalamic projection terminates in thalamic regions that are parts of other circuits. Finally, ‘hot spots’ of convergence between terminals from different cortical regions may also occur in the thalamus as is seen in the striatum. Thus, via several different pathways, the thalamus may serve as an important center of integration of networks that underlie the ability to modulate behaviors.


title: Cognitive Measurements of Graph Aesthetics

abstract: A large class of diagrams can be informally characterized as node-link diagrams. Typically nodes represent entities, and links represent relationships between them. The discipline of graph drawing is concerned with methods for drawing abstract versions of such diagrams. At the foundation of the discipline are a set of graph aesthetics (rules for graph layout) that, it is assumed, will produce graphs that can be clearly understood. Examples of aesthetics include minimizing edge crossings and minimizing the sum of the lengths of the edges. However, with a few notable exceptions, these aesthetics are taken as axiomatic, and have not been empirically tested. We argue that human pattern perception can tell us much that is relevant to the study of graph aesthetics including providing a more detailed understanding of aesthetics and suggesting new ones. In particular, we find the importance of good continuity (ie keeping multi-edge paths as straight as possible) has been neglected. We introduce a methodology for evaluating the cognitive cost of graph aesthetics and we apply it to the task of finding the shortest paths in spring layout graphs. The results suggest that after the length of the path the two most important factors are continuity and edge crossings, and we provide cognitive cost estimates for these parameters. Another important factor is the number of branches emanating from nodes on the path.


title: Who's in and why? A typology of stakeholder analysis methods for natural resource management.

abstract: Stakeholder analysis means many things to different people. Various methods and approaches have been developed in different fields for different purposes, leading to confusion over the concept and practice of stakeholder analysis. This paper asks how and why stakeholder analysis should be conducted for participatory natural resource management research. This is achieved by reviewing the development of stakeholder analysis in business management, development and natural resource management. The normative and instrumental theoretical basis for stakeholder analysis is discussed, and a stakeholder analysis typology is proposed. This consists of methods for: i) identifying stakeholders; ii) differentiating between and categorising stakeholders; and iii) investigating relationships between stakeholders. The range of methods that can be used to carry out each type of analysis is reviewed. These methods and approaches are then illustrated through a series of case studies funded through the Rural Economy and Land Use (RELU) programme. These case studies show the wide range of participatory and non-participatory methods that can be used, and discuss some of the challenges and limitations of existing methods for stakeholder analysis. The case studies also propose new tools and combinations of methods that can more effectively identify and categorise stakeholders and help understand their inter-relationships.


title: Transfer Neural Trees for Heterogeneous Domain Adaptation

abstract: Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose Transfer Neural Trees (TNT) which jointly solves cross-domain feature mapping, adaptation, and classification in a NN-based architecture. As the prediction layer in TNT, we further propose Transfer Neural Decision Forest (Transfer-NDF), which effectively adapts the neurons in TNT for adaptation by stochastic pruning. Moreover, to address semi-supervised HDA, a unique embedding loss term for preserving prediction and structural consistency between target-domain data is introduced into TNT. Experiments on classification tasks across features, datasets, and modalities successfully verify the effectiveness of our TNT.


Multimerization of Drosophila sperm protein Mst77F causes a unique condensed chromatin structure


title: High-frequency induction heating for small-foreign-metal particles Using SiC-MOSFETs inverter

abstract: This paper presents a detection method of small-foreign-metal particles using a 400 kHz SiC-MOSFETs high-frequency inverter. A 400 kHz SiC-MOSFETs high-frequency inverter is developed and applied to the small-foreign-metal particles detection on high-performance chemical films (HPCFs). HPCFs are manufactured with continuous production lines in industries. A new arrangement of IH coils are proposed, which is applicable for the practical production-lines of HPCFs. A prototype experimental model is constructed and tested. Experimental results demonstrate that the newly proposed IH coils with the constructed 400 kHz SiC-MOSFETs can heat small-foreign-metal particles and the heated small-foreign-metal particles can be detected by a thermographic camera. Experimental results with a new arrangement of IH coils also demonstrate that the proposed detection method of small-foreign-metal particles using 400 kHz SiC-MOSFETs high-frequency inverter can be applicable for the practical production lines of HPCFs.


title: Evolution of the mid range theory of comfort for outcomes research

abstract: The developmental stages of the mid range theory of comfort are discussed in this article, which includes its philosophic orientation and its inductive, deductive, and retroductive reasoning. Other steps that are described are the concept analysis of comfort, the operationalization of the outcome of patient comfort, the application of the theory in previous nursing studies, and the evaluation of the current theory as it has been adapted for outcomes research. This article is a guide that shows how a concept grows, becomes embedded in theory, is tested, and is adapted for the rapidly changing health care environment. The theory of comfort also offers a way to reconceptualize nurse productivity.


title: Accounting for genetic architecture in single- and multipopulation genomic prediction using weights from genomewide association studies in pigs

abstract: We studied the effect of including GWAS results on the accuracy of single- and multipopulation genomic predictions. Phenotypes (backfat thickness) and genotypes of animals from two sire lines (SL1, n = 1146 and SL3, n = 1264) were used in the analyses. First, GWAS were conducted for each line and for a combined data set (both lines together) to estimate the genetic variance explained by each SNP. These estimates were used to build matrices of weights (D), which was incorporated into a GBLUP method. Single population evaluated with traditional GBLUP had accuracies of 0.30 for SL1 and 0.31 for SL3. When weights were employed in GBLUP, the accuracies for both lines increased (0.32 for SL1 and 0.34 for SL3). When a multipopulation reference set was used in GBLUP, the accuracies were higher (0.36 for SL1 and 0.32 for SL3) than in single-population prediction. In addition, putting together the multipopulation reference set and the weights from the combined GWAS provided even higher accuracies (0.37 for SL1, and 0.34 for SL3). The use of multipopulation predictions and weights estimated from a combined GWAS increased the accuracy of genomic predictions.


title: Velocity kinematics based control of rocker-bogie type planetary rover

abstract: Kinematics of space rovers has been studied by several past researchers. However most studies assume that all the wheels of the rover is always in contact with the terrain. As the lunar terrain is very uneven and contains ash, dust, rocks, etc this assumption need not always be valid. Hence in this study we have analyzed the kinematics of a rover in which one wheel can loose contact with the ground or slide. The model considered in this paper is a bicycle model of the rover with an assumption that the yaw angle would be negligible and thus the results give us a fair insight of 3-D modeling of rovers. A quasi static analysis of the robot was used to develop a slip control chart that guides the amount of slip expected while traversing different terrains.


title: An Operational Approach to Information Leakage

abstract: Given two random variables $X$ and $Y$ , an operational approach is undertaken to quantify the “leakage” of information from $X$ to $Y$ . The resulting measure $\mathcal {L}\left ({X \!\! \to \!\! Y}\right)$ is called maximal leakage , and is defined as the multiplicative increase, upon observing $Y$ , of the probability of correctly guessing a randomized function of $X$ , maximized over all such randomized functions. A closed-form expression for $\mathcal {L}\left ({X \!\! \to \!\! Y}\right)$ is given for discrete $X$ and $Y$ , and it is subsequently generalized to handle a large class of random variables. The resulting properties are shown to be consistent with an axiomatic view of a leakage measure, and the definition is shown to be robust to variations in the setup. Moreover, a variant of the Shannon cipher system is studied, in which performance of an encryption scheme is measured using maximal leakage. A single-letter characterization of the optimal limit of (normalized) maximal leakage is derived and asymptotically-optimal encryption schemes are demonstrated. Furthermore, the sample complexity of estimating maximal leakage from data is characterized up to subpolynomial factors. Finally, the guessing framework used to define maximal leakage is used to give operational interpretations of commonly used leakage measures, such as Shannon capacity, maximal correlation, and local differential privacy.


title: Information-oriented Online Shopping Behavior in Electronic Commerce Environment

abstract: This study attempts to identify the information-oriented shopping behavior of online consumers in electronic commerce environment. Rich information revealed on the Internet leads to the change of consumers’ purchase decision-making processes. Findings from the consumer focus group interviews support that consumers do have tendency to perform search online before committing to any purchases. Also, it suggests a two-phased information search and evaluation model. In the proposed model, online shoppers tend to place more focus on different marketing mix elements in each phase. Most online shoppers evaluate the product in the first phase and compare prices in the second. While the decision of sales channel selection is postponed, both traditional and electronic alternatives are jointly evaluated in retail markets. Managerial implications from this study contribute insight about modern day consumers and retailing markets.


title: Sodium dodecyl sulfate/β-cyclodextrin vesicles embedded in chitosan gel for insulin delivery with pH-selective release

abstract: In an answer to the challenge of enzymatic instability and low oral bioavailability of proteins/peptides, a new type of drug-delivery vesicle has been developed. The preparation, based on sodium dodecyl sulfate (SDS) and β-cyclodextrin (β-CD) embedded in chitosan gel, was used to successfully deliver the model drug-insulin. The self-assembled SDS/β-CD vesicles were prepared and characterized by particle size, zeta potential, appearance, microscopic morphology and entrapment efficiency. In addition, both the interaction of insulin with vesicles and the stability of insulin loaded in vesicles in the presence of pepsin were investigated. The vesicles were crosslinked into thermo-sensitive chitosan/β-glycerol phosphate solution for an in-situ gel to enhance the dilution stability. The in vitro release characteristics of insulin from gels in media at different pH values were investigated. The insulin loaded vesicles–chitosan hydrogel (IVG) improved the dilution stability of the vesicles and provided pH-selective sustained release compared with insulin solution–chitosan hydrogel (ISG). In vitro, IVG exhibited slow release in acidic solution and relatively quick release in neutral solutions to provide drug efficacy. In simulated digestive fluid, IVG showed better sustained release and insulin protection properties compared with ISG. Thus IVG might improve the stability of insulin during its transport in vivo and contribute to the bioavailability and therapeutic effect of insulin.


title: Salient pole wound field synchronous machine for marine propulsors with outboard drives: Analysis and design

abstract: Propulsors with outboard drives (PODs) are propeller propulsion units situated under the hull of a marine vessel. They contain an electric motor which is directly connected to the propeller. With the absence of the gearbox, higher efficiency, lower noise and vibration can be achieved. Presently, the wound rotor synchronous motors dominate a large share of the podded propulsion market due to their high efficiency. In this paper, the design process of a 5-MW salient pole wound field synchronous motor for application in POD marine propulsion is presented. The paper contains preliminary design evaluation to describe motor specifications and the main steps of electromagnetic design. Prediction of motor performances are also presented and discussed.


title: Comparing static bug finders and statistical prediction

abstract: The all-important goal of delivering better software at lower cost has led to a vital, enduring quest for ways to find and remove defects efficiently and accurately. To this end, two parallel lines of research have emerged over the last years. Static analysis seeks to find defects using algorithms that process well-defined semantic abstractions of code. Statistical defect prediction uses historical data to estimate parameters of statistical formulae modeling the phenomena thought to govern defect occurrence and predict where defects are likely to occur. These two approaches have emerged from distinct intellectual traditions and have largely evolved independently, in “splendid isolation”. In this paper, we evaluate these two (largely) disparate approaches on a similar footing. We use historical defect data to apprise the two approaches, compare them, and seek synergies. We find that under some accounting principles, they provide comparable benefits; we also find that in some settings, the performance of certain static bug-finders can be enhanced using information provided by statistical defect prediction.


title: Improved algorithm for mining maximum frequent patterns based on FP-Tree

abstract: Mining association rule is an important matter in data mining, in which mining maximum frequent patterns is a key problem. Many of the previous algorithms mine maximum frequent patterns by producing candidate patterns firstly, then pruning. But the cost of producing candidate patterns is very high, especially when there exists long patterns. In this paper, the structure of a FP-tree is improved, we propose a fast algorithm based on FP-Tree for mining maximum frequent patterns, the algorithm does not produce maximum frequent candidate patterns and is more effectively than other improved algorithms. The new FP-Tree is a one-way tree and only retains pointers to point its father in each node, so at least one third of memory is saved. Experiment results show that the algorithm is efficient and saves memory space. Keywordsdata mining;association rule;maximum frequent pattern;FP-Tree


title: A Comprehensive Active Contour Model

abstract: Image segmentation is one of the substantial techniques in the field of image processing. It is excessively used in the field of medicine provides visual means for identification, inspection and tracking of diseases for surgical planning and simulation. Active contours or snakes are used extensively for image segmentation and processing applications, particularly to locate object boundaries. Active contours are regarded as promising and vigorously researched model-based approach to computer assisted medical image analysis. However, its utility is limited due to poor convergence of concavities and small capture range. Many subsequent models have been introduced in order to overcome these problems. This paper reviews the traditional model, the Gradient vector flow (GVF) model and the balloon model for different images and proposes a model which can provide the most accurate segmentation.


title: Design and Fabrication of a High-Gain 60-GHz Cavity-Backed Slot Antenna Array Fed by Inverted Microstrip Gap Waveguide

abstract: This communication deals with the design of a $16\times 16$ slot array antenna fed by inverted microstrip gap waveguide (IMGW). The whole structure designed in this communication consists of radiating slots, a groove gap cavity layer, a distribution feeding network, and a transition from standard WR-15 waveguide to the IMGW. First, a $2\times 2$ cavity-backed slot subarray is designed with periodic boundary condition to achieve good performances of radiation pattern and directivity. Then, a complete IMGW feeding network with a transition from WR-15 rectangular waveguide to the IMGW has been realized to excite the radiating slots. The complete antenna array is designed at 60-GHz frequency band and fabricated using Electrical Discharging Machining Technology. The measurements show that the antenna has a 16.95% bandwidth covering 54–64-GHz frequency range. The measured gain of the antenna is more than 28 dBi with the efficiency higher than 40% covering 54–64-GHz frequency range.


title: Recommendation as Classification: Using Social and Content-Based Information in Recommendation

abstract: Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals, and use nearest-neighbor techniques to make recommendations to a user concerning new artifacts. However, these methods do not use the significant amount of other information that is often available about the nature of each artifact - such as cast lists o r movie reviews, for example. This paper presents an inductive learning approach to recommendation that is able to use both ratings information and other forms of information about each artifact in predicting user preferences. We show that our method outperforms an existing social-filtering method in the domain of movie recommendations on a dataset of more than 45,000 movie ratings collected from a community of over 250 users.


title: The ICSI RT07s Speaker Diarization System.

abstract: A cryostatic device comprising an inner wall and an outer wall connected at their upper parts by a filling orifice, a vacuum being made between the two walls, wherein the tank of cryogenic liquid, which is defined by the inner wall, is divided by a separating partition into two tanks, a primary tank and a secondary tank, the secondary tank surrounding all or part of the primary tank, the filling orifice opening into the secondary tank, and the primary tank and the secondary tank communicating via an orifice located in the separating partition at a level such that the volume of the primary tank located below said orifice is approximately equal to the volume located above this same orifice in this same tank.


title: Effect of tattoos on perceptions of credibility and attractiveness.

abstract: Summary This study examined the effects of tattooing on perception of a male's and a female's credibility and attractiveness. 74 undergraduates viewed a photograph of a tattooed or nontattooed male or female model and then rated dimensions of the models' credibility and attractiveness. Analysis indicated that, although the models' attractiveness ratings were not affected by having a tattoo, their credibility ratings were generally lower when wearing a tattoo than when not wearing one.


title: On-line trajectory clustering for anomalous events detection

abstract: In this paper, we propose a trajectory clustering algorithm suited for video surveillance systems. Trajectories are clustered on-line, as the data are collected, and clusters are organized in a tree-like structure that, augmented with probability information, can be used to perform behaviour analysis, since it allows the identification of anomalous events.


title: Comparison and evaluation of digital signature schemes employed in ndn network

abstract: It is well known that Named Data networking ensure data integrity so that every important data has to be signed by its owner in order to send it safely inside the network. Similarly, in NDN we have to assure that none could open the data except authorized users. Since only the endpoints have the right to sign the data or check its validity during the verification process , we have considered that the data could be requested from various types of devices used by different people, these devices could be anything like a smartphone, PC, sensor node etc.b, with a different CPU descriptions, parameters, and memory sizes, however their ability to check the high traffic of a data during the key generation and/or verification period is definitely a hard task and it could exhaust the systems with low computational resources. RSA and ECDSA as digital signature algorithms have proven their efficiency against cyber-attacks, they are characterized by their speed to encrypt and decrypt data, in addition to their competence at checking the data integrity. The main purpose of our research was to find the optimal algorithm that avoids the system’s overhead and offers the best time during the signature scheme


title: A Hybrid Fragmentation Approach for Distributed Deductive Database Systems

abstract: Fragmentation of base relations in distributed database management systems increases the level of concurrency and therefore system throughput for query processing. Algorithms for horizontal and vertical fragmentation of relations in relational, object-oriented and deductive databases exist; however, hybrid fragmentation techniques based on variable bindings appearing in user queries and query-access-rule dependency are lacking for deductive database systems. In this paper, we propose a hybrid fragmentation approach for distributed deductive database systems. Our approach first considers the horizontal partition of base relations according to the bindings imposed on user queries, and then generates vertical fragments of the horizontally partitioned relations and clusters rules using affinity of attributes and access frequency of queries and rules. The proposed fragmentation technique facilitates the design of distributed deductive database systems.