title: Hybrid Message-Passing and Shared-Memory Programming in a Molecular Dynamics Application On Multicore Clusters

abstract: Hybrid programming, whereby shared-memory and message-passing programming techniques are combined within a single parallel application, has often been discussed as a method for increasing code performance on clusters of symmetric multiprocessors (SMPs). This paper examines whether the hybrid model brings any performance benefits for clusters based on multicore processors. A molecular dynamics application has been parallelized using both MPI and hybrid MPI/OpenMP programming models. The performance of this application has been examined on two high-end multicore clusters using both Infiniband and Gigabit Ethernet interconnects. The hybrid model has been found to perform well on the higher-latency Gigabit Ethernet connection, but offers no performance benefit on low-latency Infiniband interconnects. The changes in performance are attributed to the differing communication profiles of the hybrid and MPI codes.


title: Intrusion detection of distributed denial of service attack in cloud

abstract: Security issue in cloud environment is one of the major obstacle in cloud implementation. Network attacks make use of the vulnerability in the network and the protocol to damage the data and application. Cloud follows distributed technology; hence it is vulnerable for intrusions by malicious entities. Intrusion detection systems (IDS) has become a basic component in network protection infrastructure and a necessary method to defend systems from various attacks. Distributed denial of service (DDoS) attacks are a great problem for a user of computers linked to the Internet. Data mining techniques are widely used in IDS to identify attacks using the network traffic. This paper presents and evaluates a Radial basis function neural network (RBF-NN) detector to identify DDoS attacks. Many of the training algorithms for RBF-NNs start with a predetermined structure of the network that is selected either by means of a priori knowledge or depending on prior experience. The resultant network is frequently inadequate or needlessly intricate and a suitable network structure could be configured only by trial and error method. This paper proposes Bat algorithm (BA) to configure RBF-NN automatically. Simulation results demonstrate the effectiveness of the proposed method.


title: Wireless Mesh Networks: Current Challenges and Future Directions of Web-In-The-Sky

abstract: Within the short span of a decade, Wi-Fi hotspots have revolutionized Internet service provisioning. With the increasing popularity and rising demand for more public Wi-Fi hotspots, network service providers are facing a daunting task. Wi-Fi hotspots typically require extensive wired infrastructure to access the backhaul network, which is often expensive and time consuming to provide in such situations. wireless mesh networks (WMNs) offer an easy and economical alternative for providing broadband wireless Internet connectivity and could be called the web-in-the-sky. In place of an underlying wired backbone, a WMN forms a wireless backhaul network, thus obviating the need for extensive cabling. They are based on multihop communication paradigms that dynamically form a connected network. However, multihop wireless communication is severely plagued by many limitations such as low throughput and limited capacity. In this article we point out key challenges that are impeding the rapid progress of this upcoming technology. We systematically examine each layer of the network and discuss the feasibility of some state-of-the-art technologies/protocols for adequately addressing these challenges. We also provide broader and deeper insight to many other issues that are of paramount importance for the successful deployment and wider acceptance of WMNs.


EFFECTIVENESS OF EGYPTIAN PROPOLIS ON DENTAL PLAQUE FORMATION IN HIGH CARIES RISK CHILDREN


title: Development and initial validation of an expanded and revised version of the Short-form McGill Pain Questionnaire (SF-MPQ-2)

abstract: Abstract The objective of the present research was to develop a single measure of the major symptoms of both neuropathic and non-neuropathic pain that can be used in studies of epidemiology, natural history, pathophysiologic mechanisms, and treatment response. We expanded and revised the Short-form McGill Pain Questionnaire 1 (SF-MPQ) pain descriptors by adding symptoms relevant to neuropathic pain and by modifying the response format to a 0–10 numerical rating scale to provide increased responsiveness in longitudinal studies and clinical trials. The reliability, validity, and subscale structure of the revised SF-MPQ (SF-MPQ-2 1 ) were examined in responses from 882 individuals with diverse chronic pain syndromes and in 226 patients with painful diabetic peripheral neuropathy who participated in a randomized clinical trial. The data suggest that the SF-MPQ-2 has excellent reliability and validity, and the results of both exploratory and confirmatory factor analyses provided support for four readily interpretable subscales—continuous pain, intermittent pain, predominantly neuropathic pain, and affective descriptors. These results provide a basis for use of the SF-MPQ-2 in future clinical research, including clinical trials of treatments for neuropathic and non-neuropathic pain conditions.


title: CampusSense — A smart vehicle parking monitoring and management system using ANPR cameras and android phones

abstract: Vehicle parking monitoring and management has become a big challenge for educational institutions with increasing enrollments, high percentage of vehicle ownership and decreasing parking supply which in result triggering blockage of vehicle, congestion, wastage of time and money. In university campuses particularly in Kingdom of Saudi Arabia, vehicle parking monitoring and management problem is getting worse and more frustrating due to the fact that majority of students, faculty and staff members own cars and drive through them to the university campuses. These common problems include finding out people (evidence) who are responsible for the damages (hitting, scraping, scratching and dents) to other cars and the blockage of car due to wrong car parking which takes much time to locate the owner of the car. Moreover, locating or forgetting their car park location another difficulty that is often faced by the students, faculty and staff members. The existing cameras located at the parking lots are only for video surveillance and cannot help in such situations as there is a lack of proper vehicle parking monitoring and management system. To cope with above mentioned problems and to ensure a better parking experience by accommodating increasing number of vehicles in a proper convenient manner, we propose a smart vehicle parking monitoring and management system called CampusSense. In CampusSense, Automatic Number Plate Recognition (ANPR) cameras and android based mobile application is developed to efficiently monitor, manage and protect the parking facilities at university campuses. Parking problems around the university campus faced by the students, faculty and staff members are analyzed by conducting a survey.


title: Catch the moment: maintaining closed frequent itemsets over a data stream sliding window

abstract: This paper considers the problem of mining closed frequent itemsets over a data stream sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets. However, monitoring only frequent itemsets will make it impossible to detect new itemsets when they become frequent. In this paper, we introduce a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of itemsets over a sliding window. The selected itemsets contain a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non-frequent to frequent) must occur through the boundary. Because the boundary is relatively stable, the cost of mining closed frequent itemsets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET. Our experiments show that our algorithm performs much better than representative algorithms for the sate-of-the-art approaches.


title: A New Distance Measure for Non-Identical Data with Application to Image Classification

abstract: Distance measures are part and parcel of many computer vision algorithms. The underlying assumption in all existing distance measures is that feature elements are independent and identically distributed. However, in real-world settings, data generally originate from heterogeneous sources even if they do possess a common data-generating mechanism. Since these sources are not identically distributed by necessity, the assumption of identical distribution is inappropriate. Here, we use statistical analysis to show that feature elements of local image descriptors are indeed non-identically distributed. To test the effect of omitting the unified distribution assumption, we created a new distance measure called the Poisson-Binomial radius (PBR). PBR is a bin-to-bin distance which accounts for the dispersion of bin-to-bin information. PBR's performance was evaluated on twelve benchmark data sets covering six different classification and recognition applications: texture, material, leaf, scene, ear biometrics and category-level image classification. Results from these experiments demonstrate that PBR outperforms state-of-the-art distance measures for most of the data sets and achieves comparable performance on the rest, suggesting that accounting for different distributions in distance measures can improve performance in classification and recognition tasks. HighlightsEmpirical evidence is provided that real-world data is non-identically distributed.PBR, the first distance measure to account for non-identical data is proposed.PBR was tested in 6 test applications using 12 benchmark data sets.PBR outperforms state-of-the-art measures for most data sets.Avoiding the identical distribution assumption can improve classification.


The depth of inflammation in frontal fibrosing alopecia and lichen planopilaris: A potential distinguishing feature


title: Aurora: Adaptive Block Replication in Distributed File Systems

abstract: Distributed file systems such as Google File System and Hadoop Distributed File System have been used to store large volumes of data in Cloud data centers. These systems divide data sets in blocks of fixed size and replicate them over multiple machines to achieve both reliability and efficiency. Recent studies have shown that data blocks tend to have a wide disparity in data popularity. In this context, the naive block replication schemes used by these systems often cause an uneven load distribution across machines, which reduces the overall I/O throughput of the system. While many replication algorithms have been proposed, existing solutions have not carefully studied the placement of data blocks that balances the load across machines, while ensuring node and rack-level reliability requirements are satisfied. In this paper, we study the dynamic data replication problem with the goal of balancing machine load while ensuring machine and rack-level reliability requirements are met. We propose several local search algorithms that provide constant approximation guarantees, yet simple and practical for implementation. We further present Aurora, a dynamic block placement mechanism that implements these algorithms in the Hadoop Distributed File System with minimal overhead. Through experiments using workload traces from Yahoo! and Facebook, we show Aurora reduces machine load imbalance by up to 26.9% compared to existing solutions, while satisfying node and rack-level reliability requirements.


title: SOI CMOS tunable capacitors for RF antenna aperture tuning

abstract: This paper provides a detailed analysis of a SOI CMOS tunable capacitor for antenna tuning. Design expressions for a switched capacitor network are given and quality factor of the whole network is expressed as a function of design parameters. Application to antenna aperture tuning is described by combining a 130 nm SOI CMOS tunable capacitor with a printed notch antenna. The proposed tunable multiband antenna can be tuned from 420 MHz to 790 MHz, with an associated radiation efficiency in the 33–73% range.


title: Technological Frames, Organizational Capabilities, and IT Use: An Empirical Investigation of Electronic Procurement

abstract: The process by which organizations incorporate technological innovations into existing routines and use them on a regular basis persists as a central concern in the literature. Although we now have a fairly robust understanding of the drivers of innovation adoption, the use of innovations is less understood. In this paper, we draw on two streams of literature, managerial and organizational sensemaking, and organizational capabilities that have hitherto been used independently, to investigate organizational use of information technology (IT)-based innovations. Building on and extending prior work, we posit that organizational capabilities serve as complements to managers' technological frames related to an innovation. We focus on the use of an important technological innovation---business-to-business (B2B) electronic markets for procurement. We examine interactions between three technological frames---benefits frame, threat frame, and adjustment frame, and two organizational capabilities---technological opportunism and technological sophistication, and their relationship with the use of B2B electronic markets in firms. We test our research model using survey data collected from 292 firms. Results largely support the proposed conceptualization and shed new light on the key factors associated with firms' use of B2B electronic markets. Theoretical and practical implications of the findings are discussed.


title: Why Share Knowledge? The Influence of ICT on the Motivation for Knowledge Sharing

abstract: Information and communication technology (ICT) can enhance knowledge sharing by lowering temporal and spatial barriers between knowledge workers, and improving access to information about knowledge. Looking at ICT for knowledge sharing in this light, however, has limited value, because it ignores when and how the quality of knowledge sharing will be enhanced. A more encompassing perspective will come about if ICT is studied with relation to the motivation for knowledge sharing. The article explores this perspective by developing a theoretical model identifying and linking the variables involved. By presenting the outcomes of an empirical investigation, it also illustrates the differential effects of ICT on the motivation for knowledge sharing in different settings.


title: Barriers to Adoption of Mobile banking : Evidence from Ghana

abstract: The purpose of this research is to investigate the perceived barriers to adoption of mobile banking among consumers, and to assess whether the usage of M-Banking is constraint on the basis of diffe ...


Test cases for plagiarism detection software


title: Decapitation in suicidal hanging--vital reaction patterns.

abstract: Complete or incomplete decapitation as a consequence of suicidal hanging is very rare, few cases having been reported in the worldwide literature. Posthanging decapitation is typically related to a drop of several meters. Three cases of complete decapitation and one case of incomplete decapitation by suicidal hanging are reported with particular emphasis on internal findings and vital reaction patterns. Personal, circumstantial, autopsy, and toxicological data were analyzed to define basic characteristics of such extreme injuries. The crucial factor for the state of decapitation itself is the kinetic energy of the falling body, the strength of the human neck tissue, and the diameter and elasticity of the used ligature. Results of our case study suggest Simon's hemorrhage and air embolism as useful autopsy findings in posthanging beheading cases. Simon's hemorrhage was demonstrated in three cases of four. The test for air embolism was positive in all four cases.


title: Scientific Articles Exploration System Model based in Immersive Virtual Reality and Natural Language Processing Techniques

abstract: After having carried out a historical review and identifying the state of the art in relation to the interfaces for the exploration of scientific articles, the authors propose a model based in an immersive virtual environment, natural user interfaces and natural language processing, which provides an excellent experience for the user and allows for better use of some of its capabilities, for example, intuition and cognition in 3-dimensional environments. In this work, the Oculus Rift and Leap Motion Hardware devices are used. This work aims to contribute to the proposal of a tool which would facilitate and optimize the arduous task of reviewing literature in scientific databases. The case study is the exploration and information retrieval of scientific articles using ALICIA (Scientific database of Peru). Finally, conclusions and recommendations for future work are laid out and discussed.


title: Inkjet-Printed Flexible mm-Wave Van-Atta Reflectarrays: A Solution for Ultralong-Range Dense Multitag and Multisensing Chipless RFID Implementations for IoT Smart Skins

abstract: In this effort, the authors implement the first ultralong-range chipless sensing sticker, by providing more than an order of magnitude increase in reading range, compared with the state of the art. The theoretical advantages of the use of millimeter-wave frequencies for high-performance chipless radio-frequency identification (RFID) sensor implementations are first argued before both a new fully inkjet-printed flexible device, based on the Van-Atta reflectarray structure, as well as a new chipless RFID polarimetric interrogation, and time-frequency data-processing approach is then presented and implemented, for operation in the Ka-band. The array, fully inkjet printed on Kapton HN polyimide, was demonstrated as being robust to variations of interrogation angle (between ±70° from normal), as well as to bending. With its demonstrated range, in excess of 30 m, and its proven adequacy for dense multitag and multisensing implementations in indoor environments, the structure may set the foundation for the emergence of flexible printable low-cost sensing smart skins for the Internet of Things.


title: Hovering flight and vertical landing control of a VTOL Unmanned Aerial Vehicle using optical flow

abstract: This paper presents a nonlinear controller for hovering flight and touchdown control for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using inertial optical flow. The VTOL vehicle is assumed to be a rigid body, equipped with a minimum sensor suite (camera and IMU), manoeuvring over a textured flat target plane. Two different tasks are considered in this paper: the first concerns the stability of hovering flight and the second one concerns regulation of automatic landing using the divergent optical flow as feedback information. Experimental results on a quad-rotor UAV demonstrate the performance of the proposed control strategy.


title: Sensing muscle activities with body-worn sensors

abstract: We demonstrate that simple, unobtrusive sensors attached to the lower arm can be used to capture muscle activations during specific hand and arm activities such as grasping. Specifically, we investigate the use of force sensitive resistors and fabric stretch sensors, that can both be easily integrated into clothing. We use the above sensors to detect the contractions of arm muscles. We present and compare the signals that both sensors produce for a set of typical hand actions. We finally argue that they can provide important information for activity recognition.


title: An exact bit-parallel algorithm for the maximum clique problem

abstract: This paper presents a new exact maximum clique algorithm which improves the bounds obtained in state of the art approximate coloring by reordering the vertices at each step. Moreover, the algorithm can make full use of bit strings to sort vertices in constant time as well as to compute graph transitions and bounds efficiently, exploiting the ability of CPUs to process bitwise operations in blocks of size the ALU register word. As a result it significantly outperforms a current leading algorithm.


title: Towards Neural Phrase-based Machine Translation

abstract: In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time. Our experiments show that NPMT achieves superior performances on IWSLT 2014 German-English/English-German and IWSLT 2015 English-Vietnamese machine translation tasks compared with strong NMT baselines. We also observe that our method produces meaningful phrases in output languages.


title: FA ] 2 N ov 2 00 3 An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

abstract: We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary pre-assigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted l^p-penalties on the coefficients of such expansions, with 1<or = p<or =2, still regularizes the problem. If p<2, regularized solutions of such l^p-penalized problems will have sparser expansions, with respect to the basis under consideration. To compute the corresponding regularized solutions we propose an iterative algorithm that amounts to a Landweber iteration with thresholding (or nonlinear shrinkage) applied at each iteration step. We prove that this algorithm converges in norm. We also review some potential applications of this method.


title: Studying connections in the living human brain with diffusion MRI

abstract: The purpose of this article is to explain how the random walks of water molecules undergoing diffusion in living tissue may be exploited to garner information on the white matter of the human brain and its connections. We discuss the concepts underlying diffusion-weighted (DW) imaging, and diffusion tensor imaging before exploring fibre tracking, or tractography, which aims to reconstruct the three-dimensional trajectories of white matter fibres non-invasively. The two main classes of algorithm – deterministic and probabilistic tracking – are compared and example results are presented. We then discuss methods to resolve the ‘crossing fibre’ issue which presents a problem when using the tensor model to characterize diffusion behaviour in complex tissue. Finally, we detail some of the issues that remain to be resolved before we can reliably characterize connections of the living human brain in vivo.


Wisdom of Students: A Consistent Automatic Short Answer Grading Technique.


title: Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker

abstract: Object tracking is a hot topic in computer vision. Thanks to the booming of the very high resolution (VHR) remote sensing techniques, it is now possible to track targets of interests in satellite videos. However, since the targets in the satellite videos are usually too small in comparison with the entire image, and too similar with the background, most state-of-the-art algorithms failed to track the target in satellite videos with a satisfactory accuracy. Due to the fact that optical flow shows great potential to detect even the slight movement of the targets, we proposed a multiframe optical flow tracker for object tracking in satellite videos. The Lucas–Kanade optical flow method was fused with the HSV color system and integral image to track the targets in the satellite videos, while multiframe difference method was utilized in the optical flow tracker for a better interpretation. The experiments with five VHR remote sensing satellite video datasets indicate that compared with state-of-the-art object tracking algorithms, the proposed method can track the target more accurately.


title: XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

abstract: Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce XGAN ("Cross-GAN"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.


title: BitDrones: Towards Levitating Programmable Matter Using Interactive 3D Quadcopter Displays

abstract: In this paper, we present BitDrones, a platform for the construction of interactive 3D displays that utilize nano quadcopters as self-levitating tangible building blocks. Our prototype is a first step towards supporting interactive mid-air, tangible experiences with physical interaction techniques through multiple building blocks capable of physically representing interactive 3D data.


title: Self-standardizing cognitive profile based on gardner's multiple intelligence theory

abstract: There are different theories to estimate cognitive abilities. Gardner proposed a model, where intelligence is defined by multiple fields instead of using one specific number. However, the characterization and automated evaluation of these fields are still open issues. If we can make standalone measurements in separate intelligence fields, the numerical modeling of the competencies is possible. Observing the cognitive abilities of children can lead us to an optimized learning experience in performance and engagement. Our aim is to automatically match problem sets and intelligence fields in order to achieve a faster and deeper learning experience. The amount of data to be collected makes the automation of this process a focus point of these studies. In this paper we present an automatic cognitive profiling system with the help of smart devices and AdaptEd — an adaptive educational gaming framework. With the presented system the learning methods and tasks can be fine-tuned, and a personalized and more effective learning environment can be achieved.


title: Camera based visible light communication system for underwater applications

abstract: Visible light can be used to implement an underwater communication link, as an alternative for the current acoustic based communication methods. This paper presents the design and implementation of a unidirectional underwater visible light communication (VLC) system where a camera receiver is used. Specifically, data is transmitted from a submerged system to a floating body. In order to characterize the performance of this system, we first model the channel under zero water turbulence conditions for different transmission distances. Also algorithms and experimental results of detection are discussed. Our results show that, the proposed system can only achieve low data speeds compared to traditional VLC underwater systems, where photo detectors are used at the receiver. However, reasonable bit error rate performance can be achieved using the proposed VLC system design. Hence the proposed system can be developed further and can be used as a successful alternative for acoustic based under water communication systems.


title: "Now, i have a body": uses and social norms for mobile remote presence in the workplace

abstract: As geographically distributed teams become increasingly common, there are more pressing demands for communication work practices and technologies that support distributed collaboration. One set of technologies that are emerging on the commercial market is mobile remote presence (MRP) systems, physically embodied videoconferencing systems that remote workers use to drive through a workplace, communicating with locals there. Our interviews, observations, and survey results from people, who had 2-18 months of MRP use, showed how remotely-controlled mobility enabled remote workers to live and work with local coworkers almost as if they were physically there. The MRP supported informal communications and connections between distributed coworkers. We also found that the mobile embodiment of the remote worker evoked orientations toward the MRP both as a person and as a machine, leading to formation of new usage norms among remote and local coworkers.


title: The Human Microbiome: Our Second Genome*

abstract: The human genome has been referred to as the blueprint of human biology. In this review we consider an essential but largely ignored overlay to that blueprint, the human microbiome, which is composed of those microbes that live in and on our bodies. The human microbiome is a source of genetic diversity, a modifier of disease, an essential component of immunity, and a functional entity that influences metabolism and modulates drug interactions. Characterization and analysis of the human microbiome have been greatly catalyzed by advances in genomic technologies. We discuss how these technologies have shaped this emerging field of study and advanced our understanding of the human microbiome. We also identify future challenges, many of which are common to human genetic studies, and predict that in the future, analyzing genetic variation and risk of human disease will sometimes necessitate the integration of human and microbial genomic data sets.


title: 3DUNDERWORLD-SLS: An Open-Source Structured-Light Scanning System for Rapid Geometry Acquisition

abstract: Recently, there has been an increase in the demand of virtual 3D objects representing real-life objects. A plethora of methods and systems have already been proposed for the acquisition of the geometry of real-life objects ranging from those which employ active sensor technology, passive sensor technology or a combination of various techniques. ::: In this paper we present the development of a 3D scanning system which is based on the principle of structured-light, without having particular requirements for specialized equipment. We discuss the intrinsic details and inherent difficulties of structured-light scanning techniques and present our solutions. Finally, we introduce our open-source scanning software system "3DUNDERWORLD-SLS" which implements the proposed techniques both in CPU and GPU. We have performed extensive testing with a wide range of models and report the results. Furthermore, we present a comprehensive evaluation of the system and a comparison with a high-end commercial 3D scanner.


title: Efficient Mobile Implementation of A CNN-based Object Recognition System

abstract: Because of the recent progress on deep learning studies, Convolutional Neural Network (CNN) based method have outperformed conventional object recognition methods with a large margin. However, it requires much more memory and computational costs compared to the conventional methods. Therefore, it is not easy to implement a CNN-based object recognition system on a mobile device where memory and computational power are limited. In this paper, we examine CNN architectures which are suitable for mobile implementation, and propose multi-scale network-in-networks (NIN) in which users can adjust the trade-off between recognition time and accuracy. We implemented multi-threaded mobile applications on both iOS and Android employing either NEON SIMD instructions or the BLAS library for fast computation of convolutional layers, and compared them in terms of recognition time on mobile devices. As results, it has been revealed that BLAS is better for iOS, while NEON is better for Android, and that reducing the size of an input image by resizing is very effective for speedup of CNN-based recognition.


title: A Comparative Study of Off-Line Deep Learning Based Network Intrusion Detection

abstract: Network intrusion detection systems (NIDS) are essential security building-blocks for today's organizations to ensure safe and trusted communication of information. In this paper, we study the feasibility of off-line deep learning based NIDSes by constructing the detection engine with multiple advanced deep learning models and conducting a quantitative and comparative evaluation of those models. We first introduce the general deep learning methodology and its potential implication on the network intrusion detection problem. We then review multiple machine learning solutions to two network intrusion detection tasks (NSL-KDD and UNSW-NB15 datasets). We develop a TensorFlow-based deep learning library, called NetLearner, and implement a handful of cutting-edge deep learning models for NIDS. Finally, we conduct a quantitative and comparative performance evaluation of those models using NetLearner.


title: Distributed storage allocation problems

abstract: We investigate the problem of using several storage nodes to store a data object, subject to an aggregate storage budget or redundancy constraint. It is challenging to find the optimal allocation that maximizes the probability of successful recovery by the data collector because of the large space of possible symmetric and nonsymmetric allocations, and the nonconvexity of the problem. For the special case of probability-1 recovery, we show that the optimal allocation that minimizes the required budget is symmetric. We further explore several storage allocation and access models, and determine the optimal symmetric allocation in the high-probability regime for a case of interest. Based on our experimental investigation, we make a general conjecture about a phase transition on the optimal allocation.


title: A study of organic food consumption behavior using the decomposed theory of planned behavior

abstract: Organic food is an achievement of agricultural scientific and technological innovations, in order to ensure the application of agricultural science and technology and innovation from organic agricultural producers and participants benefit. This study employed the decomposed theory of planned behavior to analyze the antecedent factors that influence consumer purchase of organic food by deconstruction of the constructs related to attitude, the subjective norm, and the perceived behavioral control. Based on these influencing effects, the study aimed to provide marketing suggestions to popularize organic food. In total, 441 effective questionnaires were collected. The data was analyzed using the structural equation model. The study results indicate that consumers with ethical consciousness will consider environmental protection when they make food purchasing decisions, and have a comparatively positive attitude towards the purchase of organic food. One of the main motives for purchasing organic food is the requirement that food ingredients are safe and natural. Additionally, with respect to external influences, consumers tend to trust information delivered by TV media, experts, and Internet word-of-mouth. Nonetheless, consumers with high or low involvement obviously have significant differences in the relationships between facilitating conditions and perceived behavioral control. Therefore, intensifying the environmental resources of organic food will enhance the purchasing intention of consumers who have low involvement.


title: An AREA-Oriented Heuristic for Scheduling DAGs on Volatile Computing Platforms

abstract: Many modern computing platforms—notably clouds and desktop grids—exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and dynamically, even in the midst of a computation. We introduce a new quality metric, AREA, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. AREA measures the average number of chores that a schedule renders eligible for execution at each step of a computation. Even though the definition of AREA does not mention any properties of host platforms (such as volatility), intuition suggests that rendering chores eligible at a faster rate will have a benign impact on the performance of volatile platforms. We report on simulation experiments that support this intuition. Earlier work has derived the basic properties of the AREA metric and has shown how to efficiently craft AREA-maximizing ( A-M ) schedules for several classes of significant computations. Even though A-M schedules always exist for every computation, it is not always known how to derive such schedules efficiently. In response, the current study develops an efficient algorithm that produces AREA-Oriented ( A-O ) schedules, which aim to efficiently approximate the AREAs of A-M schedules on arbitrary computations. The simulation experiments reported on here suggest that, in common with A-M schedules, A-O schedules complete computations on volatile heterogeneous platforms faster than a variety of heuristics that range from lightweight ones to computationally intensive ones—albeit not to the same degree as A-M schedules do. Our experiments suggest that schedules having larger AREAs have smaller completion times—but no proof of that yet exists.


title: 3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks

abstract: The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods. The common solution of transforming the data into a 3D voxel grid introduces its own challenges, mainly large memory size. In this paper we propose a novel 3D point cloud representation called 3D Modified Fisher Vectors (3DmFV). Our representation is hybrid as it combines the discrete structure of a grid with continuous generalization of Fisher vectors, in a compact and computationally efficient way. Using the grid enables us to design a new CNN architecture for point cloud classification and part segmentation. In a series of experiments we demonstrate competitive performance or even better than state-of-the-art on challenging benchmark datasets.


title: Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data

abstract: Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.


title: Spatial Sensory Organization and Body Representation in Pain Perception

abstract: Pain is a subjective experience that protects the body. This function implies a special relation between the brain mechanisms underlying pain perception and representation of the body. All sensory systems involve the body for the trivial reason that sensory receptors are located in the body. The nociceptive system of detecting noxious stimuli comprises two classes of peripheral afferents, Aδ and C nociceptors, that cover almost the entire body surface. We review evidence from experimental studies of pain in humans and other animals suggesting that Aδ skin nociceptors project to a spatially-organised, somatotopic map in the primary somatosensory cortex. While the relation between pain perception and homeostatic regulation of bodily systems is widely acknowledged, the organization of nociceptive information into spatial maps of the body has received little attention. Importantly, the somatotopic neural organization of pain systems can shed light on pain-related plasticity and pain modulation. Finally, we show that the neural coding of noxious stimuli, and consequent experience of pain, are both strongly influenced when cognitive representations of the body are activated by viewing the body, as opposed to viewing another object - an effect we term 'visual analgesia'. We argue that pain perception involves some of the representational properties of exteroceptive senses, such as vision and touch. Pain, however, has the unique feature that the content of representation is the body itself, rather than any external object of perception. We end with some suggestions regarding how linking pain to body representation could shed light on clinical conditions, notably chronic pain.


title: Exploiting Social Network to Enhance Human-to-Human Infection Analysis without Privacy Leakage

abstract: Human-to-human infection, as a type of fatal public health threats, can rapidly spread, resulting in a large amount of labor and health cost for treatment, control and prevention. To slow down the spread of infection, social network is envisioned to provide detailed contact statistics to isolate susceptive people who has frequent contacts with infected patients. In this paper, we propose a novel human-to-human infection analysis approach by exploiting social network data and health data that are collected by social network and e-healthcare technologies. We enable the social cloud server and health cloud server to exchange social contact information of infected patients and user's health condition in a privacy-preserving way. Specifically, we propose a privacy-preserving data query method based on conditional oblivious transfer to guarantee that only the authorized entities can query users’ social data and the social cloud server cannot infer anything during the query. In addition, we propose a privacy-preserving classification-based infection analysis method that can be performed by untrusted cloud servers without accessing the users’ health data. The performance evaluation shows that the proposed approach achieves higher infection analysis accuracy with the acceptable computational overhead.


title: Domain ontology based semantic search for efficient information retrieval through automatic query expansion

abstract: To achieve semantic search, a search engine is needed which can interpret the meaning of a user's query and the relations among the concepts that a document contains with respect to a particular domain. We are presenting the skeleton of such a system based on ontology. In this system, a user enters a query from which the meaningful concepts are extracted; using these concepts and domain ontology, query expansion is performed. For all the terms (expanded and initial query terms), SPARQL query is built and then it is fired on the knowledge base that finds appropriate RDF triples in knowledge Base. Web documents relevant to the requested concepts and individuals specified in these triples are then retrieved. Finally, the retrieved documents are ranked according to their relevance to the user's query and then are sent to the user. If a user wants to find specific information; can search with another module of our system that works without query expansion. The approach of query expansion makes use of query concepts as well as synonyms of these concepts and the new terms relate with the original query terms within a threshold.


title: Reengineering, Simulation and Data Analysis of an RFID System

abstract: We present a discrete event simulation model reproducing the adoption of Radio Frequency Identification (RFID) technology for the optimal management of common logistics processes of a Fast Moving Consumer Goods (FMCG) warehouse. In this study, simulation is exploited as a powerful tool to replicate both the reengineered RFID logistics processes and the flows of Electronic Product Code (EPC) data generated by such processes. Moreover, a complex tool has been developed to analyze data resulting from the simulation runs, thus addressing the issue of how the flows of EPC data generated by RFID technology can be exploited to provide value-added information for optimally managing the logistics processes. Specifically, an EPCIS compliant Data Warehouse has been designed to act as EPCIS Repository and store EPC data resulting from simulation. Starting from EPC data, properly designed tools, referred to as Business Intelligence Modules, provide value-added information for processes optimization. Due to the newness of RFID adoption in the logistics context and to the lack of real case examples that can be examined, we believe that both the model and the data management system developed can be very useful to understand the practical implications of the technology and related information flow, as well as to show how to leverage EPC data for process management. Results of the study can provide a proof-of-concept to substantiate the adoption of RFID technology in the FMCG industry.


title: Camera calibration with known rotation

abstract: We address the problem of using external rotation information with uncalibrated video sequences. The main problem addressed is, what is the benefit of the orientation information for camera calibration? It is shown that in case of a rotating camera the camera calibration problem is linear even in the case that all intrinsic parameters vary. For arbitrarily moving cameras the calibration problem is also linear but underdetermined for the general case of varying all intrinsic parameters. However, if certain constraints are applied to the intrinsic parameters the camera calibration can be computed linearly. It is analyzed which constraints are needed for camera calibration of freely moving cameras. Furthermore we address the problem of aligning the camera data with the rotation sensor data in time. We give an approach to align these data in case of a rotating camera.


title: Compact Microstrip-to-CPS Transition for UWB Application

abstract: A novel ultra-wideband (UWB) microstrip-to-CPS (coplanar stripline) transition has been developed. This transition or balun structure has several attractive advantages such as good impedance transformation, compact size and wide bandwidth. After the parallel-coupled line section between the microstrip line and CPS is investigated under varied traversal dimensions, a wide transmitting band is well achieved with the emergence of two transmission poles. Next, such a single transition circuit is optimally designed to cover the whole UWB band (3.1 GHz to 10.6 GHz). To verify the predicted results in experiment, the two back-to-back transitions with the same 50 Omega microstrip feed lines are fabricated and tested. Measured results exhibit the return loss close to 10.0 dB over a band from 3.5 GHz to 10.0 GHz.


title: Scenario planning - lessons for practice from teaching and learning

abstract: Abstract This paper begins with an introduction to the scenario concept in relation to futures thinking, Soft Operational Research and strategic development. A brief overview of the range of scenario development methods available is then presented. The paper goes on to introduce a particular scenario development methodology as advocated in the 1970s and 1980s, which for a number of years has formed a core component of a strategic development course taught at the University of Warwick. A large number of participants from a variety of backgrounds have experienced the course over a 15 year period and have developed scenarios for real organisations in workshops and live settings. This paper draws from such experiences, by identifying a number of common pitfalls concerning scenario development, and then recommending a revised methodology that addresses them. A selection of scenarios developed by participants are used both to illustrate the pitfalls observed and the improvements achieved.


title: Bilevel Model-Based Discriminative Dictionary Learning for Recognition

abstract: Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the $\ell _{0}$ or $\ell _{1}$ norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn–Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.


title: Don't worry, be happy: The role of positive emotionality and adaptive emotion regulation strategies for youth depressive symptoms

abstract: OBJECTIVES ::: Low positive emotionality (PE) represents a temperamental vulnerability to depression in youth. Until now, little research has examined the mechanisms linking PE to depressive symptoms. Starting from integrated cognitive-affective models of depression, we aimed to study adaptive emotion regulation (ER) as a key underlying mechanism in the temperament-depression relationship. ::: ::: ::: METHODS ::: This study investigated whether adaptive ER strategies mediate the association between PE and depressive symptoms in a large community-based sample of youth, using a cross-sectional design. Participants were 1,655 youth (54% girls; 7-16 years, M = 11.41, SD = 1.88) who filled out a set of questionnaires assessing temperament, adaptive ER strategies, and depressive symptoms. ::: ::: ::: RESULTS ::: Results revealed that low PE was significantly related to higher depressive symptoms among youth and that a lack of total adaptive ER abilities mediated this relationship. More specifically, the infrequent use of problem-solving appeared to be of significant importance. Problems in positive refocusing and a deficient use of forgetting mediated the relationships between low PE and high negative emotionality (NE) in predicting depressive symptoms. Reappraisal and distraction were not significant mediators. ::: ::: ::: CONCLUSION ::: Results highlight the need to account for temperamental PE and adaptive ER strategies when studying youth depression. The findings contribute to a more nuanced understanding on the differential role of temperamental risk factors for developing depressive symptoms at an early stage and advocate for greater attention to adaptive ER strategies. ::: ::: ::: PRACTITIONER POINTS ::: Clinical interventions for youth depression may be improved by incorporating adaptive emotion regulation (ER) strategies and enhancing positive emotions. Youth low in positive emotionality (PE) may especially benefit from learning adaptive ER skills. Clinical practitioners should focus on alleviating negative emotions and enhancing positive emotions, especially among youth low in PE.


title: Addressing the empathy deficit: Beliefs about the malleability of empathy predict effortful responses when empathy is challenging.

abstract: Empathy is often thought to occur automatically. Yet, empathy frequently breaks down when it is difficult or distressing to relate to people in need, suggesting that empathy is often not felt reflexively. Indeed, the United States as a whole is said to be displaying an empathy deficit. When and why does empathy break down, and what predicts whether people will exert effort to experience empathy in challenging contexts? Across 7 studies, we found that people who held a malleable mindset about empathy (believing empathy can be developed) expended greater empathic effort in challenging contexts than did people who held a fixed theory (believing empathy cannot be developed). Specifically, a malleable theory of empathy�whether measured or experimentally induced�promoted (a) more self-reported effort to feel empathy when it is challenging (Study 1); (b) more empathically effortful responses to a person with conflicting views on personally important sociopolitical issues (Studies 2�4); (c) more time spent listening to the emotional personal story of a racial outgroup member (Study 5); and (d) greater willingness to help cancer patients in effortful, face-to-face ways (Study 6). Study 7 revealed a possible reason for this greater empathic effort in challenging contexts: a stronger interest in improving one�s empathy. Together, these data suggest that people�s mindsets powerfully affect whether they exert effort to empathize when it is needed most, and these data may represent a point of leverage in increasing empathic behaviors on a broad scale. (PsycINFO Database Record (c) 2014 APA, all rights reserved)


OF J AVA S CRIPT-BASED DEEP LEARNING PLATFORM AND APPLICATION TO DISTRIBUTED TRAINING


title: Novel coaching cricket bat: can it be used to enhance the backlift and performance of junior cricket batsmen?

abstract: Background In the current literature, it is questionable whether cricket bats in their current form and dimensions allow a young cricketer to hit the ball effectively. The aim of this study was to test the effectiveness of a novel coaching cricket bat among junior cricket batsmen with regard to enhancing performance and the direction of the backlift. Methods A cross-sectional research study with analytical research methods was employed, in which 2 groups (coached: n=12 and uncoached: n=35) of participants (ages 9–13) took part in a pilot and intervention study. Participants were required to use a novel coaching cricket bat in a coaching game format. Biomechanical and video analysis was conducted in the frontal and lateral planes. Effect sizes (ES) were calculated to determine the effectiveness and the level of significance was set at p Results Pilot study results demonstrated that participants scored an additional 100 runs when using the coaching cricket bat compared with a conventional cricket bat (p=0.003). 6 weeks postintervention (training with the coaching cricket bat), the experimental group displayed improved performance (ES=5.41). Players9 backlifts had subsequently become more lateral, which may have promoted more effective ball striking as a result of this training effect. Conclusions The recommendation from this study is that coaches should encourage young cricketers to use the coaching cricket bat as it is perceived to be a potentially significant training aid for enhancing their performance and the direction of their backlift when they use conventional cricket bats in match play.


title: An operating system for multicore and clouds: mechanisms and implementation

abstract: Cloud computers and multicore processors are two emerging classes of computational hardware that have the potential to provide unprecedented compute capacity to the average user. In order for the user to effectively harness all of this computational power, operating systems (OSes) for these new hardware platforms are needed. Existing multicore operating systems do not scale to large numbers of cores, and do not support clouds. Consequently, current day cloud systems push much complexity onto the user, requiring the user to manage individual Virtual Machines (VMs) and deal with many system-level concerns. In this work we describe the mechanisms and implementation of a factored operating system named fos. fos is a single system image operating system across both multicore and Infrastructure as a Service (IaaS) cloud systems. fos tackles OS scalability challenges by factoring the OS into its component system services. Each system service is further factored into a collection of Internet-inspired servers which communicate via messaging. Although designed in a manner similar to distributed Internet services, OS services instead provide traditional kernel services such as file systems, scheduling, memory management, and access to hardware. fos also implements new classes of OS services like fault tolerance and demand elasticity. In this work, we describe our working fos implementation, and provide early performance measurements of fos for both intra-machine and inter-machine operations.


title: Object Detection with Discriminatively Trained Part-Based Models

abstract: We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.


title: An FSS-Backed Ku/Ka Quad-Band Reflectarray Antenna for Satellite Communications

abstract: A shared aperture quad-band high-gain reflectarray antenna (RA) is proposed. It has a stacked structure consisting of a Ka-band dual-band circularly polarized RA, a Ku-band wideband linearly polarized RA, and a triband double-screen frequency-selective surface in between. It is able to simultaneously transmit and receive signals at both Ku- and Ka-bands. A prototype with a circular aperture of 400 mm in diameter is designed, fabricated, and tested for experimental verification. The measured gains are 31 dBi at 12.5 GHz, 32 dBi at 14.25 GHz, 36.1 dBic at 20.4 GHz, and 39.4 dBic at 30.2 GHz, respectively, and the corresponding aperture efficiencies are 45.6%, 44%, 56%, and 54.8%, respectively. The radiation performance demonstrated in this communication validates the feasibility of the proposed quad-band RA design for the future satellite communications at both Ku- and Ka-bands.


A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness


title: A High-Throughput Energy-Efficient Implementation of Successive Cancellation Decoder for Polar Codes Using Combinational Logic

abstract: This paper proposes a high-throughput energy-efficient Successive Cancellation (SC) decoder architecture for polar codes based on combinational logic. The proposed combinational architecture operates at relatively low clock frequencies compared to sequential circuits, but takes advantage of the high degree of parallelism inherent in such architectures to provide a favorable tradeoff between throughput and energy efficiency at short to medium block lengths. At longer block lengths, the paper proposes a hybrid-logic SC decoder that combines the advantageous aspects of the combinational decoder with the low-complexity nature of sequential-logic decoders. Performance characteristics on ASIC and FPGA are presented with a detailed power consumption analysis for combinational decoders. Finally, the paper presents an analysis of the complexity and delay of combinational decoders, and of the throughput gains obtained by hybrid-logic decoders with respect to purely synchronous architectures.


title: FusionFlow: Discrete-continuous optimization for optical flow estimation

abstract: Accurate estimation of optical flow is a challenging task, which often requires addressing difficult energy optimization problems. To solve them, most top-performing methods rely on continuous optimization algorithms. The modeling accuracy of the energy in this case is often traded for its tractability. This is in contrast to the related problem of narrow-baseline stereo matching, where the top-performing methods employ powerful discrete optimization algorithms such as graph cuts and message-passing to optimize highly non-convex energies. In this paper, we demonstrate how similar non-convex energies can be formulated and optimized discretely in the context of optical flow estimation. Starting with a set of candidate solutions that are produced by fast continuous flow estimation algorithms, the proposed method iteratively fuses these candidate solutions by the computation of minimum cuts on graphs. The obtained continuous-valued fusion result is then further improved using local gradient descent. Experimentally, we demonstrate that the proposed energy is an accurate model and that the proposed discrete-continuous optimization scheme not only finds lower energy solutions than traditional discrete or continuous optimization techniques, but also leads to flow estimates that outperform the current state-of-the-art.


title: Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction

abstract: To accomplish forecasting of mood in real-world situations, affective computing systems need to collect and learn from multimodal data collected over weeks or months of daily use. Such systems are likely to encounter frequent data loss, e.g. when a phone loses location access, or when a sensor is recharging. Lost data can handicap classifiers trained with all modalities present in the data. This paper describes a new technique for handling missing multimodal data using a specialized denoising autoencoder: the Multimodal Autoencoder (MMAE). Empirical results from over 200 participants and 5500 days of data demonstrate that the MMAE is able to predict the feature values from multiple missing modalities more accurately than reconstruction methods such as principal components analysis (PCA). We discuss several practical benefits of the MMAE's encoding and show that it can provide robust mood prediction even when up to three quarters of the data sources are lost.


Using Mapping Studies in Software Engineering


title: Memristor-CMOS hybrid integrated circuits for reconfigurable logic

abstract: Hybrid reconfigurable logic circuits were fabricated by integrating memristor-based crossbars onto a foundry-built CMOS (complementary metal-oxide-semiconductor) platform using nanoimprint lithography, as well as materials and processes that were compatible with the CMOS. Titanium dioxide thin-film memristors served as the configuration bits and switches in a data routing network and were connected to gate-level CMOS components that acted as logic elements, in a manner similar to a field programmable gate array. We analyzed the chips using a purpose-built testing system, and demonstrated the ability to configure individual devices, use them to wire up various logic gates and a flip-flop, and then reconfigure devices.


title: Broadband MIMO-OFDM wireless communications

abstract: Orthogonal frequency division multiplexing (OFDM) is a popular method for high data rate wireless transmission. OFDM may be combined with antenna arrays at the transmitter and receiver to increase the diversity gain and/or to enhance the system capacity on time-varying and frequency-selective channels, resulting in a multiple-input multiple-output (MIMO) configuration. The paper explores various physical layer research challenges in MIMO-OFDM system design, including physical channel measurements and modeling, analog beam forming techniques using adaptive antenna arrays, space-time techniques for MIMO-OFDM, error control coding techniques, OFDM preamble and packet design, and signal processing algorithms used to perform time and frequency synchronization, channel estimation, and channel tracking in MIMO-OFDM systems. Finally, the paper considers a software radio implementation of MIMO-OFDM.


title: langid.py: An Off-the-shelf Language Identification Tool

abstract: We present langid.py, an off-the-shelf language identification tool. We discuss the design and implementation of langid.py, and provide an empirical comparison on 5 long-document datasets, and 2 datasets from the microblog domain. We find that langid.py maintains consistently high accuracy across all domains, making it ideal for end-users that require language identification without wanting to invest in preparation of in-domain training data.


title: Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions

abstract: Hands appear very often in egocentric video, and their appearance and pose give important cues about what people are doing and what they are paying attention to. But existing work in hand detection has made strong assumptions that work well in only simple scenarios, such as with limited interaction with other people or in lab settings. We develop methods to locate and distinguish between hands in egocentric video using strong appearance models with Convolutional Neural Networks, and introduce a simple candidate region generation approach that outperforms existing techniques at a fraction of the computational cost. We show how these high-quality bounding boxes can be used to create accurate pixelwise hand regions, and as an application, we investigate the extent to which hand segmentation alone can distinguish between different activities. We evaluate these techniques on a new dataset of 48 first-person videos of people interacting in realistic environments, with pixel-level ground truth for over 15,000 hand instances.


title: Countering Adversarial Images using Input Transformations

abstract: This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier. Our experiments on ImageNet show that total variance minimization and image quilting are very effective defenses in practice, in particular, when the network is trained on transformed images. The strength of those defenses lies in their non-differentiable nature and their inherent randomness, which makes it difficult for an adversary to circumvent the defenses. Our best defense eliminates 60% of strong gray-box and 90% of strong black-box attacks by a variety of major attack methods.


title: Listen, attend and spell: A neural network for large vocabulary conversational speech recognition

abstract: We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional speech recognizers. In LAS, the neural network architecture subsumes the acoustic, pronunciation and language models making it not only an end-to-end trained system but an end-to-end model. In contrast to DNN-HMM, CTC and most other models, LAS makes no independence assumptions about the probability distribution of the output character sequences given the acoustic sequence. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits each character conditioned on all previous characters, and the entire acoustic sequence. On a Google voice search task, LAS achieves a WER of 14.1% without a dictionary or an external language model and 10.3% with language model rescoring over the top 32 beams. In comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0% on the same set.


title: Efficient and Privacy-Preserving Min and $k$ th Min Computations in Mobile Sensing Systems

abstract: Protecting the privacy of mobile phone user participants is extremely important for mobile phone sensing applications. In this paper, we study how an aggregator can expeditiously compute the minimum value or the $k$ th minimum value of all users’ data without knowing them. We construct two secure protocols using probabilistic coding schemes and a cipher system that allows homomorphic bitwise XOR computations for our problems. Following the standard cryptographic security definition in the semi-honest model, we formally prove our protocols’ security. The protocols proposed by us can support time-series data and need not to assume the aggregator is trusted. Moreover, different from existing protocols that are based on secure arithmetic sum computations, our protocols are based on secure bitwise XOR computations, thus are more efficient.


title: Bivariate shrinkage with local variance estimation

abstract: The performance of image-denoising algorithms using wavelet transforms can be improved significantly by taking into account the statistical dependencies among wavelet coefficients as demonstrated by several algorithms presented in the literature. In two earlier papers by the authors, a simple bivariate shrinkage rule is described using a coefficient and its parent. The performance can also be improved using simple models by estimating model parameters in a local neighborhood. This letter presents a locally adaptive denoising algorithm using the bivariate shrinkage function. The algorithm is illustrated using both the orthogonal and dual tree complex wavelet transforms. Some comparisons with the best available results are given in order to illustrate the effectiveness of the proposed algorithm.


title: Real Vulnerabilities and Practical Solutions

abstract: The convenience of 802.11-based wireless access networks has led to widespread deployment in the consumer, industrial and military sectors. However, this use is predicated on an implicit assumption of confidentiality and availability. While the security flaws in 802.11's basic confidentially mechanisms have been widely publicized, the threats to network availability are far less widely appreciated. In fact, it has been suggested that 802.11 is highly susceptible to malicious denial-of-service (DoS) attacks targeting its management and media access protocols. This paper provides an experimental analysis of such 802.11-specific attacks - their practicality, their efficacy and potential low-overhead implementation changes to mitigate the underlying vulnerabilities.


title: Traffic accident segmentation by means of latent class clustering.

abstract: Traffic accident data are often heterogeneous, which can cause certain relationships to remain hidden. Therefore, traffic accident analysis is often performed on a small subset of traffic accidents or several models are built for various traffic accident types. In this paper, we examine the effectiveness of a clustering technique, i.e. latent class clustering, for identifying homogenous traffic accident types. Firstly, a heterogeneous traffic accident data set is segmented into seven clusters, which are translated into seven traffic accident types. Secondly, injury analysis is performed for each cluster. The results of these cluster-based analyses are compared with the results of a full-data analysis. This shows that applying latent class clustering as a preliminary analysis can reveal hidden relationships and can help the domain expert or traffic safety researcher to segment traffic accidents.


Clustering correlated, sparse data streams to estimate a localized housing price index


title: Smart Home Definition and Security Threats

abstract: The home of the future should be a smart one, to support us in our daily life. Up to now only a few security incidents in that area are known. Depending on different security analyses, this fact is rather a result of the low spread of Smart Home products than the success of such systems security. Given that Smart Homes become more and more popular, we will consider current incidents and analyses to estimate potential security threats in the future. The definitions of a Smart Home drift widely apart. Thus we first need to define Smart Home for ourselves and additionally provide a way to categorize the big mass of products into smaller groups.


title: A Comparative Study of Bug Patterns in Java Cloned and Non-cloned Code

abstract: Code cloning via copy-and-paste is a common practice in software engineering. Traditionally, this practice has been considered harmful, and a symptom that some important design abstraction is being ignored. As such, many previous studies suggest approaches to facilitate the discovery, removal, and refactoring of clones. However, not many studies exist that empirically investigate the relationship of code clones with code quality. In this paper, we conduct an empirical study of 31 open source Java projects (1.7 MSLOC) to explore the relationship between code clones and a set of bug patterns reported by Find Bugs. We found that: (i) the defect density in cloned code is 3.7 times less than that of the rest of the code, (ii) 66% of the bug patterns associated with code clones are related to issues in coding style and practice, the two least problematic of the Find Bugs' categories, while that number is 49% for non-cloned code, and (iii) 75% of the bug patterns in cloned code are duplicated without any changes, while 25% are only present in one of the clones. These results show that, when using Find Bugs to detect bug patterns, there is a positive differentiation of cloned code with respect to the rest of the code: the cloned code has considerably less, and less problematic, bug patterns. While our study does not unveil any explanation for this, results from other, more qualitative studies indicate that developers use copy-and-paste intentionally and wisely, which may explain the quantitative observations of our study. Overall, these research results suggest that the practice of code cloning in Java, and possibly in all other object-oriented languages, needs to be given serious consideration on the part of tool designers.


title: A novel approach for analysis of attack graph

abstract: Attack graph technique is a common tool for the evaluation of network security. However, attack graphs are generally too large and complex to be understood and interpreted by security administrators. This paper proposes an analysis framework for security attack graphs for a given IT infrastructure system. First, in order to facilitate the discovery of interconnectivities among vulnerabilities in a network, multi-host multi-stage vulnerability analysis (MulVAL) is employed to generate an attack graph for a given network topology. Then a novel algorithm is applied to refine the attack graph and generate a simplified graph called a transition graph. Next, a Markov model is used to project the future security posture of the system. Finally, the framework is evaluated by applying it on a typical IT network scenario with specific services, network configurations, and vulnerabilities.


title: The impenetrability of 5-(N-hexadecanoyl)aminofluoroscein through endothelial cell monolayers is dependent upon its solution properties, not the presence of tight junctions.

abstract: Abstract The solution properties of two fluorescent lipophilic analogues were examined in conjunction with their ability to penetrate the tight junctions of bovine aortic endothelial cell monolayers. 5-(N-dodecanoyl) aminofluoroscein was shown to label both the apical and basolateral plasma membrane domains of confluent monolayers at 4°C and pH 7.3, but 5-(N-hexadecanoyl) aminofluoroscein was shown to label only the apical membrane domain. When used under more soluble conditions at 20°C and pH 8.5, both probes labeled apical and basolateral plasma membrane domains more equally. This indicates that solubility conditions, and not tight junctions, dictate the penetration of 5-(N-hexadecanoyl) aminofluoroscein from the apical to the basolateral plasma membrane domain.


title: Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study

abstract: Nowadays, the whole world is being concerned with a major health problem, which is diabetes. A very common symptom of diabetes is the diabetic foot ulcer (DFU). The early detection of such foot complications can protect diabetic patients from any dangerous stages that develop later and may require foot amputation. This work aims at building a mobile thermal imaging system that can be used as an indicator for possible developing ulcers. The proposed system consists of a thermal camera connected to a Samsung smart phone, which is used to acquire thermal images. This thermal imaging system has a simulated temperature gradient of more than 2.2 °C, which represents the temperature difference (in the literature) than can indicate a possible development of ulcers. The acquired images are processed and segmented using basic image processing techniques. The analysis and interpretation is conducted using two techniques: Otsu thresholding technique and Point-to-Point mean difference technique. The proposed system was implemented under MATLAB Mobile platform and thermal images were analyzed and interpreted. Four testing images (feet images) were used to test this procedure; one image with any temperature variation to the feet, and three images with skin temperature increased to more than 2.2 °C introduced at different locations. With the two techniques applied during the analysis and interpretation stage, the system was successful in identifying the location of the temperature increase. This work successfully implemented a mobile thermal imaging system that includes an automated method to identify possible ulcers in diabetic patients. This may give diabetic patients the ability for a frequent self-check of possible ulcers. Although this work was implemented in simulated conditions, it provides the necessary feasibility to be further developed and tested in a clinical environment.


title: National nutrition surveys in Asian countries: surveillance and monitoring efforts to improve global health.

abstract: Asian regions have been suffering from growing double burden of nutritional health problems, such as undernutrition and chronic diseases. National nutrition survey plays an essential role in helping to improve both national and global health and reduce health disparities. The aim of this review was to compile and present the information on current national nutrition surveys conducted in Asian countries and suggest relevant issues in implementation of national nutrition surveys. Fifteen countries in Asia have conducted national nutrition surveys to collect data on nutrition and health status of the population. The information on national nutrition survey of each country was obtained from government documents, international organizations, survey website of governmental agencies, and publications, including journal articles, books, reports, and brochures. The national nutrition survey of each country has different variables and procedures. Variables of the surveys include sociodemographic and lifestyle variables; foods and beverages intake, dietary habits, and food security of individual or household; and health indicators, such as anthropometric and biochemical variables. The surveys have focused on collecting data about nutritional health status in children aged under five years and women of reproductive ages, nutrition intake adequacy and prevalence of obesity and chronic diseases for all individuals. To measure nutrition and health status of Asian populations accurately, improvement of current dietary assessment methods with various diet evaluation tools is necessary. The information organized in this review is important for researchers, policy makers, public health program developers, educators, and consumers in improving national and global health.


title: A front-illuminated stacked global-shutter CMOS image sensor with multiple chip-on-chip integration

abstract: A front-illuminated global-shutter CMOS image sensor has been developed with super 35-mm optical format. We have developed a chip-on-chip integration process to realize a front-illuminated image sensor stacked with 2 diced logic chips through 38K micro bump interconnections. The global-shutter pixel achieves a parasitic light sensitivity of −99.6dB. The stacked device allows highly parallel column ADCs and high-speed output interfaces to attain a frame rate of 480 fps with 8.3M-pixel resolution.


title: Fit4life: the design of a persuasive technology promoting healthy behavior and ideal weight

abstract: This is a critical design paper offering a possible scenario of use intended to provoke reflection about values and politics of design in persuasive computing. We describe the design of a system - Fit4Life - that encourages individuals to address the larger goal of reducing obesity in society by promoting individual healthy behaviors. Using the Persuasive Systems Design Model [26], this paper outlines the Fit4Life persuasion context, the technology, its use of persuasive messages, and an experimental design to test the system's efficacy. We also contribute a novel discussion of the ethical and sociocultural considerations involved in our design, an issue that has remained largely unaddressed in the existing persuasive technologies literature [29].


title: Emotions shape memory suppression in trait anxiety

abstract: The question that motivated this study was to investigate the relation between trait anxiety, emotions and memory control. To this aim, memory suppression was explored in high and low trait anxiety individuals with the Think/No-think paradigm. After learning associations between neutral words and emotional scenes (negative, positive, and neutral), participants were shown a word and were requested either to think about the associated scene or to block it out from mind. Finally, in a test phase, participants were again shown each word and asked to recall the paired scene. The results show that memory control is influenced by high trait anxiety and emotions. Low trait anxiety individuals showed a memory suppression effect, whereas there was a lack of memory suppression in high trait anxious individuals, especially for emotionally negative scenes. Thus, we suggest that individuals with anxiety may have difficulty exerting cognitive control over memories with a negative valence. These findings provide evidence that memory suppression can be impaired by anxiety thus highlighting the crucial relation between cognitive control, emotions, and individual differences in regulating emotions.


title: A new approach for data clustering using hybrid artificial bee colony algorithm

abstract: Data clustering is a popular data analysis technique needed in many fields. Recent years, some swarm intelligence-based approaches for clustering were proposed and achieved encouraging results. This paper presents a Hybrid Artificial Bee Colony (HABC) algorithm for data clustering. The incentive mechanism of HABC is enhancing the information exchange (social learning) between bees by introducing the crossover operator of Genetic Algorithm (GA) to ABC. With a test on ten benchmark functions, the proposed HABC algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms. The HABC algorithm is then employed for data clustering. Six real datasets selected from the UCI machine learning repository are used. The results show that the HABC algorithm achieved better results than other algorithms and is a competitive approach for data clustering.


title: Experimenting with an Ad Hoc wireless network on campus: insights and experiences

abstract: Ad hoc wireless networks are new communication networks that can be dynamically formed and deformed on-the-fly, anytime and anywhere. User data is routed with the help of an ad hoc mobile routing protocol. Before the deployment of ad hoc mobile services, the communication performance of such networks has to be evaluated to demonstrate the practicality limits based on today's hardware and innovative communication software. This paper describes the realization of an ad hoc wireless testbed and the various experimental field trials performed on campus. In particular, throughput, end-to-end delay, route discovery time, and the impact of varying source packet size and beaconing intervals are examined.


Technology Supported Learning and Teaching: A Staff Perspective.


title: Three computer-based models of storytelling: BRUTUS, MINSTREL and MEXICA

abstract: This paper attempts to establish criteria to analyse and evaluate computer models of creativity in writing. The paper provides a brief review of the antecedents of automatic story-generation and offers a proposal for the analysis and evaluation of computer models of creativity in writing. It reviews three major projects to develop computer-based storywriters published between 1993 and 2000 and analyses their approach, similarities, differences and contributions. It compares the three approaches and discusses implications for the modelling of creativity in writing and the design of future story generation systems.


title: Adaptive Parameter Estimation and Inner and External Precision

abstract: The stochastic model can be adaptively adjusted according to inner precision,external accuracy and semi external accuracy.The statistics correspond to the adaptive estimation developed these years are analyzed respectively at first.It is shown by analysis that the existing adaptive parameter estimators are related to the inner precision,external accuracy and semi external accuracy.Thus the corresponding adaptive estimators are divided into inner precision-based,external accuracy-based and semi external accuracy-based adaptive estimators respectively.The characteristics of the different adaptive estimators are analyzed.Some examples are performed for analyzing their main properties.Finally,the premise of applications and some existed problems are pointed out.


title: Medication administration errors by nurses: adherence to guidelines

abstract: AIMS & OBJECTIVES ::: Medication administration errors represent one of the major concerns in patient safety. We aimed to study the rate using more robust methods for the correct results. ::: ::: ::: BACKGROUNDS ::: Very few studies have been carried out on medication administration error frequency. Previous studies of medication error frequency have used mainly surveys of clinical nurses, which may result in substantial undercounts. ::: ::: ::: DESIGN & METHODS ::: We developed a checklist using basic medication guidelines including the Five Rights, infection recommendations and medication recording rules. After validity and reliability were confirmed, we performed direct observation using a checklist to evaluate the medication activities of clinical nurses. ::: ::: ::: RESULTS ::: We observed total 293 cases of medication activities, collected data and calculated adherence ratios per item. Only 45·6% of nurses verified the amount of medication indicated on the vial at least once for at least one-second. In addition, only 6·5% read the name of the patient from the wristband. Administering the medication at the correct time guideline was observed 41·0% of the time. The guideline regarding hand washing before external and oral medications was followed only 4·5% of the time, although this figure was much higher for intravenous medications at 96·6%. Overall, among 31 categories regarding drug administration, 17·2 (± 3·6) items per person were followed, whereas 5·7 (± 1·2) items per person were violated. ::: ::: ::: CONCLUSION ::: Thus, the results overall showed low rates of adherence to guidelines, suggesting that many medication administration guidelines are not strictly followed. We found key instances in which nurses did not follow the guidelines, including many from the Five Rights. About one in four elements were violated overall. ::: ::: ::: RELEVANCE TO CLINICAL PRACTICE ::: The results of this study could be adopted to make guidelines of medication administration more practical for the clinical nurses to adhere.


title: Smart Health Care: An Edge-Side Computing Perspective

abstract: Increasing health awareness and rapid technological advancements have resulted in significant growth in an emerging sector: smart health care. By the end of 2020, the total number of smart health-care devices is expected to reach 808.9 million (646 million devices without wearables and the remaining with wearables) [1], [2]. This proliferation of devices is expected to revolutionize health care by speeding up treatment and diagnostic processes, decreasing physician visit costs, and enhancing patient care quality.


title: Power management in energy harvesting sensor networks

abstract: Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.


The effects of music therapy on reducing agitation in patients with Alzheimer's disease, a pre‐post study


title: Support Vector Machines for Regression Problems with Sequential Minimal Optimization

abstract: Training a support vector machine (SVM) is usually done by mapping the underlying optimization problem into a quadratic programming (QP) problem. Unfortunately, high quality QP solvers are not readily available, which makes research into the area of SVMs difficult for the those without a QP solver. Recently, the Sequential Minimal Optimization algorithm (SMO) was introduced [1, 2]. SMO reduces SVM training down to a series of smaller QP subproblems that have an analytical solution and, therefore, does not require a general QP solver. SMO has been shown to be very efficient for classification problems using linear SVMs and/or sparse data sets. This work shows how SMO can be generalized to handle regression problems.


title: Fractional-slot concentrated-windings: A paradigm shift in electrical machines

abstract: Fractional-slot concentrated-winding (FSCW) synchronous permanent magnet (PM) machines have been gaining a lot of interest over the last few years. This is mainly due to the several advantages that this type of windings provides. These include high power density, high efficiency, short end turns, high slot fill factor especially when coupled with segmented stator structures, low cogging torque, flux-weakening capability, and fault-tolerance This paper is going to provide an update of the key activities and research trends in this area over the past few years. These include efforts to reduce losses especially rotor losses in FSCW machines, exploring FSCW for machine topologies other than PM machines as well as some of the commercial products that utilize FSCW.


title: From information security to cyber security

abstract: The term cyber security is often used interchangeably with the term information security. This paper argues that, although there is a substantial overlap between cyber security and information security, these two concepts are not totally analogous. Moreover, the paper posits that cyber security goes beyond the boundaries of traditional information security to include not only the protection of information resources, but also that of other assets, including the person him/herself. In information security, reference to the human factor usually relates to the role(s) of humans in the security process. In cyber security this factor has an additional dimension, namely, the humans as potential targets of cyber attacks or even unknowingly participating in a cyber attack. This additional dimension has ethical implications for society as a whole, since the protection of certain vulnerable groups, for example children, could be seen as a societal responsibility.


title: Quantum Structure in Cognition: Fundamentals and Applications

abstract: Experiments in cognitive science and decision theory show that the ways in which people combine concepts and make decisions cannot be described by classical logic and probability theory. This has serious implications for applied disciplines such as information retrieval, artificial intelligence and robotics. Inspired by a mathematical formalism that generalizes quantum mechanics the authors have constructed a contextual framework for both concept representation and decision making, together with quantum models that are in strong alignment with experimental data. The results can be interpreted by assuming the existence in human thought of a double-layered structure, a 'classical logical thought' and a 'quantum conceptual thought', the latter being responsible of the above paradoxes and nonclassical effects. The presence of a quantum structure in cognition is relevant, for it shows that quantum mechanics provides not only a useful modeling tool for experimental data but also supplies a structural model for human and artificial thought processes. This approach has strong connections with theories formalizing meaning, such as semantic analysis, and has also a deep impact on computer science, information retrieval and artificial intelligence. More specifically, the links with information retrieval are discussed in this paper.


title: 40-Gb/s Operation of a 1.3-/1.55-μm InGaAlAs Electroabsorption Modulator Integrated With DFB Laser in Compact TO-CAN Package

abstract: We have developed compact, cost-effective transistor-outline (TO)-CAN modules based on a 1.3-/1.55-μm InGaAlAs electroabsorption modulator integrated with a distributed feedback laser (EML), and realized the 40-Gb/s operation of these modules for the first time. We propose a new method for connecting a straight RF substrate and a TO-CAN stem that minimizes the air gap between them, and that suppresses the impedance mismatch at the connection interface. We also employ a liquid crystal polymer as an RF-line substrate, and apply a borosilicate glass-based coaxial RF pin, which is compatible with K connector glass beads to the TO-CAN stem. By using these modules, we confirm experimentally that the impedance of the entire RF transmission line is approximately 50 Ω throughout the module, and achieve an electrical-to-optical 3-dB-down frequency bandwidth ( f3dB) of over 33 GHz. We also achieve 40-Gb/s clear eye openings for both 1.3- and 1.55-μm EML-based modules.


Make Evasion Harder: An Intelligent Android Malware Detection System