title: A Multibody Factorization Method for Independently Moving Objects

abstract: The structure-from-motion problem has been extensively studied in the field of computer vision. Yet, the bulk of the existing work assumes that the scene contains only a single moving object. The more realistic case where an unknown number of objects move in the scene has received little attention, especially for its theoretical treatment. In this paper we present a new method for separating and recovering the motion and shape of multiple independently moving objects in a sequence of images. The method does not require prior knowledge of the number of objects, nor is dependent on any grouping of features into an object at the image level. For this purpose, we introduce a mathematical construct of object shapes, called the shape interaction matrix, which is invariant to both the object motions and the selection of coordinate systems. This invariant structure is computable solely from the observed trajectories of image features without grouping them into individual objects. Once the matrix is computed, it allows for segmenting features into objects by the process of transforming it into a canonical form, as well as recovering the shape and motion of each object. The theory works under a broad set of projection models (scaled orthography, paraperspective and affine) but they must be linear, so it excludes projective “cameras”.


10th Rib Cartilage: Another Option of the Costal Cartilage Graft for Rhinoplasty


title: A Survey on Content-Aware Video Analysis for Sports

abstract: Sports data analysis is becoming increasingly large scale, diversified, and shared, but difficulty persists in rapidly accessing the most crucial information. Previous surveys have focused on the methodologies of sports video analysis from the spatiotemporal viewpoint instead of a content-based viewpoint, and few of these studies have considered semantics. This paper develops a deeper interpretation of content-aware sports video analysis by examining the insight offered by research into the structure of content under different scenarios. On the basis of this insight, we provide an overview of the themes particularly relevant to the research on content-aware systems for broadcast sports. Specifically, we focus on the video content analysis techniques applied in sportscasts over the past decade from the perspectives of fundamentals and general review, a content hierarchical model, and trends and challenges. Content-aware analysis methods are discussed with respect to object-, event-, and context-oriented groups. In each group, the gap between sensation and content excitement must be bridged using proper strategies. In this regard, a content-aware approach is required to determine user demands. Finally, this paper summarizes the future trends and challenges for sports video analysis. We believe that our findings can advance the field of research on content-aware video analysis for broadcast sports.


title: Aboutness: towards foundations for the Information Artifact Ontology

abstract: The Information Artifact Ontology (IAO) was created to serve as a domain‐neutral resource for the representation of types of information content entities (ICEs) such as documents, data‐bases, and digital im‐ ages. We identify a series of problems with the current version of the IAO and suggest solutions designed to advance our understanding of the relations between ICEs and associated cognitive representations in the minds of human subjects. This requires embedding IAO in a larger framework of ontologies, including most importantly the Mental Func‐ tioning Ontology (MFO). It also requires a careful treatment of the aboutness relations between ICEs and associated cognitive representa‐ tions and their targets in reality.


title: Down‐regulation of dopamine transporter by iron chelation in vitro is mediated by altered trafficking, not synthesis

abstract: Neurological development and functioning of dopamine (DA) neurotransmission is adversely affected by iron deficiency in early life. Iron-deficient rats demonstrate significant elevations in extracellular DA and a reduction in dopamine transporter (DAT) densities in the caudate putamen and nucleus accumbens. To explore possible mechanisms by which cellular iron concentrations control DAT functioning, endogenous DAT-expressing PC12 cells were used to determine the effect of iron chelation on DAT protein and mRNA expression patterns. In addition, we used human DAT (hDAT)-transfected Neuro2a (N2A) cells to examine DAT degradation and trafficking patterns. A 50 µm treatment for 24 h with the iron chelator, desferrioxamine (DFO), significantly decreased dopamine uptake in a dose-dependent manner, with no apparent change in Km, in both PC12 and N2A cells. Reduced DA uptake was accompanied by concentration- and time-dependent reductions in total DAT protein levels in both cell lines. Exposure to increasing concentrations of DFO did not significantly alter DAT mRNA in either PC12 or N2A cells. However, DAT degradation rates increased three–fivefold in both cell types exposed to 50 µm DFO for 24 h. Biotinylation studies in N2A cells indicate a more dramatic loss of DAT in the membrane fraction, while OptiPrep fractionation experiments revealed an increase in lysosomal DAT with iron chelation. Inhibition of protein kinase C activation with staurosporin prevented the effect of iron chelation on DAT function, suggesting that in vitro iron chelation affects DAT primarily through the effects on trafficking rather than on synthesis.


title: Upper limb exoskeleton control based on sliding mode control and feedback linearization

abstract: Exoskeletons have potential to improve human quality of life by relieving loads on the human musculoskeletal system or by helping motor rehabilitation. Controllers that were initially developed for industrial applications are also applied to control an exoskeleton, despite of major differences in requirements. Nevertheless, which controller has better performance for this specific application remains an open question. This paper presents a comparison between sliding mode control and feedback linearization control. The implementation of the sliding mode controller assumes a complete measurement of the biological system's dynamics. On the other hand, the feedback linearization method does not need any information from the biological system. Our results indicate that a model combining biological control and dynamics could improve several characteristics of robotic exoskeleton, such as energy consumption.


title: Enhanced Pulse-Density-Modulated Power Control for High-Frequency Induction Heating Inverters

abstract: This paper presents a 100-kW 100-kHz insulated-gate bipolar transistor (IGBT) series resonant inverter for induction heating applications that uses an improved power control scheme based on the standard pulse density modulation (PDM). This standard power control is a good solution for the design of high-frequency inverters because the output power factor is near to unity in a wide range of output power, resulting in great reduction of switching losses and electromagnetic noise. However, the output current can be in discontinuous mode, particularly for resonant loads of low quality factor or for low output power or low-load operation. This output current fluctuation produces a high output current ripple that can lead to an increase in power losses and loss of accuracy of the response of the frequency tracking control. The proposed control strategy, which is called enhanced PDM (EPDM), provides twice less output current ripple, thus resulting in an improved inverter behavior in terms of frequency tracking accuracy and energy efficiency. Experimental tests have been made, in order to compare the EPDM strategy with standard power control schemes.


title: 1 X IMPROVING BUSINESS AGILITY THROUGH TECHNICAL SOLUTIONS : A Case Study on Test-Driven Development in Mobile Software Development

abstract: This Paper maintains that efficient business agility requires actions from all levels of the organization in order to strive for success in a turbulent business environment. Agility and agile software development solutions are suggested as yielding benefit in a volatile environment, which is characterized by continuously changing requirements and unstable development technologies. Test-driven development (TDD) is an agile practice where the tests are written before the actual program code. TDD is a technical enabler for increasing agility at the developer and product project levels. Existing empirical literature on TDD has demonstrated increased productivity and more robust code, among other important benefits. This paper reports results of a case study where a mobile application was developed for global markets, using the TDD approach. Our first results show that the adoption of TDD is difficult and the potential agility benefits may not be readily available. The lessons learned from the case study are presented.


Detection Based on Structural Properties


title: Contextual semantics for sentiment analysis of Twitter

abstract: We propose a semantic sentiment representation of words called SentiCircle.SentiCircle captures the contextual semantic of words from their co-occurrences.SentiCircle updates the sentiment of words based on their contextual semantics.SentiCircle can be used to perform entity- and tweet-level level sentiment analysis. Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.


Supercritical pyrolysis of the endothermic fuels methylcyclohexane, decalin, and tetralin


title: Literature Review of Mobile Applications Testing on Cloud from Information Security Perspective

abstract: adoption of mobile application is increasing at enormous rate due to their improved functionality and features. Increased storage and computing power has augmented its utility. However, these mobile applications are still intrinsically limited by a relative lack of bandwidth, computing power, storage and energy compared to desktops. To overcome these limitations, the concept of Mobile Cloud Computing (MCC) providing abundant computing power and sufficient storage space besides large infrastructure has evolved. Cloud computing has its impact on all the stages of Software life cycle, including testing of mobile devices. In this paper we have reviewed Testing as a Service (TaaS) provided by Cloud computing. Testing of mobile application is more complex and time consuming as compared to traditional desktop applications. This paper reviews some published results in two major research fields ie cloud computing and TaaS and discusses architecture of cloud computing and TaaS in terms of necessity, features, emerging trends, benefits and gaps while focussing on security and privacy issues for mobile application. When we run our applications on the cloud, we are sharing our critical data with cloud and, therefore, security and privacy of data is a very serious issue to be considered. KeywordsComputing, Mobile Cloud Computing (MCC), TaaS,


Automatic Labeling of Issues on Github – A Machine learning Approach


title: MIMO Performance at 700MHz: Field Trials of LTE with Handheld UE

abstract: MIMO is one of the techniques used in LTE Release 8 to achieve very high data rates. A field trial was performed in a pre-commercial LTE network. The objective is to investigate how well MIMO works with realistically designed handhelds in band 13 (746-756 MHz in downlink). In total, three different handheld designs were tested using antenna mockups. In addition to the mockups, a reference antenna design with less stringent restrictions on physical size and excellent properties for MIMO was used. The trial comprised test drives in areas with different characteristics and with different network load levels. The effects of hands holding the devices and the effect of using the device inside a test vehicle were also investigated. In general, it is very clear from the trial that MIMO works very well and gives a substantial performance improvement at the tested carrier frequency if the antenna design of the hand-held is well made with respect to MIMO. In fact, the best of the handhelds performed similar to the reference antenna.


title: High-Performance Quasi-Monte Carlo Financial Simulation: FPGA vs. GPP vs. GPU

abstract: Quasi-Monte Carlo simulation is a special Monte Carlo simulation method that uses quasi-random or low-discrepancy numbers as random sample sets. In many applications, this method has proved advantageous compared to the traditional Monte Carlo simulation method, which uses pseudo-random numbers, thanks to its faster convergence and higher level of accuracy. This article presents the design and implementation of a massively parallelized Quasi-Monte Carlo simulation engine on an FPGA-based supercomputer, called Maxwell. It also compares this implementation with equivalent graphics processing units (GPUs) and general purpose processors (GPP)-based implementations. The detailed comparison between these three implementations (FPGA vs. GPP vs. GPU) is done in the context of financial derivatives pricing based on our Quasi-Monte Carlo simulation engine. Real hardware implementations on the Maxwell machine show that FPGAs outperform equivalent GPP-based software implementations by 2 orders of magnitude, with the speed-up figure scaling linearly with the number of processing nodes used (FPGAs/GPPs). The same implementations show that FPGAs achieve a ~ 3x speedup compared to equivalent GPU-based implementations. Power consumption measurements also show FPGAs to be 336x more energy efficient than CPUs, and 16x more energy efficient than GPUs.


title: Generation of a Precise Roadway Map for Autonomous Cars

abstract: This paper proposes a map generation algorithm for a precise roadway map designed for autonomous cars. The roadway map generation algorithm is composed of three steps, namely, data acquisition, data processing, and road modeling. In the data acquisition step, raw trajectory and motion data for map generation are acquired through exploration using a probe vehicle equipped with GPS and on-board sensors. The data processing step then processes the acquired trajectory and motion data into roadway geometry data. GPS trajectory data are unsuitable for direct roadway map use by autonomous cars due to signal interruptions and multipath; therefore, motion information from the on-board sensors is applied to refine the GPS trajectory data. A fixed-interval optimal smoothing theory is used for a refinement algorithm that can improve the accuracy, continuity, and reliability of road geometry data. Refined road geometry data are represented into the B-spline road model. A gradual correction algorithm is proposed to accurately represent road geometry with a reduced amount of control parameters. The developed map generation algorithm is verified and evaluated through experimental studies under various road geometry conditions. The results show that the generated roadway map is sufficiently accurate and reliable to utilize for autonomous driving.


title: Comparing reading speed for horizontal and vertical English text.

abstract: There are three formats for arranging English text for vertical readingVupright letters arranged vertically (marquee), andhorizontal text rotated 90- clockwise or counterclockwise. Previous research has shown that reading is slower for all threevertical formats than for horizontal text, with marquee being slowest (M. D. Byrne, 2002). It has been proposed that the sizeof the visual spanVthe number of letters recognized with high accuracy without moving the eyesVis a visual factor limitingreading speed. We predicted that reduced visual-span size would be correlated with the slower reading for the three verticalformats. We tested this prediction with uppercase and lowercase letters. Reading performance was measured using twopresentation methods: RSVP (Rapid Serial Visual Presentation) and ﬂashcard (a block of text on four lines). On average,reading speed for horizontal text was 139% faster than marquee text and 81% faster than the rotated texts. Size of thevisual span was highly correlated with changes in reading speed for both lowercase and uppercase letters and for bothRSVP and ﬂashcard reading. Our results are consistent with the view that slower reading of vertical text is due to adecrease in the size of the visual span for vertical reading.Keywords: visual span, letter recognition, reading speed, vertical readingCitation: Yu, D., Park, H., Gerold, D., & Legge, G. E. (2010). Comparing reading speed for horizontal and vertical Englishtext. Journal of Vision, 10(2):21, 1


title: An Extended Model of Cybercrime Investigations

abstract: A comprehensive model of cybercrime investigations is important for standardising terminology, defining requirements, and supporting the development of new techniques and tools for investigators. In this paper a model of investigations is presented which combines the existing models, generalises them, and extends them by explicitly addressing certain activities not included in them. Unlike previous models, this model explicitly represents the information flows in an investigation and captures the full scope of an investigation, rather than only the processing of evidence. The results of an evaluation of the model by practicing cybercrime investigators are presented. This new model is compared to some important existing models and applied to a real investigation.


title: Factor Structure of the Rosenberg Self-Esteem Scale

abstract: This study evaluated the factor structure of the Rosenberg Self-Esteem Scale (RSES) with a diverse sample of 1,248 European American, Latino, Armenian, and Iranian adolescents. Adolescents completed the 10-item RSES during school as part of a larger study on parental influences and academic outcomes. Findings suggested that method effects in the RSES are more strongly associated with negatively worded items across three diverse groups but also more pronounced among ethnic minority adolescents. Findings also suggested that accounting for method effects is necessary to avoid biased conclusions regarding cultural differences in self-esteem and how predictors are related to the RSES. Moreover, the two RSES factors (positive self-esteem and negative self-esteem) were differentially predicted by parenting behaviors and academic motivation. Substantive and methodological implications of these findings for cross-cultural research on adolescent self-esteem are discussed.


title: End-to-end joint learning of natural language understanding and dialogue manager

abstract: Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding (NLU) and system action prediction (SAP) as a pipeline that is sensitive to noisy outputs of error-prone NLU. To address the issues, we propose an end-to-end deep recurrent neural network with limited contextual dialogue memory by jointly training NLU and SAP on DSTC4 multi-domain human-human dialogues. Experiments show that our proposed model significantly outperforms the state-of-the-art pipeline models for both NLU and SAP, which indicates that our joint model is capable of mitigating the affects of noisy NLU outputs, and NLU model can be refined by error flows backpropagating from the extra supervised signals of system actions.


title: Crowdfunding Success Factors: The Characteristics of Successfully Funded Projects on Crowdfunding Platforms

abstract: Crowdfunding platforms offer promising opportunities for project founders to publish their project ideas and to collect money in order to be able to realize them. Consequently, the question of what influences the successful funding of projects, i.e., reaching the target amount of money, is very important. Building upon media richness theory and the concept of reciprocity, we extend previous research in the field of crowdfunding success factors. We provide a comprehensive view on factors influencing crowdfunding success by both focusing on project-specific as well as founder-specific aspects. Analyzing a sample of projects of the crowdfunding platform kickstarter.com, we find that the project description, related images and videos as well as the question of whether the founder has previously backed other projects influence funding success. Interestingly, the question of whether the founder has previously created other projects has no significant influence. Our results are of high interest for the stakeholders on crowdfunding platforms.


title: A compact ultra-wideband slot antenna with band notch characteristics

abstract: In this study, a compact wideband slot antenna with band notch performance is designed. An M-shaped slot on the feed line increases the impedance bandwidth of the square slot antenna. A stop-band notch can be achieved by using two L-shaped protruded strip inside the square-ring stub. The designed slot antenna has a compact size of 20×20 mm, showing wideband characteristics in the frequency band of 4.4 to over 10.7 GHz with a band rejection performance in the frequency band of 5.0 to 5.4 GHz.


title: Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction

abstract: Anomaly detection from a driver's perspective when driving is important to autonomous vehicles. As a part of Advanced Driver Assistance Systems (ADAS), it can remind the driver about dangers in a timely manner. Compared with traditional studied scenes such as a university campus and market surveillance videos, it is difficult to detect an abnormal event from a driver's perspective due to camera waggle, abidingly moving background, drastic change of vehicle velocity, etc. To tackle these specific problems, this paper proposes a spatial localization constrained sparse coding approach for anomaly detection in traffic scenes, which first measures the abnormality of motion orientation and magnitude, respectively, and then fuses these two aspects to obtain a robust detection result. The main contributions are threefold, as follows. 1) This work describes the motion orientation and magnitude of the object, respectively, in a new way, which is demonstrated to be better than the traditional motion descriptors. 2) The spatial localization of an object is taken into account considering the sparse reconstruction framework, which utilizes the scene's structural information and outperforms the conventional sparse coding methods. 3) Results of motion orientation and magnitude are adaptively weighted and fused by a Bayesian model, which makes the proposed method more robust and able to handle more kinds of abnormal events. The efficiency and effectiveness of the proposed method are validated by testing on nine difficult video sequences that we captured ourselves. Observed from the experimental results, the proposed method is more effective and efficient than the popular competitors and yields a higher performance.


title: Learning Object-to-Class Kernels for Scene Classification

abstract: High-level image representations have drawn increasing attention in visual recognition, e.g., scene classification, since the invention of the object bank. The object bank represents an image as a response map of a large number of pretrained object detectors and has achieved superior performance for visual recognition. In this paper, based on the object bank representation, we propose the object-to-class (O2C) distances to model scene images. In particular, four variants of O2C distances are presented, and with the O2C distances, we can represent the images using the object bank by lower-dimensional but more discriminative spaces, called distance spaces, which are spanned by the O2C distances. Due to the explicit computation of O2C distances based on the object bank, the obtained representations can possess more semantic meanings. To combine the discriminant ability of the O2C distances to all scene classes, we further propose to kernalize the distance representation for the final classification. We have conducted extensive experiments on four benchmark data sets, UIUC-Sports, Scene-15, MIT Indoor, and Caltech-101, which demonstrate that the proposed approaches can significantly improve the original object bank approach and achieve the state-of-the-art performance.


title: A Crosstab-based Statistical Method for Effective Fault Localization

abstract: Fault localization is the most expensive activity in program debugging. Traditional ad-hoc methods can be time-consuming and ineffective because they rely on programmers' intuitive guesswork, which may neither be accurate nor reliable. A better solution is to utilize a systematic and statistically well-defined method to automatically identify suspicious code that should be examined for possible fault locations. We present a crosstab-based statistical method using the coverage information of each executable statement and the execution result (success or failure) with respect to each test case. A crosstab is constructed for each executable statement and a statistic is computed to determine the suspiciousness of the corresponding statement. Statements with a higher suspiciousness are more likely to contain bugs and should be examined before those with a lower suspiciousness. Three case studies using the Siemens suite, the Space program, and the Unix suite, respectively, are conducted. Our results suggest that the crosstab-based method is effective in fault localization and performs better (in terms of a smaller percentage of executable statements that have to be examined until the first statement containing the fault is reached) than other methods such as Tarantula. The difference in efficiency (computational time) between these two methods is very small.


title: Steep Slope Field-Effect Transistors With Ag/TiO2-Based Threshold Switching Device

abstract: In this letter, we demonstrate a steep slope field-effect transistor (FET) using a threshold switching (TS) device. The Ag/TiO2-based TS device reported in our previous work was implemented in series with the drain region of a transistor. Since the TS device has an abrupt transition between the OFF- and ON-states and vice versa, the transistor has a 5-mV/decade subthreshold slope and a high ON/OFF-current ratio ( $I_{\mathrm{\scriptscriptstyle ON}}/I_{\mathrm{\scriptscriptstyle OFF}}$ ) of $> 10^{7}$ with a low drain voltage (0.3 V). Furthermore, the threshold voltage ( $V_{\mathrm {th, FET}})$ of the transistor can be tuned by controlling the thickness of the TS device.


Progressive Tunnelizations in Neck Face Lift Detachment


title: Tensor Networks for Latent Variable Analysis. Part I: Algorithms for Tensor Train Decomposition

abstract: Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model which represents data as an ordered network of sub-tensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network decomposition has been long studied in quantum physics and scientific computing. In this study, we present novel algorithms and applications of tensor network decompositions, with a particular focus on the tensor train decomposition and its variants. The novel algorithms developed for the tensor train decomposition update, in an alternating way, one or several core tensors at each iteration, and exhibit enhanced mathematical tractability and scalability to exceedingly large-scale data tensors. The proposed algorithms are tested in classic paradigms of blind source separation from a single mixture, denoising, and feature extraction, and achieve superior performance over the widely used truncated algorithms for tensor train decomposition.


title: New solver and optimal anticipation strategies design based on evolutionary computation for the game of MasterMind

abstract: This paper presents and compares several evolutionary solutions for the well-known MasterMind game, a classic board game invented in the 1970s. First, we propose a novel evolutionary approach (which we call nested hierarchical evolutionary search) to solve the MasterMind game, comparing the obtained results with that of existing algorithms. Second, we show how to design novel game anticipation strategies for the MasterMind game, by applying genetic programming. In this case we compare the performance of the new obtained strategies with that of the classical ones, obtaining advantages in all the cases tested.


title: Domain Adaptive Neural Networks for Object Recognition

abstract: We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks.


title: Abuse rates and routes of administration of reformulated extended-release oxycodone: initial findings from a sentinel surveillance sample of individuals assessed for substance abuse treatment.

abstract: UNLABELLED ::: Oxycodone hydrochloride controlled-release, also known as extended-release oxycodone (ER oxycodone), was reformulated with physicochemical barriers to crushing and dissolving intended to reduce abuse through nonoral routes of administration (ROAs) that require tampering (eg, injecting and snorting). Manufacturer shipments of original ER oxycodone (OC) stopped on August 5, 2010, and reformulated ER oxycodone (ORF) shipments started August 9, 2010. A sentinel surveillance sample of 140,496 individuals assessed for substance abuse treatment at 357 U.S. centers between June 1, 2009, and March 31, 2012, was examined for prevalence and prescription-adjusted prevalence rates of past-30-day abuse via any route, as well as abuse through oral, nonoral, and specific ROAs for ER oxycodone and comparators (ER morphine and ER oxymorphone) before and after ORF introduction. Significant reductions occurred for 8 outcome measures of ORF versus OC historically. Abuse of ORF was 41% lower (95% CI: -44 to -37) than historical abuse for OC, with oral abuse 17% lower (95% CI: -23 to -10) and nonoral abuse 66% lower (95% CI: -69 to -63). Significant reductions were not observed for comparators. Observations were consistent with the goals of a tamper resistant formulation for an opioid. Further research is needed to determine the persistence and generalizability of these findings. ::: ::: ::: PERSPECTIVE ::: This article presents preliminary findings indicating that 8 outcome measures of abuse of a reformulated ER oxycodone were lower than that for original ER oxycodone historically, particularly through nonoral ROAs that require tampering (ie, injection, snorting, smoking), in a sentinel sample of individuals assessed for substance use problems for treatment planning.


title: A Descriptive Framework of Workspace Awareness for Real-Time Groupware

abstract: Supporting awareness of others is an idea that holds promise for improving the usability of real-time distributed groupware. However, there is little principled information available about awareness that can be used by groupware designers. In this article, we develop a descriptive theory of awareness for the purpose of aiding groupware design, focusing on one kind of group awareness called i>workspace awareness. We focus on how small groups perform generation and execution tasks in medium-sized shared workspaces – tasks where group members frequently shift between individual and shared activities during the work session. We have built a three-part framework that examines the concept of workspace awareness and that helps designers understand the concept for purposes of designing awareness support in groupware. The framework sets out elements of knowledge that make up workspace awareness, perceptual mechanisms used to maintain awareness, and the ways that people use workspace awareness in collaboration. The framework also organizes previous research on awareness and extends it to provide designers with a vocabulary and a set of ground rules for analysing work situations, for comparing awareness devices, and for explaining evaluation results. The basic structure of the theory can be used to describe other kinds of awareness that are important to the usability of groupware.


Chinese Word Segmentation as Character Tagging


title: SOCS1, a Negative Regulator of Cytokine Signals and TLR Responses, in Human Liver Diseases

abstract: Toll-like receptor (TLR) signaling pathways are strictly coordinated by several mechanisms to regulate adequate innate immune responses. Recent lines of evidence indicate that the suppressor of cytokine signaling (SOCS) family proteins, originally identified as negative-feedback regulators in cytokine signaling, are involved in the regulation of TLR-mediated immune responses. SOCS1, a member of SOCS family, is strongly induced upon TLR stimulation. Cells lacking SOCS1 are hyperresponsive to TLR stimulation. Thus, SOCS1 is an important regulator for both cytokine and TLR-induced responses. As an immune organ, the liver contains various types of immune cells such as T cells, NK cells, NKT cells, and Kupffer cells and is continuously challenged with gut-derived bacterial and dietary antigens. SOCS1 may be implicated in pathophysiology of the liver. The studies using SOCS1-deficient mice revealed that endogenous SOCS1 is critical for the prevention of liver diseases such as hepatitis, cirrhosis, and cancers. Recent studies on humans suggest that SOCS1 is involved in the development of various liver disorders in humans. Thus, SOCS1 and other SOCS proteins are potential targets for the therapy of human liver diseases.


title: Field-to-Wire Coupling Model for the Common Mode in Random Bundles of Twisted-Wire Pairs

abstract: A multiconductor-transmission-line (MTL) model of field coupling to a random bundle composed of twisted-wire pairs (TWPs) running above ground is proposed for the computation of the induced common-mode (CM) noise. The nonuniform bundle is effectively represented by cascading uniform MTL sections, whose models are derived from a single reference solution through permutations of rows and columns in matrices and vectors. This is achieved by exploiting the concepts of: 1) averaged per-unit-length parameters, 2) reference cross-section, 3) minimum-distance shifts of TWP positions in the reference cross-section. Repeated-run analysis is used to outline the high sensitivity (tens of dBs) of the induced CM noise to random configurations of electrically long bundles, and to derive statistical estimates of CM voltages induced by a plane-wave field at each TWP terminal. Accuracy and computational efficiency of the proposed approach are proved by comparison versus full-wave simulations.


Acoustical modeling of gunshots including directional information and reflections


title: Towards Participatory Action Design Research: Adapting Action Research and Design Science Research Methods for Urban Informatics

abstract: This paper proposes a new research method, Participatory Action Design Research (PADR), for studies in the Urban Informatics (UI) domain. PADR supports UI research in developing new technological means (e.g. using mobile and ubiquitous computing) to resolve contemporary issues or support everyday life in urban environments. Situated in a socio-technical context, UI requires a close dialogue between social and design-oriented fields of research as well as their methods. PADR combines Action Research and Design Science Research, both of which are used in Information Systems, another field with a strong socio-technical emphasis, and further adapts them to the cross-disciplinary needs and research context of UI.


title: Spectral Representations for Convolutional Neural Networks

abstract: Discrete Fourier transforms provide a significant speedup in the computation of convolutions in deep learning. In this work, we demonstrate that, beyond its advantages for efficient computation, the spectral domain also provides a powerful representation in which to model and train convolutional neural networks (CNNs). We employ spectral representations to introduce a number of innovations to CNN design. First, we propose spectral pooling, which performs dimensionality reduction by truncating the representation in the frequency domain. This approach preserves considerably more information per parameter than other pooling strategies and enables flexibility in the choice of pooling output dimensionality. This representation also enables a new form of stochastic regularization by randomized modification of resolution. We show that these methods achieve competitive results on classification and approximation tasks, without using any dropout or max-pooling. ::: ::: Finally, we demonstrate the effectiveness of complex-coefficient spectral parameterization of convolutional filters. While this leaves the underlying model unchanged, it results in a representation that greatly facilitates optimization. We observe on a variety of popular CNN configurations that this leads to significantly faster convergence during training.


title: MotionBeam: designing for movement with handheld projectors

abstract: In this paper we present a novel interaction metaphor for handheld projectors we label MotionBeam. We detail a number of interaction techniques that utilize the physical movement of a handheld projector to better express the motion and physicality of projected objects. Finally we present the first iteration of a projected character design that uses the MotionBeam metaphor for user interaction.


title: Automatic tongue image segmentation based on histogram projection and matting

abstract: This paper mainly discusses how to use histogram projection and LBDM (Learning Based Digital Matting) to extract a tongue from a medical image, which is one of the most important steps in diagnosis of traditional Chinese Medicine. We firstly present an effective method to locate the tongue body, getting the convinced foreground and background area in form of trimap. Then, use this trimap as the input for LBDM algorithm to implement the final segmentation. Experiment was carried out to evaluate the proposed scheme, using 480 samples of pictures with tongue, the results of which were compared with the corresponding ground truth. Experimental results and analysis demonstrated the feasibility and effectiveness of the proposed algorithm.


title: A Multi-Criteria Taxonomy of Business Models in Electronic Commerce

abstract: Looking at the ongoing evolution in electronic commerce there are more and more business models becoming significant. E-shops, e-auctions or e-tendering are not the only possibilities for a company to be active in electronic commerce. This article presents many relevant business models and systematically classifies them. Mainly the classification is based on the type of business subjects like suppliers, customers and mediators and their active or passive role as initiators and carrier of a business model. Another basis for the classification is the breakdown of the concept of electronic commerce and an explicit modeling. This allows to build up a taxonomy using multiple criteria and the presentation and subsumption of individual business models. The taxonomy can be used to analyze and enhance existing systems and business models as well as to develop new internet strategies for companies. An example of the implementation of the business model e-portal concludes the article.


title: Knowledge of removable partial denture wearers on denture hygiene

abstract: BACKGROUND ::: Regular good denture hygiene by individuals with removable partial dentures (RPDs) is an important component of oral health and in the prevention of further dental problems. These individuals should be provided with advice on the importance of denture care and be aware of this information. ::: ::: ::: AIM ::: To establish deficiencies in patient knowledge surrounding denture hygiene by RPD wearers. ::: ::: ::: METHODOLOGY ::: The study was undertaken as an audit. Data was collected from April 2012 to October 2012 via a questionnaire completed by 196 RPD wearers attending as patients at the University Dental Hospital Wales and the dental units at St David's Hospital and Cynon Valley Hospital. The audit criterion was patients with RPDs should have knowledge of denture hygiene, with the standard set at 100%. ::: ::: ::: RESULTS ::: While 91.8% of participants stated they were provided with instructions on denture hygiene when provided with their current prosthesis, 60.2% were shown to have less than an appropriate level of denture cleanliness, with 9.2% reporting that they slept wearing their prosthesis. ::: ::: ::: CONCLUSION ::: The audit criterion and standard set were not achieved. A lack of knowledge surrounding denture hygiene was demonstrated among participants. As a part of the audit process the health education of RPD wearers' hygiene needs to be improved and awareness levels of the whole dental team needs to be raised. All partial dentures should receive information and regular reinforcement of key dental hygiene messages.


title: Sequence Symmetry Analysis and Disproportionality Analyses: What Percentage of Adverse Drug Reaction do they Signal?

abstract: Background: Sequence Symmetry Analysis (SSA) is a method to detect Adverse Event (AE) signals using administrative claims data. Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) are methods to detect AE signals using spontaneous reporting data. The proportion of AEs detected by all four methods is unknown. Objective: To determine sensitivity, specificity and predictive values of SSA, PRR, ROR and BCPNN for a set of medicine-AE pairs. Methods: All AEs identified in published Randomised Controlled Trials (RCTs) and Product Information (PI) were extracted for 19 medicines. Gold standard positive AEs were events identified in powered RCTs and gold standard negative AEs were events not listed in the PI for that medicine or any other medicines in the class. SSA was performed for each medicine-AE pairs using Australian Goverrnment Department of Veterans Affairs’ data, while the PRR, ROR and BCPNN, was calculated using the Food and Drug Administration Adverse Events Reporting System data. Results: A total of 157 medicine-AE pairs (43 positive and 114 negative) were identified and tested. SSA, PRR, ROR and BCPNN had a sensitivity of 65%, 1 9%, 49% and 51% respectively. Specificities across all methods were similar; 89%-97%. Thirty percent of true positive pairs were detected by all methods. SSA detected an additional 35% different true positive pairs while PRR, ROR and BCPNN methods detected an additional 21% different true positive pairs. Conclusions: Using the combination of signalling methods and data sources, more adverse drug reactions can be detected and could potentially strengthen the safety surveillance of post-marketing medicine.


title: Mutual Reinforcement of Academic Performance Prediction and Library Book Recommendation

abstract: The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in MOOCs and intelligent tutoring systems. Academic performance could be affected with factors like personality, skills, social environment, the use of library books and so on. However, it is still less investigated that how could the use of library books affect academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also promotes library book recommendation by recommending "right" books for students based on their performance levels and book meta information. Finally, we evaluate the proposed model on three years of the book-loan history and cumulative grade point average of 13,047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance is not only predictable from the book-loan history but also improves the recommendation of library books for students.


title: SUPERPIXEL-BASED UNSUPERVISED CHANGE DETECTION USING MULTI-DIMENSIONAL CHANGE VECTOR ANALYSIS AND SVM-BASED CLASSIFICATION

abstract: In this paper, a novel superpixel-based approach is introduced for unsupervised change detection using remote sensing images. The proposed approach contains three steps: 1) Superpixel segmentation. The simple linear iterative cluster (SLIC) algorithm is applied to obtain lattice-like homogenous superpixels. To avoid discordances of the superpixel boundaries obtained from bi-temporal images, the two images are firstly fused using principle component analysis. And then, the SLIC algorithm is applied on the first three principle components, which contain the main information of the two images. 2) For each superpixel, which is considered as the basic unit of the image space, the multi-dimensional change vector is computed from spectral, textural and structural features. 3) The superpixels are classified into two type: changed and unchanged through two progressive classification processes. The superpixels are firstly cataloged into three types: changed, unchanged and undefined by thresholding the change vectors and a voting process. And then the undefined superpixels are further classified into two classes: changed and unchanged, using a SVM-based classifier, which is trained by the derived changed and unchanged superpixels from the former step. The experiment using Indonesia data set has confirmed that the proposed approach is able to detect the changes automatically, by exploiting multiple change features.


title: The evolution to 4 G cellular systems : LTE-Advanced

abstract: This paper provides an in-depth view on the technologies being considered for Long Term Evolution-Advanced (LTE-Advanced). First, the evolution from third generation (3G) to fourth generation (4G) is described in terms of performance requirements and main characteristics. The new network architecture developed by the Third Generation Partnership Project (3GPP), which supports the integration of current and future radio access technologies, is highlighted. Then, the main technologies for LTE-Advanced are explained, together with possible improvements, their associated challenges, and some approaches that have been considered to tackle those challenges.


title: Non linear Image segmentation using fuzzy c means clustering method with thresholding for underwater images

abstract: The quality of underwater images is directly affected by water medium, atmosphere, pressure and temperature. This emphasizes the necessity of image segmentation, which divides an image into parts that have strong correlations with objects to reflect the actual information collected from the real world. Image segmentation is the most practical approach among virtually all automated image recognition systems. Clustering of numerical data forms the basis of many classification and system modelling algorithms. The purpose of clustering is to identify natural groupings of data from a large data set to produce a concise representation of a system's behaviour. In this paper we propose fuzzy c means clustering method with thresholding for underwater image segmentation. This paper focuses on comparison of fuzzy c means clustering algorithms with proposed method for underwater images. To evaluate the nonlinear image region segmentation, quantitative statistical measures have been used, such as the gray level energy, discrete entropy, relative entropy, mutual information and information redundancy. The assessment measures will further quantify the impact from image segmentation. The objective assessment approach has the potential to solve other image processing issues.The proposed method gives desirable results on the basis of energy, entropy, mutual information, redundancy, percentage of simplification and computer efficiency for underwater images.


title: The Use of Augmented Reality Pop-Up Book to Increase Motivation in English Language Learning For National Primary School

abstract: This research study describes the use of Augmented Reality (AR) as an emerging form of experience in which the real world is enhanced by computer-generated content. AR pop-up books for instance will help students bridge the gap between the digital and physical world. Students are able to use the AR pop-up book as the primary interface, changing perspective or direction by moving the book through their webcam where a marker detection which is a two-dimensional patterns to carry information that are attached to the book page. AR technology is adopted in this research study because it has the potential to motivate and support students in English language learning. In learning a second language, research has established that it is utmost important that students receive maximum support in terms of supportive and conducive learning environment. The AR pop-up book is developed using ZooBurst tool and the design is incorporated with a problem solving approach which is Keller's ARCS model: Attention, Relevance, Confidence, and Satisfaction. The development process is enhanced using Keller's ARCS model of motivational design. The result of the study is obtained from observations of Year One primary school students using the AR pop-up book and followed by semi-structured interview.


title: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

abstract: Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) low- level information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and (3) domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian classifier to generate a likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domain- specific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. In this paper we demonstrate the utility of our glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of (a) prostate cancer, (b) breast cancer, and (c) distinguishing between cancerous and benign breast histology specimens. The efficacy of our segmentation algorithm is evaluated by comparing breast and prostate cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei.


title: Electrical characteristics of conductive yarns and textile electrodes for medical applications

abstract: Clothing with conductive textiles for health care applications has in the last decade been of an upcoming research interest. An advantage with the technique is its suitability in distributed and home health care. The present study investigates the electrical properties of conductive yarns and textile electrodes in contact with human skin, thus representing a real ECG-registration situation. The yarn measurements showed a pure resistive characteristic proportional to the length. The electrodes made of pure stainless steel (electrode A) and 20% stainless steel/80% polyester (electrode B) showed acceptable stability of electrode potentials, the stability of A was better than that of B. The electrode made of silver plated copper (electrode C) was less stable. The electrode impedance was lower for electrodes A and B than that for electrode C. From an electrical properties point of view we recommend to use electrodes of type A to be used in intelligent textile medical applications.


Endo-Testing: Unit Testing with Mock Objects


title: The monitoring and early detection of Internet worms

abstract: After many Internet-scale worm incidents in recent years, it is clear that a simple self-propagating worm can quickly spread across the Internet and cause severe damage to our society. Facing this great security threat, we need to build an early detection system that can detect the presence of a worm in the Internet as quickly as possible in order to give people accurate early warning information and possible reaction time for counteractions. This paper first presents an Internet worm monitoring system. Then, based on the idea of "detecting the trend, not the burst" of monitored illegitimate traffic, we present a "trend detection" methodology to detect a worm at its early propagation stage by using Kalman filter estimation, which is robust to background noise in the monitored data. In addition, for uniform-scan worms such as Code Red, we can effectively predict the overall vulnerable population size, and estimate accurately how many computers are really infected in the global Internet based on the biased monitored data. For monitoring a nonuniform scan worm, especially a sequential-scan worm such as Blaster, we show that it is crucial for the address space covered by the worm monitoring system to be as distributed as possible.


title: A Novel Spectrum-Scheduling Scheme for Multichannel Cognitive Radio Network and Performance Analysis

abstract: In this paper, we develop an opportunistic spectrum-scheduling scheme for multichannel cognitive radio networks. In the proposed scheme, the primary user activity and channel quality (signal-to-noise ratio (SNR) at the receiving secondary user) vary on a slot-by-slot basis. The scheduling is performed at the beginning of the frame, which consists of multiple slots. The scheduling algorithm estimates the expected number of packets that can be transmitted over the frame by each secondary user for each licensed channel. We present a Markov chain formulation to calculate the expected number of packets that can be transmitted over the frame for a secondary user that corresponds to each licensed channel. Based on these expected packet transmissions, a central scheduler allocates the licensed channels to the secondary users. The objective of the scheduling algorithm is to allocate the licensed channels to maximize the aggregate throughput of the secondary users. Compared with the existing dynamic spectrum access schemes, the proposed scheduling scheme incurs smaller scheduling overhead and achieves higher throughput. We also present an analytical framework that models the proposed algorithm using the Markov chain analysis. The performance of the proposed scheduling scheme is evaluated in terms of the average user throughput and blocking probability.


title: ILQUA--An IE-Driven Question Answering System

abstract: ILQUA first participated in TREC QA main task in 2003. This year we have made modifications to the system by removing some components with poor performance and enhanced the system with new methods and new components. The newly built ILQUA is an IE-driven QA system. To answer “Factoid” and “List” questions, we apply our answer extraction methods on NE-tagged passages. The answer extraction methods adopted here are surface text pattern matching, n-gram proximity search and syntactic dependency matching. Surface text pattern matching has been applied in some previous TREC QA systems. However, the patterns used in ILQUA are automatically generated by a supervised learning system and represented in a format of regular expressions which can handle up to 4 question terms. N-gram proximity search and syntactic dependency matching are two steps of one component. N-grams of question terms are matched around every named entity in the candidate passages and a list of named entities are generated as answer candidate. These named entities go through a multi-level syntactic dependency matching until a final answer is generated. To answer “Other” questions, we parse the answer sentences of “Other” questions in 2004 main task and built syntactic patterns combined with semantic features. These patterns are applied to the parsed candidate sentences to extract answers of “Other” questions. The evaluation results showed ILQUA has reached an accuracy of 30.9% for factoid questions. ILQUA is an IE-driven QA system without any pre-compiled knowledge base of facts and it doesn’t get reference from any other external search engine such as Google. The disadvantage of an IE-driven QA system is that there are some types of questions that can’t be answered because the answer in the passages can’t be tagged as appropriate NE types. Figure 1 shows the diagram of the ILQUA architecture.


title: Assessing Creativity With Divergent Thinking Tasks: Exploring the Reliability and Validity of New Subjective Scoring Methods

abstract: Divergent thinking is central to the study of individual differences in creativity, but the traditional scoring systems (assigning points for infrequent responses and summing the points) face well-known problems. After critically reviewing past scoring methods, this article describes a new approach to assessing divergent thinking and appraises its reliability and validity. In our new Top 2 scoring method, participants complete a divergent thinking task and then circle the two responses that they think are their most creative responses. Raters then evaluate the responses on a 5-point scale. Regarding reliability, a generalizability analysis showed that subjective ratings of unusual-uses tasks and instances tasks yield dependable scores with only 2 or 3 raters. Regarding validity, a latent-variable study (n = 226) predicted divergent thinking from the Big Five factors and their higher-order traits (Plasticity and Stability). Over half of the variance in divergent thinking could be explained by dimensions of personality. The article presents instructions for measuring divergent thinking with the new method.


title: Leaky-Wave Theory, Techniques, and Applications: From Microwaves to Visible Frequencies

abstract: Leaky waves have been among the most active areas of research in microwave engineering over the second half of the 20th century. They have been shown to dominate the near-field of several open wave-guiding structures, of great interest to tailor their radiation, guidance and filtering properties. The elegant theoretical analyses and deep physical insights in this area, developed in an era in which computational resources were limited, represent a fundamental scientific legacy that is still extremely relevant in today's engineering society and beyond. In this regard, the relevance of leaky-wave concepts has been increasingly recognized in recent times over a broader scientific community, including optics and physics societies. In this paper, after revisiting the fundamental concepts of leaky-wave theory, we discuss and connect different relevant research activities in which leaky-wave concepts have been applied, with the goal of facilitating multidisciplinary interactions on these topics. In addition to the canonical microwave applications of leaky waves, particular attention is devoted to a few areas of interest in modern optics, such as directive optical antennas, extraordinary optical transmission, and embedded scattering eigenvalues, in which leaky waves play a fundamental role.


title: A Threat for Tablet PCs in Public Space: Remote Visualization of Screen Images Using EM Emanation

abstract: The use of tablet PCs is spreading rapidly, and accordingly users browsing and inputting personal information in public spaces can often be seen by third parties. Unlike conventional mobile phones and notebook PCs equipped with distinct input devices (e.g., keyboards), tablet PCs have touchscreen keyboards for data input. Such integration of display and input device increases the potential for harm when the display is captured by malicious attackers. This paper presents the description of reconstructing tablet PC displays via measurement of electromagnetic (EM) emanation. In conventional studies, such EM display capture has been achieved by using non-portable setups. Those studies also assumed that a large amount of time was available in advance of capture to obtain the electrical parameters of the target display. In contrast, this paper demonstrates that such EM display capture is feasible in real time by a setup that fits in an attache case. The screen image reconstruction is achieved by performing a prior course profiling and a complemental signal processing instead of the conventional fine parameter tuning. Such complemental processing can eliminate the differences of leakage parameters among individuals and therefore correct the distortions of images. The attack distance, 2 m, makes this method a practical threat to general tablet PCs in public places. This paper discusses possible attack scenarios based on the setup described above. In addition, we describe a mechanism of EM emanation from tablet PCs and a countermeasure against such EM display capture.


title: Data Poisoning Attacks against Online Learning

abstract: We consider data poisoning attacks, a class of adversarial attacks on machine learning where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier satisfy certain objectives. While there has been much prior work on data poisoning, most of it is in the offline setting, and attacks for online learning, where training data arrives in a streaming manner, are not well understood. In this work, we initiate a systematic investigation of data poisoning attacks for online learning. We formalize the problem into two settings, and we propose a general attack strategy, formulated as an optimization problem, that applies to both with some modifications. We propose three solution strategies, and perform extensive experimental evaluation. Finally, we discuss the implications of our findings for building successful defenses.


title: A wide-range VCO with optimum temperature adaptive tuning

abstract: This paper presents an integrated wide-range VCO with a modified tuning scheme to deal with VCO frequency drift over temperature. In this approach, during the coarse-tune operation, VCO tune voltage is a function of temperature such that it resembles the inverse function of VCO fine-tune characteristic. Without degrading VCO performance, the proposed temperature adaptive tuning optimizes the maximum tolerable VCO temperature frequency drift over which PLL remains locked. As a result, VCO gain can be reduced significantly, making VCO less sensitive to PLL tune voltage noise. Integrated in a multi-standard multi-band transceiver with a small VCO gain of 50MHz/V at 3.90GHz, PLL remains locked despite 45MHz frequency drift of VCO over [−30°C, 85°C]. Using an on-chip inductor, VCO covers from 3.15GHz to 4.60GHz, achieving −138.0dBc/Hz phase noise at 3.0MHz at 3.90GHz by drawing just 8.5mA from 1.60V supply in 0.13u CMOS process.


title: Child face detection using age specific luminance invariant geometric descriptor

abstract: While considerable research have been conducted on age-wise age estimation using skin detection most often with other visual cues, relatively little research has looked closely at the subject. In this paper, we present a new framework for interpreting facial image patterns that can be employed in categorical age estimation. The aim is to propose a novel approach to investigate and implement a child face detection technique that is able to estimate age categorically — adult or child based on a new hybrid feature descriptor. The novel hybrid feature descriptor LIGD (the luminance invariant geometric descriptor) is composed of some low and high level features, which are found to be effective in characterizing the local appearance. In local appearance estimation, chromaticity, texture, and positional information of few facial visual cues can be employed simultaneously. Compared to the results published in a recent work, our proposed approach yields the highest precision and recall, and overall accuracy in recognition.


title: Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents

abstract: Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning. In this paper, we propose a simple but effective neural language grounding module for embodied agents that can be trained end to end from scratch taking raw pixels, unstructured linguistic commands, and sparse rewards as the inputs. We model the language grounding process as a language-guided transformation of visual features, where latent sentence embeddings are used as the transformation matrices. In several language-directed navigation tasks that feature challenging partial observability and require simple reasoning, our module significantly outperforms the state of the art. We also release XWorld3D, an easy-to-customize 3D environment that can potentially be modified to evaluate a variety of embodied agents.


title: Dual Band-Reject UWB Antenna With Sharp Rejection of Narrow and Closely-Spaced Bands

abstract: An ultrawideband (UWB) antenna that rejects extremely sharply the two narrow and closely-spaced U.S. WLAN 802.11a bands is presented. The antenna is designed on a single surface (it is uniplanar) and uses only linear sections for easy scaling and fine-tuning. Distributed-element and lumped-element equivalent circuit models of this dual band-reject UWB antenna are presented and used to support the explanation of the physical principles of operation of the dual band-rejection mechanism thoroughly. The circuits are evaluated by comparing with the response of the presented UWB antenna that has very high selectivity and achieves dual-frequency rejection of the WLAN 5.25 GHz and 5.775 GHz bands, while it receives signal from the intermediate band between 5.35-5.725 GHz. The rejection is achieved using double open-circuited stubs, which is uncommon and are chosen based on their narrowband performance. The antenna was fabricated on a single side of a thin, flexible, LCP substrate. The measured achieved rejection is the best reported for a dual-band reject antenna with so closely-spaced rejected bands. The measured group delay of the antenna validates its suitability for UWB links. Such antennas improve both UWB and WLAN communication links at practically zero cost.


title: OptiSpace: Automated Placement of Interactive 3D Projection Mapping Content

abstract: We present OptiSpace, a system for the automated placement of perspectively corrected projection mapping content. We analyze the geometry of physical surfaces and the viewing behavior of users over time using depth cameras. Our system measures user view behavior and simulates a virtual projection mapping scene users would see if content were placed in a particular way. OptiSpace evaluates the simulated scene according to perceptual criteria, including visibility and visual quality of virtual content. Finally, based on these evaluations, it optimizes content placement, using a two-phase procedure involving adaptive sampling and the covariance matrix adaptation algorithm. With our proposed architecture, projection mapping applications are developed without any knowledge of the physical layouts of the target environments. Applications can be deployed in different uncontrolled environments, such as living rooms and office spaces.


title: HCI meets Material Science: A Literature Review of Morphing Materials for the Design of Shape-Changing Interfaces

abstract: With the proliferation of flexible displays and the advances in smart materials, it is now possible to create interactive devices that are not only flexible but can reconfigure into any shape on demand. Several Human Computer Interaction (HCI) and robotics researchers have started designing, prototyping and evaluating shape-changing devices, realising, however, that this vision still requires many engineering challenges to be addressed. On the material science front, we need breakthroughs in stable and accessible materials to create novel, proof-of-concept devices. On the interactive devices side, we require a deeper appreciation for the material properties and an understanding of how exploiting material properties can provide affordances that unleash the human interactive potential. While these challenges are interesting for the respective research fields, we believe that the true power of shape-changing devices can be magnified by bringing together these communities. In this paper we therefore present a review of advances made in shape-changing materials and discuss their applications within an HCI context.


title: Information Recovery in Shuffled Graphs via Graph Matching

abstract: While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understanding the practical impact that errorfully observed vertex correspondences can have on subsequent inference, and the capacity of graph matching methods to recover the lost vertex alignment and inferential performance. Working in the correlated stochastic blockmodel setting, we establish a duality between the loss of mutual information due to an errorfully observed vertex correspondence and the ability of graph matching algorithms to recover the true correspondence across graphs. In the process, we establish a phase transition for graph matchability in terms of the correlation across graphs, and we conjecture the analogous phase transition for the relative information loss due to shuffling vertex labels. We demonstrate the practical effect that graph shuffling—and matching—can have on subsequent inference, with examples from two sample graph hypothesis testing and joint spectral graph clustering.


title: An overview of development GPS navigation for autonomous car

abstract: An autonomous car navigation system based on Global Positioning System (GPS) is a new and promising technology, which uses real time geographical data received from several GPS satellites to calculate longitude, latitude, speed and course to help navigate a car. The goal of the project is to make an auto-navigational car model that can route through known or pre-programmed co-ordinates autonomously without any control by human. It is designed to demonstrate the feasibility of using a low-cost strap-down inertial measurement unit (IMU) to navigate between intermittent GPS fixes. The present hardware consists of a GPS receiver, IMU, compass and a data processing computer. This research paper demonstrates a prototype development of autonomous car to enable the aerial vehicle accomplish the required autonomy and maintain satisfactory automated driving operation. Design objectives include simplicity, robustness, versatility and improved operational utility. The driverless car features an advanced, highly autonomous drive control system with an anti-drift from its course algorithms. The discovery of interacting hardware and software had led to breakthrough improvements in the efficiency of maintaining planned route on the track.


title: Interactive Image Segmentation Using Semi-transparent Wearable Glasses

abstract: Since it is difficult to automatically and precisely extract an object of interest, interactive image segmentation techniques exploit user-provided segmentation seeds. In previous interactive segmentation applications, the segmentation seeds are typically provided by mouse clicks or finger touches. In this paper, the segmentation of an object is studied from the scene that the user sees through semi-transparent wearable glasses. In this application scenario, a front-view camera is used to obtain the segmentation seeds from the user's fingertip position. In particular, two segmentation methodologies called transparent segmentation and semi-transparent segmentation are considered to determine an effective segmentation scheme for the wearable glasses. Extensive user studies are performed to evaluate the user preferences and the segmentation accuracies of the two methodologies.


Operating System Process and Thread Migration in Heterogeneous Platforms


title: Unsupervised segmentation based on robust estimation and color active contour models

abstract: One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L/sub 2/E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L/sub 2/E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.


title: Brief Communication MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0

abstract: We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www. megasoftware.net free of charge.


title: Adaptive game AI with dynamic scripting

abstract: Online learning in commercial computer games allows computer-controlled opponents to adapt to the way the game is being played. As such it provides a mechanism to deal with weaknesses in the game AI, and to respond to changes in human player tactics. We argue that online learning of game AI should meet four computational and four functional requirements. The computational requirements are speed, effectiveness, robustness and efficiency. The functional requirements are clarity, variety, consistency and scalability. This paper investigates a novel online learning technique for game AI called `dynamic scripting', that uses an adaptive rulebase for the generation of game AI on the fly. The performance of dynamic scripting is evaluated in experiments in which adaptive agents are pitted against a collection of manually-designed tactics in a simulated computer roleplaying game. Experimental results indicate that dynamic scripting succeeds in endowing computer-controlled opponents with adaptive performance. To further improve the dynamic-scripting technique, an enhancement is investigated that allows scaling of the difficulty level of the game AI to the human player's skill level. With the enhancement, dynamic scripting meets all computational and functional requirements. The applicability of dynamic scripting in state-of-the-art commercial games is demonstrated by implementing the technique in the game Neverwinter Nights. We conclude that dynamic scripting can be successfully applied to the online adaptation of game AI in commercial computer games.


title: Gene expression inference with deep learning

abstract: Motivation: Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. ::: ::: Results: We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. ::: ::: Availability and implementation: D-GEX is available at https://github.com/uci-cbcl/D-GEX. ::: ::: Contact: ude.icu.sci@xhx ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach

abstract: We consider the problem of optimizing an expensive objective function when a finite budget of total evaluations is prescribed. In that context, the optimal solution strategy for Bayesian optimization can be formulated as a dynamic programming instance. This results in a complex problem with uncountable, dimension-increasing state space and an uncountable control space. We show how to approximate the solution of this dynamic programming problem using rollout, and propose rollout heuristics specifically designed for the Bayesian optimization setting. We present numerical experiments showing that the resulting algorithm for optimization with a finite budget outperforms several popular Bayesian optimization algorithms.


title: Hempseed as a nutritional resource: An overview

abstract: The seed of Cannabis sativa L. has been an important source of nutrition for thousands of years in Old World cultures. Non-drug varieties of Cannabis, commonly referred to as hemp, have not been studied extensively for their nutritional potential in recent years, nor has hempseed been utilized to any great extent by the industrial processes and food markets that have developed during the 20th century. Technically a nut, hempseed typically contains over 30% oil and about 25% protein, with considerable amounts of dietary fiber, vitamins and minerals. Hempseed oil is over 80% in polyunsaturated fatty acids (PUFAs), and is an exceptionally rich source of the two essential fatty acids (EFAs) linoleic acid (18:2 omega-6) and alpha-linolenic acid (18:3 omega-3). The omega-6 to omega-3 ratio (n6/n3) in hempseed oil is normally between 2:1 and 3:1, which is considered to be optimal for human health. In addition, the biological metabolites of the two EFAs, gamma-linolenic acid (18:3 omega-6; ‘GLA’) and stearidonic acid (18:4 omega-3; ‘SDA’), are also present in hempseed oil. The two main proteins in hempseed are edestin and albumin. Both of these high-quality storage proteins are easily digested and contain nutritionally significant amounts of all essential amino acids. In addition, hempseed has exceptionally high levels of the amino acid arginine. Hempseed has been used to treat various disorders for thousands of years in traditional oriental medicine. Recent clinical trials have identified hempseed oil as a functional food, and animal feeding studies demonstrate the long-standing utility of hempseed as an important food resource.


title: Review of solid state transformer in the distribution system: From components to field application

abstract: The emergence of high power converters makes the modern power grid more active than it was before. One of the research directions in this area is the solid state transformer, which aims at replacing the traditional 50/60 Hz power transformer by means of high frequency isolated AC/AC solid state conversion techniques. This paper presents a systematical technology review essential for the development of solid state transformer in the distribution system, especially focusing on the following four areas: high voltage and high frequency power devices, high power and high frequency transformers, AC/AC converter topologies, and applications of solid state transformer in the distribution system. For each category, the state-of-art technologies are reviewed and possible research directions are presented. It is concluded that the solid state transformer is an emerging technology for the modernization of the future smart grid.


title: A Practical Approach to 3D Scanning in the Presence of Interreflections, Subsurface Scattering and Defocus

abstract: Global or indirect illumination effects such as interreflections and subsurface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors in structured light, caused by both long-range (interreflections) and short-range (subsurface scattering) indirect illumination. The errors depend on the frequency of the projected patterns, and the nature of indirect illumination. In particular, we show that long-range effects cause decoding errors for low-frequency patterns, whereas short-range effects affect high-frequency patterns. ::: ::: Based on this analysis, we present a practical 3D scanning system which works in the presence of a broad range of indirect illumination. First, we design binary structured light patterns that are resilient to individual indirect illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows detecting any residual errors that are corrected by acquiring a few additional images. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results for several scenes with complex shape and material properties.


title: EFFECT OF EXAMINATION STRESS ON THE PLASMA CORTISOL LEVEL

abstract: Background: Medical profession has been always a noble and prestigious path but the endeavour behind it has been truly known by the persons who undergone the training of becoming a doctor. Medical students face many stresses in their academic life. This study is carried out to provide data and re-establish the effect of academic examination stress on the plasma cortisol levels. Methods: A longitudinal follow up study was carried out on the first MBBS medical students who were appearing for their first credit examination by measuring their plasma cortisol levels in pre-examination and post-examination stage in fasting condition. Serum Cortisol was estimated by using Byer’s Advia Centuse advanced Chemiluminescence’s technique with inbuilt calibrators and controls; the results obtained were statistically analysed using paired ‘t’ test. Results: On statistically analysing the results of our study we found that medical students in stage – I had significantly higher values of plasma cortisol than when they were in stage – II. Conclusion: The results cover a significant correlation of examination stress factors to changes in plasma cortisol values. It is important for medical students to use stress reducing measures, or reduce them as much as possible in order to avoid factors that can affect themselves and their patients in stressful way.


title: Image Classification Using Generative Neuro Evolution for Deep Learning

abstract: Research into deep learning has demonstrated performance competitive with humans on some visual tasks, however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus neuroevolution for deep learning is investigated in this paper. In particular, the Hypercube-based Neuro Evolution of Augmenting Topologies is a NE approach that can effectively learn large neural structures by training an indirect encoding that compresses the artificial neural network (ANN) weight pattern as a function of geometry. The methodologies are tested on a traditional image classification task as well as one tailored to overhead satellite imagery. The results show that Hyper NEAT struggles with performing image classification by itself, but can be effective in training a feature extractor that other ML approaches can learn from. Thus Neuro Evolution combined with other ML methods provides an intriguing area of research that can replicate the processes in nature.


title: Low-rank matrix recovery with structural incoherence for robust face recognition

abstract: We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recognition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix approximation algorithm with structural incoherence for robust face recognition. Our method not only decomposes raw training data into a set of representative basis with corresponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are encouraged to be as independent as possible due to the regularization on structural incoherence. We show that this provides additional discriminating ability to the original low-rank models for improved performance. Experimental results on public face databases verify the effectiveness and robustness of our method, which is also shown to outperform state-of-the-art SRC based approaches.


title: AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security

abstract: Abstract Benefits of Internet of Things and cloud–fog-edge computing are associated with the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this article, we propose a configurable, reliable, and confidential distributed data storage scheme with the ability to process encrypted data and control results of computations. Our system utilizes Redundant Residue Number System (RRNS) with new method of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank of a number (AR), which allows us to reduce the computational complexity of the decoding from RNS to binary representation, and size of the coefficients. Based on the properties of the approximate value and arithmetic properties of RNS, we introduce AR-RRNS method for error detection, correction, and controlling computational results. We provide a theoretical basis to configure probability of information loss, data redundancy, speed of encoding and decoding to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that by appropriate selection of RRNS parameters, the proposed scheme allows not only increasing safety, reliability, and reducing an overhead of data storage, but also processing of encrypted data.


A Multi-Resonant Forward Converter Based on Non-Ideal Coupling of the Transformer


title: Violent video game effects on children and adolescents. A review of the literature.

abstract: Studies of violent video games on children and adolescents were reviewed to: 1) determine the multiple effects; 2) to offer critical observations about common strengths and weaknesses in the literature; 3) to provide a broader perspective to understand the research on the effects of video games. The review includes general theoretical and methodological considerations of media violence, and description of the general aggression model (GAM). The literature was evaluated in relation to the GAM. Published literature, including meta-analyses, are reviewed, as well as relevant unpublished material, such as conference papers and dissertations. Overall, the evidence supports hypotheses that violent video game play is related to aggressive affect, physiological arousal, aggressive cognitions, and aggressive behaviours. The effects of video game play on school performance are also evaluated, and the review concludes with a dimensional approach to video game effects. The dimensional approach evaluates video game effects in terms of amount, content, form, and mechanics, and appears to have many advantages for understanding and predicting the multiple types of effects demonstrated in the literature.


title: Robust Detection of Abandoned and Removed Objects in Complex Surveillance Videos

abstract: Tracking-based approaches for abandoned object detection often become unreliable in complex surveillance videos due to occlusions, lighting changes, and other factors. We present a new framework to robustly and efficiently detect abandoned and removed objects based on background subtraction (BGS) and foreground analysis with complement of tracking to reduce false positives. In our system, the background is modeled by three Gaussian mixtures. In order to handle complex situations, several improvements are implemented for shadow removal, quick-lighting change adaptation, fragment reduction, and keeping a stable update rate for video streams with different frame rates. Then, the same Gaussian mixture models used for BGS are employed to detect static foreground regions without extra computation cost. Furthermore, the types of the static regions (abandoned or removed) are determined by using a method that exploits context information about the foreground masks, which significantly outperforms previous edge-based techniques. Based on the type of the static regions and user-defined parameters (e.g., object size and abandoned time), a matching method is proposed to detect abandoned and removed objects. A person-detection process is also integrated to distinguish static objects from stationary people. The robustness and efficiency of the proposed method is tested on IBM Smart Surveillance Solutions for public safety applications in big cities and evaluated by several public databases, such as The Image library for intelligent detection systems (i-LIDS) and IEEE Performance Evaluation of Tracking and Surveillance Workshop (PETS) 2006 datasets. The test and evaluation demonstrate our method is efficient to run in real-time, while being robust to quick-lighting changes and occlusions in complex environments.


title: An Efficient Shared Multi-Class Detection Cascade

abstract: We propose a novel multi-class object detector, that optimizes the detection costs while retaining a desired detection rate. The detector uses a cascade that unites the handling of similar object classes while separating off classes at appropriate levels of the cascade. No prior knowledge about the relationship between classes is needed as the classifier structure is automatically determined during the training phase. The detection nodes in the cascade use Haar wavelet features and Gentle AdaBoost, however the approach is not dependent on the specific features used and can easily be extended to other cases. Experiments are presented for several numbers of object classes and the approach is compared to other classifying schemes. The results demonstrate a large efficiency gain that is particularly prominent for a greater number of classes. Also the complexity of the training scales well with the number of classes.


title: Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT

abstract: Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment. We developed a fracture classification system by acquiring quantitative morphologic and bone density determinants of fracture progression through the use of automated measurements from longitudinal studies. A total of 250 CT studies were acquired for the task, each having previously identified VCFs with osteoporosis or neoplasm. Thirty-six features for each identified VCF were computed and classified using a committee of support vector machines. Ten-fold cross validation on 695 identified fractured vertebrae showed classification accuracies of 0.812, 0.665, and 0.820 for the measured, longitudinal, and combined feature sets respectively.


title: Swarm‐based spatial sorting

abstract: Purpose – The purpose of this paper is to present an algorithm for spatially sorting objects into an annular structure.Design/methodology/approach – A swarm‐based model that requires only stochastic agent behaviour coupled with a pheromone‐inspired “attraction‐repulsion” mechanism.Findings – The algorithm consistently generates high‐quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature.Research limitations/implications – Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects.Practical implications – The algorithm may find applications in distributed robotics.Originality/value – The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction‐repulsion algorithms and underlying natural processes.


title: Location-based Resource Allocation for mobile D2D communications in multicell deployments

abstract: Cooperative intelligent traffic systems (C-ITS) will help improve the safety and efficiency of ground transportation by enabling the cooperation between traffic participants. Applications based on the C-ITS paradigm rely on vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-device (collectively, V2X) communication for the exchange of critical information and have very stringent Quality of Service (QoS) requirements on the reliability and availability of the communication links. In order to enable V2X communication using the envisioned device-to-device underlay of future 5G cellular networks, we have introduced a Location Dependent Resource Allocation Scheme (LDRAS) in [1]. In this paper, we enhance and extend LDRAS to multicell deployments, and quantify its performance based on extensive system level simulations. The results show that LDRAS significantly improves the QoS for future C-ITS applications, from radio resource management point of view. However, satisfying the requirements of such services necessitates further improvements of 5G networks over current 4G deployments.