title: Markov Random Field model for single image defogging

abstract: Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white background, called the atmospheric veil. As pointed in [1], Advanced Driver Assistance Systems (ADAS) based on the display of defogged images from a camera may help the driver by improving objects visibility in the image and thus may lead to a decrease of fatality and injury rates. In the last few years, the problem of single image defogging has attracted attention in the image processing community. Being an ill-posed problem, several methods have been proposed. However, a few among of these methods are dedicated to the processing of road images. One of the first exception is the method in [2], [1] where a planar constraint is introduced to improve the restoration of the road area, assuming an approximately flat road. The single image defogging problem being ill-posed, the choice of the Bayesian approach seems adequate to set this problem as an inference problem. A first Markov Random Field (MRF) approach of the problem has been proposed recently in [3]. However, this method is not dedicated to road images. In this paper, we propose a novel MRF model of the single image defogging problem which applies to all kinds of images but can also easily be refined to obtain better results on road images using the planar constraint. A comparative study and quantitative evaluation with several state-of-the-art algorithms is presented. This evaluation demonstrates that the proposed MRF model allows to derive a new algorithm which produces better quality results, in particular in case of a noisy input image.


title: Semantic Technologies for the IoT - An Inter-IoT Perspective

abstract: The Inter-IoT project is aiming at the design and implementation of, and experimentation with, an open cross-layer framework and associated methodology, to provide voluntary interoperability among heterogeneous Internet of Things (IoT) platforms. The project is initially driven by uses cases from two domains: (e/m) Health and transportation and logistics in a port environment. While the Inter-IoT will provide interoperability across the software stack, here, we focus our attention on the semantic interoperability. In this context, we present a concise overview of existing IoT-related semantic approaches, which might either be directly applicable to, or serve as a source of inspiration for, the Inter-IoT applications.


title: Consequences of loneliness on physical activity and mortality in older adults and the power of positive emotions.

abstract: OBJECTIVES ::: This study examined the longitudinal relationships between loneliness, physical activity, and mortality in older adults. This study also tested the implication of Fredrickson's Broaden and Build Theory (1998, 2001) that positive emotions (happiness) might serve to "undo" the detrimental effects of negative emotions (loneliness). ::: ::: ::: METHOD ::: Participants (n = 228; 62% female; aged 77-96 years) took part in the Aging in Manitoba Study (2001) and the Successful Aging Study (2003). Mortality information was assessed in 2008. ::: ::: ::: RESULTS ::: Regression analyses showed that loneliness longitudinally predicted perceived physical activity and mortality. Moreover, in support of Fredrickson's theory, happiness moderated these relationships, suggesting that happiness had the power to "undo" the detrimental effects of loneliness on activity and mortality. ::: ::: ::: CONCLUSIONS ::: Loneliness is an independent risk factor for mortality and reduced physical activity among older adults; however, being happy may offset the negative consequences of being lonely. Future interventions could target positive emotions and loneliness as a way of ultimately enhancing the lifespan and wellspan of older adults.


title: Webometric Study of World Class Universities Websites

abstract: The objective of this study is to determine whether there are differences in terms of visibility and accessibility of the top thirty and the bottom thirty world class universities websites. Using software Alexa, it is possible to gauge the volume of web traffic in order to determine the visibility of the website. In this study Alexa is applied to study the visibility of these universities websites. The development of any website has to comply with the existing accessibility guidelines. It is futile to have a website that is difficult to access by users owing to non-compliance with the existing standards on accessibility. To check for such irregularities a software EvalAccess was used. By comparing the state of visibility and accessibility among the top and bottom world class universities websites, it was found that those top rank universities websites are more visible and accessible as compared to the others.


The Token Economy : A Recent Review and Evaluation


FastSpMM: An Efficient Library for Sparse Matrix Matrix Product on GPUs


title: Fabrication of Micro Electrode Array by UV-LIGA and its Application in Micro ECM

abstract: In this paper, a fabrication process of ultra-high metal micro electrode array by means of UV-LIGA technology and its application in micro Electrochemical Machining (ECM) was investigated, and a new aided method, which can completely remove the ultra-thick cross-linked SU-8 resist without leaving remnants of the resist or destroying the electroplated microstructures, was advanced. During the lithography process, 1mm thick SU-8 moulds of different patterns were fabricated on the substrate. Before the electroforming process, a through-mask electrochemical micromachining (through-mask EMM) procedure was carried out to process micro roots on the substrate, the processed micro roots could improve the adhesion of micro electrode array electroformed on the substrate. After the electroforming process, cross-linked SU-8 resist was removed by the hot remover PG (MicroChem). Under the optimal experiment conditions of this fabrication process, the electrode arrays up to 800μm high of different shapes were fabricated. Finally, the fabricated electrode arrays was used as cathode to machine micro hole arrays on stainless steel by micro ECM.


title: Automatic Train Control System Development and Simulation for High-Speed Railways

abstract: Research and development on high-speed railway systems and particularly its automatic control systems, are introduced. Numerical modeling of high-speed trains in the Chinese high-speed train system and its associate automatic control systems are described in detail. Moreover, modeling and simulation of train operation systems are analyzed and demonstrated.


title: Deep Triplet Ranking Networks for One-Shot Recognition

abstract: Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class. To tackle this natural and important challenge, one-shot learning, which aims to exploit a set of well labeled base classes to build classifiers for the new target classes that have only one observed instance per class, has recently received increasing attention from the research community. In this paper we propose a novel end-to-end deep triplet ranking network to perform one-shot learning. The proposed approach learns class universal image embeddings on the well labeled base classes under a triplet ranking loss, such that the instances from new classes can be categorized based on their similarity with the one-shot instances in the learned embedding space. Moreover, our approach can naturally incorporate the available one-shot instances from the new classes into the embedding learning process to improve the triplet ranking model. We conduct experiments on two popular datasets for one-shot learning. The results show the proposed approach achieves better performance than the state-of-the- art comparison methods.


title: A Survey on Activities of Daily Living and Occupations of Upper Extremity Amputees

abstract: Objective ::: To assess prosthetic use by upper extremity amputees, and their difficulties with prostheses in activities of daily living and occupations.


title: Knowledge Protection Challenges of Social Media Encountered by Organizations

abstract: Although social media (SM) represents a new means of creating and sharing knowledge, it also presents new challenges for protecting confidential information and other data that companies do not want to share. However, knowledge protection and security-oriented knowledge management processes related to SM have received little attention in previous studies. This research attempts to close that gap by examining which information and knowledge protection challenges arise from employees' use of SM, why they arise, and how organizations can address them. The main contribution of this study is a framework that integrates three types of knowledge protection challenges (information security challenges; reputation challenge; management challenges) with five special characteristics of SM (information distribution speed; blurry audience; merging of private and professional identity; easily collectible information; generation transition), which explain why these challenges arise. In addition, the framework presents ei...


title: Neurobiological Foundations of Acupuncture: The Relevance and Future Prospect Based on Neuroimaging Evidence

abstract: Acupuncture is currently gaining popularity as an important modality of alternative and complementary medicine in the western world. Modern neuroimaging techniques such as functional magnetic resonance imaging, positron emission tomography, and magnetoencephalography open a window into the neurobiological foundations of acupuncture. In this review, we have summarized evidence derived from neuroimaging studies and tried to elucidate both neurophysiological correlates and key experimental factors involving acupuncture. Converging evidence focusing on acute effects of acupuncture has revealed significant modulatory activities at widespread cerebrocerebellar brain regions. Given the delayed effect of acupuncture, block-designed analysis may produce bias, and acupuncture shared a common feature that identified voxels that coded the temporal dimension for which multiple levels of their dynamic activities in concert cause the processing of acupuncture. Expectation in acupuncture treatment has a physiological effect on the brain network, which may be heterogeneous from acupuncture mechanism. "Deqi" response, bearing clinical relevance and association with distinct nerve fibers, has the specific neurophysiology foundation reflected by neural responses to acupuncture stimuli. The type of sham treatment chosen is dependent on the research question asked and the type of acupuncture treatment to be tested. Due to the complexities of the therapeutic mechanisms of acupuncture, using multiple controls is an optimal choice.


title: Pornography headache

abstract: The headache associated with intercourse or masturbatory activity is a well-recognized clinical entity but pornography headache is barely mentioned. We report a young man who suffered preorgasmic headache pertaining only to pornography of specific erotic contents but not to other sexual or nonsexual act. An antecedent activation of sexual arousal and vasoconstriction during pain were found. Finally, oral indomethacin favorably prevented the pain. Therefore, pornography headache is a distinguished headache disorder distinct from other sexual-related headache disorders. Sexual arousal-mediated cerebrovascular dysregulation consequence to visuoneural uncoupling in response to erotic stimulus is proposed. Pornography headache may be underestimated in population as pain-killer overuse may mask the actual incidence in real world.


title: Multi-static passive SAR imaging based on Bayesian compressive sensing

abstract: Passive radar systems, which utilize broadcast and navigation signals as sources of opportunity, have attracted significant interests in recent years due to their low cost, covertness, and the availability of different illuminator sources. In this paper, we propose a novel method for synthetic aperture imaging in multi-static passive radar systems based on a group sparse Bayesian learning technique. In particular, the problem of imaging sparse targets is formulated as a group sparse signal reconstruction problem, which is solved using a complex multi- task Bayesian compressive sensing (CMT-BCS) method to achieve a high resolution. The proposed approach significantly improves the imaging resolution beyond the range resolution. Compared to the other group sparse signal reconstruction methods, such as the block orthogonal matching pursuit (BOMP) and group Lasso, the CMT-BCS provides significant performance improvement for the reconstruction of sparse targets in the redundant dictionary with high coherence. Simulations results demonstrate the superior performance of the proposed approach.


title: Chiron-1: a software architecture for user interface development, maintenance, and run-time support

abstract: The Chiron-1 user interface system demonstrates key techniques that enable a strict separation of an application from its user interface. These techniques include separating the control-flow aspects of the application and user interface: they are concurrent and may contain many threads. Chiron also separates windowing and look-and-feel issues from dialogue and abstract presentation decisions via mechanisms employing a client-server architecture. To separate application code from user interface code, user interface agents called artists are attached to instances of application abstract data types (ADTs). Operations on ADTs within the application implicitly trigger user interface activities within the artists. Multiple artists can be attached to ADTs, providing multiple views and alternative forms of access and manipulation by either a single user or by multiple users. Each artist and the application run in separate threads of control. Artists maintain the user interface by making remote calls to an abstract depiction hierarchy in the Chiron server, insulting the user interface code from the specifics of particular windowing systems and toolkits. The Chiron server and clients execute in separate processes. The client-server architecture also supports multilingual systems: mechanisms are demonstrated that support clients written in programming languages other than that of the server while nevertheless supporting object-oriented server concepts. The system has been used in several universities and research and development projects. It is available by anonymous ftp.


title: Measuring Universal Intelligence in Agent-Based Systems Using the Anytime Intelligence Test

abstract: This paper aims to quantify and analyze the intelligence of artificial agent collectives. A universal metric is proposed and used to empirically measure intelligence for several different agent decision controllers. Accordingly, the effectiveness of various algorithms is evaluated on a per-agent basis over a selection of abstracted, canonical tasks of different algorithmic complexities. Results reflect the different settings over which cooperative multiagent systems can be significantly more intelligent per agent than others. We identify and discuss some of the factors influencing the collective performance of these systems.


title: Control of Systems Integrating Logic, Dynamics, and Constraints

abstract: This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as mixed logical dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include linear hybrid systems, finite state machines, some classes of discrete event systems, constrained linear systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through mixed integer quadratic programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported.


title: A novel approach to CAD system for the detection of lung nodules in CT images

abstract: Grouping of nodules helped in the reduction of false positive rate and computational time.Shape specific morphological operations were used for the refinement of juxtavascular nodules.Intensity based, geometrical and statistical features assisted in the elimination of false positives. Display Omitted Detection of pulmonary nodule plays a significant role in the diagnosis of lung cancer in early stage that improves the chances of survival of an individual. In this paper, a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural nodules. Lungs are segmented from computed tomography (CT) images using intensity thresholding; brief analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules; shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system's efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features; sensitivity of the system for small nodules is 83.33%. SVM classifier is used for the classification of large nodules, for which the sensitivity of the proposed system is 93.8% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity of the system is 91.65% with 3.19 FPs per case, and accuracy is 96.22%. The system took 3.8 seconds to analyze each image.


title: Neighbourhood Components Analysis

abstract: In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set. It can also learn a low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, our classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them. The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.


title: An improved ASIFT algorithm for matching repeated patterns

abstract: Image matching is a well researched topic of computer vision. Several new algorithms have been developed in recent times to deal with repetitive pattern matching and affine invariant matching. This paper presents two improvements over the state-of-the-art Affine-Scale Invariant Feature Transform (ASIFT) algorithm. The first improvement enables ASIFT to match repetitive patterns through the use of Graph Transformation Matching. The second increases the accuracy of matching by estimating the transformation between views more precisely. Results show that the proposed method is able to successfully match repetitive patterns such as the checkerboard. An increase in the number of matches can also be seen for matching views under severe affine transformations or projections.


title: Hand posture and gesture recognition technology

abstract: Hand gestures that are performed by one or two hands can be categorized according to their applications into different categories including conversational, controlling, manipulative and communicative gestures. Generally, hand gesture recognition aims to identify specific human gestures and use them to convey information. The process of hand gesture recognition composes mainly of four stages: hand gesture images collection, gesture image preprocessing using some techniques including edge detection, filtering and normalization, capture the main characteristics of the gesture images and the evaluation (or classification) stage where the image is classified to its corresponding gesture class. There are many methods that have been used in the classification stage of hand gesture recognition such as Artificial Neural Networks, template matching, Hidden Markov Models and Dynamic Time Warping. This exploratory survey aims to provide a progress report on hand posture and gesture recognition technology.


title: Using Context-Based Password Strength Meter to Nudge Users' Password Generating Behavior: A Randomized Experiment

abstract: Encouraging users to create stronger passwords has always been one of the key issues in password-based authentication. It is particularly important as passwords are still the most common user authentication method. Furthermore, prior works have highlighted that most passwords are significantly weak. In this paper, we seek to mitigate such an issue by proposing a context-based password strength meter and investigating its effectiveness on users' password generating behavior. We conduct a randomized experiment on Amazon MTurk involving hypothetical account creating scenarios. We observe the change in users' behavior in terms of the number of occasions where users change their password after seeing the warning message, the number of occasions where users want to learn more about creating stronger passwords, and the changes in password strength. We find that our proposed password strength meter is significantly effective. Users exposed to our password strength meter are more likely to change their password, and those new passwords are stronger. Furthermore, if the information is readily available, users are willing to invest their time to learn about creating a stronger password, even in a traditional password strength meter setting. Our findings suggest that simply incorporating a contextual information to password strength meters could be one of potential methods in promoting secure behaviors among end users.


title: Microformats: a pragmatic path to the semantic web

abstract: Microformats are a clever adaptation of semantic XHTML that makes it easier to publish, index, and extract semi-structured information such as tags, calendar entries, contact information, and reviews on the Web. This makes it a pragmatic path towards achieving the vision set forth for the Semantic Web.Even though it sidesteps the existing "technology stack" of RDF, ontologies, and Artificial Intelligence-inspired processing tools, various microformats have emerged that parallel the goals of several well-known Semantic Web projects. This poster compares their prospects to the Semantic Web according to Rogers' Diffusion of Innovation model.


The protocol engine project


title: Refugee stress and folk belief: Hmong sudden deaths

abstract: Since the first reported death in 1977, scores of seemingly healthy Hmong refugees have died mysteriosly and without warning from what has come to be known as Sudden Unexpected Nocturnal Death Syndrome (SUNDS). To date medical research has provided no adequate explanation for these sudden deaths. This study is an investigation into the changing impact of traditional beliefs as they manifest during the stress of traumatic relocation. In Stockton, California, 118 Hmong men and women were interviewed regarding their awareness of and personal experience with a traditional nocturnal spirit encounter. An analysis of this data reveals that the supranormal attack acts as a trigger for Hmong SUNDS.


title: Approximate Transformations as Mutation Operators

abstract: Mutation testing is a well-established approach for evaluating test-suite quality by modifying code using syntax-changing (and potentially semantics-changing) transformations, called mutation operators. This paper proposes approximate transformations as new mutation operators that can give novel insights about the code and tests. Approximate transformations are semantics-changing transformations used in the emerging area of approximate computing, but so far they were not evaluated for mutation testing. We found that approximate transformations can be effective mutation operators. We compared three approximate transformations with a set of conventional mutation operators from the literature, on nine open-source Java subjects. The results showed that approximate transformations change program behavior differently from conventional mutation operators. Our analysis uncovered code patterns in which approximate mutants survivedand showed the practical value of approximate transformations for both understanding code amenable to approximations and discovering bad tests. We submitted 11 pull requests to fix bad tests. Seven have already been integrated by the developers.


title: An Adaptive SVR for High-Frequency Stock Price Forecasting

abstract: In order to mitigate investments, stock price forecasting has attracted more attention in recent years. Aiming at the discreteness, non-normality, high-noise in high-frequency data, a support vector machine regression (SVR) algorithm is introduced in this paper. However, the characteristics in different periods of the same stock, or the same periods of different stocks are significantly different. So, SVR with fixed parameters is difficult to satisfy with the constantly changing data flow. To tackle this problem, an adaptive SVR was proposed for stock data at three different time scales, including daily data, 30-min data, and 5-min data. Experiments show that the improved SVR with dynamic optimization of learning parameters by particle swarm optimization can get a better result than compared methods including SVR and back-propagation neural network.


title: Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction

abstract: We propose automatic contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement. We automatically set the clip point for CLAHE based on textureness of a block. Also, we introduce dual gamma correction into CLAHE to achieve contrast enhancement while preserving naturalness. First, we redistribute the histogram of the block in CLAHE based on the dynamic range of each block. Second, we perform dual gamma correction to enhance the luminance, especially in dark regions while reducing over-enhancement artifacts. Since automatic CLAHE adaptively enhances contrast in each block while boosting luminance, it is very effective in enhancing dark images and daylight ones with strong dark shadows. Moreover, automatic CLAHE is computationally efficient, i.e., more than 35 frames/s at $1024\times682$ resolution, due to the independent block processing for contrast enhancement. Experimental results demonstrate that automatic CLAHE with dual gamma correction achieves good performance in contrast enhancement and outperforms state-of-the-art methods in terms of visual quality and quantitative measures.


title: The snowmelt of Mt. Hermon and its contribution to the sources of the Jordan river.

abstract: Abstract The water resources of the upper Jordan River Basin, particularly the main springs, are partially fed by the snowmelt of Mt. Hermon. Mount Hermon is an uplifted anticline extending along the northern border of Israel, and covering an area of ∼ 1200 km 2 , of which 750 km 2 is included in the Jordan River watershed. Most of Mt. Hermon is composed of karstic limestone of Jurassic age, > 2000 m thick. The annual amount of precipitation over the Jordan River sources watershed is estimated to be 8.5 × 10 8 m 3 . The water yield of the Jordan tributaries is 5 × 10 8 m 3 , with 4 × 10 8 m 3 of it being discharge from karstic springs. To date, there are almost no data or estimates of the magnitude of snowmelt recharge and its areal and temporal distribution. Inaccessibility to most parts of the mountain is the main reason for the lack of data. During the last four years research has been conducted to find characteristic parameters that represent the relationship between the Mt. Hermon snowcover and its contribution to dry weather discharge of the Jordan River tributaries. Snow depth and water content were measured at several locations in the altitude range 1500–2100 m above sea level (a.s.l.). The snow-covered area was obtained from Landsat images and from aerial photographs. The snow lines were delineated on 1:250 000 scale maps and their area was measured. These data were compared with the dry weather water yield of the Jordan tributaries and the main springs. Significant contribution from the snowpack was found only above 1400 m. The areal extent of the pertinent snowcover is 185 km 2 , representing about 25% of the total drainage basin of the Jordan head waters. The mean water content at the snow gaging sites increased from 10 cm at an elevation of 1500 m–65 cm at an altitude of 2100 m a.s.l. Based on these values, a preliminary estimate yielded a volume of 5 × 10 7 m 3 , which is about 30% of the dry weather discharge. This may be considered as the potential snowmelt recharge to the Jordan River main springs.


title: Quad-Band Probe-Fed Stacked Annular Patch Antenna for GNSS Applications

abstract: A novel probe-fed stacked annular patch antenna is proposed for Global Navigation Satellite System (GNSS) applications. The antenna has been designed to operate at the satellite navigation frequency bands including GPS, GLONASS, BDS-1, and BDS-2. In the design, four annular patches are stacked to achieve multiband operation and compactness. Quadrature phase feed network comprising four 90° stripline baluns is employed to ensure circularly polarized (CP) radiation. Another key feature of our design is the stepped-radius shorting pillar to reduce the well-known inductance due to longer feeding probes. The final antenna exhibits good impedance matching, broad pattern coverage, and pure CP performance at all designed bands. Details of design considerations and experimental results are presented and discussed.


title: Infection in acute leukemia: an analysis of 433 episodes.

abstract: Two hundred fifty-seven patients with acute leukemia were admitted into our hospital on 346 occasions. There were 433 episodes of infection, with an average of 1.25 episodes per hospitalization; 126 patients (49.0%) died. Infection was the major predisposing factor and cause of death. The incidence of infection increased significantly with prolonged duration of hospitalization, the degree of granulocytopenia, the degree of chemotherapeutic failure, and the use of glucocorticoids. Pulmonary infection (17.3%) was the most common type of infection. Septicemia (12.0%) was also common and was associated with high mortality (71.2%). Of 22 patients with perirectal abscess, 10 had septicemia. Gram-negative bacilli were responsible for 66.1% of laboratory-documented bacterial infections. For the treatment of infection, empiric use of an aminoglycoside combined with either an antipseudomonal penicillin or a cephalosporin at the first sign of infection was emphasized. Antibiotic administration should be continued for at least 7 days after the patient's temperature becomes normal.


title: Real-time staircase detection from a wearable stereo system

abstract: We address the problem of staircase detection, in the context of a navigation aid for the visually impaired. The requirements for such a system are robustness to viewpoint, distance, scale, real-time operation, high detection rate and low false alarm rate. Our approach uses classifiers trained using Haar features and Ad-aboost learning. This first stage does detect staircases, but produces many false alarms. The false alarm rate is drastically reduced by using spatial context in the form of the estimated ground plane, and by enforcing temporal consistency. We have validated our approach on many real sequences under various weather conditions, and are presenting some of the quantitative results here.


title: End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding.

abstract: Spoken language understanding (SLU) is a core component of a spoken dialogue system. In the traditional architecture of dialogue systems, the SLU component treats each utterance independent of each other, and then the following components aggregate the multi-turn information in the separate phases. However, there are two challenges: 1) errors from previous turns may be propagated and then degrade the performance of the current turn; 2) knowledge mentioned in the long history may not be carried into the current turn. This paper addresses the above issues by proposing an architecture using end-to-end memory networks to model knowledge carryover in multi-turn conversations, where utterances encoded with intents and slots can be stored as embeddings in the memory and the decoding phase applies an attention model to leverage previously stored semantics for intent prediction and slot tagging simultaneously. The experiments on Microsoft Cortana conversational data show that the proposed memory network architecture can effectively extract salient semantics for modeling knowledge carryover in the multi-turn conversations and outperform the results using the state-of-the-art recurrent neural network framework (RNN) designed for single-turn SLU.


title: ICE-TEA: in-context expansion and translation of English abbreviations

abstract: The wide use of abbreviations in modern texts poses interesting challenges and opportunities in the field of NLP. In addition to their dynamic nature, abbreviations are highly polysemous with respect to regular words. Technologies that exhibit some level of language understanding may be adversely impacted by the presence of abbreviations. This paper addresses two related problems: (1) expansion of abbreviations given a context, and (2) translation of sentences with abbreviations. First, an efficient retrieval-based method for English abbreviation expansion is presented. Then, a hybrid system is used to pick among simple abbreviation-translation methods. The hybrid system achieves an improvement of 1.48 BLEU points over the baseline MT system, using sentences that contain abbreviations as a test set.


title: 280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification

abstract: We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach leverages the interlanguage links of Wikipedia followed by character-level classifiers to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages.


Survey of State-of-the-art in Inter-VM Communication Mechanisms


title: Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems

abstract: Automatic recording of user behavior within a system (instrumentation) to develop and test theories has a rich history in psychology and system design. Often, researchers analyze instrumented behavior in isolation from other data. The problem with collecting instrumented behaviors without attitudinal, demographic, and contextual data is that researchers have no way to answer the 'why' behind the 'what'. We have combined the collection and analysis of behavioral instrumentation with other HCI methods to develop a system for Tracking Real-Time User Experience (TRUE). Using two case studies as examples, we demonstrate how we have evolved instrumentation methodology and analysis to extensively improve the design of video games. It is our hope that TRUE is adopted and adapted by the broader HCI community, becoming a useful tool for gaining deep insights into user behavior and improvement of design for other complex systems.


title: Determination of Vascular Waterfall Phenomenon by Bedside Measurement of Mean Systemic Filling Pressure and Critical Closing Pressure in the Intensive Care Unit

abstract: BACKGROUND: Mean systemic filling pressure (Pmsf) can be determined at the bedside by measuring central venous pressure (Pcv) and cardiac output (CO) during inspiratory hold maneuvers. Critical closing pressure (Pcc) can be determined using the same method measuring arterial pressure (Pa) and CO. If Pcc > Pmsf, there is then a vascular waterfall. In this study, we assessed the existence of a waterfall and its implications for the calculation of vascular resistances by determining Pmsf and Pcc at the bedside. METHODS: In 10 mechanically ventilated postcardiac surgery patients, inspiratory hold maneuvers were performed, transiently increasing Pcv and decreasing Pa and CO to 4 different steady-state levels. For each patient, values of Pcv and CO were plotted in a venous return curve to determine Pmsf. Similarly, Pcc was determined with a ventricular output curve plotted for Pa and CO. Measurements were performed in each patient before and after volume expansion with 0.5 L colloid, and vascular resistances were calculated. RESULTS: For every patient, the relationship between the 4 measurements of Pcv and CO and of Pa and CO was linear. Baseline Pmsf was 18.7 ± 4.0 mm Hg (mean ± SD) and differed significantly from Pcc 45.5 ± 11.1 mm Hg (P < 0.0001). The difference of Pcc and Pmsf was 26.8 ± 10.7 mm Hg, indicating the presence of a systemic vascular waterfall. Volume expansion increased Pmsf (26.3 ± 3.2 mm Hg), Pcc (51.5 ± 9.0 mm Hg), and CO (5.5 ± 1.8 to 6.8 ± 1.8 L · min). Arterial (upstream of Pcc) and venous (downstream of Pmsf) vascular resistance were 8.27 ± 4.45 and 2.75 ± 1.23 mm Hg · min · L; the sum of both (11.01 mm Hg · min · L) was significantly different from total systemic vascular resistance (16.56 ± 8.57 mm Hg · min · L; P = 0.005). Arterial resistance was related to total resistance. CONCLUSIONS: Vascular pressure gradients in cardiac surgery patients suggest the presence of a vascular waterfall phenomenon, which is not affected by CO. Thus, measures of total systemic vascular resistance may become irrelevant in assessing systemic vasomotor tone. Copyright © 2012 International Anesthesia Research Society.


title: Study of Partial Discharge Measurement in Power Equipment Using Acoustic Technique and Wavelet Transform

abstract: This study proposes a noncontact type of acoustic measurement system and applies wavelet transform to noise suppression in order to raise the correct partial discharge (PD) signal identification rate. We first investigate an acoustic measurement method in the laboratory and apply the wavelet transform to suppress noise. A wavelet mother function most similar to the acoustic PD signals is chosen to set the filtering threshold value for the wavelet transform. Finally, the proposed acoustic measurement system is applied on line to epoxy-resin transformers, power distributors and the like. The superior measurement results we obtained will be able to help maintenance personnel eliminate field noise to correctly identify the PD fault types of power equipment.


The influence of strength of drive on functional fixedness and perceptual recognition.


Dermoscopy of Poikilodermatous Mycosis Fungoides in Pigmented Skin


title: Identification of rice diseases using deep convolutional neural networks

abstract: Abstract The automatic identification and diagnosis of rice diseases are highly desired in the field of agricultural information. Deep learning is a hot research topic in pattern recognition and machine learning at present, it can effectively solve these problems in vegetable pathology. In this study, we propose a novel rice diseases identification method based on deep convolutional neural networks (CNNs) techniques. Using a dataset of 500 natural images of diseased and healthy rice leaves and stems captured from rice experimental field, CNNs are trained to identify 10 common rice diseases. Under the 10-fold cross-validation strategy, the proposed CNNs-based model achieves an accuracy of 95.48%. This accuracy is much higher than conventional machine learning model. The simulation results for the identification of rice diseases show the feasibility and effectiveness of the proposed method.


title: A Unified Approach to Zero-Crossing Point Detection of Back EMF for Brushless DC Motor Drives without Current and Hall Sensors

abstract: The main theme of this paper is to present a unified approach to back electromotive force (EMF) detection of brushless dc motor (BLDCM) drives without using any current and Hall sensors. Pulsewidth modulation (PWM) techniques and theoretical analysis of back EMF detection are presented, and followed by the proposed unified back EMF detection method. It will be shown that the back EMF detection depends upon the PWM techniques and the method is required to be slightly modified as the PWM technique is changed. Experimental results derived from BLDCM drives without using any current and Hall sensors fully confirm the theoretical analysis.


title: Estimation and projection of institutional building electricity consumption

abstract: Abstract This study provides understanding of the influence of outside temperature on intuitional buildings in an urban landscape. We derive heating and cooling degree days for each of the buildings by identifying an appropriate choice of balance point temperatures and perform multiple linear regression seasonally to characterize the temperature-electricity use relations. Our study reveals considerable differences in temperature-electricity relationships based on building use and characteristics. In addition to outside temperature, the weekday effect has large influences on electricity consumption of buildings. Summer months have greater influence of outside temperature on electricity consumption followed by transition months and winter as an increase in one cooling degree day (CDD) increases daily electricity consumption by 0.124 kW/m2 whereas during winter, one heating degree day (HDD) increases daily electricity consumption by 0.025 kW/m2 and by 0.099 kW/m2 during transition months. We apply two kinds of models to project electricity demand for projected temperature profiles in 2030. Both cases strongly suggest higher electricity demand not just in summer months but also during transition periods in spring and fall. Overall, electricity demand increases by 0.95% under a low emission scenario (RCP 4.5) and 2.03% under a high emission scenario (RCP 8.5), which is likely to put immense pressure on the United States electric grid system and an overall increase in the energy cost by 2030.


title: Simulation-Based Vehicular Traffic Lights Optimization

abstract: A great challenge today in urban areas and dense populated cities is determining an optimal traffic light system that can maximize the number of passing vehicles in a minimum amount of time. In this paper, we have addressed the issue of traffic flow management in urban areas by proposing as a solution a traffic light optimization method. The proposed approach uses the SUMO simulator as a cost-effective solution and a PSO optimization technique for traffic lights cycle program. A series of experimental simulations were performed with different number of vehicles in different time tables. The results obtained shows significant improvements in terms of increasing the number of vehicles that complete the simulation (4,5% to 10,5% of gain) and average journey time necessary for the vehicles to reach their destination (5,37% to 21,53% less time loss).


title: Hyracks: A flexible and extensible foundation for data-intensive computing

abstract: Hyracks is a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. Hyracks allows users to express a computation as a DAG of data operators and connectors. Operators operate on partitions of input data and produce partitions of output data, while connectors repartition operators' outputs to make the newly produced partitions available at the consuming operators. We describe the Hyracks end user model, for authors of dataflow jobs, and the extension model for users who wish to augment Hyracks' built-in library with new operator and/or connector types. We also describe our initial Hyracks implementation. Since Hyracks is in roughly the same space as the open source Hadoop platform, we compare Hyracks with Hadoop experimentally for several different kinds of use cases. The initial results demonstrate that Hyracks has significant promise as a next-generation platform for data-intensive applications.


title: Scalable and Efficient Web Search Result Diversification

abstract: It has been shown that top-k retrieval quality can be considerably improved by taking not only relevance but also diversity into account. However, currently proposed diversification approaches have not put much attention on practical usability in large-scale settings, such as modern web search systems. In this work, we make two contributions toward this goal. First, we propose a combination of optimizations and heuristics for an implicit diversification algorithm based on the desirable facility placement principle, and present two algorithms that achieve linear complexity without compromising the retrieval effectiveness. Instead of an exhaustive comparison of documents, these algorithms first perform a clustering phase and then exploit its outcome to compose the diverse result set. Second, we describe and analyze two variants for distributed diversification in a computing cluster, for large-scale IR where the document collection is too large to keep in one node. Our contribution in this direction is pioneering, as there exists no earlier work in the literature that investigates the effectiveness and efficiency of diversification on a distributed setup. Extensive evaluations on a standard TREC framework demonstrate a competitive retrieval quality of the proposed optimizations to the baseline algorithm while reducing the processing time by more than 80p and up to 97p, and shed light on the efficiency and effectiveness tradeoffs of diversification when applied on top of a distributed architecture.


Modeling high-frequency limit order book dynamics with support vector machines


title: Large-scale complex IT systems

abstract: This paper explores the issues around the construction of large-scale complex systems which are built as 'systems of systems' and suggests that there are fundamental reasons, derived from the inherent complexity in these systems, why our current software engineering methods and techniques cannot be scaled up to cope with the engineering challenges of constructing such systems. It then goes on to propose a research and education agenda for software engineering that identifies the major challenges and issues in the development of large-scale complex, software-intensive systems. Central to this is the notion that we cannot separate software from the socio-technical environment in which it is used.


title: The Hidden Image of the City : Sensing Community Well-Being from Urban Mobility

abstract: A key facet of urban design, planning, and monitoring is measuring communities' well-being. Historically, researchers have established a link between well-being and visibility of city neighbourhoods and have measured visibility via quantitative studies with willing participants, a process that is invariably manual and cumbersome. However, the influx of the world's population into urban centres now calls for methods that can easily be implemented, scaled, and analysed. We propose that one such method is offered by pervasive technology: we test whether urban mobility--as measured by public transport fare collection sensors--is a viable proxy for the visibility of a city's communities. We validate this hypothesis by examining the correlation between London urban flow of public transport and census-based indices of the well-being of London's census areas. We find that not only are the two correlated, but a number of insights into the flow between areas of varying social standing can be uncovered with readily available transport data. For example, we find that deprived areas tend to preferentially attract people living in other deprived areas, suggesting a segregation effect.


title: Doppler Radar Fall Activity Detection Using the Wavelet Transform

abstract: We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.


title: High-efficiency high-power-density 48/1V sigma converter voltage regulator module

abstract: A high efficiency and high power density sigma converter for 48/1V voltage regulator module (VRM) is proposed in this paper. The Sigma converter is a quasi-parallel converter that uses a high efficiency unregulated converter to deliver most power to the load with small power flowing through buck converter responsible for regulating the output voltage. The unregulated isolated converter is LLC converter designed with matrix transformer structure integrating 4 transformers in one core structure with integrating the Synchronous Rectifiers (SRs) with the winding to minimize the termination losses of the transformer so a high efficiency can be achieved. The buck converter is designed with discrete GaN devices and PCB winding inductor to regulate the output voltage. The designed Sigma converter is 48/1V-80A achieving a power density of 420W/in3 and maximum efficiency of 93.4%.


title: Classification of Various Neighborhood Operations for the Nurse Scheduling Problem

abstract: Since the nurse scheduling problem (NSP) is a problem of finding a feasible solution, the solution space must include infeasible solutions to solve it using a local search algorithm. However, the solution space consisting of all the solutions is so large that the search requires much CPU time. In the NSP, some constraints have higher priority. Thus, we can define the solution space to be the set of solutions satisfying some of the important constraints, which are called the elementary constraints. The connectivity of the solution space is also important for the performance. However, the connectivity is not obvious when the solution space consists only of solutions satisfying the elementary constraints and is composed of small neighborhoods. This paper gives theoretical support for using 4-opt-type neighborhood operations by discussing the connectivity of its solution space and the size of the neighborhood. Another interesting point in our model is a special case of the NSP corresponds to the bipartite transportation problem, and our result also applies to it.


Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings


Agents Teaching Humans in Reinforcement Learning Tasks


title: Improving Efficiency in Convolutional Neural Network with Multilinear Filters

abstract: Abstract The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters.


title: Face Spoofing Detection Using Colour Texture Analysis

abstract: Research on non-intrusive software-based face spoofing detection schemes has been mainly focused on the analysis of the luminance information of the face images, hence discarding the chroma component, which can be very useful for discriminating fake faces from genuine ones. This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis. We exploit the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces. More specifically, the feature histograms are computed over each image band separately. Extensive experiments on the three most challenging benchmark data sets, namely, the CASIA face anti-spoofing database, the replay-attack database, and the MSU mobile face spoof database, showed excellent results compared with the state of the art. More importantly, unlike most of the methods proposed in the literature, our proposed approach is able to achieve stable performance across all the three benchmark data sets. The promising results of our cross-database evaluation suggest that the facial colour texture representation is more stable in unknown conditions compared with its gray-scale counterparts.


title: AN INTERNATIONAL COMPARISON OF THE FACTORS AFFECTING ENVIRONMENTAL STRATEGY AND PERFORMANCE.

abstract: An environmental management system (EMS) consists of internal policies, assessments, and implementation actions that affect the entire enterprise and its relationship with the natural environment. Such systems are increasingly being recognized as comprehensive mechanisms for improving environmental and business performance. However, little is known about the factors affect their international adoption and whether EMSs enhance a company's performance. This study draws on institutional theory and the resource-based view to evaluate when a facility's EMS improves environmental and business performance.


title: A Cosmetic Prosthetic Hand with Tendon Driven Under-Actuated Mechanism and Compliant Joints: Ongoing Research and Preliminary Results

abstract: This paper presents recent results aimed at developing a functional prosthetic hand characterized by an EMG-control and by a simple and low cost fabrication technology. In order to overcome some limitations of current prosthetic hands mainly related to the poor functionality and controllability, the prosthetic hand has been designed following a biomechatronic approach based on biologically-inspired design solutions. The core of the project described in this paper is the fabrication of a compliant under-actuated prosthetic hand: the structure of the hand (both palm and fingers) is moulded as a soft polymeric single part with compliant joints and embedded tendon driven underactuated mechanism for providing adaptive grasp. In order to make user trials, the hand is equipped with simple but functional EMG-based control of the single motor incorporated in the hand, and is integrated with a prosthesis socket. The paper presents the biomechatronics design, the fabrication process, the integration of the prosthetic device and first experimental results.


title: BI4BI: A continuous evaluation system for Business Intelligence systems

abstract: Abstract Business Intelligence (BI) allows supporting decision making by providing methods and tools to easily access and manage business information. Indeed, it is crucial to maintain BI systems and make them evolve in order to fit business and users’ needs. For this purpose, applying BI for BI is suggested. It helps to analyze the current BI environment and to make decisions about its use and evolution. Then, the main contribution of this paper consists in developing a novel system called BI4BI which is based on both BI systems’ data and BI users’ feedbacks. As a result, based on a user centered approach, an automated system for BI evaluation is developed helping its continuous evolution. To this end, system-based and user-based solutions provide two complementary data sources, required to effectively gather insights on the use of BI systems and the way they should evolve. A case study in a semiconductor manufacturer company demonstrates the impact of our solution on the evaluation and evolution of BI systems. Hence, thanks to the involvement of the company’s BI users during the solution design, a set of evaluation criteria responding to their objectives for evaluating the BI system are identified and validated. Furthermore, the results of this case study show that our proposed solution is better than existing ones, in terms of level of focus of its BI evaluation criteria, its level of automation, and its continuous processing.


title: The Use of Knowledge in Society

abstract: AbstractWhat is the problem we wish to solve when we try to construct a rational economic order?On certain familiar assumptions the answer is simple enough. If we possess all the relevant information, if we can start out from a given system of preferences and if we command complete knowledge of available means, the problem which remains is purely one of logic. That is, the answer to the question of what is the best use of the available means is implicit in our assumptions. The conditions which the solution of this optimum problem must satisfy have been fully worked out and can be stated best in mathematical form: put at their briefest, they are that the marginal rates of substitution between any two commodities or factors must be the same in all their different uses.


Final Report Calculation of vertical tidal datums for the tidal Hudson River north of


title: Understanding vehicular traffic behavior from video: a survey of unsupervised approaches

abstract: Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provided.


title: AUV local path planning based on virtual potential field

abstract: We build a local path planning algorithm using virtual force for free local minimum. Potential field is used as a basic platform for the path planning since it has the advantages of simplicity, real-time computation. However, there is one shortcoming in the potential field: it may cause local minimum whenever the curvature of the repulsive equipotential curve is less than the curvature of the attractive equipotential curve at the same configuration. To get rid of the local minimum in the presence of obstacles, we present a navigation algorithm, which integrates virtual force concept with a potential-field-based method to maneuver autonomous underwater vehicle (AUV) in unknown or unstructured environments. This study focuses on the free local minimum in potential-field based navigation. We mainly consider the potential-field method in conjunction with virtual force concept as the basis of our navigation algorithm. Simulation and experiments of our algorithm shows good performance and ability to overcome the local minimum problem associated with potential field methods.


title: Cyber security threat analysis and modeling of an unmanned aerial vehicle system

abstract: Advances in technology for miniature electronic military equipment and systems have led to the emergence of unmanned aerial vehicles (UAVs) as the new weapons of war and tools used in various other areas. UAVs can easily be controlled from a remote location. They are being used for critical operations, including offensive, reconnaissance, surveillance and other civilian missions. The need to secure these channels in a UAV system is one of the most important aspects of the security of this system because all information critical to the mission is sent through wireless communication channels. It is well understood that loss of control over these systems to adversaries due to lack of security is a potential threat to national security. In this paper various security threats to a UAV system is analyzed and a cyber-security threat model showing possible attack paths has been proposed. This model will help designers and users of the UAV systems to understand the threat profile of the system so as to allow them to address various system vulnerabilities, identify high priority threats, and select mitigation techniques for these threats.


title: Worm-level Control through Search-based Reinforcement Learning

abstract: Through natural evolution, nervous systems of organisms formed near-optimal structures to express behavior. Here, we propose an effective way to create control agents, by \textit{re-purposing} the function of biological neural circuit models, to govern similar real world applications. We model the tap-withdrawal (TW) neural circuit of the nematode, \textit{C. elegans}, a circuit responsible for the worm's reflexive response to external mechanical touch stimulations, and learn its synaptic and neural parameters as a policy for controlling the inverted pendulum problem. For reconfiguration of the purpose of the TW neural circuit, we manipulate a search-based reinforcement learning. We show that our neural policy performs as good as existing traditional control theory and machine learning approaches. A video demonstration of the performance of our method can be accessed at \url{this https URL}.


title: Real-time loop closure in 2D LIDAR SLAM

abstract: Portable laser range-finders, further referred to as LIDAR, and simultaneous localization and mapping (SLAM) are an efficient method of acquiring as-built floor plans. Generating and visualizing floor plans in real-time helps the operator assess the quality and coverage of capture data. Building a portable capture platform necessitates operating under limited computational resources. We present the approach used in our backpack mapping platform which achieves real-time mapping and loop closure at a 5 cm resolution. To achieve realtime loop closure, we use a branch-and-bound approach for computing scan-to-submap matches as constraints. We provide experimental results and comparisons to other well known approaches which show that, in terms of quality, our approach is competitive with established techniques.


title: A confirmatory factor analysis of IS employee motivation and retention

abstract: Abstract It is widely recognized that the relationships between organizations and their IS departments are changing. This trend threatens to undermine the retention of IS workers and the productivity of IS operations. In the study reported here, we examine IS employees’ motivation and intent to remain using structural equation modeling. A survey was conducted among existing IS employees and analyzed with LISREL VIII. Results showed that latent motivation has an impact on latent retention, with job satisfaction and perceptions of management on career development as indicator variables for the former, and burnout, loyalty, and turnover intent as indicator variables for the latter. Implications for management in developing strategies for the retention of IS employees are provided.


title: Joint Learning Improves Semantic Role Labeling

abstract: Despite much recent progress on accurate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label sequence models via Viterbi decoding. This stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependencies between arguments. We show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative log-linear models. This system achieves an error reduction of 22% on all arguments and 32% on core arguments over a state-of-the art independent classifier for gold-standard parse trees on PropBank.


title: Human action recognition using multi-view image sequences

abstract: Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image sequences in different viewing angles that uses the Cartesian component of optical flow velocity and human body shape feature vector information. We use principal component analysis to reduce the higher dimensional shape feature space into low dimensional shape feature space. We represent each action using a set of multidimensional discrete hidden Markov model and model each action for any viewing direction. We performed experiments of the proposed method by using KU gesture database. Experimental results based on this database of different actions show that our method is robust.


title: Ambient Occlusion for Animated Characters

abstract: We present a novel technique for approximating ambient occlusion of animated objects. Our method automatically determines the correspondence between animation parameters and per-vertex ambient occlusion using a set of reference poses as its input. Then, at runtime, the ambient occlusion is approximated by taking a dot product between the current animation parameters and static per-vertex coefficients. According to our results, both the computational and storage requirements are low enough for the technique to be directly applicable to computer games running on current graphics hardware. The resulting images are also significantly more realistic than the commonly used static ambient occlusion solutions.


title: Experimental analysis of eligibility traces strategies in temporal difference learning

abstract: Temporal difference (TD) learning is a model-free reinforcement learning technique, which adopts an infinite horizon discount model and uses an incremental learning technique for dynamic programming. The state value function is updated in terms of sample episodes. Utilising eligibility traces is a key mechanism in enhancing the rate of convergence. TD(λ) represents the use of eligibility traces by introducing the parameter λ. However, the underlying mechanism of eligibility traces with an approximation function has not been well understood, either from theoretical point of view or from practical point of view. The TD(λ) method has been proved to be convergent with local tabular state representation. Unfortunately, proving convergence of TD(λ) with function approximation is still an important open theoretical question. This paper aims to investigate the convergence and the effects of different eligibility traces. In this paper, we adopt Sarsa(λ) learning control algorithm with a large, stochastic and dynamic simulation environment called SoccerBots. The state value function is represented by a linear approximation function known as tile coding. The performance metrics generated from the simulation system can be used to analyse the mechanism of eligibility traces.


Recurrent Inference Machines for Accelerated MRI Reconstruction


title: SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules

abstract: Simplified Molecular Input Line Entry System (SMILES) is a single line text representation of a unique molecule. One molecule can however have multiple SMILES strings, which is a reason that canonical SMILES have been defined, which ensures a one to one correspondence between SMILES string and molecule. Here the fact that multiple SMILES represent the same molecule is explored as a technique for data augmentation of a molecular QSAR dataset modeled by a long short term memory (LSTM) cell based neural network. The augmented dataset was 130 times bigger than the original. The network trained with the augmented dataset shows better performance on a test set when compared to a model built with only one canonical SMILES string per molecule. The correlation coefficient R2 on the test set was improved from 0.56 to 0.66 when using SMILES enumeration, and the root mean square error (RMS) likewise fell from 0.62 to 0.55. The technique also works in the prediction phase. By taking the average per molecule of the predictions for the enumerated SMILES a further improvement to a correlation coefficient of 0.68 and a RMS of 0.52 was found.


Principles of Guidance, Navigation and Control of UAVs


title: Accelerating the Cloud Backup Using GPU Based Data Deduplication

abstract: Data deduplication is an efficient technique for data reduction. It has been adopted and widely used in cloud backup service. However deduplication requires high computing capability since it has to calculate the fingerprint of data many times using a hash algorithm. This can be a performance bottleneck for the system. In this paper, we propose to use Graphics Processing Unit (GPU), a high computing architecture, to accelerate the fingerprint generating process in deduplication. Our experiment shows that GPU can significantly improve the performance of deduplication especially when running on large data, where we achieve an acceleration ratio up to 53 times greater compared to with CPU.


title: Video gender recognition using temporal coherent face descriptor

abstract: In this paper, we propose a new temporal coherent face descriptor for video gender recognition. The proposed face descriptor is constructed from detected faces of continuous video frames. Because it describes detected faces under variant changes in continuous video frames and provides a unified feature description, face normalization and alignment processes can be avoided during gender recognition. Based on the face descriptor, a support vector machine classifier is applied to identify the gender of the subject in the videos. As shown in the experiments, our method not only achieves better results compared to the state-of-the-art methods but also the real-time performance for video processing.


Outflow : Visualizing Patient Flow by Symptoms and Outcome


title: Exploiting query reformulations for web search result diversification

abstract: When a Web user's underlying information need is not clearly specified from the initial query, an effective approach is to diversify the results retrieved for this query. In this paper, we introduce a novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query. In particular, we diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole. We thoroughly evaluate our framework in the context of the diversity task of the TREC 2009 Web track. Moreover, we exploit query reformulations provided by three major Web search engines (WSEs) as a means to uncover different query aspects. The results attest the effectiveness of our framework when compared to state-of-the-art diversification approaches in the literature. Additionally, by simulating an upper-bound query reformulation mechanism from official TREC data, we draw useful insights regarding the effectiveness of the query reformulations generated by the different WSEs in promoting diversity.


title: Composite Bloom Filters for Secure Record Linkage

abstract: The process of record linkage seeks to integrate instances that correspond to the same entity. Record linkage has traditionally been performed through the comparison of identifying field values ( e.g. , Surname ), however, when databases are maintained by disparate organizations, the disclosure of such information can breach the privacy of the corresponding individuals. Various private record linkage (PRL) methods have been developed to obscure such identifiers, but they vary widely in their ability to balance competing goals of accuracy, efficiency and security. The tokenization and hashing of field values into Bloom filters (BF) enables greater linkage accuracy and efficiency than other PRL methods, but the encodings may be compromised through frequency-based cryptanalysis. Our objective is to adapt a BF encoding technique to mitigate such attacks with minimal sacrifices in accuracy and efficiency. To accomplish these goals, we introduce a statistically-informed method to generate BF encodings that integrate bits from multiple fields, the frequencies of which are provably associated with a minimum number of fields. Our method enables a user-specified tradeoff between security and accuracy. We compare our encoding method with other techniques using a public dataset of voter registration records and demonstrate that the increases in security come with only minor losses to accuracy.


title: Planar Phased-Array Antennas

abstract: This article will deal with the impact of mutual coupling on the far-field peak sidelobe performance of a finite planar phased-array antenna having a circular aperture shape and containing 2,066 elements arranged in a triangular grid. The radiator that is used is of the open-ended rectangular waveguide type, vertically polarized. The computation of the far-field performance of the array antenna is based on the use of embedded element patterns (EEPs). The effect of mutual coupling on the EEPs is taken into account through measured coupling coefficients.


title: Hybrid Models with Deep and Invertible Features

abstract: We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets | features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Moreover the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning.


title: A Survey on Non-transferable Anonymous Credentials

abstract: There are at least two principal approaches to prevent users from sharing their anonymous credentials: adding valuable secrets into the system the user does not want to share or embedding biometric access control. This paper seeks to identify possible fields of application and to compare both approaches with respect to the credentials’ non-transferability.


title: The Syllabus Based Web Content Extractor (SBWCE)

abstract: Syllabus Based Web Content Extractor (SBWCE) introduces a new technique of Syllabus Based Web Content Mining. It makes the Syllabus Based Web Content Extraction easy and creates an instant online book view based on the links relevant to the given Syllabus. Three important contributions are made by the current work. First, as multiple format educational information is needed for Syllabus based content; the technique used makes the finding of such content easier. Second, a new approach for capturing and recording the heuristics involved during searching by experts is used. Third, the grouping of Syllabus Words for precise extraction is exploited. This paper introduces SBWCE and presents the related details.


title: Efficient Higher-Order CRFs for Morphological Tagging

abstract: Training higher-order conditional random fields is prohibitive for huge tag sets. We present an approximated conditional random field using coarse-to-fine decoding and early updating. We show that our implementation yields fast and accurate morphological taggers across six languages with different morphological properties and that across languages higher-order models give significant improvements over 1-order models.


title: FlipNet: Modeling Covert and Persistent Attacks on Networked Resources

abstract: Persistent and zero-day attacks have increased considerably in the recent past in terms of scale and impact. Security experts can no longer rely only on known defenses and thereby protect their resources permanently. It is increasingly common now to observe attackers being able to repeatedly break systems exploiting new vulnerabilities and defenders hardening systems with new measures. To model this phenomenon of the repeated takeover of the computing resources by system administrators and malicious attackers, a novel game framework, FlipIt, has been proposed by (Van Dijk et al. 2013) for a system consisting of a single resource. In this paper, we extend this and develop FlipNet, which is a repeated game framework for a networked system of multiple resources. This game involves two players-a defender and an attacker. Each player's objective is to maximize its gain (i.e., its control over the nodes in the network with stealthy moves), while minimizing the cost for making those moves. This leads to a novel and natural game formulation, with a very complex strategy space, that depends on the network structure. We show that finding the best response strategy for both the defender and attacker is NP-hard. In a key result in this study, we show that the attacker's gain for an instance of the game has a type of diminishing marginal return property, which leads to a near-optimal algorithm for maximizingthe attacker's gain. We examine the impact of network structure on the strategy space using simulations.


title: A study on the priority decision making of IT goals in COBIT 5 goals cascade

abstract: Nowadays, as IT becomes a key element in most business models, it is very important for countries and corporations to create effective business / IT value from IT-related investments. To do so, the process and structure for business / IT value creation must be designed appropriately for the organization's goals and context. For this, the IT governance models that organizations can use typically include COBIT(Control Objectives for Information and Related Technologies) 5, ITIL (IT Infrastructure Library), and TOGAF (The Open Group Architecture Framework). This research helps to use the COBIT 5 Goals Cascade when companies use COBIT 5 to design processes for business / IT value creation. The COBIT 5 Goals Cascade is a mechanism that translates the needs of stakeholders into actionable and customized enterprise goals, IT-related goals and enabler goals. However, this mechanism does not give priority to what goals should be prioritized. According to the COBIT 5 Enterprise Goals to IT-related Goals Mapping table, multiple IT-related goals must be fulfilled to achieve a single enterprise goal. As a result, the IT-related goals for some enterprise goals will be much larger, confusing which goals should be undertaken. To contribute to this problem, this research presents a prioritized methodology that determines the IT-related goals that are more relevant to the organization's goals in realizing IT-related goals. This prioritization methodology is based on the AHP(Analytic Hierarchy Process) using the 'Balanced Scorecard' and 'Governance Objectives' as indicators.


title: Haptic feature extraction from a biomimetic tactile sensor: Force, contact location and curvature

abstract: The BioTac® is a biomimetic tactile sensor for grip control and object characterization. It has three sensing modalities: thermal flux, microvibration and force. In this paper, we discuss feature extraction and interpretation of the force modality data. The data produced by this force sensing modality during sensor-object interaction are monotonic but non-linear. Algorithms and machine learning techniques were developed and validated for extracting the radius of curvature (ROC), point of application of force (PAF) and force vector (FV). These features have varying degrees of usefulness in extracting object properties using only cutaneous information; most robots can also provide the equivalent of proprioceptive sensing. For example, PAF and ROC is useful for extracting contact points for grasp and object shape as the finger depresses and moves along an object; magnitude of FV is useful in evaluating compliance from reaction forces when a finger is pushed into an object at a given velocity while direction is important for maintaining stable grip.


title: A technique to preserve the shape of the calcaneus after massive osteomyelitis

abstract: Abstract Osteomyelitis of the os calcis challenges the reconstructive surgeon. We present the case of a patient in which massive medullary osteomyelitis of the calcaneus was treated with a technique that preserves the bone shape. The medullary cavity of the bone was first emptied, preserving the cortical shell. Then a free muscle flap was used to fill up the dead space. Three months later autologous bone grafts were packed into the cavity. Full weight-bearing was commenced 4 months after this procedure. In the latest follow-up, 2 1 / 2 years, the patient has had no recurrence or difficulty with walking.


title: An end-to-end voting-system based on bitcoin

abstract: In this work we re-adapt the Bitcoin e-payment system and propose it as a decentralised end-to-end voting platform (from voters to candidates). We describe the main architectural choices behind the implementation, which consists of the pre-voting, voting, and post-voting phases. The resulting implementation is completely decentralised: it is possible to directly cast a vote in the block-chain without any collecting intermediate-level. All the votes can be verified by anyone reading such a public ledger. We also exploit digital asset coins to directly keep track of votes (through the Open Asset Protocol), and we show the election cost for n voters.


title: Adaptive generic learning for face recognition from a single sample per person

abstract: Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.


title: Corporate Governance, Board Diversity, and Firm Value

abstract: This study examines the relationship between board diversity and firm value for Fortune 1000 firms. Board diversity is defined as the percentage of women, African-Americans, Asians, and Hispanics on the board of directors. This research is important because it presents the first empirical evidence examining whether board diversity is associated with improved financial value. After controlling for size, industry, and other corporate governance measures, we find significant positive relationships between the fraction of women or minorities on the board and firm value. We also find that the proportion of women and minorities on boards increases with firm size and board size but decreases as the number of insiders increases.


title: Dynamics of quartz tuning fork force sensors used in scanning probe microscopy

abstract: We have performed an experimental characterization of the dynamics of oscillating quartz tuning forks which are being increasingly used in scanning probe microscopy as force sensors. We show that tuning forks can be described as a system of coupled oscillators. Nevertheless, this description requires knowledge of the elastic coupling constant between the prongs of the tuning fork, which has not yet been measured. Therefore, tuning forks have usually been described within the single oscillator or the weakly coupled oscillators approximation that neglects the coupling between the prongs. We propose three different procedures to measure the elastic coupling constant: an opto-mechanical method, a variation of the Cleveland method and a thermal noise based method. We find that the coupling between the quartz tuning fork prongs has a strong influence on the dynamics and the measured motion is in remarkable agreement with a simple model of coupled harmonic oscillators. The precise determination of the elastic coupling between the prongs of a tuning fork allows us to obtain a quantitative relation between the resonance frequency shift and the force gradient acting at the free end of a tuning fork prong.


title: A Survey on Security Issues in Cloud Computing

abstract: Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for the IT-based solutions and services that the industry uses. It promises to provide a flexible IT architecture, accessible through internet for lightweight portable devices. This would allow many-fold increase in the capacity or capabilities of the existing and new software. In a cloud computing environment, the entire data reside over a set of networked resources, enabling the data to be accessed through virtual machines. Since these data centers may lie in any corner of the world beyond the reach and control of users, there are multifarious security and privacy challenges that need to be understood and taken care of. Also, one can never deny the possibility of a server breakdown that has been witnessed, rather quite often in the recent times. There are various issues that need to be dealt with respect to security and privacy in a cloud computing scenario. This extensive survey paper aims to elaborate and analyze the numerous unresolved issues threatening the Cloud computing adoption and diffusion affecting the various stake-holders linked to it.


title: Controller team possibilities for sectorless air traffic management

abstract: Sectorless air traffic management is an en-route concept, which eliminates the need for control sectors. Instead of assigning a geographic area (sector), air traffic controllers are assigned certain aircraft. Controllers are responsible for their assigned aircraft all the way from entry to exit.


title: A Novel Ultra-Lightweight Multiband Rectenna on Paper for RF Energy Harvesting in the Next Generation LTE Bands

abstract: This paper introduces a novel compact ultralightweight multiband RF energy harvester fabricated on a paper substrate. The proposed rectenna is designed to operate in all recently released LTE bands (range 0.79–0.96 GHz; 1.71–2.17 GHz; and 2.5–2.69 GHz). High compactness and ease of integration between antenna and rectifier are achieved by using a topology of nested annular slots. The proposed rectifier features an RF-to-dc conversion efficiency in the range of 5%–16% for an available input power of −20 dBm in all bands of interest, which increases up to 11%–30% at −15 dBm. The rectenna has been finally tested both in laboratory and in realistic scenarios featuring a superior performance to other state-of-the-art RF harvesters on flexible substrates.


title: Interim measures for meeting needs for health sector data: births, deaths, and causes of death

abstract: Most developing countries do not have fully effective civil registration systems to provide necessary information about population health. Interim approaches—both innovative strategies for collection of data, and methods of assessment or estimation of these data—to fi ll the resulting information gaps have been developed and refined over the past four decades. To respond to the needs for data for births, deaths, and causes of death, data collection systems such as population censuses, sample vital registration systems, demographic surveillance sites, and internationally-coordinated sample survey programmes in combination with enhanced methods of assessment and analysis have been successfully implemented to complement civil registration systems. Methods of assessment and analysis of incomplete information or indirect indicators have also been improved, as have approaches to ascertainment of cause of death by verbal autopsy, disease modelling, and other strategies. Our knowledge of demography and descriptive epidemiology of populations in developing countries has been greatly increased by the widespread use of these interim approaches; although gaps remain, particularly for adult mortality. However,these approaches should not be regarded as substitutes for complete civil registration but rather as complements,essential parts of any fully comprehensive health information system. International organisations, national governments, and academia all have responsibilities in ensuring that data continue to be collected and that methods continue to be improved.


title: A CMOS Passive LPTV Nonmagnetic Circulator and Its Application in a Full-Duplex Receiver

abstract: Recently, we demonstrated the first CMOS nonmagnetic nonreciprocal passive circulator based on N-path filters that uses time variance to break reciprocity. Here, the analysis of performance metrics, such as loss, isolation, linearity, and tuning range, is presented in terms of the design parameters. The analysis is verified by the measured performance of a 65-nm CMOS circulator prototype that exhibits 1.7 dB of loss in the transmitter-antenna (TX-ANT) and antenna-receiver (ANT-RX) paths, and has high isolation [TX–RX, up to 50 dB through tuning and 20-dB bandwidth (BW) of 32 MHz] and a tuning range of 610–850 MHz. Through an architectural feature specifically designed to enhance TX linearity, the circulator achieves an in-band TX-ANT input-referred third-order intercept point (IIP3) of +27.5 dBm, nearly two orders of magnitude higher than the ANT-RX IIP3 of +8.7 dBm. The circulator is also integrated with a self-interference-canceling full-duplex (FD) RX featuring an analog baseband (BB) SI canceller. The FD RX achieves 42-dB on-chip SI suppression across the circulator and analog BB domains over a 12-MHz signal BW. In conjunction with digital SI and its input-referred third-order intermodulation (IM3) cancellation, the FD RX demonstrates 85-dB overall SI suppression, enabling an FD link budget of −7-dBm TX average output power and −92-dBm noise floor.


title: Robust Object Tracking Based on Temporal and Spatial Deep Networks

abstract: Recently deep neural networks have been widely employed to deal with the visual tracking problem. In this work, we present a new deep architecture which incorporates the temporal and spatial information to boost the tracking performance. Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net. The Feature Net extracts general feature representations of the target. With these feature representations, the Temporal Net encodes the trajectory of the target and directly learns temporal correspondences to estimate the object state from a global perspective. Based on the learning results of the Temporal Net, the Spatial Net further refines the object tracking state using local spatial object information. Extensive experiments on four of the largest tracking benchmarks, including VOT2014, VOT2016, OTB50, and OTB100, demonstrate competing performance of the proposed tracker over a number of state-of-the-art algorithms.


title: 15.7 A 1.65mW 0.16mm2 dynamic zoom-ADC with 107.5dB DR in 20kHz BW

abstract: Audio codecs for automotive applications and smartphones require up to five stereo channels to achieve effective acoustic noise and echo cancellation, thus demanding ADCs with low power and minimal die area. Zoom-ADCs should be well suited for such applications, since they combine compact and energy-efficient SAR ADCs with low-distortion ΔΣ ADCs to simultaneously achieve high energy efficiency, small die area, and high linearity [1,2]. However, previous implementations were limited to the conversion of quasi-static signals, since the two ADCs were operated sequentially, with a coarse SAR conversion followed by, a much slower, fine ΔΣ conversion. This work describes a zoom-ADC with a 20kHz bandwidth, which achieves 107.5dB DR and 104.4dB SNR while dissipating 1.65mW and occupying 0.16mm2. A comparison with recent state-of-the-art ADCs with similar resolution and bandwidth [3–7] shows that the ADC achieves significantly improved energy and area efficiency. These advances are enabled by the use of concurrent fine and coarse conversions, dynamic error-correction techniques, and an inverter-based OTA.


title: Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks

abstract: Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients. As such, RECIST annotations must be accurate. However, RECIST annotations manually labeled by radiologists require professional knowledge and are time-consuming, subjective, and prone to inconsistency among different observers. To alleviate these problems, we propose a cascaded convolutional neural network based method to semi-automatically label RECIST annotations and drastically reduce annotation time. The proposed method consists of two stages: lesion region normalization and RECIST estimation. We employ the spatial transformer network (STN) for lesion region normalization, where a localization network is designed to predict the lesion region and the transformation parameters with a multi-task learning strategy. For RECIST estimation, we adapt the stacked hourglass network (SHN), introducing a relationship constraint loss to improve the estimation precision. STN and SHN can both be learned in an end-to-end fashion. We train our system on the DeepLesion dataset, obtaining a consensus model trained on RECIST annotations performed by multiple radiologists over a multi-year period. Importantly, when judged against the inter-reader variability of two additional radiologist raters, our system performs more stably and with less variability, suggesting that RECIST annotations can be reliably obtained with reduced labor and time.


title: Cogging Torque Reduction in Permanent Magnet Machines

abstract: This paper examines two methods - magnet shifting and optimizing the magnet pole arc - for reducing cogging torque in permanent magnet machines. The methods were applied to existing machine designs and their performance was calculated using finite-element analysis (FEA). Prototypes of the machine designs were constructed and experimental results obtained. It is shown that the FEA predicted the cogging torque to be nearly eliminated using the two methods. However, there was some residual cogging in the prototypes due to manufacturing difficulties. In both methods, the back electromotive force was improved by reducing harmonics while preserving the magnitude.