title: FitLoc: Fine-grained and low-cost device-free localization for multiple targets over various areas

abstract: Device-free localization (DfL) techniques, which can localize targets without carrying any wireless devices, have attracting an increasing attentions. Most current DfL approaches, however, have two main drawbacks hindering their practical applications. First, one needs to collect large number of measurements to achieve a high localization accuracy, inevitably causing a high deployment cost, and the areas variety will further exacerbate this problem. Second, as the pre-obtained Received Signal Strength (RSS) from each location {i.e., radio-map) in a specific area cannot be directly applied to new areas for localization, the calibration process of different areas will lead to the high human effort cost. In this paper, we propose, FitLoc, a fine-grained and low cost DfL approach that can localize multiple targets in various areas. By taking advantage of the compressive sensing (CS) theory, FitLoc decreases the deployment cost by collecting only a few of RSS measurements and performs a fine-grained localization. Further, FitLoc employs a rigorously designed transfer scheme to unify the radio-map over various areas, thus greatly reduces the human effort cost. Theoretical analysis about the effectivity of the problem formulation is provided. Extensive experimental results illustrate the effectiveness of FitLoc.


title: Mapping discrete and dimensional emotions onto the brain: controversies and consensus

abstract: A longstanding controversy in the field of emotion research has concerned whether emotions are better conceptualized in terms of discrete categories, such as fear and anger, or underlying dimensions, such as arousal and valence. In the domain of neuroimaging studies of emotion, the debate has centered on whether neuroimaging findings support characteristic and discriminable neural signatures for basic emotions or whether they favor competing dimensional and psychological construction accounts. This review highlights recent neuroimaging findings in this controversy, assesses what they have contributed to this debate, and offers some preliminary conclusions. Namely, although neuroimaging studies have identified consistent neural correlates associated with basic emotions and other emotion models, they have ruled out simple one-to-one mappings between emotions and brain regions, pointing to the need for more complex, network-based representations of emotion.


title: Transparent air filter for high-efficiency PM2.5 capture.

abstract: Particulate matter (PM) pollution has raised serious concerns for public health. Although outdoor individual protection could be achieved by facial masks, indoor air usually relies on expensive and energy-intensive air-filtering devices. Here, we introduce a transparent air filter for indoor air protection through windows that uses natural passive ventilation to effectively protect the indoor air quality. By controlling the surface chemistry to enable strong PM adhesion and also the microstructure of the air filters to increase the capture possibilities, we achieve transparent, high air flow and highly effective air filters of ~90% transparency with >95.00% removal of PM2.5 under extreme hazardous air-quality conditions (PM2.5 mass concentration >250 μg m(-3)). A field test in Beijing shows that the polyacrylonitrile transparent air filter has the best PM2.5 removal efficiency of 98.69% at high transmittance of ~77% during haze occurrence.


title: Evading android runtime analysis via sandbox detection

abstract: The large amounts of malware, and its diversity, have made it necessary for the security community to use automated dynamic analysis systems. These systems often rely on virtualization or emulation, and have recently started to be available to process mobile malware. Conversely, malware authors seek to detect such systems and evade analysis. In this paper, we present techniques for detecting Android runtime analysis systems. Our techniques are classified into four broad classes showing the ability to detect systems based on differences in behavior, performance, hardware and software components, and those resulting from analysis system design choices. We also evaluate our techniques against current publicly accessible systems, all of which are easily identified and can therefore be hindered by a motivated adversary. Our results show some fundamental limitations in the viability of dynamic mobile malware analysis platforms purely based on virtualization.


title: Memory-Guided Attention: Independent Contributions of the Hippocampus and Striatum

abstract: Memory can strongly influence how attention is deployed in future encounters. Though memory dependent on the medial temporal lobes has been shown to drive attention, how other memory systems could concurrently and comparably enhance attention is less clear. Here, we demonstrate that both reinforcement learning and context memory facilitate attention in a visual search task. Using functional magnetic resonance imaging, we dissociate the mechanisms by which these memories guide attention: trial by trial, the hippocampus (not the striatum) predicted attention benefits from context memory, while the striatum (not the hippocampus) predicted facilitation from rewarded stimulus-response associations. Responses in these regions were also distinctly correlated with individual differences in each type of memory-guided attention. This study provides novel evidence for the role of the striatum in guiding attention, dissociable from hippocampus-dependent context memory.


title: Beat the Machine: Challenging Humans to Find a Predictive Model's “Unknown Unknowns”

abstract: We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings, traditional methods for evaluating predictive models tend to miss rare but important errors—most importantly, cases for which the model is confident of its prediction (but wrong). In this article, we present a system that, in a game-like setting, asks humans to identify cases that will cause the predictive model-based system to fail. Such techniques are valuable in discovering problematic cases that may not reveal themselves during the normal operation of the system and may include cases that are rare but catastrophic. We describe the design of the system, including design iterations that did not quite work. In particular, the system incentivizes humans to provide examples that are difficult for the model to handle by providing a reward proportional to the magnitude of the predictive model's error. The humans are asked to “Beat the Machine” and find cases where the automatic model (“the Machine”) is wrong. Experiments show that the humans using Beat the Machine identify more errors than do traditional techniques for discovering errors in predictive models, and, indeed, they identify many more errors where the machine is (wrongly) confident it is correct. Furthermore, those cases the humans identify seem to be not simply outliers, but coherent areas missed completely by the model. Beat the Machine identifies the “unknown unknowns.” Beat the Machine has been deployed at an industrial scale by several companies. The main impact has been that firms are changing their perspective on and practice of evaluating predictive models. “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.” --Donald Rumsfeld


title: Rule extraction from linear support vector machines

abstract: We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike the original classifier, can be easily interpreted by humans. Each iteration of the rule extraction algorithm is formulated as a constrained optimization problem that is computationally inexpensive to solve. We discuss various properties of the algorithm and provide proof of convergence for two different optimization criteria We demonstrate the performance and the speed of the algorithm on linear classifiers learned from real-world datasets, including a medical dataset on detection of lung cancer from medical images. The ability to convert SVM's and other "black-box" classifiers into a set of human-understandable rules, is critical not only for physician acceptance, but also to reducing the regulatory barrier for medical-decision support systems based on such classifiers.


title: Epilogue: School-located Influenza Vaccination During the 2009–2010 Pandemic and Beyond

abstract: * Abbreviations: ::: ACIP — : Advisory Committee on Immunization Practices ::: CDC — : Centers for Disease Control and Prevention ::: SLV — : school-located vaccination ::: ::: As the preceding articles indicate, school-located vaccination (SLV) shows great promise as a method to quickly and efficiently vaccinate large numbers of school-aged children against influenza. This approach can both relieve health care providers who lack sufficient capacity to effectively vaccinate their patients annually, in accordance with the 2008 recommendations of Advisory Committee on Immunization Practices (ACIP),1 as well as provide a convenient option for parents and an opportunity for children without a medical home to be vaccinated. Importantly, SLV may be appropriate for routine vaccination against seasonal influenza, as well as during a public health emergency or pandemic. ::: ::: The utility of SLV was demonstrated on a large scale in response to the 2009–2010 H1N1 influenza pandemic. In July 2009, intense efforts were underway to procure H1N1 vaccine and prepare for implementing a large-scale national vaccination program. At that time, the ACIP published recommendations that defined the highest-priority target groups to receive vaccine when it first became available, which included school-aged children.2 Accordingly, public health units, educational institutions, and others in local communities joined together to hold SLV clinics throughout the United States, with several states implementing SLV statewide. Based on a National Association of County and City Health Officials survey of local health department officials that was conducted in the summer of 2010, an estimated 85% of local health departments held at least 1 H1N1 influenza SLV clinic in their jurisdiction (National Association of County and City Health Officials, unpublished data). The Centers for Disease Control and Prevention’s (CDC) National 2009 H1N1 Flu Survey is a nationally representative telephone-based survey designed to collect vaccination coverage from US … ::: ::: Address correspondence to Tara M. Vogt, PhD, MPH, 1600 Clifton Rd NE, MS A19, Atlanta, GA 30333. E-mail: tcv3{at}cdc.gov


Web Marketing Tourism Destinations


title: A robotic manipulator design with novel soft actuators

abstract: Soft robots are inherently compliant and adaptive, therefore they are promising candidates for interacting with humans. However robotic manipulators utilizing soft actuators are often constrained by a series of actuator performance limitations. In this work we design a novel linear soft robotic actuator with significantly improved performances over the existing products, achieving 300% deformation ratio, quasi-constant output force over a wide motion range, while maintaining passive compliance and adaptability. Moreover, the novel actuator is less prone to friction, and could be fabricated using inject molding and 3D printing, hence having high repeatability at very low cost. An analytical model was developed to characterize the actuator behavior and provide a guideline for actuator design according to performance specifications. A 6 DOF soft manipulator was designed and fabricated utilizing the novel soft actuator. The manipulator arm had a serial kinematic structure with a biomimetic wrist and was driven by 12 soft actuators mounted onto the arm links. With 1.2m workspace radius and 1kg payload, the working air pressure could be as low as 1bar. Preliminary results have shown the validity of the novel soft actuator and manipulator designs, as well as the strong potential of soft robots in human-oriented applications.


title: The effect of human resource management high performance work practices on organisational outcomes: a Sri Lankan perspective

abstract: High performance work practices (HPWPs) in human resource management (HRM) are practices considered to contribute to higher levels of employee performance and organisational productivity. They may be implemented through a best practice (universalistic), best fit (contingent) or integrated bundling (configurational) strategy. This study explores the extent to which the level of adoption of HPWPs in HRM in Sri Lankan organisations influences selected measures of performance outcomes covering a broad spectrum of divisions, hierarchical levels and processes, including those reflecting the effectiveness of the HRM strategy. In addition, the relationship between seven groups of HPWPs and the selected performance outcomes are researched. The findings add support to existing research in a number of countries linking HPWP adoption with improved levels of performance outcomes. Although HPWPs influence some outcomes positively they also may have negative effects upon others. The results have indicated which practices most significantly impact upon different outcomes thereby providing organisational decision makers with a guide as to how HPWPs can be employed most effectively to achieve organisational objectives.


title: Connecting mathematical logic and computation , it ensures that some aspects of programming are absolute

abstract: Connecting mathematical logic and computation, it ensures that some aspects of programming are absolute.


title: Human Identification Using Palm-Vein Images

abstract: This paper presents two new approaches to improve the performance of palm-vein-based identification systems presented in the literature. The proposed approach attempts to more effectively accommodate the potential deformations, rotational and translational changes by encoding the orientation preserving features and utilizing a novel region-based matching scheme. We systematically compare the previously proposed palm-vein identification approaches with our proposed ones on two different databases that are acquired with the contactless and touch-based imaging setup. We evaluate the performance improvement in both verification and recognition scenarios and analyze the influence of enrollment size on the performance. In this context, the proposed approaches are also compared for its superiority using single image enrollment on two different databases. The rigorous experimental results presented in this paper, on the databases of 100 and 250 subjects, consistently conforms the superiority of the proposed approach in both the verification and recognition scenario.


title: Instrumentality, aesthetics and symbolism of physical artifacts as triggers of emotion

abstract: Building on qualitative data collected from three groups of professionals who assessed the green colour of a public transportation bus, this paper develops a model of the relationship between physical artifacts and emotions. The model suggests that artifacts need to be analysed according to three conceptually distinct aspects: instrumentality, aesthetics and symbolism. These three aspects are suggested to arouse emotion through different mechanisms: a hygiene, a sensory and an associative mechanism. The model opens an arena for extensive future research on the role and influence of physical artifacts in general and on emotions in particular.


title: How users’ Internet experience affects the adoption of mobile payment: a mediation model

abstract: ABSTRACTMobile payment becomes a convenient way to complete transactions due to the increasing popularity of mobile devices and the maturity of related technologies. Users’ experience of using computers and the Internet in financial activities largely affects their intention to use mobile payment. Combining the Technology Acceptance Model and Innovation Diffusion Theory, we examined the mediating effect of five factors, that is, perceived usefulness, perceived ease of use, compatibility, risk, and privacy concern, in the relationship between Internet experience and the adoption of mobile payment. Data from 922 mobile users supported the partial mediating effects of the five factors.


title: Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

abstract: Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches. We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations. We show that flipout is effective at regularizing LSTMs, and outperforms previous methods. Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.


title: A Survey on Fault Detection, Isolation, and Reconfiguration Methods in Electric Ship Power Systems

abstract: One of the major challenges with electric shipboard power systems (SPS) is preserving the survivability of the system under fault situations. Some minor faults in SPS can result in catastrophic consequences. Therefore, it is essential to investigate available fault management techniques for SPS applications that can enhance SPS robustness and reliability. Many recent studies in this area take different approaches to address fault tolerance in SPSs. This paper provides an overview of the concepts and methodologies that are utilized to deal with faults in the electric SPS. First, a taxonomy of the types of faults and their sources in SPS is presented; then, the methods that are used to detect, identify, isolate, and manage faults are reviewed. Furthermore, common techniques for designing a fault management system in SPS are analyzed and compared. This paper also highlights several possible future research directions.


Responsibility for Crashes of Autonomous Vehicles: An Ethical Analysis


title: Magnetic resonance wireless power transfer using three-coil system with single planar receiver for laptop applications

abstract: This paper presents a magnetic resonance wireless power transfer (WPT) system that uses three coils, a planar receiver and operates at 6.78 MHz,. Effective power transfer is ensured by establishing an impedance matching condition for this WPT system. A metamaterial (MTM) array having dimensions of 20 cm Ⅹ 30 cm is also positioned near the load coil to concentrate the magnetic field and enhance the transfer efficiency. The result is a maximal improvement of 27% in the transfer efficiency at a transfer distance of 50 cm. The impact of a ground plane on the transfer efficiency is also examined. By utilizing the MTM array, making slits on the ground plane and increasing the gap between the ground plane and the load coil, it is possible to mitigate this impact. The highest transfer efficiency improvement is about 55% at a distance of 20 cm with the ground plane. A practical laptop model is fabricated to verify the impact of the load coil angle and position on the transfer efficiency. The result shows that the maximum transfer efficiency with the laptop model is 47.58% with the load coil angle of 90 degree.


title: Multidisciplinary creativity: the case of Herbert A. Simon

abstract: In the twentieth century, no person epitomized more dramatically the “Renaissance mind” than Herbert A. Simon (1916–2001). In a working life spanning over 60 years, Simon made seminal contributions to administrative theory, axiomatic foundations of physics, economics, sociology, econometrics, cognitive psychology, logic of scientific discovery, and artificial intelligence. Simon’s life of the mind, thus, affords nothing less than a “laboratory” in which to observe and examine at close quarters the phenomenon of multidisciplinary creativity. In this paper, we attempt to shed some light on the nature of Simon’s creativity and the nature of his particular Renaissance mind. In particular, we have attempted here to articulate the cognitive style underlying Simon’s multidisciplinary creativity. © 2003 Cognitive Science Society, Inc. All rights reserved.


title: Development of a genderless and fail-safe connection system for autonomous modular robots

abstract: One of the most challenging tasks on developing hardware of modular robots is to design a reliable and flexible connection system. This paper presents the design and implementation of a GENderless and FAil-safe (GENFA) connection system for autonomous modular robots that possesses self-assembly, self-reconfiguration and self-healing capabilities. By using the GENFA connector, a group of autonomous modular robots can assemble into a single robotic organism. With robust mechanical and electrical connection, a single robotic organism can disassemble into a set of disconnected units. In this paper, the detailed design of a connection system is presented. Then, mechanical strength and misalignment tolerance are studied theoretically. Some experimental tests are conducted with miniature mobile robots, and some experimental results show that the GENFA connection system is power efficient, fail-safe, and has limited misalignment tolerance.


title: Ghrelin Enhances Appetite and Increases Food Intake in Humans

abstract: Ghrelin is a recently identified endogenous ligand for the growth hormone secretagogue receptor. It is synthesized predominantly in the stomach and found in the circulation of healthy humans. Ghrelin has been shown to promote increased food intake, weight gain and adiposity in rodents. The effect of ghrelin on appetite and food intake in man has not been determined. We investigated the effects of intravenous ghrelin (5.0 pmol/kg/min) or saline infusion on appetite and food intake in a randomised double-blind cross-over study in nine healthy volunteers. There was a clear-cut increase in energy consumed by every individual from a free-choice buffet (mean increase 28 +/- 3.9%, p<0.001) during ghrelin compared with saline infusion. Visual analogue scores for appetite were greater during ghrelin compared to saline infusion. Ghrelin had no effect on gastric emptying as assessed by the paracetamol absorption test. Ghrelin is the first circulating hormone demonstrated to stimulate food intake in man. Endogenous ghrelin is a potentially important new regulator of the complex systems controlling food intake and body weight.


title: Scheduling processes with release times, deadlines, precedence and exclusion relations

abstract: An algorithm that finds an optimal schedule on a single processor for a given set of processes is presented. Each process starts executing after its release time and completes its computation before its deadline and a given set of precedence relations and exclusion relations defined on ordered pairs of process segments are satisfied. This algorithm can be applied to the important and previously unsolved problem of automated pre-run-time scheduling of processes with arbitrary precedence and exclusion in hard-real-time systems. >


title: FSO-Based Vertical Backhaul/Fronthaul Framework for 5G+ Wireless Networks

abstract: The presence of a super high rate, but also cost-efficient, easy-to-deploy, and scalable, backhaul/fronthaul framework is essential in the upcoming fifth-generation (5G) wireless networks \& beyond. Motivated by the mounting interest in the unmanned flying platforms of various types including unmanned aerial vehicles (UAVs), drones, balloons, and high-altitude/medium-altitude/low-altitude platforms (HAPs/MAPs/LAPs), which we refer to as the networked flying platforms (NFPs), for providing communications services and the recent advances in free-space optics (FSO), this article investigates the feasibility of a novel vertical backhaul/fronthaul framework where the NFPs transport the backhaul/fronthaul traffic between the access and core networks via point-to-point FSO links. The performance of the proposed innovative approach is investigated under different weather conditions and a broad range of system parameters. Simulation results demonstrate that the FSO-based vertical backhaul/fronthaul framework can offer data rates higher than the baseline alternatives, and thus can be considered as a promising solution to the emerging backhaul/fronthaul requirements of the 5G+ wireless networks, particularly in the presence of ultra-dense heterogeneous small cells. The paper also presents the challenges that accompany such a novel framework and provides some key ideas towards overcoming these challenges.


title: The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

abstract: In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.


title: The social mediascape of transnational Korean pop culture: Hallyu 2.0 as spreadable media practice

abstract: While it has been more than 15 years since the Korean pop culture phenomenon known as the Korean wave or hallyu emerged, academic analyses have not sufficiently addressed its dimension as a media environment from a global perspective. In this regard, drawing on qualitative interviews with North American fans of the recent Korean wave, this study explores how the hallyu phenomenon is integrated into a social media-driven cultural landscape, which will be referred to as the social mediascape. The social mediascape of hallyu reveals that the technological affordances of social media platforms and fans’ sociality interplay with each other, resulting in the rapid spread of hallyu as a set of impure cultural forms.


title: Improved non-Probabilistic Roadmap method for determination of shortest nautical navigation path

abstract: Probabilistic Roadmap (PRM) is one of the methods for finding the shortest path between Beginning and destination way-points on maritime shipping routes. The main behavior of the algorithm is based on assigning randomly distributed nodes on search space, finding the alternative routes, and then selecting the shortest one among them. As its name indicates, these path candidates are determined according to the position of the randomly distributed nodes at the beginning of the PRM algorithm. Therefore, it is not possible to obtain the global (or near global) shortest path. Hence, in this paper, two issues are considered i) omit the randomness with the previously proposed methodologies and ii) reduce the total track mileage with the aid of Hooke-Jeeves algorithm which is one of the classical optimization algorithm. To present the performance of this proposed framework, 20 different scenarios are defined. The PRM-HJ couple and PRM are applied to all these test problems and results are compared with respect to the total track mileage.


Morphchecker for non-standard data : a tool for morphological error correction in learner corpora


title: Development of a MatLab based GPS constellation simulation for navigation algorithm developments

abstract: This thesis is a part of a PhD project called Algorithm for Coupled Navigation Using an Inertial Measurement Unit and the Global Positioning System. The aim of the PhD project is to study a con ...


title: A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system

abstract: In this paper, we first present a self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data. Next, we prove the convergence of this algorithm. Two examples are presented to demonstrate the validity of our algorithm with model selection. Finally, we apply our algorithm to a data set collected from a P300-based brain computer interface (BCI) speller. This algorithm is shown to be able to significantly reduce training effort of the P300-based BCI speller.


title: A Hybrid model of Neural Network Approach for Speaker independent Word Recognition

abstract: Speech Recognition by computer is a process where speech signals are automatically converted into the corresponding sequence of words in text. When the training and testing conditions are not similar, statistical speech recognition algorithms suffer from severe degradation in recognition accuracy. So we depend on intelligent and recognizable sounds for common communications. In this research, word inputs are recognized by the system and executed in the form of text corresponding to the input word. In this paper, we propose a hybrid model by using a fully connected hidden layer between the input state nodes and the output. We have proposed a new objective function for the neural network using a combined framework of statistical and neural network based classifiers. We have used the hybrid model of Radial Basis Function and the Pattern Matching method. The system was trained by Indian English word consisting of 50 words uttered by 20 male speakers and 20 female speakers. The test samples comprised 30 words spoken by a different set of 20 male speakers and 20 female speakers. The recognition accuracy is found to be 91% which is well above the previous results.


title: EXPORT BARRIERS AND EXPORT PERFORMANCE: EMPIRICAL EVIDENCE FROM THE COMMERCIAL RELATIONSHIP BETWEEN GREECE AND IRAN

abstract: The purpose of this paper is to examine the relationship between export barriers and the export performance of Greek firms targeting the Iranian market. Reviewing the literature and using expert opinions, 18 variables were identified. A structured questionnaire was applied to 141 Greek firms and exploratory and confirmatory factor analyses were used to categorize variables in 6 dimensions. Then, a structural equation model was developed to determine which dimension has a greater effect on export performance. This model identifies the operational dimension (0.65), environmental dimension (0.60), financial dimension (0.58), source dimension (0.44), legal dimension (0.35) and logistic dimension (0.27) as effective export barriers to the export performance of Greek firms.


title: Personalizing recommendation diversity based on user personality

abstract: In recent years, diversity has attracted increasing attention in the field of recommender systems because of its ability of catching users’ various interests by providing a set of dissimilar items. There are few endeavors to personalize the recommendation diversity being tailored to individual users’ diversity needs. However, they mainly depend on users’ behavior history such as ratings to customize diversity, which has two limitations: (1) They neglect taking into account a user’s needs that are inherently caused by some personal factors such as personality; (2) they fail to work well for new users who have little behavior history. In order to address these issues, this paper proposes a generalized, dynamic personality-based greedy re-ranking approach to generating the recommendation list. On one hand, personality is used to estimate each user’s diversity preference. On the other hand, personality is leveraged to alleviate the cold-start problem of collaborative filtering recommendations. The experimental results demonstrate that our approach significantly outperforms related methods (including both non-diversity-oriented and diversity-oriented methods) in terms of metrics measuring recommendation accuracy and personalized diversity degree, especially in the cold-start setting.


title: The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution

abstract: While implicit generative models such as GANs have shown impressive results in high quality image reconstruction and manipulation using a combination of various losses, we consider a simpler approach leading to surprisingly strong results. We show that texture loss alone allows the generation of perceptually high quality images. We provide a better understanding of texture constraining mechanism and develop a novel semantically guided texture constraining method for further improvement. Using a recently developed perceptual metric employing"deep features"and termed LPIPS, the method obtains state-of-the-art results. Moreover, we show that a texture representation of those deep features better capture the perceptual quality of an image than the original deep features. Using texture information, off-the-shelf deep classification networks (without training) perform as well as the best performing (tuned and calibrated) LPIPS metrics. The code is publicly available.


title: Routing Metrics of Cognitive Radio Networks: A Survey

abstract: The majority of work in cognitive radio networks have focused on single-hop networks with mainly challenges at the physical and MAC layers. Recently, multi-hop secondary networks have gained attention as a promising design to leverage the full potential of cognitive radio networks. One of the main features of routing protocols in multi-hop networks is the routing metric used to select the best route for forwarding packets. In this paper, we survey the state-of-the-art routing metrics for cognitive radio networks. We start by listing the challenges that have to be addressed in designing a good routing metric for cognitive radio networks. We then provide a taxonomy of the different metrics and a survey of the way they have been used in different routing protocols. Then we present a case study to compare different classes of metrics. After that, we discuss how to combine individual routing metrics to obtain a global one. We end the paper with a discussion of the open issues in the design of future metrics for cognitive radio networks.


title: Underwater object detection with efficient shadow-removal for side scan sonar images

abstract: Side scan sonar has been widely used in ocean investigations, underwater object detection by side scan sonar is one of the most essential and fundamental tasks these years. In this paper, we present one simplified underwater object detection scheme with the help of shadow removal of side scan sonar images. The fuzzy C-mean clustering (FCM) algorithm is first taken to partition all pixels from the side scan sonar images into a collection of C fuzzy clusters, which makes shadow regions be segmented. The Criminisi algorithm based on isophote-driven image sampling process is then made full use of to undertake the shadow removal by filling the shadow region. Through the Otsu algorithm by choosing threshold value automatically, to segment the object and background we complete the object detection. It is shown from the simulation experiments that the proposed approach could achieve great performances in the object detection with both robustness and effectiveness.


XANES Study of Fe and Nb Oxidation in Zr-2 . 5 Nb Oxide Layers


Urdu Word Embeddings.


title: Health effects of vegetarian and vegan diets.

abstract: Vegetarian diets do not contain meat, poultry or fish; vegan diets further exclude dairy products and eggs. Vegetarian and vegan diets can vary widely, but the empirical evidence largely relates to the nutritional content and health effects of the average diet of well-educated vegetarians living in Western countries, together with some information on vegetarians in non-Western countries. In general, vegetarian diets provide relatively large amounts of cereals, pulses, nuts, fruits and vegetables. In terms of nutrients, vegetarian diets are usually rich in carbohydrates, n-6 fatty acids, dietary fibre, carotenoids, folic acid, vitamin C, vitamin E and Mg, and relatively low in protein, saturated fat, long-chain n-3 fatty acids, retinol, vitamin B(12) and Zn; vegans may have particularly low intakes of vitamin B(12) and low intakes of Ca. Cross-sectional studies of vegetarians and vegans have shown that on average they have a relatively low BMI and a low plasma cholesterol concentration; recent studies have also shown higher plasma homocysteine concentrations than in non-vegetarians. Cohort studies of vegetarians have shown a moderate reduction in mortality from IHD but little difference in other major causes of death or all-cause mortality in comparison with health-conscious non-vegetarians from the same population. Studies of cancer have not shown clear differences in cancer rates between vegetarians and non-vegetarians. More data are needed, particularly on the health of vegans and on the possible impacts on health of low intakes of long-chain n-3 fatty acids and vitamin B(12). Overall, the data suggest that the health of Western vegetarians is good and similar to that of comparable non-vegetarians.


title: Towards Big Topic Modeling

abstract: To solve the big topic modeling problem, we need to reduce both time and space complexities of batch latent Dirichlet allocation (LDA) algorithms. Although parallel LDA algorithms on the multi-processor architecture have low time and space complexities, their communication costs among processors often scale linearly with the vocabulary size and the number of topics, leading to a serious scalability problem. To reduce the communication complexity among processors for a better scalability, we propose a novel communication-efficient parallel topic modeling architecture based on power law, which consumes orders of magnitude less communication time when the number of topics is large. We combine the proposed communication-efficient parallel architecture with the online belief propagation (OBP) algorithm referred to as POBP for big topic modeling tasks. Extensive empirical results confirm that POBP has the following advantages to solve the big topic modeling problem: 1) high accuracy, 2) communication-efficient, 3) fast speed, and 4) constant memory usage when compared with recent state-of-the-art parallel LDA algorithms on the multi-processor architecture.


title: Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG

abstract: This paper proposed a multimodal fusion between brain and peripheral signals for emotion detection. The input signals were electroencephalogram, galvanic skin resistance, temperature, blood pressure and respiration, which can reflect the influence of emotion on the central nervous system and autonomic nervous system respectively. The acquisition protocol is based on a subset of pictures which correspond to three specific areas of valance-arousal emotional space (positively excited, negatively excited, and calm). The features extracted from input signals, and to improve the results, correlation dimension as a strong nonlinear feature is used for brain signals. The performance of the Quadratic Discriminant Classifier has been evaluated on different feature sets: peripheral signals, EEG's, and both. In comparison among the results of different feature sets, EEG signals seem to perform better than other physiological signals, and the results confirm the interest of using brain signals as peripherals in emotion assessment. According to the improvement in EEG results compare in each raw of the table, it seems that nonlinear features would lead to better understanding of how emotional activities work.


title: Compliant Joint Actuator With Dual Spiral Springs

abstract: The main motivation behind the creation of a compliant actuation system is to provide safety, capability of storing energy, and improved performance levels in dynamic tasks. The choice of the proper compliance level pertaining to robots depends on the specific purposes of the robots. In this paper, a dual-spiral-spring actuation system (DSSAS) is proposed to provide high compliance and deformation values for wearable robots. Among many different applications of wearable robotic systems, we aim for systems in which safety and compliance are more important than the control bandwidth of the joints. A spiral spring is selected to take advantage of the characteristics of high levels of compliance and deformation compared to other elastic components. The proposed dual-spiral-spring actuation system uses two spiral springs in opposite directions, as the spiral spring generates torque only in one direction. First, we present the characteristics of the spiral spring for a compliant joint. Then, the design and performance of the DSSAS are demonstrated by experiments.


title: Diabetic retinopathy classification using deeply supervised ResNet

abstract: Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population all over the world. Classifying DR is a time-consuming work, which requires an experienced ophthalmologist to evaluate color fundus photographs of retina. In this paper, we propose a deeply supervised ResNet approach to classify the severity of DR automatically. In this new convolutional neural networks (CNN) architecture, we add three sets of additional side-output layers to intermediate hidden layers of a 11-layer ResNet. By introducing these deeply supervised layers, we can provide additional regularization during training network. More importantly, we can perform multi-scale learning by leveraging the predictions of intermediate supervised layers, thus improving the final performance. Furthermore, to combat the issue of class-imbalance in the dataset, we adopt cost sensitive learning and an oversampling method of cropping images. We train and evaluate our network on the publicly available Kaggle dataset, and the results show that our method outperforms the state-of-the-art method.


title: An efficient wikipedia-based approach for better understanding of natural language text related to user requirements

abstract: Requirements engineering is the initial step in the development of software. Eliciting and understanding requirements are crucial in that the quality of the resulting system is highly dependent on a clear understanding of the customers' needs. Customers frequently express requirements in natural language, and software engineers then transform these natural language requirements to a more formalized representation that is useful during the remaining steps of software development. This transformation process has the potential to introduce errors and misunderstandings in the requirements because of ambiguities and incompleteness found in the natural language requirements. A communication gap typically exists between customers and software developers who may not share the same technical expertise. Software developers typically have computer science backgrounds, while the software application areas and thus the expertise of the customers may be from different domains, including such domains as chemistry, aerospace, healthcare, and economics. This communication gap can lead to software failures that can have devastating consequences in many different domains, including the aerospace domain, in terms of time, money and mission-critical efforts. Numerous research efforts are found in the literature that describe how a large textual corpus such as Wikipedia can be useful for helping fill that communication gap between the customer and the developer when natural language requirements are used in the requirements engineering process [1][2][3]. Wikipedia is a large online information repository that has an interface that emulates a paper encyclopedia in its coverage of diverse topics. It provides a large number of real-world concepts organized in hierarchical semantic structures. The quantity of the information and the ability to search the information in Wikipedia make it a valuable resource for research related to requirements and natural language processing [4][5][6]; however, the quantity of information makes searching it time consuming. In this paper, we describe research to determine the most efficient approach to retrieve data from Wikipedia for use in improving the requirements engineering process. We refer to this step of retrieving the information of interest as knowledge acquisition. We present our analysis and compare time efficiency of the use of regular expressions and string search algorithms for decreasing the knowledge acquisition bottleneck caused by the high computational time needed to retrieve these data from Wikipedia.


title: Inferring method specifications from natural language API descriptions

abstract: Application Programming Interface (API) documents are a typical way of describing legal usage of reusable software libraries, thus facilitating software reuse. However, even with such documents, developers often overlook some documents and build software systems that are inconsistent with the legal usage of those libraries. Existing software verification tools require formal specifications (such as code contracts), and therefore cannot directly verify the legal usage described in natural language text of API documents against the code using that library. However, in practice, most libraries do not come with formal specifications, thus hindering tool-based verification. To address this issue, we propose a novel approach to infer formal specifications from natural language text of API documents. Our evaluation results show that our approach achieves an average of 92% precision and 93% recall in identifying sentences that describe code contracts from more than 2500 sentences of API documents. Furthermore, our results show that our approach has an average 83% accuracy in inferring specifications from over 1600 sentences describing code contracts.


title: Wireless underground sensor networks : Research challenges

abstract: This work introduces the concept of a Wireless Underground Sensor Network (WUSN). WUSNs can be used to monitor a variety of conditions, such as soil properties for agricultural applications and toxic substances for environmental monitoring. Unlike existing methods of monitoring underground conditions, which rely on buried sensors connected via wire to the surface, WUSN devices are deployed completely belowground and do not require any wired connections. Each device contains all necessary sensors, memory, a processor, a radio, an antenna, and a power source. This makes their deployment much simpler than existing underground sensing solutions. Wireless communication within a dense substance such as soil or rock is, however, significantly more challenging than through air. This factor, combined with the necessity to conserve energy due to the difficulty of unearthing and recharging WUSN devices, requires that communication protocols be redesigned to be as efficient as possible. This work provides an extensive overview of applications and design challenges for WUSNs, challenges for the underground communication channel including methods for predicting path losses in an underground link, and challenges at each layer of the communication protocol stack. � 2006 Elsevier B.V. All rights reserved.


Security in the GSM network


title: Vision Based Fall Detector Exploiting Deep Learning

abstract: In this paper we propose a vision based fall detection algorithm. Our scheme exploits a deep learning paradigm in order to isolate human's object from the background and then to perform tracking. Deep learning better emulates our brain by propagating the raw sensory inputs into "deep" levels of hierarchies. Network adaptation dynamically re-configures the network to fit current environment visual properties. This way, object classification accuracy and tracking is enriched. In the following step, geometrically properties from the detected human object takes place. This is performed by extracting real 3D measurements from the captured 2D image planes. Camera self-calibration methods through the extraction of vanishing points are considered in this context. The derived features are filtered using autoregressive models and filtered feature sequence are exploited as feature in a time delay neural network for performing the final fall detection. Semi-supervised learning strategies are exploited to enhance classification efficiency. Experimental results indicate the efficiency of our proposed algorithm.


title: Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints

abstract: We present a discriminative model for single-document summarization that integrally combines compression and anaphoricity constraints. Our model selects textual units to include in the summary based on a rich set of sparse features whose weights are learned on a large corpus. We allow for the deletion of content within a sentence when that deletion is licensed by compression rules; in our framework, these are implemented as dependencies between subsentential units of text. Anaphoricity constraints then improve cross-sentence coherence by guaranteeing that, for each pronoun included in the summary, the pronoun's antecedent is included as well or the pronoun is rewritten as a full mention. When trained end-to-end, our final system outperforms prior work on both ROUGE as well as on human judgments of linguistic quality.


title: Feature Representations for Scene Text Character Recognition: A Comparative Study

abstract: Recognizing text character from natural scene images is a challenging problem due to background interferences and multiple character patterns. Scene Text Character (STC) recognition, which generally includes feature representation to model character structure and multi-class classification to predict label and score of character class, mostly plays a significant role in word-level text recognition. The contribution of this paper is a complete performance evaluation of image-based STC recognition, by comparing different sampling methods, feature descriptors, dictionary sizes, coding and pooling schemes, and SVM kernels. We systematically analyze the impact of each option in the feature representation and classification. The evaluation results on two datasets CHARS74K and ICDAR2003 demonstrate that Histogram of Oriented Gradient (HOG) descriptor, soft-assignment coding, max pooling, and Chi-Square Support Vector Machines (SVM) obtain the best performance among local sampling based feature representations. To improve STC recognition, we apply global sampling feature representation. We generate Global HOG (GHOG) by computing HOG descriptor from global sampling. GHOG enables better character structure modeling and obtains better performance than local sampling based feature representations. The GHOG also outperforms existing methods in the two benchmark datasets.


title: 3-D Eddy Current Analysis of Induction Heating Apparatus Considering Heat Emission, Heat Conduction, and Temperature Dependence of Magnetic Characteristics

abstract: The main issue of a billet heater using induction heating is to avoid billets that were not heated at a desired temperature. In order to improve the induction heating system, it is necessary to clarify the heating property of an object due to eddy current loss and to investigate the temperature distribution in an object by the magneto-thermal coupled analysis. In this paper, the eddy current and temperature distribution of a billet heater is analyzed considering the heat emission, heat conduction, and temperature dependence of magnetic characteristics of the billet. It is shown that the calculated values of temperature in the center and surface of a billet are in good agreement with measured values. The precise analysis is possible by considering the temperature dependence of magnetic characteristics, heat conductivity, etc. The detailed behavior of the heat generation in the billet is clarified. The skin depth is increased because the resistivity of the billet is increased and the permeability is decreased at high temperature. As a result, the flux in the billet is reduced, and then the power (eddy current loss) in the billet is decreased.


title: Mecanum-wheel vehicle systems based on position corrective control

abstract: This paper introduces a position corrective feedback control method using a vision sensor on Mecanum-wheel omnidirectional vehicles. Recently, this omnidirectional vehicle has been developed, and it is sometimes employed in wheelchairs. We believe that by exploiting its unique features, it can be more widely employed as a vehicle robot. The control system is based on unique kinematics under holonomic constraints. A CCD camera, which is a part of the vision sensor system, is suspended from the ceiling of the lab housing the moving vehicle. The vision system is used to correct the vehicle position. The position data of the vehicle is calculated using the visual data. Further, the position of the vehicle is corrected smoothly by continuously entering the data into the position feedback loop. This paper describes the structure of the control system and a position correction feedback method. Further, experimental results obtained from the evaluation of this method are presented.


title: Processed meat and colorectal cancer: a review of epidemiologic and experimental evidence

abstract: Processed meat intake may be involved in the etiology of colorectal cancer, a major cause of death in affluent countries. The epidemiologic studies published to date conclude that the excess risk in the highest category of processed meat-eaters is comprised between 20% and 50% compared with non-eaters. In addition, the excess risk per gram of intake is clearly higher than that of fresh red meat. Several hypotheses, which are mainly based on studies carried out on red meat, may explain why processed meat intake is linked to cancer risk. Those that have been tested experimentally are (i) that high-fat diets could promote carcinogenesis via insulin resistance or fecal bile acids; (ii) that cooking meat at a high temperature forms carcinogenic heterocyclic amines and polycyclic aromatic hydrocarbons; (iii) that carcinogenic N-nitroso compounds are formed in meat and endogenously; (iv) that heme iron in red meat can promote carcinogenesis because it increases cell proliferation in the mucosa, through lipoperoxidation and/or cytotoxicity of fecal water. Nitrosation might increase the toxicity of heme in cured products. Solving this puzzle is a challenge that would permit to reduce cancer load by changing the processes rather than by banning processed meat.


title: Analytical Modeling of Mode Selection and Power Control for Underlay D2D Communication in Cellular Networks

abstract: Device-to-device (D2D) communication enables the user equipments (UEs) located in close proximity to bypass the cellular base stations (BSs) and directly connect to each other, and thereby, offload traffic from the cellular infrastructure. D2D communication can improve spatial frequency reuse and energy efficiency in cellular networks. This paper presents a comprehensive and tractable analytical framework for D2D-enabled uplink cellular networks with a flexible mode selection scheme along with truncated channel inversion power control. The developed framework is used to analyze and understand how the underlaying D2D communication affects the cellular network performance. Through comprehensive numerical analysis, we investigate the expected performance gains and provide guidelines for selecting the network parameters.


title: Probabilistic scan matching for motion estimation in unstructured environments

abstract: This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence.


title: SNIFFER: A high-accuracy malware detector for enterprise-based systems

abstract: In the continual battle between malware attacks and antivirus technologies, both sides strive to deploy their techniques at always lower layers in the software system stack. The goal is to monitor and control the software executing in the levels above their own deployment, to detect attacks or to defeat defenses. Recent antivirus solutions have gone even below the software, by enlisting hardware support. However, so far, they have only mimicked classic software techniques by monitoring software clues of an attack. As a result, malware can easily defeat them by employing metamorphic manifestation patterns. With this work, we propose a hardware-monitoring solution, SNIFFER, which tracks malware manifestations in system-level behavior, rather than code patterns, and it thus cannot be circumvented unless malware renounces its very nature, that is, to attack. SNIFFER leverages in-hardware feature monitoring, and uses machine learning to assess whether a system shows signs of an attack. Experiments with a virtual SNIFFER implementation, which supports 13 features and tests against five common network-based malicious behaviors, show that SNIFFER detects malware nearly 100% of the time, unless the malware aggressively throttle its attack. Our experiments also highlight the need for machine-learning classifiers employing a range of diverse system features, as many of the tested malware require multiple, seemingly disconnected, features for accurate detection.


Classifying the Informative Behaviour of Emoji in Microblogs


title: Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks

abstract: Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve the creative task of the designer. For this, we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web. Training Generative Adversarial Networks (GANs) for logo synthesis on such multi-modal data is not straightforward and results in mode collapse for some state-of-the-art methods. We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training. We are able to generate a high diversity of plausible logos and we demonstrate latent space exploration techniques to ease the logo design task in an interactive manner. Moreover, we validate the proposed clustered GAN training on CIFAR 10, achieving state-of-the-art Inception scores when using synthetic labels obtained via clustering the features of an ImageNet classifier. GANs can cope with multi-modal data by means of synthetic labels achieved through clustering, and our results show the creative potential of such techniques for logo synthesis and manipulation. Our dataset and models will be made publicly available at this https URL


title: Wound Biofilm: Current Perspectives and Strategies on Biofilm Disruption and Treatments

abstract: The presence of bio lm remains a challenging factor that contributes to the delayed healing of many chronic wounds. The major threat of chronic wound bio lms is their substantial protection from host immunities and extreme tolerance to antimicrobial agents. To help guide the development of wound treatment strategies, a panel of experts experienced in clinical and laboratory aspects of biofilm convened to discuss what is understood and not yet understood about biofilms and what is needed to better identify and treat chronic wounds in which biofilm is suspected. This article reviews evidence of the problem of biofilms in chronic wounds, summarizes literature-based and experience-based recommendations from the panel meeting, and identities future and emerging technologies needed to address the current gaps in knowledge. While currently there is insufficient evidence to provide an accurate comparison of the effectiveness of current therapies/products in reducing or removing biofilm, research has shown that in addition to debridement, appropriate topical antimicrobial application can suppress biofilm reformation. Because the majority of the resistance of bacteria in a biofilm population is expressed by its own secreted matrix of extracellular polymeric substance (EPS), panel members stressed the need for a paradigm shift toward biofilm treatment strategies that disrupt this shield. High-osmolarity surfactant solution technology is emerging as a potential multimodal treatment that has shown promise in EPS disruption and prevention of biofilm formation when used immediately post debridement. Panel members advocated incorporating an EPS-disrupting technology into an antibiofilm treatment approach for all chronic wounds. The activity of this panel is a step toward identifying technology and research needed to improve biofilm management of chronic wounds.


title: Understanding the Limits of LoRaWAN

abstract: Low-power wide area networking technology offers long-range communication, which enables new types of services. Several solutions exist; LoRaWAN is arguably the most adopted. It promises ubiquitous connectivity in outdoor IoT applications, while keeping network structures and management simple. This technology has received a lot of attention in recent months from network operators and solution providers. However, the technology has limitations that need to be clearly understood to avoid inflated expectations and disillusionment. This article provides an impartial and fair overview of the capabilities and limitations of LoRaWAN. We discuss those in the context of use cases, and list open research and development questions.


title: Scalable Kernel Density Classification via Threshold-Based Pruning

abstract: Density estimation forms a critical component of many analytics tasks including outlier detection, visualization, and statistical testing. These tasks often seek to classify data into high and low-density regions of a probability distribution. Kernel Density Estimation (KDE) is a powerful technique for computing these densities, offering excellent statistical accuracy but quadratic total runtime. In this paper, we introduce a simple technique for improving the performance of using a KDE to classify points by their density (density classification). Our technique, thresholded kernel density classification (tKDC), applies threshold-based pruning to spatial index traversal to achieve asymptotic speedups over naive KDE, while maintaining accuracy guarantees. Instead of exactly computing each point's exact density for use in classification, tKDC iteratively computes density bounds and short-circuits density computation as soon as bounds are either higher or lower than the target classification threshold. On a wide range of dataset sizes and dimensions, tKDC demonstrates empirical speedups of up to 1000x over alternatives.


SVM and Neural Network based Diagnosis of Diabetic


title: Reactive and Real-Time Systems Course: How to Get the Most Out of it

abstract: The paper describes the syllabus and the students’ projects from a graduate course on the subject of “Reactive and Real-Time Systems”, taught at Tel-Aviv University and at the Open University of Israel. The course focuses on the development of provably correct reactive real-time systems. The course combines theoretical issues with practical implementation experience, trying to make things as tangible as possible. Hence, the mathematical and logical frameworks introduced are followed by presentation of relevant software tools and the students’ projects are implemented using these tools. The course is planned so that no special purpose hardware is needed and so that all software tools used are freely available from various Internet sites and can be installed quite easily. This makes our course attractive to institutions and instructors for which purchasing and maintaining a special lab is not feasible due to budget, space, or time limitations (as in our case). In the paper we elaborate on the rationale behind the syllabus and the selection of the students’ projects, presenting an almost complete description of a sample design of one team’s project.


title: Modeling Narrative Discourse

abstract: This thesis describes new approaches to the formal modeling of narrative discourse. Although narratives of all kinds are ubiquitous in daily life, contemporary text processing techniques typically do not leverage the aspects that separate narrative from expository discourse. We describe two approaches to the problem. The first approach considers the conversational networks to be found in literary fiction as a key aspect of discourse coherence; by isolating and analyzing these networks, we are able to comment on longstanding literary theories. The second approach proposes a new set of discourse relations that are specific to narrative. By focusing on certain key aspects, such as agentive characters, goals, plans, beliefs, and time, these relations represent a theory-of-mind interpretation of a text. We show that these discourse relations are expressive, formal, robust, and through the use of a software system, amenable to corpus collection projects through the use of trained annotators. We have procured and released a collection of over 100 encodings, covering a set of fables as well as longer texts including literary fiction and epic poetry. We are able to inferentially find similarities and analogies between encoded stories based on the proposed relations, and an evaluation of this technique shows that human raters prefer such a measure of similarity to a more traditional one based on the semantic distances between story propositions.


title: 140 GHz High-Gain LTCC-Integrated Transmit-Array Antenna Using a Wideband SIW Aperture-Coupling Phase Delay Structure

abstract: This paper presents the complete design of a wideband transmit-array (TA) antenna with high gain and high efficiency for D-band applications based on the low-temperature co-fired ceramic technology. The proposed unit cell is composed of a pair of wideband magnetoelectric dipoles as the receive/transmit elements, together with a substrate-integrated waveguide (SIW) aperture-coupling transmission structure for independent phase adjustability. A 360° phase coverage is obtained by the proposed phasing element, and its phase response curves are nearly parallel within a broad frequency band, which indicates a wideband performance. To verify the design, the fabricated prototype is measured by using a vector network analyzer in a terahertz compact-range anechoic chamber. The measured peak gain is 33.45 dBi at 150 GHz with the aperture efficiency of 44.03%, and the measured 3 dB gain bandwidth is 124–158 GHz (24.29%). The good radiation performance ensures that the proposed SIW aperture-coupling TA antenna is a promising candidate for D-band applications.


title: Multilingual Language Processing From Bytes

abstract: We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start positions, lengths, and labels are separate entries in our vocabulary. Because we operate directly on unicode bytes rather than language-specific words or characters, we can analyze text in many languages with a single model. Due to the small vocabulary size, these multilingual models are very compact, but produce results similar to or better than the state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that use only the provided training datasets (no external data sources). Our models are learning "from scratch" in that they do not rely on any elements of the standard pipeline in Natural Language Processing (including tokenization), and thus can run in standalone fashion on raw text.


title: Design Guidelines of Horn Antennas That Combine Horizontal and Vertical Corrugations for Satellite Communications

abstract: Today, telecommunication satellite horn antennas are required to be wideband mainly because of the additional bandwidth necessary to accommodate more transponders to increase the capacity of the new satellite services. In this aspect, horn antennas play a key role in the development of wider bandwidth services on board satellites because now-a-days, with recent advances in orthomode junctions and transducers, they are usually the bandwidth limiting component of a satellite antenna as often low crosspolar level is the toughest specification parameter. In this paper, the design guidelines for horn antennas that combine horizontal and vertical corrugations are explained, the method to prepare a preliminary profile for optimization is shown, and an example of a satellite feedhorn with severe requirements is presented.


title: JackHare: a framework for SQL to NoSQL translation using MapReduce

abstract: As data exploration has increased rapidly in recent years, the datastore and data processing are getting more and more attention in extracting important information. To find a scalable solution to process the large-scale data is a critical issue in either the relational database system or the emerging NoSQL database. With the inherent scalability and fault tolerance of Hadoop, MapReduce is attractive to process the massive data in parallel. Most of previous researches focus on developing the SQL or SQL-like queries translator with the Hadoop distributed file system. However, it could be difficult to update data frequently in such file system. Therefore, we need a flexible datastore as HBase not only to place the data over a scale-out storage system, but also to manipulate the changeable data in a transparent way. However, the HBase interface is not friendly enough for most users. A GUI composed of SQL client application and database connection to HBase will ease the learning curve. In this paper, we propose the JackHare framework with SQL query compiler, JDBC driver and a systematical method using MapReduce framework for processing the unstructured data in HBase. After importing the JDBC driver to a SQL client GUI, we can exploit the HBase as the underlying datastore to execute the ANSI-SQL queries. Experimental results show that our approaches can perform well with efficiency and scalability.


title: Product review summarization through question retrieval and diversification

abstract: Product reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers to digest them and make informed choices. In our study, we aim to help customers who want to quickly capture the main idea of a lengthy product review before they read the details. In contrast with existing work on review analysis and document summarization, we aim to retrieve a set of real-world user questions to summarize a review. In this way, users would know what questions a given review can address and they may further read the review only if they have similar questions about the product. Specifically, we design a two-stage approach which consists of question selection and question diversification. For question selection phase, we first employ probabilistic retrieval models to locate candidate questions that are relevant to a given review. A Recurrent Neural Network Encoder---Decoder is utilized to measure the "answerability" of questions to a review. We then design a set function to re-rank the questions with the goal of rewarding diversity in the final question set. The set function satisfies submodularity and monotonicity, which results in an efficient greedy algorithm of submodular optimization. Evaluation on product reviews from two categories shows that the proposed approach is effective for discovering meaningful questions that are representative of individual reviews.


Sepic Topology Based High Step-Up Step down Soft Switching Bidirectional DC-DC Converter for Energy Storage Applications


title: A 5.75 to 44 Gb/s Quarter Rate CDR With Data Rate Selection in 90 nm Bulk CMOS

abstract: This paper presents a quarter-rate clock and data recovery (CDR) circuit for plesiochronous serial I/O-links. The 2times-oversampling phase-tracking CDR, implemented in 90 nm bulk CMOS technology, covers the whole range of data rates from 5.75 to 44 Gb/s realized in a single IC by the novel feature of a data rate selection logic. Input data are sampled with eight parallel differential master-slave flip-flops, where bandwidth enhancement techniques were necessary for 90 nm CMOS. Precise and low-jitter local clock phases are generated by an analog delay-locked loop. These clock phases are aligned to the incoming data by four parallel phase rotators. The phase-tracking loop of the CDR is realized as a digital delay-locked loop and is therefore immune against process tolerances. The CDR is able to track a maximum frequency deviation of plusmn615 ppm between incoming data and a local reference clock and fulfills the extended XAUI jitter tolerance mask. A bit error rate <10-12 was verified up to 38 Gb/s using a 27 -1 PRBS pattern. With a low power consumption per data rate of only 5.74 mW/(Gb/s) the CDR meets the specifications of the International Technology Roadmap for Semiconductors for 90 nm CMOS serial I/O-links at the maximal data rate of 44 Gb/s. The CDR occupies a chip area of 0.2 mm2.


title: No Need to Stop What You’re Doing: Exploring No-Handed Smartwatch Interaction

abstract: Smartwatches have the potential to enable quick micro-interactions throughout daily life. However, because they require both hands to operate, their full potential is constrained, particularly in situations where the user is actively performing a task with their hands. We investigate the space of no-handed interaction with smartwatches in scenarios where one or both hands are not free. Specifically, we present a taxonomy of scenarios in which standard touchscreen interaction with smartwatches is not possible, and discuss the key constraints that limit such interaction. We then implement a set of interaction techniques and evaluate them via two user studies: one where participants viewed video clips of the techniques and another where participants used the techniques in simulated hand-constrained scenarios. Our results found a preference for foot-based interaction and reveal novel design considerations to be mindful of when designing for no-handed smartwatch interaction scenarios.


title: Flexpad: highly flexible bending interactions for projected handheld displays

abstract: Flexpad is an interactive system that combines a depth camera and a projector to transform sheets of plain paper or foam into flexible, highly deformable, and spatially aware handheld displays. We present a novel approach for tracking deformed surfaces from depth images in real time. It captures deformations in high detail, is very robust to occlusions created by the user's hands and fingers, and does not require any kind of markers or visible texture. As a result, the display is considerably more deformable than in previous work on flexible handheld displays, enabling novel applications that leverage the high expressiveness of detailed deformation. We illustrate these unique capabilities through three application examples: curved cross-cuts in volumetric images, deforming virtual paper characters, and slicing through time in videos. Results from two user studies show that our system is capable of detecting complex deformations and that users are able to perform them quickly and precisely.


title: Hybrid Multi-Channel MAC Protocol for Wireless Sensor Networks: Interference Rate Evaluation

abstract: This paper evaluates a channel allocation algorithm of a Hybrid Multi-Channel MAC protocol, HMC-MAC, that tries to improve communication performance in Wireless Sensor Networks (WSNs). We also analyze different techniques that are used in multi-channel MAC protocols proposed for WSNs. These protocols often use either a schedule-based or a contention based MAC protocol to communicate with each others. HMC-MAC takes advantage of the synchronous activity of TDMA to achieve collision free communications, of the flexibility offered by CSMA/CA to offer a scalable network, and of the parallel transmissions over different channels provided by FDMA to enhance the overall throughput. HMC-MAC assumes that sensor nodes are equipped with one half-duplex radio interface. We define a new method for channel allocation that enables nodes to choose the most convenient channel in its three-hop neighborhood in order to minimize interference as much possible. Simulation results demonstrate that HMC-MAC provides smaller interference rate and outperforms other mechanisms.


title: Foveated instant preview for progressive rendering

abstract: Progressive rendering, for example Monte Carlo rendering of 360° content for virtual reality headsets, is a time-consuming task. If the 3D artist notices an error while previewing the rendering, he or she must return to editing mode, do the required changes, and restart rendering. Restart is required because the rendering system cannot know which pixels are affected by the change. We propose the use of eye-tracking-based optimization to significantly speed up previewing the artist's points of interest. Moreover, we derive an optimized version of the visual acuity model, which follows the original model more accurately than previous work. The proposed optimization was tested with a comprehensive user study. The participants felt that preview with the proposed method converged instantly, and the recorded split times show that the preview is 10 times faster than conventional preview. In addition, the system does not have measurable drawbacks on computational performance.


title: Picture tags and world knowledge: learning tag relations from visual semantic sources

abstract: This paper studies the use of everyday words to describe images. The common saying has it that 'a picture is worth a thousand words', here we ask which thousand? The proliferation of tagged social multimedia data presents a challenge to understanding collective tag-use at large scale -- one can ask if patterns from photo tags help understand tag-tag relations, and how it can be leveraged to improve visual search and recognition. We propose a new method to jointly analyze three distinct visual knowledge resources: Flickr, ImageNet/WordNet, and ConceptNet. This allows us to quantify the visual relevance of both tags learn their relationships. We propose a novel network estimation algorithm, Inverse Concept Rank, to infer incomplete tag relationships. We then design an algorithm for image annotation that takes into account both image and tag features. We analyze over 5 million photos with over 20,000 visual tags. The statistics from this collection leads to good results for image tagging, relationship estimation, and generalizing to unseen tags. This is a first step in analyzing picture tags and everyday semantic knowledge. Potential other applications include generating natural language descriptions of pictures, as well as validating and supplementing knowledge databases.


title: Contrast enhancement for images in turbid water.

abstract: Absorption, scattering, and color distortion are three major degradation factors in underwater optical imaging. Light rays are absorbed while passing through water, and absorption rates depend on the wavelength of the light. Scattering is caused by large suspended particles, which are always observed in an underwater environment. Color distortion occurs because the attenuation ratio is inversely proportional to the wavelength of light when light passes through a unit length in water. Consequently, underwater images are dark, low contrast, and dominated by a bluish tone. In this paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a robust color lines-based ambient light estimator and a locally adaptive filtering algorithm for enhancing underwater images in shallow oceans. Furthermore, we propose a spectral characteristic-based color correction algorithm to recover the distorted color. The enhanced images have a reasonable noise level after the illumination compensation in the dark regions, and demonstrate an improved global contrast by which the finest details and edges are enhanced significantly.


title: Modern Aircraft Flight Control

abstract: Generally on automatic flight control.- Complete model of aircraft perturbed motion.- Nominal aircraft dynamics.- Stabilization of nominal motion.- Application of dynamic flight control to realization of aircraft spatial flight manoeuvre.


title: Fighting game artificial intelligence competition platform

abstract: Game playing has provided an interesting framework for developing and testing artificial intelligence (AI) algorithms, with well-known examples such as Go and Chess. Since the first chess programs were written, there has been steady progress in the level of play to the point where current systems can challenge top-class human players. Recently, academia groups have created competitions to evaluate and compare AI methods, such as Ms Pac-Man versus Ghost Team and Student StarCraft AI Tournament. Those tournaments define their rules and goals based on the game they selected, according to which intelligent techniques and technologies are developed. We focus on a genre called fighting game. Examples of fighting games are Street Fighter, Tekken, and Mortal Kombat. To select the winner in the fighting game is simple: the winner is the character with less damage or the last one standing. In this paper we introduce a brand new competition based on a 2D fighting game written in Java.


title: Collaborative Physical User Interfaces

abstract: Unlike the 'traditional' computer that is based on a screen, mouse and keyboard, physical interfaces for collaborative interaction are special purpose devices that can be situated in a real-world setting and are designed for particular collaborative contexts and uses. However this is a new design genre; developers do not yet know what these devices should do and what they should look like. In this chapter, I hint at the variety of design categories for collaborative physical interfaces, as suggested and illustrated by a collection of working prototypes created by researchers and students at the University of Calgary. We will also see that two toolkits encouraged people to explore creative ideas in this new genre. Through Phidgets, people rapidly prototype physical user interfaces under computer control. Through the Grouplab Collabrary, people had an easy means to share data between interconnected devices and software. These combined tools became a media form that allowed researchers and students to create and develop new ideas concerning physical interfaces for collaborative interaction, or to vary already established ideas in interesting ways.


Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts


title: EMTerms 1.0: A Terminological Resource for Crisis Tweets

abstract: We present the first release of EMTerms (Emergency Management Terms), the largest crisis-related terminological resource to date, containing over 7,000 terms used in Twitter to describe various crises. This resource can be used by practitioners to search for relevant messages in Twitter during crises, and by computer scientists to develop new automatic methods for crises in Twitter. The terms have been collected from a seed set of terms manually annotated by a linguist and an emergency manager from tweets broadcast during 4 crisis events. A Conditional Random Fields (CRF) method was then applied to tweets from 35 crisis events, in order to expand the set of terms while overcoming the difficulty of getting more emergency managers’ annotations. The terms are classified into 23 information-specific categories, by using a combination of expert annotations and crowdsourcing. This article presents the detailed terminology extraction methodology, as well as final results.


title: Working Toward the Experimenter : Reconceptualizing Obedience Within the Milgram Paradigm as Identification-Based Followership

abstract: The behavior of participants within Milgram’s obedience paradigm is commonly understood to arise from the propensity to cede responsibility to those in authority and hence to obey them. This parallels a belief that brutality in general arises from passive conformity to roles. However, recent historical and social psychological research suggests that agents of tyranny actively identify with their leaders and are motivated to display creative followership in working toward goals that they believe those leaders wish to see fulfilled. Such analysis provides the basis for reinterpreting the behavior of Milgram’s participants. It is supported by a range of material, including evidence that the willingness of participants to administer 450-volt shocks within the Milgram paradigm changes dramatically, but predictably, as a function of experimental variations that condition participants’ identification with either the experimenter and the scientific community that he represents or the learner and the general commu...


Update on psoriasis immunopathogenesis and targeted immunotherapy


title: Application of Flexible OLED Display Technology for Electro-Optical Stimulation and/or Silencing of Neural Activity

abstract: This paper presents a new biophotonic application for large-area, high-resolution, flexible organic light-emitting diode (OLED) display technology currently used to manufacture low-cost color flexible displays on plastic substrates. The new concept uses a fully addressable high resolution flexible OLED pixel array on a thin, mechanically compliant biocompatible plastic substrate to selectively stimulate and/or silence small groups of neurons on either the cortical surface or, alternatively, within the deep brain. Optical measurements from a 455 nm blue flexible OLED test structure demonstrated the ability to emit 1 ${\hbox{mW/mm}}^{2}$ of instantaneous light intensity using a 13 V, 20 Hz pulse, which meets the minimum reported intensity at $\sim$ 450 nm to induce optical stimulation in genetically modified neural tissue. Biocompatibility was successfully demonstrated by the ability to grow human epithelial cells on the surface of a full TFT process flow plastic flexible display substrate. Additionally, a new active matrix array display architecture was designed to support pulsed mode OLED operation. These preliminary results demonstrate the initial viability of extending flexible plastic substrate OLED display technology to the development of large-area, high-resolution emissive active matrix arrays for chronic optogenetic applications.


title: The Evolution of MAC Protocols in Wireless Sensor Networks: A Survey

abstract: Wireless Sensor Networks (WSNs) have become a leading solution in many important applications such as intrusion detection, target tracking, industrial automation, smart building and so on. Typically, a WSN consists of a large number of small, low-cost sensor nodes that are distributed in the target area for collecting data of interest. For a WSN to provide high throughput in an energy-efficient way, designing an efficient Medium Access Control (MAC) protocol is of paramount importance because the MAC layer coordinates nodes' access to the shared wireless medium. To show the evolution of WSN MAC protocols, this article surveys the latest progresses in WSN MAC protocol designs over the period 2002-2011. In the early development stages, designers were mostly concerned with energy efficiency because sensor nodes are usually limited in power supply. Recently, new protocols are being developed to provide multi-task support and efficient delivery of bursty traffic. Therefore, research attention has turned back to throughput and delay. This article details the evolution of WSN MAC protocols in four categories: asynchronous, synchronous, frame-slotted, and multichannel. These designs are evaluated in terms of energy efficiency, data delivery performance, and overhead needed to maintain a protocol's mechanisms. With extensive analysis of the protocols many future directions are stated at the end of this survey. The performance of different classes of protocols could be substantially improved in future designs by taking into consideration the recent advances in technologies and application demands.


title: Concatenated coded OFDM system over AWGN channel with H.264 source coding

abstract: This paper presents a robust concatenated coding scheme for OFDM with 64 QAM over AWGN channel. At the forward error correction unit, our proposed concatenated coding scheme employs standard form of BCH code as outer code and LDPC code as inner code. Varying from the code rates of BCH codes, we can find the minimum requirement of signal to noise ratio in the proposed concatenated coding scheme. In addition, our proposed scheme can yield better performance than that using BCH (7200, 7032) code in ETSI EN 302 775. Finally, we apply the H.264 source coding via our platform for demonstrations.


title: MIST: Multiple Instance Spatial Transformer Network

abstract: We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant top-K patches, and feeds these patches to a task-specific network -- e.g., auto-encoder or classifier -- to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurrent structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art.


title: Collocation least-squares polynomial chaos method

abstract: The polynomial chaos (PC) method has been used in many engineering applications to replace the traditional Monte Carlo (MC) approach for uncertainty quantification (UQ) due to its better convergence properties. Many researchers seek to further improve the efficiency of PC, especially in higher dimensional space with more uncertainties. The intrusive PC Galerkin approach requires the modification of the deterministic system, which leads to a stochastic system with a much bigger size. The non-intrusive collocation approach imposes the system to be satisfied at a set of collocation points to form and solve the linear system equations. Compared with the intrusive approach, the collocation method is easy to implement, however, choosing an optimal set of the collocation points is still an open problem. In this paper, we first propose using the low-discrepancy Hammersley/Halton dataset and Smolyak datasets as the collocation points, then propose a least-squares (LS) collocation approach to use more collocation points than the required minimum to solve for the system coefficients. We prove that the PC coefficients computed with the collocation LS approach converges to the optimal coefficients. The numerical tests on a simple 2-dimensional problem show that PC collocation LS results using the Hammersley/Halton points approach to optimal result.


title: Active learning for automatic classification of software behavior

abstract: A program's behavior is ultimately the collection of all its executions. This collection is diverse, unpredictable, and generally unbounded. Thus it is especially suited to statistical analysis and machine learning techniques. The primary focus of this paper is on the automatic classification of program behavior using execution data. Prior work on classifiers for software engineering adopts a classical batch-learning approach. In contrast, we explore an active-learning paradigm for behavior classification. In active learning, the classifier is trained incrementally on a series of labeled data elements. Secondly, we explore the thesis that certain features of program behavior are stochastic processes that exhibit the Markov property, and that the resultant Markov models of individual program executions can be automatically clustered into effective predictors of program behavior. We present a technique that models program executions as Markov models, and a clustering method for Markov models that aggregates multiple program executions into effective behavior classifiers. We evaluate an application of active learning to the efficient refinement of our classifiers by conducting three empirical studies that explore a scenario illustrating automated test plan augmentation.


title: On Training Targets for Supervised Speech Separation

abstract: Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation.


title: Readability assessment for text simplification: From analysing documents to identifying sentential simplifications

abstract: Readability assessment can play a role in the evaluation of a simplification algorithm as well as in the identification of what to simplify. While some previous research used traditional readability formulas to evaluate text simplification, there is little research into the utility of readability assessment for identifying and analyzing sentence level targets for text simplification. We explore this aspect in our paper by first constructing a readability model that is generalizable across corpora and across genres and later adapting this model to make sentence-level readability judgments.First, we report on experiments establishing that the readability model integrating a broad range of linguistic features works well at a document level, performing on par with the best systems on a standard test corpus. Next, the model is confirmed to be transferable to different text genres. Moving from documents to sentences, we investigate the model’s ability to correctly identify the difference in reading level between a sentence and its human simplified version. We conclude that readability models can be useful for identifying simplification targets for human writers and for evaluating machine generated simplifications.


title: A Bayesian Method for the Induction of Probabilistic Networks from Data

abstract: This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.