title: SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis

abstract: This paper describes our SeemGo system for the task of Aspect Based Sentiment Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditional Random Fields that obtains the F-score of 0.683 for Laptops and 0.791 for Restaurants by exploiting both word-based features and context features. The other three subtasks are solved by the Maximum Entropy model, with the occurrence counts of unigram and bigram words of each sentence as features. The subtask of aspect category detection obtains the best result when applying the Boosting method on the Maximum Entropy model, with the precision of 0.869 for Restaurants. The Maximum Entropy model also shows good performance in the subtasks of both aspect term and aspect category polarity classification.


GENDER IN TWITTER: STYLES, STANCES, AND SOCIAL NETWORKS


title: The future vision of simulation in health care

abstract: Simulation is a technique-not a technology-to replace or amplify real experiences with guided experiences that evoke or replicate substantial aspects of the real world in a fully interactive manner. The diverse applications of simulation in healthcare can be categorized by 11 dimensions: aims and purposes of the simulation activity; unit of participation; experience level of participants; healthcare domain; professional discipline of participants; type of knowledge, skill, attitudes, or behaviors addressed; the simulated patient's age; technology applicable or required; site of simulation; extent of direct participation; and method of feedback used. Using simulation to improve safety will require full integration of its applications into the routine structures and practices of healthcare. The costs and benefits of simulation are difficult to determine, especially for the most challenging applications, where long-term use may be required. Various driving forces and implementation mechanisms can be expected to propel simulation forward, including professional societies, liability insurers, healthcare payers, and ultimately the public. The future of simulation in healthcare depends on the commitment and ingenuity of the healthcare simulation community to see that improved patient safety using this tool becomes a reality.


title: Named Entity Recognition with Extremely Limited Data

abstract: Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity taggers themselves are typically trained on thousands or tens of thousands of examples labeled by humans. However, there is a long tail of named entities classes, and for these cases, labeled data may be impossible to find or justify financially. We propose exploring named entity recognition as a search task, where the named entity class of interest is a query, and entities of that class are the relevant"documents". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries.


title: Bayesian Gaussian Process Classification with the EM-EP Algorithm

abstract: Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically related to the value of some latent function at that location. Starting from a Gaussian process prior over this latent function, data are used to infer both the posterior over the latent function and the values of hyperparameters to determine various aspects of the function. Recently, the expectation propagation (EP) approach has been proposed to infer the posterior over the latent function. Based on this work, we present an approximate EM algorithm, the EM-EP algorithm, to learn both the latent function and the hyperparameters. This algorithm is found to converge in practice and provides an efficient Bayesian framework for learning hyperparameters of the kernel. A multiclass extension of the EM-EP algorithm for GPCs is also derived. In the experimental results, the EM-EP algorithms are as good or better than other methods for GPCs or support vector machines (SVMs) with cross-validation


title: Menu Positioning on Web Pages. Does it Matter

abstract: This paper concerns an investigation by the authors into the efficiency and user opinions of menu positioning in web pages. While the idea and use of menus on web pages is not new, the authors feel there is not enough empirical evidence to help designers choose an appropriate menu position. We therefore present the design and results of an empirical experiment, investigating the usability of menu positioning on web pages. A four condition experiment was conducted by the authors. Each condition tested a different menu position. The menu positions tested were left vertical, right vertical, top horizontal and bottom horizontal. The context was a fictitious online store. The results, based on statistical analysis and statistically significant findings, suggest that the top horizontal and left vertical positioned menus incurred fewer errors and fewer mouse clicks. Furthermore, the user satisfaction ratings were in line with the efficiency aspects observed.


title: AVP: Physics-informed Data Generation for Small-data Learning

abstract: Deep neural networks have achieved great success in multiple learning problems, and attracted increasing attention from the medicine community. In reality, however, the limited availability and high costs of medical data is a major challenge of applying deep neural networks to computer-aided diagnosis and treatment planning. We address this challenge with adaptive virtual patients (AVPs) and the associated physics-informed learning framework. Specifically, the original training dataset is fused with an additional dataset of AVPs, which are generated by a data-driven model and the associated supervision (e.g., labels) is obtained by a physics-based approach. A key novelty in the proposed framework is the bidirectional and uncoupled generative invertible networks (GIN), which can extract pathophysiological features from the training medical image and generate pathophysiologically meaningful virtual patients. In order to mitigate the possibly high labeling cost of physical experiments, a $\mu$-measure design is conducted: this allows the AVPs to not only further explore the uncertain regions, but also balance the label distribution. We then discuss the pathophysiological interpretability of GIN both theoretically and experimentally, and demonstrate the effectiveness of AVPs using a real medical image dataset, in which the proposed AVPs lower the labeling cost by 90% while achieving a 15% improvement in prediction accuracy.


title: Trust Dynamics: How Trust Is Influenced by Direct Experiences and by Trust Itself

abstract: In this paper we will examine two main aspects of trust dynamics: a) How direct experiences involving trust, with their successes or failures, influence the future trust of an agent about similar facts. We challenge the trivial idea that always success increases trust while failure decreases it. Of course, this primitive view cannot be avoided till Trust is modeled just as a simple index, a dimension, a number; for example reduced to mere subjective probability. We claim that a cognitive attribution process is needed in order to update trust on the basis of an 'interpretation' of the outcome of A's reliance on B and of B's performance (failure or success). b) How the fact that A trusts B and relies on it in situation Ω can actually (objectively) influence B's trustworthiness in the /Spl Omega/ situation. Either trust is a selffulfilling prophecy that modifies the subjective probability of the predicted event; or it is a self-defeating strategy by negatively influencing the events. These phenomena are very crucial in human societies (states, market, groups), but also in computer mediated Organizations, Interactions (EC), Cooperation (CSCW) and even in Multi-Agent Systems with autonomous agents. We present a formal model of these dynamic nontrivial aspects.


title: Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image

abstract: We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.


title: Identification of bioaccessible and uptaken phenolic compounds from strawberry fruits in in vitro digestion/Caco-2 absorption model.

abstract: Strawberry fruits are highly valued for their taste and nutritional value. However, results describing the bioaccessibility and intestinal absorption of phenolic compounds from strawberries are still scarce. In our study, a combined in vitro digestion/Caco-2 absorption model was used to mimic physiological conditions in the gastrointestinal track and identify compounds transported across intestinal epithelium. In the course of digestion, the loss of anthocyanins was noted whilst pelargonidin-3-glucoside remained the most abundant compound, amounting to nearly 12 mg per 100 g of digested strawberries. Digestion increased the amount of ellagic acid available by nearly 50%, probably due to decomposition of ellagitannins. Only trace amounts of pelargonidin-3-glucoside were found to be absorbed in the intestine model. Dihydrocoumaric acid sulphate and p-coumaric acid were identified as metabolites formed in enterocytes and released at the serosal side of the model.


Finding experts in community-based question-answering services


title: Mapping and Analysis of Crime in Aurangabad City using GIS

abstract: The study of Earth and its features with the help of Remote Sensing has lead to a fast growing and effective technology called Geographic Information System (GIS) where digital Maps and spatial data can be used for visualizing, analyzing and taking decision based on the analysis. Crime Analysis has become an integral part of GIS where the Analyst uses the spatial and temporal aspect of Crime information for analysis and forecasting. Using Hotspot analysis technique the Hotspot means the area where the concentration of Crime is more can be found. There are different classes of Hotspot detection such as Spatial Analysis, Interpolation and Spatial Autocorrelation for finding out the Crime Hotspot. In this paper Kernel Density Estimation, Inverse Distance Weighted and Getis-Ord Gi* methods from each classes respectively are discussed. These methods are applied on Aurangabad city of Maharashtra state, India for finding out the hotspots for crime incidence like Murder, Day House Break and Night House Break.


title: A meta-learning framework for algorithm recommendation in software fault prediction

abstract: Software fault prediction is a significant part of software quality assurance and it is commonly used to detect faulty software modules based on software measurement data. Several machine learning based approaches have been proposed for generating predictive models from collected data, although none has become standard given the specificities of each software project. Hence, we believe that recommending the best algorithm for each project is much more important and useful than developing a single algorithm for being used in any project. For achieving that goal, we propose in this paper a novel framework for recommending machine learning algorithms that is capable of automatically identifying the most suitable algorithm according to the software project that is being considered. Our solution, namely SFP-MLF, makes use of the meta-learning paradigm in order to learn the best learner for a particular project. Results show that the SFP-MLF framework provides both the best single algorithm recommendation and also the best ranking recommendation for the software fault prediction problem.


title: Smiles When Lying

abstract: Subtle differences among forms of smiling distinguished when subjects were truthful and when they lied about experiencing pleasant feelings. Expressions that included muscular activity around the eyes in addition to the smiling lips occurred more often when people were actually enjoying themselves as compared with when enjoyment was feigned to conceal negative emotions. Smiles that included traces of muscular actions associated with disgust, fear, contempt, or sadness occurred more often when subjects were trying to mask negative emotions with a happy mask. When these differences among types of smiling were ignored and smiling was treated as a unitary phenomenon, there was no difference between truthful and deceptive behavior.


title: Monocular Extraction of 2.1D Sketch Using Constrained Convex Optimization

abstract: This paper presents an approach to estimating the 2.1D sketch from monocular, low-level visual cues. We use a low-level segmenter to partition the image into regions, and, then, estimate their 2.1D sketch, subject to figure-ground and similarity constraints between neighboring regions. The 2.1D sketch assigns a depth ordering to image regions which are expected to correspond to objects and surfaces in the scene. This is cast as a constrained convex optimization problem, and solved within the optimization transfer framework. The optimization objective takes into account the curvature and convexity of parts of region boundaries, appearance, and spatial layout properties of regions. Our new optimization transfer algorithm admits a closed-form expression of the duality gap, and thus allows explicit computation of the achieved accuracy. The algorithm is efficient with quadratic complexity in the number of constraints between image regions. Quantitative and qualitative results on challenging, real-world images of Berkeley segmentation, Geometric Context, and Stanford Make3D datasets demonstrate our high accuracy, efficiency, and robustness.


title: The residual cognitive effects of heavy marijuana use in college students.

abstract: OBJECTIVE ::: To assess whether frequent marijuana use is associated with residual neuropsychological effects. ::: ::: ::: DESIGN ::: Single-blind comparison of regular users vs infrequent users of marijuana. ::: ::: ::: PARTICIPANTS ::: Two samples of college undergraduates: 65 heavy users, who had smoked marijuana a median of 29 days in the last 30 days (range, 22 to 30 days) and who also displayed cannabinoids in their urine, and 64 light users, who had smoked a median of 1 day in the last 30 days (range, 0 to 9 days) and who displayed no urinary cannabinoids. ::: ::: ::: INTERVENTION ::: Subjects arrived at 2 PM on day 1 of their study visit, then remained at our center overnight under supervision. Neuropsychological tests were administered to all subjects starting at 9 AM on day 2. Thus, all subjects were abstinent from marijuana and other drugs for a minimum of 19 hours before testing. ::: ::: ::: MAIN OUTCOME MEASURES ::: Subjects received a battery of standard neuropsychological tests to assess general intellectual functioning, abstraction ability, sustained attention, verbal fluency, and ability to learn and recall new verbal and visuospatial information. ::: ::: ::: RESULTS ::: Heavy users displayed significantly greater impairment than light users on attention/executive functions, as evidenced particularly by greater perseverations on card sorting and reduced learning of word lists. These differences remained after controlling for potential confounding variables, such as estimated levels of premorbid cognitive functioning, and for use of alcohol and other substances in the two groups. ::: ::: ::: CONCLUSIONS ::: Heavy marijuana use is associated with residual neuropsychological effects even after a day of supervised abstinence from the drug. However, the question remains open as to whether this impairment is due to a residue of drug in the brain, a withdrawal effect from the drug, or a frank neurotoxic effect of the drug. from marijuana


title: Probabilistic online POMDP decision making for lane changes in fully automated driving

abstract: The Stadtpilot project aims at fully automated driving on Braunschweig's inner city ring road. The TU Braunschweig's research vehicle “Leonie” is one of the first vehicles having the ability of fully automated driving in real urban traffic scenarios. This paper shows our decision making approach for performing lane changes while driving fully automated in urban environments. We apply an online Partially Observable Markov Decision Process (POMDP) to accommodate inevitable sensor noise to be faced in urban traffic scenarios. In this paper we propose a two step algorithm to keep the complexity of the POMDP low enough for real-time decision making while driving. The presented approach has been integrated in our vehicle and was evaluated in real urban traffic.


title: Duality of Graphical Models and Tensor Networks

abstract: In this article we show the duality between tensor networks and undirected graphical models with discrete variables. We study tensor networks on hypergraphs, which we call tensor hypernetworks. We show that the tensor hypernetwork on a hypergraph exactly corresponds to the graphical model given by the dual hypergraph. We translate various notions under duality. For example, marginalization in a graphical model is dual to contraction in the tensor network. Algorithms also translate under duality. We show that belief propagation corresponds to a known algorithm for tensor network contraction. This article is a reminder that the research areas of graphical models and tensor networks can benefit from interaction.


title: A Permanent-Magnet Linear Motion Driven Kinetic Energy Harvester

abstract: This manuscript presents a double-sided flat-type permanent magnetic linear energy harvester to scavenge kinetic power from linear motions. The dynamic system model of the linear generator is analytically driven and analyzed by an innovative state-based approach. The analytical equations on maximum power generation specific for non-resonant applications such as human foot motion are derived. Both magnetic circuit analysis and the finite-element analysis simulation are carried out on the linear machine to investigate its performance. Under the typical horizontal foot motion velocity of 4.5 m/s, the proposed energy harvester generates 674 mW electric power with the power density as high as 3.4 mW/cm3. A macro-scale proof-of-concept machine is prototyped to verify the performance of the linear machine and validate the analysis, simulation, and the proposed modeling approach.


title: Characterizing and classifying IoT traffic in smart cities and campuses

abstract: Campuses and cities of the near future will be equipped with vast numbers of IoT devices. Operators of such environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This paper proposes the use of network traffic analytics to characterize IoT devices, including their typical behaviour mode. We first collect and synthesize traffic traces from a smart-campus environment instrumented with a diversity of IoT devices including cameras, lights, appliances, and health-monitors; our traces, collected over a period of 3 weeks, are released as open data to the public. We then analyze the traffic traces to characterize statistical attributes such as data rates and burstiness, activity cycles, and signalling patterns, for over 20 IoT devices deployed in our environment. Finally, using these attributes, we develop a classification method that can not only distinguish IoT from non-IoT traffic, but also identify specific IoT devices with over 95% accuracy. Our study empowers operators of smart cities and campuses to discover and monitor their IoT assets based on their network behaviour.


title: Massive point cloud data management : Design , implementation and execution of a point cloud benchmark

abstract: Point cloud data are important sources for 3D geo-information. An inventory of the point cloud data management user requirements has been compiled using structured interviews with users from different background: government, industry and academia. Based on these requirements a benchmark has been developed to compare various point cloud data management solutions with regard to functionality and performance. The main test dataset is the second national height map of the Netherlands, AHN2, with 6-10 samples for every square meter of the country, resulting in 640 billion points. At the database level, a data storage model based on grouping the points in blocks is available in Oracle and PostgreSQL. This model is compared with the 'flat table' model, where each point is stored in a table row, in Oracle, PostgreSQL and the column-store MonetDB. In addition, the commonly used file-based solution Rapidlasso LAStools is used for comparison with the database solutions. The results of executing the benchmark on different platforms are presented as obtained during the increasingly challenging stages with more functionality and more data: mini (20 million points), medium (20 billion points), and full benchmark (the complete AHN2).During the design, the implementation and the execution of the benchmarks, a number of point cloud data management improvements were proposed and partly tested: Morton/Hilbert code for ordering data (especially in flat model), two algorithms for parallel query execution, and a unique vario-scale LoD data organization avoiding the density jumps of the well-known discrete LoD data organizations. Display Omitted HighlightsDesign of point cloud benchmark based on requirements from different groups of users within government, industry and academia.Analysing various data management systems: PostgreSQL, MonetDB, Oracle, and LAStools.New techniques for point cloud management: Morton code and Morton-ranges, algorithms for parallel query, and vario-LoD organization.


title: Taming Parallelism in a Multi-Variant Execution Environment

abstract: Exploit mitigations, by themselves, do not stop determined and well-resourced adversaries from compromising vulnerable software through memory corruption. Multi-variant execution environments (MVEEs) add additional assurance by executing multiple, diversified copies (variants) of the same program in lockstep while monitoring their behavior for signs of attacks (divergence). While executing multiple copies of the same program requires additional computational resources, modern MVEEs run many workloads at near-native speed and can detect adversaries before they leak secrets or achieve persistence on the host system. Multi-threaded programs are challenging to execute in lockstep by an MVEE. If the threads in a set of variants are not scheduled in the exact same order, the variants will diverge from each other in terms of the system calls they make. While benign, such divergence undermines the MVEEs ability detect divergence caused by malicious program inputs. To address this problem, we developed an MVEE-specific synchronization scheme that lets us execute a set of multithreaded variants in lockstep without causing benign divergence. Our fully-fledged MVEE runs the PARSEC 2.1 and SPLASH-2x parallel benchmarks (with four worker threads per variant) with a slowdown of less than 15% relative to unprotected execution. Addressing this longstanding compatibility issue makes MVEEs a viable defense for a far greater range of realistic workloads.


title: Clothed and naked human shapes estimation from a single image

abstract: This paper presents a novel method to simultaneously estimate the clothed and naked 3D shapes of a person. The method needs only a single photograph of a person wearing clothing. Firstly, we learn a deformable model of human clothed body shapes from a database. Then, given an input image, the deformable model is initialized with a few user-specified 2D joints and contours of the person. And the correspondence between 3D shape and 2D contours is established automatically. Finally, we optimize the parameters of the deformable model in an iterative way, and then obtain the clothed and naked 3D shapes of the person simultaneously. The experimental results on real images demonstrate the effectiveness of our method.


title: Correlated discrete data generation using adversarial training

abstract: Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach are presented over two datasets; job-seeking candidates skill set (private dataset) and MNIST (public dataset). From quantitative and qualitative analysis of these results, we show that our model performs better as it leverages inherent correlation in the data, than an existing model that overlooks correlation.


title: 3D-printing of non-assembly, articulated models

abstract: Additive manufacturing (3D printing) is commonly used to produce physical models for a wide variety of applications, from archaeology to design. While static models are directly supported, it is desirable to also be able to print models with functional articulations, such as a hand with joints and knuckles, without the need for manual assembly of joint components. Apart from having to address limitations inherent to the printing process, this poses a particular challenge for articulated models that should be posable: to allow the model to hold a pose, joints need to exhibit internal friction to withstand gravity, without their parts fusing during 3D printing. This has not been possible with previous printable joint designs. In this paper, we propose a method for converting 3D models into printable, functional, non-assembly models with internal friction. To this end, we have designed an intuitive work-flow that takes an appropriately rigged 3D model, automatically fits novel 3D-printable and posable joints, and provides an interface for specifying rotational constraints. We show a number of results for different articulated models, demonstrating the effectiveness of our method.


title: A Dataset for Quality Assessment of Camera Captured Document Images

abstract: With the proliferation of cameras on mobile devices there is an increased desire to image document pages as an alternative to scanning. However, the quality of captured document images is often lower than its scanned equivalent due to hardware limitations and stability issues. In this context, automatic assessment of the quality of captured images is useful for many applications. Although there has been a lot of work on developing computational methods and creating standard datasets for natural scene image quality assessment, until recently quality estimation of camera captured document images has not been given much attention. One traditional quality indicator for document images is the Optical Character Recognition (OCR) accuracy. In this work, we present a dataset of camera captured document images containing varying levels of focal-blur introduced manually during capture. For each image we obtained the character level OCR accuracy. Our dataset can be used to evaluate methods for predicting OCR quality of captured documents as well as enhancements. In order to make the dataset publicly and freely available, originals from two existing datasets - University of Washington dataset and Tobacco Database were selected. We present a case study with three recent methods for predicting the OCR quality of images on our dataset.


title: State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing

abstract: The Internet of Things (IoT) is a paradigm based on the Internet that comprises many interconnected technologies like RFID (Radio Frequency IDentification) and WSAN (Wireless Sensor and Actor Networks) in order to exchange information. The current needs for better control, monitoring and management in many areas, and the ongoing research in this field, have originated the appearance and creation of multiple systems like smart-home, smart-city and smart-grid. However, the limitations of associated devices in the IoT in terms of storage, network and computing, and the requirements of complex analysis, scalability, and data access, require a technology like Cloud Computing to supplement this field. Moreover, the IoT can generate large amounts of varied data and quickly when there are millions of things feeding data to Cloud Computing. The latter is a clear example of Big Data, that Cloud Computing needs to take into account. This paper presents a survey of integration components: Cloud platforms, Cloud infrastructures and IoT Middleware. In addition, some integration proposals and data analytics techniques are surveyed as well as different challenges and open research issues are pointed out.


title: A DC-2.5GHz wide dynamic-range attenuator in 0.13/spl mu/m CMOS technology

abstract: A CMOS attenuator with wide dynamic range has been designed and fabricated in a 0.13/spl mu/m CMOS process. The design employs two nonidentical cascaded T-stages that are activated consecutively to improve linearity. The design operates in the frequency band of DC-2.5GHz with 0.9-3.5dB insertion loss and 42dB maximum attenuation in the entire frequency range. Worst case SII is -8.2dB across the frequency band. The design achieves an IIP3 of +20 dBm at mid-attenuation.


title: Aromatherapy Facts and Fictions: A Scientific Analysis of Olfactory Effects on Mood, Physiology and Behavior

abstract: A systematic review of scientific experimentation addressing olfactory effects on mood, physiology and behavior was undertaken. From this review, 18 studies meeting stringent empirical criteria were then analyzed in detail and it was found that credible evidence that odors can affect mood, physiology and behavior exists. To explain these effects, pharmacological and psychological mechanisms were explored and a psychological interpretation of the data was found to be more comprehensive. Methodological problems regarding dependent measures and stimuli, which led to inconsistencies in the data were discussed, as were the mediating variables of culture, experience, sex differences, and personality.


title: Learning Single-Image Depth From Videos Using Quality Assessment Networks

abstract: Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild.


title: Powering the internet of things

abstract: Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. Further, many of them will have stringent size constraints. With no cord for power and limited space for a battery, powering these devices (to achieve several months to possibly years of unattended operation) becomes a daunting challenge. This paper highlights some promising directions for addressing this challenge, focusing on three main building blocks: (a) the design of ultra-low power hardware platforms that integrate computing, sensing, storage, and wireless connectivity in a tiny form factor, (b) the development of intelligent system-level power management techniques, and (c) the use of environmental energy harvesting to make IoT devices self-powered, thus decreasing -- in some cases, even eliminating -- their dependence on batteries. We discuss these building blocks in detail and illustrate case-studies of systems that use them judiciously, including the QUBE wireless embedded platform, which exploits the characteristics of emerging non-volatile memory technologies to seamlessly and efficiently enable long-running computations in systems that experience frequent power loss (i.e., intermittently powered systems).


title: Space vector modulation strategy based on volt-second balancing for three-level neutral point clamped inverter

abstract: In this paper, a methodology to apply space vector modulation (SVM) to a three-level neutral point clamped (NPC) inverter based on volt-second balancing principle is presented. The proposed scheme, switching-state selection of the three-level NPC inverter by using a nearest three vector scheme, is fully described. A mathematical analysis of the duty cycle is determined by utilizing the calculated switching time method in order to generate the duty cycle signals for both the continuous SVM (CSVM) and the discontinuous SVM (DSVM) conditions. Compared with the CSVM scheme, the DSVM scheme is the reduction in the number of commutations of switches by one third which leads to a further reduction in switching losses. Simulated results are presented to validate the proposal, which demonstrates good steady-state and dynamic performance.


title: High resolution radar tomographic imaging using single-tone CW signals

abstract: Radar tomographic imaging is a special radar imaging technique which can achieve very high spatial resolution (up to ¼ wavelength) but with very narrow signal bandwidth. Although the theory of radar tomography had been developed for more than 20 years, very few experimental results can be found in public literatures. In this paper, the radar tomographic imaging is investigated and some measurement results of radar tomographic imaging using single-tone CW signals are presented. The theoretical spatial resolution is achieved and good agreement is obtained between the measured and simulated results.


title: Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

abstract: Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.


title: Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification

abstract: Video-based person re-identification matches video clips of people across non-overlapping cameras. Most existing methods tackle this problem by encoding each video frame in its entirety and computing an aggregate representation across all frames. In practice, people are often partially occluded, which can corrupt the extracted features. Instead, we propose a new spatiotemporal attention model that automatically discovers a diverse set of distinctive body parts. This allows useful information to be extracted from all frames without succumbing to occlusions and misalignments. The network learns multiple spatial attention models and employs a diversity regularization term to ensure multiple models do not discover the same body part. Features extracted from local image regions are organized by spatial attention model and are combined using temporal attention. As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence. Extensive evaluations on three datasets show that our framework outperforms the state-of-the-art approaches by large margins on multiple metrics.


title: Constraints in the IoT: The World in 2020 and Beyond

abstract: The Internet of Things (IoT), often referred as the future Internet; is a collection of interconnected devices integrated into the world-wide network that covers almost everything and could be available anywhere. IoT is an emerging technology and aims to play an important role in saving money, conserving energy, eliminating gap and better monitoring for intensive management on a routine basis. On the other hand, it is also facing certain design constraints such as technical challenges, social challenges, compromising privacy and performance tradeoffs. This paper surveys major technical limitations that are hindering the successful deployment of the IoT such as standardization, interoperability, networking issues, addressing and sensing issues, power and storage restrictions, privacy and security, etc. This paper categorizes the existing research on the technical constraints that have been published in the recent years. With this categorization, we aim to provide an easy and concise view of the technical aspects of the IoT. Furthermore, we forecast the changes influenced by the IoT. This paper predicts the future and provides an estimation of the world in year 2020 and beyond.


title: doi:10.1155/2010/263593 Review Article Emotion on the Road—Necessity, Acceptance, and Feasibility of Affective Computing in the Car

abstract: Besides reduction of energy consumption, which implies alternate actuation and light construction, the main research domain in automobile development in the near future is dominated by driver assistance and natural driver-car communication. The ability of a car to understand natural speech and provide a human-like driver assistance system can be expected to be a factor decisive for market success on par with automatic driving systems. Emotional factors and affective states are thereby crucial for enhanced safety and comfort. This paper gives an extensive literature overview on work related to influence of emotions on driving safety and comfort, automatic recognition, control of emotions, and improvement of in-car interfaces by affect sensitive technology. Various use-case scenarios are outlined as possible applications for emotion-oriented technology in the vehicle. The possible acceptance of such future technology by drivers is assessed in a Wizard-Of-Oz user study, and feasibility of automatically recognising various driver states is demonstrated by an example system for monitoring driver attentiveness. Thereby an accuracy of 91.3% is reported for classifying in real-time whether the driver is attentive or distracted.


title: A Secure Big Data Stream Analytics Framework for Disaster Management on the Cloud

abstract: Cloud computing and big data analysis are gaining lots of interest across a range of applications including disaster management. These two technologies together provide the capability of real-time data analysis not only to detect emergencies in disaster areas, but also to rescue the affected people. This paper presents a framework that supports emergency event detection and alert generation by analyzing the data stream, which includes efficient data collection, data aggregation and alert dissemination. One of the goals for such a framework is to support an end-to-end security architecture to protect the data stream from unauthorized manipulation as well as leakage of sensitive information. The proposed system provides support for both data security punctuation and query security punctuation. This paper presents the proposed architecture with a specific focus on data stream security. It also briefly describes the implementation of security aspects of the architecture.


title: Extending Bagging for Imbalanced Data

abstract: Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside its neighbourhood. Experiments indicate that this proposal is competitive to best undersampling bagging extensions.


title: An Investigation of Generative Design for Heating, Ventilation, and Air-Conditioning

abstract: Energy consumption in buildings contributes to 41% of global carbon dioxide emissions through electricity and heat production, making the design of mechanical systems in buildings of paramount importance. Industry practice for design of mechanical systems is currently limited in the conceptual design phase, often leading to sub-optimal designs. By using Generative Design (GD), many design options can be created, optimized and evaluated, based on system energy consumption and life-cycle cost (LCC). By combining GD for Architecture with GD for HVAC, two areas of building design can be analyzed and optimized simultaneously, resulting in novel designs with improved energy performance. This paper presents GD for HVAC, a Matlab script developed to create improved zone level mechanical systems for improved energy efficiency. Through experiments, GD methodologies are explored and their applicability and effect on building HVAC design is evaluated.


title: Minimum Bayes Risk decoding and system combination based on a recursion for edit distance

abstract: Abstract: In this paper we describe a method that can be used for Minimum Bayes Risk (MBR) decoding for speech recognition. Our algorithm can take as input either a single lattice, or multiple lattices for system combination. It has similar functionality to the widely used Consensus method, but has a clearer theoretical basis and appears to give better results both for MBR decoding and system combination. Many different approximations have been described to solve the MBR decoding problem, which is very difficult from an optimization point of view. Our proposed method solves the problem through a novel forward-backward recursion on the lattice, not requiring time markings. We prove that our algorithm iteratively improves a bound on the Bayes risk.


title: Coherent MIMO radar: The phased array and orthogonal waveforms

abstract: Coherent multiple-input multiple-output (MIMO) radar is a natural extension of the phased array antenna that has been used by radar systems for decades. This tutorial unifies concepts from the literature and provides a framework for the analysis of an arbitrary suite of MIMO radar waveforms. A number of gain patterns are introduced, which quantify the antenna performance of a MIMO radar, and the impact of the waveform characteristics (e.g., range sidelobes) is discussed.


title: Extracting and Analyzing Hidden Graphs from Relational Databases

abstract: Analyzing interconnection structures among underlying entities or objects in a dataset through the use of graph analytics can provide tremendous value in many application domains. However, graphs are not the primary representation choice for storing most data today, and in order to have access to these analyses, users are forced to manually extract data from their data stores, construct the requisite graphs, and then load them into some graph engine in order to execute their graph analysis task. Moreover, in many cases (especially when the graphs are dense), these graphs can be significantly larger than the initial input stored in the database, making it infeasible to construct or analyze such graphs in memory. In this paper we address both of these challenges by building a system that enables users to declaratively specify graph extraction tasks over a relational database schema and then execute graph algorithms on the extracted graphs. We propose a declarative domain specific language for this purpose, and pair it up with a novel condensed, in-memory representation that significantly reduces the memory footprint of these graphs, permitting analysis of larger-than-memory graphs. We present a general algorithm for creating such a condensed representation for a large class of graph extraction queries against arbitrary schemas. We observe that the condensed representation suffers from a duplication issue, that results in inaccuracies for most graph algorithms. We then present a suite of in-memory representations that handle this duplication in different ways and allow trading off the memory required and the computational cost for executing different graph algorithms. We also introduce several novel deduplication algorithms for removing this duplication in the graph, which are of independent interest for graph compression, and provide a comprehensive experimental evaluation over several real-world and synthetic datasets illustrating these trade-offs.


title: Symbolic execution for software testing in practice: preliminary assessment

abstract: We present results for the "Impact Project Focus Area" on the topic of symbolic execution as used in software testing. Symbolic execution is a program analysis technique introduced in the 70s that has received renewed interest in recent years, due to algorithmic advances and increased availability of computational power and constraint solving technology. We review classical symbolic execution and some modern extensions such as generalized symbolic execution and dynamic test generation. We also give a preliminary assessment of the use in academia, research labs, and industry.


title: Hyperledger fabric: a distributed operating system for permissioned blockchains

abstract: Hyperledger Fabric is a modular and extensible open-source system for deploying and operating permissioned blockchains. Fabric is currently used in more than 400 prototypes and proofs-of-concept of distributed ledger technology, as well as several production systems, across different industries and use cases. Starting from the premise that there are no"one-size-fits-all"solutions, Fabric is the first truly extensible blockchain system for running distributed applications. It supports modular consensus protocols, which allows the system to be tailored to particular use cases and trust models. Fabric is also the first blockchain system that runs distributed applications written in general-purpose programming languages, without systemic dependency on a native cryptocurrency. This stands in sharp contrast to existing blockchain platforms for running smart contracts that require code to be written in domain-specific languages or rely on a cryptocurrency. Furthermore, it uses a portable notion of membership for realizing the permissioned model, which may be integrated with industry-standard identity management. To support such flexibility, Fabric takes a novel approach to the design of a permissioned blockchain and revamps the way blockchains cope with non-determinism, resource exhaustion, and performance attacks. This paper describes Fabric, its architecture, the rationale behind various design decisions, its security model and guarantees, its most prominent implementation aspects, as well as its distributed application programming model. We further evaluate Fabric by implementing and benchmarking a Bitcoin-inspired digital currency. We show that Fabric achieves end-to-end throughput of more than 3500 transactions per second in certain popular deployment configurations, with sub-second latency.


Voice Activation Control with Digital Assistant for Humanoid Robot Torso


Time-of-flight Optical Ranging Sensor Based on a Current Assisted Photonic Demodulator


title: Designing resilient system-of-systems networks

abstract: The networked nature of many system-of-systems (SoS) requires consideration of resilience in their design and analysis. We present an approach for the design space exploration of such resilient SoS networks. We model candidate network designs using complex network methods, and generate and analyze a network design space with Design of Experiments. Results show an interaction between adaptation strategy and threat type, indicating that the best adaptation strongly depends on the threat faced. However, no interaction is seen with initial topology, suggesting that random initial topologies provide more resilience than scale-free ones regardless of the threat or adaptation strategy.


Intracranial Pressure, Mean Arterial Pressure, and Heart Rate Following Midazolam or Thiopental in Humans with Brain Tumors


title: Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction

abstract: Detection of moving objects in the presence of complex scenes such as dynamic background (e.g, swaying vegetation, ripples in water, spouting fountain), illumination variation, and camouflage is a very challenging task. In this context, we propose a robust background subtraction technique with three contributions. First, we present the use of color difference histogram (CDH) in the background subtraction algorithm. This is done by measuring the color difference between a pixel and its neighbors in a small local neighborhood. The use of CDH reduces the number of false errors due to the non-stationary background, illumination variation and camouflage. Secondly, the color difference is fuzzified with a Gaussian membership function. Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering algorithm in CDH reduces the large dimensionality of the histogram bins in the computation and also lessens the effect of intensity variation generated due to the fake motion or change in illumination of the background. The proposed algorithm is tested with various complex scenes of some benchmark publicly available video sequences. It exhibits better performance over the state-of-the-art background subtraction techniques available in the literature in terms of classification accuracy metrics like $MCC$ and $PCC$ .


title: The Most Dangerous Place: Pro-Life Politics and the Rhetoric of Slavery

abstract: In recent years, comparisons between abortion and slavery have become increasingly common in American pro-life politics. Some have compared the struggle to extinguish abortion rights to the struggle to end slavery. Others have claimed that Roe v Wade is the Dred Scott of our time. Still others have argued that abortion is worse than slavery; it is a form of genocide. This paper tracks the abortion = slavery meme from Ronald Reagan to the current personhood movement, drawing on work by Orlando Patterson, Hortense Spillers, and Saidiya Hartman to develop a discourse of reproductive justice that grapples with the wounded kinship of slavery and racism.


title: Audiovisual Speech Source Separation: An overview of key methodologies

abstract: The separation of speech signals measured at multiple microphones in noisy and reverberant environments using only the audio modality has limitations because there is generally insufficient information to fully discriminate the different sound sources. Humans mitigate this problem by exploiting the visual modality, which is insensitive to background noise and can provide contextual information about the audio scene. This advantage has inspired the creation of the new field of audiovisual (AV) speech source separation that targets exploiting visual modality alongside the microphone measurements in a machine. Success in this emerging field will expand the application of voice-based machine interfaces, such as Siri, the intelligent personal assistant on the iPhone and iPad, to much more realistic settings and thereby provide more natural human?machine interfaces.


title: Determinism and Total Explanation in the Biological and Behavioral Sciences

abstract: Should we think of our universe as law-governed and ‘clockwork’-like or as disorderly and ‘soup’-like? Alternatively, should we consciously and intentionally synthesise these two extreme pictures? More concretely, how deterministic are the postulated causes and how rigid are the modelled properties of the best statistical methodologies used in the biological and behavioural sciences? The charge of this entry is to explore thinking about causation in the temporal evolution of biological and behavioural systems. Regression analysis and path analysis are simply explicated with reference to a thought experiment of painting three universes (clockwork, soup and conscious) useful for imagining our actual universe. Attention to historical, structural and mechanistic explanatory perspectives broadens the palette of methodologies available for analysing determinism in, and explanation of, biological and behavioural systems. Each justified methodology provides a partial perspective on complex reality. Is a total explanation of any system ever possible and what would it require? ::: ::: ::: ::: Key Concepts: ::: ::: ::: ::: A thought experiment delineating three universes – clockwork, soup, and conscious – assists in understanding statistical methodologies. ::: ::: ::: ::: ::: Neither extreme rigid determinism nor radical chancy indeterminism permit accurate depictions of a ‘Laplacian chance’, conscious universe. ::: ::: ::: ::: ::: Standard statistical methodologies related to the General Linear Model (GLM), such as regression analysis and path analysis, synthesise determinism and indeterminism in important ways (e.g. via error terms). ::: ::: ::: ::: ::: History of science illustrates the plurality of scientific methods. ::: ::: ::: ::: ::: Inferring causes using statistical analysis is not impossible. ::: ::: ::: ::: ::: Indeed, randomised experiments and rigorous statistical methodologies can licence causal claims. ::: ::: ::: ::: ::: An analogy from the physical sciences, displacement of springs, provides a running example for the variety of questions asked of system behaviour from statistical, historical, structural, and mechanistic perspectives. ::: ::: ::: ::: ::: Historical explanation and mechanistic explanation remain autonomous explanatory perspectives interested, respectively, in the origin and part-whole structure of a biological or behavioural system. ::: ::: ::: ::: ::: The general methodological analyses here presented should be complemented with more concrete case studies of, for example, sexuality, intelligence, autism, and ‘criminality’. ::: ::: ::: ::: ::: Are explanations from distinct explanatory perspectives ‘miscible’ or do we reach an ‘impasse when we attempt to integrate and mix them? ::: ::: ::: ::: ::: ::: ::: Keywords: ::: ::: models; ::: causes; ::: explanation; ::: statistics; ::: regression analysis; ::: path analysis; ::: history; ::: mechanism; ::: scientific methods; ::: genetics


title: Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics

abstract: Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact the performance of such an architecture. This paper focuses on detecting those adversarial examples by analyzing whether they come from the same distribution as the normal examples. Instead of directly training a deep neural network to detect adversarials, a much simpler approach was proposed based on statistics on outputs from convolutional layers. A cascade classifier was designed to efficiently detect adversarials. Furthermore, trained from one particular adversarial generating mechanism, the resulting classifier can successfully detect adversarials from a completely different mechanism as well. The resulting classifier is non-subdifferentiable, hence creates a difficulty for adversaries to attack by using the gradient of the classifier. After detecting adversarial examples, we show that many of them can be recovered by simply performing a small average filter on the image. Those findings should lead to more insights about the classification mechanisms in deep convolutional neural networks.


title: An Alternative Proof of The Schwartz-Zippel Lemma.

abstract: We show an alternative proof of the Schwartz-Zippel lemma. The lemma bounds the number of zeros of a multivariate low degree polynomial over a finite field.


title: Diagonal based feature extraction for handwritten character recognition system using neural network

abstract: An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.


title: Group decision making under multiple criteria

abstract: Note: Bibliogr. : p. 355-400 Reference Record created on 2004-09-07, modified on 2016-08-08


title: Increasing Cognitive Load to Facilitate Lie Detection: The Benefit of Recalling an Event in Reverse Order

abstract: In two experiments, we tested the hypotheses that (a) the difference between liars and truth tellers will be greater when interviewees report their stories in reverse order than in chronological order, and (b) instructing interviewees to recall their stories in reverse order will facilitate detecting deception. In Experiment 1, 80 mock suspects told the truth or lied about a staged event and did or did not report their stories in reverse order. The reverse order interviews contained many more cues to deceit than the control interviews. In Experiment 2, 55 police officers watched a selection of the videotaped interviews of Experiment 1 and made veracity judgements. Requesting suspects to convey their stories in reverse order improved police observers’ ability to detect deception and did not result in a response bias.


title: Detecting Web Attacks Using Multi-stage Log Analysis

abstract: Web-based applications have gained universal acceptance in every sector of lives, including social, commercial, government, and academic communities. Even with the recent emergence of cloud technology, most of cloud applications are accessed and controlled through web interfaces. Web security has therefore continued to be fundamentally important and extremely challenging. One major security issue of web applications is SQL-injection attacks. Most existing solutions for detecting these attacks use log analysis, and employ either pattern matching or machine learning methods. Pattern matching methods can be effective, dynamic, they however cannot detect new kinds of attacks. Supervised machine learning methods can detect new attacks, yet they need to rely on an off-line training phase. This work proposes a multi-stage log analysis architecture, which combines both pattern matching and supervised machine learning methods. It uses logs generated by the application during attacks to effectively detect attacks and to help preventing future attacks. The architecture is described in detail, a proof-of-concept prototype is implemented and hosted on Amazon AWS, using Kibana for pattern matching and Bayes Net for machine learning. It is evaluated on 10,000 logs for detecting SQL injection attacks. Experiment results show that the two-stage system has combined the advantages of both systems, and has substantially improved the detection accuracy. The proposed work is significant in advancing web securities, while the multi-stage log analysis concept would be highly applicable to many intrusion detection applications.


title: Spectral embedding of graphs

abstract: In this paper we explore how to embed symbolic relational graphs with unweighted edges in a pattern-space. We adopt a graph-spectral approach. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties. These include the eigenmode perimeter, eigenmode volume, Cheeger number, inter-mode adjacency matrices and intermode edge-distance. We embed these vectors in a pattern-space using two contrasting approaches. The first of these involves performing principal or independent components analysis on the covariance matrix for the spectral pattern vectors. The second approach involves performing multidimensional scaling on the L2 norm for pairs of pattern vectors. We illustrate the utility of the embedding methods on neighbourhood graphs representing the arrangement of corner features in 2D images of 3D polyhedral objects. Two problems are investigated. The first of these is the clustering of graphs representing distinct objects viewed from different directions. The second is the identification of characteristic views of single objects. These two studies reveal that both embedding methods result in well-structured view spaces for graph-data extracted from 2D views of 3D objects.


title: Detecting crypto-ransomware in IoT networks based on energy consumption footprint

abstract: An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed approach outperforms K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure.


title: Autonomous Autorotation of an RC Helicopter

abstract: In case of engine failure, skilled pilots can save a helicopter from crashing by executing an emergency procedure known as autorotation. In autorotation, rather than relying on the engine to drive the main rotor, the pilot has to control the helicopter such that potential energy from altitude is transferred to rotor speed. In fact, maintaining a sufficiently high rotor speed is critical to retain sufficient control of the helicopter to land safely. In this paper, we present the first autonomous controller to successfully pilot a remotely controlled (RC) helicopter during an autorotation descent and landing.


title: Convolutional Social Pooling for Vehicle Trajectory Prediction

abstract: Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic context, i.e., the motion and relative spatial configuration of neighboring vehicles. In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. We evaluate our model using the publicly available NGSIM US-101 and I-80 datasets. Our results show improvement over the state of the art in terms of RMS values of prediction error and negative log-likelihoods of true future trajectories under the model's predictive distribution. We also present a qualitative analysis of the model's predicted distributions for various traffic scenarios.


title: SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines

abstract: Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with weighted finite-state automata (WFSAs) to learn a soft version of traditional surface patterns. We show that SoPa is an extension of a one-layer CNN, and that such CNNs are equivalent to a restricted version of SoPa, and accordingly, to a restricted form of WFSA. Empirically, on three text classification tasks, SoPa is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline, and is particularly useful in small data settings.


title: Brain Age from the Electroencephalogram of Sleep

abstract: Abstract The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as “brain age (BA),” which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18–80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40–80). The model obtains a mean absolute deviation of 7.6 years between BA and chronological age (CA) in healthy participants in the MGH data set. As validation, a subset of SHHS containing longitudinal EEGs 5.2 years apart shows an average of 5.4 years increase in BA. Participants with significant neurological or psychiatric disease exhibit a mean excess BA, or “brain age index” (BAI = BA-CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess BA of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging.


title: Mimicking Word Embeddings using Subword RNNs

abstract: Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised character-based model in low-resource settings.


title: Spontaneous regression of a large calcified thoracic disk extrusion

abstract: Although spontaneous regression of herniated disk material has been reported in the lumbar and cervical spine, reports of complete spontaneous regression of calcified thoracic disk herniations are exceedingly rare. In symptomatic patients, surgery is typically the treatment of choice; however, conservative therapy with surveillance may allow time for spontaneous resolution and potentially avoid high-risk surgery. We report a 40-year-old woman with complete spontaneous resolution of a large calcified thoracic disk extrusion with conservative management over an 8-month period.


title: Multiparty computation from somewhat homomorphic encryption

abstract: We propose a general multiparty computation protocol secure against an active adversary corrupting up to $$n-1$$ of the n players. The protocol may be used to compute securely arithmetic circuits over any finite field $$\mathbb {F}_{p^k}$$. Our protocol consists of a preprocessing phase that is both independent of the function to be computed and of the inputs, and a much more efficient online phase where the actual computation takes place. The online phase is unconditionally secure and has total computational and communication complexity linear in n, the number of players, where earlier work was quadratic in n. Moreover, the work done by each player is only a small constant factor larger than what one would need to compute the circuit in the clear. We show this is optimal for computation in large fields. In practice, for 3 players, a secure 64-bit multiplication can be done in 0.05 ms. Our preprocessing is based on a somewhat homomorphic cryptosystem. We extend a scheme by Brakerski et al., so that we can perform distributed decryption and handle many values in parallel in one ciphertext. The computational complexity of our preprocessing phase is dominated by the public-key operations, we need $$On^2/s$$ operations per secure multiplication where s is a parameter that increases with the security parameter of the cryptosystem. Earlier work in this model needed $$\varOmega n^2$$ operations. In practice, the preprocessing prepares a secure 64-bit multiplication for 3 players in about 13 ms.


title: Low cost QFN package design for millimeter-wave applications

abstract: This paper focuses on the design and development of a low-cost QFN package that is based on wirebond interconnects. One of the design goals is to extend the frequency at which the package can be used to 40–50 GHz (above the K band), in the millimeter-wave range. Owing to the use of mass production assembly protocols and materials, such as commercially available QFN in a mold compound, the design that is outlined in this paper significantly reduces the cost of assembly of millimeter wave modules. To operate the package at 50 GHz or a higher frequency, several key design features are proposed. They include the use of through vias (backside vias) and ground bondwires to provide ground return currents. This paper also provides rigorous validation steps that we took to obtain the key high frequency characteristics. Since a molding compound is used in conventional QFN packages, the material and its effectiveness in determining the signal propagation have to be incorporated in the overall design. However, the mold compound creates some extra challenges in the de-embedding task. For example, the mold compound must be removed to expose the probing pads so the effect of the microstrip on the GaAs chip can be obtained and de-embedded. Careful simulation and experimental validation reveal that the proposed QFN design achieves a return loss of −10 dB and an insertion loss of −1.5 dB up to 50 GHz.


title: Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition

abstract: This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle. The primary goal of this challenge is accurate semantic segmentation of different classes in satellite imagery. Our approach is based on an adaptation of fully convolutional neural network for multispectral data processing. In addition, we defined several modifications to the training objective and overall training pipeline, e.g. boundary effect estimation, also we discuss usage of data augmentation strategies and reflectance indices. Our solution scored third place out of 419 entries. Its accuracy is comparable to the first two places, but unlike those solutions, it doesn't rely on complex ensembling techniques and thus can be easily scaled for deployment in production as a part of automatic feature labeling systems for satellite imagery analysis.


title: A Geometry-Appearance-Based Pupil Detection Method for Near-Infrared Head-Mounted Cameras

abstract: This paper presents, for the first time, a novel pupil detection method for near-infrared head-mounted cameras, which relies not only on image appearance to pursue the shape and gradient variation of the pupil contour, but also on structure principle to explore the mechanism of pupil projection. There are three main characteristics in the proposed method. First, in order to complement the pupil projection information, an eyeball center calibration method is proposed to build an eye model. Second, by utilizing the deformation model of pupils under head-mounted cameras and the edge gradients of a circular pattern, we find the best fitting ellipse describing the pupil boundary. Third, an eye-model-based pupil fitting algorithm with only three parameters is proposed to fine-tune the final pupil contour. Consequently, the proposed method extracts the geometry-appearance information, effectively boosting the performance of pupil detection. Experimental results show that this method outperforms the state-of-the-art ones. On a widely used public database (LPW), our method achieves 72.62% in terms of detection rate up to an error of five pixels, which is superior to the previous best one.


title: Stress disorders following prolonged critical illness in survivors of severe sepsis.

abstract: Objectives:To examine the frequency of acute stress disorder and posttraumatic stress disorder in chronically critically ill patients with a specific focus on severe sepsis, to classify different courses of stress disorders from 4 weeks to 6 months after transfer from acute care hospital to postacut


title: Macro- and micro-expression spotting in long videos using spatio-temporal strain

abstract: We propose a method for the automatic spotting (temporal segmentation) of facial expressions in long videos comprising of macro- and micro-expressions. The method utilizes the strain impacted on the facial skin due to the non-rigid motion caused during expressions. The strain magnitude is calculated using the central difference method over the robust and dense optical flow field observed in several regions (chin, mouth, cheek, forehead) on each subject's face. This new approach is able to successfully detect and distinguish between large expressions (macro) and rapid and localized expressions (micro). Extensive testing was completed on a dataset containing 181 macro-expressions and 124 micro-expressions. The dataset consists of 56 videos collected at USF, 6 videos from the Canal-9 political debates, and 3 low quality videos found on the internet. A spotting accuracy of 85% was achieved for macro-expressions and 74% of all micro-expressions were spotted.


title: A hierarchical image clustering cosegmentation framework

abstract: Given the knowledge that the same or similar objects appear in a set of images, our goal is to simultaneously segment that object from the set of images. To solve this problem, known as the cosegmentation problem, we present a method based upon hierarchical clustering. Our framework first eliminates intra-class heterogeneity in a dataset by clustering similar images together into smaller groups. Then, from each image, our method extracts multiple levels of segmentation and creates connections between regions (e.g. superpixel) across levels to establish intra-image multi-scale constraints. Next we take advantage of the information available from other images in our group. We design and present an efficient method to create inter-image relationships, e.g. connections between image regions from one image to all other images in an image cluster. Given the intra & inter-image connections, we perform a segmentation of the group of images into foreground and background regions. Finally, we compare our segmentation accuracy to several other state-of-the-art segmentation methods on standard datasets, and also demonstrate the robustness of our method on real world data.


title: An Analysis on Time- and Session-aware Diversification in Recommender Systems

abstract: In modern recommender systems, diversity has been widely acknowledged as an important factor to improve user experience and, more recently, intent-aware approaches to diversification have been proposed to provide the user with a list of recommendations covering different aspects of her behavior. In this paper, we propose and analyze the performances of two diversification methods taking into account temporal aspects of the user profile: in the first one we adopt a temporal decay function to emphasize the importance of more recent items in the user profile while in the second one we perform an evaluation based on the identification and analysis of temporal sessions. The two proposed methods have been implemented as temporal variants of the well-known xQuAD framework. In both cases, experimental results on Netflix 100M show an improvement in terms of accuracy-diversity balance.


title: The World report on ageing and health: a policy framework for healthy ageing

abstract: Although populations around the world are rapidly ageing, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. A coherent and focused public health response that spans multiple sectors and stakeholders is urgently needed. To guide this global response, WHO has released the first World report on ageing and health, reviewing current knowledge and gaps and providing a public health framework for action. The report is built around a redefinition of healthy ageing that centres on the notion of functional ability: the combination of the intrinsic capacity of the individual, relevant environmental characteristics, and the interactions between the individual and these characteristics. This Health Policy highlights key findings and recommendations from the report.


title: simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions

abstract: The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensiveness, have been separately proposed to ground the caption on the image. In this paper, we propose the Stepwise Image-Topic Merging Network (simNet) that makes use of the two kinds of attention at the same time. At each time step when generating the caption, the decoder adaptively merges the attentive information in the extracted topics and the image according to the generated context, so that the visual information and the semantic information can be effectively combined. The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art performances.(The code is available at https://github.com/lancopku/simNet)


title: Audit Games

abstract: Effective enforcement of laws and policies requires expending resources to prevent and detect offenders, as well as appropriate punishment schemes to deter violators. In particular, enforcement of privacy laws and policies in modern organizations that hold large volumes of personal information (e.g., hospitals, banks) relies heavily on internal audit mechanisms. We study economic considerations in the design of these mechanisms, focusing in particular on effective resource allocation and appropriate punishment schemes. We present an audit game model that is a natural generalization of a standard security game model for resource allocation with an additional punishment parameter. Computing the Stackelberg equilibrium for this game is challenging because it involves solving an optimization problem with non-convex quadratic constraints. We present an additive FPTAS that efficiently computes the solution.


title: A familiar face(book): profile elements as signals in an online social network

abstract: Using data from a popular online social network site, this paper explores the relationship between profile structure (namely, which fields are completed) and number of friends, giving designers insight into the importance of the profile and how it works to encourage connections and articulated relationships between users. We describe a theoretical framework that draws on aspects of signaling theory, common ground theory, and transaction costs theory to generate an understanding of why certain profile fields may be more predictive of friendship articulation on the site. Using a dataset consisting of 30,773 Facebook profiles, we determine which profile elements are most likely to predict friendship links and discuss the theoretical and design implications of our findings.


title: Severe Hyperacusis, Photophobia, and Skin Hypersensitivity

abstract: We report a case of a patient with severe hyperacusis, photophobia, and skin hypersensitivity. The patient was initially treated with sound therapy and medical therapy for 4 months and successfully with a selective serotonin reuptake inhibitor (SSRI) and cognitive behavioral therapy which improved her mood and the tolerance for sounds and light.


title: Analytics in Motion: High Performance Event-Processing AND Real-Time Analytics in the Same Database

abstract: Modern data-centric flows in the telecommunications industry require real time analytical processing over a rapidly changing and large dataset. The traditional approach of separating OLTP and OLAP workloads cannot satisfy this requirement. Instead, a new class of integrated solutions for handling hybrid workloads is needed. This paper presents an industrial use case and a novel architecture that integrates key-value-based event processing and SQL-based analytical processing on the same distributed store while minimizing the total cost of ownership. Our approach combines several well-known techniques such as shared scans, delta processing, a PAX-fashioned storage layout, and an interleaving of scanning and delta merging in a completely new way. Performance experiments show that our system scales out linearly with the number of servers. For instance, our system sustains event streams of 100,000 events per second while simultaneously processing 100 ad-hoc analytical queries per second, using a cluster of 12 commodity servers. In doing so, our system meets all response time goals of our telecommunication customers; that is, 10 milliseconds per event and 100 milliseconds for an ad-hoc analytical query. Moreover, our system beats commercial competitors by a factor of 2.5 in analytical and two orders of magnitude in update performance.


title: Stress recognition using noninvasive technology

abstract: The need to provide computers with the ability to estimate the affective state of their users is a major requirement for the practical implementation of Affective Computing concepts. This research aims at sensing and recognizing typical negative emotional states, especially “stress”, when the user is interacting with the computer. An integrated hardware – software setup has been developed to achieve automatic assessment of the affective status of a computer user. A computer-based “Paced Stroop Test” is designed to act as a stimulus to elicit emotional stress in the subject. Four signals: Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Pupil Diameter (PD) and Skin Temperature (ST) are monitored and analyzed to differentiate affective states in the user. Several signal processing techniques are applied to the signals collected to extract the most relevant features in the physiological responses and feed them into learning systems, to accomplish the affective state classification. Three learning algorithms are applied to this classification process and their performance is compared. Results indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in emotional state of our experimental subjects.


title: High confidence off-policy evaluation

abstract: Many reinforcement learning algorithms use trajectories collected from the execution of one or more policies to propose a new policy. Because execution of a bad policy can be costly or dangerous, techniques for evaluating the performance of the new policy without requiring its execution have been of recent interest in industry. Such off-policy evaluation methods, which estimate the performance of a policy using trajectories collected from the execution of other policies, heretofore have not provided confidences regarding the accuracy of their estimates. In this paper we propose an off-policy method for computing a lower confidence bound on the expected return of a policy.


title: TeamWork: synchronizing threads globally to detect real deadlocks for multithreaded programs

abstract: This paper presents the aim of TeamWork, our ongoing effort to develop a comprehensive dynamic deadlock confirmation tool for multithreaded programs. It also presents a refined object abstraction algorithm that refines the existing stack hash abstraction.


title: Characteristics of health IT outage and suggested risk management strategies: An analysis of historical incident reports in China

abstract: Abstract Background The healthcare industry has become increasingly dependent on using information technology (IT) to manage its daily operations. Unexpected downtime of health IT systems could therefore wreak havoc and result in catastrophic consequences. Little is known, however, regarding the nature of failures of health IT. Objective To analyze historical health IT outage incidents as a means to better understand health IT vulnerabilities and inform more effective prevention and emergency response strategies. Methods We studied news articles and incident reports publicly available on the internet describing health IT outage events that occurred in China. The data were qualitatively analyzed using a deductive grounded theory approach based on a synthesized IT risk model developed in the domain of information systems. Results A total of 116 distinct health IT incidents were identified. A majority of them (69.8%) occurred in the morning; over 50% caused disruptions to the patient registration and payment collection functions of the affected healthcare facilities. The outpatient practices in tertiary hospitals seem to be particularly vulnerable to IT failures. Software defects and overcapacity issues, followed by malfunctioning hardware, were among the principal causes. Conclusions Unexpected health IT downtime occurs more and more often with the widespread adoption of electronic systems in healthcare. Risk identification and risk assessments are essential steps to developing preventive measures. Equally important is institutionalization of contingency plans as our data show that not all failures of health IT can be predicted and thus effectively prevented. The results of this study also suggest significant future work is needed to systematize the reporting of health IT outage incidents in order to promote transparency and accountability.


title: Characterizing Client Behavior of Commercial Mobile Video Streaming Services

abstract: HTTP Adaptive Streaming dominates most of the traffic on the Internet today and a large fraction is driven by video consumption on mobile phones and tablet devices. The client player implementations from different popular commercial services make different parameter and architectural choices, which result from heterogeneous APIs and device limitations. As a result, they exhibit quite different behaviors even for the same service, with very different traffic consumption characteristics. In this paper, we examine three major streaming services -- Netflix, Youtube, and Hulu, over the two dominant mobile platforms -- iOS and Android, in order to understand the impact of these design choices. We infer detailed session behavior based on passively collected packet traces over a large set of experiments across the providers and network types. We discover varying amounts of "redundant" traffic in the presence of bandwidth adaptation across the services, which negatively impacts network resources. We also find these design choices lead to unfairness in bandwidth consumption on shared networks across different platforms. In particular, we find the Android Netflix player is able to take a larger fraction of shared bandwidth when competing with the iOS implementation.


title: Students' attitudes toward playing games and using games in education: Comparing Scotland and the Netherlands

abstract: Games-based learning has captured the interest of educationalists and industrialists who seek to exploit the characteristics of computer games as they are perceived by some to be a potentially effective approach for teaching and learning. Despite this interest in using games-based learning there is a dearth of empirical evidence supporting the validity of the approach covering the wider context of gaming and education. This study presents a large scale gaming survey, involving 887 students from 13 different Higher Education (HE) institutes in Scotland and the Netherlands, which examines students' characteristics related to their gaming preferences, game playing habits, and their perceptions and thoughts on the use of games in education. It presents a comparison of three separate groups of students: a group in regular education in a Scottish university, a group in regular education in universities in the Netherlands and a distance learning group from a university in the Netherlands. This study addresses an overall research question of: Can computer games be used for educational purposes at HE level in regular and distance education in different countries? The study then addresses four sub-research questions related to the overall research question:*What are the different game playing habits of the three groups? *What are the different motivations for playing games across the three groups? *What are the different reasons for using games in HE across the three groups? *What are the different attitudes towards games across the three groups? To our knowledge this is the first in-depth cross-national survey on gaming and education. We found that a large number of participants believed that computer games could be used at HE level for educational purposes and that further research in the area of game playing habits, motivations for playing computer games and motivations for playing computer games in education are worthy of extensive further investigation. We also found a clear distinction between the views of students in regular education and those in distance education. Regular education students in both countries rated all motivations for playing computer games as significantly more important than distance education students. Also the results suggest that Scottish students aim to enhance their social experience with regards to competition and cooperation, while Dutch students aim to enhance their leisurely experience with regards to leisure, feeling good, preventing boredom and excitement.


title: In search of cultural competence in psychotherapy and counseling.

abstract: The characteristics involved in cultural competency in psychotherapy and counseling have been difficult to specify. This article describes attempts to study factors associated with cultural competency and addresses 3 questions. First, is ethnic match between therapists and clients associated with treatment outcomes? Second, do clients who use ethnic-specific services exhibit more favorable outcomes than those who use mainstream services? Third, is cognitive match between therapists and clients a predictor of outcomes? The research suggests that match is important in psychotherapy. The cultural competency research has also generated some controversy, and lessons learned from the controversy are discussed. Finally, it is suggested that important and orthogonal ingredients in cultural competency are therapists' scientific mindedness, dynamic-sizing skills, and culturespecific expertise.


title: High reliability 1.3-μm buried heterostructure AlGaInAs-MQW DFB laser operated at 28-Gbit/s direct modulation

abstract: Clear eye opening at 28-Gbit/s direct modulation up to 75°C is achieved with 1.3-μm buried heterostructure AlGaInAs-MQW DFB laser. No failure had occurred at high optical output power aging test over 7000 hours.


title: Question Asking as Program Generation

abstract: A hallmark of human intelligence is the ability to ask rich, creative, and revealing questions. Here we introduce a cognitive model capable of constructing human-like questions. Our approach treats questions as formal programs that, when executed on the state of the world, output an answer. The model specifies a probability distribution over a complex, compositional space of programs, favoring concise programs that help the agent learn in the current context. We evaluate our approach by modeling the types of open-ended questions generated by humans who were attempting to learn about an ambiguous situation in a game. We find that our model predicts what questions people will ask, and can creatively produce novel questions that were not present in the training set. In addition, we compare a number of model variants, finding that both question informativeness and complexity are important for producing human-like questions.


title: Array-Fed Partially Reflective Surface Antenna With Independent Scanning and Beamwidth Dynamic Control

abstract: In this communication we present a concept of a partially reflective surface (PRS) antenna capable of independent beam-scanning and beamwidth dynamic control. The beamwidth is controlled by reconfiguring the PRS reflectivity via embedded varactor diodes, while the beam is steered by using a small phased array in the H-plane as a source antenna exciting the Fabry-Perot cavity. A fully operational prototype is fabricated and measured upon reconfiguration, demonstrating the viability of the concept and good agreement between simulated and measured results. The achieved beamwidth variation is from 18.7° to 22.4°, with the scanning range of 15° and 20° for the narrowest and the widest beamwidth, respectively.


title: Learning to Estimate 3D Hand Pose from Single RGB Images

abstract: Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images. In this paper, we present an approach that estimates 3D hand pose from regular RGB images. This task has far more ambiguities due to the missing depth information. To this end, we propose a deep network that learns a network-implicit 3D articulation prior. Together with detected keypoints in the images, this network yields good estimates of the 3D pose. We introduce a large scale 3D hand pose dataset based on synthetic hand models for training the involved networks. Experiments on a variety of test sets, including one on sign language recognition, demonstrate the feasibility of 3D hand pose estimation on single color images.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Who Knows What About a Person? The Self–Other Knowledge Asymmetry (SOKA) Model

abstract: This article tests a new model for predicting which aspects of personality are best judged by the self and which are best judged by others. Previous research suggests an asymmetry in the accuracy of personality judgments: Some aspects of personality are known better to the self than others and vice versa. According to the self– other knowledge asymmetry (SOKA) model presented here, the self should be more accurate than others for traits low in observability (e.g., neuroticism), whereas others should be more accurate than the self for traits high in evaluativeness (e.g., intellect). In the present study, 165 participants provided self-ratings and were rated by 4 friends and up to 4 strangers in a round-robin design. Participants then completed a battery of behavioral tests from which criterion measures were derived. Consistent with SOKA model predictions, the self was the best judge of neuroticism-related traits, friends were the best judges of intellect-related traits, and people of all perspectives were equally good at judging extraversionrelated traits. The theoretical and practical value of articulating this asymmetry is discussed.