title: A Colonel Blotto Game for Anti-Jamming in the Internet of Things

abstract: The Internet of Things (IoT) is envisioned to be a large-scale system that interconnects sensors, mundane objects, and other physical devices via an effective communication infrastructure. Given the heterogeneous and large-scale nature of the IoT, security has emerged as a key challenge. This challenge is further exacerbated by the fact that security solutions for the IoT must account for the limited computational capabilities of the IoT's nodes. That makes enhancing the security at the physical layer level an attractive solution for IoT networks. In this paper, a novel anti- jamming mechanism is proposed to enable a fusion center to defend the IoT from a malicious radio jamming attack. The problem is formulated as a Colonel Blotto game in which the fusion center, acting as defender, aims to detect the jamming attack by increasing the number of bits allocated to certain nodes for reporting their measured interference level, while the jammer aims to disturb the network performance and still be undetected. To solve this game, an algorithm based on fictitious play is proposed to reach the equilibrium of the game. Simulation results show that the proposed mechanism outperforms the mechanism of allocating the available bits in a random manner for two different cases of network architecture.


title: Recurrent Neural Network Based Classification of ECG Signal Features for Obstruction of Sleep Apnea Detection

abstract: This paper introduces an OSA detection method based on Recurrent Neural network. At the first step, RR interval (time interval from one R wave to the next R wave) is employed to extract the signals from Apnea- Electrocardiogram (ECG) where all extracted features are then used as an input for the designed deep model. Then an architecture having four recurrent layers and batch normalization layers are designed and trained with the extracted features for OSA detection. Apnea-ECG datasets from physionet.org are used for training and testing our model. Experimental results reveal that our automatic OSA detection model provides better classification accuracy.


title: Generalized Canonical Time Warping

abstract: Temporal alignment of human motion has been of recent interest due to its applications in animation, tele-rehabilitation and activity recognition. This paper presents generalized canonical time warping (GCTW), an extension of dynamic time warping (DTW) and canonical correlation analysis (CCA) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GCTW extends previous work on DTW and CCA in several ways: (1) it combines CCA with DTW to align multi-modal data (e.g., video and motion capture data); (2) it extends DTW by using a linear combination of monotonic functions to represent the warping path, providing a more flexible temporal warp. Unlike exact DTW, which has quadratic complexity, we propose a linear time algorithm to minimize GCTW. (3) GCTW allows simultaneous alignment of multiple sequences. Experimental results on aligning multi-modal data, facial expressions, motion capture data and video illustrate the benefits of GCTW. The code is available at http://humansensing.cs.cmu.edu/ctw .


title: The Application of Deep Convolutional Denoising Autoencoder for Optical Character Recognition Preprocessing

abstract: The process of converting physical documents into digital texts generally requires a scanner tool to obtain high-quality document images. These high quality images will be read by OCR software to get digital text results. The weakness of this method is that OCR software requires a high quality document with low blur noise and no parallax in the image to have high accuracy. We developed an application to increase the document image quality with the help of Deep Convolutional Denoising Autoencoder, afterwards read by the OCR application. The final product of this program is a digital text converted from a document image which has been taken from a smartphone. There is an increase in accuracy using this application by 26.68% in a blurred image compare to standard Tesseract OCR and outperformed Simple OCR in average accuracy testing.


title: Stock trend prediction relying on text mining and sentiment analysis with tweets

abstract: Stock trend prediction based on text has gained much attention from researchers in recent years. According to investment theories, investors' behaviors will influence the stock market, and the way people invest their money is based on the history trend and information they hold. On account of this indirectly influential relationship between information of stock and stock trend, stock trend prediction based on text has been done by many researchers. However, due to the serious feature sparse problem in tweets and unreliability of using average sentiment score to indicate one day's sentiment, this work proposed a text-sentiment based stock trend prediction model with a hybrid feature selection method. Instead of applying sentiment analysis to add sentiment related features, this paper uses SentiWordNet to give an additional weight to the selected features. Besides, this work also compares the results with those of other learning algorithms. SVM linear algorithm based on leave-one-out cross validation yields the best performance of 90.34%.


title: A sub-nyquist analog front-end with subarray beamforming for ultrasound imaging

abstract: Traditional ultrasound imagers suffer from a large data rate between their analog-to-digital converter (ADC) front-end and digital beamforming backend. This high aggregate data rate (typically exceeding 30 Gb/s) is holding back the design of imagers with high transducer counts, such as 2D imaging arrays. Prior work has demonstrated sub-Nyquist sampling techniques in software, which take advantage of the low innovation rate to sample well below the physical bandwidth of the signal. This work demonstrates a PCB-based hardware implementation of a 32-channel analog front-end (AFE) employing sub-Nyquist sampling. We additionally demonstrate a mixer-based subarray beamforming technique that exploits the narrowband nature of the receiver to beamform received signals prior to digitization. Together, these techniques reduce the aggregate data rate by a factor of 43x when compared to a conventional system.


title: Permanent and Temporary Policy Incentives for Conservation under Stochastic Returns from Competing Land Uses

abstract: Rapid conversion of private land for agricultural and urban use has raised concern worldwide over the loss of ecological services. To inform conservation policy, we model privately optimal land use decisions using a real options framework that assumes both the ecological and commercial values of land are stochastic and that land conversion is irreversible. We analyze permanent and temporary land conservation policy incentives using this dynamic framework to guide policy makers interested in designing efficient payment mechanisms to achieve ecological preservation goals. We find that while the financial cost of temporary policy incentives is generally much lower than permanent policy incentives, this difference is very small in scenarios with high discount rates and lower uncertainties in conversion returns and lower expected trend in conservation returns. Alternately, the financial cost of temporary conservation is substantially lower than that of permanent conservation when the expected trend in conservation returns and uncertainties in conservation and conversion returns is high and the discount rate is low. Comparison with net present value and single-source uncertainty models indicates that the presence of a second uncertainty increases the option value of delaying conversion, and shows that permanent and temporary policy incentives based on either of the two simpler models can be seriously misguided if multiple uncertainties are present. We illustrate the analytical results with a case study of tropical deforestation in Indonesia where private landowners can either conserve forests and earn carbon credits, or convert to palm oil agriculture and earn profits from the sale of palm oil.


title: Suicide and Self-Harm by Drowning: A Review of the Literature.

abstract: The objectives of this study were to carry out a comprehensive review of the worldwide literature on suicidal behaviour by drowning. Systematic electronic searches of databases using various search terms were carried out. Recent trends in suicide and undetermined deaths due to drowning in England and Wales are described. The characteristics of patients presenting to the general hospital in Oxford, UK following attempted drowning are compared with self-poisoning patients. A total of 20 studies containing empirical data about suicide by drowning were identified, mainly concerning Western countries. Drowning suicides have declined in most countries in recent years. The proportion of undetermined deaths remains high. Drowning suicides and self-harm patients tend to be older, with only a small excess of males compared to those using other methods. This is an under-researched area that deserves good quality studies focusing upon prevention.


title: RABAC: role-centric attribute-based access control

abstract: Role-based access control (RBAC) is a commercially dominant model, standardized by the National Institute of Standards and Technology (NIST). Although RBAC provides compelling benefits for security management it has several known deficiencies such as role explosion, wherein multiple closely related roles are required (e.g., attending-doctor role is separately defined for each patient). Numerous extensions to RBAC have been proposed to overcome these shortcomings. Recently NIST announced an initiative to unify and standardize these extensions by integrating roles with attributes, and identified three approaches: use attributes to dynamically assign users to roles, treat roles as just another attribute, and constrain the permissions of a role via attributes. The first two approaches have been previously studied. This paper presents a formal model for the third approach for the first time in the literature. We propose the novel role-centric attribute-based access control (RABAC) model which extends the NIST RBAC model with permission filtering policies. Unlike prior proposals addressing the role-explosion problem, RABAC does not fundamentally modify the role concept and integrates seamlessly with the NIST RBAC model. We also define an XACML profile for RABAC based on the existing XACML profile for RBAC.


title: Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

abstract: We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy, but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters problematic. The computation is dominated by the convolution operations in the lower layers of the model. We exploit the redundancy present within the convolutional filters to derive approximations that significantly reduce the required computation. Using large state-of-the-art models, we demonstrate speedups of convolutional layers on both CPU and GPU by a factor of 2 x, while keeping the accuracy within 1% of the original model.


title: Sparse representation and learning in visual recognition: Theory and applications

abstract: Sparse representation and learning has been widely used in computational intelligence, machine learning, computer vision and pattern recognition, etc. Mathematically, solving sparse representation and learning involves seeking the sparsest linear combination of basis functions from an overcomplete dictionary. A rational behind this is the sparse connectivity between nodes in human brain. This paper presents a survey of some recent work on sparse representation, learning and modeling with emphasis on visual recognition. It covers both the theory and application aspects. We first review the sparse representation and learning theory including general sparse representation, structured sparse representation, high-dimensional nonlinear learning, Bayesian compressed sensing, sparse subspace learning, non-negative sparse representation, robust sparse representation, and efficient sparse representation. We then introduce the applications of sparse theory to various visual recognition tasks, including feature representation and selection, dictionary learning, Sparsity Induced Similarity (SIS) measures, sparse coding based classification frameworks, and sparsity-related topics.


Human-robot Collaboration for Bin-picking Tasks to Support Low-volume Assemblies


title: Contextual Dueling Bandits

abstract: We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner's goal is to find the best policy, or way of behaving, in some space of policies, although "best" is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game theory, called a von Neumann winner, a randomized policy that beats or ties every other policy. We show that this notion overcomes important limitations of existing solutions, particularly the Condorcet winner which has typically been used in the past, but which requires strong and often unrealistic assumptions. We then present three efficient algorithms for online learning in our setting, and for approximating a von Neumann winner from batch-like data. The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space. The other two algorithms require time and space only logarithmic in the size of the policy space when provided access to an oracle for solving classification problems on the space.


title: Acoustic Chord Transcription and Key Extraction From Audio Using Key-Dependent HMMs Trained on Synthesized Audio

abstract: We describe an acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results. We avoid the extremely laborious task of human annotation of chord names and boundaries-which must be done to provide machine learning models with ground truth-by performing automatic harmony analysis on symbolic music files. In parallel, we synthesize audio from the same symbolic files and extract acoustic feature vectors which are in perfect alignment with the labels. We, therefore, generate a large set of labeled training data with a minimal amount of human labor. This allows for richer models. Thus, we build 24 key-dependent HMMs, one for each key, using the key information derived from symbolic data. Each key model defines a unique state-transition characteristic and helps avoid confusions seen in the observation vector. Given acoustic input, we identify a musical key by choosing a key model with the maximum likelihood, and we obtain the chord sequence from the optimal state path of the corresponding key model, both of which are returned by a Viterbi decoder. This not only increases the chord recognition accuracy, but also gives key information. Experimental results show the models trained on synthesized data perform very well on real recordings, even though the labels automatically generated from symbolic data are not 100% accurate. We also demonstrate the robustness of the tonal centroid feature, which outperforms the conventional chroma feature.


title: From judgment to calculation

abstract: We only regard a system or a process as being “scientific” if it displays the three predominant characteristics of the natural sciences: predictability, repeatability and quantifiability. This by definition precludes intuition, subjective judgement, tacit knowledge, heuristics, dreams, etc. in other words, those attributes which are peculiarly human. Furthermore, this is resulting in a shift from judgment to calculation giving rise, in some cases, to an abject dependency on the machine and an inability to disagree with the outcome or even question it. To tolerate such a situation could be seen as an abdication of professional responsibility. In complex technological and scientific environments, it is sometimes said that those who make best use of computers already know what the answer is (in ball park terms) before the calculation.


title: All gadget and no representation makes Jack a dull environment

abstract: In an Intelligent Environment, the user and the environment work together in a unique manner; the user expresses what he wishes to do, and the environment recognizes his intentions and helps out however it can. If well-implemented, such an environment allows the user to interact with it in the manner that is most natural for him personally. He should need virtually no time to learn to use it and should be more productive once he has. But to implement a useful and natural Intelligent Environment, the designers are faced with a daunting task: they must design a software system that senses what its users do, understands their intentions, and then responds appropriately. In this paper we argue that, in order to function reasonably in any of these ways, an Intelligent Environment must make use of declarative representations of what the user might do. We present our evidence in the context of the Intelligent Classroom, a facility that aids a speaker in this way and uses its understanding to produce a video of his presentation.


title: FusionNet: 3D Object Classification Using Multiple Data Representations

abstract: High-quality 3D object recognition is an important component of many vision and robotics systems. We tackle the object recognition problem using two data representations, to achieve leading results on the Princeton ModelNet challenge. The two representations: 1. Volumetric representation: the 3D object is discretized spatially as binary voxels - $1$ if the voxel is occupied and $0$ otherwise. 2. Pixel representation: the 3D object is represented as a set of projected 2D pixel images. Current leading submissions to the ModelNet Challenge use Convolutional Neural Networks (CNNs) on pixel representations. However, we diverge from this trend and additionally, use Volumetric CNNs to bridge the gap between the efficiency of the above two representations. We combine both representations and exploit them to learn new features, which yield a significantly better classifier than using either of the representations in isolation. To do this, we introduce new Volumetric CNN (V-CNN) architectures.


title: Visualization Model for Learning of Pronunciation with an Approach from Human Computer Interaction

abstract: The fields of Human Computer Interaction (HCI) and Information Visualization (InfoVis) could complement one another, rather than being treated as two different disciplines, in order to provide visual and interactive aspects for improved information visualization. A model is proposed in this light that allows both researchers and users to analyze a pronunciation learning-oriented task, based on analysis of three reference models for information visualization Card, McKinlay-Shneiderman; Chi; and Wunsche. These models are linear and although they offer data representation-focused approaches, they recognize the role of the user in their different stages. The three visualization models highlight such important aspects as representation, interaction and perception - elements of study that are essential in the field of HCI - in order to create a suitable representation of the information that enables a detailed understanding to be acquired and developed, and experiences to be communicated, wherein the user can interact more easily with the information and get the most from the visualization tool. This article aims to address both research fields. Information visualization is focused on visual representation of acoustic signals of the voice, based upon multidimensional data, while HCI takes into account user-oriented visual and interactive aspects. It is sought to integrate the two research fields in the proposed model, so that acoustic voice signal data are represented on a two dimensional plane and the user can visually comprehend as many aspects of the voice as possible, being able to recognize the quality of the pronunciation through information succinctly represented by graphic attributes. The model is comprised of stages and interaction mechanisms that combine to represent four views. Each view has a different makeup, in order to structure the data and display them using graphical attributes, such as color, position, shape, size, text, orientation and texture. These help in analysis and evaluation and in how to represent the connections between the various visual components in order to establish relationships between data. The main objective of the model is to represent a large number of aspects of the voice, so that in pronunciation training tasks a person can understand pronunciation quality visually. The model represents the results of comparing an input signal recorded by a microphone with a correctly pronounced signal set. Views constitute the final stage of the model. Data are thus presented so that the user can see and interpret representations of phoneme pronunciations. The views enable the user to learn about different aspects of the voice and recognize pronunciation quality visually. Each view represents different information: the first reveals information rapidly using facial gestures to represent moods - a happy face for good pronunciation and a sad face when the pronunciation is poor, making use of facial attributes such as eyes, eyebrows, nose and mouth. These represent values of voice characteristics through their size, shape, position or orientation. The second view permits a quick inspection of data, making comparison between correct pronunciation prototypes and the test signal and employing graphical attributes such as color and shape to interpret degrees of similarity of characteristics between phonemes. A third view illustrates similarities between data associated with a color, position and shape, using a visualization technique called Self-organizing Maps. Finally, a fourth view shows a diagonal line to indicate correlations between a set of phonemes relative to the phoneme pronounced. Each view has a level of interpretation and understanding depending on the degree of experience of the user - from a first view oriented for a child to a fourth view for a person skilled in signal modeling. The four views offer different visual explorations and levels of interpretation, so that the user can understand aspects of the voice and gain knowledge about the quality of the pronunciation. The model involves interaction with the user, which takes account of key aspects that might contribute to an easy decoding of information, in order to provide a better understanding of the task. They also allow the user to learn about the different aspects of the acoustic signal of the voice by means of various combinations of visualization and help an understanding of the data, taking full advantage of visual perception skills to discover patterns. The proposed model considers aspects of design in the display interface, aspects that could reduce the cognitive effort required to understand the graphic representation, so that users can devote their cognitive ability to understanding that which is being represented. Information visualization and HCI are two areas of research that can support each another with the aim of creating a visual representation oriented to the user, in such a way that as much information as possible can be gained and interpreted with the minimum of effort.


title: The zygorbicular dissection in composite rhytidectomy: an ideal midface plane.

abstract: Abstract Composite rhytidectomy added the repositioning of the orbicularis oculi muscle to the deep plane face lift to achieve a more harmonious appearance of the face by adding periorbital rejuvenation. By not separating the orbicularis oculi from the zygomaticus minor and by extending the dissection under medial portions of the zygomaticus major and minor muscles, a more significant improvement in composite rhytidectomy can now be achieved. A thin nonrestrictive mesentery between the deep plane face lift dissection and the zygorbicular dissection still allows vertical movement of the composite face lift flap without interrupting the intimate relationship between the platysma, cheek fat, and orbicularis oculi muscle. This modification eliminates the occasional prolonged edema and occasional temporary dystonia previously observed. It allows the continuation of the use of the arcus marginalis release, which has also been modified by resetting the septum orbitale over the orbital rim. These two modifications allow a more predictable and impressive result. They reinforce the concept of periorbital rejuvenation as an integral part of facial rejuvenation, which not only produces a more harmonious immediate result but prevents the possible unfavorable sequelae of conventional rhytidectomy and lower blepharoplasty.


Recent Trend in Crypto Currency in India Vs Global : Issues and Challenges-Overview


title: Business simulation game for teaching multi-echelon supply chain management

abstract: This paper presents a new business game which helps to understand concepts developed in the ECLIPS project of the European Comission. The game provides an insight into different aspects of supply chain management, i.e., general supply chain mechanisms, non-cyclic and cyclic inventory replenishment policies. This allows for people who have no deep notion in this area to better understand project concepts and evaluate their efficiency in practice. Demonstrating concepts in a playful way is considered as more powerful and effective than purely explaining the underlying theory. The paper describes game rules, playing process and provides results of game test sessions.


title: Twitter for crisis communication: lessons learned from Japan's tsunami disaster

abstract: Two weeks after the Great Tohoku earthquake followed by the devastating tsunami, we have sent open-ended questionnaires to a randomly selected sample of Twitter users and also analysed the tweets sent from the disaster-hit areas. We found that people in directly affected areas tend to tweet about their unsafe and uncertain situation while people in remote areas post messages to let their followers know that they are safe. Our analysis of the open-ended answers has revealed that unreliable retweets (RTs) on Twitter was the biggest problem the users have faced during the disaster. Some of the solutions offered by the respondents included introducing official hash tags, limiting the number of RTs for each hash tag and adding features that allow users to trace information by maintaining anonymity.


title: U.S. and Canadian industrial production indices as coupled oscillators

abstract: This paper explores the impact of different types of dynamical linkages (coupling) between the indices of industrial production for the U.S. and Canada. The Ozaki model provides an appropriate empirical framework for analyzing the dynamic path of each economy's productive activity because it provides an effective approximation to continuous time differential equations. We examine a combination of six different types of linkage between the indices of production. Major questions we study include whether the linkages increase or decrease the stability of the equilibrium paths, whether the linkages encourage or discourage business cycle oscillations, and whether the oscillations are synchronized.


title: Normalizing historical orthography for OCR historical documents using LSTM

abstract: Historical text presents numerous challenges for contemporary different techniques, e.g. information retrieval, OCR and POS tagging. In particular, the absence of consistent orthographic conventions in historical text presents difficulties for any system which requires reference to a fixed lexicon accessed by orthographic form. For example, language modeling or retrieval engine for historical text which is produced by OCR systems, where the spelling of words often differ in various way, e.g. one word might have different spellings evolved over time. It is very important to aid those techniques with the rules for automatic mapping of historical wordforms. In this paper, we propose a new technique to model the target modern language by means of a recurrent neural network with long-short term memory architecture. Because the network is recurrent, the considered context is not limited to a fixed size especially due to memory cells which are designed to deal with long-term dependencies. In the set of experiments conducted on the Luther bible database and transform wordforms from Early New High German (ENHG) 14th - 16th centuries to the corresponding modern wordforms in New High German (NHG). We compare our proposed supervised model LSTM to various methods for computing word alignments using statistical, heuristic models. Our new proposed LSTM outperforms the other three state-of-the-art methods. The evaluation shows the accuracy of our model on the known wordforms is 93.90% and on the unknown wordforms is 87.95%, while the accuracy of the existing state-of-the-art combined approach of the wordlist-based and rule-based normalization models is 92.93% for known and 76.88% for unknown tokens. Our proposed LSTM model outperforms on normalizing the modern wordform to historical wordform. The performance on seen tokens is 93.4%, while for unknown tokens is 89.17%.


title: An application of minimum description length clustering to partitioning learning curves

abstract: We apply a minimum description length-based clustering technique to the problem of partitioning a set of learning curves. The goal is to partition experimental data collected from different sources into groups of sources that are statistically the same. We solve this problem by defining statistical models for the data generating processes, then partitioning them using the normalized maximum likelihood criterion. Unlike many alternative model selection methods, this approach is optimal (in a minimax coding sense) for data of any sample size. We present an application of the method to the cognitive modeling problem of partitioning of human learning curves for different categorization tasks


title: ANTIGLYCATION, ANTIOXIDANT AND ANTIDIABETIC ACTIVITY OF MATURE STRAWBERRY (FRAGARIA × ANANASSA) FRUITS

abstract: A comprehensive in vitro study involving antiglycation, antioxidant and anti-diabetic assays was carried out in mature fruits of strawberry. The effect of aqueous extract of mature strawberry fruits on glycation of guanosine with glucose and fructose with or without oxidizing entities like reactive oxygen species was analyzed. Spectral studies showed that glycation and/or fructation of guanosine was significantly inhibited by aqueous extract of strawberry. The UV absorbance of the glycation reactions was found to be maximum at 24 hrs. and decreased consecutively for 48, 72 and 96 hours. Inhibition of oxidative damage due to reactive oxygen species was also observed in presence of the plant extract. To our knowledge, antiglycation activity of strawberry fruit with reference to guanosine is being demonstrated for the first time. To determine the antioxidant activity of the plant extract, in vitro antioxidant enzymes assays (catalase, peroxidase, polyphenol oxidase and ascorbic acid oxidase) and antioxidant assays (DPPH, superoxide anion scavenging activity and xanthine oxidase) were performed. Maximum inhibition activity of 79.36%, 65.62% and 62.78% was observed for DPPH, superoxide anion scavenging and xanthine oxidase, respectively. In antidiabetic assays, IC50 value for alpha - amylase and alpha - glucosidase activity of fruit extract of strawberry was found to be 86.47 ± 1.12µg/ml and 76.83 ± 0.93 µg/ml, respectively. Thus, the aqueous extract of strawberry showed antiglycation, antioxidant and antidiabetic properties indicating that strawberry fruits, as a dietary supplement, may be utilized towards management of diabetes.


title: How to Build Templates for RDF Question/Answering: An Uncertain Graph Similarity Join Approach

abstract: A challenging task in the natural language question answering (Q/A for short) over RDF knowledge graph is how to bridge the gap between unstructured natural language questions (NLQ) and graph-structured RDF data (GOne of the effective tools is the "template", which is often used in many existing RDF Q/A systems. However, few of them study how to generate templates automatically. To the best of our knowledge, we are the first to propose a join approach for template generation. Given a workload D of SPARQL queries and a set N of natural language questions, the goal is to find some pairs q, n, for q∈ D ∧ n ∈, N, where SPARQL query q is the best match for natural language question n. These pairs provide promising hints for automatic template generation. Due to the ambiguity of the natural languages, we model the problem above as an uncertain graph join task. We propose several structural and probability pruning techniques to speed up joining. Extensive experiments over real RDF Q/A benchmark datasets confirm both the effectiveness and efficiency of our approach.


title: Obsessive± compulsive disorder after closed head injury: review of literature and report of four cases

abstract: An increasing number of recent reports has pointed to the underlying neuropathological substrate for obsessive-compulsive disorder OCD. Neuropsychological tseting may suggest underlying organicity even though the neuroimaging studies and the neurological examination may be normal. Earlier reports are not in agreement about the laterality of deficits. Abnormalities in frontal regions, limbic areas and basal ganglia are noted in functional neuroimaging and neuropsychological studies. In closed head injury the damage tends to be diffuse, and it is not easy to clearly localize deficits or to determine their laterality. In this paper we review the various theories and literature on OCD and organicity, and report on four individuals who developed OCD symptoms after closed head injury. We also discuss their neuroimaging and neuropsychological testing results.


title: The interim test effect: testing prior material can facilitate the learning of new material.

abstract: A wealth of prior research has shown that testing can improve subsequent learning of the initially tested material. In contrast, only one recent study has shown that an interim test over prior material can improve learning of subsequent new material (i.e., an interim-test effect). Five experiments replicated and extended this initial work by exploring the extent to which interim test effects generalize to complex text material. Participants were prompted to recall each section of an expository text before moving on to study the next section, or were only prompted to recall after the final section. In all experiments, recall of the final, target section was greater when prior sections had received interim tests versus no interim tests. Experiment 3 established that the effect was due to interim testing in particular rather than to intervening activity in general. Experiment 4 established that the effect was not due to test expectancy differences. In contrast to prior research, Experiment 4 also provided evidence that the effect is not due to release from proactive interference. We discuss other possible mechanisms underlying interim-test effects with text, including shifting to more effective encoding strategies.


title: Living with Seal Robots in a Care House - Evaluations of Social and Physiological Influences

abstract: Robot therapy for elderly residents in a care house has been conducted since June 2005. Two therapeutic seal robots were introduced and activated for over 9 hours every day to interact with the residents. This paper presents the results of this experiment. In order to investigate the psychological and social effects of the robots, the activities of the residents in public areas were recorded by video cameras during daytime hours (8:30?18:00) for over 2 months. In addition, the hormones 17-Ketosteroid sulfate (17-KS-S) and 17-hydroxycorticosteroids (17-OHCS) in the residents' urine were obtained and analyzed. The results indicate that their social interaction was increased through interaction with the seal robots. Furthermore, the results of the urinary tests showed that the reactions of the subjects' vital organs to stress improved after the introduction of the robots.


title: Monocular Object Detection Using 3D Geometric Primitives

abstract: Multiview object detection methods achieve robustness in adverse imaging conditions by exploiting projective consistency across views. In this paper, we present an algorithm that achieves performance comparable to multiview methods from a single camera by employing geometric primitives as proxies for the true 3D shape of objects, such as pedestrians or vehicles. Our key insight is that for a calibrated camera, geometric primitives produce predetermined location-specific patterns in occupancy maps. We use these to define spatially-varying kernel functions of projected shape. This leads to an analytical formation model of occupancy maps as the convolution of locations and projected shape kernels. We estimate object locations by deconvolving the occupancy map using an efficient template similarity scheme. The number of objects and their positions are determined using the mean shift algorithm. The approach is highly parallel because the occupancy probability of a particular geometric primitive at each ground location is an independent computation. The algorithm extends to multiple cameras without requiring significant bandwidth. We demonstrate comparable performance to multiview methods and show robust, realtime object detection on full resolution HD video in a variety of challenging imaging conditions.


Deep Spectral Clustering Learning


title: Facial Component-Landmark Detection With Weakly-Supervised LR-CNN

abstract: In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously. Most of the existing course-to-fine facial detectors fail to detect landmark accurately without lots of fully labeled data, which are costly to obtain. We can handle the task with a small amount of finely labeled data. First, deep convolutional generative adversarial networks are utilized to generate training samples with weak labels, as data preparation. Then, through weakly supervised learning, our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Detecting unblocked components first helps us to focus on the informative area, resulting in a better performance. Additionally, to improve the performance of the above tasks, we design two models as follows: 1) we add AnchorAlign in the region proposal networks to accurately localize components and 2) we propose a two-branch model consisting classification branch and regression branch to detect landmark. Extensive evaluations on benchmark datasets indicate that our proposed approach is able to complete the multi-task facial detection and outperforms the state-of-the-art facial component and landmark detection algorithms.


title: Topic Modelling in Parallel and Comparable Fiction Texts (the case study of English and Russian prose)

abstract: The paper is devoted to processing parallel and comparable corpora by means of topic modelling. We focus our attention on Russian and English parallel and comparable texts. We use Latent Dirichlet Allocation (LDA) algorithm for building topic models of fiction texts, evaluation of compatibility for the original text and its translation(s), selection of possible translation equivalents.


title: Laser-assisted facial contouring using a thermally confined 1444-nm Nd-YAG laser: a new paradigm for facial sculpting and rejuvenation.

abstract: The micropulsed 1444-nm neodymium-doped lipolysis laser exhibits favorable characteristics for novel application in facial contouring. The study described herein is the first clinical report of laser-assisted facial contouring (LAFC). We retrospectively reviewed records of 478 LAFC patients (mean age 52) who underwent contouring of 1278 individual mid- and lower facial treatment sites over 18 months. Along with clinical assessment, study parameters evaluated among "original" and "modified" (where protocol updates included deep dermal soft tissue coagulation as an optional step) protocol groups included laser power, pulse energy, and total energy delivery as well as lipoaspirate volume at each treatment site. Mean power and pulse energy were similar (within 5%) and total energy use was greater (70% higher for mid- and lower face) in the original protocol group. Lipoaspirate volume was similar for both groups for the midface (within 10%) but elevated in the modified protocol group for the lower face (40% higher). Treatment complications were observed in 47 of 363 treatment sites (13%) in the original and in 12 of 915 treatment sites (1%) in the modified protocol group with the majority (63%) of the complications comprising over- versus undercorrections of desired tissue contour. Clinical efficacy varied with improvements of mid- and/or lower facial contour ranging from marginal to subtle to very apparent. LAFC as detailed herein is a novel treatment modality that enables selective soft tissue removal for greater precision in three-dimensional contouring of the face. Protocol modifications based on laboratory and observed tissue photothermodynamics have improved LAFC safety.


title: ‘A tale of two cases:’ The health, illness, and physical activity stories of two children living with cystic fibrosis

abstract: Storytelling is perennial, and central to the human condition. Although illness may shatter identity and one’s role and place in the broader social world, narrative may aid in the process of self-reparation. Despite the merits of the narrative approach, it has been underutilized with children who are living with cystic fibrosis (CF). The role that illness narratives may play in influencing CF youths’ physical activity also remains poorly investigated. This article drew on the qualitative case study methodological tradition to narrate the stories of two children living with CF at a children’s hospital in Canada. The findings beg researchers to consider (a) how children with life-limiting diseases borrow multiple illness narrative types, (b) the role of development in influencing the kinds of stories that children can tell, and (c) the impact of illness narratives on physical activity. By rendering the tales of two CF youth in this study, we respond to Aurthur Frank’s call; taking a multiple narrative turn,...


title: Deep Learning for Video Game Playing

abstract: In this paper, we review recent deep learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.


title: Superjunction IGBT vs. FS IGBT for 200°C operation

abstract: Adverse effects on the temperature-dependent properties of Insulated Gate Bipolar Transistors (IGBTs) are often observed when the junction temperature exceeds 175°C. It is believed that attempts in enabling the operation of IGBTs at 200°C will inevitably introduce penalties in some electrical properties of the device. Therefore, the trade-off relationship between the key parameters must be carefully considered. In this paper, we present for the first time that the Superjunction (SJ) IGBT can deliver the best performance trade-offs between the conduction and turn-off losses while maintaining low leakage current at 200°C when compared with other IGBTs. Moreover, the output and switching characteristics of SJ IGBTs depend on the SJ pillar geometry and the doping level of the SJ pillars. We discover that the structure with disconnected p-body and p-pillar with moderate pillar doping concentration (D pn ) is the key in achieving excellent turn-off behavior without sacrificing the on-state voltage drop (V on ) at 200°C.


Computer virus-antivirus coevolution


title: Ensemble of keyword extraction methods and classifiers in text classification

abstract: Text classification is a domain with high dimensional feature space.Extracting the keywords as the features can be extremely useful in text classification.An empirical analysis of five statistical keyword extraction methods.A comprehensive analysis of classifier and keyword extraction ensembles.For ACM collection, a classification accuracy of 93.80% with Bagging ensemble of Random Forest. Automatic keyword extraction is an important research direction in text mining, natural language processing and information retrieval. Keyword extraction enables us to represent text documents in a condensed way. The compact representation of documents can be helpful in several applications, such as automatic indexing, automatic summarization, automatic classification, clustering and filtering. For instance, text classification is a domain with high dimensional feature space challenge. Hence, extracting the most important/relevant words about the content of the document and using these keywords as the features can be extremely useful. In this regard, this study examines the predictive performance of five statistical keyword extraction methods (most frequent measure based keyword extraction, term frequency-inverse sentence frequency based keyword extraction, co-occurrence statistical information based keyword extraction, eccentricity-based keyword extraction and TextRank algorithm) on classification algorithms and ensemble methods for scientific text document classification (categorization). In the study, a comprehensive study of comparing base learning algorithms (Naive Bayes, support vector machines, logistic regression and Random Forest) with five widely utilized ensemble methods (AdaBoost, Bagging, Dagging, Random Subspace and Majority Voting) is conducted. To the best of our knowledge, this is the first empirical analysis, which evaluates the effectiveness of statistical keyword extraction methods in conjunction with ensemble learning algorithms. The classification schemes are compared in terms of classification accuracy, F-measure and area under curve values. To validate the empirical analysis, two-way ANOVA test is employed. The experimental analysis indicates that Bagging ensemble of Random Forest with the most-frequent based keyword extraction method yields promising results for text classification. For ACM document collection, the highest average predictive performance (93.80%) is obtained with the utilization of the most frequent based keyword extraction method with Bagging ensemble of Random Forest algorithm. In general, Bagging and Random Subspace ensembles of Random Forest yield promising results. The empirical analysis indicates that the utilization of keyword-based representation of text documents in conjunction with ensemble learning can enhance the predictive performance and scalability of text classification schemes, which is of practical importance in the application fields of text classification.


title: An approach to online identification of Takagi-Sugeno fuzzy models

abstract: An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.


title: Assessment of algorithms for mitosis detection in breast cancer histopathology images

abstract: The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.


title: Adapting the Tesseract open source OCR engine for multilingual OCR

abstract: We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages. Effort has been concentrated on enabling generic multi-lingual operation such that negligible customization is required for a new language beyond providing a corpus of text. Although change was required to various modules, including physical layout analysis, and linguistic post-processing, no change was required to the character classifier beyond changing a few limits. The Tesseract classifier has adapted easily to Simplified Chinese. Test results on English, a mixture of European languages, and Russian, taken from a random sample of books, show a reasonably consistent word error rate between 3.72% and 5.78%, and Simplified Chinese has a character error rate of only 3.77%.


title: Effect of facial makeup style recommendation on visual sensibility

abstract: As ubiquitous commerce using IT convergence technologies is coming, it is important for the strategy of cosmetic sales to investigate the sensibility and the degree of preference in the environment for which the makeup style has changed focusing on being consumer centric. The users caused the diversification of the facial makeup styles, because they seek makeup and individuality to satisfy their needs. In this paper, we proposed the effect of the facial makeup style recommendation on visual sensibility. Development of the facial makeup style recommendation system used a user interface, sensibility analysis, weather forecast, and collaborative filtering for the facial makeup styles to satisfy the user's needs in the cosmetic industry. Collaborative filtering was adopted to recommend facial makeup style of interest for users based on the predictive relationship discovered between the current user and other previous users. We used makeup styles in the survey questionnaire. The pictures of makeup style details, such as foundation, color lens, eye shadow, blusher, eyelash, lipstick, hairstyle, hairpin, necklace, earring, and hair length were evaluated in terms of sensibility. The data were analyzed by SPSS using ANOVA and factor analysis to discover the most effective types of details from the consumer's sensibility viewpoint. Sensibility was composed of three concepts: contemporary, mature, and individual. The details of facial makeup styles were positioned in 3D-concept space to relate each type of detail to the makeup concept regarding a woman's cosmetics. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity of this system.


title: Facility-imposed barriers to early utilization of focused antenatal care services in Mangochi District, Malawi – a mixed methods assessment

abstract: Focused Antenatal Care (FANC) is advocated by the World Health Organization (WHO) as a key service approach to improving the health of pregnant women. Four targeted visits to antenatal clinics are recommended starting in the first trimester. First trimester attendance for FANC in Mangochi District, Malawi was low at 8%. FANC has mainly been promoted through health facility based communication activities with less emphasis on activities at community level. We developed and tested a community focused health communication approach “Community Driven Total FANC Attendance (CDTFA)” with the aim of increasing FANC clinic attendance. We included a research component in order to understand the context and responses of community members to this intervention. CDTFA meetings were designed in parallel with data gathering with approval of the local research ethics committee and community stakeholders. Participants in both the CDTFA meetings and data gathering activities, undertaken from December, 2015 to June, 2016 were of reproductive age (15–49 years). Data were collected through flexible interactive processes from participants through recording on pre-designed forms. Quantitative data were processed and analyzed in Microsoft Excel, while qualitative data were manually analyzed to identify themes. In total, 403 CDTFA meetings were held. In the course of interactions with community members, some barriers that affected early utilization of FANC services were identified. Women who did not bring their partners and those who could not bring along with them cloth wraps for the newborn to clinics were not allowed to access FANC services. Payment for authorization letters from village heads for women who have no partners and user fees in non-governmental health facilities were also identified as barriers. Despite the benefits of FANC services, health authorities in the District should ensure that use and promotion of the approach does not inadvertently bar some pregnant women from accessing services. There is a need to explore strategies and redesign an approach to health promotion that will promote uptake of the integrated services in FANC clinics without infringing on women’s rights to access health care.


title: Automatic semantic classification of scientific literature according to the hallmarks of cancer

abstract: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html.simon.baker@cl.cam.ac.uk.


title: Machine Learning Based Detection of Clickbait Posts in Social Media

abstract: Clickbait (headlines) make use of misleading titles that hide critical information from or exaggerate the content on the landing target pages to entice clicks. As clickbaits often use eye-catching wording to attract viewers, target contents are often of low quality. Clickbaits are especially widespread on social media such as Twitter, adversely impacting user experience by causing immense dissatisfaction. Hence, it has become increasingly important to put forward a widely applicable approach to identify and detect clickbaits. In this paper, we make use of a dataset from the clickbait challenge 2017 (clickbait-challenge.com) comprising of over 21,000 headlines/titles, each of which is annotated by at least five judgments from crowdsourcing on how clickbait it is. We attempt to build an effective computational clickbait detection model on this dataset. We first considered a total of 331 features, filtered out many features to avoid overfitting and improve the running time of learning, and eventually selected the 60 most important features for our final model. Using these features, Random Forest Regression achieved the following results: MSE=0.035 MSE, Accuracy=0.82, and F1-sore=0.61 on the clickbait class.


title: High speed fixed point DSOGI PLL implementation on FPGA for synchronization of grid connected power converters

abstract: Power converters are the subject of extensive research because of their ability to work under different operating conditions, providing bidirectional power flow, high dynamic range and fast response. These advantages allow them to be used as an interface between the electric grid and many high power applications such as motors, energy storage, active filters and renewable energy sources. To be able to connect to the utility grid, every power converter must be provided with a synchronization method. The synchronization algorithms are mostly based on the well known Synchronous Reference Frame Phase Locked Loop (SRF-PLL) plus some pre-filter stage that can be achieved by different algorithms of variable complexity, usually implemented on Digital Signal Procesors (DSP) or Microcontrollers. In this paper a simple and highly effective Field Programable Gate Array (FPGA) implementation of a Phase Locked Loop (PLL) algorithm based on a Dual Second Order Generalized Integrator PLL (DSOGI-PLL) is presented in detail, along with the auxiliary circuitry needed to acquire the grid voltage information. Using a fast-prototype high-level synthesis tool, design time is drastically reduced without the need of any Hardware Description Language (HDL) code. Both simulation with MATLAB Simulink and experimental results on a Xilinx FPGA, show a robust behaviour even against frequency steps, severe distortion and unbalances in the power input.


title: EEG classification of imagined syllable rhythm using Hilbert spectrum methods

abstract: We conducted an experiment to determine whether the rhythm with which imagined syllables are produced may be decoded from EEG recordings. High density EEG data were recorded for seven subjects while they produced in imagination one of two syllables in one of three different rhythms. We used a modified second-order blind identification (SOBI) algorithm to remove artefact signals and reduce data dimensionality. The algorithm uses the consistent temporal structure along multi-trial EEG data to blindly decompose the original recordings. For the four primary SOBI components, joint temporal and spectral features were extracted from the Hilbert spectra (HS) obtained by a Hilbert–Huang transformation (HHT). The HS provide more accurate time-spectral representations of non-stationary data than do conventional techniques like short-time Fourier spectrograms and wavelet scalograms. Classification of the three rhythms yields promising results for inter-trial transfer, with performance for all subjects significantly greater than chance. For comparison, we tested classification performance of three averaging-based methods, using features in the temporal, spectral and time–frequency domains, respectively, and the results are inferior to those of the SOBI-HHT-based method. The results suggest that the rhythmic structure of imagined syllable production can be detected in non-invasive brain recordings and provide a step towards the development of an EEG-based system for communicating imagined speech.


title: Vision through other senses: Practical use of Sensory Substitution devices as assistive technology for visual rehabilitation

abstract: Visual-to-auditory Sensory Substitution Devices (SSDs) are non-invasive sensory aids that provide visual information to the blind via their functioning senses, such as audition. For years SSDs have been confined to laboratory settings, but we believe the time has come to use them also for their original purpose of real-world practical visual rehabilitation. Here we demonstrate this potential by presenting for the first time new features of the EyeMusic SSD, which gives the user whole-scene shape, location & color information. These features include higher resolution and attempts to overcome previous stumbling blocks by being freely available to download and run from a smartphone platform. We demonstrate with use the EyeMusic the potential of SSDs in noisy real-world scenarios for tasks such as identifying and manipulating objects. We then discuss the neural basis of using SSDs, and conclude by discussing other steps-in-progress on the path to making their practical use more widespread.


title: CLOUDLET: towards mapreduce implementation on virtual machines

abstract: The existing MapReduce framework in virtualized environment suffers from poor performance, due to the heavy overhead of I/O virtualization, and management difficulty for storage and computation. To address the problems, we propose Cloudlet, a novel MapReduce framework on virtual machines. The aim of Cloudlet design is to overcome the overhead of VM while benefiting of the other features of VM (i.e. management and reliability issues).


Space, social networks and the neighbourhood in digital production: the role of events in Tech City, London


title: A design of high-gain Vivaldi antenna and its array research

abstract: In order to meet the request of ultra wideband (UWB) system to antenna, In this paper, a kind of Vivaldi antenna with ripple is designed, and its array is studied. Its working frequency band is from 400MHz to 900MHz, the gain of the antenna array unit in the low frequency band is 5.11dB, and 8.68dB in the high frequency band. In addition, we studied the antenna array respectively according to Dolph-chebyshef series distribution array of sequence distribution, the test results showed that Dolph-Chebyshef series distribution array can improve the gain of the antenna array and can reduce the side lobe level, the gain in the low frequency band can reach 26.7dB and gain at high frequency band to 28.4dB. The simulation results show that the array antenna has ideal gain effect and excellent radiation performance in the operating frequency band.


title: Experiences While Selecting, Adapting and Implementing ERP Systems in SMEs: A Case Study

abstract: Enterprise Resource Planning (ERP) systems are important for all kinds of enterprises. The selection and implementation of such systems are very difficult and many projects do not meet their expectations. While large companies have financial and human resources to engage in such a project, the situation for smaller sized enterprises is different. They have only limited budget, human resources and experiences. Hence, this case study describes an EPR selection and implementation project at a small-sized enterprise by analyzing the selection and implementation procedure and its critical success factors.


title: Future Logistics: What to Expect, How to Adapt

abstract: As a result of global societal and economic as well as technological developments logistics and supply chains face unprecedented challenges. Climate change, the need for more sustainable products and processes, major political changes, the advance of “Industry 4.0” and cyber-physical system are some of the challenges that require radical solutions, but also present major opportunities. The authors argue that logistics has to reinvent itself, not only to address these chal-lenges but also to cope with mass individualization on the one hand while exploit-ing broad-fielded business applications of artificial intelligence on the other hand. An essential challenge will be to find a compromise between these two develop-ments – in line and in combination with the known triple-bottom line for sustaina-bility – that will define supply chains and logistics concepts of the future.


title: Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks

abstract: Driver’s status is crucial because one of the main reasons for motor vehicular accidents is related to driver’s inattention or drowsiness. Drowsiness detector on a car can reduce numerous accidents. Accidents occur because of a single moment of negligence, thus driver monitoring system which works in real-time is necessary. This detector should be deployable to an embedded device and perform at high accuracy. In this paper, a novel approach towards real-time drowsiness detection based on deep learning which can be implemented on a low cost embedded board and performs with a high accuracy is proposed. Main contribution of our paper is compression of heavy baseline model to a light weight model deployable to an embedded board. Moreover, minimized network structure was designed based on facial landmark input to recognize whether driver is drowsy or not. The proposed model achieved an accuracy of 89.5% on 3-class classification and speed of 14.9 frames per second (FPS) on Jetson TK1.


title: Diffusion MRI at 25: Exploring brain tissue structure and function

abstract: Diffusion MRI (or dMRI) came into existence in the mid-1980s. During the last 25 years, diffusion MRI has been extraordinarily successful (with more than 300,000 entries on Google Scholar for diffusion MRI). Its main clinical domain of application has been neurological disorders, especially for the management of patients with acute stroke. It is also rapidly becoming a standard for white matter disorders, as diffusion tensor imaging (DTI) can reveal abnormalities in white matter fiber structure and provide outstanding maps of brain connectivity. The ability to visualize anatomical connections between different parts of the brain, non-invasively and on an individual basis, has emerged as a major breakthrough for neurosciences. The driving force of dMRI is to monitor microscopic, natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. Water molecules are thus used as a probe that can reveal microscopic details about tissue architecture, either normal or in a diseased state.


title: Theory and Prosecution Evidence

abstract: If investors are not fully rational, what can smart money do? This paper provides an example in which smart money can strategically take advantage of investors' behavioral biases and manipulate the price process to make profit. The paper considers three types of traders, behavior-driven investors who are less willing to sell losers than to sell winners (dispositional effect), arbitrageurs, and a manipulator who can influence asset prices. We show that, due to the investors' behavioral biases and the limit of arbitrage, the manipulator can profit from a "pump-and-dump" trading strategy by accumulating the speculative asset while pushing the asset price up, and then selling the asset at high prices. Since nobody has private information, manipulation here is completely trade-based. The paper also endogenously derives several asset-pricing anomalies, including excess volatility, momentum and reversal. As an empirical test, the paper presents some empirical evidence from the U.S. SEC prosecution of "pump-and-dump" manipulation cases that are consistent with our model.


title: By the Community & For the Community: A Deep Learning Approach to Assist Collaborative Editing in Q&A Sites

abstract: Community edits to questions and answers (called post edits) plays an important role in improving content quality in Stack Overflow. Our study of post edits in Stack Overflow shows that a large number of edits are about formatting, grammar and spelling. These post edits usually involve small-scale sentence edits and our survey of trusted contributors suggests that most of them care much or very much about such small sentence edits. To assist users in making small sentence edits, we develop an edit-assistance tool for identifying minor textual issues in posts and recommending sentence edits for correction. We formulate the sentence editing task as a machine translation problem, in which an original sentence is "translated" into an edited sentence. Our tool implements a character-level Recurrent Neural Network (RNN) encoder-decoder model, trained with about 6.8 millions original-edited sentence pairs from Stack Overflow post edits. We evaluate our edit assistance tool using a large-scale archival post edits, a field study of assisting a novice post editor, and a survey of trusted contributors. Our evaluation demonstrates the feasibility of training a deep learning model with post edits by the community and then using the trained model to assist post editing for the community.


title: Religious Meaning and Subjective Well-Being in Late Life

abstract: Objectives. The purpose of this study is to examine the relationship between religious meaning and subjective well-being. A major emphasis is placed on assessing race differences in the relationship between these constructs. Methods. Interviews were conducted with a nationwide sample of older White and older Black adults. Survey items were administered to assess a sense of meaning in life that is derived specifically from religion. Subjective well-being was measured with indices of life satisfaction, self-esteem, and optimism. Results. The findings suggest that older adults who derive a sense of meaning in life from religion tend to have higher levels of life satisfaction, self-esteem, and optimism. The data further reveal that older Black adults are more likely to find meaning in religion than older White adults. In addition, the relationships among religious meaning, life satisfaction, self-esteem, and optimism tend to be stronger for older African Americans persons than older White persons. Discussion. Researchers have argued for some time that religion may be an important source of resilience for older Black adults, but it is not clear how these beneficial effects arise. The data from this study suggest that religious meaning may be an important factor.


Acute Gastric Dilatation, Necrosis and Perforation Complicating Restrictive-Type Anorexia Nervosa


title: A Multi-ESD-Path Low-Noise Amplifier With a 4.3-A TLP Current Level in 65-nm CMOS

abstract: This paper studies the electrostatic discharge (ESD)-protected RF low-noise amplifiers (LNAs) in 65-nm CMOS technology. Three different ESD designs, including double-diode, modified silicon-controlled rectifier (SCR), and modified-SCR with double-diode configurations, are employed to realize ESD-protected LNAs at 5.8 GHz. By using the modified-SCR in conjunction with double-diode, a 5.8-GHz LNA with multiple ESD current paths demonstrates a 4.3-A transmission line pulse (TLP) failure level, corresponding to a ~ 6.5-kV Human-Body-Mode (HBM) ESD protection level. Under a supply voltage of 1.2 V and a drain current of 6.5 mA, the proposed ESD-protected LNA demonstrates a noise figure of 2.57 dB with an associated power gain of 16.7 dB. The input third-order intercept point (IIP3) is - 11 dBm, the input and output return losses are greater than 15.9 and 20 dB, respectively.


title: Submanifold Sparse Convolutional Networks

abstract: Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for instance, photos), many other data sources are inherently sparse. Examples include pen-strokes forming on a piece of paper, or (colored) 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera. Standard "dense" implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce a sparse convolutional operation tailored to processing sparse data that differs from prior work on sparse convolutional networks in that it operates strictly on submanifolds, rather than "dilating" the observation with every layer in the network. Our empirical analysis of the resulting submanifold sparse convolutional networks shows that they perform on par with state-of-the-art methods whilst requiring substantially less computation.


title: Extracting and Visualising Biographical Events from Wikipedia

abstract: This work presents a proposal for the development of a natural language processing module for event and temporal analysis of biographies as available in Wikipedia. At the current level of development, we restricted the extraction to temporally anchored events as they represent salient information which can be further used to extract additional events and facilitate their chronological ordering and the representation of a person’s timeline. Visualising data about basic facts concerning groups of people helps with historical reasoning and enables comparisons among them.


title: Relational Algebra Teaching Support Tool

abstract: In recent years, there has been an increasing supply of digital, pedagogical tools, known as Digital Learning Objects (DLO) – digital resources (image, film, animation, etc.) and software developed specifically for educational purposes. In the area of Computer Science, teaching Databases present a particular challenge because of a lack of quality tools to work with Relational Algebra. The present study proposes a web-based tool to support teaching and learning Relational Algebra – an important subject that is particularly difficult for students to understand. The purpose of the proposed tool is to provide an alternative method for teaching Relational Algebra operations, such as: selection, projection, union, set difference, rename, intersection, Cartesian product, natural join, division and some aggregate functions. In addition, we propose a graphic definition of a database schema (using features such as drag and drop, column highlights, lines, fields, etc.), so students can use the tool easily, and in conjunction with the theory taught regarding the definition languages (DDL) and data manipulation (DML). We intend for this tool to serve as an appropriate means for teaching and learning Relational Algebra, contributing to the development of new teaching skills, as well motivating the students in the process of learning.


title: An Overview of Reconfigurable Hardware in Embedded Systems

abstract: Over the past few years, the realm of embedded systems has expanded to include a wide variety of products, ranging from digital cameras, to sensor networks, to medical imaging systems. Consequently, engineers strive to create ever smaller and faster products, many of which have stringent power requirements. Coupled with increasing pressure to decrease costs and time-to-market, the design constraints of embedded systems pose a serious challenge to embedded systems designers. Reconfigurable hardware can provide a flexible and efficient platform for satisfying the area, performance, cost, and power requirements of many embedded systems. This article presents an overview of reconfigurable computing in embedded systems, in terms of benefits it can provide, how it has already been used, design issues, and hurdles that have slowed its adoption.


title: A detail analysis on intrusion detection datasets

abstract: To simulate an efficient Intrusion Detection System (IDS) model, enormous amount of data are required to train and testing the model. To improve the accuracy and efficiency of the model, it is essential to infer the statistical properties from the observable elements of th e dataset. In this work, we have proposed some data preprocessing techniques such as filling the missing values, removing redundant samples, reduce the dimension, selecting most relevant features and finally, normalize the samples. After data preprocessing, we have simulated and tested the dataset by applying various data mining algorithms such as Support Vector Machine (SVM), Decision Tree, K nearest neighbor, K-Mean and Fuzzy C-Mean Clustering which provides better result in less computational time.


title: An Approach For Transforming of Relational Databases to OWL Ontology

abstract: Rapid growth of documents, web pages, and other types of text content is a huge challenge for the modern content management systems. One of the problems in the areas of information storage and retrieval is the lacking of semantic data. Ontologies can present knowledge in sharable and repeatedly usable manner and provide an effective way to reduce the data volume overhead by encoding the structure of a particular domain. Metadata in relational databases can be used to extract ontology from database in a special domain. According to solve the problem of sharing and reusing of data, approaches based on transforming relational database to ontology are proposed. In this paper we propose a method for automatic ontology construction based on relational database. Mining and obtaining further components from relational database leads to obtain knowledge with high semantic power and more expressiveness. Triggers are one of the database components which could be transformed to the ontology model and increase the amount of power and expressiveness of knowledge by presenting part of the knowledge dynamically


title: Interactive Learning with Convolutional Neural Networks for Image Labeling

abstract: Recently, deep learning models, such as Convolutional Neural Networks, have shown to give good performance for various computer vision tasks. A pre-requisite for such models is to have access to lo ...


title: Learning Discriminative Reconstructions for Unsupervised Outlier Removal

abstract: We study the problem of automatically removing outliers from noisy data, with application for removing outlier images from an image collection. We address this problem by utilizing the reconstruction errors of an autoencoder. We observe that when data are reconstructed from low-dimensional representations, the inliers and the outliers can be well separated according to their reconstruction errors. Based on this basic observation, we gradually inject discriminative information in the learning process of an autoencoder to make the inliers and the outliers more separable. Experiments on a variety of image datasets validate our approach.


title: Sentence completion task using web-scale data

abstract: We propose a method to automatically answer SAT-style sentence completion questions using web-scale data. Web-scale da-ta have been used in many language studies and have been found to be a very useful resource for improving accuracy in sentence completion task. Our method employs assorted N-gram probability information for each candidate word. We also proposed back-off strategy was used to remove zero probabilities. We found that the accuracy of our proposed method improved by 52-87% over the current state-of-the-art.


title: Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods

abstract: Motivation: Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows identifying the sequenced community members and reconstructing taxonomic bins with sequence data for the individual taxa. For the massive datasets generated by next-generation sequencing technologies, this cannot be performed with de-novo phylogenetic inference methods. We describe an algorithm and the accompanying software, taxator-tk, which performs taxonomic sequence assignment by fast approximate determination of evolutionary neighbors from sequence similarities. Results: Taxator-tk was precise in its taxonomic assignment across all ranks and taxa for a range of evolutionary distances and for short as well as for long sequences. In addition to the taxonomic binning of metagenomes, it is well suited for profiling microbial communities from metagenome samples because it identifies bacterial, archaeal and eukaryotic community members without being affected by varying primer binding strengths, as in marker gene amplification, or copy number variations of marker genes across different taxa. Taxator-tk has an efficient, parallelized implementation that allows the assignment of 6 Gb of sequence data per day on a standard multiprocessor system with 10 CPU cores and microbial RefSeq as the genomic reference data. Availability and implementation: Taxator-tk source and binary program files are publicly available at http://algbio.cs.uni-duesseldorf.de/software/. Contact: Alice.McHardy@uni-duesseldorf.de Supplementary information: Supplementary data are available at Bioinformatics online.


title: EEG and MEG Data Analysis in SPM8

abstract: SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.


title: Analyzing expert behaviors in collaborative networks

abstract: Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns. In particular, we observe an interesting phenomenon that an expert tends to transfer a task to someone whose knowledge is neither too similar to nor too different from his own. Based on this observation, an expertise difference based routing pattern is developed. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to combine them. For a held-out set of tasks, our model not only explains their real routing sequences very well, but also accurately predicts their completion time. Under three different quality measures, our method significantly outperforms all the alternatives with more than 75% accuracy gain. In practice, with the help of our model, hypotheses on how to improve a collaborative network can be tested quickly and reliably, thereby significantly easing performance improvement of collaborative networks.


title: CAN DEEP MACHINE LEARNING OUTSMART THE MARKET? A COMPARISON BETWEEN ECONOMETRIC MODELLING AND LONG- SHORT TERM MEMORY

abstract: Using long-short term memory (LSTM) recurrent neural network (RNN) architecture, we analyse data from the Romanian stock markets in the attempt to forecast its future trend. Then we try to compare the results using the classical statistical modelling tools, further employing back testing to prove our findings. We believe that the LSTM should be the next tool in balancing portfolios and reducing market risk.


title: ACCDIST: an accent similarity metric for accent recognition and diagnosis

abstract: ACCDIST is a metric of the similarity between speakers’ accents that is largely uninfluenced by the individual characteristics of the speakers’ voices. In this article we describe the ACCDIST approach and contrast its performance with formant and spectral-envelope similarity measures. Using a database of 14 regional accents of the British Isles, we show that the ACCDIST metric outperforms linear discriminant analysis based on either spectral-envelope or normalised formant features. Using vowel measurements from 10 male and 10 female speakers in each accent, the best spectral-envelope metric assigned the correct accent group to a held-out speaker 78.8% of the time, while the best normalised formant-frequency metric was correct 89.4% of the time. The ACCDIST metric based on spectral-envelope features, scored 92.3%. ACCDIST is also effective in clustering speakers by accent and has applications in speech technology, language learning, forensic phonetics and accent studies.


title: Green spaces and cognitive development in primary schoolchildren

abstract: Exposure to green space has been associated with better physical and mental health. Although this exposure could also influence cognitive development in children, available epidemiological evidence on such an impact is scarce. This study aimed to assess the association between exposure to green space and measures of cognitive development in primary schoolchildren. This study was based on 2,593 schoolchildren in the second to fourth grades (7-10 y) of 36 primary schools in Barcelona, Spain (2012-2013). Cognitive development was assessed as 12-mo change in developmental trajectory of working memory, superior working memory, and inattentiveness by using four repeated (every 3 mo) computerized cognitive tests for each outcome. We assessed exposure to green space by characterizing outdoor surrounding greenness at home and school and during commuting by using high-resolution (5 m × 5 m) satellite data on greenness (normalized difference vegetation index). Multilevel modeling was used to estimate the associations between green spaces and cognitive development. We observed an enhanced 12-mo progress in working memory and superior working memory and a greater 12-mo reduction in inattentiveness associated with greenness within and surrounding school boundaries and with total surrounding greenness index (including greenness surrounding home, commuting route, and school). Adding a traffic-related air pollutant (elemental carbon) to models explained 20-65% of our estimated associations between school greenness and 12-mo cognitive development. Our study showed a beneficial association between exposure to green space and cognitive development among schoolchildren that was partly mediated by reduction in exposure to air pollution.


title: Mobile Phone Usage Detection by ANN Trained with a Metaheuristic Algorithm †

abstract: Artificial neural networks (ANN) are widely used to classify high non-linear systems by using a set of input/output data. Moreover, they are trained using several optimization methodologies and this paper presents a novel algorithm for training ANN through an earthquake optimization method. Usually, gradient optimization method is implemented for the training process, with perhaps the large number of iterations leading to slow convergence, and not always achieving the optimal solution. Since metaheuristic optimization methods deal with searching for weight values in a broad optimization space, the training computational effort is reduced and ensures an optimal solution. This work shows an efficient training process that is a suitable solution for detection of mobile phone usage while driving. The main advantage of training ANN using the Earthquake Algorithm (EA) lies in its versatility to search in a fine or aggressive way, which extends its field of application. Additionally, a basic example of a linear classification is illustrated using the proposal-training method, so the number of applications could be expanded to nano-sensors, such as reversible logic circuit synthesis in which a genetic algorithm had been implemented. The fine search is important for the studied logic gate emulation due to the small searching areas for the linear separation, also demonstrating the convergence capabilities of the algorithm. Experimental results validate the proposed method for smart mobile phone applications that also can be applied for optimization applications.


title: Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video

abstract: We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American sign language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.


title: Planar ultra wide band slot antenna with frequency band notch function

abstract: The development of ultra wide band (UWB) antennas for high resolution and high data transmission rates in modern communication systems is growing rapidly due to the difficult requirements of UWB antennas such as small size, non-dispersive and ultra wide-band characteristics. This paper describes a planar UWB slot antenna having frequency band notch characteristics. By inserting a V-shaped thin slot on the planar antenna, a narrow frequency band notch function has been created. Also, the notched frequency band can be varied by adjusting the length of the slot. Antenna performance including VSWR and azimuthal gain pattern of the new antenna are presented.


title: Linear chirp generator based on direct digital synthesis and frequency multiplication for airborne FMCW snow probing radar

abstract: This paper presents a linear chirp generator for synthesizing ultra-wideband signals for use in an FM-CW radar being used for airborne snow thickness measurements. Ultra-wideband chirp generators with rigorous linearity requirements are needed for long-range FMCW radars. The chirp generator is composed of a direct digital synthesizer and a frequency multiplier chain. The implementation approach combines recently available high-speed digital, mixed signal, and microwave components along with a frequency pre-distortion technique to synthesize a 6-GHz chirp signal over 240 μs with a <;0.02 MHz/μs deviation from linearity.


title: Cost-Benefit Analysis, the Death Penalty, and Rationales for Punishment

abstract: The paper contends that the use of cost-benefit analysis (CBA) for or against capital punishment is problematic insofar as CBA (1) commodifies and thus reduces the value of human life and (2) cannot quantify all costs and benefits. The paramount theories of punishment, retribution and utilitarianism, which are used as rationales for capital punishment, do not justify the use of cost-benefit analysis as part of that rationale. Calling on the theory of restorative justice, the paper recommends a change in the linguistic register used to describe the value of human beings. In particular, abolitionists should emphasize that human beings have essential value. INTRODUCTION Advocates of the death penalty use economics to justify the use of capital punishment. Scott Turow, an Illinois-based lawyer says it well when he comments that two arguments frequently used by death penalty advocates are that, “the death penalty is a deterrent to others and it is more cost effective than keeping an individual in jail for life” (Turow). Edward Elijas takes the point further in writing the following, “Let’s imagine for a moment there was no death penalty. The only reasonable sentence would a life sentence. This would be costly to the tax payers, not only for the cost of housing and feeding the prisoner but because of the numerous appeals which wastes man hours and money. By treating criminals in this manner, we are encouraging behavior that will result in a prison sentence. If there is no threat of death to one who commits a murder, than that person is guaranteed to be provided with a decent living environment until their next parole hearing. They are definitely not getting the punishment they deserve” (http://www.cwrl.utexas.edu/). According to the argument, whether a person convicted


title: Direct Antenna Modulation (DAM) for Enhanced Capacity Performance of Near-Field Communication (NFC) Link

abstract: Shannon's theorem suggests that the capacity of a wireless link is determined by both the power received and the bandwidth of the system. However, in a conventional setup of an inductively coupled near-field communication (NFC) link, there is a trade-off between the power coupled through and the bandwidth. Direct antenna modulation (DAM) is a feasible scheme to mitigate this dilemma. With DAM utilized in a NFC link, the power and bandwidth product limit in a high Q system can be circumvented because the non-linear/time-varying nature of the operation allows high speed modulations decoupled from the charging and discharging process of the high-Q resonator. In this paper, the theory of NFC link with DAM on the transmitter is presented and validated with an experimental setup. Improvement in reception of the high-speed modulation information is observed in the experiment, implying that a superior capacity performance of a NFC link is achieved through DAM versus the traditional scheme.


title: Internet scale string attribute publish/subscribe data networks

abstract: With this work we aim to make a three-fold contribution. We first address the issue of supporting efficiently queries over string-attributes involving prefix, suffix, containment, and equality operators in large-scale data networks. Our first design decision is to employ distributed hash tables (DHTs) for the data network's topology, harnessing their desirable properties. Our next design decision is to derive DHT-independent solutions, treating DHT as a black box. Second, we exploit this infrastructure to develop efficient content based publish/subscribe systems. The main contribution here are algorithms for the efficient processing of queries (subscriptions) and events (publications). Specifically, we show that our subscription processing algorithms require O(logN) messages for a N-node network, and our event processing algorithms require O(l x logN) messages (with l being the average string length).Third, we develop algorithms for optimizing the processing of multi-dimensional events, involving several string attributes. Further to our analysis, we provide simulation-based experiments showing promising performance results in terms of number of messages, required bandwidth, load balancing, and response times.


title: Leading Edge Review The Inflammasomes

abstract: Inflammasomes are molecular platforms activated upon cellular infection or stress that trigger the maturation of proinflammatory cytokines such as interleukin-1beta to engage innate immune defenses. Strong associations between dysregulated inflammasome activity and human heritable and acquired inflammatory diseases highlight the importance this pathway in tailoring immune responses. Here, we comprehensively review mechanisms directing normal inflammasome function and its dysregulation in disease. Agonists and activation mechanisms of the NLRP1, NLRP3, IPAF, and AIM2 inflammasomes are discussed. Regulatory mechanisms that potentiate or limit inflammasome activation are examined, as well as emerging links between the inflammasome and pyroptosis and autophagy.


title: Reducing Interruptions at Work: A Large-Scale Field Study of FlowLight

abstract: Due to the high number and cost of interruptions at work, several approaches have been suggested to reduce this cost for knowledge workers. These approaches predominantly focus either on a manual and physical indicator, such as headphones or a closed office door, or on the automatic measure of a worker's interruptibilty in combination with a computer-based indicator. Little is known about the combination of a physical indicator with an automatic interruptibility measure and its long-term impact in the workplace. In our research, we developed the FlowLight, that combines a physical traffic-light like LED with an automatic interruptibility measure based on computer interaction data. In a large-scale and long-term field study with 449 participants from 12 countries, we found, amongst other results, that the FlowLight reduced the interruptions of participants by 46%, increased their awareness on the potential disruptiveness of interruptions and most participants never stopped using it.