title: A survey of collaborative filtering based social recommender systems

abstract: Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several representative algorithms.


title: A neuroscience approach to optimizing brain resources for human performance in extreme environments

abstract: Extreme environments requiring optimal cognitive and behavioral performance occur in a wide variety of situations ranging from complex combat operations to elite athletic competitions. Although a large literature characterizes psychological and other aspects of individual differences in performances in extreme environments, virtually nothing is known about the underlying neural basis for these differences. This review summarizes the cognitive, emotional, and behavioral consequences of exposure to extreme environments, discusses predictors of performance, and builds a case for the use of neuroscience approaches to quantify and understand optimal cognitive and behavioral performance. ::: ::: Extreme environments are defined as an external context that exposes individuals to demanding psychological and/or physical conditions, and which may have profound effects on cognitive and behavioral performance. Examples of these types of environments include combat situations, Olympic-level competition, and expeditions in extreme cold, at high altitudes, or in space. Optimal performance is defined as the degree to which individuals achieve a desired outcome when completing goal-oriented tasks. It is hypothesized that individual variability with respect to optimal performance in extreme environments depends on a well “contextualized” internal body state that is associated with an appropriate potential to act. This hypothesis can be translated into an experimental approach that may be useful for quantifying the degree to which individuals are particularly suited to performing optimally in demanding environments.


title: Budgeted Batch Bayesian Optimization With Unknown Batch Sizes

abstract: Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical grid search or trial-error methods are exponentially expensive in large parameter spaces, and Bayesian optimization (BO) offers an elegant alternative for global optimization of black box functions. In situations where the black box function can be evaluated at multiple points simultaneously, batch Bayesian optimization is used. Current batch BO approaches are restrictive in that they fix the number of evaluations per batch, and this can be wasteful when the number of specified evaluations is larger than the number of real maxima in the underlying acquisition function. We present the Budgeted Batch Bayesian Optimization (B3O) for hyper-parameter tuning and experimental design - we identify the appropriate batch size for each iteration in an elegant way. To set the batch size flexible, we use the infinite Gaussian mixture model (IGMM) for automatically identifying the number of peaks in the underlying acquisition functions. We solve the intractability of estimating the IGMM directly from the acquisition function by formulating the batch generalized slice sampling to efficiently draw samples from the acquisition function. We perform extensive experiments for both synthetic functions and two real world applications - machine learning hyper-parameter tuning and experimental design for alloy hardening. We show empirically that the proposed B3O outperforms the existing fixed batch BO approaches in finding the optimum whilst requiring a fewer number of evaluations, thus saving cost and time.


title: Inducing Deterministic Prolog Parsers from Treebanks: A Machine Learning Approach

abstract: This paper presents a method for constructing deterministic Prolog parsers from corpora of parsed sentences. Our approach uses recent machine learning methods for inducing Prolog rules from examples (inductive logic programming). We discuss several advantages of this method compared to recent statistical methods and present results on learning complete parsers from portions of the ATIS corpus.


title: High efficiency bidirectional LLC resonant converter for 380V DC power distribution system using digital control scheme

abstract: A bidirectional full-bridge LLC resonant converter with a new symmetric LLC-type resonant network using a digital control scheme is proposed for a 380V dc power distribution system. This converter can operate under high power conversion efficiency since the symmetric LLC resonant network has zero voltage switching capability for primary power switches and soft commutation capability for output rectifiers. In addition, the proposed topology does not require any clamp circuits to reduce the voltage stress of the switches because the switch voltage of the primary inverting stage is confined by the input voltage, and that of the secondary rectifying stage is limited by the output voltage. Therefore, the power conversion efficiency of any directions is exactly the same as each other. In addition, intelligent digital control schemes such as dead-band control and switch transition control are proposed to regulate output voltage for any power flow directions. A prototype converter designed for a high-frequency galvanic isolation of 380V dc buses was developed with a rated power rating of 5kW using a digital signal processor to verify the performance of the proposed topology and algorithms. The maximum power conversion efficiency was 97.8% during bidirectional operations.


title: The economics of information security

abstract: The economics of information security has recently become a thriving and fast-moving discipline. As distributed systems are assembled from machines belonging to principals with divergent interests, we find that incentives are becoming as important as technical design in achieving dependability. The new field provides valuable insights not just into “security” topics (such as bugs, spam, phishing, and law enforcement strategy) but into more general areas such as the design of peer-to-peer systems, the optimal balance of effort by programmers and testers, why privacy gets eroded, and the politics of digital rights management.


title: Category-Independent Object Proposals with Diverse Ranking

abstract: We propose a category-independent method to produce a bag of regions and rank them, such that top-ranked regions are likely to be good segmentations of different objects. Our key objectives are completeness and diversity: Every object should have at least one good proposed region, and a diverse set should be top-ranked. Our approach is to generate a set of segmentations by performing graph cuts based on a seed region and a learned affinity function. Then, the regions are ranked using structured learning based on various cues. Our experiments on the Berkeley Segmentation Data Set and Pascal VOC 2011 demonstrate our ability to find most objects within a small bag of proposed regions.


title: Experimental and simulated transport of phosphorus through sandy soils

abstract: Reversible equilibrium adsorption-desorption relationships were inadequate for describing the transport of orthophosphate through water-saturated and water-unsaturated cores from surface A1 and subsurface A2 horizons of Oldsmar fine sand. Using a kinetic model with nonlinear reversible adsorption-desorption improved descriptions of phosphorus transport through these soils. Phosphorus effluent concentrations were described best by using an irreversible sink for chemical immobilization or precipitation with a nonlinear reversible kinetic adsorption-desorption equation.


Racing Bib Numbers Recognition


title: A lock-free B+tree

abstract: Lock-free data structures provide a progress guarantee and are known for facilitating scalability, avoiding deadlocks and livelocks, and providing guaranteed system responsiveness. In this paper we present a design for a lock-free balanced tree, specifically, a B+tree. The B+tree data structure has an important practical applications, and is used in various storage-system products. As far as we know this is the first design of a lock-free, dynamic, and balanced tree, that employs standard compare-and-swap operations.


title: SCI-FI: Shape-Changing Interfaces, Future Interactions

abstract: Shape-changing interfaces (SCI) are rapidly evolving and creating new interaction paradigms in human-computer interaction (HCI). However, empirical research in SCI is still bound to present technological limitations and existing prototypes can only show a limited number of potential applications for shape change. In this paper we attempt to broaden the pool of examples of what shape change may be good for by investigating SCI using Science Fiction (Sci-Fi) movies. We look at 340 Sci-Fi movies to identify instances of SCI and analyze their behavioral patterns and the context in which they are used. The result of our analysis presents four emerging behavioral patterns of shape change: (1) Reconfiguration, (2) Transformation, (3) Adaptation, and (4) Physicalization. We report a selection of SCI instances from Sci-Fi movies, which show how these behavioral patterns model functionalities of shape change and what they can do. Finally, we conclude by providing a discussion on how our results can inspire the design of SCI.


Theory for the Localized Setting of Learning Kernels


title: Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

abstract: Advances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available github.com/cortex-lab/Kilosort.


title: When a Red Herring in Not a Red Herring: Using Compositional Methods to Detect Non-Compositional Phrases.

abstract: Non-compositional phrases such as `red herring' and weakly compositional phrases such as `spelling bee' are an integral part of natural language (Sag, 2002). They are also the phrases that are difficult, or even impossible, for good compositional distributional models of semantics. Compositionality detection therefore provides a good testbed for compositional methods. We compare an integrated compositional distributional approach, using sparse high dimensional representations, with the ad-hoc compositional approach of applying simple composition operations to state-of-the-art neural embeddings.


title: Smartphones and Cognition: A Review of Research Exploring the Links between Mobile Technology Habits and Cognitive Functioning

abstract: While smartphones and related mobile technologies are recognized as flexible and powerful tools that, when used prudently, can augment human cognition, there is also a growing perception that habitual involvement with these devices may have a negative and lasting impact on users’ ability to think, remember, pay attention, and regulate emotion. The present review considers an intensifying, though still limited, area of research exploring the potential cognitive impacts of smartphone-related habits, and seeks to determine in which domains of functioning there is accruing evidence of a significant relationship between smartphone technology and cognitive performance, and in which domains the scientific literature is not yet mature enough to endorse any firm conclusions. We focus our review primarily on three facets of cognition that are clearly implicated in public discourse regarding the impacts of mobile technology – attention, memory, and delay of gratification – and then consider evidence regarding the broader relationships between smartphone habits and everyday cognitive functioning. Along the way, we highlight compelling findings, discuss limitations with respect to empirical methodology and interpretation, and offer suggestions for how the field might progress toward a more coherent and robust area of scientific inquiry.


Autonomous Exploitation of System Binaries using Symbolic Analysis


title: Low power SC CMFB folded cascode OTA optimization

abstract: This paper describes a design of a switched capacitor common mode feedback (SC CMFB) folded cascode operational transconductance amplifier for low power and high-speed sigma-delta modulators. An algorithmic driven methodology is developed ending to the optimal transistor geometries. Using a 0.35 mum CMOS process, the OTA circuit has been designed to achieve 82.94 dB DC gain, 526 MHz unity-gain frequency, 560 V/mus slew rate with 1.8 V power supply voltage and a power consumption of only 1.19 mW. The simulated OTA circuit has been applied in a SC integrator to illustrate the versatility of the circuit.


title: Bin packing and cutting stock problems: Mathematical models and exact algorithms

abstract: We review the most important mathematical models and algorithms developed for the exact solution of the one-dimensional bin packing and cutting stock problems, and experimentally evaluate, on state-of-the art computers, the performance of the main available software tools.


title: Magnetics on silicon: an enabling technology for power supply on chip

abstract: Data from the ITRS2003 roadmap for 2010 predicts voltages for microprocessors in hand-held electronics will decrease to 0.8V with current and power increasing to 4A and 3W, respectively. Consequently, low power converters will move to multimegahertz frequencies with a resulting reduction in capacitor and inductor values by factors of 5 and 20, respectively. Values required at 10 MHz, for a low power buck converter, are estimated at 130 nH and 0.6 uF, compatible with the integration of magnetics onto silicon and the concept of power supply-on-chip (PSOC). A review of magnetics-on-silicon shows that inductance values of 20 to 40nH/mm/sup 2/ can be achieved for winding resistances less than 1/spl Omega/. A 1-/spl mu/H inductance can be achieved at 5 MHz with dc resistance of 1/spl Omega/ and a Q of four. Thin film magnetic materials, compatible with semiconductor processing, offer power loss density that is lower than ferrite by a factor of 5 at 10 MHz. Other data reported includes, lowest dc resistance values of 120 m/spl Omega/ for an inductance of 120 nH; highest Q of 15 for an inductance of 350 nH and a current of 1 A for a 1- /spl mu/H inductor. Future technology challenges include reducing losses using high resistivity, laminated magnetic materials, and increasing current carrying capability using high aspect-ratio, electroplated copper conductors. Compatible technologies are available in the power switch, control, and packaging space. Integrated capacitor technology is still a long-term challenge with maximum reported values of 400 nF/cm/sup 2/.


title: Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD

abstract: Majority of the existing copy-move forgery detection algorithms operate based on the principle of image block matching. However, such detection becomes complicated when an intelligent adversary blurs the edges of forged region(s). To solve this problem, the authors present a novel approach for detection of copy-move forgery using stationary wavelet transform (SWT) which, unlike most wavelet transforms (e.g. discrete wavelet transform), is shift invariant, and helps in finding the similarities, i.e. matches and dissimilarities, i.e. noise, between the blocks of an image, caused due to blurring. The blocks are represented by features extracted using singular value decomposition (SVD) of an image. Also, the concept of colour-based segmentation used in this work helps to achieve blur invariance. The authors' experimental results prove the efficiency of the proposed method in detection of copy-move forgery involving intelligent edge blurring. Also, their experimental results prove that the performance of the proposed method in terms of detection accuracy is considerably higher compared with the state-of-the-art.


title: Current and future mobile and wearable device use by people with visual impairments

abstract: With the increasing popularity of mainstream wearable devices, it is critical to assess the accessibility implications of such technologies. For people with visual impairments, who do not always need the visual display of a mobile phone, alternative means of eyes-free wearable interaction are particularly appealing. To explore the potential impacts of such technology, we conducted two studies. The first was an online survey that included 114 participants with visual impairments and 101 sighted participants; we compare the two groups in terms of current device use. The second was an interview and design probe study with 10 participants with visual impairments. Our findings expand on past work to characterize a range of trends in smartphone use and accessibility issues therein. Participants with visual impairments also responded positively to two eyes-free wearable device scenarios: a wristband or ring and a glasses-based device. Discussions on projected use of these devices suggest that small, easily accessible, and discreet wearable input could positively impact the ability of people with visual impairments to access information on the go and to participate in certain social interactions.


CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities


title: An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

abstract: Abstract While deep learning methods have demonstrated performance comparable to human readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a “black-box.” The lack of model interpretability hinders them from being fully understood by end users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level semantic features; and 2) a high-level prediction of nodule malignancy. The low-level outputs reflect diagnostic features often reported by radiologists and serve to explain how the model interprets the images in an expert-interpretable manner. The information from these low-level outputs, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level output. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves better results compared to using a 3D CNN alone.


title: Generalized neural network for nonsmooth nonlinear programming problems

abstract: In 1988 Kennedy and Chua introduced the dynamical canonical nonlinear programming circuit (NPC) to solve in real time nonlinear programming problems where the objective function and the constraints are smooth (twice continuously differentiable) functions. In this paper, a generalized circuit is introduced (G-NPC), which is aimed at solving in real time a much wider class of nonsmooth nonlinear programming problems where the objective function and the constraints are assumed to satisfy only the weak condition of being regular functions. G-NPC, which derives from a natural extension of NPC, has a neural-like architecture and also features the presence of constraint neurons modeled by ideal diodes with infinite slope in the conducting region. By using the Clarke's generalized gradient of the involved functions, G-NPC is shown to obey a gradient system of differential inclusions, and its dynamical behavior and optimization capabilities, both for convex and nonconvex problems, are rigorously analyzed in the framework of nonsmooth analysis and the theory of differential inclusions. In the special important case of linear and quadratic programming problems, salient dynamical features of G-NPC, namely the presence of sliding modes , trajectory convergence in finite time, and the ability to compute the exact optimal solution of the problem being modeled, are uncovered and explained in the developed analytical framework.


title: Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

abstract: For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive), direct-to-peripheral-nerve (invasive), and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals. The peripheral nerve interfaces are highly dependent on interface designs and are required to be biocompatible with the nerves to achieve prolonged stability and longevity. Another criterion is the selection of nerves that allows minimal invasiveness and damages as well as high selectivity for a large number of nerve fascicles. In this paper, we review the nerve-machine interface modalities noted above with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback. The invasive interfaces for recording and stimulation of electro-neurographic signals include intra-fascicular, regenerative-type interfaces that provide multiple contact channels to a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable interaction with many axons around the periphery of the nerve. Section 9 summarizes the advancements made to date in the field of neuroprosthetics toward the achievement of a bidirectional nerve-machine interface with more focus on sensory feedback. In Section 10, the authors propose a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques.


title: Typicality Matching for Pairs of Correlated Graphs

abstract: In this paper, the problem of matching pairs of correlated random graphs with multi-valued edge attributes is considered. Graph matching problems of this nature arise in several settings of practical interest including social network de-anonymization, study of biological data, web graphs, etc. An achievable region for successful matching is derived by analyzing a new matching algorithm that we refer to as typicality matching. The algorithm operates by investigating the joint typicality of the adjacency matrices of the two correlated graphs. Our main result shows that the achievable region depends on the mutual information between the variables corresponding to the edge probabilities of the two graphs. The result is based on bounds on the typicality of permutations of sequences of random variables that might be of independent interest.


title: Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data

abstract: Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.


title: Unsupervised learning of digit recognition using spike-timing-dependent plasticity

abstract: In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.


title: What characterizes a shadow boundary under the sun and sky?

abstract: Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets from Lalonde et al. and Zhu et al., our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning.


title: Agent based patient scheduling system

abstract: The continuous evolution of technology can be used to further improvements in our daily lives. Embedded smart technologies follow the same increasing trend of evolution. These technologies can greatly improve medical systems where, along with the services they provide, they can mimic intelligent behavior in hospitals. One such behavior allows doctors to be contacted in a short period of time in order to respond to the patient's needs. This paper presents a system designed for this purpose, that uses embedded smart devices, tailored for hospitals, clinics, ambulatory services or treatment centers. The system drives to a patient scheduling scheme which can be used to store and distribute medical information according to its nature and importance. Emergencies are the main target of our system, due to the requirements of locating a relevant physician in a minimum period of time. In such cases, the proposed device can be used to locate a nearby doctor and notify him about the emergency within a few seconds.


title: Named Entity Recognition for Indian Languages: A Survey

abstract: Named Entity Recognition (NER) is a sub task of Information Extraction (IE) used to identify and classify the names in any given data. Earlier studies were mostly based on hand written rules where as now-a-days Machine Learning models such as Hidden Markov Model (HMM), Maximum Entropy (MaxEnt), Maximum Entropy Markov model (MEMM), Support Vector Machine (SVM), Conditional Random Fields (CRFs) are used to develop NER systems. In this paper we are presenting a survey of NER's for Indian Languages which has been developed using the above learning algorithms. Work on NER has been done only for few Indian Languages like Bengali, Hindi, Tamil, Telugu, Oriya, Punjabi, Urdu and Kannada among 22 official languages. In this paper, we discuss the strategies adopted and performance of these NER'S with respect to recall, precision and F-measure.


title: Low-power arithmetic unit for DSP applications

abstract: DSP algorithms are one of the most important components of modern embedded computer systems. These applications generally include fixed point and floating-point arithmetic operations and trigonometric functions which have long latencies and high power consumption. Nonetheless, DSP applications enjoy from some interesting characteristics such as tolerating slight loss of accuracy and high degree of value locality which can be exploited to improve their power consumption and performance. In this paper, we present an application-specific result-cache that aims to reduce the power consumption and latency of DSP algorithms by reusing the results of the arithmetic operations executed on the same (or approximately the same) inputs. Our proposal improves the area overhead and hit ratio of previously proposed result-caches by figuring out the frequent operands of the arithmetic operations and allocating the cache entries to them. This mechanism exploits the fact that in most DSP applications, one of the operands of the arithmetic operations is input-data independent and takes its value from a finite set of coefficients. The experimental results show that the proposed approach can significantly reduce the power consumption of arithmetic operations by providing a high hit ratio using a small cache.


title: Memristive fuzzy edge detector

abstract: Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this difficulty by proposing a new method for the implementation of the fuzzy rule-based inference systems. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing a new concept called the fuzzy minterm. Although many applications can be realized through the use of our proposed system, in this study we only show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. One main advantage of our memristive fuzzy edge detector (implemented in analog form) compared to other commonly used edge detectors is it can be implemented in parallel form, which makes it a powerful device for real-time applications.


title: How to bridge the gap in human resources for health

abstract: Human resources are the crucial core of a health system, but they have been a neglected component of health-system development. The demands on health systems have escalated in low income countries, in the form of the Millennium Development Goals and new targets for more access to HIV/AIDS treatment. Human resources are in very short supply in health systems in low and middle income countries compared with high income countries or with the skill requirements of a minimum package of health interventions. Equally serious concerns exist about the quality and productivity of the health workforce in low income countries. Among available strategies to address the problems, expansion of the numbers of doctors and nurses through training is highly constrained. This is a difficult issue involving the interplay of multiple factors and forces.


title: Forensics for partially double compressed doctored JPEG images

abstract: Digital image forensics is required to investigate unethical use of doctored images by recovering the historic information of an image. Most of the cameras compress the image using JPEG standard. When this image is decompressed and recompressed with different quantization matrix, it becomes double compressed. Although in certain cases, e.g. after a cropping attack, the image can be recompressed with the same quantization matrix too. This JPEG double compression becomes an integral part of forgery creation. The detection and analysis of double compression in an image help the investigator to find the authenticity of an image. In this paper, a two-stage technique is proposed to estimate the first quantization matrix or steps from the partial double compressed JPEG images. In the first stage of the proposed approach, the detection of the double compressed region through JPEG ghost technique is extended to the automatic isolation of the doubly compressed part from an image. The second stage analyzes the doubly compressed part to estimate the first quantization matrix or steps. In the latter stage, an optimized filtering scheme is also proposed to cope with the effects of the error. The results of proposed scheme are evaluated by considering partial double compressed images based on the two different datasets. The partial double compressed datasets have not been considered in the previous state-of-the-art approaches. The first stage of the proposed scheme provides an average percentage accuracy of 95.45%. The second stage provides an error less than 1.5% for the first 10 DCT coefficients, hence, outperforming the existing techniques. The experimental results consider the partial double compressed images in which the recompression is done with different quantization matrix.


title: Embeddings for Word Sense Disambiguation: An Evaluation Study

abstract: Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features.


title: Reducing Illumination Based on Nonlinear Gamma Correction

abstract: Illumination always affects image quality seriously in practice. To weaken illumination effect on image quality, this paper proposes an adaptive gamma correction method. First, a mapping between pixel and gamma values is built. The gamma values are then revised using two non-linear functions to prevent image distortion. Experimental results demonstrate that the proposed method performs better in readjusting image illumination condition and improving image quality.


title: A segmentation method and classification of diagnosis for thyroid nodules

abstract: Heterogeneous features of thyroid nodules in ultrasound images is very difficult task when radiologists and physicians manually draw a complete shape of nodule, size and shape, image or distinguish what type of nodule is exist. Segmentation and classification is important methods for medical image processing. Ultrasound imaging is the best way to prediction of which type of thyroid is there. In this paper, uses the groups Benign (non-cancerous) and Malignant (cancerous) Thyroid Nodules images were used. The texture feature method like GLCM are very useful for classifying texture of images and these features are used to train the classifiers such as SVM, KNN and Bayesian. The experimental result shows the performance of the classifiers and shows the best predictive value and positively identify the percentage of the non-cancerous or cancerous people and shows the best performance accuracy using the SVM classifier as compare to the KNN and Bayesian classifier.


Using Variance to Analyze Visual Cryptography Schemes ∗


title: Harmful devices considered harmless.

abstract: Attacks on host computers by malicious peripherals are a growing problem; the inexorable advance of powerful, small, cheap peripherals, which attach to plug-and-play buses, has made such attacks easy to mount. At the same time, commodity operating systems lack systematic defenses. ::: We present Cinch, a pragmatic response to this threat. Cinch transforms USB peripherals to untrusted network endpoints, thereby allowing users and administrators to deploy a rich, existing toolbox from network security (firewalls, VPNs, DPIs, etc.) to defend the computer in a systematic way. Cinch uses virtualization technology to place the hardware in a logically separate and untrusted machine, and includes an interposition layer between the untrusted machine and the protected one. This layer accepts or rejects interaction with devices and enforces strict standards-compliant behavior, presenting to the protected machine the abstraction of "well-behaved" peripherals. We show that Cinch can be easily implemented on existing operating systems, concisely expresses policies that thwart many real-world attacks, and has low overhead.


title: The Relationship of a Measure of Critical Thinking Ability to Personality Variables and to Indicators of Academic Achievement

abstract: Involving use of 100 graduate student subjects, this study was concerned with the interrelationships between scores on selected scales of the Minnesota Multiphasic Personality Inventory (MMPI), those on the Cornell Critical Thinking Test (Level Z), and academic success as measured one year later by Grade Point Average (GPA) and total number of graduate units completed. Measures of ego development and critical thinking were significantly related for the sample of females, but not for that of males. Measures of personality maladjustment were not significantly related in either sample to a measure in ability in critical thinking. Finally, in each sample, critical thinking ability was predictive of success in graduate school.


title: Low-complexity equalization of OFDM in doubly selective channels

abstract: Orthogonal frequency division multiplexing (OFDM) systems may experience significant inter-carrier interference (ICI) when used in time- and frequency-selective, or doubly selective, channels. In such cases, the classical symbol estimation schemes, e.g., minimum mean-squared error (MMSE) and zero-forcing (ZF) estimation, require matrix inversion that is prohibitively complex for large symbol lengths. An analysis of the ICI generation mechanism leads us to propose a novel two-stage equalizer whose complexity (apart from the FFT) is linear in the OFDM symbol length. The first stage applies optimal linear preprocessing to restrict ICI support, and the second stage uses iterative MMSE estimation to estimate finite-alphabet frequency-domain symbols. Simulation results indicate that our equalizer has significant performance and complexity advantages over the classical linear MMSE estimator in doubly selective channels.


title: The effect of plyometric training on distance running performance

abstract: Previous research has reported that plyometric training improves running economy (RE) and ultimately distance-running performance, although the exact mechanism by which this occurs remains unclear. This study examined whether changes in running performance resulting from plyometric training were related to alterations in lower leg musculotendinous stiffness (MTS). Seventeen male runners were pre- and post-tested for lower leg MTS, maximum isometric force, rate of force development, 5-bound distance test (5BT), counter movement jump (CMJ) height , RE, V˙O2max, lactate threshold (Thla), and 3-km time. Subjects were randomly split into an experimental (E) group which completed 6 weeks of plyometric training in conjunction with their normal running training, and a control (C) group which trained as normal. Following the training period, the E group significantly improved 3-km performance (2.7%) and RE at each of the tested velocities, while no changes in V˙O2max or Thla were recorded. CMJ height, 5BT, and MTS also increased significantly. No significant changes were observed in any measures for the C group. The results clearly demonstrated that a 6-week plyometric programme led to improvements in 3-km running performance. It is postulated that the increase in MTS resulted in improved RE. We speculate that the improved RE led to changes in 3-km running performance, as there were no corresponding alterations in V˙O2max or Thla.


title: Control under communication constraints

abstract: There is an increasing interest in studying control systems employing multiple sensors and actuators that are geographically distributed. Communication is an important component of these distributed and networked control systems. Hence, there is a need to understand the interactions between the control components and the communication components of the distributed system. In this paper, we formulate a control problem with a communication channel connecting the sensor to the controller. Our task involves designing the channel encoder and channel decoder along with the controller to achieve different control objectives. We provide upper and lower bounds on the channel rate required to achieve these different control objectives. In many cases, these bounds are tight. In doing so, we characterize the "information complexity" of different control objectives.


title: Ironing in the Dark

abstract: This paper presents the first polynomial-time algorithm for position and matroid auction environments that learns, from samples from an unknown distribution, an auction with expected revenue arbitrarily close to the maximum possible. In contrast to most previous work, our results do not assume that the unknown distribution is regular, and require only that the distribution does not have an extremely heavy tail (a necessary assumption for any non-trivial results). Our performance guarantee is with respect to the strongest possible benchmark, the Myerson-optimal auction. Learning a near-optimal auction for an irregular distribution is technically challenging because it requires learning the appropriate "ironed intervals", a delicate global property of the distribution.


title: A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks

abstract: More and more network traffic data have brought great challenge to traditional intrusion detection system. The detection performance is tightly related to selected features and classifiers, but traditional feature selection algorithms and classification algorithms can’t perform well in massive data environment. Also the raw traffic data are imbalanced, which has a serious impact on the classification results. In this paper, we propose a novel network intrusion detection model utilizing convolutional neural networks (CNNs). We use CNN to select traffic features from raw data set automatically, and we set the cost function weight coefficient of each class based on its numbers to solve the imbalanced data set problem. The model not only reduces the false alarm rate (FAR) but also improves the accuracy of the class with small numbers. To reduce the calculation cost further, we convert the raw traffic vector format into image format. We use the standard NSL-KDD data set to evaluate the performance of the proposed CNN model. The experimental results show that the accuracy, FAR, and calculation cost of the proposed model perform better than traditional standard algorithms. It is an effective and reliable solution for the intrusion detection of a massive network.


title: Analyzing and Comparing the Protection Quality of Security Enhanced Operating Systems

abstract: Host compromise is a serious computer security problem today. To better protect hosts, several Mandatory Access Control systems, such as Security Enhanced Linux (SELinux) and AppArmor, have been introduced. In this paper we propose an approach to analyze and compare the quality of protection offered by these different MAC systems. We introduce the notion of vulnerability surfaces under attack scenarios as the measurement of protection quality, and implement a tool called VulSAN for computing such vulnerability surfaces. In VulSAN, we encode security policies, system states, and system rules using logic programs. Given an attack scenario, VulSAN computes a host attack graph and the vulnerability surface. We apply our approach to compare SELinux and AppArmor policies in several Linux distributions and discuss the results. Our tool can also be used by Linux system administrators as a system hardening tool. Because of its ability to analyze SELinux as well as AppArmor policies, it can be used for most enterprise Linux distributions and home user distributions.


title: Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

abstract: Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches.


title: Impact of some invertebrates on eggs and hatchlings of the Loggerhead Turtle, Caretta caretta, in Turkey

abstract: Abstract The damage caused by some invertebrates to the eggs and hatchlings of Loggerhead Turtles, Caretta caretta, was investigated during 1999 and 2000 on Fethiye beach, Turkey. Nematoda, Acarina, Myrmeleonidae, Elateridae, Scarabeidae, Muscidae and Tenebrionidae were recorded as infesting nests of Loggerhead Turtles. Tenebrionidae (Coleoptera) larvae were found to have the heaviest impact on Loggerhead Turtle eggs and hatchlings. A total of 185 randomly selected nests was examined. Of the nests examined in 2000, almost 50% contained tenebrionid larvae. Tenebrionid larval damage was recorded in 8.1% of the eggs that were counted in the nests containing larvae and in 0.6% of the hatchlings. However, it could not be determined whether the larvae destroy viable eggs and hatchlings. In comparison to previous years, a remarkable increase in tenebrionid larval damage was recorded.


title: Construction of BPMN-based Business Process Model Base

abstract: On the purpose of managing process models to make them more practical and effective in enterprises, a construction of BPMN-based Business Process Model Base is proposed. Considering Business Process Modeling Notation (BPMN) is used as a standard of process modeling, based on BPMN, the process model transformation is given, and business blueprint modularization management methodology is used for process management. Therefore, BPMN-based Business Process Model Base provides a solution of business process modeling standardization, management and execution so as to enhance the business process reuse.


title: Punica granatum (pomegranate) and its potential for prevention and treatment of inflammation and cancer.

abstract: The last 7 years have seen over seven times as many publications indexed by Medline dealing with pomegranate and Punica granatum than in all the years preceding them. Because of this, and the virtual explosion of interest in pomegranate as a medicinal and nutritional product that has followed, this review is accordingly launched. The pomegranate tree, Punica granatum, especially its fruit, possesses a vast ethnomedical history and represents a phytochemical reservoir of heuristic medicinal value. The tree/fruit can be divided into several anatomical compartments: (1) seed, (2) juice, (3) peel, (4) leaf, (5) flower, (6) bark, and (7) roots, each of which has interesting pharmacologic activity. Juice and peels, for example, possess potent antioxidant properties, while juice, peel and oil are all weakly estrogenic and heuristically of interest for the treatment of menopausal symptoms and sequellae. The use of juice, peel and oil have also been shown to possess anticancer activities, including interference with tumor cell proliferation, cell cycle, invasion and angiogenesis. These may be associated with plant based anti-inflammatory effects, The phytochemistry and pharmacological actions of all Punica granatum components suggest a wide range of clinical applications for the treatment and prevention of cancer, as well as other diseases where chronic inflammation is believed to play an essential etiologic role.


title: SECURITY MANAGEMENT IN CROSS -ORGANIZATIONAL SETTINGS : A DESIGN SCIENCE APPROACH Research-in-Progress

abstract: The challenges of managing security have increased substantially with the advent of outsourcing and cloud computing. Service providers need to ensure that security controls correctly address the complex sets of requirements demanded by their clients. Auditors find it difficult to check whether service providers are compliant with standard security guidelines as well as organization-specific security requirements. This paper reports research-in-progress of a design science project that addresses security management in cross-organizational settings. Based on a sequence of empirical studies involving interviews and an online survey, we analyze critical activities associated with security management in cross-organizational settings from the perspective of service providers and auditors and discuss the support provided by software. The paper also lays out our plans for developing design artifacts and the theoretical framework for their evaluation.


title: Optimizing the Distribution of Software Services in Infrastructure Clouds

abstract: In the public perception, cloud computing is frequently associated with almost unlimited elasticity and scalability of computing capacity. However, Infrastructure as a Service in the form of virtual machines provides limited supplies of virtual resources, due to restrictions of the underlying physical hardware. At the same time, the execution of Software as a Service instances leads to a specific demand for these resources. Based on this observation, I introduce the Software Service Distribution Problem, i.e., the challenge of (cost-)efficiently distributing the execution of software service instances across available cloud infrastructure providers and virtual machine types under resource constraints. I outline my research approach, which aims at the development of optimization algorithms in the context of an integrated Software Service Distribution Broker, and report the progress made to date.


title: A Scalable Vector Symbolic Architecture Approach for Decentralized Workflows

abstract: Vectors Symbolic Architectures (VSAs) are distributed representations that combine random patterns, representing atomic symbols across a hyper-dimensional vector space, into new symbolic vector representations that semantically represent the component vectors and their relationships. In this paper, we extend the VSA approach and apply it to decentralized workflows, capable of executing distributed compute nodes and their interdependencies. To achieve this goal, services must be discovered and orchestrated in a decentralized way with the minimum communication overhead whilst providing detailed information about the workflow - tasks, dependencies, location, metadata, and so on. To this end, we extended VSAs using a hierarchical vector chunking scheme that enables semantic matching at each level and provides scaling up to tens of thousands of services. We then show how VSAs can be used to encode complex workflows by building primitives that represent sequences (pipelines) and then extend this to support full Directed Acyclic Graphs (DAGs) and apply this to five well-known Pegasus scientific workflows to demonstrate the approach


title: The Measurement of Student Engagement: A Comparative Analysis of Various Methods and Student Self-report Instruments

abstract: One of the challenges with research on student engagement is the large variation in the measurement of this construct, which has made it challenging to compare findings across studies. This chapter contributes to our understanding of the measurement of student in engagement in three ways. First, we describe strengths and limitations of different methods for assessing student engagement (i.e., self-report measures, experience sampling techniques, teacher ratings, interviews, and observations). Second, we compare and contrast 11 self-report survey measures of student engagement that have been used in prior research. Across these 11 measures, we describe what is measured (scale name and items), use of measure, samples, and the extent of reliability and validity information available on each measure. Finally, we outline limitations with current approaches to measurement and promising future directions.


title: Efficient extraction of schemas for XML documents

abstract: In this paper, we present a technique for efficient extraction of concise and accurate schemas for XML documents. By restricting the schema form and applying some heuristic rules, we achieve the efficiency and conciseness. The result of an experiment with real-life DTDs shows that our approach attains high accuracy and is 20 to 200 times faster than existing approaches.


title: Small Array Reproduction Method for Ambisonic Encodings Using Headtracking

abstract: Compensated Amplitude Panning (CAP) is a spatial audio reproduction method for loudspeakers that takes the listener head orientation into account. It can produce stable images in any direction with as few as two loudspeakers. In its original form CAP is inherently an object-based method, where each image is produced separately. A direct method is presented here for dynamically decoding a first order Ambisonic encoding that is equivalent to using CAP to separately reproduce the constituents of the encoding. Both the stereo and multichannel cases are considered. Ambisonic decoding enables complex scenes to be reproduced with little cost. Ambisonic encodings are now used widely for 360° video, and other applications.


title: SMEM Algorithm for Mixture Models

abstract: We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly perform simultaneous split-and-merge operations using a new criterion for efficiently selecting the split-and-merge candidates. We apply the proposed algorithm to the training of gaussian mixtures and mixtures of factor analyzers using synthetic and real data and show the effectiveness of using the split- and-merge operations to improve the likelihood of both the training data and of held-out test data. We also show the practical usefulness of the proposed algorithm by applying it to image compression and pattern recognition problems.


Mobile Computing: Data Management Issues


title: Low-voltage, high-speed CMOS analog latched voltage comparator using the "flipped voltage follower" as input stage

abstract: The design and characterization of a low-voltage, high-speed CMOS analog latched voltage comparator based on the flipped voltage follower (FVF) cell and input signal regeneration is presented. The proposed circuit consists of a differential input stage with a common-mode signal detector, followed by a regenerative latch and a Set-Reset (S-R) latch. It is suitable for successive-approximation type's analog-to-digital converters (ADC), but can also be adapted for use in flash-type ADCs. The circuit was fabricated using [email protected] CMOS technology, and its measured performance shows 12-bit resolution at 20MHz comparison rate and 1V single supply voltage, with a total power consumption of [email protected]


title: Management and conservation of the kelp speciesLaminaria digitata: using genetic tools to explore the potential exporting role of the MPA “Parc naturel marin d’Iroise”

abstract: Marine protected areas (MPAs) have been depicted as a useful tool for improving fishery manage- ment and protecting biodiversity. For example, by acting as source populations, MPAs may provide a spillover of adults/propagules, enhancing recruitment to surrounding, non-protected waters. However, such positive effect will de- pend on how a MPAs design matches population biology and dispersal abilities of the target species. High levels of intraspecific genetic diversity without pronounced structure have been found in many marine organisms over large spa- tial scales (from hundreds to thousands kilometres) but do not hold for other ecologically and economically important species such as coral reef fishes, seagrasses and kelps. In particular, the limited potential for dispersal of many macroal- gae suggests the occurrence of self-sustaining, "closed" populations that contrast with the "open" populations of many fishes and invertebrates. Consequently, the high resilience of fish/invertebrate populations cannot be generalized to the whole marine realm. In the present work, we use genetic tools to explore the role of the MPA Parc naturel marin d'Iroise in the management of the emblematic kelp Laminaria digitata. While kelps have received much more scientific attention than any other seaweed group, there is still critical baseline knowledge which needs to be addressed for their effective management. Our genetic survey of 12 L. digitata populations along the Brittany coast, four of them located within the boundaries of the Parc naturel marin d'Iroise, allowed us to assess the conservation value of these kelp populations. In particular, classical genetic metrics as well as more recent clustering approaches were used to identify and characterize Management Units. Moreover, assignment tests were employed to determine contemporary migration events and source/sink populations. Our results show that populations within this MPA have high conservation value. However, we also identify other populations of conservation interest.


title: A GPU-based implementation of motion detection from a moving platform

abstract: We describe a GPU-based implementation of motion detection from a moving platform. Motion detection from a moving platform is inherently difficult as the moving camera induces 2D motion field in the entire image. A step compensating for camera motion is required prior to estimating of the background model. Due to inevitable registration errors, the background model is estimated according to a sliding window of frames to avoid the case where erroneous registration influences the quality of the detection for the whole sequence. However, this approach involves several characteristics that put a heavy burden on real-time CPU implementation. We exploit GPU to achieve significant acceleration over standard CPU implementations. Our GPU-based implementation can build the background model and detect motion regions at around 18 fps on 320times240 videos that are captured for a moving camera.


title: Assessing insider threats to information security using technical, behavioural and organisational measures

abstract: The UK government took a bruising in the headlines (Sep 2008) after a Home Office contractor lost a USB stick containing unencrypted data on all 84,000 prisoners in England and Wales. As a result, the Home Office terminated the @$1.5 million contract with the management consultancy firm. The world woke up to the largest attempted bank fraud ever when the UK's National Hi-Tech Crime Unit foiled the world's largest potential bank robbery in March 2005. With the help of the security supervisor, thieves masquerading as cleaning staff installed hardware keystroke loggers on computers within the London branch of a Japanese bank, to steal @$220m. It is indeed sobering to imagine that any organisation could fall victim to such events and the damage an insider can do. The consulting firm lost the contract worth @$1.5 million due to a small mistake by an employee. The London branch of the Japanese Bank would have lost @$220 million had not the crime been foiled. Insider threat is a reality. Insiders commit fraud or steal sensitive information when motivated by money or revenge. Well-meaning employees can compromise the security of an organisation with their overzealousness in getting their job done. Every organisation has a varied mix of employees, consultants, management, partners and complex infrastructure and that makes handling insider threats a daunting challenge. With insider attacks, organisations face potential damage through loss of revenue, loss of reputation, loss of intellectual property or even loss of human life. The insider threat problem is more elusive and perplexing than any other threat. Assessing the insider threat is the first step to determine the likelihood of any insider attack. Technical solutions do not suffice since insider threats are fundamentally a people issue. Therefore, a three-pronged approach - technological, behavioural and organisational assessment is essential in facilitating the prediction of insider threats and pre-empt any insider attack thus improving the organization's security, survivability, and resiliency in light of insider threats.


title: Prevalence of female genital mutilation among school girls in ElMansoura Center, El-Dakahlia Governorate, Egypt

abstract: Background: Female genital mutilation, has received growing attention from governmental, international organizations and researchers over the last decades due to its impact on women’s health. Objectives: To estimate; the prevalence, some socio-demographic determinants and post-practice complication associated with FGM among secondary school girls in El-Mansoura Center, El-Dakahlia Governorate, Egypt Subjects & Methods: A cross-sectional study conducted during the year 2013. Total number of girls included in the study were 721 (369 from urban areas and 352 from the rural areas), with mean age of 16.29±1.53 years. The questionnaire was filled by the participants themselves over a period of one month (through February 2013). Results: The prevalence of FGMwas 53.7%. In the rural areas the prevalence was (65.6%), compared to (42.3%) in the urban areas. Factors associated with FGM included; rural residence (P< 0.05), lower educational levels of the both parents (P< 0.05), and those living in lowest economic levels(P< 0.05).The procedure was predominantly performed by a physician in the urban area (71.8%), compared to (59.3.5%) in the rural areas. The mothers were the main decision-makers for the procedure of FGM. More than one half of respondents (55.1%) were against continuation of the practice. The most common reasons under continuation of the practice were; religious requirement (32.1%), cultural and social traditional (23.1%), restraining sexual desire (17.0%), cleanliness for girls (12.3%), chastity (9.0%) and evidence of feminist (6.5%). The most common post-practice complication were; bleeding (30.5%), infection (27.4%), pain (20.4%), shock (12.9%) and injury to adjacent tissues (8.8%). Conclusion: FGM is widespread among school girls in Egypt. Factors associated with FGM included; rural residence, lower educational levels of the both parents, and those living in lowest economic levels.The medical physicians were the main performers ofFGM. There was a considerable support for the continuation of the practice particularly in the rural areas.


title: Spiking Neuron Models: Single Neurons, Populations, Plasticity

abstract: 1. Introduction Part I. Single Neuron Models: 2. Detailed neuron models 3. Two-dimensional neuron models 4. Formal spiking neuron models 5. Noise in spiking neuron models Part II. Population Models: 6. Population equations 7. Signal transmission and neuronal coding 8. Oscillations and synchrony 9. Spatially structured networks Part III. Models of Synaptic Plasticity: 10. Hebbian models 11. Learning equations 12. Plasticity and coding Bibliography Index.


title: Convex Optimization Procedure for Clustering: Theoretical Revisit

abstract: In this paper, we present theoretical analysis of SON - a convex optimization procedure for clustering using a sum-of-norms (SON) regularization recently proposed in [8, 10, 11, 17]. In particular, we show if the samples are drawn from two cubes, each being one cluster, then SON can provably identify the cluster membership provided that the distance between the two cubes is larger than a threshold which (linearly) depends on the size of the cube and the ratio of numbers of samples in each cluster. To the best of our knowledge, this paper is the first to provide a rigorous analysis to understand why and when SON works. We believe this may provide important insights to develop novel convex optimization based algorithms for clustering.


title: Cause and analysis of stator and rotor failures in 3-phase squirrel cage induction motors

abstract: The authors attempt to identify the various causes of stator and rotor failures in three-phase squirrel cage induction motors. A specific methodology is proposed to facilitate an accurate analysis of these failures. It is noted that, due to the destructive nature of most failures, it is not easy, and is sometimes impossible, to determine the primary cause of failure. By a process of elimination, one can usually be assured of properly identifying the most likely cause of the failure. It is pointed out that the key point in going through this process of elimination is to use the basic steps of analyzing the failure class and pattern, noting the general motor appearance, identifying the operating condition at the time of failure, and gaining knowledge of the past history of the motor and application. >


Preparation of Powder Precursors and Evaporation of Photoconductive Indium Sulfide Films


title: Legion scribe: real-time captioning by non-experts

abstract: The promise of affordable, automatic approaches to real-time captioning imagines a future in which deaf and hard of hearing (DHH) users have immediate access to speech in the world around them my simply picking up their phone or other mobile device. While the challenges of processing highly variable natural language has prevented automated approaches from completing this task reliably enough for use in settings such as classrooms or workplaces [4], recent work in crowd-powered approaches have allowed groups of non-expert captionists to provide a similarly-flexible source of captions for DHH users. This is in contrast to current human-powered approaches, which use highly-trained professional captionists who can type up to 250 words per minute (WPM), but also can cost over $100/hr. In this paper, we describe a real-time demo of Legion:Scribe (or just "Scribe"), a crowd-powered captioning system that allows untrained participants and volunteers to provide reliable captions with less than 5 seconds of latency by computationally merging their input into a single collective answer that is more accurate and more complete than any one worker could have generated alone.


title: Analyzing crowd workers in mobile pay-for-answer q&a

abstract: Despite the popularity of mobile pay-for-answer Q&A services, little is known about the people who answer questions on these services. In this paper we examine 18.8 million question and answer pairs from Jisiklog, the largest mobile pay-foranswer Q&A service in Korea, and the results of a complementary survey study of 245 Jisiklog workers. The data are used to investigate key motivators of participation, working strategies of experienced users, and longitudinal interaction dynamics. We find that answerers are rarely motivated by social factors but are motivated by financial incentives and intrinsic motives. Additionally, although answers are provided quickly, an answerer's topic selection tends to be broad, with experienced workers employing unique strategies to answer questions and judge relevance. Finally, analysis of longitudinal working patterns and community dynamics demonstrate the robustness of mobile pay-for-answer Q&A. These findings have significant implications on the design of mobile pay-for-answer Q&A.


title: Variance-based Gradient Compression for Efficient Distributed Deep Learning

abstract: Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth connections. A few methods have been proposed to compress gradient for efficient communication, but they either suffer a low compression ratio or significantly harm the resulting model accuracy, particularly when applied to convolutional neural networks. To address these issues, we propose a method to reduce the communication overhead of distributed deep learning. Our key observation is that gradient updates can be delayed until an unambiguous (high amplitude, low variance) gradient has been calculated. We also present an efficient algorithm to compute the variance with negligible additional cost. We experimentally show that our method can achieve very high compression ratio while maintaining the result model accuracy. We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.


title: C-Band Single-Chip Radar Front-End in AlGaN/GaN Technology

abstract: This paper presents the design and measurement results of a single-chip front-end monolithic microwave integrated circuit (MMIC), incorporating a high-power amplifier, transmit–receive switch, low-noise amplifier, and calibration coupler, realized in 0.25 $\mu \text{m}$ AlGaN/GaN-on-SiC MMIC technology of UMS (GH25-10). The MMIC is operating in C-band (5.2–5.6 GHz) and is targeting the next generation spaceborne synthetic aperture radar. The use of GaN technology has resulted in a design that is robust against antenna load variation in transmit as well as against high received power levels, without the need for an additional limiter. By including a transmit-receive switch on the MMIC there is no need for an external circulator, resulting in a significant size and weight reduction of the transmit–receive module. The measured output power in transmit is higher than 40 W with 36% PAE. The receive gain is higher than 31 dB with better than 2.4 dB noise figure. To the best of the author’s knowledge this is the first time such performance has been demonstrated for a single-chip implementation of a C-band transmit–receive front-end.


title: Health‐related quality of life and peer relationships in adolescents with developmental coordination disorder and attention‐deficit–hyperactivity disorder

abstract: AIM ::: Health-related quality of life (HRQoL) and peer relationships were investigated in adolescents with developmental coordination disorder (DCD) and attention-deficit-hyperactivity disorder (ADHD). ::: ::: ::: METHOD ::: Adolescents with DCD (n=9), ADHD (n=9), DCD and ADHD (n=10), and typically developing adolescents (n=16) completed the following questionnaires: KIDSCREEN-52 Health-Related Quality of Life Questionnaire and Peer Relations Questionnaire for Children. Twenty-five participants took part in semi-structured interviews. ::: ::: ::: RESULTS ::: Adolescents with DCD and ADHD had lower HRQoL on the mood and emotions, school environment, and financial resources scales of the KIDSCREEN-52 than adolescents in the DCD and typically developing groups (all p<0.05). On the Peer Relations Questionnaire for Children, the DCD and ADHD group reported significantly higher victimization compared with those in the typically developing (p=0.030) and DCD (p=0.010) groups. Qualitative interviews among young people with DCD and ADHD revealed feelings of marginalization and victimization. Descriptors such as 'misfits', 'oddballs', 'weird', and 'the rejects' were used to describe themselves. ::: ::: ::: INTERPRETATION ::: HRQoL and peer relationships are negatively affected in adolescents with DCD and ADHD. WHAT THIS PAPER ADDS?: Children with developmental coordination disorder (DCD) do not display poorer overall health-related quality of life (HRQoL) versus typically developing controls. Having DCD and attention-deficit-hyperactivity disorder (ADHD) was associated with poorer HRQoL. Adolescents with DCD and ADHD experience significantly higher levels of peer victimization than typically developing adolescents. HRQoL and peer relationships are significantly associated in adolescent respondents.


title: Development of Zika Virus Vaccines

abstract: Zika virus (ZIKV) is a mosquito-borne flavivirus that emerged as a global threat following the most recent outbreak in Brazil in 2015. ZIKV infection of pregnant women is associated with fetal abnormalities such as microcephaly, and infection of adults can lead to Guillain–Barre syndrome, an autoimmune disease characterized by neurological deficits. Although there are currently licensed vaccines for other flaviviruses, there remains an urgent need for preventative vaccines against ZIKV infection. Herein we describe the current efforts to accelerate the development of ZIKV vaccines using various platforms, including live attenuated virus, inactivated virus, DNA and RNA, viral vectors, and in silico-predicted immunogenic viral epitopes. Many of these approaches have leveraged lessons learned from past experience with Dengue and other flavivirus vaccines.


Patterns of muscle involvement in Pompe disease: A whole-body MRI study


title: Spectrum-efficient and scalable elastic optical path network: architecture, benefits, and enabling technologies

abstract: The sustained growth of data traffic volume calls for an introduction of an efficient and scalable transport platform for links of 100 Gb/s and beyond in the future optical network. In this article, after briefly reviewing the existing major technology options, we propose a novel, spectrum- efficient, and scalable optical transport network architecture called SLICE. The SLICE architecture enables sub-wavelength, superwavelength, and multiple-rate data traffic accommodation in a highly spectrum-efficient manner, thereby providing a fractional bandwidth service. Dynamic bandwidth variation of elastic optical paths provides network operators with new business opportunities offering cost-effective and highly available connectivity services through time-dependent bandwidth sharing, energy-efficient network operation, and highly survivable restoration with bandwidth squeezing. We also discuss an optical orthogonal frequency-division multiplexing-based flexible-rate transponder and a bandwidth-variable wavelength cross-connect as the enabling technologies of SLICE concept. Finally, we present the performance evaluation and technical challenges that arise in this new network architecture.


title: Continuum robot arms inspired by cephalopods

abstract: In this paper, we describe our recent results in the development of a new class of soft, continuous backbone ("continuum") robot manipulators. Our work is strongly motivated by the dexterous appendages found in cephalopods, particularly the arms and suckers of octopus, and the arms and tentacles of squid. Our ongoing investigation of these animals reveals interesting and unexpected functional aspects of their structure and behavior. The arrangement and dynamic operation of muscles and connective tissue observed in the arms of a variety of octopus species motivate the underlying design approach for our soft manipulators. These artificial manipulators feature biomimetic actuators, including artificial muscles based on both electro-active polymers (EAP) and pneumatic (McKibben) muscles. They feature a "clean" continuous backbone design, redundant degrees of freedom, and exhibit significant compliance that provides novel operational capacities during environmental interaction and object manipulation. The unusual compliance and redundant degrees of freedom provide strong potential for application to delicate tasks in cluttered and/or unstructured environments. Our aim is to endow these compliant robotic mechanisms with the diverse and dexterous grasping behavior observed in octopuses. To this end, we are conducting fundamental research into the manipulation tactics, sensory biology, and neural control of octopuses. This work in turn leads to novel approaches to motion planning and operator interfaces for the robots. The paper describes the above efforts, along with the results of our development of a series of continuum tentacle-like robots, demonstrating the unique abilities of biologically-inspired design.


title: Automatic detection of unsafe component loadings

abstract: Dynamic loading of software components (e.g., libraries or modules) is a widely used mechanism for improved system modularity and flexibility. Correct component resolution is critical for reliable and secure software execution, however, programming mistakes may lead to unintended or even malicious components to be resolved and loaded. In particular, dynamic loading can be hijacked by placing an arbitrary file with the specified name in a directory searched before resolving the target component. Although this issue has been known for quite some time, it was not considered serious because exploiting it requires access to the local file system on the vulnerable host. Recently such vulnerabilities started to receive considerable attention as their remote exploitation became realistic; it is now important to detect and fix these vulnerabilities. In this paper, we present the first automated technique to detect vulnerable and unsafe dynamic component loadings. Our analysis has two phases: 1) apply dynamic binary instrumentation to collect runtime information on component loading (online phase); and 2) analyze the collected information to detect vulnerable component loadings (offline phase). For evaluation, we implemented our technique to detect vulnerable and unsafe DLL loadings in popular Microsoft Windows software. Our results show that unsafe DLL loading is prevalent and can lead to serious security threats. Our tool detected more than 1,700 unsafe DLL loadings in 28 widely used software and discovered serious attack vectors for remote code execution. Microsoft has opened a Microsoft Security Response Center (MSRC) case on our reported issues and is working with us and other affected software vendors to develop necessary patches.


title: A demonstration of the being and attributes of God and other writings

abstract: A Demonstration of the Being and Attributes of God 1. Several letters to the Reverend Dr Clarke 2. The answer to a sixth letter 3. The answer to a seventh letter 4. Letters to Dr Clarke concerning liberty and necessity 5. From Remarks Upon a Book 6. From Clarke's Sermons on Several Subjects 7. From A Discourse concerning the Unchangeable Obligations of Natural Religion 8. From Four Defences of a Letter to Mr Dodwell 9. From a collection of papers which passed between the late learned Mr Leibniz and Dr Clarke.


title: Students performance prediction using KNN and Naïve Bayesian

abstract: Data mining techniques is rapidly increasing in the research of educational domains. Educational data mining aims to discover hidden knowledge and patterns about student performance. This paper proposes a student performance prediction model by applying two classification algorithms: KNN and Naive Bayes on educational data set of secondary schools, collected from the ministry of education in Gaza Strip for 2015 year. The main objective of such classification may help the ministry of education to improve the performance due to early prediction of student performance. Teachers also can take the proper evaluation to improve student learning. The experimental results show that Naive Bayes is better than KNN by receiving the highest accuracy value of 93.6%.


title: Reconfigurable multiport amplifiers for in-orbit use

abstract: A highly reliable and economical system design is presented for a multiport amplifier (MPA) system, which has attracted considerable attention for its potential use in multibeam mobile satellite communications. An MPA is composed of multiple input/output ports and an array of multiple high-power amplifier (HPAs). Since the HPAs are shared among multiple beams, this design solves the problems of traffic imbalance among beams, traffic changes due to terminal movement, and changes in propagation conditions, and can efficiently utilize the total transmitting power with maximum flexibility and minimum hardware. The problem is that HPA failures degrade the MPA beam isolation, causing multipath interference in the surrounding area due to leakage through adjacent beams. To address this problem, we investigated the MPA performance and survival probability when HPAs fail, by taking the specific properties of the MPA configuration into account. Based on our analysis, we found that there is an optimum operational HPA on/off mode in the event of HPA failure, and we were able to obtain optimal performance and reliability by reconfiguring the HPA on/off states in orbit upon the occurrence of HPA failures. Our proposed self-redundant, reconfigurable MPA can achieve high reliability without any need for additional redundant units or switches.


title: Fast Sparse Adaptive Filtering Algorithms for Acoustic Echo Cancellation

abstract: In this communication, fast adaptive algorithms are suggested to enhance the performance of the Fast- Normalized Least Mean Square (FNLMS) algorithm in Acoustic Echo Cancellation (AEC) applications with a sparse system. We propose two new algorithms, the first one is the Zero-Attracting (ZA) FNLMS which gives a better performance when the unknown system is extremely sparse. However, by decreasing the sparsity of the system, the Mean Square Error (MSE) got significantly worse than that of the FNLMS algorithm. To overcome this issue, another algorithm named Reweighted Zero-Attracting FNLMS (RZA-FNLMS) algorithm is proposed in this paper. Simulation results with stationary and non-stationary inputs under different Signal to Noise Ratio (SNR) values of additive noise and change in the impulse response lengths show an improvement in the convergence speed.


title: Counselling of a couple faced with a prenatal diagnosis of Klinefelter syndrome

abstract: When a prenatal diagnosis of Klinefelter syndrome (KS) is made, a couple is faced with an unfamiliar and unexpected diagnosis. The aim of this article is to give clues to prenatal counselling in this situation. The information provided to couples facing a prenatal diagnosis of KS should ideally be based on longitudinal studies of unselected individuals, including those diagnosed prenatally. Indeed, there are several reasons to think that the phenotype of individuals diagnosed prenatally is globally less severe than in those diagnosed postnatally. Based on these studies, the evidence to be explained to couples to help them make an informed decision about the pregnancy is the following: except for rather tall height, generally normal appearance throughout life; increased risk of learning disabilities; spontaneous puberty, reduced testicular size, usual need for testosterone supplementation from adolescence onward; increased risk of gynecomastia; sexual orientation similar to the general male population; infertility, but with the possibility of having biological offspring with assisted reproductive techniques. In this article, we review the evidence about the phenotype of KS according to the circumstances of diagnosis and its use in counselling couples faced with a prenatal diagnosis of this common condition. Conclusion: Cohort studies including individuals with KS diagnosed prenatally are still lacking.


title: Real-Time EMG driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy

abstract: This paper presents a new approach to control a wearable knee joint exoskeleton driven through the wearer's intention. A realistic bio-inspired musculoskeletal knee joint model is used to control the exoskeleton. This model takes into account changes in muscle length and joint moment arms as well as the dynamics of muscle activation and muscle contraction during lower limb movements. Identification of the model parameters is done through an unconstrained optimization problem formulation. A control law strategy based on the principle of assistance as needed is proposed. This approach guarantees asymptotic stability of the knee joint orthosis and adaptation to human-orthosis interaction. Moreover, the proposed control law is robust with respect to external disturbances. Experimental validations are conducted online on a healthy subject during flexion and extenion of their knee joint. The proposed control strategy has shown satisfactory performances in terms of tracking trajectory and adaptation to human tasks completion.


title: Intuitive Surgical Navigation System for Dental Implantology by Using Retinal Imaging Display

abstract: As dental implants have become an established treatment, their application to aggressive cases with insufficient quantity and quality of bone has increased. To perform safe and precise surgery, overcoming these difficulties, computer-assisted systems have been developed (Jabero et al., 2006). There are two major classes of these systems, that is, a computer-guided (static) system (Schneider et al., 2009) and a computer-navigated (dynamic) system (Jung et al., 2009). The computer-guided system such as SimPlant software and SurgiGuide (Materialize Dental Inc., USA), NobelGuide (Nobel Biocare Management AG, Sweden), and 10DR (10DR JAPAN Co., Ltd., Japan) are a surgical template based system which can simulate an optimal implant location, angle, direction, depth and size pre-operatively. Our system, BoneNavi (Bionic Inc., Japan), has also been developed to simulate implant placement and the surgical template fabrication for dental implant surgery (Ohtani et al., 2009) and applied in more than 200 clinical cases in Japan (Sohmura et al., 2009). On the other hand, the computer-navigated system such as IGI system (Image Navigation Ltd., USA) offers freehand implant navigation empowered by highly accurate motion tracking technology that tracks the positions of the dental drill and patient throughout the surgery (Casap et al., 2004). It has also already been applied in clinical cases (Casap et al., 2005 & 2008). Advantages and disadvantages of the computerguided system and the computer-navigated system are shown in Table 1. Unlike an open chest surgery and an open abdominal surgery, looking away from the oral cavity (narrow space (Fig.1)) intra-operatively involve a risk of operation errors in case of a dental implant surgery. However, with conventional systems, surgeons have to manipulate instruments in the patient’s oral cavity while watching a surgical monitor as shown in Fig.2(a) and they feel anxious during operations. To overcome this problem, we have been developed a novel surgical navigation system by combining the retinal imaging display (RID) and the augmented reality (AR) techniques


title: A proposed semantic machine translation system for translating Arabic text to Arabic sign language

abstract: Arabic Sign Language (ArSL) is the native language for the Arabic deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feelings. Opposite to spoken languages, Sign Language (SL) depends on hands and facial expression to express person thoughts instead of sounds. In recent years, interest in automatically translating text to sign language for different languages has increased. However, a small set of these works are specialized in ArSL. Basically, these works translate word by word without taking care of the semantics of the translated sentence or the translation rules of Arabic text to Arabic sign language. In this paper we will present a proposed system for translating Arabic text to Arabic sign language in the jurisprudence of prayer domain. The proposed system will translate Arabic text to ArSL by applying ArSL translation rules as well as using domain ontology.


abstract: The high availability of extreme events serves resource-rational decision-making Falk Lieder (falk.lieder@berkeley.edu) Helen Wills Neuroscience Institute, University of California at Berkeley, CA, USA Ming Hsu (mhsu@haas.berkeley.edu) Haas School of Business, University of California at Berkeley, CA, USA Thomas L. Griffiths (tom griffiths@berkeley.edu) Department of Psychology, University of California at Berkeley, CA, USA Sampling as a Decision Strategy Abstract To evaluate a potential action a, decision makers should in- tegrate the probabilities P(o|A = a) of possible outcomes o with their utilities u(o) into the action’s expected utility E p(O|A=a) [u(O)] (Von Neumann & Morgenstern, 1944). In the real-world–unlike in the laboratory–each action has in- finitely many possible outcomes. As a consequence, the ex- pected utility of action a becomes an integral: Extreme events come to mind very easily and people over- estimate their probability and overweight them in decision- making. In this paper we show that rational use of limited cog- nitive resources can generate these ’availability biases.’ We hy- pothesize that availability helps people to quickly make good decisions in very risky situations. Our analysis shows that agents who decide by simulating a finite number of possible outcomes (sampling) should over-sample outcomes with ex- treme utility. We derive a cognitive strategy with connections to fast-and-frugal heuristics, and we experimentally confirm its prediction that an event’s extremity increases the factor by which people overestimate its frequency. Our model also explains three context effects in decision-making under risk: framing effects, the Allais paradox, and preference reversals. Z p(o|a) · u(o) do. E p(O|A=a) [u(O)] = Keywords: availability; Bayesian; bounded rationality; cog- nitive biases; heuristics; judgment and decision-making Introduction People overestimate the probability of extreme events such as terrorism (Sunstein & Zeckhauser, 2011) and other threats (Lichtenstein, Slovic, Fischhoff, Layman, & Combs, 1978; Rothman, Klein, & Weinstein, 1996) and overreact accord- ingly (Sunstein & Zeckhauser, 2011). This phenomenon has been explained by the availability bias (Tversky & Kahne- man, 1973) according to which people overestimate the prob- ability of events that come to mind very easily. The availability bias appears irrational, but here we ar- gue that it reflects the rational use of finite time (resource- rationality; Lieder, Griffiths, & Goodman, 2013, Griffiths, Lieder, & Goodman, in revision). In brief, we hypothesize that the high availability of extreme events helps decision- makers to allocate their finite time towards considering the most important consequences their decision might have. We model the strategy that might determine which events come to mind first and how they influence judgment and decision- making. Our model explains not only why people overesti- mate the probability of extreme events, but it also explains three context effects in decision-making under risk. The plan of this paper is as follows: The first section intro- duces the theoretical and empirical background. The second section derives a rational model of decision-making in high- risk situations. The third section presents an experiment test- ing the model’s predictions for frequency judgment, and the fourth section applies the model to explain context-effects in decision-making under risk. The final section discusses our results and their implications. In the general case, this integral is intractable to compute, but it can be approximated by sampling methods (Hammersley & Handscomb, 1964). Mental simulations of a decision’s poten- tial consequences can be thought of as samples. In fact, there is neural evidence (Fiser, Berkes, Orb´an, & Lengyel, 2010) and behavioral evidence (Vul, Goodman, Griffiths, & Tenen- baum, 2014; Denison, Bonawitz, Gopnik, & Griffiths, 2013; Griffiths & Tenenbaum, 2006) that the brain handles uncer- tainty by sampling. For instance, people’s predictions of an uncertain quantity X given partial information y are roughly distributed according to the posterior distribution p(X|y) as if they were sampled from it (Griffiths & Tenenbaum, 2006; Vul et al., 2014). These results suggest that people often use only one or very few samples, and this is what rational agents with finite computational resources (bounded rational agents) should do (Vul et al., 2014). The evidence for sam- pling in human cognition raises the question which sampling algorithm(s) are implemented in the brain. Importance sampling is a popular sampling algorithm in computer science and statistics (Hammersley & Handscomb, 1964; Geweke, 1989), and it has been shown to have connec- tions to both neural network (Shi & Griffiths, 2009) and psy- chological process models (Shi, Griffiths, Feldman, & San- born, 2010). Self-normalizing importance sampling estimates the expected value of a function by the weighted average of the function’s values at points drawn from a distribution q: X 1 , · · · , X s ∼ q, w j = IS E p [ f (X)] ≈ E ˆ q,s p(x j ) q(x j ) ∑ sj=1 w j s · ∑ w j · f (x j ), j=1 where w i is the weight of the i th sample. With finitely many samples, this estimate is generally biased. Following Zabaras


title: A Survey Paper: Areas, Techniques and Challenges of Opinion Mining

abstract: Opinion Mining is a promising discipline, defined as an intersection of information retrieval and computational linguistic techniques to deal with the opinions expressed in a document. The field aims at solving the problems related to opinions about products, Politian in newsgroup posts, review sites, comments on Facebook posts and twitter etc. This paper is about to covers the techniques, applications, long and short future research areas, Research gaps and future challenges in opinion mining. Further, an attempt has been made to discuss in detail the use of supervised, unsupervised, machine learning and case based reasoning techniques in opinion mining to perform computational treatment of sentiments.


title: Exploring architectures, data and units for streaming end-to-end speech recognition with RNN-transducer

abstract: We investigate training end-to-end speech recognition models with the recurrent neural network transducer (RNN-T): a streaming, all-neural, sequence-to-sequence architecture which jointly learns acoustic and language model components from transcribed acoustic data. We explore various model architectures and demonstrate how the model can be improved further if additional text or pronunciation data are available. The model consists of an ‘encoder’, which is initialized from a connectionist temporal classification-based (CTC) acoustic model, and a ‘decoder’ which is partially initialized from a recurrent neural network language model trained on text data alone. The entire neural network is trained with the RNN-T loss and directly outputs the recognized transcript as a sequence of graphemes, thus performing end-to-end speech recognition. We find that performance can be improved further through the use of sub-word units ('wordpieces') which capture longer context and significantly reduce substitution errors. The best RNN-T system, a twelve-layer LSTM encoder with a two-layer LSTM decoder trained with 30,000 wordpieces as output targets achieves a word error rate of 8.5% on voice-search and 5.2% on voice-dictation tasks and is comparable to a state-of-the-art baseline at 8.3% on voice-search and 5.4% voice-dictation.


Probabilistic Terrain Analysis For High-Speed Desert Driving