title: Proactive edge computing in latency-constrained fog networks

abstract: In this paper, the fundamental problem of distribution and proactive caching of computing tasks in fog networks is studied under latency and reliability constraints. In the proposed scenario, computing can be executed either locally at the user device or offloaded to an edge cloudlet. Moreover, cloudlets exploit both their computing and storage capabilities by proactively caching popular task computation results to minimize computing latency. To this end, a clustering method to group spatially proximate user devices with mutual task popularity interests and their serving cloudlets is proposed. Then, cloudlets can proactively cache the popular tasks' computations of their cluster members to minimize computing latency. Additionally, the problem of distributing tasks to cloudlets is formulated as a matching game in which a cost function of computing delay is minimized under latency and reliability constraints. Simulation results show that the proposed scheme guarantees reliable computations with bounded latency and achieves up to 91% decrease in computing latency as compared to baseline schemes.


title: Automated assessment of short free-text responses in computer science using latent semantic analysis

abstract: In the last few decades, much research has focused on the evaluation and assessment of students' knowledge. The idea that computers can now be used to aid assessment is appealing. While implementing automatic marking of multiple choice questions is trivial, most educators agree that such form of assessment provides only limited insight into students' knowledge. Due to this limitation, teachers prefer to use unstructured questions in assessments. These questions, however, are much more complicated to mark as the semantic meaning of a response is more important than any of the individual keywords. Latent Semantic Analysis (LSA) has a remarkable ability to infer meaning from a text in this way. This paper describes the design, implementation and evaluation of an automatic marking system based on LSA and designed to grade paragraph responses to exam questions. In addition to presenting the algorithm and the theoretical basis of the system, we describe the tests that were conducted to test its efficacy. The tests included comparing the marks for several computer science courses exams generated by the system with the original grades awarded by a human examiner. The various settings of the system are studied to understand their effect on the accuracy. Using this understanding, along with trial and error, good configurations for each question are found. Under ideal configurations the system is capable of generating marks with correlations above 0.80 compared to the human examiner's grades. This is considered an acceptable variance. Generating these ideal configurations is nontrivial but possible by designing effective ways to extract appropriate settings from features of the data. If there is enough training data the system easily performs at rates that match the inter-correlation between human markers.


Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal


title: The Relationship between Impulsivity and Internet Gaming Disorder in Young Adults: Mediating Effects of Interpersonal Relationships and Depression

abstract: Background: This study aimed to explore relationships between impulsivity, interpersonal relationships, depression, and Internet Gaming Disorder (IGD) symptoms. Methods: A total of 118 young adults participated in this study: 67 IGD patients who met five or more of the DSM-5 diagnostic criteria for IGD and 56 healthy controls. We administered questionnaires to assess IGD symptoms (Young's Internet Addiction Test; Y-IAT), impulsivity (Barratt Impulsiveness Scale; BIS-11), interpersonal relationship (Relationship Change Scale; RCS), and depression (Beck Depression Inventory; BDI). We used PROCESS macro in SPSS to perform mediation analysis. Results: IGD symptom was positively related to depression and impulsivity, and negatively related to the quality of interpersonal relationships. Mediation analysis revealed full mediation effects of interpersonal relationships and depression on the association between impulsivity and IGD symptoms in the IGD group. Specifically, even after adjusting for gender as a covariate, high impulsivity was associated with greater difficulty with interpersonal relationships; which further affected depression and increased the risk of IGD. Conclusions: These results demonstrate the importance of early intervention in IGD patients, particularly in young adults with high impulsivity. When intervening in adults' IGD, we should consider not only individual factors (e.g., depression) but also socioenvironmental factors (e.g., interpersonal relationships).


title: IT Governance and Sarbanes-Oxley: The Latest Sales Pitch or Real Challenges for the IT Function?

abstract: Building on an analysis of the Sarbanes-Oxley Act, literature, as well as private and public interviews, this paper hopes to shed light on the potential impact of Sarbanes-Oxley for IT governance, IT budgets, and relationships with vendors and outsourcers. Findings have implications for research as well as practical lessons-learned for American firms, and for IT vendors or other companies doing business with American companies.


title: Real-time News Story Detection and Tracking with Hashtags

abstract: Computing News Storylines Workshop at EMNLP 2016, Austin, Texas, United States of America, 2-6 November 2016


title: NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

abstract: This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.


A Novel Method to Treat Primary Anorgasmia: Vestibuloplasty: A Case Report


title: Three-Level Bidirectional Converter for Fuel-Cell/Battery Hybrid Power System

abstract: A novel three-level (3L) bidirectional converter (BDC) is proposed in this paper. Compared with the traditional BDC, the inductor of the 3L BDC can be reduced significantly so that the dynamic response is greatly improved. Hence, the proposed converter is very suitable for fuel-cell/battery hybrid power systems. In addition, the voltage stress on the switch of the proposed converter is only half of the voltage on the high-voltage side, so it is also suitable for high-voltage applications. The operation principle and the implementation of the control circuit are presented in detail. This paper also proposes a novel bidirectional soft-start control strategy for the BDC. A 1-kW prototype converter is built to verify the theoretical analysis.


title: Determinants of successful Website design: relative importance and recommendations for effectiveness

abstract: The success of e-commerce for any company, especially if it is not a well-known name, is greatly dependent on the appropriate design of its Web site. This study investigates the determinants of an effective Web site. A literature survey indicated that the major categories of determinants are: page loading speed, business content, navigation efficiency, security and marketing/customer focus. The relative importance of each category was determined by counting the number of citations in the literature, and by soliciting the opinions of end users. The paper concentrates on experts' recommendations of how to create an effective Web site from an e-commerce point of view.


title: A Boost Converter With Capacitor Multiplier and Coupled Inductor for AC Module Applications

abstract: The grid-connected AC module is an alternative solution in photovoltaic (PV) generation systems. It combines a PV panel and a micro-inverter connected to grid. The use of a high step-up converter is essential for the grid-connected micro-inverter because the input voltage is about 15 V to 40 V for a single PV panel. The proposed converter employs a Zeta converter and a coupled inductor, without the extreme duty ratios and high turns ratios generally needed for the coupled inductor to achieve high step-up voltage conversion; the leakage-inductor energy of the coupled inductor is efficiently recycled to the load. These features improve the energy-conversion efficiency. The operating principles and steady-state analyses of continuous and boundary conduction modes, as well as the voltage and current stresses of the active components, are discussed in detail. A 25 V input voltage, 200 V output voltage, and 250 W output power prototype circuit of the proposed converter is implemented to verify the feasibility; the maximum efficiency is up to 97.3%, and full-load efficiency is 94.8%.


title: Small-Signal Modeling of Active-Clamped ZVS Current-Fed Full-Bridge Isolated DC/DC Converter and Control System Implementation Using PSoC

abstract: The small-signal analysis of an active-clamped zero-voltage-switching current-fed isolated dc/dc converter is presented in this paper. State-space averaging is used to derive the small-signal model. A closed-loop controller is designed to regulate the converter output over a wide range of power (1:10) and source voltage (1:2) with fixed frequency duty cycle modulation. Bode plots are presented using MATLAB and PSIM 9.0 to show the frequency response of the converter with and without a controller. The designed controller is implemented on the mixed-signal (analog and digital) processor Cypress Programmable System-on-Chip (PSoC) 5. All components are integrated in Cypress PSoC 5 which is compact, economical, and easily configurable. A 300-W converter prototype is developed and tested with the implemented controller to observe the performance of the system. Experimental results for step change in load for different input voltage conditions show the high performance of the controller, satisfactory transient performance of the converter, and voltage regulation with continued safe operation.


title: Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation

abstract: Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are presented, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.


title: An Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns

abstract: Cortical minicolumns are considered a model of cortical organization. Their function is still a source of research and not reflected properly in modern architecture of nets in algorithms of Artificial Intelligence. We assume its function and describe it in this article. Furthermore, we show how this proposal allows to construct a new architecture, that is not based on convolutional neural networks, test it on MNIST data and receive close to Convolutional Neural Network accuracy. We also show that the proposed architecture possesses an ability to train on a small quantity of samples. To achieve these results, we enable the minicolumns to remember context transformations.


title: Compression, SIMD, and Postings Lists

abstract: The three generations of postings list compression strategies (Variable Byte Encoding, Word Aligned Codes, and SIMD Codecs) are examined in order to test whether or not each truly represented a generational change -- they do. Some weaknesses of the current SIMD-based schemes are identified and a new scheme, QMX, is introduced to address both space and decoding inefficiencies. Improvements are examined on multiple architectures and it is shown that different SSE implementations (Intel and AMD) perform differently.


title: The ATIS Spoken Language Systems Pilot Corpus

abstract: Speech research has made tremendous progress in the past using the following paradigm:• define the research problem,• collect a corpus to objectively measure progress, and• solve the research problem.Natural language research, on the other hand, has typically progressed without the benefit of any corpus of data with which to test research hypotheses. We describe the Air Travel Information System (ATIS) pilot corpus, a corpus designed to measure progress in Spoken Language Systems that include both a speech and natural language component. This pilot marks the first full-scale attempt to collect such a corpus and provides guidelines for future efforts.


title: Process models in the practice of distributed software development: A systematic review of the literature

abstract: Context: Distributed Software Development (DSD) has recently become an active research area. Although considerable research effort has been made in this area, as yet, no agreement has been reached as to an appropriate process model for DSD. Purpose: This paper is intended to identify and synthesize papers that describe process models for distributed software development in the context of overseas outsourcing, i.e. ''offshoring''. Method: We used a systematic review methodology to search seven digital libraries and one topic-specific conference. Results: We found 27 primary studies describing stage-related DSD process models. Only five of such studies looked into outsourcing to a subsidiary company (i.e. ''internal offshoring''). Nineteen primary studies addressed the need for DSD process models. Eight primary studies and three literature surveys described stage-based DSD process models, but only three of such models were empirically evaluated. Conclusion: We need more research aimed at internal offshoring. Furthermore, proposed models need to be empirically validated.


title: Explaining the Enigmatic Anchoring Effect: Mechanisms of Selective Accessibility

abstract: Results of 3 studies support the notion that anchoring is a special case of semantic priming; specifically, information that is activated to solve a comparative anchoring task will subsequently be more accessible when participants make absolute judgments. By using the logic of priming research, in Study 1 the authors showed that the strength of the anchor effect depends on the applicability of activated information. Study 2 revealed a contrast effect when the activated information was not representative for the absolute judgment and the targets of the 2 judgment tasks were sufficiently different. Study 3 demonstrated that generating absolute judgments requires more time when comparative judgments include an implausible anchor and can therefore be made without relevant target information that would otherwise be accessible.


title: Alteration of the platelet serotonin transporter in romantic love

abstract: Background. The evolutionary consequences of love are so important that there must be some long-established biological process regulating it. Recent findings suggest that the serotonin (5-HT) transporter might be linked to both neuroticism and sexual behaviour as well as to obsessive–compulsive disorder (OCD). The similarities between an overvalued idea, such as that typical of subjects in the early phase of a love relationship, and obsession, prompted us to explore the possibility that the two conditions might share alterations at the level of the 5-HT transporter. Methods. Twenty subjects who had recently (within the previous 6 months) fallen in love, 20 unmedicated OCD patients and 20 normal controls, were included in the study. The 5-HT transporter was evaluated with the specific binding of 3 H-paroxetine ( 3 H-Par) to platelet membranes. Results. The results showed that the density of 3 H-Par binding sites was significantly lower in subjects who had recently fallen in love and in OCD patients than in controls. Discussion. The main finding of the present study is that subjects who were in the early romantic phase of a love relationship were not different from OCD patients in terms of the density of the platelet 5-HT transporter, which proved to be significantly lower than in the normal controls. This would suggest common neurochemical changes involving the 5-HT system, linked to psychological dimensions shared by the two conditions, perhaps at an ideational level.


title: A Multi-objective Deep Reinforcement Learning Approach for Stock Index Future’s Intraday Trading

abstract: Modern artificial intelligence has been widely discussed to practice in automated financial asserts trading. Automated intraday trading means that the agent can react to the market conditions automatically, while simultaneously make the right decisions. Besides, the profits will be made within a day considering transaction cost charged by the brokerage company. In this paper, we introduce a multiobjective deep reinforcement learning approach for intraday financial signal representation and trading. We design the deep neural networks to automatically discover the dynamic market features, then a reinforcement learning method implemented by a special kind of recurrent neural network (LSTM) is applied to make continuous trading decisions. In terms of balancing the profit and risk, we implement a multi-objective structure which includes two objectives with different weights. We conduct experiments on stock index futures data, and our analysis and experiments not only offer insights into financial market features mining, but also provide a straightforward and reliable method to make profits, which sheds light on its wide application on automated financial trading.


title: Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels

abstract: The operating system kernel is often the security foundation for the whole system. To prevent attacks, control-flow integrity (CFI) has been proposed to ensure that any control transfer during the program’s execution never deviates from its control-flow graph (CFG). Existing CFI solutions either work in user space or are coarse-grained; thus they cannot be readily deployed in kernels or are vulnerable to state-of-the-art attacks. In this paper, we present Fine-CFI, a system that enforces fine-grained CFI for operating system kernels. Unlike previous systems, Fine-CFI constructs the kernel’s fine-grained CFG with a retrofitted context-sensitive and field-sensitive pointer analysis, then enforces CFI with this CFG. At the same time, Fine-CFI provides comprehensive protection to the control data in the kernel’s interrupt context. Combining the above two kinds of protection, we can thus defeat those formidable ret2usr and kernel code-reuse attacks. We have developed a compiler-based prototype and implemented this technique in Linux 3.14 kernel. Our evaluation indicates that Fine-CFI prevents all the gadgets found by an open-source gadget-finding tool from being misused, as well as all the attacks from the RIPE benchmark and malicious attempts to modify control data in the interrupt context; and it also reduces the number of indirect control-flow targets by 99.998%, thus largely raising the bar for attackers. Our evaluation also shows that the performance overhead introduced by Fine-CFI is less than 10% on average.


title: New parameterized foot trajectory shape for multi-gait quadruped locomotion with state machine-based approach for executing gait transitions

abstract: This paper presents a method of generating adjustable robot foot trajectories to provide forward open-loop locomotion for different gaits of a planar quadruped robot. The trajectory is described by a small set of tunable parameters that are related to the motion animals perform. This allows faster and easier adjusting of the foot trajectories for different quadruped robots. This method was tested on a simulation model while performing different type of gaits: walk, trot, pace and bound gait. The proposed trajectory shape exhibited great results in simulation. Robot locomotion speed was controlled by scaling the obtained trajectory execution time. This way robot can achieve a wide range of speeds over all of the observed gaits. Additionally, a state-machine based approach for controlling trajectory execution and gait switching is presented. By covering a certain amount of possible events along with ground contact feedback, a quadruped can execute gait transition in a controlled way at overlapping configurations between gaits. The gait transition is performed by defining a set of rules which must be satisfied in order for the gait transition to take place. The simulation results have confirmed that robot maintains stable locomotion after executing gait transition.


title: Percutaneous retrograde posterior column acetabular fixation: is the sciatic nerve safe? A cadaveric study.

abstract: OBJECTIVES ::: The purpose of this cadaveric study was to determine the proximity of the neurologic structures to the path of the screw inserted percutaneously into the ischial tuberosity. ::: ::: ::: DESIGN ::: Cadaver study. ::: ::: ::: INTERVENTION ::: Ten screws were inserted in 10 limbs (5 cadavers) under fluoroscopic guidance. Dissection was then performed to expose the head of the screw and was extended laterally to expose the sciatic nerve, the posterior cutaneous nerve of the thigh, and its inferior cluneal branches. ::: ::: ::: MAIN OUTCOME MEASURE ::: The distance from the screw head to the sciatic nerve, posterior cutaneous nerve of the thigh, and the inferior cluneal nerves. ::: ::: ::: RESULTS ::: The distance from the center of the screw head to the sciatic nerve averaged 58 mm (range, 40-70 mm). The average distance between the screw head and the posterior cutaneous nerve of the thigh was 42 mm (range, 30-60 mm). The inferior cluneal branches were the closest to the path of the screw with an average distance of 3.5 mm in 6 specimens (range, 1-6 mm) and were injured by the screw in 3 and could not be located in another specimen. ::: ::: ::: CONCLUSIONS ::: The sciatic nerve and the posterior cutaneous nerve of the thigh appear to be safe during retrograde percutaneous screw fixation of a posterior column acetabular fracture through a central entry point in the ischial tuberosity. However, the inferior cluneal nerves that are responsible for the cutaneous sensitivity of the lower half of the gluteal region are at risk of injury.


title: Tracking the power port of remote radio unit (RRU) using computer vision

abstract: In this paper, problem of identifying and tracking the power port of remote radio unit (RRU) is addressed. The testing of RRU requires the inspection robot to insert the probes into its power and network ports. In order to solve this problem, an experimental setup of visual servoing with 6 degrees of freedom (DoF) manipulator has been established. The initial problem of recognizing and tracking the power port of RRU has been resolved using template matching and camshift tracking algorithms. Furthermore, camshift tracking algorithm has been improved to work more accurately in this application. Modified algorithm addresses the problem of swapping of major and minor axes of camshift and enhances its application to 6 DoF from 4 DoF. Experimental results have been presented to support the research claims, and computational comparison of modified tracking algorithm with camshift has been shown.


The Interdisciplinary Journal of Problem-based Learning Problem-Based Learning : An Inquiry Approach


title: A systematic and comprehensive investigation of methods to build and evaluate fault prediction models

abstract: This paper describes a study performed in an industrial setting that attempts to build predictive models to identify parts of a Java system with a high fault probability. The system under consideration is constantly evolving as several releases a year are shipped to customers. Developers usually have limited resources for their testing and would like to devote extra resources to faulty system parts. The main research focus of this paper is to systematically assess three aspects on how to build and evaluate fault-proneness models in the context of this large Java legacy system development project: (1) compare many data mining and machine learning techniques to build fault-proneness models, (2) assess the impact of using different metric sets such as source code structural measures and change/fault history (process measures), and (3) compare several alternative ways of assessing the performance of the models, in terms of (i) confusion matrix criteria such as accuracy and precision/recall, (ii) ranking ability, using the receiver operating characteristic area (ROC), and (iii) our proposed cost-effectiveness measure (CE). The results of the study indicate that the choice of fault-proneness modeling technique has limited impact on the resulting classification accuracy or cost-effectiveness. There is however large differences between the individual metric sets in terms of cost-effectiveness, and although the process measures are among the most expensive ones to collect, including them as candidate measures significantly improves the prediction models compared with models that only include structural measures and/or their deltas between releases - both in terms of ROC area and in terms of CE. Further, we observe that what is considered the best model is highly dependent on the criteria that are used to evaluate and compare the models. And the regular confusion matrix criteria, although popular, are not clearly related to the problem at hand, namely the cost-effectiveness of using fault-proneness prediction models to focus verification efforts to deliver software with less faults at less cost.


title: A Fully-Integrated 77-GHz UWB Pseudo-Random Noise Radar Transceiver With a Programmable Sequence Generator in SiGe Technology

abstract: This paper describes a fully-integrated 77-GHz ultra-wideband pseudo-random noise (PRN) radar transceiver in a Silicon-Germanium technology. The transceiver is equipped with a programmable pseudo-random binary sequence (PRBS) generator, which is realized in a current-mode logic topology and can be operated with a clock rate of up to 4.25 GHz to enable a range resolution of 3.5 cm. The signal generation unit is simplified by including a frequency multiplier to create a 76.5-GHz carrier signal from a single 4.25-GHz input, that is also used as a clock for the PRBS generator. The transceiver achieves a phase noise of -105.3 dBc/Hz at 1-MHz offset frequency, a transmit output power of 6.2 dBm, a receive gain of 24 dB and an input-referred 1-dB compression point of -14 dBm. Track&hold circuits included in the receive path allow the use of a sub-sampling technique to reduce the IF data rate down to 1 MHz. Radar measurements with two PRN transceivers with different primitive polynomials were done concurrently to show a fundamental function of the programmable PRBS generator. Radar measurements with the PRN and the frequency-modulated continuous-wave (FMCW) principles show comparable results and the PRN radar proves to be a real alternative to the FMCW radar.


title: Bandits with Knapsacks

abstract: Multi-armed bandit problems are the predominant theoretical model of exploration-exploitation tradeoffs in learning, and they have countless applications ranging from medical trials, to communication networks, to Web search and advertising. In many of these application domains the learner may be constrained by one or more supply (or budget) limits, in addition to the customary limitation on the time horizon. The literature lacks a general model encompassing these sorts of problems. We introduce such a model, called "bandits with knapsacks", that combines aspects of stochastic integer programming with online learning. A distinctive feature of our problem, in comparison to the existing regret-minimization literature, is that the optimal policy for a given latent distribution may significantly outperform the policy that plays the optimal fixed arm. Consequently, achieving sub linear regret in the bandits-with-knapsacks problem is significantly more challenging than in conventional bandit problems. We present two algorithms whose reward is close to the information-theoretic optimum: one is based on a novel "balanced exploration" paradigm, while the other is a primal-dual algorithm that uses multiplicative updates. Further, we prove that the regret achieved by both algorithms is optimal up to polylogarithmic factors. We illustrate the generality of the problem by presenting applications in a number of different domains including electronic commerce, routing, and scheduling. As one example of a concrete application, we consider the problem of dynamic posted pricing with limited supply and obtain the first algorithm whose regret, with respect to the optimal dynamic policy, is sub linear in the supply.


title: Gunrock: a high-performance graph processing library on the GPU

abstract: For large-scale graph analytics on the GPU, the irregularity of data access/control flow and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock," our high-level bulk-synchronous graph-processing system targeting the GPU, takes a new approach to abstracting GPU graph analytics: rather than designing an abstraction around computation, Gunrock instead implements a novel data-centric abstraction centered on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We evaluate Gunrock on five graph primitives (BFS, BC, SSSP, CC, and PageRank) and show that Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.


A Voice Conversion Framework with Tandem Feature Sparse Representation and Speaker-Adapted WaveNet Vocoder


title: A Joint Model of Conversational Discourse and Latent Topics on Microblogs

abstract: Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.


title: Phishing-Aware: A Neuro-Fuzzy Approach for Anti-Phishing on Fog Networks

abstract: Phishing detection is recognized as a criminal issue of Internet security. By deploying a gateway anti-phishing in the networks, these current hardware-based approaches provide an additional layer of defense against phishing attacks. However, such hardware devices are expensive and inefficient in operation due to the diversity of phishing attacks. With promising technologies of virtualization in fog networks, an anti-phishing gateway can be implemented as software at the edge of the network and embedded robust machine learning techniques for phishing detection. In this paper, we use uniform resource locator features and Web traffic features to detect phishing websites based on a designed neuro-fuzzy framework (dubbed Fi-NFN). Based on the new approach, fog computing as encouraged by Cisco, we design an anti-phishing model to transparently monitor and protect fog users from phishing attacks. The experiment results of our proposed approach, based on a large-scale dataset collected from real phishing cases, have shown that our system can effectively prevent phishing attacks and improve the security of the network.


title: Evolutionary psychology: Controversies, questions, prospects, and limitations

abstract: Evolutionary psychology has emerged over the past 15 years as a major theoretical perspective, generating an increasing volume of empirical studies and assuming a larger presence within psychological science. At the same time, it has generated critiques and remains controversial among some psychologists. Some of the controversy stems from hypotheses that go against traditional psychological theories; some from empirical findings that may have disturbing implications; some from misunderstandings about the logic of evolutionary psychology; and some from reasonable scientific concerns about its underlying framework. This article identifies some of the most common concerns and attempts to elucidate evolutionary psychology's stance pertaining to them. These include issues of testability and falsifiability; the domain specificity versus domain generality of psychological mechanisms; the role of novel environments as they interact with evolved psychological circuits; the role of genes in the conceptual structure of evolutionary psychology; the roles of learning, socialization, and culture in evolutionary psychology; and the practical value of applied evolutionary psychology. The article concludes with a discussion of the limitations of current evolutionary psychology.


title: The development of a word-learning strategy

abstract: Two studies investigated young infants’ use of the word-learning principle Mutual Exclusivity. In Experiment 1, a linear relationship between age and performance was discovered. Seventeen-monthold infants successfully used Mutual Exclusivity to map novel labels to novel objects in a preferential looking paradigm. That is, when presented a familiar and a novel object (e.g. car and phototube) and asked to “look at the dax”, 17-month-olds increased looking to the novel object (i.e. phototube) above baseline preference. On these trials, 16-month-olds were at chance. And, 14month-olds systematically increased looking to the familiar object (i.e. car) in response to hearing the novel label “dax”. Experiment 2 established that this increase in looking to the car was due solely to hearing the novel label “dax”. Several possible interpretations of the surprising form of failure at 14 months are discussed. q 2002 Elsevier Science B.V. All rights reserved.


title: Psychosocial symptoms in very young children assessed for sexual abuse : A qualitative analysis from the ASAC study

abstract: Abstract Child sexual abuse (CSA) is a worldwide problem with serious consequences. No recognizable symptom pattern for suspected CSA has yet been identified in very young children. We aim to investigate psychosocial symptoms in a sample of children with confirmed or strongly suspected CSA and the interpretations given to such symptoms by independent clinical experts. Secondly we examined whether experts were able to identify confirmed victims of severe CSA. A qualitative study including inductive content analysis of medical files and focus group discussions with independent experts on the interpretation of psychosocial symptoms was conducted. We included 125 children (76 boys, 60.8%, and 49 girls, 39.2%; median age 3.3 years; age range 0–11) who were involved in the Amsterdam sexual abuse case (ASAC) and had been examined for strongly suspected CSA. We identified four themes among the psychosocial symptoms: problems concerning emotions, behavior, toilet training, and development, whether or not associated with the daycare center or the perpetrator. Clinical experts identified signs of posttraumatic stress disorder (PTSD), regression in continence skills (not otherwise explained), and problems triggered by exposure to the perpetrator or the abuse location as concerning symptoms for CSA. Less concerning symptoms were designated as worrisome if they were numerous and there was no clear explanation for these symptoms. A clear symptom pattern was lacking and about half of the confirmed severe victims of CSA did not display any psychosocial problems. Therefore, it is difficult for experts to identify confirmed CSA victims. Thus, the assessment of suspected CSA should be over time and multidisciplinary.


title: Hybrid Graphene-M

abstract: The concept and analysis of a graphene-metal reconfigurable terahertz dipole antenna are presented. The hybrid graphene-metal implementation is proposed to combine i) dynamic frequency reconfiguration using graphene field effect and ii) high radiation efficiency enabled by metal. The good direct matching to terahertz continuous wave sources, the large tuning range, and the improved efficiency demonstrate that graphene-metal structures are very promising for reconfigurable terahertz antennas.


title: Multimodal Neural Language Models

abstract: We introduce two multimodal neural language models: models of natural language that can be conditioned on other modalities. An image-text multimodal neural language model can be used to retrieve images given complex sentence queries, retrieve phrase descriptions given image queries, as well as generate text conditioned on images. We show that in the case of image-text modelling we can jointly learn word representations and image features by training our models together with a convolutional network. Unlike many of the existing methods, our approach can generate sentence descriptions for images without the use of templates, structured prediction, and/or syntactic trees. While we focus on imagetext modelling, our algorithms can be easily applied to other modalities such as audio.


title: Fall detection for elderly in assisted environments: Video surveillance systems and challenges

abstract: Fall Detection is an important challenge in the field of healthcare. Especially for elderly people who live alone. Therefore, reliable surveillance to reduce the effects of falls is an essential requirement. Video surveillance systems provide important areas for detecting unusual events such as falls, with the growing use of cameras in assisted environments. In this paper first, the fall detection techniques are expressed in video sequences. Then, the available challenges are introduced in a proposed classification. Finally, the presented techniques are evaluated and analyzed in terms of addressing the challenges. Understanding the challenges and ways to handle them can lead to a comparison and assessment of the presented approaches. It will also direct researchers to the accurate recognition and improvement of existing approaches in the future.


title: The Pathobiology of Diabetic Complications: A Unifying Mechanism

abstract: It’s a great honor to join the exceptional club of Banting Award winners, many of whom were my role models and mentors. In addition, giving the Banting Lecture also has a very personal meaning to me, because without Frederick Banting, I would have died from type 1 diabetes when I was 8 years old. However, it was already apparent at the time I was diagnosed that for too many people like me, Banting’s discovery of insulin only allowed them to live just long enough to develop blindness, renal failure, and coronary disease. For example, when I started college, the American Diabetes Association’s Diabetes Textbook had this to say to my parents: “The person with type 1 diabetes can be reassured that it is highly likely that he will live at least into his 30s.” Not surprisingly, my parents did not find this particularly reassuring. ::: ::: At the same time we were reading this in 1967, however, the first basic research discovery about the pathobiology of diabetic complications had just been published in Science the previous year. In my Banting Lecture today, I am thus going to tell you a scientific story that is also profoundly personal. ::: ::: I’ve divided my talk into three parts. The first part is called “pieces of the puzzle,” and in it I describe what was learned about the pathobiology of diabetic complications starting with that 1966 Science paper and continuing through the end of the 1990s. In the second part, I present a unified mechanism that links together all of the seemingly unconnected pieces of the puzzle. Finally, in the third part, I focus on three examples of novel therapeutic approaches for the prevention and treatment of diabetic complications, which are all based on the new paradigm of a unifying mechanism for the pathogenesis of diabetic complications. …


title: An Algorithm that Learns What's in a Name

abstract: In this paper, we present IdentiFinderTM, a hidden Markov model that learns to recognize and classify names, dates, times, and numerical quantities. We have evaluated the model in English (based on data from the Sixth and Seventh Message Understanding Conferences [MUC-6, MUC-7] and broadcast news) and in Spanish (based on data distributed through the First Multilingual Entity Task [MET-1]), and on speech input (based on broadcast news). We report results here on standard materials only to quantify performance on data available to the community, namely, MUC-6 and MET-1. Results have been consistently better than reported by any other learning algorithm. IdentiFinder‘s performance is competitive with approaches based on handcrafted rules on mixed case text and superior on text where case information is not available. We also present a controlled experiment showing the effect of training set size on performance, demonstrating that as little as 100,000 words of training data is adequate to get performance around 90% on newswire. Although we present our understanding of why this algorithm performs so well on this class of problems, we believe that significant improvement in performance may still be possible.


title: Corrections of the NIST Statistical Test Suite for Randomness

abstract: It is well known that the NIST statistical test suite was used for the evaluation of AES candidate algorithms. We have found that the test setting of Discrete Fourier Transform test and Lempel-Ziv test of this test suite are wrong. We give four corrections of mistakes in the test settings. This suggests that re-evaluation of the test results should be needed.


title: Object proposal using 3D point cloud for DRC-HUBO+

abstract: We present an object proposal method which utilizes the 3D data obtained from a depth sensor as well as the color information of images. Our object proposal method is designed to improve the performance of the object detection for a mobile robot equipped with a camera and a laser scanner. Compared to traditional object proposal methods using only 2D images, the proposed method provides much less number of candidate windows for object detection. We show less than 100 object proposal windows per image using the proposed method result in high recall tested on the public dataset. Our method presents object proposals in 3D space as well as in 2D image thus it can further be applied to following tasks for mobile robots such as 3D location and pose estimation of the target object after successful object detection. We validate our method using the real-world object detection dataset for outdoor mobile robots captured during the DRC Finals 2015 and the public dataset for comparison with the previous methods.


title: Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation

abstract: Though many tasks in computer vision can be formulated elegantly as pixel-labeling problems, a typical challenge discouraging such a discrete formulation is often due to computational efficiency. Recent studies on fast cost volume filtering based on efficient edge-aware filters have provided a fast alternative to solve discrete labeling problems, with the complexity independent of the support window size. However, these methods still have to step through the entire cost volume exhaustively, which makes the solution speed scale linearly with the label space size. When the label space is huge, which is often the case for (sub pixel-accurate) stereo and optical flow estimation, their computational complexity becomes quickly unacceptable. Developed to search approximate nearest neighbors rapidly, the Patch Match method can significantly reduce the complexity dependency on the search space size. But, its pixel-wise randomized search and fragmented data access within the 3D cost volume seriously hinder the application of efficient cost slice filtering. This paper presents a generic and fast computational framework for general multi-labeling problems called Patch Match Filter (PMF). For the very first time, we explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation, i.e., Based-based randomized search and efficient edge-aware image filtering. By decompositing an image into compact super pixels, we also propose super pixel-based novel search strategies that generalize and improve the original Patch Match method. Focusing on dense correspondence field estimation in this paper, we demonstrate PMF's applications in stereo and optical flow. Our PMF methods achieve state-of-the-art correspondence accuracy but run much faster than other competing methods, often giving over 10-times speedup for large label space cases.


title: A Novel 10-Bit 2.8-mW TDC Design Using SAR With Continuous Disassembly Algorithm

abstract: This brief introduces a successive approximation time-to-digital converter based on a novel algorithm denoted as successive approximation register with continuous disassembly (SAR-CD). The main advantage of the proposed SAR-CD algorithm is that it moves the conditioning between the evaluated bits to the digital domain, after all the bits are evaluated. Simulation results show promising enhancements in power consumption compared with the state-of-the-art designs. A full 10-bit architecture is introduced using 65-nm CMOS technology as a case study with simulation power consumption of 2.8 mW at a sampling rate of 29.4 Msample/s from 1-V power supply with an effective number of bits value of 8.63 bits and a maximum differential nonlinearity of 1 least significant bit.


title: Implement role based access control with attribute certificates

abstract: Nowadays more and more activities are performed over the Internet. But as more people are involved in the transaction circle, security and authorization control becomes one of the biggest concerns. Hence, We are motivated by the need 10 manage and to enforce a strong authorization mechanism in large-scale web-environment. Role based access control (RBAC) provides some flexibility to security management. Public key infrastructure (PKI) can provide a strong authentication. Privilege management infrastructure (PMI) as a new technology can provide strong authorization. In order to satisfy mentioned security requirements, we have established a role based access control infrastructure and developed a prototype that uses X.509 public key certificates (PKCs) and attribute certificates (ACs). Access control is performed by access control policies that are written in XML. Policies and roles are stored in ACs. PKCs and AO are all stored in LDAP servers. A new solution for policy management is described. The main components of the prototype are administration tool and access control engine. The access control engine provides a service that mediates the data between the users and the resources, which is also responsible for authentication and authorization. The administration tool can create key pairs, PKCs and ACs, manage users' information, and so on.


title: Circle detection using isosceles triangles sampling

abstract: Detection of circular objects in digital images is an important problem in several vision applications. Circle detection using randomized sampling has been developed in recent years to reduce the computational intensity. Randomized sampling, however, is sensitive to noise that can lead to reduced accuracy and false-positive candidates. This paper presents a new circle detection method based upon randomized isosceles triangles sampling to improve the robustness of randomized circle detection in noisy conditions. It is shown that the geometrical property of isosceles triangles provide a robust criterion to find relevant edge pixels and thereby efficiently provide an estimation of the circle center and radii. The estimated results given by the isosceles triangles sampling from each connected component of edge map were analyzed using a simple clustering approach for efficiency. To further improve on the accuracy we applied a two-step refinement process using chords and linear error compensation with gradient information of the edge pixels. Extensive experiments using both synthetic and real images were presented and results were compared to leading state-of-the-art algorithms and showed that the proposed algorithm: are efficient in finding circles with a low number of iterations; has high rejection rate of false-positive circle candidates; and has high robustness against noise, making it adaptive and useful in many vision applications.


title: The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events.

abstract: IMPORTANCE The study provides novel data to inform the mechanisms by which poverty negatively impacts childhood brain development. OBJECTIVE To investigate whether the income-to-needs ratio experienced in early childhood impacts brain development at school age and to explore the mediators of this effect. DESIGN, SETTING, AND PARTICIPANTS This study was conducted at an academic research unit at the Washington University School of Medicine in St Louis. Data from a prospective longitudinal study of emotion development in preschool children who participated in neuroimaging at school age were used to investigate the effects of poverty on brain development. Children were assessed annually for 3 to 6 years prior to the time of a magnetic resonance imaging scan, during which they were evaluated on psychosocial, behavioral, and other developmental dimensions. Preschoolers included in the study were 3 to 6 years of age and were recruited from primary care and day care sites in the St Louis metropolitan area; they were annually assessed behaviorally for 5 to 10 years. Healthy preschoolers and those with clinical symptoms of depression participated in neuroimaging at school age/early adolescence. EXPOSURE Household poverty as measured by the income-to-needs ratio. MAIN OUTCOMES AND MEASURES Brain volumes of children's white matter and cortical gray matter, as well as hippocampus and amygdala volumes, obtained using magnetic resonance imaging. Mediators of interest were caregiver support/hostility measured observationally during the preschool period and stressful life events measured prospectively. RESULTS Poverty was associated with smaller white and cortical gray matter and hippocampal and amygdala volumes. The effects of poverty on hippocampal volume were mediated by caregiving support/hostility on the left and right, as well as stressful life events on the left. CONCLUSIONS AND RELEVANCE The finding that exposure to poverty in early childhood materially impacts brain development at school age further underscores the importance of attention to the well-established deleterious effects of poverty on child development. Findings that these effects on the hippocampus are mediated by caregiving and stressful life events suggest that attempts to enhance early caregiving should be a focused public health target for prevention and early intervention. Findings substantiate the behavioral literature on the negative effects of poverty on child development and provide new data confirming that effects extend to brain development. Mechanisms for these effects on the hippocampus are suggested to inform intervention.


title: Disentangling the initiation from the response in joint attention: an eye-tracking study in toddlers with autism spectrum disorders

abstract: Joint attention (JA), whose deficit is an early risk marker for autism spectrum disorder (ASD), has two dimensions: (1) responding to JA and (2) initiating JA. Eye-tracking technology has largely been used to investigate responding JA, but rarely to study initiating JA especially in young children with ASD. The aim of this study was to describe the differences in the visual patterns of toddlers with ASD and those with typical development (TD) during both responding JA and initiating JA tasks. Eye-tracking technology was used to monitor the gaze of 17 children with ASD and 15 age-matched children with TD during the presentation of short video sequences involving one responding JA and two initiating JA tasks (initiating JA-1 and initiating JA-2). Gaze accuracy, transitions and fixations were analyzed. No differences were found in the responding JA task between children with ASD and those with TD, whereas, in the initiating JA tasks, different patterns of fixation and transitions were shown between the groups. These results suggest that children with ASD and those with TD show different visual patterns when they are expected to initiate joint attention but not when they respond to joint attention. We hypothesized that differences in transitions and fixations are linked to ASD impairments in visual disengagement from face, in global scanning of the scene and in the ability to anticipate object's action.


CA-RNN: Using Context-Aligned Recurrent Neural Networks for Modeling Sentence Similarity


title: A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction

abstract: We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. The network is initialized with embeddings that make use of character N-gram information to better suit this task. When evaluated on common benchmark test data sets (CoNLL-2014 and JFLEG), our model substantially outperforms all prior neural approaches on this task as well as strong statistical machine translation-based systems with neural and task-specific features trained on the same data. Our analysis shows the superiority of convolutional neural networks over recurrent neural networks such as long short-term memory (LSTM) networks in capturing the local context via attention, and thereby improving the coverage in correcting grammatical errors. By ensembling multiple models, and incorporating an N-gram language model and edit features via rescoring, our novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach, both in terms of grammaticality and fluency.


title: Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

abstract: Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos. In this paper, we formulate video summarization as a sequential decision-making process and develop a deep summarization network (DSN) to summarize videos. DSN predicts for each video frame a probability, which indicates how likely a frame is selected, and then takes actions based on the probability distributions to select frames, forming video summaries. To train our DSN, we propose an end-to-end, reinforcement learning-based framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries and does not rely on labels or user interactions at all. During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative summaries. Since labels are not required, our method can be fully unsupervised. Extensive experiments on two benchmark datasets show that our unsupervised method not only outperforms other state-of-the-art unsupervised methods, but also is comparable to or even superior than most of published supervised approaches.


title: Manga colorization

abstract: This paper proposes a novel colorization technique that propagates color over regions exhibiting pattern-continuity as well as intensity-continuity. The proposed method works effectively on colorizing black-and-white manga which contains intensive amount of strokes, hatching, halftoning and screening. Such fine details and discontinuities in intensity introduce many difficulties to intensity-based colorization methods. Once the user scribbles on the drawing, a local, statistical based pattern feature obtained with Gabor wavelet filters is applied to measure the pattern-continuity. The boundary is then propagated by the level set method that monitors the pattern-continuity. Regions with open boundaries or multiple disjointed regions with similar patterns can be sensibly segmented by a single scribble. With the segmented regions, various colorization techniques can be applied to replace colors, colorize with stroke preservation, or even convert pattern to shading. Several results are shown to demonstrate the effectiveness and convenience of the proposed method.


title: Haptic Device Using a Newly Developed Redundant Parallel Mechanism

abstract: A number of haptic devices have recently become available on the commercial market, and these devices are becoming common not only in research but in consumer use as well. In this paper, a new parallel mechanism, referred to herein as DELTA-R (meaning DELTA-Redundant, formerly referred to as DELTA-4) is proposed for a new haptic device having high-quality force display capability and operability. DELTA-R allows three-degree-of-freedom (DOF) translational motions. The key features of DELTA-R, as compared with conventional parallel mechanisms, are redundant actuation, a smaller footprint, a larger working area, and improved access to the end effector. The prototype is equipped with a 3-DOF rotation mechanism, the center of motion of which is located on the wrist position of the operator. An evaluation test of the force display was conducted using a prototype of the proposed mechanism. This paper describes the kinematic design, kinematic modeling, kinematic analysis, prototype implementation, and evaluations.


title: Coupling design and performance analysis of rim-driven integrated motor propulsor

abstract: Coupling design of rim-driven integrated motor propulsor for vessels and underwater vehicle is presented in this paper. The main characteristic of integrated motor propulsor is that the motor is integrated in the duct of propulsor. So the coupling design of motor and propulsor is the key to the overall design. Considering the influence of the motor and duct size, the propeller was designed, and the CFD Method was used to analyze the hydrodynamic performance of the propulsor. Based on the air-gap magnetic field of permanent magnet motor and the equivalent magnetic circuit, the integrated motor electromagnetic model was proposed, and the finite element method was used to analyze the motor electromagnetic field. Finally, the simulation of the integrated motor starting process with the load of propulsor torque was carried out, and the results meets the design specifications.


title: BATCHING: A Design Pattern for Efficient and Flexible Client-Server Interaction

abstract: The Batching design pattern consists of a common piece of design and implementation that is shared by a wide variety of well-known techniques in Computing such as gather/scatter for input/output, code downloading for system extension, message batching, mobile agents, and deferred calls for disconnected operation. ::: ::: All techniques mentioned above are designed for applications running across multiple domains (e.g., multiple processes or multiple nodes in a network). In these techniques, multiple operations are bundled together and then sent to a different domain, where they are executed. In some cases, the objective is to reduce the number of domain-crossings. In other cases, it is to enable dynamic server extension. ::: ::: In this article, we present the Batching pattern, discuss the circumstances in which the pattern should and should not be used, and identify eight classes of existing techniques that instantiate it.


title: Compositional real-time scheduling framework

abstract: Our goal is to develop a compositional real-time scheduling framework so that global (system-level) timing properties can be established by composing independently (specified and) analyzed local (component-level) timing properties. The two essential problems in developing such a framework are: (1) to abstract the collective real-time requirements of a component as a single real-time requirement and (2) to compose the component demand abstraction results into the system-level real-time requirement. In our earlier work, we addressed the problems using the Liu and Layland periodic model. In this paper, we address the problems using another well-known model, a bounded-delay resource partition model, as a solution model to the problems. To extend our framework to this model, we develop an exact feasibility condition for a set of bounded-delay tasks over a bounded-delay resource partition. In addition, we present simulation results to evaluate the overheads that the component demand abstraction results incur in terms of utilization increase. We also present utilization bound results on a bounded-delay resource model.


title: An Accurate and Robust Flux Splitting Scheme for Shock and Contact Discontinuities

abstract: A flux splitting scheme (AUSMDV) has been constructed with an aim at removing numerical dissipation of the Van Leer-type flux vector splittings on a contact discontinuity. The obtained scheme is also recognized as an improved advection upstream splitting method (AUSM) by Liou and Steffen. The proposed scheme has the following favorable properties: accurate and robust resolution for shock and contact (steady and moving) discontinuities; conservation of enthalpy for steady flows; algorithmic simplicity; and easy extension to general conservation laws such as that for chemically reacting flows. A simple shock fix is presented to cure the numerical shock instability associated with the ``carbuncle phenomenon'' and an entropy fix to remove an expansion shock or glitch at the sonic point. Extensive numerical experiments were conducted to validate the proposed scheme for a wide range of problems, and the results are compiled for comparison with several recent upwind methods.


title: Functional structure identification of scientific documents in computer science

abstract: The increasing number of open-access full-text scientific documents promotes the transformation from metadata- to content-based studies, which is more detailed and semantic. Along with the benefits of ample data, the confused internal structure introduces great difficulties to data organization and analysis. Each unit in scientific documents has its own function in expressing authors’ research ideas, such as introducing motivations, describing methods, stating related work, and drawing conclusions; these could be used to identify functional structure of scientific documents. This paper firstly proposes a clustering method to generate domain-specific structures based on high-frequency section headers in scientific documents of a domain. To automatically identify the structure of scientific documents, we categorize scientific documents into three types: (1) strong-structure documents; (2) weak-structure documents; and (3) no-structure documents. We further divide the identification into three levels—section header-based identification, section content-based identification, and paragraph-based identification—corresponding to the three types of documents. Our experiments on documents in the field of computer science show that: (1) section header-based identification is the most direct and simplest method, but its accuracy is limited by unknown words in section headers; (2) section content-based identification is more stable and obtains good performance; and (3) paragraph-based identification is promising in identifying functions of no-structure documents. Additionally, we apply our methods to two tasks: academic search and keyword extraction. Both tasks demonstrate the effectiveness of functional structure.


title: Espresso: Efficient Forward Propagation for BCNNs

abstract: There are many applications scenarios for which the computational performance and memory footprint of the prediction phase of Deep Neural Networks (DNNs) needs to be optimized. Binary Neural Networks (BDNNs) have been shown to be an effective way of achieving this objective. In this paper, we show how Convolutional Neural Networks (CNNs) can be implemented using binary representations. Espresso is a compact, yet powerful library written in C/CUDA that features all the functionalities required for the forward propagation of CNNs, in a binary file less than 400KB, without any external dependencies. Although it is mainly designed to take advantage of massive GPU parallelism, Espresso also provides an equivalent CPU implementation for CNNs. Espresso provides special convolutional and dense layers for BCNNs, leveraging bit-packing and bit-wise computations for efficient execution. These techniques provide a speed-up of matrix-multiplication routines, and at the same time, reduce memory usage when storing parameters and activations. We experimentally show that Espresso is significantly faster than existing implementations of optimized binary neural networks ($\approx$ 2 orders of magnitude). Espresso is released under the Apache 2.0 license and is available at this http URL.


title: Interactive Topic Graph Extraction and Exploration of Web Content

abstract: In the following, we present an approach using interactive topic graph extraction for the exploration of Web content. The initial information request, in the form of a query topic description, is issued online by a user to the system. The topic graph is then constructed from N Web snippets that are produced by a standard search engine. We consider the extraction of a topic graph to be a specific empirical collocation extraction task, where collocations are extracted between chunks. Our measure of association strength is based on the pointwise mutual information between chunk pairs which explicitly takes their distance into account. This topic graph can then be further analyzed by users so that they can request additional background information with the help of interesting nodes and pairs of nodes in the topic graph, e.g., explicit relationships extracted from Wikipedia or those automatically extracted from additional Web content as well as conceptual information of the topic in form of semantically oriented clusters of descriptive phrases. This information is presented to the users, who can investigate the identified information nuggets to refine their information search. An initial user evaluation shows that our approach is especially helpful for finding new interesting information on topics about which the user has only a vague idea or no idea, at all.


title: Estimation of primary quantization matrix in double compressed jpeg images

abstract: In this report, we present a method for estimation of primary quantization matrix from a double compressed JPEG image. We first identify characteristic features that occur in DCT histograms of individual coefficients due to double compression. Then, we present 3 different approaches that estimate the original quantization matrix from double compressed images. Finally, most successful of them Neural Network classifier is discussed and its performance and reliability is evaluated in a series of experiments on various databases of double compressed images. It is also explained in this paper, how double compression detection techniques and primary quantization matrix estimators can be used in steganalysis of JPEG files and in digital forensic analysis for detection of digital forgeries.


title: Multi-source information fusion for drowsy driving detection based on wireless sensor networks

abstract: Drowsy driving is a major cause of road accidents. This paper analyses the drivers' behavior in the state of fatigue driving and introduces the latest developments of drowsy driving detection technology. In this study we also propose a drowsy driving detection based on the driver's physiological signals such as eye activity measures, the inclination of the driver's head, sagging posture, heart beat rate, skin electric potential, and electroencephalographic (EEG) activities, as well as response characteristics, decline in gripping force on the steering wheel and lane keeping characteristics. By developing a hierarchical model that is able to collect the sensing data, analyze the driving behavior and the reactions to the driver, it can provide a safe and a comfortable driving environment. Combining different indications of drowsiness and processing the contextual information to predict whether a driver is drowsy, the system not only issues a warning for the driver, but also provides the drowsy driving information to transportation control center and other vehicles if necessary.


title: Monocular precrash vehicle detection: features and classifiers

abstract: Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.


title: Completed Local Binary Count for Rotation Invariant Texture Classification

abstract: In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.


Secreted Frizzled-Related Protein 4 Inhibits Glioma Stem-Like Cells by Reversing Epithelial to Mesenchymal Transition, Inducing Apoptosis and Decreasing Cancer Stem Cell Properties


title: An anticipation model of potential customers' purchasing behavior based on clustering analysis and association rules analysis

abstract: This paper proposes an anticipation model of potential customers' purchasing behavior. This model is inferred from past purchasing behavior of loyal customers and the web server log files of loyal and potential customers by means of clustering analysis and association rules analysis. Clustering analysis collects key characteristics of loyal customers' personal information; these are used to locate other potential customers. Association rules analysis extracts knowledge of loyal customers' purchasing behavior, which is used to detect potential customers' near-future interest in a star product. Despite using offline analysis to filter out potential customers based on loyal customers' personal information and generate rules of loyal customers' click streams based on loyal customers' web log data, an online analysis which observes potential customers' web logs and compares it with loyal customers' click stream rules can more readily target potential customers who may be interested in the star products in the near future.


title: Force Analysis of Connected Differential Mechanisms: Application to Grasping

abstract: In this paper, a methodology is proposed for the analysis of the force capabilities of connected differential mechanisms. These systems are the key elements used to extend the principle of underactuation in grasping from the fingers to the hand itself. The concept of under-actuation in robotic grasping--with fewer actuators than degrees of freedom (DOF)--allows the hand to adjust itself to an irregularly shaped object without complex control strategies and sensors. Several technological solutions have been proposed in the past but no theoretical background has been provided to analyze their characteristics, especially with respect to the forces generated. The purpose of this paper is to provide such a theoretical foundation and to illustrate its usefulness with examples applied to grasping. First, several differential elements are presented and studied. Second, a mathematical method to obtain the output force capabilities of connected differential mechanisms is presented. Finally, the technique presented is applied to two types of underactuated robotic hands.


title: An ambient assisted living framework for mobile environments

abstract: Ambient assisted living (AAL) delivers IT solutions that aim to facilitate and improve lives of the disabled, elderly, and chronically ill people. Mobility is a key issue for elderly people because their physical activity, in general, improves their quality of life and keep health status. Then, this paper presents an AAL framework for caregivers and elderly people that allow them to maintain an active lifestyle without limiting their mobility. This framework includes four AAL tools for mobility environments: i) a fall detection mobile application; ii) a biofeedback monitoring system trough wearable sensors; iii) an outdoor location service through a shoe equipped with Global Positioning System (GPS); and iv) a mobile application for caregivers that take care of several elders confined to a home environment. The proposal is evaluated and demonstrated and it is ready for use.


title: Plagiarism Detection Using the Levenshtein Distance and Smith-Waterman Algorithm

abstract: Plagiarism in texts is issues of increasing concern to the academic community. Now most common text plagiarism occurs by making a variety of minor alterations that include the insertion, deletion, or substitution of words. Such simple changes, however, require excessive string comparisons. In this paper, we present a hybrid plagiarism detection method. We investigate the use of a diagonal line, which is derived from Levenshtein distance, and simplified Smith-Waterman algorithm that is a classical tool in the identification and quantification of local similarities in biological sequences, with a view to the application in the plagiarism detection. Our approach avoids globally involved string comparisons and considers psychological factors, which can yield significant speed-up by experiment results. Based on the results, we indicate the practicality of such improvement using Levenshtein distance and Smith-Waterman algorithm and to illustrate the efficiency gains. In the future, it would be interesting to explore appropriate heuristics in the area of text comparison.


title: Electromagnetic Performance of Double-Stator Flux-Modulation Permanent-Magnet Motor

abstract: This paper proposes a novel double-stator flux-modulation permanent-magnet (DS-FMPM) motor for electric vehicles. The key of the proposed motor is the double-stator and flux-modulation structure. Due to its special design, more PMs can be adopted, and the useful harmonics of the air-gap flux density are increased, thus improving the torque capability of the proposed DS-FMPM motor. Since the inner stator is located inside, the motor space is fully used. Due to the absence of PMs and windings on the rotor, the cooling condition of the proposed motor is improved. The electromagnetic characteristics of the proposed motor are analyzed by using the finite-element method, confirming the validity of the theoretical analysis.


title: An improved fuzzy clustering method using modified Fukuyama-Sugeno cluster validity index

abstract: The objective of clustering algorithms is to group similar patterns in one class and dissimilar patterns in disjoint classes. This article proposes a novel algorithm for fuzzy partitional clustering with an aim to minimize a composite objective function, defined using the Fukuyama-Sugeno cluster validity index. The optimization of this objective function tries to minimize the separation between clusters of a data set and maximize the compactness of a certain cluster. But in certain cases, such as a data set having overlapping clusters, this approach leads to poor clustering results. Thus we introduce a new parameter in the objective function which enables us to yield more accurate clustering results. The algorithm has been validated with some artificial and real world datasets.


title: High Accuracy Time of Flight Measurement Using Digital Signal Processing Techniques for Subsea Applications

abstract: The techniques widely used in ultrasonic measurements are based on the determination of the time of flight (T.o.F). A short train of waves is transmitted and same transducer is used for reception of the reflected signal for the pulse-echo measurement applications. The amplitude of the received waveform is an envelope which starts from zero reaches to a peak and then dies out. The echoes are mostly detected by simple threshold crossing technique, which is also cause of error. In this paper digital signal processing is used to calculate the time delay in reception i.e. T.o.F, for which a maximum similarity between the reference and the delayed echo signals is obtained. To observe the effect of phase uncertainties and frequency shifts (Doppler), this processing is carried out, both directly on the actual wave shape and after extracting the envelopes of the reference and delayed echo signals. Several digital signal processing algorithms are considered and the effects of different factors such as sampling rate, resolution of digitization and S/N ratio are analyzed. Result show accuracy, computing time and cost for different techniques.


title: Detection of Partially Occluded Face Using Support Vector Machines

abstract: Partially occluded face detection is need, because although the technology of the Automated Teller Machines and face detection is increased, we cannot control the people who wear sunglasses or mask for the crime. To reject the occluded face, we first trained the features of the normal faces and the occluded faces that wear sunglasses or mask using Principal Component Analysis and Support Vector Machines to reduce the dimension and classify eficiently. Then we decide that the detected face is normal or partially occluded face using the scheme that integrates the Principal Component Analysis and Support Vector Machines. In the experiments, we trained the 3200 normal face images that have the variations of illumination and expression and each 2900 and 4500 partially occluded face images that wear the sunglasses or mask with 60*25, and 60*35 resolution. We get the 95.2% and 98.8% partially occluded face detection ratio after face detection and 2.5% and 0% false alarm ratio from the experiments based on the Purdue University Face DB. The proposed algorithm which is incorporated the face detection system can help the security using the Digital Video Recorder System and face recognition


title: Document Retrieval Model Through Semantic Linking

abstract: This paper addresses the task of document retrieval based on the degree of document relatedness to the meanings of a query by presenting a semantic-enabled language model. Our model relies on the use of semantic linking systems for forming a graph representation of documents and queries, where nodes represent concepts extracted from documents and edges represent semantic relatedness between concepts. Based on this graph, our model adopts a probabilistic reasoning model for calculating the conditional probability of a query concept given values assigned to document concepts. We present an integration framework for interpolating other retrieval systems with the presented model in this paper. Our empirical experiments on a number of TREC collections show that the semantic retrieval has a synergetic impact on the results obtained through state of the art keyword-based approaches, and the consideration of semantic information obtained from entity linking on queries and documents can complement and enhance the performance of other retrieval models.


title: Structured Overcomplete Sparsifying Transform Learning with Convergence Guarantees and Applications

abstract: In recent years, sparse signal modeling, especially using the synthesis model has been popular. Sparse coding in the synthesis model is however, NP-hard. Recently, interest has turned to the sparsifying transform model, for which sparse coding is cheap. However, natural images typically contain diverse textures that cannot be sparsified well by a single transform. Hence, in this work, we propose a union of sparsifying transforms model. Sparse coding in this model reduces to a form of clustering. The proposed model is also equivalent to a structured overcomplete sparsifying transform model with block cosparsity, dubbed OCTOBOS. The alternating algorithm introduced for learning such transforms involves simple closed-form solutions. A theoretical analysis provides a convergence guarantee for this algorithm. It is shown to be globally convergent to the set of partial minimizers of the non-convex learning problem. We also show that under certain conditions, the algorithm converges to the set of stationary points of the overall objective. When applied to images, the algorithm learns a collection of well-conditioned square transforms, and a good clustering of patches or textures. The resulting sparse representations for the images are much better than those obtained with a single learned transform, or with analytical transforms. We show the promising performance of the proposed approach in image denoising, which compares quite favorably with approaches involving a single learned square transform or an overcomplete synthesis dictionary, or gaussian mixture models. The proposed denoising method is also faster than the synthesis dictionary based approach.


title: A C3D-Based Convolutional Neural Network for Frame Dropping Detection in a Single Video Shot

abstract: Frame dropping is a type of video manipulation where consecutive frames are deleted to omit content from the original video. Automatically detecting dropped frames across a large archive of videos while maintaining a low false alarm rate is a challenging task in digital video forensics. We propose a new approach for forensic analysis by exploiting the local spatio-temporal relationships within a portion of a video to robustly detect frame removals. In this paper, we propose to adapt the Convolutional 3D Neural Network (C3D) for frame drop detection. In order to further suppress the errors due by the network, we produce a refined video-level confidence score and demonstrate that it is superior to the raw output scores from the network. We conduct experiments on two challenging video datasets containing rapid camera motion and zoom changes. The experimental results clearly demonstrate the efficacy of the proposed approach.


title: Mobile Cloud Computing: A Survey, State of Art and Future Directions

abstract: In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a phenomenal increase in the usage and deployment of smartphone platforms and applications worldwide. This paper discusses the current state of the art in the merger of these two popular technologies, that we refer to as Mobile Cloud Computing (MCC). We illustrate the applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias. We further identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security and privacy.


title: An Impedance and Multi-Wavelength Near-Infrared Spectroscopy IC for Non-Invasive Blood Glucose Estimation

abstract: A multi-modal spectroscopy IC combining impedance spectroscopy (IMPS) and multi-wavelength near-infrared spectroscopy (mNIRS) is proposed for high precision non-invasive glucose level estimation. A combination of IMPS and mNIRS can compensate for the glucose estimation error to improve its accuracy. The IMPS circuit measures dielectric characteristics of the tissue using the RLC resonant frequency and the resonant impedance to estimate the glucose level. To accurately find resonant frequency, a 2-step frequency sweep sinusoidal oscillator (FSSO) is proposed: 1) 8-level coarse frequency switching (f STEP = 9.4 kHz) in 10–76 kHz, and 2) fine analog frequency sweep in the range of 18.9 kHz. During the frequency sweep, the adaptive gain control loop stabilizes the output voltage swing (400 mV p-p ). To improve accuracy of mNIRS, three wavelengths, 850 nm, 950 nm, and 1,300 nm, are used. For highly accurate glucose estimation, the measurement data of the IMPS and mNIRS are combined by an artificial neural network (ANN) in external DSP. The proposed ANN method reduces the mean absolute relative difference to 8.3% from 15% of IMPS, and 15–20% of mNIRS in 80–180 mg/dL blood glucose level. The proposed multi-modal spectroscopy IC occupies 12.5 mm 2 in a 0.18 µm 1P6M CMOS technology and dissipates a peak power of 38 mW with the maximum radiant emitting power of 12.1 mW.


title: Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective

abstract: Machine-to-machine (M2M) communications enables networked devices to exchange information among each other as well as with business application servers and therefore creates what is known as the Internet-of-Things (IoT). The research community has a consensus for the need of a standardized protocol stack for M2M communications. On the other hand, cognitive radio technology is very promising for M2M communications due to a number of factors. It is expected that cognitive M2M communications will be indispensable in order to realize the vision of IoT. However cognitive M2M communications requires a cognitive radio-enabled protocol stack in addition to the fundamental requirements of energy efficiency, reliability, and Internet connectivity. The main objective of this paper is to provide the state of the art in cognitive M2M communications from a protocol stack perspective. This paper covers the emerging standardization efforts and the latest developments on protocols for cognitive M2M networks. In addition, this paper also presents the authors’ recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks. These protocols explicitly account for the peculiarities of cognitive radio environments. Performance evaluation demonstrates that the proposed protocols not only ensure protection to the primary users (PUs) but also fulfil the utility requirements of the secondary M2M networks.


title: Unveiling Zeus: automated classification of malware samples

abstract: Malware family classification is an age old problem that many Anti-Virus (AV) companies have tackled. There are two common techniques used for classification, signature based and behavior based. Signature based classification uses a common sequence of bytes that appears in the binary code to identify and detect a family of malware. Behavior based classification uses artifacts created by malware during execution for identification. In this paper we report on a unique dataset we obtained from our operations and classified using several machine learning techniques using the behavior-based approach. Our main class of malware we are interested in classifying is the popular Zeus malware. For its classification we identify 65 features that are unique and robust for identifying malware families. We show that artifacts like file system, registry, and network features can be used to identify distinct malware families with high accuracy - in some cases as high as 95 percent.


title: LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs

abstract: We present LemmaTag, a featureless neural network architecture that jointly generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. We demonstrate that both tasks benefit from sharing the encoding part of the network, predicting tag subcategories, and using the tagger output as an input to the lemmatizer. We evaluate our model across several languages with complex morphology, which surpasses state-of-the-art accuracy in both part-of-speech tagging and lemmatization in Czech, German, and Arabic.


title: Optimal State Reference Computation With Constrained MTPA Criterion for PM Motor Drives

abstract: This research proposes a procedure that maps a PMSM torque request onto optimal state (current) references. Combining the procedure with a dynamic (current) controller yields a torque controller. The maximum torque per ampere (MTPA) criterion is used to minimize conduction and switching losses. This research extends the concept to field-weakening operation to obtain high efficiency at any machine speed. The resulting constrained MTPA criterion is formalized as an optimization problem. Since it is difficult to solve directly, the maximum and intersection torque subproblems are identified. An algorithm is obtained that maps a torque onto an optimal state reference, and it is sufficiently efficient for real-time implementation. This method is compatible with a variety of state (current) controllers with/without PWM, SPM and IPM machines with saliency and reverse saliency, and a variable dc-link voltage. The proposed procedure relies on a sufficiently accurate torque model that may not be provided using rated machine parameters. Thus, an approach to compute locally optimized machine parameters is proposed that takes magnetic saturation into account. The concept is developed on a software-in-the-loop platform and evaluated on an experimental test bench.


title: The IBM 2016 Speaker Recognition System

abstract: In this paper we describe the recent advancements made in the IBM i-vector speaker recognition system for conversational speech. In particular, we identify key techniques that contribute to significant improvements in performance of our system, and quantify their contributions. The techniques include: 1) a nearest-neighbor discriminant analysis (NDA) approach that is formulated to alleviate some of the limitations associated with the conventional linear discriminant analysis (LDA) that assumes Gaussian class-conditional distributions, 2) the application of speaker- and channel-adapted features, which are derived from an automatic speech recognition (ASR) system, for speaker recognition, and 3) the use of a deep neural network (DNN) acoustic model with a large number of output units (~10k senones) to compute the frame-level soft alignments required in the i-vector estimation process. We evaluate these techniques on the NIST 2010 speaker recognition evaluation (SRE) extended core conditions involving telephone and microphone trials. Experimental results indicate that: 1) the NDA is more effective (up to 35% relative improvement in terms of EER) than the traditional parametric LDA for speaker recognition, 2) when compared to raw acoustic features (e.g., MFCCs), the ASR speaker-adapted features provide gains in speaker recognition performance, and 3) increasing the number of output units in the DNN acoustic model (i.e., increasing the senone set size from 2k to 10k) provides consistent improvements in performance (for example from 37% to 57% relative EER gains over our baseline GMM i-vector system). To our knowledge, results reported in this paper represent the best performances published to date on the NIST SRE 2010 extended core tasks.


title: Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation

abstract: Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other works, we treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without reactant-reagent split and including stereochemistry, which makes our method universally applicable.


title: Investigation and Design of On-Chip Power-Rail ESD Clamp Circuits Without Suffering Latchup-Like Failure During System-Level ESD Test

abstract: On-chip power-rail electrostatic discharge (ESD) protection circuit designed with active ESD detection function is the key role to significantly improve ESD robustness of CMOS integrated circuits (ICs). Four power-rail ESD clamp circuits with different ESD-transient detection circuits were fabricated in a 0.18-mum CMOS process and tested to compare their system-level ESD susceptibility, which are named as power-rail ESD clamp circuits with typical RC-based detection, PMOS feedback, NMOS+PMOS feedback, and cascaded PMOS feedback in this work. During the system-level ESD test, where the ICs in a system have been powered up, the feedback loop used in the power-rail ESD clamp circuits provides the lock function to keep the ESD-clamping NMOS in a ldquolatch-onrdquo state. The latch-on ESD-clamping NMOS, which is often drawn with a larger device dimension to sustain high ESD level, conducts a huge current between the power lines to perform a latchup-like failure after the system-level ESD test. A modified power-rail ESD clamp circuit is proposed to solve this problem. The proposed power-rail ESD clamp circuit can provide high enough chip-level ESD robustness, and without suffering the latchup-like failure during the system-level ESD test.