title: DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

text: Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.


title: A socially assistive robot exercise coach for the elderly

text: We present a socially assistive robot (SAR) system designed to engage elderly users in physical exercise. We discuss the system approach, design methodology, and implementation details, which incorporate insights from psychology research on intrinsic motivation, and we present five clear design principles for SAR-based therapeutic interventions. We then describe the system evaluation, consisting of a multi-session user study with older adults (n = 33), to evaluate the effectiveness of our SAR exercise system and to investigate the role of embodiment by comparing user evaluations of similar physically and virtually embodied coaches. The results validate the system approach and effectiveness at motivating physical exercise in older adults according to a variety of user performance and outcomes measures. The results also show a clear preference by older adults for the physically embodied robot coach over the virtual coach in terms of enjoyableness, helpfulness, and social attraction, among other factors.


title: Polyphase filters - A model for teaching the art of discovery in DSP

text: By its very nature DSP is a mathematically heavy topic and to fully understand it students need to understand the mathematical developments underlying DSP topics. However, relying solely on mathematical developments often clouds the true nature of the foundation of a result. It is likely that students who master the mathematics may still not truly grasp the key ideas of a topic. Furthermore, teaching DSP topics by merely “going through the mathematics” deprives students of learning the art of discovery that will make them good researchers. This paper uses the topic of polyphase decimation and interpolation to illustrate how it is possible to maintain rigor yet teach using less mathematical approaches that show students how researchers think when developing new ideas.


title: Explanation-Based Generalization: A Unifying View

text: The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.


title: Broadband Micro-Coaxial Wilkinson Dividers

text: This paper presents several micro-coaxial broadband 2 : 1 Wilkinson power dividers operating from 2 to 22 GHz, a 11 : 1 bandwidth. Circuits are fabricated on silicon with PolyStrata technology, and are implemented with 650 mum times 400 mum air-supported micro-coaxial lines. The measured isolation between the output ports is greater than 11 dB and the return loss at each port is more than 13 dB over the entire bandwidth. The footprints of these dividers can be miniaturized due to the high isolation between adjacent coaxial lines and their tight bend radius. For higher power handling, larger lines with a cross section of 1050 mum times 850 mum are also demonstrated. The effect of mismatch at the output ports is investigated in order to find the power loss in the resistors.


title: Distributed Graph Routing for WirelessHART Networks

text: Communication reliability in a Wireless Sensor and Actuator Network (WSAN) has a high impact on stability of industrial process monitoring and control. To make reliable and real-time communication in highly unreliable environments, industrial WSANs such as those based on WirelessHART adopt graph routing. In graph routing, each packet is scheduled on multiple time slots using multiple channels, on multiple links along multiple paths on a routing graph between a source and a destination. While high redundancy is crucial to reliable communication, determining and maintaining graph routing is challenging in terms of execution time and energy consumption for resource constrained WSAN. Existing graph routing algorithms use centralized approach, do not scale well in terms of these metrics, and are less suitable under network dynamics. To address these limitations, we propose the first distributed graph routing protocol for WirelessHART networks. Our distributed protocol is based on the Bellman-Ford Algorithm, and generates all routing graphs together using a single algorithm. We prove that our proposed graph routing can include a path between a source and a destination with cost (in terms of hop-count) at most 3 times the optimal cost. We implemented our proposed routing algorithm on TinyOS and evaluated through experiments on TelosB motes and simulations using TOSSIM. The results show that it is scalable and consumes at least 40% less energy and needs at least 65% less time at the cost of 1kB of extra memory compared to the state-of-the-art centralized approach for generating routing graphs.


title: Traffic Surveillance using Multi-Camera Detection and Multi-Target Tracking

text: Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements). Different views of the same area by more than one camera sensor is necessary, because of the typical limitations of single camera systems, resulting from the occlusion effect of other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras is done using a tracking method. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system provides new derived and consolidated information about traffic participants. Thus, this approach is also beneficial for a description of individual traffic participants.


title: Designing Password Policies for Strength and Usability

text: Password-composition policies are the result of service providers becoming increasingly concerned about the security of online accounts. These policies restrict the space of user-created passwords to preclude easily guessed passwords and thus make passwords more difficult for attackers to guess. However, many users struggle to create and recall their passwords under strict password-composition policies, for example, ones that require passwords to have at least eight characters with multiple character classes and a dictionary check. Recent research showed that a promising alternative was to focus policy requirements on password length instead of on complexity. In this work, we examine 15 password policies, many focusing on length requirements. In doing so, we contribute the first thorough examination of policies requiring longer passwords. We conducted two online studies with over 20,000 participants, and collected both usability and password-strength data. Our findings indicate that password strength and password usability are not necessarily inversely correlated: policies that lead to stronger passwords do not always reduce usability. We identify policies that are both more usable and more secure than commonly used policies that emphasize complexity rather than length requirements. We also provide practical recommendations for service providers who want their users to have strong yet usable passwords.


title: Mimir: a Market-based Real-time Question and Answer Service

text:
Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.
 In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.


title: Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach

text: Despite the popularity, the tuning aspect of proportional–integral-derivative (PID) controllers is a challenge for researchers and plant operators. Various controllers tuning methodologies have been proposed in the literature such as auto-tuning, self-tuning, pattern recognition, artificial intelligence, and optimization methods. Chaotic optimization algorithms as an emergent method of global optimization have attracted much attention in engineering applications. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local optimum, is a promising tool for engineering applications. In this paper, a tuning method for determining the parameters of PID control for an automatic regulator voltage (AVR) system using a chaotic optimization approach based on Lozi map is proposed. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed chaotic optimization introduces chaos mapping using Lozi map chaotic sequences which increases its convergence rate and resulting precision. Simulation results are promising and show the effectiveness of the proposed approach. Numerical simulations based on proposed PID control of an AVR system for nominal system parameters and step reference voltage input demonstrate the good performance of chaotic optimization. 2007 Elsevier Ltd. All rights reserved.


title: What Constitutes Normal Sleeping Patterns ? Childhood to Adolescence 2 . 1 Developmental Patterns in Sleep Duration : Birth to Adolescence

text: Healthy, adequate sleep is integral to the process of growth and development during adolescence. At puberty, maturational changes in th e underlying homeostatic and circadian sleep regulatory mechanisms influence the sleep-wak e p tterns of adolescents. These changes interact with psychosocial factors, such as increas ing academic demands, hours spent in paid employment, electronic media use, and social opport unities, and constrict the time available for adolescents to sleep. Survey studies reveal tha t adolescents’ habitual sleep schedules are associated with cumulative sleep loss. As a consequ ence, there is growing concern about the effects of insufficient sleep on adolescents’ wakin g function. This review identifies and examines the characteristics of sleep and sleep los s in adolescents. It highlights the need for more research into the effects of chronic partial s leep deprivation in adolescents, and the process of extending sleep on weekends to recover t he effects of sleep debt. An understanding of chronic sleep deprivation and recovery sleep in adolescents will facilitate the development of evidence-based sleep guidelines and recommendati o s for recovery sleep opportunities when habitual sleep times are insufficient.


title: Search-based Neural Structured Learning for Sequential Question Answering

text: Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semistructured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.


title: Crowdfunding Success Factors: The Characteristics of Successfully Funded Projects on Crowdfunding Platforms

text: Crowdfunding platforms offer promising opportunities for project founders to publish their project ideas and to collect money in order to be able to realize them. Consequently, the question of what influences the successful funding of projects, i.e., reaching the target amount of money, is very important. Building upon media richness theory and the concept of reciprocity, we extend previous research in the field of crowdfunding success factors. We provide a comprehensive view on factors influencing crowdfunding success by both focusing on project-specific as well as founder-specific aspects. Analyzing a sample of projects of the crowdfunding platform kickstarter.com, we find that the project description, related images and videos as well as the question of whether the founder has previously backed other projects influence funding success. Interestingly, the question of whether the founder has previously created other projects has no significant influence. Our results are of high interest for the stakeholders on crowdfunding platforms.


title: Predicting Song Popularity

text: Future Work Dataset and Features Music has been an integral part of our culture all throughout human history. In 2012 alone, the U.S. music industry generated $15 billion. Having a fundamental understanding of what makes a song popular has major implications to businesses that thrive on popular music, namely radio stations, record labels, and digital and physical music market places. Many private companies in these industries have solutions for this problem, but details have been kept private for competitive reasons. For our project, we will predict song popularity based on an song’s audio features and metadata. Methods & Results


title: Multi-task Deep Reinforcement Learning with PopArt

text: The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy with a single set of weights that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.


title: Many-objective optimization algorithm applied to history matching

text: Reservoir model calibration, called history matching in the petroleum industry, is an important task to make more accurate predictions for better reservoir management. Providing an ensemble of good matched reservoir models from history matching is essential to reproduce the observed production data from a field and to forecast reservoir performance. The nature of history matching is multi-objective because there are multiple match criteria or misfit from different production data, wells and regions in the field. In many cases, these criteria are conflicting and can be handled by the multi-objective approach. Moreover, multi-objective provides faster misfit convergence and more robust towards stochastic nature of optimization algorithms. However, reservoir history matching may feature far too many objectives that can be efficiently handled by conventional multi-objective algorithms, such as multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm II (NSGA II). Under an increasing number of objectives, the performance of multi-objective history matching by these algorithms deteriorates (lower match quality and slower misfit convergence). In this work, we introduce a recently proposed algorithm for many-objective optimization problem, known as reference vector-guided evolutionary algorithm (RVEA), to history matching. We apply the algorithm to history matching a synthetic reservoir model and a real field case study with more than three objectives. The paper demonstrates the superiority of the proposed RVEA to the state of the art multi-objective history matching algorithms, namely MOPSO and NSGA II.


title: An improved LLC resonant inverter for induction heating with asymmetrical control

text: This paper proposes a modified LLC resonant load configuration of a full-bridge inverter for induction heating applications by using asymmetrical voltage cancellation control. The proposed control method is implemented in a full-bridge inverter topology. With the use of a phase-locked loop control, the operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load parameter variation. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed with a matching transformer in between the series inductor and paralleled LC resonant tank for short circuit protection capability of the matching transformer and the induction coil. The validity of the proposed method is verified through computer simulation and hardware experiment at the operating frequency of 108.7 to 110.6 kHz.


title: An active battery cell balancing topology without using external energy storage elements

text: Cell balancing circuits are important to extent life-cycle of batteries and to extract maximum power from the batteries. A lot of power electronics topology has been tried for cell balancing in the battery packages. Active cell balancing topologies transfer energy from the cells showing higher performance to the cells showing lower performance to balance voltages across the cells of the battery using energy storage elements like combination of inductor-capacitor or transformer-capacitor or switched capacitor or switched inductor. In this study an active balancing topology without using any energy storage element is proposed. The idea is similar to the switched capacitor topology in which a capacitor or capacitor banks is switched across the cells of battery to balance the voltages. Since a basic battery cell model includes capacitance because of capacitive effect of the cell, this capacitive effect can be utilized in cell balancing. Hence the equalizer capacitors in switched capacitor topology can be eliminated and the cells of battery can be switched with each other. This allows faster energy transfer and hence results in quick equalization. The proposed topology removes the need of extra energy storage elements like capacitors which frequently fails in power electronic circuits, reduces the losses inserted by extra energy storage elements and cost and volume of the circuits and simplifies control algorithm. The proposed balancing circuit can be implemented according to the application requirement. The proposed topology is simulated in MATLAB/Simulink environment and showed better results in terms of balancing speed in comparison to switched capacitor topologies.


title: Energy-Efficient Resource Allocation in Cellular Networks With Shared Full-Duplex Relaying

text: Recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy-efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where an FDR relay is deployed at the intersection of several adjacent cell sectors. First, a simple but practical transmission strategy is proposed to deal with the involved interference, i.e., multiaccess interference, multiuser interference, and self-interference. Then, the problem of joint power and subcarrier allocation is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem with high computation complexity, we use Dinkelbach and discrete stochastic optimization methods to solve the energy-efficient resource-allocation problem efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.


title: A novel wideband waveguide-to-microstrip transition with waveguide stepped impedance transformer

text: A novel wideband waveguide-to-microstrip transition is presented in this paper. A printed fan-shaped probe is used, while a waveguide stepped impedance transformer is utilized to broaden the bandwidth further. With an optimized design, a relative bandwidth of 27.8% for -20dB return loss with a center frequency of 15GHz is achieved. The simulated insertion loss in this bandwidth is less than 0.29dB. A back-to-back transition is fabricated and measured. The measured result is similar to that of simulation.


title: Graph based E-Government web service composition

text: Nowadays, e-government has emerged as a government policy to improve the quality and efficiency of public administrations. By exploiting the potential of new information and communication technologies, government agencies are providing a wide spectrum of online services. These services are composed of several web services that comply with well defined processes. One of the big challenges is the need to optimize the composition of the elementary web services. In this paper, we present a solution for optimizing the computation effort in web service composition. Our method is based on Graph Theory. We model the semantic relationship between the involved web services through a directed graph. Then, we compute all shortest paths using for the first time, an extended version of the FloydWarshall algorithm.


title: Classification of Mixed-Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks

text: In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed-type defect patterns, i.e., two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed-type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WBMs.


title: Information Retrieval

text: The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …


title: Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors

text: Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.


title: Deep Learning for Natural Language Processing

text: 1Student,Dept. of Computer Engineering, VESIT, Maharashtra, India ---------------------------------------------------------------------------***-------------------------------------------------------------------Abstract Deep Learning has come into existence as a new area for research in Machine Learning. It aims to act like a human brain, having the ability to learn and process from complex data and also tries solving intricate tasks as well. Due to this capability, its been used in various fields like text, sound, images etc. Natural language process has started to being impacted by the deep learning techniques. This research paper highlights Deep Learning’s recent developments and applications in Natural Language Processing.


title: Image Segmentation by Cascaded Region Agglomeration

text: We propose a hierarchical segmentation algorithm that starts with a very fine over segmentation and gradually merges regions using a cascade of boundary classifiers. This approach allows the weights of region and boundary features to adapt to the segmentation scale at which they are applied. The stages of the cascade are trained sequentially, with asymetric loss to maximize boundary recall. On six segmentation data sets, our algorithm achieves best performance under most region-quality measures, and does it with fewer segments than the prior work. Our algorithm is also highly competitive in a dense over segmentation (super pixel) regime under boundary-based measures.


title: Continuous and Cuffless Blood Pressure Monitoring Based on ECG and SpO2 Signals ByUsing Microsoft Visual C Sharp

text:
BACKGROUND
One of the main problems especially in operating room and monitoring devices is measurement of Blood Pressure (BP) by sphygmomanometer cuff. Objective :In this study we designed a new method to measure BP changes continuously for detecting information between cuff inflation times by using vital signals in monitoring devices. This will be achieved by extraction of the time difference between each cardiac cycle and a relative pulse wave.

METHODS
Finger pulse and ECG signals in lead I were recorded by a monitoring device. The output of monitoring device wasinserted in a computer by serial network communication. A software interface (Microsoft Visual C#.NET ) was used to display and process the signals in the computer. Time difference between each cardiac cycle and pulse signal was calculated throughout R wave detection in ECG and peak of pulse signal by the software. The relation between time difference in two waves and BP was determined then the coefficients of equation were obtained in different physical situations. The results of estimating BP were compared with the results of sphygmomanometer method and the error rate was calculated.

RESULTS
In this study, 25 subjects participated among them 15 were male and 10 were female. The results showed that BP was linearly related to time difference. Average of coefficient correlation was 0.9±0.03 for systolic and 0.82±0.04 for diastolic blood pressure. The highest error percentage was calculated 8% for male and 11% for female group. Significant difference was observed between the different physical situation and arm movement changes. The relationship between time difference and age was estimated in a linear relationship with a correlation coefficient of 0.76.

CONCLUSION
By determining linear relation values with high accuracy, BP can be measured with insignificant error. Therefore it can be suggested as a new method to measure the blood pressure continuously.


title: Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems

text: We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation of a predictive model for quality forecasting of products on an assembly line.


title: LDA-based document models for ad-hoc retrieval

text: Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.


title: Number of articles Economy Health Care Taxes Federal Budget Jobs State Budget Education Candidate Biography Elections Immigration Foreign Policy Crime Energy History Job Accomplishments Legal Issues Environment Terrorism Military Guns

text: We report on attempts to use currently available automated text analysis tools to identify possible biased treatment by Politifact of Democratic vs. Republican speakers, through language. We begin by noting that there is no established method for detecting such differences, and indeed that “bias” is complicated and difficult to operationalize into a measurable quantity. This report includes several analyses that are representative of the tools available from natural language processing at this writing. In each case, we offer (i) what we would expect to see in the results if the method picked up on differential treatment between Democrats vs. Republicans, (ii) what we actually observe, and (iii) potential problems with the analysis; in some cases we also suggest (iv) future analyses that might be more revelatory.


title: PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

text: The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.


title: Class Activation Map Generation by Representative Class Selection and Multi-Layer Feature Fusion

text: Existing method generates class activation map (CAM) by a set of fixed classes (i.e., using all the classes), while the discriminative cues between class pairs are not considered. Note that activation maps by considering different class pair are complementary, and therefore can provide more discriminative cues to overcome the shortcoming of the existing CAM generation that the highlighted regions are usually local part regions rather than global object regions due to the lack of object cues. In this paper, we generate CAM by using a few of representative classes, with aim of extracting more discriminative cues by considering each class pair to obtain CAM more globally. The advantages are twofold. Firstly, the representative classes are able to obtain activation regions that are complementary to each other, and therefore leads to generating activation map more accurately. Secondly, we only need to consider a small number of representative classes, making the CAM generation suitable for small networks. We propose a clustering based method to select the representative classes. Multiple binary classification models rather than a multiple class classification model are used to generate the CAM. Moreover, we propose a multi-layer fusion based CAM generation method to simultaneously combine high-level semantic features and low-level detail features. We validate the proposed method on the PASCAL VOC and COCO database in terms of segmentation groundtruth. Various networks such as classical network (Resnet-50, Resent-101 and Resnet-152) and small network (VGG-19, Resnet-18 and Mobilenet) are considered. Experimental results show that the proposed method improves the CAM generation obviously.


title: Processing of Reference and the Structure of Language: An Analysis of Complex Noun Phrases

text: Five experiments used self-paced reading time to examine the ways in which complex noun phrases (both conjoined NPs and possessive NPs) inuence the interpretation of referentially dependent expressions. The experimental conditions contrasted the reading of repeated names and pronouns referring to components of a complex NP and to the entire complex NP. The results indicate that the entity introduced by a major constituent of a sentence is more accessible as a referent than the entities introduced by component noun phrases. This pattern of accessibility departs from the advantage of rst mention that has been demonstrated using probe-word recognition tasks. It supports the idea that reduced expressions are interpreted as referring directly to prominent entities in a mental model whereas reference by names to entities that are already represented in a mental model is mediated by additional processes. The same interpretive processes appear to operate on coreference within and between sentences.


title: Predicting the Price of Used Cars using Machine Learning Techniques

text: In this paper, we investigate the application of supervised machine learning techniques to predict the price of used cars in Mauritius. The predictions are based on historical data collected from daily newspapers. Different techniques like multiple linear regression analysis, k-nearest neighbours, naïve bayes and decision trees have been used to make the predictions. The predictions are then evaluated and compared in order to find those which provide the best performances. A seemingly easy problem turned out to be indeed very difficult to resolve with high accuracy. All the four methods provided comparable performance. In the future, we intend to use more sophisticated algorithms to make the predictions. Keywords-component; formatting; style; styling; insert (key words)


title: Beyond Doctors: Future Health Prediction from Multimedia and Multimodal Observations

text: Although chronic diseases cannot be cured, they can be effectively controlled as long as we understand their progressions based on the current observational health records, which is often in the form of multimedia data. A large and growing body of literature has investigated the disease progression problem. However, far too little attention to date has been paid to jointly consider the following three observations of the chronic disease progression: 1) the health statuses at different time points are chronologically similar; 2) the future health statuses of each patient can be comprehensively revealed from the current multimedia and multimodal observations, such as visual scans, digital measurements and textual medical histories; and 3) the discriminative capabilities of different modalities vary significantly in accordance to specific diseases. In the light of these, we propose an adaptive multimodal multi-task learning model to co-regularize the modality agreement, temporal progression and discriminative capabilities of different modalities. We theoretically show that our proposed model is a linear system. Before training our model, we address the data missing problem via the matrix factorization approach. Extensive evaluations on a real-world Alzheimer's disease dataset well verify our proposed model. It should be noted that our model is also applicable to other chronic diseases.


title: Rapid diagnostic tests for malaria parasites.

text: Malaria presents a diagnostic challenge to laboratories in most countries. Endemic malaria, population movements, and travelers all contribute to presenting the laboratory with diagnostic problems for which it may have little expertise available. Drug resistance and genetic variation has altered many accepted morphological appearances of malaria species, and new technology has given an opportunity to review available procedures. Concurrently the World Health Organization has opened a dialogue with scientists, clinicians, and manufacturers on the realistic possibilities for developing accurate, sensitive, and cost-effective rapid diagnostic tests for malaria, capable of detecting 100 parasites/microl from all species and with a semiquantitative measurement for monitoring successful drug treatment. New technology has to be compared with an accepted "gold standard" that makes comparisons of sensitivity and specificity between different methods. The majority of malaria is found in countries where cost-effectiveness is an important factor and ease of performance and training is a major consideration. Most new technology for malaria diagnosis incorporates immunochromatographic capture procedures, with conjugated monoclonal antibodies providing the indicator of infection. Preferred targeted antigens are those which are abundant in all asexual and sexual stages of the parasite and are currently centered on detection of HRP-2 from Plasmodium falciparum and parasite-specific lactate dehydrogenase or Plasmodium aldolase from the parasite glycolytic pathway found in all species. Clinical studies allow effective comparisons between different formats, and the reality of nonmicroscopic diagnoses of malaria is considered.


title: Automatic Keyword Extraction for Text Summarization in Multi-document e-Newspapers Articles

text: Summarization is the way towards lessening the content of a text file to make it brief that holds all the critical purposes in the content of original text file. In the process of extractive summarization, one extracts only those sentences which are the most relevant sentences in the text document and that conveys the moral of the content. The extractive summarization techniques usually revolve around the idea of discovering most relevant and frequent keywords and then extract the sentences based on those keywords. Manual extraction or explanation of relevant keywords are a dreary procedure overflowing with errors including loads of manual exertion and time. In this paper, we proposed a hybrid approach to extract keyword automatically for multi-document text summarization in enewspaper articles. The performance of the proposed approach is compared with three additional keyword extraction techniques namely, term frequency-inverse document frequency (TF-IDF), term frequency-adaptive inverse document frequency (TF-AIDF), and a number of false alarm (NFA) for automatic keyword extraction and summarization in e-newspapers articles for better analysis. Finally, we showed that our proposed techniques had been outperformed over other techniques for automatic keyword extraction and summarization.


title: Software-Defined “Hardware” Infrastructures: A Survey on Enabling Technologies and Open Research Directions

text: This paper provides an overview of software-defined “hardware” infrastructures (SDHI). SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource disaggregation breaks today’s physical server-oriented model where the use of a physical resource (e.g., processor or memory) is constrained to a physical server’s chassis. SDHI extends the definition of of software-defined infrastructures (SDI) and brings greater modularity, flexibility, and extensibility to cloud infrastructures, thus allowing cloud operators to employ resources more efficiently and allowing applications not to be bounded by the physical infrastructure’s layout. This paper aims to be an initial introduction to SDHI and its associated technological advancements. This paper starts with an overview of the cloud domain and puts into perspective some of the most prominent efforts in the area. Then, it presents a set of differentiating use-cases that SDHI enables. Next, we state the fundamentals behind SDI and SDHI, and elaborate why SDHI is of great interest today. Moreover, it provides an overview of the functional architecture of a cloud built on SDHI, exploring how the impact of this transformation goes far beyond the cloud infrastructure level in its impact on platforms, execution environments, and applications. Finally, an in-depth assessment is made of the technologies behind SDHI, the impact of these technologies, and the associated challenges and potential future directions of SDHI.


title: A Study On Unmanned Vehicles and Cyber Security

text: During the past years Unmanned Aerial Vehicle (UAV) usage has grown in military and civilian fields. Every year, emergency response operations are more and more dependent of these unmanned vehicles. Lacking direct human interference, the correction of onboard errors and security breaches is a growing concern in unmanned vehicles. One of the concerns raised by first responders using unmanned vehicles is the security and privacy of the victims they are rescuing. Video channels used for the control of unmanned vehicles are of particular importance, having great susceptibility to hijack, jamming and spoofing attacks. It is assumed the video feeds are not protected by any type of encryption and are therefore vulnerable to hacking and/or spoofing. This summer we have conducted a preliminary survey of the security vulnerabilities and their implications on the operation of unmanned vehicles from a network security perspective.


title: A stochastic data discrimination based autoencoder approach for network anomaly detection

text: Machine learning based network anomaly detection methods, which are already effective defense mechanisms against known network intrusion attacks, have also proven themselves to be more successful on the detection of zero-day attacks compared to other types of detection methods. Therefore, research on network anomaly detection using deep learning is getting more attention constantly. In this study we created an anomaly detection model based on a deterministic autoencoder, which discriminates normal and abnormal data by using our proposed stochastic threshold determination approach. We tested our proposed anomaly detection model on the NSL-KDD's test dataset KDDTest+ and obtained an accuracy of 88.28%. The experimental results show that our proposed anomaly detection model can perform almost the same as the most successful and up-to-date machine learning based anomaly detection models in the literature.


title: Electrophysiology in the age of light

text: Electrophysiology, the 'gold standard' for investigating neuronal signalling, is being challenged by a new generation of optical probes. Together with new forms of microscopy, these probes allow us to measure and control neuronal signals with spatial resolution and genetic specificity that already greatly surpass those of electrophysiology. We predict that the photon will progressively replace the electron for probing neuronal function, particularly for targeted stimulation and silencing of neuronal populations. Although electrophysiological characterization of channels, cells and neural circuits will remain necessary, new combinations of electrophysiology and imaging should lead to transformational discoveries in neuroscience.


title: Tagging Ingush - Language Technology For Low-Resource Languages Using Resources From Linguistic Field Work

text: This paper presents on-going work on creating NLP tools for under-resourced languages from very sparse training data coming from linguistic field work. In this work, we focus on Ingush, a Nakh-Daghestanian language spoken by about 300,000 people in the Russian republics Ingushetia and Chechnya. We present work on morphosyntactic taggers trained on transcribed and linguistically analyzed recordings and dependency parsers using English glosses to project annotation for creating synthetic treebanks. Our preliminary results are promising, supporting the goal of bootstrapping efficient NLP tools with limited or no task-specific annotated data resources available.


title: Vertex-Centric Graph Processing: The Good, the Bad, and the Ugly

text: We study distributed graph algorithms that adopt an iterati ve vertexcentric framework for graph processing, popularized by Goo gle’s Pregel system. Since then, there are several attempts to imp lement many graph algorithms in a vertex-centric framework, a s well as efforts to design optimization techniques for improving the efficiency. However, to the best of our knowledge, there has not been any systematic study to compare these vertex-centric i mplementations with their sequential counterparts. Our paper a dd esses this gap in two ways. (1) We analyze the computational complexity of such implementations with the notion of time-pro cessor product, and benchmark several vertex-centric graph algor ithms whether they perform more work with respect to their best-kn ow sequential solutions. (2) Employing the concept of balance d practical Pregel algorithms, we study if these implementations suffer from imbalanced workload and large number of iterations. Ou r findings illustrate that with the exception of Euler tour tre e algorithm, all other algorithms either perform asymptotically more work than their best-known sequential approach, or suffer from i mbalanced workload/ large number of iterations, or even both. We also emphasize on graph algorithms that are fundamentally diffic ult to be expressed in vertex-centric frameworks, and conclude by discussing the road ahead for distributed graph processing.


title: Vision-Based Classification of Skin Cancer using Deep Learning

text: This study proposes the use of deep learning algorithms to detect the presence of skin cancer, specifically melanoma, from images of skin lesions taken by a standard camera. Skin cancer is the most prevalent form of cancer in the US where 3.3 million people get treated each year. The 5-year survival rate of melanoma is 98% when detected and treated early yet over 10,000 people are lost each year due mostly to late-stage diagnoses [2]. Thus, there is a need to make melanoma screening and diagnoses methods cheaper, quicker, simpler, and more accessible. This study aims to produce an inexpensive and fast computer-vision based machine learning tool that can be used by doctors and patients to track and classify suspicious skin lesions as benign or malignant with adequate accuracy using only a cell phone camera. The data set was trained on 3 separate learning models with increasingly improved classification accuracy. The 3 models included logistic regression, a deep neural network, and a fine-tuned, pre-trained, VGG-16 Convolutional Neural Network (CNN) [7]. Preliminary results show the developed algorithm’s ability to segment moles from images with 70% accuracy and classify skin lesions as melanoma with 78% balanced accuracy using a fine-tuned VGG-16 CNN.


title: Ceiling continuum arm with extensible pneumatic actuators for desktop workspace

text: We propose an extensible pneumatic continuum arm that elongates to perform reaching movements and object grasping, and is suspended on the ceiling to prevent interference with human workers in a desktop workspace. The selected actuators with bellows aim to enhance the arm motion capabilities. A single actuator can provide a maximum tension force of 150 N, and the proposed arm has a three-segment structure with a bundle of three actuators per segment. We measured the three-dimensional motion at the arm tip by using an optical motion-capture system. The corresponding results show that the arm can grasp objects with approximate radius of 80 mm and reach any point on the desktop. Furthermore, the maximum elongation ratio is 180%, with length varying between 0.75 m and 2.1 m. Experiments verified that the arm can grasp objects of various sizes and shapes. Moreover, we demonstrate the vertical transportation of objects taking advantage of the arm extensibility. We expect to apply the proposed arm for tasks such as grasping objects, illuminating desktops, and physically interacting with users.


title: Eventually Returning to Strong Consistency

text: Eventually and weakly consistent distributed systems have emerged in the past decade as an answer to scalability and availability issues associated with strong consistency semantics, such as linearizability. However, systems offering strong consistency semantics have an advantage over systems based on weaker consistency models, as they are typically much simpler to reason about and are more intuitive to developers, exhibiting more predictable behavior. Therefore, a lot of research and development effort is being invested lately into the re-engineering of strongly consistent distributed systems, as well as into boosting their scalability and performance. This paper overviews and discusses several novel directions in the design and implementation of strongly consistent systems in industries and research domains such as cloud computing, data center networking and blockchain. It also discusses a general trend of returning to strong consistency in distributed systems, when system requirements permit so.


title: Food Recognition using Fusion of Classifiers based on CNNs

text: With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional ceural cetworks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models.


title: Implementation of PID controller and pre-filter to control non-linear ball and plate system

text: In this paper, the authors try to make PID controller with Pre-filter that is implemented at ball and plate system. Ball and plate system will control the position of ball's axis in pixels value by using servo motor as its actuator and webcam as its sensor of position. PID controller with Pre-filter will have a better response than conventional PID controller. Eventhough the response of PID with Pre-filter is slower than conventional PID, the effect of Pre-filter in the system will give the less overshoot response.


title: Comparing a Scalable SDN Simulation Framework Built on ns-3 and DCE with Existing SDN Simulators and Emulators

text: As software-defined networking (SDN) grows beyond its original aim to simply separate the control and data network planes, it becomes useful both financially and analytically to provide adequate mechanisms for simulating this new paradigm. A number of simulation/emulation tools for modeling SDN, such as Mininet, are already available. A new, novel framework for providing SDN simulation has been provided in this work using the network simulator ns-3. The ns-3 module Direct Code Execution (DCE) allows real-world network applications to be run within a simulated network topology. This work employs DCE for running the SDN controller library POX and its applications on nodes in a simulated network topology. In this way, real-world controller applications can be completely portable between simulation and actual deployment. This work also describes a user-defined ns-3 application mimicking an SDN switch supporting OpenFlow 1.0 that can interact with real-world controllers. To evaluate its performance, this ns-3 DCE SDN framework is compared against Mininet as well as some other readily available SDN simulation/emulation tools. Metrics such as realtime performance, memory usage, and reliability in terms of packet loss are analyzed across the multiple simulation/emulation tools to gauge how they compare.


title: Pose optimization in edge distance field for textureless 3D object tracking

text: This paper presents a monocular model-based 3D tracking approach for textureless objects. Instead of explicitly searching for 3D-2D correspondences as previous methods, which unavoidably generates individual outlier matches, we aim to minimize the holistic distance between the predicted object contour and the query image edges. We propose a method that can directly solve 3D pose parameters in unsegmented edge distance field. We derive the differentials of edge matching distance with respect to the pose parameters, and search the optimal 3D pose parameters using standard gradient-based non-linear optimization techniques. To avoid being trapped in local minima and to deal with potential large inter-frame motions, a particle filtering process with a first order autoregressive state dynamics is exploited. Occlusions are handled by a robust estimator. The effectiveness of our approach is demonstrated using comparative experiments on real image sequences with occlusions, large motions and cluttered backgrounds.


title: Designing games with a purpose

text: Data generated as a side effect of game play also solves computational problems and trains AI algorithms.


title: A web-based IDE for IDP

text: IDP is a knowledge base system based on first order logic. It is finding its way to a larger public but is still facing practical challenges. Adoption of new languages requires a newcomer-friendly way for users to interact with it. Both an online presence to try to convince potential users to download the system and offline availability to develop larger applications are essential. We developed an IDE which can serve both purposes through the use of web technology. It enables us to provide the user with a modern IDE with relatively little effort.


title: The Stanford CoreNLP Natural Language Processing Toolkit

text: We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.


title: Marijuana and Coronary Heart Disease

text: Marijuana is the most commonly abused drug in the United States.1 It mainly acts on cannabinoid receptors. There are two types of cannabinoid receptors in humans: cannabinoid receptor type 1 (CB1) and cannabinoid receptor type 2 (CB2). CB1 receptor activation is pro-atherogenic, and CB2 receptor activation is largely anti-atherogenic. Marijuana use is also implicated as a trigger for myocardial infarction (MI) in patients with stable coronary artery disease (CAD).


title: Health of Things Algorithms for Malignancy Level Classification of Lung Nodules

text: Lung cancer is one of the leading causes of death world wide. Several computer-aided diagnosis systems have been developed to help reduce lung cancer mortality rates. This paper presents a novel structural co-occurrence matrix (SCM)-based approach to classify nodules into malignant or benign nodules and also into their malignancy levels. The SCM technique was applied to extract features from images of nodules and classifying them into malignant or benign nodules and also into their malignancy levels. The computed tomography exams from the lung image database consortium and image database resource initiative datasets provide information concerning nodule positions and their malignancy levels. The SCM was applied on both grayscale and Hounsfield unit images with four filters, to wit, mean, Laplace, Gaussian, and Sobel filters creating eight different configurations. The classification stage used three well-known classifiers: multilayer perceptron, support vector machine, and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm and applied them to two tasks: (<inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>) to classify the nodule images into malignant or benign nodules and (<inline-formula> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula>) to classify the lung nodules into malignancy levels (1 to 5). The results of this approach were compared to four other feature extraction methods: gray-level co-occurrence matrix, local binary patterns, central moments, and statistical moments. Moreover, the results here were also compared to the results reported in the literature. Our approach outperformed the other methods in both tasks; it achieved 96.7% for both accuracy and F-Score metrics in the first task, and 74.5% accuracy and 53.2% F-Score in the second. These experimental results reveal that the SCM successfully extracted features of the nodules from the images and, therefore may be considered as a promising tool to support medical specialist to make a more precise diagnosis concerning the malignancy of lung nodules.


title: Maximum Margin Clustering

text: We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidefinite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering. Experimental results show that our maximum margin clustering technique often obtains more accurate results than conventional clustering methods. The real benefit of our approach, however, is that it leads naturally to a semi-supervised training method for support vector machines. By maximizing the margin simultaneously on labeled and unlabeled training data, we achieve state of the art performance by using a single, integrated learning principle.


title: A MapReduce Implementation of C 4 . 5 Decision Tree Algorithm

text: Recent years have witness the development of cloud computing and the big data era, which brings up challenges to traditional decision tree algorithms. First, as the size of dataset becomes extremely big, the process of building a decision tree can be quite time consuming. Second, because the data cannot fit in memory any more, some computation must be moved to the external storage and therefore increases the I/O cost. To this end, we propose to implement a typical decision tree algorithm, C4.5, using MapReduce programming model. Specifically, we transform the traditional algorithm into a series of Map and Reduce procedures. Besides, we design some data structures to minimize the communication cost. We also conduct extensive experiments on a massive dataset. The results indicate that our algorithm exhibits both time efficiency and scalability.


Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.


title: The McGill pain questionnaire: from description to measurement.

text: On the language of pain. By Ronald Melzack, Warren S. Torgerson. Anesthesiology 1971; 34:50-9. Reprinted with permission. The purpose of this study was to develop new approaches to the problem of describing and measuring pain in human subjects. Words used to describe pain were brought together and categorized, and an attempt was made to scale them on a common intensity dimension. The data show that: 1) there are many words in the English language to describe the varieties of pain experience; 2) there is a high level of agreement that the words fall into classes and subclasses that represent particular dimensions or properties of pain experience; 3) substantial portions of the words have approximately the same relative positions on a common intensity scale for people who have widely divergent backgrounds. The word lists provide a basis for a questionnaire to study the effects of anesthetic and analgesic agents on the experience of pain.


title: Low-Power Near-Threshold 10T SRAM Bit Cells With Enhanced Data-Independent Read Port Leakage for Array Augmentation in 32-nm CMOS

text: The conventional six-transistor static random access memory (SRAM) cell allows high density and fast differential sensing but suffers from half-select and read-disturb issues. Although the conventional eight-transistor SRAM cell solves the read-disturb issue, it still suffers from low array efficiency due to deterioration of read bit-line (RBL) swing and  $\text{I}_{\mathbf {on}}/\text{I}_{\mathbf {off}}$  ratio with increase in the number of cells per column. Previous approaches to solve these issues have been afflicted by low performance, data-dependent leakage, large area, and high energy per access. Therefore, in this paper, we present three iterations of SRAM bit cells with nMOS-only based read ports aimed to greatly reduce data-dependent read port leakage to enable 1k cells/RBL, improve read performance, and reduce area and power over conventional and 10T cell-based works. We compare the proposed work with other works by recording metrics from the simulation of a 128-kb SRAM constructed with divided-wordline-decoding architecture and a 32-bit word size. Apart from large improvements observed over conventional cells, up to 100-mV improvement in read-access performance, up to 19.8% saving in energy per access, and up to 19.5% saving in the area are also observed over other 10T cells, thereby enlarging the design and application gamut for memory designers in low-power sensors and battery-enabled devices.


title: Trainable Sentence Planning for Complex Information Presentations in Spoken Dialog Systems

text: A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH’s template-based generator even for quite complex information presentations.


title: Complications associated with injectable soft-tissue fillers: a 5-year retrospective review.

text:
IMPORTANCE
Even when administered by experienced hands, injectable soft-tissue fillers can cause various unintended reactions, ranging from minor and self-limited responses to severe complications requiring prompt treatment and close follow-up.

OBJECTIVES
To review the complications associated with injectable soft-tissue filler treatments administered in the Williams Rejuva Center during a 5-year period and to discuss their management.

DESIGN AND SETTING
Retrospective medical record review in a private practice setting.

PARTICIPANTS
Patients receiving injectable soft-tissue fillers and having a treatment-related complication.

INTERVENTIONS
Injectable soft-tissue filler treatments.

MAIN OUTCOME MEASURES
A retrospective medical record review was conducted of patients undergoing treatment with injectable soft-tissue fillers between January 1, 2007, and December 31, 2011, and identified as having a treatment-related complication.

RESULTS
A total of 2089 injectable soft-tissue filler treatments were performed during the study period, including 1047 with hyaluronic acid, 811 with poly-L-lactic acid, and 231 with calcium hydroxylapatite. Fourteen complications were identified. The most common complication was nodule or granuloma formation. Treatment with calcium hydroxylapatite had the highest complication rate.

CONCLUSIONS AND RELEVANCE
Complications are rare following treatment with injectable soft-tissue fillers. Nevertheless, it is important to be aware of the spectrum of potential adverse sequelae and to be comfortable with their proper management.

LEVEL OF EVIDENCE
4.


title: On Data Clustering Analysis: Scalability, Constraints, and Validation

text: Clustering is the problem of grouping data based on similarity. While this problem has attracted the attention of many researchers for many years, we are witnessing a resurgence of interest in new clustering techniques. In this paper we discuss some very recent clustering approaches and recount our experience with some of these algorithms. We also present the problem of clustering in the presence of constraints and discuss the issue of clustering validation.


title: Better managed than memorized? Studying the Impact of Managers on Password Strength and Reuse

text: Despite their well-known security problems, passwords are still the incumbent authentication method for virtually all online services. To remedy the situation, users are very often referred to password managers as a solution to the password reuse and weakness problems. However, to date the actual impact of password managers on password strength and reuse has not been studied systematically. We provide the first large-scale study of the password managers’ influence on users’ real-life passwords. By combining qualitative data on users’ password creation and management strategies, collected from 476 participants of an online survey, with quantitative data (incl. password metrics and entry methods) collected in situ with a browser plugin from 170 users, we were able to gain a more complete picture of the factors that influence our participants’ password strength and reuse. Our approach allows us to quantify for the first time that password managers indeed influence the password security, however, whether this influence is beneficial or aggravating existing problems depends on the users’ strategies and how well the manager supports the users’ password management right from the time of password creation. Given our results, we think research should further investigate how managers can better support users’ password strategies in order to improve password security as well as stop aggravating the existing problems.


title: Advanced Beamformers for Cochlear Implant Users: Acute Measurement of Speech Perception in Challenging Listening Conditions

text:
OBJECTIVE
To investigate the performance of monaural and binaural beamforming technology with an additional noise reduction algorithm, in cochlear implant recipients.

METHOD
This experimental study was conducted as a single subject repeated measures design within a large German cochlear implant centre. Twelve experienced users of an Advanced Bionics HiRes90K or CII implant with a Harmony speech processor were enrolled. The cochlear implant processor of each subject was connected to one of two bilaterally placed state-of-the-art hearing aids (Phonak Ambra) providing three alternative directional processing options: an omnidirectional setting, an adaptive monaural beamformer, and a binaural beamformer. A further noise reduction algorithm (ClearVoice) was applied to the signal on the cochlear implant processor itself. The speech signal was presented from 0° and speech shaped noise presented from loudspeakers placed at ±70°, ±135° and 180°. The Oldenburg sentence test was used to determine the signal-to-noise ratio at which subjects scored 50% correct.

RESULTS
Both the adaptive and binaural beamformer were significantly better than the omnidirectional condition (5.3 dB±1.2 dB and 7.1 dB±1.6 dB (p<0.001) respectively). The best score was achieved with the binaural beamformer in combination with the ClearVoice noise reduction algorithm, with a significant improvement in SRT of 7.9 dB±2.4 dB (p<0.001) over the omnidirectional alone condition.

CONCLUSIONS
The study showed that the binaural beamformer implemented in the Phonak Ambra hearing aid could be used in conjunction with a Harmony speech processor to produce substantial average improvements in SRT of 7.1 dB. The monaural, adaptive beamformer provided an averaged SRT improvement of 5.3 dB.


title: Robust ground plane tracking in cluttered environments from egocentric stereo vision

text: Estimating the ground plane is often one of the first steps in geometric reasoning processes as it offers easily accessible context knowledge. Especially unconstrained platforms that capture video from egocentric viewpoints can benefit from such knowledge in various ways. A key requirement here is keeping orientation, which can be greatly achieved by keeping track of the ground. We present an approach to keep track of the ground plane in cluttered inner-urban environments using stereo vision in real-time. We fuse a planar model fit in low-resolution disparity data with the direction of the vertical vanishing point. Our experiments show how this effectively decreases the error of plane attitude estimation compared to classic least-squares fitting and allows to track the plane with camera configurations in which the ground is not visible. We evaluate the approach using ground-truth from an inertial measurement unit and demonstrate long-term stability on a dataset of challenging inner city scenes.


title: Global refinement of random forest

text: Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refinement and global pruning, to improve a pre-trained random forest. The proposed global refinement jointly relearns the leaf nodes of all trees under a global objective function so that the complementary information between multiple trees is well exploited. In this way, the fitting power of the forest is significantly enhanced. The global pruning is developed to reduce the model size as well as the over-fitting risk. The refined model has better performance and smaller storage cost, as verified in extensive experiments.


title: 3D geo-database research: Retrospective and future directions

text: 3D geo-database research is a promising field to support challenging applications such as 3D urban planning, environmental monitoring, infrastructure management, and early warning or disaster management and response. In these fields, interdisciplinary research in GIScience and related fields is needed to support the modelling, analysis, management, and integration of large geo-referenced data sets, which describe human activities and geophysical phenomena. Geo-databases may serve as platforms to integrate 2D maps, 3D geo-scientific models, and other geo-referenced data. However, current geo-databases do not provide sufficient 3D data modelling and data handling techniques. New 3D geo-databases are needed to handle surface and volume models. This article first presents a 25-year retrospective of geo-database research. Data modelling, standards, and indexing of geo-data are discussed in detail. New directions for the development of 3D geo-databases to open new fields for interdisciplinary research are addressed. Two scenarios in the fields of early warning and emergency response demonstrate the combined management of human and geophysical phenomena. The article concludes with a critical outlook on open research problems. & 2011 Elsevier Ltd. All rights reserved.


title: How Online Basic Psychological Need Satisfaction Influences Self-Disclosure Online among Chinese Adolescents: Moderated Mediation Effect of Exhibitionism and Narcissism

text: Under the basic framework of self-determination theory, the present study examined a moderated mediation model in which exhibitionism mediated the relationship between online basic psychological need satisfaction and self-disclosure on the mobile Internet, and this mediation effect was moderated by narcissism. A total of 296 Chinese middle school students participated in this research. The results revealed that exhibitionism fully mediated the association between online competence need satisfaction and self-disclosure on the mobile net, and partly mediated the association between online relatedness need satisfaction and self-disclosure on the mobile net. The mediating path from online basic psychological need satisfaction (competence and relatedness) to exhibitionism was moderated by narcissism. Compared to the low level of narcissism, online competence need satisfaction had a stronger predictive power on exhibitionism under the high level of narcissism condition. In contrast, online relatedness need satisfaction had a weaker predictive power on exhibitionism.


title: Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier

text: Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount. DNN is to directly predict the posterior of phoneme units of any personally customized key-phrase, and CTC to produce a confidence score of the given phoneme sequence as responsive decision-making mechanism. The CTC-KWS has competitive performance in comparison with purely DNN based keyword specific KWS, but not increasing any computational complexity.


title: Attacking state space explosion problem in model checking embedded TV software

text: The features of current TV sets is increasing rapidly resulting in more complicated embedded TV software that is also harder to test and verify. Using model checking in verification of embedded software is a widely accepted practice even though it is seriously affected by the exponential increase in the number of states being produced during the verification process. Using fully non-deterministic user agent models is one of the reasons that can result in a drastic increase in the number of states being produced. In order to shrink the state space being observed during the model checking process of TV software, a method is proposed that rely on using previous test logs to generate partly nondeterministic user agent model. Results show that by using partly non-deterministic user agents, the verification time of certain safety and liveness properties can be significantly decreased.


title: Bridging the Gap between Business Strategy and Software Development

text: In software-intensive organizations, an organizational management system will not guarantee organizational success unless the business strategy can be translated into a set of operational software goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of industrial settings to support quantitative software project management. However, it does not address linking software measurement goals to higher-level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher-level decisions. In this paper, we propose a GQMStrategies® measurement approach that builds on the GQM approach to plan and implement software measurement. GQMStrategies provides mechanisms for explicitly linking software measurement goals to higher-level goals for the software organization, and further to goals and strategies at the level of the entire business. An example application of the proposed method is illustrated in the context of an example measurement initiative.


title: Increased social fear and decreased fear of objects in monkeys with neonatal amygdala lesions

text: The amygdala has been implicated in the mediation of emotional and species-specific social behavior (Kling et al., 1970; Kling and Brothers, 1992; Kluver and Bucy, 1939; Rosvold et al., 1954). Humans with bilateral amygdala damage are impaired in judging negative emotion in facial expressions and making accurate judgements of trustworthiness (Adolphs et al., 1998, 1994). Amygdala dysfunction has also been implicated in human disorders ranging from social anxiety (Birbaumer et al., 1998) to depression (Drevets, 2000) to autism (Bachevalier, 1994; Baron-Cohen et al., 2000; Bauman and Kemper, 1993). We produced selective amygdala lesions in 2-week-old macaque monkeys who were returned to their mothers for rearing. At 6-8 months of age, the lesioned animals demonstrated less fear of novel objects such as rubber snakes than age-matched controls. However, they displayed substantially more fear behavior than controls during dyadic social interactions. These results suggest that neonatal amygdala lesions dissociate a system that mediates social fear from one that mediates fear of inanimate objects. Furthermore, much of the age-appropriate repertoire of social behavior was present in amygdala-lesioned infants indicating that these lesions do not produce autistic-like behavior in monkeys. Finally, amygdala lesions early in development have different effects on social behavior than lesions produced in adulthood.


title: Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders

text: Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.


title: Performance of H.264, H.265, VP8 and VP9 Compression Standards for High Resolutions

text: Recently multimedia services increase, especially in video domain, leads to requirements of quality assessment. The main factors effected that quality are compression technology and transmission link. This paper presents a coding efficiency comparison of the well-known video compression standards Advanced Video Coding (H.264/AVC), High-Efficiency Video Coding (H.265/HEVC), VP8 and VP9 using objective metrics. An extensive range of bitrates from low to high bitrates was selected and encoded. The evaluation was done for four types of sequences in high resolutions depending on content. All four video sequences were encoded by using same encoding configurations for all the examined video codecs. The results showed that the quality of all compression standards rises logarithmically with increasing bitrate - in low bitrates the quality grows faster than in high bitrates. The efficiency of the new compression standards outperforms the older ones. The efficiency of VP8 compression standard outperforms the H.264/AVC compression standard. The efficiency of H.265/HEVC and VP9 compression standards is almost the same. The results also showed that VP8 codec cannot allow encoding by low bitrates. According to the results can be also said that the efficiency of the new compression standards decreases with the resolution. The results showed that the effectiveness of compression depends on the type of test sequence.


title: Detection of Ellipses by a Modified Hough Transformation

text: The Hough transformation can detect straight lines in an edge-enhanced picture, however its extension to recover ellipses requires too long a computing time. This correspondence proposes a modified method which utilizes two properties of an ellipse in such a way that it iteratively searches for clusters in two different parameter spaces to find almost complete ellipses, then evaluates their parameters by the least mean squares method.


title: Is dark silicon useful? Harnessing the four horsemen of the coming dark silicon apocalypse

text:
Due to the breakdown of Dennardian scaling, the percentage of a silicon chip that can switch at full frequency is dropping exponentially with each process generation. This utilization wall forces designers to ensure that, at any point in time, large fractions of their chips are effectively dark or dim silicon, i.e., either idle or significantly underclocked.
 As exponentially larger fractions of a chip's transistors become dark, silicon area becomes an exponentially cheaper resource relative to power and energy consumption. This shift is driving a new class of architectural techniques that "spend" area to "buy" energy efficiency. All of these techniques seek to introduce new forms of heterogeneity into the computational stack. We envision that ultimately we will see widespread use of specialized architectures that leverage these techniques in order to attain orders-of-magnitude improvements in energy efficiency.
 However, many of these approaches also suffer from massive increases in complexity. As a result, we will need to look towards developing pervasively specialized architectures that insulate the hardware designer and the programmer from the underlying complexity of such systems. In this paper, I discuss four key approaches--the four horsemen--that have emerged as top contenders for thriving in the dark silicon age. Each class carries with its virtues deep-seated restrictions that requires a careful understanding of the underlying tradeoffs and benefits.


title: Distributed Multi-Robot Localization

text: This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.


title: Human model evaluation in interactive supervised learning

text: Model evaluation plays a special role in interactive machine learning (IML) systems in which users rely on their assessment of a model's performance in order to determine how to improve it. A better understanding of what model criteria are important to users can therefore inform the design of user interfaces for model evaluation as well as the choice and design of learning algorithms. We present work studying the evaluation practices of end users interactively building supervised learning systems for real-world gesture analysis problems. We examine users' model evaluation criteria, which span conventionally relevant criteria such as accuracy and cost, as well as novel criteria such as unexpectedness. We observed that users employed evaluation techniques---including cross-validation and direct, real-time evaluation---not only to make relevant judgments of algorithms' performance and interactively improve the trained models, but also to learn to provide more effective training data. Furthermore, we observed that evaluation taught users about what types of models were easy or possible to build, and users sometimes used this information to modify the learning problem definition or their plans for using the trained models in practice. We discuss the implications of these findings with regard to the role of generalization accuracy in IML, the design of new algorithms and interfaces, and the scope of potential benefits of incorporating human interaction in the design of supervised learning systems.


title: Teachable robots: Understanding human teaching behavior to build more effective robot learners

text: While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback — possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. Given this, we made specific modifications to the simulated RL robot, and analyzed and evaluated its learning behavior in four additional experiments with human trainers. We report significant improvements on several learning measures. This work demonstrates the importance of understanding the human-teacher/robot-learner partnership in order to design algorithms that support how people want to teach while simultaneously improving the robot’s learning behavior.


title: Natural methods for robot task learning: instructive demonstrations, generalization and practice

text: Among humans, teaching various tasks is a complex process which relies on multiple means for interaction and learning, both on the part of the teacher and of the learner. Used together, these modalities lead to effective teaching and learning approaches, respectively. In the robotics domain, task teaching has been mostly addressed by using only one or very few of these interactions. In this paper we present an approach for teaching robots that relies on the key features and the general approach people use when teaching each other: first give a demonstration, then allow the learner to refine the acquired capabilities by practicing under the teacher's supervision, involving a small number of trials. Depending on the quality of the learned task, the teacher may either demonstrate it again or provide specific feedback during the learner's practice trial for further refinement. Also, as people do during demonstrations, the teacher can provide simple instructions and informative cues, increasing the performance of learning. Thus, instructive demonstrations, generalization over multiple demonstrations and practice trials are essential features for a successful human-robot teaching approach. We implemented a system that enables all these capabilities and validated these concepts with a Pioneer 2DX mobile robot learning tasks from multiple demonstrations and teacher feedback.


title: XWand: UI for intelligent spaces

text: The XWand is a novel wireless sensor package that enables styles of natural interaction with intelligent environments. For example, a user may point the wand at a device and control it using simple gestures. The XWand system leverages the intelligence of the environment to best determine the user's intention. We detail the hardware device, signal processing algorithms to recover position and orientation, gesture recognition techniques, a multimodal (wand and speech) computational architecture and a preliminary user study examining pointing performance under conditions of tracking availability and audio feedback.


title: GloveTalkII: An Adaptive Gesture-to-Formant Interface

text: Glove-TaikII is a system which translates hand gestures-· to speech through an adaptive interface. Hand gestures are mapped continuously to 10 control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary, multiple languages in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-TaikII uses several input devices (including a Cyberglove, a ContactGlove, a polhemus sensor, and a foot-pedal), a parallel formant speech synthesizer and 3 neural networks. The gestureto-speech task is divided into vowel and consonant production by using a gating network to weight the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed, user-defined relationship between hand-position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency and stop consonants are produced with a fixed mapping from the input devices. One subject has trained for about 100 hours to speak intelligibly with Glove-TalkII. He passed through eight distinct stages while learning to speak. He speaks slowly with speech quality similar to a text-to-speech synthesizer but with far more natural-sounding pitch variations.