title: Weakly Supervised Learning for Fake News Detection on Twitter

abstract: The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor. In this paper, we discuss a weakly supervised approach, which automatically collects a large-scale, but very noisy training dataset comprising hundreds of thousands of tweets. During collection, we automatically label tweets by their source, i.e., trustworthy or untrustworthy source, and train a classifier on this dataset. We then use that classifier for a different classification target, i.e., the classification of fake and non-fake tweets. Although the labels are not accurate according to the new classification target (not all tweets by an untrustworthy source need to be fake news, and vice versa), we show that despite this unclean inaccurate dataset, it is possible to detect fake news with an F1 score of up to 0.9.


title: Millimeter Wave Receiver Efficiency: A Comprehensive Comparison of Beamforming Schemes With Low Resolution ADCs

abstract: In this work, we study the achievable rate and the energy efficiency of Analog, Hybrid and Digital Combining (AC, HC and DC) for millimeter wave (mmW) receivers. We take into account the power consumption of all receiver components, not just Analog-to-Digital Converters (ADC), determine some practical limitations of beamforming in each architecture, and develop performance analysis charts that enable comparison of different receivers simultaneously in terms of two metrics, namely, Spectral Efficiency (SE) and Energy Efficiency (EE). We present a multi-objective utility optimization interpretation to find the best SE-EE weighted trade-off among AC, DC and HC schemes. We consider an Additive Quantization Noise Model (AQNM) to evaluate the achievable rates with low resolution ADCs. Our analysis shows that AC is only advantageous if the channel rank is strictly one, the link has very low SNR, or there is a very stringent low power constraint at the receiver. Otherwise, we show that the usual claim that DC requires the highest power is not universally valid. Rather, either DC or HC alternatively result in the better SE vs EE trade-off depending strongly on the considered power consumption characteristic values for each component of the mmW receiver.


title: Deformable Image Registration based on Similarity-Steered CNN Regression

abstract: Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.


title: Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation

abstract: Sensor fusion is indispensable to improve accuracy and robustness in an autonomous navigation setting. However, in the space of end-to-end sensorimotor control, this multimodal outlook has received limited attention. In this work, we propose a novel stochastic regularization technique, called Sensor Dropout, to robustify multimodal sensor policy learning outcomes. We also introduce an auxiliary loss on policy network along with the standard DRL loss that help reduce the action variations of the multimodal sensor policy. Through empirical testing we demonstrate that our proposed policy can 1) operate with minimal performance drop in noisy environments, 2) remain functional even in the face of a sensor subset failure. Finally, through the visualization of gradients, we show that the learned policies are conditioned on the same latent input distribution despite having multiple sensory observations spaces - a hallmark of true sensor-fusion. This efficacy of a multimodal policy is shown through simulations on TORCS, a popular open-source racing car game. A demo video can be seen here: https://youtu.be/HC3TcJjXf3Q


title: On identifying leaves: A comparison of CNN with classical ML methods

abstract: Convolution neural networks (CNNs) eliminate the need for feature extraction which is one of the most important and time-consuming part of traditional machine learning (ML) methods. However, the challenge of training a deep CNN model with a limited amount of training data still remains. Transfer learning and parameter fine-tuning have emerged as solutions to this problem. Following the recent trends, we address the task of visual identification of leaves in images by modifying a trained model on a similar problem. In particular, we show that a pre-trained CNN model on a large dataset (ImageNet) can be used to train a model from a small training set (ImageCLEF2013 Plant Identification). The resulting model outperforms the classical machine learning methods using local binary patterns (LBPs), a well utilized feature in the field.


MATHEMATICAL MODELING AND SPEED TORQUE ANALYSIS OF THREE PHASE SQUIRREL CAGE INDUCTION MOTOR USING MATLAB SIMULINK FOR ELECTRICAL MACHINES LABORATORY


title: Android low entropy demystified

abstract: We look into the issue that the amount of entropy kept by the pseudorandom number generator (PRNG) of Android is constantly low. We find that the accusation against this issue of causing poor performance and low frame rate experienced by users is ungrounded. We also investigate possible security vulnerabilities resulting from this issue. We find that this issue does not affect the quality of random numbers that are generated by the PRNG and used in Android applications because recent Android devices do not lack entropy sources. However, we identify a vulnerability in which the stack canary for all future Android applications is generated earlier than the PRNG is properly setup. This vulnerability makes stack overflow simpler and threats Android applications linked with native code (through NDK) as well as Dalvik VM instances. An attacker could nullify the stack protecting mechanism, given the knowledge of the time of boot or a malicious app running on the victim device. This vulnerability also affects the address space layout randomization (ASLR) mechanism on Android, and can turn it from a weak protection to void. We discuss in this paper several possible attacks against this vulnerability as well as ways of defending. As this vulnerability is rooted in an essential Android design choice since the very first version, it is difficult to fix.


title: What the Language You Tweet Says About Your Occupation

abstract: Many aspects of people's lives are proven to be deeply connected to their jobs. In this paper, we first investigate the distinct characteristics of major occupation categories based on tweets. From multiple social media platforms, we gather several types of user information. From users' LinkedIn webpages, we learn their proficiencies. To overcome the ambiguity of self-reported information, a soft clustering approach is applied to extract occupations from crowd-sourced data. Eight job categories are extracted, including Marketing, Administrator, Start-up, Editor, Software Engineer, Public Relation, Office Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for understanding their linguistic styles, interests, and personalities. Our results suggest that people of different jobs have unique tendencies in certain language styles and interests. Our results also clearly reveal distinctive levels in terms of Big Five Traits for different jobs. Finally, a classifier is built to predict job types based on the features extracted from tweets. A high accuracy indicates a strong discrimination power of language features for job prediction task.


title: Identifying Multiple Authors in a Binary Program

abstract: Knowing the authors of a binary program has significant application to forensics of malicious software (malware), software supply chain risk management, and software plagiarism detection. Existing techniques assume that a binary is written by a single author, which does not hold true in real world because most modern software, including malware, often contains code from multiple authors. In this paper, we make the first step toward identifying multiple authors in a binary. We present new fine-grained techniques to address the tougher problem of determining the author of each basic block. The decision of attributing authors at the basic block level is based on an empirical study of three large open source software, in which we find that a large fraction of basic blocks can be well attributed to a single author. We present new code features that capture programming style at the basic block level, our approach for identifying external template library code, and a new approach to capture correlations between the authors of basic blocks in a binary. Our experiments show strong evidence that programming styles can be recovered at the basic block level and it is practical to identify multiple authors in a binary.


title: Learning Gradient Descent: Better Generalization and Longer Horizons

abstract: Training deep neural networks is a highly nontrivial task, involving carefully selecting appropriate training algorithms, scheduling step sizes and tuning other hyperparameters. Trying different combinations can be quite labor-intensive and time consuming. Recently, researchers have tried to use deep learning algorithms to exploit the landscape of the loss function of the training problem of interest, and learn how to optimize over it in an automatic way. In this paper, we propose a new learning-to-learn model and some useful and practical tricks. Our optimizer outperforms generic, hand-crafted optimization algorithms and state-of-the-art learning-to-learn optimizers by DeepMind in many tasks. We demonstrate the effectiveness of our algorithms on a number of tasks, including deep MLPs, CNNs, and simple LSTMs.


title: Forensic State Acquisition from Internet of Things (FSAIoT): A general framework and practical approach for IoT forensics through IoT device state acquisition

abstract: IoT device forensics is a difficult problem given that manufactured IoT devices are not standardized, many store little to no historical data, and are always connected; making them extremely volatile. The goal of this paper was to address these challenges by presenting a primary account for a general framework and practical approach we term Forensic State Acquisition from Internet of Things (FSAIoT). We argue that by leveraging the acquisition of the state of IoT devices (e.g. if an IoT lock is open or locked), it becomes possible to paint a clear picture of events that have occurred. To this end, FSAIoT consists of a centralized Forensic State Acquisition Controller (FSAC) employed in three state collection modes: controller to IoT device, controller to cloud, and controller to controller. We present a proof of concept implementation using openHAB -- a device agnostic open source IoT device controller -- and self-created scripts, to resemble a FSAC implementation. Our proof of concept employed an Insteon IP Camera as a controller to device test, an Insteon Hub as a controller to controller test, and a nest thermostat for a a controller to cloud test. Our findings show that it is possible to practically pull forensically relevant state data from IoT devices. Future work and open research problems are shared.


title: Non-invasive blood glucose estimation using Near-Infrared spectroscopy based on SVR

abstract: There is a nonlinear relation between the blood glucose and photoplethysmography(PPG) signal. In order to estimate the blood glucose from the photoplethysmography signal, this paper presents a non-invasive blood glucose estimation using Near-Infrared spectroscopy based on the Support Vector Regression(SVR). The wavelet transform algorithm is used to remove baseline drift and smooth signals. 22 parameters, including features obtained from PPG signal and some physiological and environmental parameters, are the input parameters of Support Vector Regression model. The comparison between estimated and reference values shows better accuracy than the multiple linear regression analysis method, partial least squares method.


title: Open Information Extraction

abstract: The research on information extraction is being developed into open information extraction,i.e.extracting open categories of entities,relations and events from open domain text resources.The methods used are also transferred from pure statistical machine learning model based on human annotated corpora into statistical learning model incorporated with knowledge bases mined from large-scaled and heterogeneous Web resources.This paper firstly reviews the history of the researches on information extraction,then detailedly introduces the task definitions,difficulties,typical methods,evaluations,performances and the challenges of three main open domain information extraction tasks,i.e.entity extraction,entity disambiguation and relation extraction.Finally,based on our researches on this field,we analyze and discuss the development directions of open information extraction research and its applications in large-scaled knowledge engineering,question answering,etc.


title: Behavior-Based Robotics as a Tool for Synthesis of Artificial Behavior and Analysis of Natural Behavior

abstract: Abstract Work in behavior-based systems focuses on functional modeling, that is, the synthesis of life-like and/or biologically inspired behavior that is robust, repeatable and adaptive. Inspiration from cognitive science, neuroscience and biology drives the development of new methods and models in behavior-based robotics, and the results tie together several related fields including artificial life, evolutionary computation, and multi-agent systems. Ideas from artificial intelligence and engineering continue to be explored actively and applied to behavior-based robots as their role in animal modeling and practical applications is being developed.


title: Incremental quantitative rule derivation by multidimensional data partitioning

abstract: By using cardinality and relevance information about a set of attributes and concept hierarchies, a top-down incremental data partitioning method is proposed for quantitative rule derivation from database in parallelism. Based on sequential incremental approach, we proposed two parallel versions of incremental partitioning algorithms. These two parallel algorithms are multidimensional-based to partition data set into multiple independent subsets for further rule derivation process. The second version of the parallel algorithm improves the first in terms of load balance.


title: Gossip: Automatically Identifying Malicious Domains from Mailing List Discussions

abstract: Domain names play a critical role in cybercrime, because they identify hosts that serve malicious content (such as malware, Trojan binaries, or malicious scripts), operate as command-and-control servers, or carry out some other role in the malicious network infrastructure. To defend against Internet attacks and scams, operators widely use blacklisting to detect and block malicious domain names and IP addresses. Existing blacklists are typically generated by crawling suspicious domains, manually or automatically analyzing malware, and collecting information from honeypots and intrusion detection systems. Unfortunately, such blacklists are difficult to maintain and are often slow to respond to new attacks. Security experts set up and join mailing lists to discuss and share intelligence information, which provides a better chance to identify emerging malicious activities. In this paper, we design Gossip, a novel approach to automatically detect malicious domains based on the analysis of discussions in technical mailing lists (particularly on security-related topics) by using natural language processing and machine learning techniques. We identify a set of effective features extracted from email threads, users participating in the discussions, and content keywords, to infer malicious domains from mailing lists, without the need to actually crawl the suspect websites. Our result shows that Gossip achieves high detection accuracy. Moreover, the detection from our system is often days or weeks earlier than existing public blacklists.


title: AD-LRU: An efficient buffer replacement algorithm for flash-based databases

abstract: Flash memory has characteristics of out-of-place update and asymmetric I/O latencies for read, write, and erase operations. Thus, the buffering policy for flash-based databases has to consider those properties to improve the overall performance. This article introduces a new approach to buffer management for flash-based databases, called AD-LRU (Adaptive Double LRU), which focuses on improving the overall runtime efficiency by reducing the number of write/erase operations and by retaining a high buffer hit ratio. We conduct trace-driven experiments both in a simulation environment and in a real DBMS, using a real OLTP trace and four kinds of synthetic traces: random, read-most, write-most, and Zipf. We make detailed comparisons between our algorithm and the best-known competitor methods. The experimental results show that AD-LRU is superior to its competitors in most cases.


title: Ultraflat Interleaved Triangular Current Mode (TCM) Single-Phase PFC Rectifier

abstract: This paper presents the analysis and realization of a topology suitable to realize a power factor correction (PFC) rectifier with a thickness of only a few millimeters. The low height of the converter requires all components to be integrated into the printed circuit board (PCB). Still reasonable dimensions of the converter PCB are feasible (221 mm × 157 mm for a 200 W PFC rectifier), since PCB-integrated inductors and capacitors allow for high energy densities due to their large surface area which facilitates a low thermal resistance to ambient. A multicell totem-pole PFC rectifier employing a soft-switching modulation scheme over the complete mains period is identified as an adequate topology. The mode of operation is entitled triangular current mode (TCM) due to the triangular-shaped inductor currents. The modulation technique requires a reliable description of the switching transition of a half-bridge in order to provide accurate timing parameters. For this purpose, a simplified model of the nonlinear MOSFETs' output capacitances facilitates closed-form analytical expressions for duty cycle and switching frequency. Furthermore, this paper details the control of three interleaved converter cells which yields a reduction of the input current ripple. A 200 W TCM PFC rectifier with a low height of 5 mm has been realized and measurement results are provided in order to validate the theoretical considerations. The presented TCM PFC rectifier achieves an efficiency of 94.6% and a power factor of 99.3% at nominal power.


title: Crowdsourced semantic annotation of scientific publications and tabular data in PDF

abstract: Significant amounts of knowledge in science and technology have so far not been published as Linked Open Data but are contained in the text and tables of legacy PDF publications. Making such information available as RDF would, for example, provide direct access to claims and facilitate surveys of related work. A lot of valuable tabular information that till now only existed in PDF documents would also finally become machine understandable. Instead of studying scientific literature or engineering patents for months, it would be possible to collect such input by simple SPARQL queries. The SemAnn approach enables collaborative annotation of text and tables in PDF documents, a format that is still the common denominator of publishing, thus maximising the potential user base. The resulting annotations in RDF format are available for querying through a SPARQL endpoint. To incentivise users with an immediate benefit for making the effort of annotation, SemAnn recommends related papers, taking into account the hierarchical context of annotations in a novel way. We evaluated the usability of SemAnn and the usefulness of its recommendations by analysing annotations resulting from tasks assigned to test users and by interviewing them. While the evaluation shows that even few annotations lead to a good recall, we also observed unexpected, serendipitous recommendations, which confirms the merit of our low-threshold annotation support for the crowd.


Novel In-flight Coarse Alignment of Low-cost Strapdown Inertial Navigation System for Unmanned Aerial Vehicle Applications


title: Assigning personality/identity to a chatting machine for coherent conversation generation

abstract: Endowing a chatbot with personality or an identity is quite challenging but critical to deliver more realistic and natural conversations. In this paper, we address the issue of generating responses that are coherent to a pre-specified agent profile. We design a model consisting of three modules: a profile detector to decide whether a post should be responded using the profile and which key should be addressed, a bidirectional decoder to generate responses forward and backward starting from a selected profile value, and a position detector that predicts a word position from which decoding should start given a selected profile value. We show that general conversation data from social media can be used to generate profile-coherent responses. Manual and automatic evaluation shows that our model can deliver more coherent, natural, and diversified responses.


title: High-performance carbon nanotube field-effect transistor with tunable polarities

abstract: State-of-the-art carbon nanotube field-effect transistors (CNFETs) behave as Schottky barrier (SB)-modulated transistors. It is known that vertical scaling of the gate oxide significantly improves the performance of these devices. However, decreasing the oxide thickness also results in pronounced ambipolar transistor characteristics and increased drain leakage currents. Using a novel device concept, we have fabricated high-performance, enhancement-mode CNFETs exhibiting n or p-type unipolar behavior, tunable by electrostatic and/or chemical doping, with excellent OFF-state performance and a steep subthreshold swing (S =63 mV/dec). The device design allows for aggressive oxide thickness and gate length scaling while maintaining the desired device characteristics.


title: News events prediction using Markov logic networks

abstract: Predicting future events from text data has been a controversial and much disputed topic in the field of text analytics. However, far too little attention has been paid to efficient prediction in textual environments. This study has aimed to develop a novel and efficient method for news event prediction. The proposed method is based on Markov logic networks (MLNs) framework, which enables us to concisely represent complex events by full expressivity of first-order logic (FOL), as well as to reason uncertain event with probabilities. In our framework, we first extract text news events via an event representation model at a semantic level and then transform them into web ontology language (OWL) as a posteriori knowledge. A set of domain-specific causal rules in FOL associated with weights were also fed into the system as a priori (common-sense) knowledge. Additionally, several large-scale ontologies including DBpedia, VerbNet and WordNet were used to model common-sense logic rules as contextual knowledge. F...


INJECTIVITY DECLINE DUE TO CALCIUM CARBONATE SCALING


title: Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions

abstract: In-depth scene descriptions and question answering tasks have greatly increased the scope of today's definition of scene understanding. While such tasks are in principle open ended, current formulations primarily focus on describing only the current state of the scenes under consideration. In contrast, in this paper, we focus on the future states of the scenes which are also conditioned on actions. We posit this as a question answering task, where an answer has to be given about a future scene state, given observations of the current scene, and a question that includes a hypothetical action. Our solution is a hybrid model which integrates a physics engine into a question answering architecture in order to anticipate future scene states resulting from object-object interactions caused by an action. We demonstrate first results on this challenging new problem and compare to baselines, where we outperform fully data-driven end-to-end learning approaches.


title: Cephalometric landmark detection in dental x-ray images using convolutional neural networks

abstract: In dental X-ray images, an accurate detection of cephalometric landmarks plays an important role in clinical diagnosis, treatment and surgical decisions for dental problems. In this work, we propose an end-to-end deep learning system for cephalometric landmark detection in dental X-ray images, using convolutional neural networks (CNN). For detecting 19 cephalometric landmarks in dental X-ray images, we develop a detection system using CNN-based coordinate-wise regression systems. By viewing x- and y-coordinates of all landmarks as 38 independent variables, multiple CNN-based regression systems are constructed to predict the coordinate variables from input X-ray images. First, each coordinate variable is normalized by the length of either height or width of an image. For each normalized coordinate variable, a CNN-based regression system is trained on training images and corresponding coordinate variable, which is a variable to be regressed. We train 38 regression systems with the same CNN structure on coordinate variables, respectively. Finally, we compute 38 coordinate variables with these trained systems from unseen images and extract 19 landmarks by pairing the regressed coordinates. In experiments, the public database from the Grand Challenges in Dental X-ray Image Analysis in ISBI 2015 was used and the proposed system showed promising performance by successfully locating the cephalometric landmarks within considerable margins from the ground truths.


title: Optimized Spatial Hashing for Collision Detection of Deformable Objects

abstract: We propose a new approach to collision and self– collision detection of dynamically deforming objects that consist of tetrahedrons. Tetrahedral meshes are commonly used to represent volumetric deformable models and the presented algorithm is integrated in a physically–based environment, which can be used in game engines and surgical simulators. The proposed algorithm employs a hash function for compressing a potentially infinite regular spatial grid. Although the hash function does not always provide a unique mapping of grid cells, it can be generated very efficiently and does not require complex data structures, such as octrees or BSPs. We have investigated and optimized the parameters of the collision detection algorithm, such as hash function, hash table size and spatial cell size. The algorithm can detect collisions and self– collisions in environments of up to 20k tetrahedrons in real–time. Although the algorithm works with tetrahedral meshes, it can be easily adapted to other object primitives, such as triangles.


title: Cloud Computing-Based Non-Invasive Glucose Monitoring for Diabetic Care

abstract: Near infrared photoacoustic spectroscopy is utilized for the development of a continuous non-invasive glucose monitoring system for diabetics. A portable embedded system for taking photoacoustic measurements on tissues to estimate glucose concentration is implemented using field programmable gate array (FPGA). The back-end architecture for high-speed data acquisition and de-noising of photoacoustic measurements operates at 274.823 MHz on a Xilinx Virtex-II Pro FPGA. The glucose measurement technique is verified in vitro on glucose solutions and in vivo on tissues, with photoacoustic signal amplitude varying linearly with sample glucose concentration. A kernel-based regression algorithm using multiple features of the photoacoustic signal is used to estimate glucose concentration from photoacoustic measurements. The calibration algorithm provides a superior performance over previous efforts with a mean absolute relative difference of 8.84% and Clarke Error Grid distribution of 92.86% and 7.14% over Zones A and B of the grid. A cloud computing platform for automated monitoring of blood glucose levels is proposed to enable individuals with diabetes to connect with doctors and caretakers. The developed system is connected to the cloud service using a mobile device, which facilitates implementation of computationally intensive calibration tasks and the storage and analysis of measurement data for treatment and monitoring.


title: Cogeneration of mechanical, electrical, and software designs for printable robots from structural specifications

abstract: Designing and fabricating new robotic systems is typically limited to experts, requiring engineering background, expensive tools, and considerable time. In contrast, to facilitate everyday users developing custom robots for personal use, this work presents a new system to easily create printable foldable robots from high-level structural specifications. A user merely needs to select electromechanical components from a library of basic building blocks and pre-designed mechanisms, then connect them to define custom robot assemblies. The system then generates complete mechanical drawings suitable for fabrication, instructions for the assembly of electronics, and software to control and drive the final robot. Several robots designed in this manner demonstrate the ability and versatility of this process. I. INTRODUCTION While robots are widely used for research and commer- cial applications, they are not yet ubiquitous in everyday life for personalized tasks. Creating a new robotic system typically involves repeated design iterations using a variety of computer-aided design tools. Domain-specific expertise is generally required to create the requisite mechanical drawings for a structural body, electronic circuits to connect sensors and actuators, and software to manage system inputs and outputs. This entire process often needs to be rerun for each new robot desired, making the design and fabrication of new robots slow and introducing a knowledge barrier for casual users. For the general public to obtain a device able to accom- plish a specified function, computational tools are required that can create robots from high-level descriptions. The long- term objective is to develop a hardware compiler that can start with functional specifications of a desired system and automatically design and fabricate a robot to accomplish those. This paper takes a step towards that vision with a system to simultaneously generate the mechanical, electrical, and software components of a robot from its structural specifications, allowing non-experts to easily design elec- tromechanical systems with custom specifications and then quickly and inexpensively fabricate the designed robot. The presented approach modularizes mechanical, electri- cal, and software components to represent them in a database suitable for hierarchical composition. These parameterized components are characterized in terms of their geometric and physical properties; while experts can directly generate new low level building blocks, casual users can simply connect existing designs to make custom electromechanical devices. The system then automatically outputs a collection of files with which the user can manufacture the specified robot: fabrication drawings get sent to a desktop cutter to generate cut-and-fold origami-inspired 3D structures, wiring instructions guide the user to assemble sensors and actuators onto that structure using plug-and-play electronic modules, and libraries and application software get loaded onto a central microcontroller. The resulting robot can be wirelessly controlled from an auto-generated user interface (UI) on a smartphone.


title: Adaptive Fault Detection for Testing Tenant Applications in Multi-tenancy SaaS Systems

abstract: SaaS (Software-as-a-Service) often uses multi-tenancy architecture (MTA) where tenant developers compose their applications online using the components stored in the SaaS database. Tenant applications need to be tested, and combinatorial testing can be used. While numerous combinatorial testing techniques are available, most of them produce static sequences of test configurations and their goal is often to provide sufficient coverage such as 2-way interaction coverage. But the goal of SaaS testing is to identify those compositions that are faulty for tenant applications. This paper proposes an adaptive test configuration generation algorithm AR (Adaptive Reasoning) that can rapidly identify those faulty combinations so that those faulty combinations cannot be selected by tenant developers for composition. The AR algorithm has been evaluated by both simulation and real experimentation using a MTA SaaS sample running on GAE (Google App Engine). Both the simulation and experiment showed show that the AR algorithm can identify those faulty combinations rapidly. Whenever a new component is submitted to the SaaS database, the AR algorithm can be applied so that any faulty interactions with new components can be identified to continue to support future tenant applications.


title: Explicit In Situ User Feedback for Web Search Results

abstract: Gathering evidence about whether a search result is relevant is a core concern in the evaluation and improvement of information retrieval systems. Two common sources of evidence for establishing relevance are judgements from trained assessors and logs of online user behavior. However, both are limited; it is hard for a trained assessor to know exactly what users want to find, and user behavior only provides an implicit and ambiguous signal. In this paper, we aim to address these limitations by collecting explicit feedback on web search results from users in situ as they search. When users return to the search result page via the browser back button after having clicked on a result, we ask them to provide a binary thumbs up or thumbs down judgment and text feedback. We collect in situ feedback from a large commercial search engine, and compare this feedback with the judgments provided by trained assessors. We find that in situ feedback differs significantly from traditional relevance judgments, and that it suggests a different interpretation of behavior signals, with the dwell time threshold between negative and positive in situ feedback being 87 seconds, longer than the more common heuristic of 30 seconds. Using text feedback from users, we discuss why user feedback may differ from editorial judgments.


title: A systematic review and comparative analysis of cross-document coreference resolution methods and tools

abstract: Information extraction (IE) is the task of automatically extracting structured information from unstructured/semi-structured machine-readable documents. Among various IE tasks, extracting actionable intelligence from an ever-increasing amount of data depends critically upon cross-document coreference resolution (CDCR) - the task of identifying entity mentions across information sources that refer to the same underlying entity. CDCR is the basis of knowledge acquisition and is at the heart of Web search, recommendations, and analytics. Real time processing of CDCR processes is very important and have various applications in discovering must-know information in real-time for clients in finance, public sector, news, and crisis management. Being an emerging area of research and practice, the reported literature on CDCR challenges and solutions is growing fast but is scattered due to the large space, various applications, and large datasets of the order of peta-/tera-bytes. In order to fill this gap, we provide a systematic review of the state of the art of challenges and solutions for a CDCR process. We identify a set of quality attributes, that have been frequently reported in the context of CDCR processes, to be used as a guide to identify important and outstanding issues for further investigations. Finally, we assess existing tools and techniques for CDCR subtasks and provide guidance on selection of tools and algorithms.


title: Resonant Charging Performance of Spiral Tesla Transformer Applied in Compact High-Voltage Repetitive Nanosecond Pulse Generator

abstract: The compact, small-sized device based on a pulsed transformer combined with a semiconductor switch is a feasible solution that generates the high-frequency and high-voltage pulse. Compared with other types of transformers, spiral Tesla transformer is much easier to achieve a high output-voltage ratio. By an in-depth analysis of the resonance circuit of Tesla transformer, the expressions of secondary voltage, transformation ratio, peak, and steepness of the primary current are deduced and analyzed in this paper. The development test of a small-size Tesla transformer prototype has been performed. The key electrical characteristics are further measured, estimated, and discussed. The specification parameter selection of the primary semiconductor switch must rely on the characteristics of primary current. Applied to a 6.0 kΩ resistor, the high-voltage repetitive pulse generator based on the Tesla transformer prototype can generate a series of pulses with a peak voltage of 100 kV and a rise time of 40 ns at the repetition rate of 200 Hz. Atmospheric air nonequilibrium plasma can be formed using the developed pulse generator and some typical images are presented.


title: Semantics-Based Repackaging Detection for Mobile Apps

abstract: While Android app stores keep growing in size and in number, app repackaging has become a major threat to the health of the mobile ecosystem. Different from many syntax-based repackaging detection techniques, in this work we propose a semantic-based approach, RepDetector, which is more robust against code obfuscation attacks. To capture an app's semantics, our approach extracts input-output states of core functions in the app and then compare function and app similarity. We implement a prototype of RepDetector, and evaluate it against various obfuscation technologies. The results show that our approach can detect repackaged apps effectively. It is also at least a hundred times faster than Androguard.


title: Cross-language lexical connections in the mental lexicon: Evidence from a case of trilingual aphasia

abstract: Abstract Despite anecdotal data on lexical interference among the languages of multilingual speakers, little research evidence about the lexical connections among multilinguals’ languages exists to date. In the present paper, two experiments with a multilingual speaker who had suffered aphasia are reported. The first experiment provides data about inter-language activation during natural conversations; the second experiment examines performance on a word-translation task. Asymmetric patterns of inter-language interference and translation are evident. These patterns are influenced by age of language learning, degree of language recovery and use, and prevalence of shared lexical items. We conclude that whereas age of language learning plays a role in language recovery following aphasia, the degrees of language use prior to the aphasia onset and of shared vocabulary determine the ease with which words are accessed. The findings emphasize the importance of patterns of language use and the relations between the language pair under investigation in understanding lexical connections among languages in bilinguals and multilinguals.


title: Mining half a billion topical experts across multiple social networks

abstract: Mining topical experts on social media is a problem that has gained significant attention due to its wide-ranging applications. Here we present the first study that combines data from four major social networks—Twitter, Facebook, Google+ and LinkedIn—along with the Wikipedia graph and Internet webpage text and metadata, to rank topical experts across the global population of users. We perform an in-depth analysis of 37 features derived from various data sources such as message text, user lists, webpages, social graphs and Wikipedia. This large-scale study includes more than 12 billion messages over a 90-day sliding window and 58 billion social graph edges. Comparison reveals that features derived from Twitter Lists, Wikipedia, Internet webpages and Twitter Followers are especially good indicators of expertise. We train an expertise ranking model using these features on a large ground-truth dataset containing almost 90,000 labels. This model is applied within a production system that ranks over 650 million experts in more than 9000 topical domains on a daily basis. We provide results and examples on the effectiveness of our expert ranking system, along with empirical validation. Finally, we make the topical expertise data available through open REST APIs for wider use.


title: Mobile applications for weight management: theory-based content analysis.

abstract: BACKGROUND ::: The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized. ::: ::: ::: PURPOSE ::: The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change. ::: ::: ::: METHODS ::: A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0-100) and the other on the Fogg Behavioral Model (score range: 0-6). Data collection and analysis occurred in November 2012. ::: ::: ::: RESULTS ::: Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc. ::: ::: ::: CONCLUSIONS ::: All apps received low overall scores for inclusion of behavioral theory-based strategies.


title: Laser stripe peak detector for 3D scanners. A FIR filter approach

abstract: The accuracy of a 3D reconstruction using laser scanners is significantly determined by the detection of the laser stripe. Since the energy pattern of such a stripe corresponds to a Gaussian profile, it makes sense to detect the point of maximum light intensity (or peak) by computing the zero-crossing point of the first derivative of such Gaussian profile. However, because noise is present in every physical process, such as electronic image formation, it is not sensitive to perform the derivative of the image of the stripe in almost any situation, unless a previous filtering stage is done. Considering that stripe scanning is an inherently row-parallel process, every row of a given image must be processed independently in order to compute its corresponding peak position in the row. This paper reports on the use of digital filtering techniques in order to cope with the scanning of different surfaces with different optical properties and different noise levels, leading to the proposal of a more accurate numerical peak detector, even at very low signal-to-noise ratios.


title: Stock Trend Prediction by Using K-Means and AprioriAll Algorithm for Sequential Chart Pattern Mining

abstract: In this paper we present a model to predict the stock trend based on a combination of sequential chart pattern, K-means and AprioriAll algorithm. The stock price sequence is truncated to charts by sliding window, then the charts are clustered by K-means algorithm to form chart patterns. Therefore, the chart sequences are converted to chart pattern sequences, and frequent patterns in the sequences can be extracted by AprioriAll algorithm. The existence of frequent patterns implies that some specific market behaviors often appear accompanied, thus the corresponding trend can be predicted. Experiment results show that the proposed system can produce better index return with fewer trades. Its annualized return is also better than award winning mutual funds. Therefore, the proposed method makes profits on the real market, even in a long-term usage.


title: Radiobots: The autonomous, self-learning future cognitive radios

abstract: A futuristic autonomous self-learning cognitive radio (CR), also called as a Radiobot, is proposed. The Radiobot is defined to be a radio device that is capable of self-managing and self-reconfiguring in real-time to match its RF environment while continuously self-learning from its past experience to achieve: 1) autonomous communication and awareness of experienced RF environment, 2) spectrum coexistence/efficiency including dynamic spectrum sharing (DSS), 3) inter-operability in heterogeneous RF network environments, 4) multi-mode operability (simultaneous operation over multiple modes/networks), and 5) power efficient green communications. In this paper, we present a system level architecture of the Radiobot and our current work on self-learning guided wide-band spectrum-sensing. We also discuss future research directions in order to make the concept a reality, including necessary cognitive algorithms critical for its operation and the need for real-time reconfigurable hardware and RF antennas.


title: What is the future of mobile learning in education?

abstract: The evolution of wireless technologies and the development of applications for mobile devices in higher education have been spectacular. For many educators, mobile technology in the field of teaching and learning has recently become one of the most important areas of research. Today, mobile learning is a strategic topic for many organizations concerned with education. In the future, more research should be conducted to transform education using mobile learning. The advent of new types of devices is disruptive to education, no matter what educators and education institutions do. Therefore, a thorough analysis, from a pedagogical and technological perspective, is key to ensuring appropriate usage and implementation of mobile learning. This Special Section of RUSC. Universities and Knowledge Society Journal presents a general overview of successful mobile learning experiences in higher education. Its aim is to share best practices and create new opportunities in universities. These mobile applications will add another layer to the learning and teaching processes.


title: Exploration by Distributional Reinforcement Learning

abstract: We propose a framework based on distributional reinforcement learning and recent attempts to combine Bayesian parameter updates with deep reinforcement learning. We show that our proposed framework conceptually unifies multiple previous methods in exploration. We also derive a practical algorithm that achieves efficient exploration on challenging control tasks.


title: Learning to Rank based on Analogical Reasoning

abstract: Object ranking or"learning to rank"is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. In this paper, we propose a new approach to object ranking based on principles of analogical reasoning. More specifically, our inference pattern is formalized in terms of so-called analogical proportions and can be summarized as follows: Given objects $A,B,C,D$, if object $A$ is known to be preferred to $B$, and $C$ relates to $D$ as $A$ relates to $B$, then $C$ is (supposedly) preferred to $D$. Our method applies this pattern as a main building block and combines it with ideas and techniques from instance-based learning and rank aggregation. Based on first experimental results for data sets from various domains (sports, education, tourism, etc.), we conclude that our approach is highly competitive. It appears to be specifically interesting in situations in which the objects are coming from different subdomains, and which hence require a kind of knowledge transfer.


title: Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos

abstract: In this paper, we investigate an approach for reconstructing storyline graphs from large-scale collections of Internet images, and optionally other side information such as friendship graphs. The storyline graphs can be an effective summary that visualizes various branching narrative structure of events or activities recurring across the input photo sets of a topic class. In order to explore further the usefulness of the storyline graphs, we leverage them to perform the image sequential prediction tasks, from which photo recommendation applications can benefit. We formulate the storyline reconstruction problem as an inference of sparse time-varying directed graphs, and develop an optimization algorithm that successfully addresses a number of key challenges of Web-scale problems, including global optimality, linear complexity, and easy parallelization. With experiments on more than 3.3 millions of images of 24 classes and user studies via Amazon Mechanical Turk, we show that the proposed algorithm improves other candidate methods for both storyline reconstruction and image prediction tasks.


title: Upward and downward physical appearance comparisons: Development of scales and examination of predictive qualities

abstract: Abstract Despite good theoretical and empirical rationale for assessing tendencies to make upward and downward physical appearance comparisons no measure for these specific constructs exists. The present work developed and tested the psychometric properties of upward and downward physical appearance comparison scales. The scales were administered to participants ( N = 224) along with measures of general appearance comparison tendencies, body image, disordered eating, Antifat and Antigay attitudes. The scales displayed good psychometric properties. Importantly, the upward but not downward physical appearance comparison scale predicted lower Appearance Evaluation and higher EAT-26 scores. Conversely, the downward but not upward physical appearance comparison scale predicted higher Appearance Evaluation and greater Antifat Attitudes (Dislike). The scales were unrelated to a nonappearance related construct. These new measures fill a gap in the literature and may be of benefit to researchers interested in body image, appearance concerns, eating disorders, social comparison, and obesity prejudice.


title: Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks

abstract: Recent advances in convolutional neural networks have shown promising results in 3D shape completion. But due to GPU memory limitations, these methods can only produce low-resolution outputs. To inpaint 3D models with semantic plausibility and contextual details, we introduce a hybrid framework that combines a 3D Encoder-Decoder Generative Adversarial Network (3D-ED-GAN) and a Long-term Recurrent Convolutional Network (LRCN). The 3D-ED-GAN is a 3D convolutional neural network trained with a generative adversarial paradigm to fill missing 3D data in low-resolution. LRCN adopts a recurrent neural network architecture to minimize GPU memory usage and incorporates an Encoder-Decoder pair into a Long Short-term Memory Network. By handling the 3D model as a sequence of 2D slices, LRCN transforms a coarse 3D shape into a more complete and higher resolution volume. While 3D-ED-GAN captures global contextual structure of the 3D shape, LRCN localizes the fine-grained details. Experimental results on both real-world and synthetic data show reconstructions from corrupted models result in complete and high-resolution 3D objects.


title: Dimension Projection Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data

abstract: For high-dimensional data, this work proposes two novel visual exploration methods to gain insights into the data aspect and the dimension aspect of the data. The first is a Dimension Projection Matrix, as an extension of a scatterplot matrix. In the matrix, each row or column represents a group of dimensions, and each cell shows a dimension projection (such as MDS) of the data with the corresponding dimensions. The second is a Dimension Projection Tree, where every node is either a dimension projection plot or a Dimension Projection Matrix. Nodes are connected with links and each child node in the tree covers a subset of the parent node's dimensions or a subset of the parent node's data items. While the tree nodes visualize the subspaces of dimensions or subsets of the data items under exploration, the matrix nodes enable cross-comparison between different combinations of subspaces. Both Dimension Projection Matrix and Dimension Project Tree can be constructed algorithmically through automation, or manually through user interaction. Our implementation enables interactions such as drilling down to explore different levels of the data, merging or splitting the subspaces to adjust the matrix, and applying brushing to select data clusters. Our method enables simultaneously exploring data correlation and dimension correlation for data with high dimensions.


title: Asset-Management of Transformers Based on Condition Monitoring and Standard Diagnosis [Feature Article]

abstract: In this paper, a methodology is developed to use data acquisition derived from condition monitoring and standard diagnosis for rehabilitation purposes of transformers. The interpretation and understanding of the test data are obtained from international test standards to determine the current condition of transformers. In an attempt to ascertain monitoring priorities, the effective test methods are selected for transformer diagnosis. In particular, the standardization of diagnostic and analytical techniques are being improved that will enable field personnel to more easily use the test results and will reduce the need for interpretation by experts. In addition, the advanced method has the potential to reduce the time greatly and increase the accuracy of diagnostics. The important aim of the standardization is to develop the multiple diagnostic models that combine results from the different tests and give an overall assessment of reliability and maintenance for transformers.


title: Automatic Generation of Implied Constraints

abstract: A well-known difficulty with solving Constraint Satisfaction Problems (CSPs) is that, while one formulation of a CSP may enable a solver to solve it quickly, a different formulation may take prohibitively long to solve. We demonstrate a system for automatically reformulating CSP solver models by combining the capabilities of machine learning and automated theorem proving with CSP systems. Our system is given a basic CSP formulation and outputs a set of reformulations, each of which includes additional constraints. The additional constraints are generated through a machine learning process and are proven to follow from the basic formulation by a theorem prover. Experimenting with benchmark problem classes from finite algebras, we show how the time invested in reformulation is often recovered many times over when searching for solutions to more difficult problems from the problem class.


title: A large scale study of programming languages and code quality in github

abstract: What is the effect of programming languages on software quality? This question has been a topic of much debate for a very long time. In this study, we gather a very large data set from GitHub (729 projects, 80 Million SLOC, 29,000 authors, 1.5 million commits, in 17 languages) in an attempt to shed some empirical light on this question. This reasonably large sample size allows us to use a mixed-methods approach, combining multiple regression modeling with visualization and text analytics, to study the effect of language features such as static v.s. dynamic typing, strong v.s. weak typing on software quality. By triangulating findings from different methods, and controlling for confounding effects such as team size, project size, and project history, we report that language design does have a significant, but modest effect on software quality. Most notably, it does appear that strong typing is modestly better than weak typing, and among functional languages, static typing is also somewhat better than dynamic typing. We also find that functional languages are somewhat better than procedural languages. It is worth noting that these modest effects arising from language design are overwhelmingly dominated by the process factors such as project size, team size, and commit size. However, we hasten to caution the reader that even these modest effects might quite possibly be due to other, intangible process factors, e.g., the preference of certain personality types for functional, static and strongly typed languages.


title: Video lens: rapid playback and exploration of large video collections and associated metadata

abstract: We present Video Lens, a framework which allows users to visualize and interactively explore large collections of videos and associated metadata. The primary goal of the framework is to let users quickly find relevant sections within the videos and play them back in rapid succession. The individual UI elements are linked and highly interactive, supporting a faceted search paradigm and encouraging exploration of the data set. We demonstrate the capabilities and specific scenarios of Video Lens within the domain of professional baseball videos. A user study with 12 participants indicates that Video Lens efficiently supports a diverse range of powerful yet desirable video query tasks, while a series of interviews with professionals in the field demonstrates the framework's benefits and future potential.


title: Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

abstract: The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.


title: A low power high speed Class-B buffer amplifier for flat panel display application

abstract: A low power, high speed, but with a large input dynamic range and output swing class-B output buffer circuit which is suitable for the flat-panel display application is proposed. The buffer draws little current during static but has a large driving capability during transients. It has been demonstrated with the TSMC 0.6 /spl mu/m CMOS technology.


title: CRNN: A Joint Neural Network for Redundancy Detection

abstract: This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character- aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the- art text classification algorithms on four popular benchmarking corpus. For instance, our model achieves competing precision rate, recall ratio, and F1 score on the Google-news data-set. For twenty- news-groups data stream, our algorithm obtains the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus, our framework obtains the best F1 score and almost equivalent precision rate and recall ratio over the top competitor. For the question classification collection, CRNN produces the optimal recall rate and F1 score and comparable precision rate. We also analyse three different RNN hidden recurrent cells' impact on performance and their runtime efficiency. We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM. For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec embeddings and report their performance differences.


title: Driver behavior quantitative models: Identification and classification of variables

abstract: In this paper we investigate driver behavior variables proposed in literature from an objective approach; we consider studies which employed numeric data that can be objectively measured by standardized tools and electronic devices, such as speed, acceleration and position. Our analysis develops in three phases; first we identify recent fields that focused on modeling driver behavior, such as Usage based insurance and governement agencies of transport. Next we extract quantitative variables from literature and classify them according to their importance; to achieve this, we identify their priority in each paper depending on their results. Finally we order them by their rate use in scientific literature, in order to have a comprehensive synthesis about the most significant and prevalent variables in literature. This paper therefore serves as reference for future studies on driver behavior; the collaboration we provide is a set of crucial variables for modeling, understanding and analysis driver behavior in quantitative research. Results show that “Speed”, “Acceleration/deceleration” and “Braking” are the most used in recent studies as well as considered priority variables.


title: oro.open.ac.uk Using Dempster-Shafer’s Evidence Theory for Query Expansion Based on Freebase Knowledge

abstract: Query expansion is generally a useful technique in improving search performance. However, some expanded query terms obtained by traditional statistical methods (e.g., pseudo-relevance feedback) may not be relevant to the user’s information need, while some relevant terms may not be contained in the feedback documents at all. Recent studies utilize external resources to detect terms that are related to the query, and then adopt these terms in query expansion. In this paper, we present a study in the use of Freebase [6], which is an open source general-purpose ontology, as a source for deriving expansion terms. FreeBase provides a graph-based model of human knowledge, from which a rich and multi-step structure of instances related to the query concept can be extracted, as a complement to the traditional statistical approaches to query expansion. We propose a novel method, based on the well-principled Dempster-Shafer’s (D-S) evidence theory, to measure the certainty of expansion terms from the Freebase structure. The expanded query model is then combined with a state of the art statistical query expansion model – the Relevance Model (RM3). Experiments show that the proposed method achieves significant improvements over RM3.


title: Infinite-Horizon Policy-Gradient Estimation

abstract: Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a {\em biased} estimate of the gradient of the {\em average reward} in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter $\beta\in [0,1)$ (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter $\beta$ is related to the {\em mixing time} of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett,&Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward


title: Prevalence of ulcerative colitis and Crohn’s disease in Japan

abstract: Purpose ::: The prevalence of inflammatory bowel diseases is much lower in Asian countries, including Japan, than in Western countries, but it is rapidly increasing. However, no recent reports describe the current prevalence of these diseases in Japan, so we performed a descriptive epidemiological study to remedy this situation and to elucidate various characteristics of inflammatory bowel diseases in this country.


title: Secure Execution via Program Shepherding

abstract: We introduce program shepherding, a method for monitoring control flow transfers during program execution to enforce a security policy. Program shepherding provides three techniques as building blocks for security policies. First, shepherding can restrict execution privileges on the basis of code origins. This distinction can ensure that malicious code masquerading as data is never executed, thwarting a large class of security attacks. Second, shepherding can restrict control transfers based on instruction class, source, and target. For example, shepherding can forbid execution of shared library code except through declared entry points, and can ensure that a return instruction only targets the instruction after a call. Finally, shepherding guarantees that sandboxing checks placed around any type of program operation will never be bypassed. We have implemented these capabilities efficiently in a runtime system with minimal or no performance penalties. This system operates on unmodified native binaries, requires no special hardware or operating system support, and runs on existing IA-32 machines under both Linux and Windows.


title: Head First: Living Labs for Ad-hoc Search Evaluation

abstract: The information retrieval (IR) community strives to make evaluation more centered on real users and their needs. The living labs evaluation paradigm, i.e., observing users in their natural task environments, offers great promise in this regard. Yet, progress in an academic setting has been limited. This paper presents the first living labs for the IR community benchmarking campaign initiative, taking as test two use-cases: local domain search on a university website and product search on an e-commerce site. There are many challenges associated with this setting, including incorporating results from experimental search systems into live production systems, and obtaining sufficiently many impressions from relatively low traffic sites. We propose that head queries can be used to generate result lists offline, which are then interleaved with results of the production system for live evaluation. An API is developed to orchestrate the communication between commercial parties and benchmark participants. This campaign acts to progress the living labs for IR evaluation methodology, and offers important insight into the role of living labs in this space.


title: Truth Discovery via Exploiting Implications from Multi-Source Data

abstract: Data veracity is a grand challenge for various tasks on the Web. Since the web data sources are inherently unreliable and may provide conflicting information about the same real-world entities, truth discovery is emerging as a countermeasure of resolving the conflicts by discovering the truth, which conforms to the reality, from the multi-source data. A major challenge related to truth discovery is that different data items may have varying numbers of true values (or multi-truth), which counters the assumption of existing truth discovery methods that each data item should have exactly one true value. In this paper, we address this challenge by exploiting and leveraging the implications from multi-source data. In particular, we exploit three types of implications, namely the implicit negative claims, the distribution of positive/negative claims, and the co-occurrence of values in sources' claims, to facilitate multi-truth discovery. We propose a probabilistic approach with improvement measures that incorporate the three implications in all stages of truth discovery process. In particular, incorporating the negative claims enables multi-truth discovery, considering the distribution of positive/negative claims relieves truth discovery from the impact of sources' behavioral features in the specific datasets, and considering values' co-occurrence relationship compensates the information lost from evaluating each value in the same claims individually. Experimental results on three real-world datasets demonstrate the effectiveness of our approach.


title: Econometrics of testing for jumps in financial economics using bipower variation

abstract: In this paper we provide an asymptotic distribution theory for some non-parametric tests of the hypothesis that asset prices have continuous sample paths. We study the behaviour of the tests using simulated data and see that certain versions of the tests have good finite sample behaviour. We also apply the tests to exchange rate data and show that the null of a continuous sample path is frequently rejected. Most of the jumps the statistics identify are associated with governmental macroeconomic announcements.


title: 1 Research Issues in Web Data Mining

abstract: In this paper, we discuss mining with respect to web data referred here as web data mining. In particular, our focus is on web data mining research in context of our web warehousing project called WHOWEDA (Warehouse of Web Data). We have categorized web data mining into threes areas; web content mining, web structure mining and web usage mining. We have highlighted and discussed various research issues involved in each of these web data mining category. We believe that web data mining will be the topic of exploratory research in near future.


title: A multichannel MMSE-based framework for joint blind source separation and noise reduction

abstract: In this paper, we propose a new framework to separate multiple speech signals and reduce the additive acoustic noise using multiple microphones. In this framework, we start by formulating the minimum-mean-square error (MMSE) criterion to retrieve each of the desired speech signals from the observed mixtures of sounds and outline the importance of multi-speaker activity detection. The latter is modeled by introducing a latent variable whose posterior probability is computed via expectation maximization (EM) combining both the spatial and spectral cues of the multichannel speech observations. We experimentally demonstrate that the resulting joint blind source separation (BSS) and noise reduction solution performs remarkably well in reverberant and noisy environments.


title: Radio planning and coverage optimization of 3 G cellular networks

abstract: Radio planning and coverage optimization are critical issues for service providers and vendors that are deploying third generation mobile networks and need to control coverage as well as the huge costs involved. Due to the peculiarities of the Code Division Multiple Access (CDMA) scheme used in 3G cellular systems like UMTS and CDMA2000, network planning cannot be based only on signal predictions, and the approach relying on classical set covering formulations adopted for second generation systems is not appropriate. ::: ::: In this paper we investigate mathematical programming models for supporting the decisions on where to install new base stations and how to select their configuration (antenna height and tilt, sector orientations, maximum emission power, pilot signal, etc.) so as to find a trade-off between maximizing coverage and minimizing costs. The overall model takes into account signal-quality constraints in both uplink and downlink directions, as well as the power control mechanism and the pilot signal. ::: ::: Since even small and simplified instances of this NP-hard problem are beyond the reach of state-of-the-art techniques for mixed integer programming, we propose a Tabu Search algorithm which provides good solutions within a reasonable computing time. Computational results obtained for realistic instances, generated according to classical propagation models, with different traffic scenarios (voice and data) are reported and discussed.


title: Various Image Segmentation Techniques: A Review

abstract: The Image segmentation is referred to as one of the most important processes of image processing. Image segmentation is the technique of dividing or partitioning an image into parts, called segments. It is mostly useful for applications like image compression or object recognition, because for these types of applications, it is inefficient to process the whole image. So, image segmentation is used to segment the parts from image for further processing. There exist several image segmentation techniques, which partition the image into several parts based on certain image features like pixel intensity value, color, texture, etc. These all techniques are categorized based on the segmentation method used. In this paper the various image segmentation techniques are reviewed, discussed and finally a comparison of their advantages and disadvantages is listed.


title: Feasibility of a bimanual, lever-driven wheelchair for people with severe arm impairment after stroke

abstract: Individuals with severe arm impairment after stroke are thought to be unable to use a manual wheelchair in the conventional bimanual fashion, because they cannot grip and push the pushrim with their impaired hand. Instead, they are often taught to propel a wheelchair with their good arm and leg, a compensatory strategy that encourages disuse and may cause asymmetric tone. Here, we show that four stroke survivors (9, 27 50 and 16 months post stroke) with severe arm impairment (upper extremity Fugl Meyer scores of 21, 17, 16 and 15 of 66 respectively) were able to propel themselves overground during ten, 3.3 meter movement trials, using a specially designed lever-driven wheelchair adapted with a splint and elastic bands. Their average speed on the tenth trial was about 0.1 m/sec. These results suggest that individuals with stroke could use bimanual wheelchair propulsion for mobility, both avoiding the problems associated with good-arm/good-leg propulsion and increasing the number of daily arm movements they achieve, which may improve arm movement recovery.


title: Building bridges between psychological science and education: Cultural stereotypes, STEM, and equity

abstract: There is a gender gap in science, technology, engineering, and mathematics (STEM) education. This presents a worldwide problem of inequity. Sociocultural stereotypes associating STEM with males act as barriers that prevent girls from developing interests in STEM. This article aims to show that we can increase equity and enhance outcomes for a broader number of children around the world by integrating psychological and educational science. The article discusses four strands of research in an effort to build a bridge between psychological science and educational practice and policy. First, it describes how stereotypes can act as barriers that prevent girls from developing interests in STEM. Second, it summarizes psychological experiments demonstrating that counteracting stereotypes can increase girls’ interest in enrolling in STEM courses. Third, it examines new results showing that children adopt the pervasive stereotypes of their culture starting from surprisingly young ages, and it shows that children’s stereotypes influence their academic attitudes and performance. Fourth, it describes innovative practical interventions that can increase and equalize motivation and engagement in STEM for both boys and girls. In each of these sections, the authors link scientific findings with educational applications. Cultural stereotypes contribute to educational inequities, but scientists, educators, and policymakers can together make a difference to reduce stereotyping and boost girls’ interest in STEM worldwide.


title: UNDERSTANDING CONTINUANCE USAGE OF SOCIAL NETWORKING SERVICES : A THEORETICAL MODEL AND EMPIRICAL STUDY OF THE CHINESE CONTEXT

abstract: Social networking services (SNS) provide innovative online platforms for social interactions and communications. In order to understand users’ continuance intention of using SNS, we first propose a comprehensive research model based on the expectation-confirmation model (ECM) of IS continuance. Our model examines direct and indirect factors affecting users’ continuance intention of SNS usage. We then apply the model in an empirical study, in which we collect and analyze survey data from the users of a major Chinese SNS website. The results of the study reveal different effects of individual motivations such as perceived usefulness and perceived enjoyment on continued usage intention (CUI) in SNS. We also find significant impact of non-individual motivation (i.e., structural embeddedness) on CUI. This research not only extends the IS continuance theory into SNS studies, but also provides IS researchers and SNS practitioners empirical insights into CUI in SNS and its underlying factors in the Chinese context.


title: Robust Background Subtraction Using Geodesic Active Contours in ICA Subspace for Video Surveillance Applications

abstract: Current background subtraction methods require background modeling to handle dynamic backgrounds. The purpose of our study is to investigate a background template substraction method to detect foreground objects in the presence of background variations. The method uses a single reference image but the change detection process allows change in the background including illumination changes and dynamic scenes. Using indoor and outdoor scenes, we compare our method to the best state-of-the art algorithms using both quantitative and qualitative evaluation. The results show that our method is in general more accurate and more effective.


title: Challenges to meat safety in the 21st century

abstract: Abstract The safety of meat has been at the forefront of societal concerns in recent years, and indications exist that challenges to meat safety will continue in the future. Major meat safety issues and related challenges include the need to control traditional as well as “new,” “emerging,” or “evolving” pathogenic microorganisms, which may be of increased virulence and low infectious doses, or of resistance to antibiotics or food related stresses. Other microbial pathogen related concerns include cross-contamination of other foods and water with enteric pathogens of animal origin, meat animal manure treatment and disposal issues, foodborne illness surveillance and food attribution activities, and potential use of food safety programs at the farm. Other issues and challenges include food additives and chemical residues, animal identification and traceability issues, the safety and quality of organic and natural products, the need for and development of improved and rapid testing and pathogen detection methodologies for laboratory and field use, regulatory and inspection harmonization issues at the national and international level, determination of responsibilities for zoonotic diseases between animal health and regulatory public health agencies, establishment of risk assessment based food safety objectives, and complete and routine implementation of HACCP at the production and processing level on the basis of food handler training and consumer education. Viral pathogens will continue to be of concern at food service, bacterial pathogens such as Escherichia coli O157:H7, Salmonella and Campylobacter will continue affecting the safety of raw meat and poultry, while Listeria monocytogenes will be of concern in ready-to-eat processed products. These challenges become more important due to changes in animal production, product processing and distribution; increased international trade; changing consumer needs and increased preference for minimally processed products; increased worldwide meat consumption; higher numbers of consumers at-risk for infection; and, increased interest, awareness and scrutiny by consumers, news media, and consumer activist groups. Issues such as bovine sponginform encephalopathy will continue to be of interest mostly as a target for eradication, while viral agents affecting food animals, such as avian influenza, will always need attention for prevention or containment.


title: SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases

abstract: BACKGROUND ::: Semantic Web has established itself as a framework for using and sharing data across applications and database boundaries. Here, we present a web-based platform for querying biological Semantic Web databases in a graphical way. ::: ::: ::: RESULTS ::: SPARQLGraph offers an intuitive drag & drop query builder, which converts the visual graph into a query and executes it on a public endpoint. The tool integrates several publicly available Semantic Web databases, including the databases of the just recently released EBI RDF platform. Furthermore, it provides several predefined template queries for answering biological questions. Users can easily create and save new query graphs, which can also be shared with other researchers. ::: ::: ::: CONCLUSIONS ::: This new graphical way of creating queries for biological Semantic Web databases considerably facilitates usability as it removes the requirement of knowing specific query languages and database structures. The system is freely available at http://sparqlgraph.i-med.ac.at.


title: Implementation of RFID Technology in Parking Lot Access Control System

abstract: Parking plays an important role in the traffic system since all vehicles require a storage location when they are not being used to transport passengers. Whether it is a parking lot or on-street parking there is a problem of parking revenue convenience. Implementation of the RFID technology could be a good solution for this problem.


title: A survey and comparison of commercial and open-source robotic simulator software

abstract: Simulators play an important role in robotics research as tools for testing the efficiency, safety, and robustness of new algorithms. This is of particular importance in scenarios that require robots to closely interact with humans, e.g., in medical robotics, and in assistive environments. Despite the increasing number of commercial and open-source robotic simulation tools, to the best of our knowledge, no comprehensive up-to-date survey paper has reviewed and compared their features. This survey paper presents a comprehensive and detailed overview and a comparison between the most recent and popular commercial and open-source robotic software for simulation and interfacing with real robots. A case-study is presented, showing the versatility in porting the control code from a simulation to a real robot. Finally, a detailed step-by-step documentation on software installation and usage has been made available publicly on the Internet, together with downloadable code examples.


title: Validation of the Oncomine™ focus panel for next-generation sequencing of clinical tumour samples

abstract: The clinical utility of next-generation sequencing (NGS) for a diverse range of targets is expanding, increasing the need for multiplexed analysis of both DNA and RNA. However, translation into daily use requires a rigorous and comprehensive validation strategy. The aim of this clinical validation was to assess the performance of the Ion Torrent Personal Genome Machine (IonPGM™) and validate the Oncomine™ Focus DNA and RNA Fusion panels for clinical application in solid tumour testing of formalin-fixed, paraffin-embedded (FFPE) tissue. Using a mixture of routine FFPE and reference material across a variety of tissue and specimen types, we sequenced 86 and 31 samples on the Oncomine™ Focus DNA and RNA Fusion assays, respectively. This validation considered a number of parameters including the clinical robustness of the bioinformatics pipeline for variant detection and interpretation. The Oncomine™ Focus DNA assay had a sample and variant-based sensitivity of 99.1 and 97.1%, respectively, and an assay specificity of 100%. The Oncomine™ Focus Fusion panel had a good sensitivity and specificity based upon the samples assessed, however requires further validation to confirm findings due to limited sample numbers. We observed a good sequencing performance based upon amplicon, gene (hotspot variants within gene) and sample specific analysis with 92% of clinical samples obtaining an average amplicon coverage above 500X. Detection of some indels was challenging for the routine IonReporter™ workflow; however, the addition of NextGENe® software improved indel identification demonstrating the importance of both bench and bioinformatic validation. With an increasing number of clinically actionable targets requiring a variety of methodologies, NGS provides a cost-effective and time-saving methodology to assess multiple targets across different modalities. We suggest the use of multiple analysis software to ensure identification of clinically applicable variants.


title: Computational origami: from flapping birds to space telescopes

abstract: In this paper, I discuss developments in computational origami, the application of computation geometry to the creation of art and technology. Origami, the Japanese art of paperfolding, is a tightly constrained art form (one sheet, no cuts in the most common form) and techniques and concepts from CG have revolutionized the art and have led to remarkable practical applications.


title: Filter Bubbles, Echo Chambers, and Online News Consumption

abstract: Online publishing, social networks, and web search have dramatically lowered the costs of producing, distributing, and discovering news articles. Some scholars argue that such technological changes increase exposure to diverse perspectives, while others worry that they increase ideological segregation. We address the issue by examining webbrowsing histories for 50,000 US-located users who regularly read online news. We find that social networks and search engines are associated with an increase in the mean ideological distance between individuals. However, somewhat counterintuitively, these same channels also are associated with an increase in an individual's exposure to material from his or her less preferred side of the political spectrum. Finally, the vast majority of online news consumption is accounted for by individuals simply visiting the home pages of their favorite, typically mainstream, news outlets, tempering the consequences -- both positive and negative -- of recent technological changes. We thus uncover evidence for both sides of the debate, while also finding that the magnitude of the effects is relatively modest


title: Towards a programmable management plane for SDN and legacy networks

abstract: Software-defined Networking (SDN) and one of its most known realization, namely OpenFlow, enabled wide-spread and vendor-neutral programmability of the control plane of modern network equipment. However, despite research and standardization efforts, the management plane is still eluding device-neutral and vendor-neutral programmability. Thus, innovation in the management plane is hampered by a dependency on human experts, domain knowledge that is hidden in human-centered manuals, and the huge amount of diverse device capabilities and configuration interfaces. Recent proposals for vendor-neutral data-models, such as OFCONF, are still lacking majority and do not provide a standardized way of device capability discovery and device status monitoring. Accordingly, we present an architecture that provides a unified interface to the management plane of heterogeneous devices, i.e., SDN and legacy devices. We discuss the properties of the chosen level of configuration abstraction and show how applications northbound of the abstraction layer are well prepared against undesired side-effects of management actions. By example of a popular approach for enabling Open-Flow in mixed-SDN/legacy networks, i.e., Panopticon, we provide a proof-of-concept implementation of the proposed architecture in a real test-bed. We show how a management application can use the abstraction layer to discover and configure QoS options in the network, monitor the devices, and prevent undesired traffic interruptions in the legacy domain, while being operated in parallel with an SDN controller.


title: Introducing Self-Oscillating Technique for a Soft-Switched LED Driver

abstract: In this paper, a self-oscillating soft-switched converter for light-emitting-diode (LED) driver application is presented. In the proposed converter, the self-oscillation conditions are provided with a low number of additional components, and this converter provides a zero-current switching (ZCS) condition for instant switch turn- OFF . This converter does not need additional output current feedback because the output current changes only 10% when the output voltage changes by 33%. The self-oscillating part of this converter applies three low-power BJT and guarantees a soft-switching condition. The proposed self-oscillating control circuit can be implemented with a low cost since it does not need any IC and power supply for the control circuit. The proposed self-oscillating circuit detects the proper moment to turn the switch OFF under the ZCS condition. In order to justify the validity of theoretical analysis, the results of the implemented prototype for driving an LED string are presented. The measured power factor of the prototype shows that, although the input current of the converter is not sinusoidal, this converter can meet the IEC61000-3-2 standard at an operating power of less than 25 W, in addition to having a simple structure and achieving the robust ZCS condition.


title: Critical Review and Evaluation of the Uterotrophic Bioassay for the Identification of Possible Estrogen Agonists and Antagonists: In Support of the Validation of the OECD Uterotrophic Protocols for the Laboratory Rodent

abstract: A current issue for regulatory agencies is endocrine-related modes of action such as those mediated by the estrogen, androgen, and thyroid nuclear receptors. At the national and international levels, the consensus recommendation for the assessment of such modes of action is a tiered series of in vitro and in vivo protocols. The tiered framework begins with screens for structural alerts and then moves to rapid, mechanistic in vitro screening assays, and then to in vivo screening bioassays. The objective of these screens is to identify substances that may warrant testing for endocrine-mediated adverse effects. The final framework tier as needed is to test these substances in long-term bioassays for adverse endocrine-mediated reproductive and/or developmental effects. The subject of this review, the rodent uterotrophic bioassay, is intended to be a rapid in vivo screening bioassay for possible estrogen agonists and antagonists, based on the response of the estrogen-sensitive uterus. The central metric of the...


title: An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification

abstract: An elementary piece of our prior knowledge about images of the physical world is that they are spatially smooth, in the sense that neighboring pixels are more likely to belong to the same object (class) than to different ones. The smoothness assumption becomes more important as sensor resolutions keep increasing, both because the radiometric variability within classes increases and because remote sensing is employed in more heterogeneous areas (e.g., cities), where shadow and shading effects, a multitude of materials, etc., degrade the measurement data, and prior knowledge plays a greater role. This paper gives a systematic overview of image classification methods, which impose a smoothness prior on the labels. Both local filtering-type approaches and global random field models developed in other fields of image processing are reviewed, and two new methods are proposed. Then follows a detailed experimental comparison and analysis of the presented methods, using two different aerial data sets from urban areas with known ground truth. A main message of the paper is that when classifying data of high spatial resolution, smoothness greatly improves the accuracy of the result-in our experiments up to 33%. A further finding is that global random field models outperform local filtering methods and should be more widely adopted for remote sensing. Finally, the evaluation confirms that all methods already oversmooth when most effective, pointing out that there is a need to include more and more complex prior information into the classification process.


title: RAPID COMMUNICATION Tracking the Hemodynamic Responses to Reward and Punishment

abstract: People rationalize the choices they make when confronted with difficult decisions by claiming they never wanted the option they did not choose. Behavioral studies on cognitive dissonance provide evidence for decision-induced attitude change, but these studies cannot fully uncover the mechanisms driving the attitude change because only pre- and post-decision attitudes are measured, rather than the process of change itself. In the first fMRI study to examine the decision phase in a decision-based cognitive dissonance paradigm, we observed that increased activity in right-inferior frontal gyrus, medial fronto-parietal regions and ventral striatum, and decreased activity in anterior insula were associated with subsequent decision-related attitude change. These findings suggest the characteristic rationalization processes that are associated with decision-making may be engaged very quickly at the moment of the decision, without extended deliberation and may involve reappraisal-like emotion regulation processes.


title: Contact System Design to Improve Energy Efficiency in Copper Electrowinning Processes

abstract: To improve energy efficiency in copper electrowinning, different technologies have been developed. These include electrode positioning capping boards and 3-D grids, electrode spacers, and segmented intercell bars. This paper introduces a design concept to avoid electrode open circuits and reduce contact resistances. The design is based on a female tooth shape for the contacts on the intercell bar. This leads to improved electrode alignment, reduced contact resistances, easier contact cleaning, and ensured electrical contact for the electrodes. It results in lower operational temperature for the electrodes, reduced plant housekeeping, increased lifespan for capping boards, and higher rate of grade A copper production. The comparative results presented should be a useful guideline for any type of intercell bar. Improvements in production levels and energy efficiency should reach 0.5% and 3%, respectively. A 3-D finite-element-based analysis and industrial measurements are used to verify the results.


title: Wireless Optical Communication for Underwater Applications

abstract: Wireless optical communication can be presented as an alternative to acoustic modems for scenarios where high speed, moderate distances, lower power and less complex communication systems are desired. Wireless communication is much more feasible solution to the problem of communicating with robotic vehicles. In this paper, wireless optical communication for the underwater criteria is designed and analysed. The focus of the paper is to construct light emitting diode based links at low cost with faster data rate of 1Mbps, wirelessly under the water. The choice of using LEDs instead of Lasers was largely economic. However, the underwater environment can be very challenging optically and many of the advantages that lasers have in terms of beam quality can be rapidly degraded by scattering and turbulence. Keyword: Moderate distance, Wireless optical, Super bright blue LED.