title: Seeing Disorder: Neighborhood Stigma and the Social Construction of “Broken Windows”*

abstract: This article reveals the grounds on which individuals form perceptions of disorder. Integrating ideas about implicit bias and statistical discrimination with a theoretical framework on neighborhood racial stigma, our empirical test brings together personal interviews, census data, police records, and systematic social observations situated within some 500 block groups in Chicago. Observed disorder predicts perceived disorder, but racial and economic context matter more. As the concentration of minority groups and poverty increases, residents of all races perceive heightened disorder even after we account for an extensive array of personal characteristics and independently observed neighborhood conditions. Seeing disorder appears to be imbued with social meanings that go well beyond what essentialist theories imply, generating self-reinforcing processes that may help account for the perpetuation of urban racial inequality.


title: What you see is what you can change: Human-centered machine learning by interactive visualization

abstract: Visual analytics (VA) systems help data analysts solve complex problems interactively, by integrating automated data analysis and mining, such as machine learning (ML) based methods, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and that puts the central relationship between automated algorithms and interactive visualizations into sharp focus. The framework is illustrated with several examples and we further elaborate on the interactive ML process by identifying key scenarios where ML methods are combined with human feedback through interactive visualization. We derive five open research challenges at the intersection of ML and visualization research, whose solution should lead to more effective data analysis.


title: Design of a Low-Torque-Ripple Fractional-Slot Interior Permanent-Magnet Motor

abstract: Despite a strong nonlinear behavior and a complex design, the interior permanent-magnet (IPM) machine is proposed as a good candidate among the PM machines owing to its interesting peculiarities, i.e., higher torque in flux-weakening operation, higher fault tolerance, and ability to adopt low-cost PMs. A second trend in designing PM machines concerns the adoption of fractional-slot (FS) nonoverlapped coil windings, which reduce the end winding length and consequently the Joule losses and the cost. Therefore, the adoption of an IPM machine with an FS winding aims to combine both advantages: high torque and efficiency in a wide operating region. However, the combination of an anisotropic rotor and an FS winding stator causes some problems. The interaction between the magnetomotive force harmonics due to the stator current and the rotor anisotropy causes a very high torque ripple. This paper illustrates a procedure in designing an IPM motor with the FS winding exhibiting a low torque ripple. The design strategy is based on two consecutive steps: at first, the winding is optimized by taking a multilayer structure, and then, the rotor geometry is optimized by adopting a nonsymmetric structure. As an example, a 12-slot 10-pole IPM machine is considered, achieving a torque ripple lower than 1.5% at full load.


title: Robot-Centric Activity Recognition ‘in the Wild’

abstract: This paper considers the problem of recognizing spontaneous human activities from a robot’s perspective. We present a novel dataset, where data is collected by an autonomous mobile robot moving around in a building and recording the activities of people in the surroundings. Activities are not specified beforehand and humans are not prompted to perform them in any way. Instead, labels are determined on the basis of the recorded spontaneous activities. The classification of such activities presents a number of challenges, as the robot’s movement affects its perceptions. To address it, we propose a combined descriptor that, along with visual features, integrates information related to the robot’s actions. We show experimentally that such information is important for classifying natural activities with high accuracy. Along with initial results for future benchmarking, we also provide an analysis of the usefulness and importance of the various features for the activity recognition task.


Living with Parkinson's and the Emerging Role of Occupational Therapy


title: Best Topic Word Selection for Topic Labelling

abstract: This paper presents the novel task of best topic word selection, that is the selection of the topic word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.


title: Dispersion Games: General Definitions and Some Specific Learning Results

abstract: Dispersion games are the generalization of the anticoordination game to arbitrary numbers of agents and actions. In these games agents prefer outcomes in which the agents are maximally dispersed over the set of possible actions. This class of games models a large number of natural problems, including load balancing in computer science, niche selection in economics, and division of roles within a team in robotics. Our work consists of two main contributions. First, we formally define and characterize some interesting classes of dispersion games. Second, we present several learning strategies that agents can use in these games, including traditional learning rules from game theory and artificial intelligence, as well as some special purpose strategies. We then evaluate analytically and empirically the performance of each of these strategies.


title: Hardware Trojan Detection by Multiple-Parameter Side-Channel Analysis

abstract: Hardware Trojan attack in the form of malicious modification of a design has emerged as a major security threat. Sidechannel analysis has been investigated as an alternative to conventional logic testing to detect the presence of hardware Trojans. However, these techniques suffer from decreased sensitivity toward small Trojans, especially because of the large process variations present in modern nanometer technologies. In this paper, we propose a novel noninvasive, multiple-parameter side-channel analysisbased Trojan detection approach. We use the intrinsic relationship between dynamic current and maximum operating frequency of a circuit to isolate the effect of a Trojan circuit from process noise. We propose a vector generation approach and several design/test techniques to improve the detection sensitivity. Simulation results with two large circuits, a 32-bit integer execution unit (IEU) and a 128-bit advanced encryption standard (AES) cipher, show a detection resolution of 1.12 percent amidst ±20 percent parameter variations. The approach is also validated with experimental results. Finally, the use of a combined side-channel analysis and logic testing approach is shown to provide high overall detection coverage for hardware Trojan circuits of varying types and sizes.


Chinese Textual Sentiment Analysis: Datasets, Resources and Tools


title: Wing Rotation and the Aerodynamic Basis of Insect

abstract: The enhanced aerodynamic performance of insects results from an interaction of three distinct yet interactive mechanisms: delayed stall, rotational circulation, and wake capture. Delayed stall functions during the translational portions of the stroke, when the wings sweep through the air with a large angle of attack. In contrast, rotational circulation and wake capture generate aerodynamic forces during stroke reversals, when the wings rapidly rotate and change direction. In addition to contributing to the lift required to keep an insect aloft, these two rotational mechanisms provide a potent means by which the animal can modulate the direction and magnitude of flight forces during steering maneuvers. A comprehensive theory incorporating both translational and rotational mechanisms may explain the diverse patterns of wing motion displayed by different species of insects.


title: Simulation Modeling and Analysis of Business Process

abstract: Web services composition technology is promising technology to efficiently integrate heterogeneous, distributed and legacy software system. Performance becomes the key issue when web services composition model complex and mission-critical application. The defect of design can result in the performance bottleneck problem in the running time. In this paper, we propose a simulation-based model for the business process defined by WS-BPEL. Our model takes advantage of General Stochastic Petri Net to simulate the control flow and the transaction behavior of WS-BPEL through constructing the state transition space of composite web service. Therefore, our simulation model can accurately obtain the performance prosperities with the smaller cost in contrast to building experimental system.


title: Global Belief Recursive Neural Networks

abstract: Recursive Neural Networks have recently obtained state of the art performance on several natural language processing tasks. However, because of their feedforward architecture they cannot correctly predict phrase or word labels that are determined by context. This is a problem in tasks such as aspect-specific sentiment classification which tries to, for instance, predict that the word Android is positive in the sentence Android beats iOS. We introduce global belief recursive neural networks (GB-RNNs) which are based on the idea of extending purely feedforward neural networks to include one feedbackward step during inference. This allows phrase level predictions and representations to give feedback to words. We show the effectiveness of this model on the task of contextual sentiment analysis. We also show that dropout can improve RNN training and that a combination of unsupervised and supervised word vector representations performs better than either alone. The feedbackward step improves F1 performance by 3% over the standard RNN on this task, obtains state-of-the-art performance on the SemEval 2013 challenge and can accurately predict the sentiment of specific entities.


title: Semi-supervised learning with an imperfect supervisor

abstract: Real-life applications may involve huge data sets with misclassified or partially classified training data. Semi-supervised learning and learning in the presence of label noise have recently emerged as new paradigms in the machine learning community to cope with this kind of problems. This paper describes a new discriminant algorithm for semi-supervised learning. This algorithm optimizes the classification maximum likelihood (CML) of a set of labeled–unlabeled data, using a discriminant extension of the Classification Expectation Maximization algorithm. We further propose to extend this algorithm by modeling imperfections in the estimated class labels for unlabeled data. The parameters of this label-error model are learned together with the semi-supervised classifier parameters. We demonstrate the effectiveness of the approach using extensive experiments on different datasets.


title: Empirical analysis and statistical modeling of attack processes based on honeypots

abstract: Honeypots are more and more used to collect data on malicious activities on the Internet and to better understand the strategies and techniques used by attackers to compromise target systems. Analysis and modeling methodologies are needed to support the characterization of attack processes based on the data collected from the honeypots. This paper presents some empirical analyses based on the data collected from the Leurr{\'e}.com honeypot platforms deployed on the Internet and presents some preliminary modeling studies aimed at fulfilling such objectives.


title: Algorithm and hardware implementation for visual perception system in autonomous vehicle

abstract: This paper briefly surveys the recent progress on visual perception algorithms and their corresponding hardware implementations for the emerging application of autonomous driving. In particular, vehicle and pedestrian detection, lane detection and drivable surface detection are presented as three important applications for visual perception. On the other hand, CPU, GPU, FPGA and ASIC are discussed as the major components to form an efficient hardware platform for real-time operation. Finally, several technical challenges are presented to motivate future research and development in the field.


title: Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams

abstract: Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.


title: A Programmable Dual-RNA-Guided DNA Endonuclease in Adaptive Bacterial Immunity

abstract: Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems provide bacteria and archaea with adaptive immunity against viruses and plasmids by using CRISPR RNAs (crRNAs) to guide the silencing of invading nucleic acids. We show here that in a subset of these systems, the mature crRNA that is base-paired to trans-activating crRNA (tracrRNA) forms a two-RNA structure that directs the CRISPR-associated protein Cas9 to introduce double-stranded (ds) breaks in target DNA. At sites complementary to the crRNA-guide sequence, the Cas9 HNH nuclease domain cleaves the complementary strand, whereas the Cas9 RuvC-like domain cleaves the noncomplementary strand. The dual-tracrRNA:crRNA, when engineered as a single RNA chimera, also directs sequence-specific Cas9 dsDNA cleavage. Our study reveals a family of endonucleases that use dual-RNAs for site-specific DNA cleavage and highlights the potential to exploit the system for RNA-programmable genome editing.


title: Pegasus: An Efficient Intermediate Representation

abstract: Abstract : This paper presents Pegasus, a compact and expressive intermediate representation for imperative languages. The representation is suitable for target architectures supporting predicated execution and aggressive speculation. In this paper, the authors present Pegasus (Predicated Explicit GAted Simple Uniform SSA), a new intermediate representation (IR) that makes explicit -- in a single representation -- the control flow, the data flow, and the synchronization of operations that interfere through side-effects. More importantly, Pegasus has a clean semantics, independent of the target architecture. This enables its use to bridge the gap between C programs and hardware implementations, enabling the conversion from an imperative, single-threaded model of computation to a highly parallel, asynchronous, explicitly synchronized target. Pegasus combines predicated static single assignment (SSA) representation and gated SSA, making explicit the switching of data values, enabling the compiler to use predication and aggressive speculation for exposing instruction-level parallelism (ILP). In the second section, the authors describe the basic components of Pegasus and how the IR can be constructed starting from an imperative language. The operational semantics of Pegasus is described informally in Section 3, and formally in Appendix A. Section 4 shows that the compactness of Pegasus enables the use of extremely simple and efficient algorithms for many major compiler optimizations; in particular, they exhibit linear-time algorithms for almost all of the scalar optimizations from Muchnick. The versatility of Pegasus is demonstrated in Section 5, where they describe its use in a compiler that translates ANSI C programs into hardware.


title: A novel architecture of the 3D stacked MRAM L2 cache for CMPs

abstract: Magnetic random access memory (MRAM) is a promising memory technology, which has fast read access, high density, and non-volatility. Using 3D heterogeneous integrations, it becomes feasible and cost-efficient to stack MRAM atop conventional chip multiprocessors (CMPs). However, one disadvantage of MRAM is its long write latency and its high write energy. In this paper, we first stackMRAM-based L2 caches directly atop CMPs and compare it against SRAM counterparts in terms of performance and energy. We observe that the direct MRAM stacking might harm the chip performance due to the aforementioned long write latency and high write energy. To solve this problem, we then propose two architectural techniques: read-preemptive write buffer and SRAM-MRAM hybrid L2 cache. The simulation result shows that our optimized MRAM L2 cache improves performance by 4.91% and reduces power by 73.5%compared to the conventional SRAM L2 cache with the similar area.


title: Clustering binary data streams with K-means

abstract: Clustering data streams is an interesting Data Mining problem. This article presents three variants of the K-means algorithm to cluster binary data streams. The variants include On-line K-means, Scalable K-means, and Incremental K-means, a proposed variant introduced that finds higher quality solutions in less time. Higher quality of solutions are obtained with a mean-based initialization and incremental learning. The speedup is achieved through a simplified set of sufficient statistics and operations with sparse matrices. A summary table of clusters is maintained on-line. The K-means variants are compared with respect to quality of results and speed. The proposed algorithms can be used to monitor transactions.


title: Automatic Text Summarization: Past, Present and Future

abstract: Automatic text summarization, the computer-based production of condensed versions of documents, is an important technology for the information society. Without summaries it would be practically impossible for human beings to get access to the ever growing mass of information available online. Although research in text summarization is over 50 years old, some efforts are still needed given the insufficient quality of automatic summaries and the number of interesting summarization topics being proposed in different contexts by end users (“domain-specific summaries”, “opinion-oriented summaries”, “update summaries”, etc.). This paper gives a short overview of summarization methods and evaluation.


abstract: Data management in an epidemiological study: experiences from the Iowa 500 field follow-up and family study. -


title: Deriving Machine Attention from Human Rationales

abstract: Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectively. To this end, we start with discrete human-annotated rationales and map them into continuous attention. Our central hypothesis is that this mapping is general across domains, and thus can be transferred from resource-rich domains to low-resource ones. Our model jointly learns a domain-invariant representation and induces the desired mapping between rationales and attention. Our empirical results validate this hypothesis and show that our approach delivers significant gains over state-of-the-art baselines, yielding over 15% average error reduction on benchmark datasets.


title: Trajectory Tracking of the Mixed conventional/braking Actuation Mobile robots using Model Predictive Control

abstract: In this paper a trajectory tracking controller for a Mixed conventional/braking Actuation Mobile Robot (MAMR) is presented. The MAMR was designed to study the use of brakes in steering mobile robots that suffer from primary actuation failure. The MAMR design may also solve some common drawbacks of the conventional mobile robots such as control complexity, weight, and cost. This paper proposes a model predictive control approach to solve the trajectory tracking problem for the MAMR. The desired trajectories between two points are pre-generated using a quintic polynomial under curvature constraints. The dynamical model of the robot is used to solve the MPC problem. The effectiveness of the proposed control algorithm is tested through simulation.


title: Automatic PCB Defects Detection and Classification using Matlab

abstract: The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment. In electronics mass production manufacturing facilities, an attempt is often to achieve 100% quality assurance. In this work Machine Vision PCB Inspection System is applied at the first step of manufacturing. In this system a PCB inspection system is proposed and the inspection algorithm mainly focuses on the defect detection and defect classification of the defects. Defect classification is essential to the identification of the defect sources. The purpose of the system is to provide the automatic defect detection of PCB and relieve the human inspectors from the tedious task of finding the defects in PCB which may lead to electric failure. We first compare a standard PCB inspection image with a PCB image to be inspected. The MATLAB tool is used to detect the defects and to classify the defects. With the solutions designed and implemented in this thesis the algorithm designed in the proposed system is able to detect and classify all the known 14 types of defects successfully with greater accuracy. The algorithm makes use of image subtraction method for defect detection and kNN classification algorithm for the classification of the defects. This thesis will present and analyze the performance of the proposed inspection algorithm. The experiment will measure the accuracy of the system.


title: Therapeutic and Diagnostic Applications of Nanotechnology in Dermatology and Cosmetics

abstract: Nanoscience is the branch of science pertaining to the study of minute particles on an atomic or molecular scale, whose size is measured in nanometers. A nanometer represents one billionth of a meter (i.e., 10-9 m). Nanotechnology is an emerging branch of engineering that involves the use of particles on a nanoscale (1-100 nm). Thus, the application of nanotechnology in the field of dermatology is the Nanodermatology. Nanodermatology represents one of the most emerging fields for which an increasing interest is rising among scientists as well as pharmaceutical companies. Nanotechnology has revolutionized the treatment of several skin diseases. It is effective in targeted delivery of active medicaments and cosmetic ingredients. The skin forms the first point of contact for a diverse number of nanomaterials. Nanomedicine in dermatology has opened a new era in the diagnosis and treatment of various skin disorders. Possible applications of nanotechnology in dermatology and cosmetics include sunscreens, moisturizers, anti-aging formulations, phototherapy, ani-sepsis, vaccines, skin cancers, hair and nail care, etc. In this review after an introduction of nanotechnology, author has described various types of nanoparticles followed by various possible indications of nanotechnology in dermatology and cosmetics. An account on safety of nanoparticles has also been added to the review.


title: Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

abstract: Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.


title: The Ethnography of Infrastructure

abstract: This article asks methodological questions about studying infrastructure with some of the tools and perspectives of ethnography. Infrastructure is both relational and ecological—it means different ...


title: Structured Prediction for Object Detection in Deep Neural Networks

abstract: Deep convolutional neural networks are currently applied to computer vision tasks, especially object detection. Due to the large dimensionality of the output space, four dimensions per bounding box of an object, classification techniques do not apply easily. We propose to adapt a structured loss function for neural network training which directly maximizes overlap of the prediction with ground truth bounding boxes. We show how this structured loss can be implemented efficiently, and demonstrate bounding box prediction on two of the Pascal VOC 2007 classes.


Approximate algorithms scheduling parallelizable tasks


title: Challenges for Transparency

abstract: Transparency is often deemed critical to enable effective real-world deployment of intelligent systems. Yet the motivations for and benefits of different types of transparency can vary significantly depending on context, and objective measurement criteria are difficult to identify. We provide a brief survey, suggesting challenges and related concerns. We highlight and review settings where transparency may cause harm, discussing connections across privacy, multi-agent game theory, economics, fairness and trust.


title: Blockchain based trust&authentication for decentralized sensor networks

abstract: Sensor networks and Wireless Sensor Networks (WSN) are key components for the development of the Internet of Things. These networks are subject of two kinds of constraints. Adaptability by the mean of mutability and evolutivity, and constrained node resources such as energy consumption, computational complexity or memory usage. In this context, none of the existing protocols and models allows reliable peer authentication and trust level management. In the field of virtual economic transactions, Bitcoin has proposed a new decentralized and evolutive way to model and acknowledge trust and data validity in a peer network by the mean of the blockchain. We propose a new security model and its protocol based on the blockchain technology to ensure validity and integrity of cryptographic authentication data and associate peer trust level, from the beginning to the end of the sensor network lifetime.


title: Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation

abstract: HCI has become particularly interested in using machine learning (ML) to improve user experience (UX). However, some design researchers claim that there is a lack of design innovation in envisioning how ML might improve UX. We investigate this claim by analyzing 2,494 related HCI research publications. Our review confirmed a lack of research integrating UX and ML. To help span this gap, we mined our corpus to generate a topic landscape, mapping out 7 clusters of ML technical capabilities within HCI. Among them, we identified 3 under-explored clusters that design researchers can dig in and create sensitizing concepts for. To help operationalize these technical design materials, our analysis then identified value channels through which the technical capabilities can provide value for users: self, context, optimal, and utility-capability. The clusters and the value channels collectively mark starting places for envisioning new ways for ML technology to improve people's lives.


title: Pencil Grasp and Children's Handwriting Legibility During Different-Length Writing Tasks

abstract: OBJECTIVE: This study examined the influence ofpencil grasp on handwriting legibility during both short and long writing tasks in 46fourth-grade students who were typically developing. Matched samples were used to controlfor variability. METHOD: Regular classroom writing assignments were scoredfor word and letter legibility, and scores were compared using a mixed repeated-measures analysis of variance design. The two independent variables were pencil grasp (dynamic tripod grasp vs. atypical grasp) and task length (short vs. long). RESULTS: A significant difference was found between the letter legibility scores on the short task and the letter legibility scores on the long task. Students' legibility was greater on the short task than on the long task across both grasp conditions. No significant difference was found in scores between students who used dynamic tripod grasps and those who used atypical grasps, nor was there a significant interaction between grasp and task length. No significant differences were found between word legibility scores. CONCLUSION: The results indicate that although the students in this study wrote more legibly on the short task than on the long task, the type of grasp they used did not affect their legibility. Because of the limited sample size, the results of this study should be interpreted cautiously. More research in handwriting performance and pencil grasp is needed to provide clear expectations and treatment options for students.


title: Network Slicing in 5G Mobile Communication Architecture, Profit Modeling, and Challenges

abstract: Efficient flexibility and higher system scalability call for enhanced network performance, better energy consumption, lower infrastructure cost, and effective resource utilization. To accomplish this, an architectural optimization and reconstruction of existing cellular network is required. Network slicing is considered to be one of the key enablers and an architectural answer of communication system of 2020 and beyond. Traditional mobile operators provide all types of services to various kinds of customers through a single network, however, with the deployment of network slicing operators are now able to divide entire network into different slices each with its own configuration and specific Quality of Service (QoS) requirements. In a slice-based network, each slice will be considered as a separate logical network. In this way, the infrastructure utilization and resource allocation will be much more energy and cost efficient in comparison to traditional network. In this paper, we provided a comprehensive discussion on concept and system architecture of network slicing with particular focus on its business aspect and profit modeling. We throughly discussed two different dimensions of profit modeling, so called Own-Slice Implementation and Resource Leasing for Outsourced Slices. We further addressed open research directions and existing challenges with the purpose of motivating new advances and adding realistic solutions to this emerging technology.


title: Efficient Physics-Based Implementation for Realistic Hand-Object Interaction in Virtual Reality

abstract: We propose an efficient physics-based method for dexterous ‘real hand’ - ‘virtual object’ interaction in Virtual Reality environments. Our method is based on the Coulomb friction model, and we show how to efficiently implement it in a commodity VR engine for realtime performance. This model enables very convincing simulations of many types of actions such as pushing, pulling, grasping, or even dexterous manipulations such as spinning objects between fingers without restrictions on the objects' shapes or hand poses. Because it is an analytic model, we do not require any prerecorded data, in contrast to previous methods. For the evaluation of our method, we conduction a pilot study that shows that our method is perceived more realistic and natural, and allows for more diverse interactions. Further, we evaluate the computational complexity of our method to show real-time performance in VR environments.


title: Investigating the factorial invariance of the 28-item DBQ across genders and age groups: An Exploratory Structural Equation Modeling Study

abstract: The Driver Behaviour Questionnaire (DBQ) is perhaps the most widely used questionnaire instrument in traffic psychology with 174 studies published by late 2010. The instrument was developed based on a plausible cognitive ergonomic theory (the Generic Error Modeling System, GEMS), but the factor structure obtained in the original study (Reason et al., 1990) did not mirror the theory's conceptual structure. This led to abandoning GEMS and adopting the obtained factor structure as a starting point for further DBQ research. This article argues that (1) certain choices in the original study, concerning statistical methodology and the wording of individual question items, may have contributed to the ways the obtained factor structure deviated from the underlying theory and (2) the analysis methods often used in DBQ studies, principal components (PC) analysis and maximum likelihood (ML) factor analysis, are not optimal choices for the non-normally distributed categorical data that is obtained using the instrument. This is because ML produces biased results when used with this type of data, while PC is by definition unable to uncover latent factors as it summarizes all variation in the measured variables. (3) Even though DBQ factor scores have been routinely compared in subgroups of men and women and respondents of different ages, DBQ's factorial invariance in these groups has not been rigorously tested. These concerns are addressed in this article by framing the results of certain previous DBQ studies as a structural equation model (SEM) and an Exploratory Structural Equation Model (ESEM) and testing measurement model fit in subgroups of respondents. The SEM analyses indicate that the model does not fit data from the whole sample of respondents as it stands, while the ESEM analyses show that a modification of the model does. However, the ESEM analyses indicate the DBQ measures different underlying latent variables in the different subgroups. Based on the analyses and a review of recent advances in attention and memory research, an update to the theory underlying the DBQ is suggested.


title: Getting Inside Your Employees’ Heads: Navigating Barriers to Internal-Crowdsourcing for Product and Service Innovation

abstract: Organizations are beginning to experiment with the use of the “crowd” to complete tasks commonly performed by employees or designated agents. While “crowdsourcing” is growing, we know little about common uses of the crowd and the challenges such initiatives present. Findings from a grounded theory study reveal four common uses by established organizations: productivity, product/service innovation, knowledge capture, and marketing/branding. Further, leveraging internal crowds for product/service innovation may present unique challenges. Based on findings, an Internal-Crowdsourcing Acceptance Model illustrates the role of proactive executive leadership in breaking through organizational structure and processes that act as barriers to acceptance and use of internal-crowdsourcing platforms. The anticipated theoretical contribution of this research is the finding that internal-crowdsourcing presents many of the same challenges to organizations as faced in open innovation initiatives. Its anticipated contribution to practice is the identification of the role proactive executive leadership plays in reducing barriers to successful outcomes.


title: Class point approach for software effort estimation using stochastic gradient boosting technique

abstract: The success of software development depends on the proper estimation of the effort required to develop the software. Project managers require a reliable approach for software effort estimation. It is especially important during the early stages of the software development life cycle. Accurate software effort estimation is a major concern in software industries. Stochastic Gradient Boosting (SGB) is one of the machine learning techniques that helps in getting improved estimated values. SGB is used for improving the accuracy of models built on decision trees. In this paper, the main goal is to estimate the effort required to develop various software projects using the class point approach. Then, optimization of the effort parameters is achieved using the SGB technique to obtain better prediction accuracy. Further- more, performance comparisons of the models obtained using the SGB technique with the Multi Layer Perceptron and the Radial Basis Function Network are presented in order to highlight the performance achieved by each method.


title: Transductive Learning via Spectral Graph Partitioning

abstract: We present a new method for transductive learning, which can be seen as a transductive version of the k nearest-neighbor classifier. Unlike for many other transductive learning methods, the training problem has a meaningful relaxation that can be solved globally optimally using spectral methods. We propose an algorithm that robustly achieves good generalization performance and that can be trained efficiently. A key advantage of the algorithm is that it does not require additional heuristics to avoid unbalanced splits. Furthermore, we show a connection to transductive Support Vector Machines, and that an effective Co-Training algorithm arises as a special case.


title: Recent advances in visual and infrared face recognition - a review

abstract: Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.


Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches


title: An empirical BSSRDF model

abstract: We present a new model of the homogeneous BSSRDF based on large-scale simulations. Our model captures the appearance of materials that are not accurately represented using existing single scattering models or multiple isotropic scattering models (e.g. the diffusion approximation). We use an analytic function to model the 2D hemispherical distribution of exitant light at a point on the surface, and a table of parameter values of this function computed at uniformly sampled locations over the remaining dimensions of the BSSRDF domain. This analytic function is expressed in elliptic coordinates and has six parameters which vary smoothly with surface position, incident angle, and the underlying optical properties of the material (albedo, mean free path length, phase function and the relative index of refraction). Our model agrees well with measured data, and is compact, requiring only 250MB to represent the full spatial- and angular-distribution of light across a wide spectrum of materials. In practice, rendering a single material requires only about 100KB to represent the BSSRDF.


title: The duval triangle for load tap changers, non-mineral oils and low temperature faults in transformers

abstract: New versions of the Duval triangle for equipment filled with mineral oil are proposed for load tap changers of the oil type, equipment filled with non- mineral oils, and for low-temperature faults where stray gassing of oils may interfere with diagnostics.


title: Modeling Clouds Shape

abstract: We propose a model for representing the shape of cumulus clouds. We draw on several approaches that we combine and extend: We store a hierarchy of quasi-spherical particles (or blobs) living on top of each other. The shape of these particles is dened by an implicit eld which deforms under the inuence of neighbor particles. We dene a set of shaders to simulate a volumetric appearance in the spirit of Gardner’s textured ellipsoids, to make the shape appear continuous, and to account for dedicated shading effects. In this paper we deal only with the denition of the shape. However, we believe that this model is well suited to be integrated with particle animation and advanced rendering.


title: Gorilla: A Fast, Scalable, In-Memory Time Series Database

abstract: Large-scale internet services aim to remain highly available and responsive in the presence of unexpected failures. Providing this service often requires monitoring and analyzing tens of millions of measurements per second across a large number of systems, and one particularly effective solution is to store and query such measurements in a time series database (TSDB). ::: ::: A key challenge in the design of TSDBs is how to strike the right balance between efficiency, scalability, and reliability. In this paper we introduce Gorilla, Facebook's in-memory TSDB. Our insight is that users of monitoring systems do not place much emphasis on individual data points but rather on aggregate analysis, and recent data points are of much higher value than older points to quickly detect and diagnose the root cause of an ongoing problem. Gorilla optimizes for remaining highly available for writes and reads, even in the face of failures, at the expense of possibly dropping small amounts of data on the write path. To improve query efficiency, we aggressively leverage compression techniques such as delta-of-delta timestamps and XOR'd floating point values to reduce Gorilla's storage footprint by 10x. This allows us to store Gorilla's data in memory, reducing query latency by 73x and improving query throughput by 14x when compared to a traditional database (HBase)-backed time series data. This performance improvement has unlocked new monitoring and debugging tools, such as time series correlation search and more dense visualization tools. Gorilla also gracefully handles failures from a single-node to entire regions with little to no operational overhead.


title: Predicting Social Security numbers from public data

abstract: Information about an individual's place and date of birth can be exploited to predict his or her Social Security number (SSN). Using only publicly available information, we observed a correlation between individuals' SSNs and their birth data and found that for younger cohorts the correlation allows statistical inference of private SSNs. The inferences are made possible by the public availability of the Social Security Administration's Death Master File and the widespread accessibility of personal information from multiple sources, such as data brokers or profiles on social networking sites. Our results highlight the unexpected privacy consequences of the complex interactions among multiple data sources in modern information economies and quantify privacy risks associated with information revelation in public forums.


title: The Financial Crimes Enforcement Network AI System (FAIS) Identifying Potential Money Laundering from Reports of Large Cash Transactions.

abstract: The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS integrates intelligent human and software agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed. FAIS's unique analytic power arises primarily from a change in view of the underlying data from a transaction-oriented perspective to a subject-oriented (that is, person or organization) perspective.


title: On the online bin packing problem

abstract: A new framework for analyzing online bin packing algorithms is presented. This framework presents a unified way of explaining the performance of algorithms based on the Harmonic approach. Within this framework, it is shown that a new algorithm, Harmonic++, has asymptotic performance ratio at most 1.58889. It is also shown that the analysis of Harmonic+1 presented in Richey [1991] is incorrect; this is a fundamental logical flaw, not an error in calculation or an omitted case. The asymptotic performance ratio of Harmonic+1 is at least 1.59217. Thus, Harmonic++ provides the best upper bound for the online bin packing problem to date.


Technology investment and business performance


title: Google Scholar revisited

abstract: Purpose – The purpose of this paper is to revisit Google Scholar.Design/methodology/approach – This paper discusses the strengths and weaknesses of Google Scholar.Findings – The Google Books project has given a massive and valuable boost to the already rich and diverse content of Google Scholar. The downside of the growth is that significant gaps remain for top ranking journals and serials, and the number of duplicate, triplicate and quadruplicate records for the same source documents (which Google Scholar cannot detect reliably) has increased.Originality/value – This paper discusses the strengths and weaknesses of Google Scholar.


title: SisFall: A Fall and Movement Dataset

abstract: Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.


title: Unite the People: Closing the Loop Between 3D and 2D Human Representations

abstract: 3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits in-the-wild. However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets. Human annotators solely sort good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich annotations. With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body. Using the 91 landmark pose estimator, we present state-of-the art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure. We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. The data, code and models are available for research purposes.


title: CIMLoc: A crowdsourcing indoor digital map construction system for localization

abstract: Indoor maps, as crucial prerequisites for many indoor localization and navigation systems, are sometimes inaccessible. The absence of an indoor map database and the high cost of manually constructing an indoor map produce a need for an inexpensive and efficient way to dynamically construct indoor maps. The ubiquity of sensor-equipped mobile devices enables us to crowdsource user trajectories, out of which indoor digital maps can be automatically constructed at low costs. Similar to other crowdsourced data, the collected user trajectories are often noisy and of low fidelity, which poses a challenge to the accurate map construction. To alleviate this problem, we propose CIMLoc — a crowdsourcing indoor map construction system for localization. The system is evaluated with real-world trajectories collected from different mobile devices. We quantify the construction errors by computing the localization errors achieved with the constructed map and the real map. Experimental results reveal that CIMLoc is able to construct accurate maps that significantly improve localization results. We believe that CIMLoc provides an effective solution to the indoor localization problems where the indoor maps are unavailable.


title: Effects of a 7-Day Meditation Retreat on the Brain Function of Meditators and Non-Meditators During an Attention Task

abstract: Abtract Meditation as a cognitive enhancement technique is of growing interest in the field of health and research on brain function. The Stroop Word-Color Task (SWCT) has been adapted for neuroimaging studies as an interesting paradigm for the understanding of cognitive control mechanisms. Performance in the SWCT requires both attention and impulse control, which is trained in meditation practices. We presented SWCT inside the MRI equipment to measure the performance of meditators compared with non-meditators before and after a meditation retreat. The aim of this study was to evaluate the effects of a seven-day Zen intensive meditation training (a retreat) on meditators and non-meditators in this task on performance level and neural mechanisms. Nineteen meditators and fourteen non-meditators were scanned before and after a 7-day Zen meditation retreat. No significant differences were found between meditators and non-meditators in the number of the correct responses and response time during SWCT before and after the retreat. Probably, due to meditators training in attention, their brain activity in the contrast incongruent>neutral during the SWCT in the anterior cingulate, ventromedial prefrontal cortex/anterior cingulate, caudate/putamen/pallidum/temporal lobe (left), insula/putamen/temporal lobe (right) and posterior cingulate before the retreat, were reduced compared with non-meditators. After the meditation retreat, non-meditators had reduced activation in these regions, becoming similar to meditators before the retreat. This result could be interpreted as an increase in the brain efficiency of non-meditators (less brain activation in attention-related regions and same behavioral response) promoted by their intensive training in meditation in only seven days. On the other hand, meditators showed an increase in brain activation in these regions after the same training. Intensive meditation training (retreat) presented distinct effects on the attention-related regions in meditators and non-meditators probably due to differences in expertise, attention processing as well as neuroplasticity.


title: Scalable, High-Quality Object Detection

abstract: Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods. However, domain agnostic proposal generation has the principal drawback that the proposals come unranked or with very weak ranking, making it hard to trade-off quality for running time. This raises the more fundamental question of whether high-quality proposal generation requires careful engineering or can be derived just from data alone. We demonstrate that learning-based proposal methods can effectively match the performance of hand-engineered methods while allowing for very efficient runtime-quality trade-offs. Using the multi-scale convolutional MultiBox (MSC-MultiBox) approach, we substantially advance the state-of-the-art on the ILSVRC 2014 detection challenge data set, with $0.5$ mAP for a single model and $0.52$ mAP for an ensemble of two models. MSC-Multibox significantly improves the proposal quality over its predecessor MultiBox~method: AP increases from $0.42$ to $0.53$ for the ILSVRC detection challenge. Finally, we demonstrate improved bounding-box recall compared to Multiscale Combinatorial Grouping with less proposals on the Microsoft-COCO data set.


title: In Vitro Approach to Assess the Potential for Risk of Idiosyncratic Adverse Reactions Caused by Candidate Drugs

abstract: Idiosyncratic adverse drug reactions (IADRs) in humans can result in a broad range of clinically significant toxicities leading to attrition during drug development as well as postlicensing withdrawal or labeling. IADRs arise from both drug and patient related mechanisms and risk factors. Drug related risk factors, resulting from parent compound or metabolites, may involve multiple contributory mechanisms including organelle toxicity, effects related to compound disposition, and/or immune activation. In the current study, we evaluate an in vitro approach, which explored both cellular effects and covalent binding (CVB) to assess IADR risks for drug candidates using 36 drugs which caused different patterns and severities of IADRs in humans. The cellular effects were tested in an in vitro Panel of five assays which quantified (1) toxicity to THLE cells (SV40 T-antigen-immortalized human liver epithelial cells), which do not express P450s, (2) toxicity to a THLE cell line which selectively expresses P450 3A4, (3) cytotoxicity in HepG2 cells in glucose and galactose media, which is indicative of mitochondrial injury, (4) inhibition of the human bile salt export pump, BSEP, and (5) inhibition of the rat multidrug resistance associated protein 2, Mrp2. In addition, the CVB Burden was estimated by determining the CVB of radiolabeled compound to human hepatocytes and factoring in both the maximum prescribed daily dose and the fraction of metabolism leading to CVB. Combining the aggregated results from the in vitro Panel assays with the CVB Burden data discriminated, with high specificity (78%) and sensitivity (100%), between 27 drugs, which had severe or marked IADR concern, and 9 drugs, which had low IADR concern, we propose that this integrated approach has the potential to enable selection of drug candidates with reduced propensity to cause IADRs in humans.


title: Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis

abstract: Learning salient representations of multiview data is an essential step in many applications such as image classification, retrieval, and annotation. Standard predictive methods, such as support vector machines, often directly use all the features available without taking into consideration the presence of distinct views and the resultant view dependencies, coherence, and complementarity that offer key insights to the semantics of the data, and are therefore offering weak performance and are incapable of supporting view-level analysis. This paper presents a statistical method to learn a predictive subspace representation underlying multiple views, leveraging both multiview dependencies and availability of supervising side-information. Our approach is based on a multiview latent subspace Markov network (MN) which fulfills a weak conditional independence assumption that multiview observations and response variables are conditionally independent given a set of latent variables. To learn the latent subspace MN, we develop a large-margin approach which jointly maximizes data likelihood and minimizes a prediction loss on training data. Learning and inference are efficiently done with a contrastive divergence method. Finally, we extensively evaluate the large-margin latent MN on real image and hotel review datasets for classification, regression, image annotation, and retrieval. Our results demonstrate that the large-margin approach can achieve significant improvements in terms of prediction performance and discovering predictive latent subspace representations.


title: Structured document segmentation and representation by the modified X-Y tree

abstract: We describe a top-down approach to the segmentation and representation of documents containing tabular structures. Examples of these documents are invoices and technical papers with tables. The segmentation is based on an extension of X-Y trees, where the regions are split by means of cuts along separators (e.g. lines), in addition to cuts along white spaces. The leaves describe regions containing homogeneous information and cutting separators. Adjacency links among leaves of the tree describe local relationships between corresponding regions.


title: Stimulation of calcium-dependent release of labelled protein from pulse-labelled mouse pituitary intermediate lobe tissue.

abstract: 1. The effect of K depolarization on the release of labelled protein from pulse-labelled intermediate lobe tissue of mouse pituitaries has been studied.2. Depolarization briefly stimulated Ca-dependent release of labelled protein. Co (2.5 mM) and verapamil (0.08 mM) reversibly blocked stimulation. Efflux of (45)Ca was also briefly stimulated by depolarization, but not in the presence of Co or verapamil, nor in the absence of Ca.3. The response to maintained depolarization quickly inactivated. Inactivation probably resulted from Ca-channel inactivation rather than exhaustion of available labelled protein, since responses different in magnitude but of constant time course could be obtained by depolarization in different concentrations of Ca. In addition, much more labelled protein could be released by exposure to Ba.4. Depolarization did not cause a response if the Ca concentration was 0.4 mM or less; neither did inactivation occur in these conditions, and a response occurred as soon as the Ca concentration was raised. Two separate responses could be generated by a stepwise increase in the Ca concentration during maintained depolarization. The magnitude of the two responses together was similar to the magnitude of one response at the higher Ca concentration.5. Recovery from inactivation was complete after about 20 min in normal K. However, recovery also occurred during maintained depolarization provided the Ca concentration was reduced sufficiently. Recovery was complete at 0.4 mM-Ca or less, and at higher concentrations the extent of recovery depended on the Ca concentration selected.6. It is concluded that depolarization opened potential-dependent Ca channels permitting Ca entry and causing the release of labelled products. The response was brief probably because the Ca channels were inactivated. Since the channels were not inactivated by depolarization in low Ca, inactivation may result from Ca entry.


title: Automatic analysis of affective postures and body motion to detect engagement with a game companion

abstract: The design of an affect recognition system for socially perceptive robots relies on representative data: human-robot interaction in naturalistic settings requires an affect recognition system to be trained and validated with contextualised affective expressions, that is, expressions that emerge in the same interaction scenario of the target application. In this paper we propose an initial computational model to automatically analyse human postures and body motion to detect engagement of children playing chess with an iCat robot that acts as a game companion. Our approach is based on vision-based automatic extraction of expressive postural features from videos capturing the behaviour of the children from a lateral view. An initial evaluation, conducted by training several recognition models with contextualised affective postural expressions, suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making our approach suitable for integration into an affect recognition system for a game companion in a real world scenario.


title: Omnidirectional-vision-based estimation for containment detection of a robotic mower

abstract: In this paper, we present an omnidirectional-vision-based localization and mapping system which can detect whether a robotic mower is contained in a permitted area. We exploit a robot-centric mapping framework that exploits a differential equation of motion of the landmarks, which are referenced with respect to the robot body frame. The estimator in our system generates a 3D point-based map with landmarks. Concurrently, the estimator defines a boundary of the mowing area with the estimated trajectory of the mower. The estimated boundary and the landmark map are provided for the estimation of the mowing location and for the containment detection. We validate the effectiveness of our system through numerical simulations and present the results of the outdoor experiment that we conducted with our robotic mower.


title: Role of self-compassion in psychological well-being among perinatal women

abstract: Self-compassion is associated with depression and anxiety in general samples. Although recent research indicates that dysfunctional maternal attitudes predict the development of perinatal depression and anxiety symptoms, no research to date has examined the construct of self-compassion and its relationship with psychological well-being in perinatal women. Pregnant and postpartum women (N = 189) completed self-report measures of depression and anxiety history, current depression and anxiety symptom severity, and self-compassion. Women with higher depression and anxiety symptom severity had significantly lower self-compassion. Additionally, women with self-reported prior history of depression or anxiety had significantly lower self-compassion even while controlling for current depression or anxiety symptom severity, respectively. Our results suggest that self-compassion warrants further attention in the study of the development, maintenance, and treatment of perinatal mood and anxiety disorders.


title: American sign language-based finger-spelling recognition using k-Nearest Neighbors classifier

abstract: This paper presents finger-spelling recognition method for American Sign Language (ASL) Alphabet using k-Nearest Neighbours (k-NN) Classifier. This research also examines the effect of PCA for dimensional reduction to k-NN performance. The empiric results show that k-NN classifier achieves the highest accuracy (99.8 percent) for k=3 when the pattern is represented by full dimensional feature. However, k-NN classifier only achieves 28.6 percent accuracy (for k=5) when the pattern is represented by PCAreduced dimensional feature. This low accuracy is due to several factors, among others, is the presence of high numbers of redundant or highly correlated features among ASL alphabet that makes PCA unable to separate data. Although kNN classifier accuracy is higher than the proposed classifier in [7], recognition time of k-NN classifier is longer than that of the method proposed in [7]. Therefore, k-NN classifier is suitable for early child education-based application such as self-assessment system for special need student who learns ASL alphabet finger-spelling.


title: Recommender systems survey

abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.


title: Analysis of Wikipedia-based Corpora for Question Answering

abstract: This paper gives comprehensive analyses of corpora based on Wikipedia for several tasks in question answering. Four recent corpora are collected,WikiQA, SelQA, SQuAD, and InfoQA, and first analyzed intrinsically by contextual similarities, question types, and answer categories. These corpora are then analyzed extrinsically by three question answering tasks, answer retrieval, selection, and triggering. An indexing-based method for the creation of a silver-standard dataset for answer retrieval using the entire Wikipedia is also presented. Our analysis shows the uniqueness of these corpora and suggests a better use of them for statistical question answering learning.


title: Exposure to violent video games and aggression in German adolescents: a longitudinal analysis

abstract: The relationship between exposure to violent electronic games and aggressive cognitions and behavior was examined in a longitudinal study. A total of 295 German adolescents completed the measures of violent video game usage, endorsement of aggressive norms, hostile attribution bias, and physical as well as indirect/relational aggression cross-sectionally, and a subsample of N= 143 was measured again 30 months later. Cross-sectional results at T1 showed a direct relationship between violent game usage and aggressive norms, and an indirect link to hostile attribution bias through aggressive norms. In combination, exposure to game violence, normative beliefs, and hostile attribution bias predicted physical and indirect/relational aggression. Longitudinal analyses using path analysis showed that violence exposure at T1 predicted physical (but not indirect/relational) aggression 30 months later, whereas aggression at T1 was unrelated to later video game use. Exposure to violent games at T1 influenced physical (but not indirect/relational) aggression at T2 via an increase of aggressive norms and hostile attribution bias. The findings are discussed in relation to social-cognitive explanations of long-term effects of media violence on aggression. Language: en


title: Building extraction from multi-source remote sensing images via deep deconvolution neural networks

abstract: Building extraction from remote sensing images is of great importance in urban planning. Yet it is a longstanding problem for many complicate factors such as various scales and complex backgrounds. This paper proposes a novel supervised building extraction method via deep deconvolution neural networks (DeconvNet). Our method consists of three steps. First, we preprocess the multi-source remote sensing images provided by the IEEE GRSS Data Fusion Contest. A high-quality Vancouver building dataset is created on pansharpened images whose ground-truth are obtained from the OpenStreetMap project. Then, we pretrain a deep deconvolution network on a public large-scale Massachusetts building dataset, which is further fine-tuned by two band combinations (RGB and NRG) of our dataset, respectively. Moreover, the output saliency maps of the fine-tuned models are fused to produce the final building extraction result. Extensive experiments on our Vancouver building dataset demonstrate the effectiveness and efficiency of the proposed method. To the best of our knowledge, it is the first work to use deconvolution networks for building extraction from remote sensing images.


All-Digital Phase Locked Loop ( ADPLL )-A Review


title: Dynamic analysis and control design of optocoupler-isolated LLC series resonant converters with wide input and load variations

abstract: LLC series resonant converters for consumer or industrial electronics frequently encounter considerable changes in both input voltage and load current requirements. This paper presents theoretical and practical details involved with the dynamic analysis and control design of LLC series resonant dc-to-dc converters operating with wide input and load variations. The accuracy of dynamic analysis and validity of control design are confirmed with both computer simulations and experimental measurements.


title: How to Generate a Good Word Embedding

abstract: The authors analyze three critical components in training word embeddings: model, corpus, and training parameters. They systematize existing neural-network-based word embedding methods and experimentally compare them using the same corpus. They then evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for supervised tasks, and using it to initialize neural networks. They also provide several simple guidelines for training good word embeddings.


title: Interleaved soft-switching buck converter with coupled inductors

abstract: This paper presents an interleaved soft-switching buck converter with coupled inductors to extend duty ratio for high step-down voltage applications. In the proposed converter, a single-capacitor turn-off snubber is introduced to limit voltage rising rate of active switches to reduce turn-off loss. To handle the energy trapped in the leakage inductance of the coupled inductors, simple passive-clamp circuits are added to the proposed converter, which can effectively recycle the energy and suppress voltage spike. To highlight the merits of the proposed converter, its performance indexes, such as voltage gain function and component stresses, are analyzed and compared with those of the conventional interleaved buck converter. In this study, a prototype of the proposed converter, of which input voltage range is 127-177 Vdc, output voltage is 12 Vdc, and power capacity rates at 240 W, has been designed and implemented to verify the feasibility. From experimental results, it has been shown that conversion efficiency up to 85% can be achieved at full load condition.


title: An inductorless wideband balun-LNA in 65nm CMOS with balanced output

abstract: An inductorless LNA with active balun is designed for multi-standard radio applications between 100 MHz and 6 GHz. It exploits a combination of a common gate stage and a common source stage with replica biasing to maximize balanced operation. The NF is designed to be around 3 dB by using the noise canceling technique. Its best performance is achieved between 300 MHz to 3.5 GHz with gain and phase errors below 0.3 dB and plusmn2 degrees, 15 dB gain, S11<-14 dB, IIP3=0 dBm and IIP2 higher than +20 dBm at a total power consumption of 21 mW. The circuit is fabricated in a baseline 65 nm CMOS process, with an active area of only 0.01 mm2. The circuit simultaneously achieves impedance matching, noise canceling and a well balanced output.


title: Heart Rate Monitoring in Team Sports—A Conceptual Framework for Contextualizing Heart Rate Measures for Training and Recovery Prescription

abstract: A comprehensive monitoring of fitness, fatigue, and performance is crucial for understanding an athlete's individual responses to training to optimize the scheduling of training and recovery strategies. Resting and exercise-related heart rate measures have received growing interest in recent decades and are considered potentially useful within multivariate response monitoring, as they provide non-invasive and time-efficient insights into the status of the autonomic nervous system (ANS) and aerobic fitness. In team sports, the practical implementation of athlete monitoring systems poses a particular challenge due to the complex and multidimensional structure of game demands and player and team performance, as well as logistic reasons, such as the typically large number of players and busy training and competition schedules. In this regard, exercise-related heart rate measures are likely the most applicable markers, as they can be routinely assessed during warm-ups using short (3-5 min) submaximal exercise protocols for an entire squad with common chest strap-based team monitoring devices. However, a comprehensive and meaningful monitoring of the training process requires the accurate separation of various types of responses, such as strain, recovery, and adaptation, which may all affect heart rate measures. Therefore, additional information on the training context (such as the training phase, training load, and intensity distribution) combined with multivariate analysis, which includes markers of (perceived) wellness and fatigue, should be considered when interpreting changes in heart rate indices. The aim of this article is to outline current limitations of heart rate monitoring, discuss methodological considerations of univariate and multivariate approaches, illustrate the influence of different analytical concepts on assessing meaningful changes in heart rate responses, and provide case examples for contextualizing heart rate measures using simple heuristics. To overcome current knowledge deficits and methodological inconsistencies, future investigations should systematically evaluate the validity and usefulness of the various approaches available to guide and improve the implementation of decision-support systems in (team) sports practice.


Design of RF based speed control system for vehicles


title: Planar surface SLAM with 3D and 2D sensors

abstract: We present an extension to our feature based mapping technique that allows for the use of planar surfaces such as walls, tables, counters, or other planar surfaces as landmarks in our mapper. These planar surfaces are measured both in 3D point clouds, as well as 2D laser scans. These sensing modalities compliment each other well, as they differ significantly in their measurable fields of view and maximum ranges. We present experiments to evaluate the contributions of each type of sensor.


title: Consensus photometric stereo

abstract: This paper describes a photometric stereo method that works with a wide range of surface reflectances. Unlike previous approaches that assume simple parametric models such as Lambertian reflectance, the only assumption that we make is that the reflectance has three properties; monotonicity, visibility, and isotropy with respect to the cosine of light direction and surface orientation. In fact, these properties are observed in many non-Lambertian diffuse reflectances. We also show that the monotonicity and isotropy properties hold specular lobes with respect to the cosine of the surface orientation and the bisector between the light direction and view direction. Each of these three properties independently gives a possible solution space of the surface orientation. By taking the intersection of the solution spaces, our method determines the surface orientation in a consensus manner. Our method naturally avoids the need for radiometrically calibrating cameras because the radiometric response function preserves these three properties. The effectiveness of the proposed method is demonstrated using various simulated and real-world scenes that contain a variety of diffuse and specular surfaces.


title: Binary Permutation Polynomial Inversion and Application to Obfuscation Techniques

abstract: Whether it is for conditional statement, constant, opaque predicate or equation obfuscation, Mixed Boolean Arithmetics (MBA) technique is a powerful tool providing concrete ways to achieve obfuscation. Recent papers ([22,1]) presented ways to mix such tools with permutation polynomials modulo 2n in order to make them more resilient to SMT solvers. However, because of limitations regarding the inversion of such permutations, the set of permutation polynomials presented suffer some restrictions. Such restrictions bring several methods of arithmetic simplification, decreasing their effectiveness at hiding information. In this work, we present general methods for permutation polynomials inversion. Those methods allow us to remove some of the restrictions presented in the literature, making simplification methods less effective. We discuss complexity and limits of these methods, and conclude that not only current simplification methods may not be as effective as we thought, but they are still many uses of polynomial permutations in obfuscation that are yet to be explored.


title: Herpes simplex virus protein targets for CD4 and CD8 lymphocyte cytotoxicity in cultured epidermal keratinocytes treated with interferon-gamma.

abstract: In early recurrent herpetic lesions, CD4 T lymphocytes are the predominant infiltrating cells, and keratinocytes expressing major histocompatibility complex (MHC) class II antigens, induced by interferon-gamma (IFN-gamma), are the major site of herpes simplex virus (HSV) replication. IFN-gamma pretreatment of human keratinocytes in vitro reduced MHC class I antigen down-regulation by HSV-1 infection and induced expression of HLA-DR that was unaltered by subsequent HSV-1 infection. Incubation of these infected keratinocytes with phosphonoacetic acid (PAA) almost completely inhibited expression of four major HSV glycoproteins, although expression of early proteins was not affected. Weak CD8 T lymphocyte cytotoxicity against IFN-gamma-stimulated, HLA-DR-expressing HSV-1-infected keratinocytes was consistently directed to the immediate early/early proteins (all 9 patients tested) but against late proteins to a lesser degree (4/9 patients). However, CD4 T lymphocyte cytotoxicity was much greater and directed predominantly against late HSV-1 glycoproteins (all 9 subjects tested) in these cells.


title: Pleasure and Orgasm in Women with Female Genital Mutilation/Cutting (FGM/C)

abstract: Introduction. Female genital mutilation/cutting (FGM/C) violates human rights. FGM/C women's sexuality is not well known and often it is neglected by gynecologists, urologists, and sexologists. In mutilated/cut women, some fundamental structures for orgasm have not been excised. Aim. The aim of this report is to describe and analyze the results of four investigations on sexual functioning in different groups of cut women. Main Outcome Measure. Instruments: semistructured interviews and the Female Sexual Function Index (FSFI). Methods. Sample: 137 adult women affected by different types of FGM/C; 58 young FGM/C ladies living in the West; 57 infibulated women; 15 infibulated women after the operation of defibulation. Results. The group of 137 women, affected by different types of FGM/C, reported orgasm in almost 86%, always 69.23%; 58 mutilated young women reported orgasm in 91.43%, always 8.57%; after defibulation 14 out of 15 infibulated women reported orgasm; the group of 57 infibulated women investigated with the FSFI questionnaire showed significant differences between group of study and an equivalent group of control in desire, arousal, orgasm, and satisfaction with mean scores higher in the group of mutilated women. No significant differences were observed between the two groups in lubrication and pain. Conclusion. Embryology, anatomy, and physiology of female erectile organs are neglected in specialist textbooks. In infibulated women, some erectile structures fundamental for orgasm have not been excised. Cultural influence can change the perception of pleasure, as well as social acceptance. Every woman has the right to have sexual health and to feel sexual pleasure for full psychophysical well-being of the person. In accordance with other research, the present study reports that FGM/C women can also have the possibility of reaching an orgasm. Therefore, FGM/C women with sexual dysfunctions can and must be cured; they have the right to have an appropriate sexual therapy. Catania L, Abdulcadir O, Puppo V, Baldaro Verde J, Abdulcadir J, and Abdulcadir D. Pleasure and orgasm in women with female genital mutilation/cutting (FGM/C). J Sex Med 2007;4:1666-1678.


title: Image denoising and inpainting with deep neural networks

abstract: We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method's performance in the image denoising task is comparable to that of KSVD which is a widely used sparse coding technique. More importantly, in blind image inpainting task, the proposed method provides solutions to some complex problems that have not been tackled before. Specifically, we can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random. Moreover, the proposed method does not need the information regarding the region that requires inpainting to be given a priori. Experimental results demonstrate the effectiveness of the proposed method in the tasks of image denoising and blind inpainting. We also show that our new training scheme for DA is more effective and can improve the performance of unsupervised feature learning.


title: Extract conceptual graphs from plain texts in patent claims

abstract: This paper develops techniques to extract conceptual graphs from a patent claim using syntactic information (POS, and dependency tree) and semantic information (background ontology). Due to plenteous technical domain terms and lengthy sentences prevailing in patent claims, it is difficult to apply a NLP Parser directly to parse the plain texts in the patent claim. This paper combines techniques such as finite state machines, Part-Of-Speech tags, conceptual graphs, domain ontology and dependency tree to convert a patent claim into a formally defined conceptual graph. The method of a finite state machine splits a lengthy patent claim sentence into a set of shortened sub-sentences so that the NLP Parser can parse them one by one effectively. The Part-Of-Speech and dependency tree of a patent claim are used to build the conceptual graph based on the pre-established domain ontology. The result shows that 99% sub-sentences split from 1700 patent claims can be efficiently parsed by the NLP Parser. There are two types of nodes in a conceptual graph, the concept and the relation nodes. Each concept or relation can be extracted directly from a patent claim and each relation can link with a fixed number of concepts in a conceptual graph. From 100 patent claims, the average precision and recall of a concept class mapping from the patent claim to domain ontology are 96% and 89%, respectively, and the average precision and recall for Real relation class mapping are 97% and 98%, respectively. For the concept linking of a relation, the average precision is 79%. Based on the extracted conceptual graphs from patents, it would facilitate automated comparison and summarization among patents for judgment of patent infringement.


title: DBCS: Density based cluster sampling for solving imbalanced classification problem

abstract: Class imbalance of instances is a common problem in the field of data mining and machine learning. A dataset is considered to be imbalanced if one of the classes (further called a minority class or positive class) contains much smaller number of instances than the remaining classes (majority classes or negative class). We describe a new approach to balance data with improved classification. Resampling approaches is a group of popular and flexible techniques to deal with class imbalance, which attempt to resample the original dataset, by oversampling the minority class and/or under sampling the majority class the imbalanced learning problem, there are various methods that solve the imbalanced data at data level and algorithm level. The purpose of this paper is to present a more detailed analysis of density based cluster oversampling and under sampling techniques in terms of density based clustering. The concept of density based cluster sampling called DBCS is to use DBSCAN algorithm for discover clutters and apply oversampling and resampling in minority and majority class respectively. The experimental results of the hybrid DBCS algorithm are compared with an original datasets and other sampling techniques. The results show that the DBCS can improve the predictive performance of the classifiers. In addition, it yields the best in the average of accuracy.


title: Naive Bayes classifier algorithm and Particle Swarm Optimization for classification of cross selling (Case study: PT TELKOM Jakarta)

abstract: Marketing has a broad range of strategies according to the needs of the company. One marketing strategy is cross-selling. The strategy can be done by offering new products. However, too many offers to customers who are not appropriate, make marketing nonoptimal. It is necessary for classification of existing telephone customers and speedy for cross-selling strategy IndiHome PT. TELKOM Jakarta. This study uses data mining classification with Naive Bayes classifier algorithm and selection of attributes' Particle Swarm Optimization to improve the classification accuracy. The result of Naive Bayes algorithm (85. 08%) can improve accuracy by using Particle Swarm Optimization as a selection attribute (89. 31%.


title: Drugs in Development for Influenza

abstract: The emergence and global spread of the 2009 pandemic H1N1 influenza virus reminds us that we are limited in the strategies available to control influenza infection. Vaccines are the best option for the prophylaxis and control of a pandemic; however, the lag time between virus identification and vaccine distribution exceeds 6 months and concerns regarding vaccine safety are a growing issue leading to vaccination refusal. In the short-term, antiviral therapy is vital to control the spread of influenza. However, we are currently limited to four licensed anti-influenza drugs: the neuraminidase inhibitors oseltamivir and zanamivir, and the M2 ion-channel inhibitors amantadine and rimantadine. The value of neuraminidase inhibitors was clearly established during the initial phases of the 2009 pandemic when vaccines were not available, i.e. stockpiles of antivirals are valuable. Unfortunately, as drug-resistant variants continue to emerge naturally and through selective pressure applied by use of antiviral drugs, the efficacy of these drugs declines. Because we cannot predict the strain of influenza virus that will cause the next epidemic or pandemic, it is important that we develop novel anti-influenza drugs with broad reactivity against all strains and subtypes, and consider moving to multiple drug therapy in the future. In this article we review the experimental data on investigational antiviral agents undergoing clinical trials (parenteral zanamivir and peramivir, long-acting neuraminidase inhibitors and the polymerase inhibitor favipiravir [T-705]) and experimental antiviral agents that target either the virus (the haemagglutinin inhibitor cyanovirin-N and thiazolides) or the host (fusion protein inhibitors [DAS181], cyclo-oxygenase-2 inhibitors and peroxisome proliferator-activated receptor agonists).


title: Digital Control of Secondary Active Clamp Phase-Shifted Full-Bridge Converters

abstract: A DSP-based self-adaptive proportional-integral (PI) controller to control a DC-DC converter is proposed in this paper. The full-bridge topology is adopted here to obtain higher power output capability and higher conversion efficiency. The converter adopts the zero-voltage-switching (ZVS) technique to reduce the conduction losses. A parallel secondary active clamp circuit is added to deal with the voltage overshoot and ringing effect on the transformer’s secondary side. A self-adaptive PI controller is proposed to replace the traditional PI controller. Moreover, the designed converter adopts the constant-current and constant-voltage (CC-CV) output control strategy. The secondary active clamp mechanism is discussed in detail. The effectiveness of the proposed converter was experimentally verified by an IGBT-based 10kW prototype.


title: Self-disclosure topic model for classifying and analyzing Twitter conversations

abstract: Self-disclosure, the act of revealing oneself to others, is an important social behavior that strengthens interpersonal relationships and increases social support. Although there are many social science studies of self-disclosure, they are based on manual coding of small datasets and questionnaires. We conduct a computational analysis of self-disclosure with a large dataset of naturally-occurring conversations, a semi-supervised machine learning algorithm, and a computational analysis of the effects of self-disclosure on subsequent conversations. We use a longitudinal dataset of 17 million tweets, all of which occurred in conversations that consist of five or more tweets directly replying to the previous tweet, and from dyads with twenty of more conversations each. We develop self-disclosure topic model (SDTM), a variant of latent Dirichlet allocation (LDA) for automatically classifying the level of self-disclosure for each tweet. We take the results of SDTM and analyze the effects of self-disclosure on subsequent conversations. Our model significantly outperforms several comparable methods on classifying the level of selfdisclosure, and the analysis of the longitudinal data using SDTM uncovers significant and positive correlation between selfdisclosure and conversation frequency and length.


title: Detecting and classifying lesions in mammograms with Deep Learning

abstract: In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms. The benefits of current CAD technologies appear to be contradictory and they should be improved to be ultimately considered useful. Since 2012 deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. Our approach described here has achieved the 2nd place in the Digital Mammography DREAM Challenge with $ AUC = 0.85 $. The proposed method also sets the state of the art classification performance on the public INbreast database, $ AUC = 0.95$. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset.


title: Altered testicular microsomal steroidogenic enzyme activities in rats with lifetime exposure to soy isoflavones

abstract: Androgen production in the testis is carried out by the Leydig cells, which convert cholesterol into androgens. Previously, isoflavones have been shown to affect serum androgen levels and steroidogenic enzyme activities. In this study, the effects of lifelong exposure to dietary soy isoflavones on testicular microsomal steroidogenic enzyme activities were examined in the rat. F1 male rats were obtained from a multi-generational study where the parental generation was fed diets containing alcohol-washed soy protein supplemented with increasing amounts of Novasoy, a commercially available isoflavone supplement. A control group was maintained on a soy-free casein protein-based diet (AIN93G). The diets were designed to approximate human consumption levels and ranged from 0 to 1046.6 mg isoflavones/kg pelleted feed, encompassing exposures representative of North American and Asian diets as well as infant fed soy-based formula. Activities of testicular 3beta-hydroxysteroid dehydrogenase (3beta-HSD), P450c17 (CYP17), 17beta-hydroxysteroid dehydrogenase (17beta-HSD) were assayed on post natal day (PND) 28, 70, 120, 240 and 360 while 5alpha-reducatase was assayed on PND 28. At PND 28, 3beta-HSD activity was elevated by approximately 50% in rats receiving 1046.6 mg total isoflavones/kg feed compared to those on the casein only diet. A similar increase in activity was observed for CYP17 in rats receiving 235.6 mg total isoflavones/kg feed, a level representative of infant exposure through formula, compared to those receiving 0mg isoflavones from the casein diet. These results demonstrate that rats fed a mixture of dietary soy isoflavones showed significantly altered enzyme activity profiles during development at PND 28 as a result of early exposure to isoflavones at levels obtainable by humans.


title: An Iterative Improvement Process for Agile Software Development

abstract: Agile software development of short iterative cycles offers an opportunity for rapid, visible and motivating software process improvement (SPI). The agile principles suggest the regular reflections of agile project teams for improving the efficiency and adaptation of the process. However, current literature provides little support or empirical evidence for conducting such improvement efficiently, systematically and in a validated manner. Thus, this article proposes an Iterative Improvement Process for conducting SPI within individual agile project teams, which aims at increasing the ability of software developers to improve the development process based on their experiences and context knowledge. The approach has been trialed within a multiple case study of five consecutive case projects where both qualitative and quantitative research data has been systematically collected. The empirical data confirms the positive effects of the Iterative Improvement Process on the software development projects and reveals the willingness of software developers to participate in the Iterative Improvement activities due to the rapid and visible changes in their working practices. Copyright © 2006 John Wiley & Sons, Ltd.


title: What Makes a Good App Description?

abstract: In the Google Play store, an introduction page is associated with every mobile application (app) for users to acquire its details, including screenshots, description, reviews, etc. However, it remains a challenge to identify what items influence users most when downloading an app. To explore users’ perspective, we conduct a survey to inquire about this question. The results of survey suggest that the participants pay most attention to the app description which gives users a quick overview of the app. Although there exist some guidelines about how to write a good app description to attract more downloads, it is hard to define a high quality app description. Meanwhile, there is no tool to evaluate the quality of app description. In this paper, we employ the method of crowdsourcing to extract the attributes that affect the app descriptions’ quality. First, we download some app descriptions from Google Play, then invite some participants to rate their quality with the score from one (very poor) to five (very good). The participants are also requested to explain every score’s reasons. By analyzing the reasons, we extract the attributes that the participants consider important during evaluating the quality of app descriptions. Finally, we train the supervised learning models on a sample of 100 app descriptions. In our experiments, the support vector machine model obtains up to 62% accuracy. In addition, we find that the permission, the number of paragraphs and the average number of words in one feature play key roles in defining a good app description.


title: Contextual Bitext-Derived Paraphrases In Automatic MT Evaluation

abstract: In this paper we present a novel method for deriving paraphrases during automatic MT evaluation using only the source and reference texts, which are necessary for the evaluation, and word and phrase alignment software. Using target language paraphrases produced through word and phrase alignment a number of alternative reference sentences are constructed automatically for each candidate translation. The method produces lexical and low-level syntactic paraphrases that are relevant to the domain in hand, does not use external knowledge resources, and can be combined with a variety of automatic MT evaluation system.