title: The ontological key: automatically understanding and integrating forms to access the deep Web

abstract: Forms are our gates to the Web. They enable us to access the deep content of Web sites. Automatic form understanding provides applications, ranging from crawlers over meta-search engines to service integrators, with a key to this content. Yet, it has received little attention other than as component in specific applications such as crawlers or meta-search engines. No comprehensive approach to form understanding exists, let alone one that produces rich models for semantic services or integration with linked open data. In this paper, we present opal, the first comprehensive approach to form understanding and integration. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems, opal advances the state of the art: For form labeling, it combines features from the text, structure, and visual rendering of a Web page. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern Web forms, opal outperforms previous approaches for form labeling by a significant margin. For form interpretation, opal uses a schema (or ontology) of forms in a given domain. Thanks to this domain schema, it is able to produce nearly perfect ( $$>$$ > 97 % accuracy in the evaluation domains) form interpretations. Yet, the effort to produce a domain schema is very low, as we provide a datalog-based template language that eases the specification of such schemata and a methodology for deriving a domain schema largely automatically from an existing domain ontology. We demonstrate the value of opal's form interpretations through a light-weight form integration system that successfully translates and distributes master queries to hundreds of forms with no error, yet is implemented with only a handful translation rules.


title: FPGA architecture for real-time barrel distortion correction of colour images

abstract: This paper presents a hardware architecture for real time barrel distortion correction of YUV 4∶2∶2 encoded color images. In our solution we are presenting an alternative implementation of the correction engine which is based on multiplications rather than trigonometric transforms. The system makes minimal use of resources thus being a good candidate for embedding into a single chip. Proposed solution is implemented in a cost-effective Spartan 3 Field Programmable Gate Array (FPGA). Our architecture is capable of processing QQVGA images at the rate of 30 frames per second (fps). Qualitative and quantitative examination confirm correct preservation of color information in processed images.


title: Multilayer Perceptron: An Intelligent Model for Classification and Intrusion Detection

abstract: Nowadays the security of computer devices is growing significantly. This is due to more and more devices areconnected to the network. For this reason, optimize the performance of systems able to detect intrusions (IDS) is a goalof common interest. The following work consists of use thegeneralizing power of neural networks to classify the attacks. In particular, we will use multilayer perceptron (MLP) withthe algorithm of back-propagation algorithm and the sigmoidalactivation function. We use a subset of the DARPA dataset, known as KDD99. It is a public dataset labeled for an IDS andpreviously processed. We will make an analysis of the resultsobtained using different configurations, varying the numberof hidden layers and the number of training epochs to obtaina low number of false results. We observe that it is requireda large number of training epochs and how, using the entiredata set consists of 41 features, the best classification is carriedout for the type of DOS and Probe attacks.


title: Online and offline handwritten Chinese character recognition: Benchmarking on new databases

abstract: Recently, the Institute of Automation of Chinese Academy of Sciences (CASIA) released the unconstrained online and offline Chinese handwriting databases CASIA-OLHWDB and CASIA-HWDB, which contain isolated character samples and handwritten texts produced by 1020 writers. This paper presents our benchmarking results using state-of-the-art methods on the isolated character datasets OLHWDB1.0 and HWDB1.0 (called DB1.0 in general), OLHWDB1.1 and HWDB1.1 (called DB1.1 in general). The DB1.1 covers 3755 Chinese character classes as in the level-1 set of GB2312-80. The evaluated methods include 1D and pseudo 2D normalization methods, gradient direction feature extraction from binary images and from gray-scale images, online stroke direction feature extraction from pen-down trajectory and from pen lifts, classification using the modified quadratic discriminant function (MQDF), discriminative feature extraction (DFE), and discriminative learning quadratic discriminant function (DLQDF). Our experiments reported the highest test accuracies 89.55% and 93.22% on the HWDB1.1 (offline) and OLHWDB1.1 (online), respectively, when using the MQDF classifier trained with DB1.1. When training with both the DB1.0 and DB1.1, the test accuracies on HWDB1.1 and OLHWDB are improved to 90.71% and 93.95%, respectively. Using DFE and DLQDF, the best results on HWDB1.1 and OLHWDB1.1 are 92.08% and 94.85%, respectively. Our results are comparable to the best results of the ICDAR2011 Chinese Handwriting Recognition Competition though we used less training samples.


title: Searchable Attribute-Based Mechanism With Efficient Data Sharing for Secure Cloud Storage

abstract: To date, the growth of electronic personal data leads to a trend that data owners prefer to remotely outsource their data to clouds for the enjoyment of the high-quality retrieval and storage service without worrying the burden of local data management and maintenance. However, secure share and search for the outsourced data is a formidable task, which may easily incur the leakage of sensitive personal information. Efficient data sharing and searching with security is of critical importance. This paper, for the first time, proposes a searchable attribute-based proxy reencryption system. When compared with the existing systems only supporting either searchable attribute-based functionality or attribute-based proxy reencryption, our new primitive supports both abilities and provides flexible keyword update service. In particular, the system enables a data owner to efficiently share his data to a specified group of users matching a sharing policy and meanwhile, the data will maintain its searchable property but also the corresponding search keyword(s) can be updated after the data sharing. The new mechanism is applicable to many real-world applications, such as electronic health record systems. It is also proved chosen ciphertext secure in the random oracle model.


title: Fourth-factor authentication: somebody you know

abstract: User authentication in computing systems traditionally depends on three factors: something you have (e.g., a hardware token), something you are (e.g., a fingerprint), and something you know (e.g., a password). In this paper, we explore a fourth factor, the social network of the user, that is, somebody you know.Human authentication through mutual acquaintance is an age-old practice. In the arena of computer security, it plays roles in privilege delegation, peer-level certification, help-desk assistance, and reputation networks. As a direct means of logical authentication, though, the reliance of human being on another has little supporting scientific literature or practice.In this paper, we explore the notion of vouching, that is, peer-level, human-intermediated authentication for access control. We explore its use in emergency authentication, when primary authenticators like passwords or hardware tokens become unavailable. We describe a practical, prototype vouching system based on SecurID, a popular hardware authentication token. We address traditional, cryptographic security requirements, but also consider questions of social engineering and user behavior.


title: Class EF2 inverters for wireless power transfer applications

abstract: Class EF inverters are hybrid inverters that combine the improved switch voltage and current waveforms of Class F and Class F−1 inverters with the efficient switching of Class E inverters. As a result, their switch voltage and current stresses are reduced, and their efficiency, output power and power-output capability can be higher than the Class E inverter. These improved features of Class EF inverters over Class E inverters can be beneficial in a wireless power transfer (WPT) via magnetic induction application. This paper demonstrates the application of a Class EF inverter operating at a 6.78MHz for a 25W WPT system. A sixth-order piecewise-linear state space model is presented to analyse the inverter and to derive values of its components to achieve optimum switching operation. Experimental results are provided to confirm validity of the state-space model and the design of the inverter and a DC-DC efficiency of 87% was achieved.


title: Robust passive dynamics of the musculoskeletal system compensate for unexpected surface changes during human hopping

abstract: When human hoppers are surprised by a change in surface stiffness, they adapt almost instantly by changing leg stiffness, implying that neural feedback is not necessary. The goal of this simulation study was first to investigate whether leg stiffness can change without neural control adjustment when landing on an unexpected hard or unexpected compliant (soft) surface, and second to determine what underlying mechanisms are responsible for this change in leg stiffness. The muscle stimulation pattern of a forward dynamic musculoskeletal model was optimized to make the model match experimental hopping kinematics on hard and soft surfaces. Next, only surface stiffness was changed to determine how the mechanical interaction of the musculoskeletal model with the unexpected surface affected leg stiffness. It was found that leg stiffness adapted passively to both unexpected surfaces. On the unexpected hard surface, leg stiffness was lower than on the soft surface, resulting in close-to-normal center of mass displacement. This reduction in leg stiffness was a result of reduced joint stiffness caused by lower effective muscle stiffness. Faster flexion of the joints due to the interaction with the hard surface led to larger changes in muscle length, while the prescribed increase in active state and resulting muscle force remained nearly constant in time. Opposite effects were found on the unexpected soft surface, demonstrating the bidirectional stabilizing properties of passive dynamics. These passive adaptations to unexpected surfaces may be critical when negotiating disturbances during locomotion across variable terrain.


title: Stoic Ethics for Artificial Agents

abstract: We present a position paper advocating the notion that Stoic philosophy and ethics can inform the development of ethical A.I. systems. This is in sharp contrast to most work on building ethical A.I., which has focused on Utilitarian or Deontological ethical theories. We relate ethical A.I. to several core Stoic notions, including the dichotomy of control, the four cardinal virtues, the ideal Sage, Stoic practices, and Stoic perspectives on emotion or affect. More generally, we put forward an ethical view of A.I. that focuses more on internal states of the artificial agent rather than on external actions of the agent. We provide examples relating to near-term A.I. systems as well as hypothetical superintelligent agents.


title: Analysis and Defense of Vulnerabilities in Binary Code

abstract: In this thesis, we develop techniques for vulnerability analysis and defense that only require access to vulnerable programs in binary form. Our approach does not use or require source code. We focus on a binary-centric approach since everyone typically has access to the binary code for the programs they run. Thus, our approach is applicable to a wider audience than previous approaches that require or utilize source code. In addition, the binary itself is often the most faithful encoding of security-relevant details since it is what is actually executed on hardware. ::: In order to demonstrate the benefits of binary-centric vulnerability analysis and defense, we first develop binary analysis techniques. We have implemented our techniques as part of a binary analysis architecture called Vine. We then demonstrate the utility of our approach, and Vine, in two typical applications of vulnerability analysis and defense. ::: First, we develop binary analysis techniques for reverse engineering a patched vulnerability. More specifically, our techniques enable an attacker to reverse engineer exploits from software patches that fix program bugs and vulnerabilities. We call this automatic patch-based exploit generation. We demonstrate automatic patch-based exploit generation on real vulnerabilities using Vine. In our experiments, it only takes a few minutes to generate an exploit from the patched program. We argue one consequence of our results is that current delayed patch distribution architectures (e.g., Windows Automatic Update) may hurt security. ::: Second, we propose methods and techniques for generating input filters based upon vulnerability analysis. An input filter is a recognizer for inputs that exploit a vulnerability. We develop the first automatic techniques for generating input filters with accuracy guarantees even when there may be restrictions on the input filtering language. We demonstrate our techniques by automatically generating input filters from vulnerable binary programs.


title: Leveraging wearables for steering and driver tracking

abstract: Given the increasing popularity of wearable devices, this paper explores the potential to use wearables for steering and driver tracking. Such capability would enable novel classes of mobile safety applications without relying on information or sensors in the vehicle. In particular, we study how wrist-mounted inertial sensors, such as those in smart watches and fitness trackers, can track steering wheel usage and angle. In particular, tracking steering wheel usage and turning angle provide fundamental techniques to improve driving detection, enhance vehicle motion tracking by mobile devices and help identify unsafe driving. The approach relies on motion features that allow distinguishing steering from other confounding hand movements. Once steering wheel usage is detected, it further uses wrist rotation measurements to infer steering wheel turning angles. Our on-road experiments show that the technique is 99% accurate in detecting steering wheel usage and can estimate turning angles with an average error within 3.4 degrees.


RIFM fragrance ingredient safety assessment, ethyl anthranilate, CAS registry number 87-25-2.


title: Clustering problems on sliding windows

abstract: We explore clustering problems in the streaming sliding window model in both general metric spaces and Euclidean space. We present the first polylogarithmic space O(1)-approximation to the metric k-median and metric k-means problems in the sliding window model, answering the main open problem posed by Babcock, Datar, Motwani and O'Callaghan [5], which has remained unanswered for over a decade. Our algorithm uses O(k3log6W) space and poly(k, log W) update time, where W is the window size. This is an exponential improvement on the space required by the technique due to Babcock, et al. We introduce a data structure that extends smooth histograms as introduced by Braverman and Ostrovsky [11] to operate on a broader class of functions. In particular, we show that using only polylogarithmic space we can maintain a summary of the current window from which we can construct an O(1)-approximate clustering solution. Merge-and-reduce is a generic method in computational geometry for adapting offline algorithms to the insertion-only streaming model. Several well-known coreset constructions are maintainable in the insertion-only streaming model using this method, including well-known coreset techniques for the k-median and k-means problems in both low-and high-dimensional Euclidean spaces [31, 15]. Previous work [27] has adapted coreset techniques to the insertion-deletion model, but translating them to the sliding window model has remained a challenge. We give the first algorithm that, given an insertion-only streaming coreset of space s (maintained using merge-and-reduce method), maintains this coreset in the sliding window model using O(s2e--2 log W) space. For clustering problems, our results constitute the first significant step towards resolving problem number 20 from the List of Open Problems in Sublinear Algorithms [39].


title: Are Mindfulness-Based Interventions Effective for Substance Use Disorders? A Systematic Review of the Evidence

abstract: Mindfulness-based interventions (MBIs) are increasingly suggested as therapeutic approaches for effecting substance use and misuse (SUM). The aim of this article is to review current evidence on the therapeutic efficacy of MBIs for SUM. A literature search was undertaken using four electronic databases and references of retrieved articles. The search included articles written in English published up to December 2011. Quality of included trials was assessed. In total, 24 studies were included, three of which were based on secondary analyses of previously investigated samples. Current evidence suggests that MBIs can reduce the consumption of several substances including alcohol, cocaine, amphetamines, marijuana, cigarettes, and opiates to a significantly greater extent than waitlist controls, non-specific educational support groups, and some specific control groups. Some preliminary evidence also suggests that MBIs are associated with a reduction in craving as well as increased mindfulness. The limited gene...


title: A syntactic approach for aspect based opinion mining

abstract: Opinion mining or sentiment analysis is the process of analysing the text about a topic written in a natural language and classify them as positive negative or neutral based on the humans sentiments, emotions, opinions expressed in it. Nowadays, the opinions expressed through reviews are increasing day by day on the web. It is practically impossible to analyse and extract opinions from such huge number of reviews manually. To solve this problem an automated opinion mining approach is needed. This task of automatic opinion mining can be done mainly at three different levels, which are document level, sentence level and aspect level. Most of the previous work is in the field of document or sentence level opinion mining. This paper focus on aspect level opinion mining and propose a new syntactic based approach for it, which uses syntactic dependency, aggregate score of opinion words, SentiWordNet and aspect table together for opinion mining. The experimental work was done on restaurant reviews. The dataset of restaurant reviews was collected from web and tagged manually. The proposed method achieved total accuracy of 78.04% on the annotated test set. The method was also compared with the method, which uses Part-Of-Speech tagger for feature extraction; the obtained results show that the proposed method gives 6% more accuracy than previous one on the annotated test set.


title: A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks

abstract: Modeling and predicting people's opinions plays an important role in today's life. For viral marketing and political strategy design, it is particularly important to be able to analyze competing opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network "behaved"' abnormally? Furthermore, can we predict how the opinions of individual users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions spread beyond users' ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously. In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen opinion dynamics model. SND has a rich semantics of a transportation problem, yet, is computable in pseudo-linear time, thereby, being applicable to large-scale social networks analysis. We demonstrate the effectiveness of SND in experiments with Twitter data.


title: Euclidean Embedding of Co-Occurrence Data

abstract: Embedding algorithms search for low dimensional structure in complex data, but most algorithms only handle objects of a single type for which pairwise distances are specified. This paper describes a method for embedding objects of different types, such as images and text, into a single common Euclidean space based on their co-occurrence statistics. The joint distributions are modeled as exponentials of Euclidean distances in the low-dimensional embedding space, which links the problem to convex optimization over positive semidefinite matrices. The local structure of our embedding corresponds to the statistical correlations via random walks in the Euclidean space. We quantify the performance of our method on two text datasets, and show that it consistently and significantly outperforms standard methods of statistical correspondence modeling, such as multidimensional scaling and correspondence analysis.


title: Articles Design Space and Evaluation Challenges of Adaptive Graphical User Interfaces

abstract: Adaptive graphical user interfaces (GUIs) have the potential to improve performance and user satisfaction by automatically tailoring the presentation of functionality to each individual user. In practice, however, many challenges exist and evaluation results of adaptive GUIs have been mixed. To guide researchers and designers in developing effective adaptive GUIs, we outline a design space and discuss three important aspects to consider when conducting user evaluations of these types of interfaces: the control and reporting of adaptive algorithm characteristics, the impact of task choice and user characteristics on the overall effectiveness of a design, and evaluation measures that are appropriate for adaptive interaction.


title: BULDP: Biomimetic Uncorrelated Locality Discriminant Projection for Feature Extraction in Face Recognition

abstract: This paper develops a new dimensionality reduction method, named Biomimetic Uncorrelated Locality Discriminant Projection (BULDP), for face recognition. It is based on unsupervised discriminant projection and two human bionic characteristics: principle of homology continuity and principle of heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency coefficient representation, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity between different samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it can be shown that we can transform the original data space into an uncorrelated discriminant subspace. A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover, we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-the-art methods on four public benchmarks for face recognition. Experimental results show that the proposed BULDP method and its nonlinear version achieve much competitive recognition performance.


title: Persistent Temporal Streams

abstract: Distributed continuous live stream analysis applications are increasingly common. Video-based surveillance, emergency response, disaster recovery, and critical infrastructure protection are all examples of such applications. They are characterized by a variety of high- and low-bandwidth streams as well as a need for analyzing both live and archived streams. We present a system called Persistent Temporal Streams (PTS) that supports a higher-level, domain-targeted programming abstraction for such applications. PTS provides a simple but expressive stream abstraction encompassing transport, manipulation and storage of streaming data. In this paper, we present a system architecture for implementing PTS. We provide an experimental evaluation which shows the system-level primitives can be implemented in a lightweight and high-performance manner, and an application-based evaluation designed to show that a representative high-bandwidth stream analysis application can be implemented relatively simply and with good performance.


title: Higher Order Compensation for Inductive-Power-Transfer Converters With Constant-Voltage or Constant-Current Output Combating Transformer Parameter Constraints

abstract: Compensation is crucial for improving performance of inductive-power-transfer (IPT) converters. With proper compensation at some specific frequencies, an IPT converter can achieve load-independent constant output voltage or current, near zero reactive power, and soft switching of power switches simultaneously, resulting in simplified control circuitry, reduced component ratings, and improved power conversion efficiency. However, constant output voltage or current depends significantly on parameters of the transformer, which is often space constrained, making the converter design hard to optimize. To free the design from the constraints imposed by the transformer parameters, this paper proposes a family of higher order compensation circuits for IPT converters that achieves any desired constant-voltage or constant-current (CC) output with near zero reactive power and soft switching. Detailed derivation of the compensation method is given for the desired transfer function not constrained by transformer parameters. Prototypes of CC IPT configurations based on a single transformer are constructed to verify the analysis with three different output specifications.


title: Wii all play: the console game as a computational meeting place

abstract: In this paper, we present results from a qualitative study of collocated group console gaming. We focus on motivations for, perceptions of, and practices surrounding the shared use of console games by a variety of established groups of gamers. These groups include both intragenerational groups of youth, adults, and elders as well as intergenerational families. Our analysis highlights the numerous ways that console games serve as a computational meeting place for a diverse population of gamers.


LEARNING ALGORITHMS FOR CLASSIFICATION: A COMPARISON ON HANDWRITTEN DIGIT RECOGNITION


title: Online Planning for Collaborative Search and Rescue by Heterogeneous Robot Teams

abstract: Collaboration is essential for effective performance by groups of robots in disaster response settings. Here we are particularly interested in heterogeneous robots that collaborate in complex scenarios with incomplete, dynamically changing information. In detail, we consider a search and rescue setting, where robots with different capabilities work together to accomplish tasks (rescue) and find information about further tasks (search) at the same time. The state of the art for such collaboration is robot control based on independent planning for robots with different capabilities and typically incorporates uncertainty with only a limited scope. In contrast, in this paper, we create a joint plan to optimise all robots' actions incorporating uncertainty about the future information gain of the robots. We evaluate our planner's performance in settings based on real disasters and find that our approach decreases the response time by 20-25% compared to state-of-the-art approaches. In addition, practical constraints are met in terms of time and resource utilisation.


title: A Machine Learning Approach to Diagnosis of Parkinson’s Disease

abstract: I will investigate applications of machine learning algorithms to medical data, adaptations of differences in data collection, and the use of ensemble techniques. Focusing on the binary classification problem of Parkinson’s Disease (PD) diagnosis, I will apply machine learning algorithms to a primary dataset consisting of voice recordings from healthy and PD subjects. Specifically, I will use Artificial Neural Networks, Support Vector Machines, and an Ensemble Learning algorithm to reproduce results from [MS12] and [GM09]. Next, I will adapt a secondary regression dataset of PD recordings and combine it with the primary binary classification dataset, testing various techniques to consolidate the data including treating the regression data as unlabeled data in a semi-supervised learning approach. I will determine the performance of the above algorithms on this consolidated dataset. Performance of algorithms will be evaluated using 10-fold cross validation and results will be analyzed in a confusion matrix. Accuracy, precision, recall, and F-score will be calculated. The expands on past related work, which has used either a regression dataset alone to predict a Unified Parkinson’s Disease Rating Scale score for PD patients, or a classification dataset to determine healthy or PD diagnosis. In past work, the datasets have not been combined, and the regression set has not been used to contribute to evaluation of healthy subjects.


title: Early Childhood Nutrition And Academic Achievement: A Longitudinal Analysis

abstract: Abstract Many policymakers propose early childhood nutrition programs as a way to increase students’ academic achievement. This paper investigates the nutrition–learning nexus using a unique longitudinal data set that follows a large sample of Filipino children from birth until the end of their primary education. We find that better nourished children perform significantly better in school, partly because they enter school earlier and thus have more time to learn but mostly because of greater learning productivity per year of schooling. Our cost–benefit analysis suggests that a dollar invested in an early childhood nutrition program in a developing country could potentially return at least three dollars worth of gains in academic achievement, and perhaps much more.


title: Plant classification system based on leaf features

abstract: This paper presents a classification approach based on Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves, so the classification approach presented in this research depends on plants leave. Leaves are different from each other by characteristics such as the shape, color, texture and the margin. The used dataset for this experiments is a database of different plant species with total of only 340 leaf images, was downloaded from UCI- Machine Learning Repository. It was used for both training and testing datasets with 10-fold cross-validation. Experimental results showed that LDA achieved classification accuracy of (92.65%) against the RF that achieved accuracy of (88.82%) with combination of shape, first order texture, Gray Level Co-occurrence Matrix (GLCM), HSV color moments, and vein features.


title: Nonnegative matrix factorization using ADMM: Algorithm and convergence analysis

abstract: The nonnegative matrix factorization (NMF) has been a popular model for a wide range of signal processing and machine learning problems. It is usually formulated as a nonconvex cost minimization problem. This work settles the convergence issue of a popular algorithm based on the alternating direction method of multipliers proposed in Boyd et al 2011. We show that the algorithm converges globally to the set of KKT solutions whenever certain penalty parameter ρ satisfies ρ > 1. We further extend the algorithm and its analysis to the problem where the observation matrix contains missing values. Numerical experiments on real and synthetic data sets demonstrate the effectiveness of the algorithms under investigation.


title: Solutions to the GSM Security Weaknesses

abstract: Recently, the mobile industry has experienced an extreme increment in number of its users. The GSM network with the greatest worldwide number of users succumbs to several security vulnerabilities. Although some of its security problems are addressed in its upper generations, there are still many operators using 2G systems. This paper briefly presents the most important security flaws of the GSM network and its transport channels. It also provides some practical solutions to improve the security of currently available 2G systems.


title: A matching algorithm for measuring the structural similarity between an XML document and a DTD and its applications

abstract: In this paper we propose a matching algorithm for measuring the structural similarity between an XML document and a DTD. The matching algorithm, by comparing the document structure against the one the DTD requires, is able to identify commonalities and differences. Differences can be due to the presence of extra elements with respect to those the DTD requires and to the absence of required elements. The evaluation of commonalities and differences gives raise to a numerical rank of the structural similarity. Moreover, in the paper, some applications of the matching algorithm are discussed. Specifically, the matching algorithm is exploited for the classification of XML documents against a set of DTDs, the evolution of the DTD structure, the evaluation of structural queries, the selective dissemination of XML documents, and the protection of XML document contents.


Multi-Frame Video Super-Resolution Using Convolutional Neural Networks


title: Universal blind quantum computation

abstract: We present a protocol which allows a client to have a server carry out a quantum computation for her such that the client's inputs, outputs and computation remain perfectly private, and where she does not require any quantum computational power or memory. The client only needs to be able to prepare single qubits randomly chosen from a finite set and send them to the server, who has the balance of the required quantum computational resources. Our protocol is interactive: after the initial preparation of quantum states, the client and server use two-way classical communication which enables the client to drive the computation, giving single-qubit measurement instructions to the server, depending on previous measurement outcomes. Our protocol works for inputs and outputs that are either classical or quantum. We give an authentication protocol that allows the client to detect an interfering server; our scheme can also be made fault-tolerant. We also generalize our result to the setting of a purely classical client who communicates classically with two non-communicating entangled servers, in order to perform a blind quantum computation. By incorporating the authentication protocol, we show that any problem in BQP has an entangled two-prover interactive proof with a purely classical verifier. Our protocol is the first universal scheme which detects a cheating server, as well as the first protocol which does not require any quantum computation whatsoever on the client's side. The novelty of our approach is in using the unique features of measurement-based quantum computing which allows us to clearly distinguish between the quantum and classical aspects of a quantum computation.


title: A Bayesian Framework for Multi-cue 3D Object Tracking

abstract: This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linear subspace models or Dynamic Point Distribution Models (DPDMs), which can deal with both continuous and discontinuous appearance changes; the representation is learned fully automatically from training data. The representation is enriched with texture information by means of intensity histograms, which are compared using the Bhattacharyya coefficient. Direct 3D measurement is furthermore provided by a stereo system.


title: The Relevance of Big Five Trait Content in Behavior to Subjective Authenticity: Do High Levels of Within-Person Behavioral Variability Undermine or Enable Authenticity Achievement?

abstract: ABSTRACT Individuals vary their behavior from moment to moment a great deal, often acting “out of character” for their traits. This article investigates the consequences for authenticity. We compared 2 hypotheses—trait consistency, that individuals feel most authentic when acting in a way consistent with their traits; and state-content significance, that some ways of acting feel more authentic because of their content and consequences, regardless of the actor's corresponding traits. Three studies using experience-sampling methodology in laboratory and natural settings, with participants ages 18–51, strongly supported the state-content significance hypothesis and did not support the trait-consistency hypothesis. Authenticity was consistently associated with acting highly extraverted, agreeable, conscientious, emotionally stable, and intellectual, regardless of the actor's traits. Discussion focuses on possible implications for within-person variability in behavior and for the nature of the self-concept.


title: Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

abstract: In this paper we examine how the Minimum Description Length (MDL) principle can be used to efficiently select aggregated views of hierarchical datasets that feature a good balance between clutter and information. We present MDL formulae for generating uneven tree cuts tailored to treemap and sunburst diagrams, taking into account the available display space and information content of the data. We present the results of a proof-of-concept implementation. In addition, we demonstrate how such tree cuts can be used to enhance drill-down interaction in hierarchical visualizations by implementing our approach in an existing visualization tool. Validation is done with the feature congestion measure of clutter in views of a subset of the current DMOZ web directory, which contains nearly half million categories. The results show that MDL views achieve near constant clutter level across display resolutions. We also present the results of a crowdsourced user study where participants were asked to find targets in views of DMOZ generated by our approach and a set of baseline aggregation methods. The results suggest that, in some conditions, participants are able to locate targets (in particular, outliers) faster using the proposed approach.


title: Convolutional Neural Network for Paraphrase Identification

abstract: We present a new deep learning architecture Bi-CNN-MI for paraphrase identification (PI). Based on the insight that PI requires comparing two sentences on multiple levels of granularity, we learn multigranular sentence representations using convolutional neural network (CNN) and model interaction features at each level. These features are then the input to a logistic classifier for PI. All parameters of the model (for embeddings, convolution and classification) are directly optimized for PI. To address the lack of training data, we pretrain the network in a novel way using a language modeling task. Results on the MSRP corpus surpass that of previous NN competitors.


title: W band 2 bit MEMS based digital phase shifter

abstract: 2 bit MEMS digital phase shifter has been implemented using IHP’s MEMS switch embedded 0.25 μm SG25H1 SiGe BiCMOS technology for W band 77 GHz The implemented phase shifter covers 0-67.5 with 22.5 phase increments in 4 states. Input/output impedances of all 4 states are matched below 14dB return loss with 8 GHz bandwidth. Maximum of 4 (RMS) phase error with insertion loss of 8.8 dB is measured at 77 GHz for 2 bit phase shifter. The fully BiCMOS embedded low-loss and small size MEMS switches enable an overall compact and low-loss phase shifter design.


title: Localized induction heating solder bonding for wafer level MEMS packaging

abstract: This paper reports a new solder bonding method for the wafer level packaging of MEMS devices. The electroplated magnetic film was heated up using the induction heating, and leaded to solder reflow. It took only several seconds to complete the solder reflow and bonding process. The measurement results showed that the temperature of device region was only 110/spl deg/C during heating. Due to the solder reflow and electroplating technique, hermetic seal of the packaged device is achieved. The bonding strength is up to 18 MPa. In addition, the electroplated thick film also performed as a spacer. In applications, the integration the MUMPs devices with the proposed packaging techniques were demonstrated.


title: Interactive Modeling and Authoring of Climbing Plants

abstract: We present a novel system for the interactive modeling of developmental climbing plants with an emphasis on efficient control and plausible physics response. A plant is represented by a set of connected anisotropic particles that respond to the surrounding environment and to their inner state. Each particle stores biological and physical attributes that drive growth and plant adaptation to the environment such as light sensitivity, wind interaction, and physical obstacles. This representation allows for the efficient modeling of external effects that can be induced at any time without prior analysis of the plant structure. In our framework we exploit this representation to provide powerful editing capabilities that allow to edit a plant with respect to its structure and its environment while maintaining a biologically plausible appearance. Moreover, we couple plants with Lagrangian fluid dynamics and model advanced effects, such as the breaking and bending of branches. The user can thus interactively drag and prune branches or seed new plants in dynamically changing environments. Our system runs in real-time and supports up to 20 plant instances with 25k branches in parallel. The effectiveness of our approach is demonstrated through a number of interactive experiments, including modeling and animation of different species of climbing plants on complex support structures.


title: Real-time lens blur effects and focus control

abstract: We present a novel rendering system for defocus blur and lens effects. It supports physically-based rendering and outperforms previous approaches by involving a novel GPU-based tracing method. Our solution achieves more precision than competing real-time solutions and our results are mostly indistinguishable from offline rendering. Our method is also more general and can integrate advanced simulations, such as simple geometric lens models enabling various lens aberration effects. These latter is crucial for realism, but are often employed in artistic contexts, too. We show that available artistic lenses can be simulated by our method. In this spirit, our work introduces an intuitive control over depth-of-field effects. The physical basis is crucial as a starting point to enable new artistic renderings based on a generalized focal surface to emphasize particular elements in the scene while retaining a realistic look. Our real-time solution provides realistic, as well as plausible expressive results.


title: The nomogram of intracranial translucency in the first trimester in singletons

abstract: OBJECTIVE ::: Measurement of intracranial translucency (IT), which is a recent earlier recognizable sonographic marker, has been suggested for detection of spina bifida. In this prospective study we aimed to determine normative values of IT in the population of Turkish singleton pregnant women during the first trimester of pregnancy. ::: ::: ::: MATERIAL AND METHODS ::: Between January 2011 and July 2011, all consecutive singleton pregnant women were screened for intracranial translucency. Pregnancies were followed until birth. Pregnancies with any detected/suspicious anatomical or genetic fetal anomalies, biochemical abnormalities, increased nuchal translucency measurements, pregnancies of artificial reproduction techniques and anomaly detected at birth were set to be excluded from the data analyses. Mean±standard deviation, median and percentile values of intracranial translucency were calculated. Linear regression analyses were done between the length of intracranial translucency and gestational week and CRL. ::: ::: ::: RESULTS ::: Data analyses included 201 fetuses. Median maternal age was 31 years. Median CRL was 62 mm. Median gestational week was 12.57 weeks. Median IT value for the first trimester was 1.7 mm (range 1.00-2.40). Calculated formulae are; IT (mm)=[0.237×Gestational Week)]-1.27, R(2)=0.302; p<0.001 and IT (mm)=[0.020×CRL (mm)]+0.44, R(2)=0.381; p<0.001). IT measurements increased with increasing CRL and advancing gestational weeks. During follow up none of the fetuses was found to have any anatomical abnormality or anomaly. ::: ::: ::: CONCLUSION ::: This study presents normative values of intracranial translucency in the first trimester screening of normal singleton pregnancies of the Turkish population. In accordance with previous reports, intracranial translucency increases linearly with advancing CRL.


Information Security Risk Management Framework for University Computing Environment


Performance Error Detection and Post-Processing for Fast and Accurate Symbolic Music Alignment


title: An efficient FMRI classification technique in cloud using multiple parallel feature selection algorithm

abstract: Extensive research is going on in the field of analyzing the applications involving functional magnetic resonance imaging (fMRI) data with the help of machine learning classifiers. Studies have proven that inspiring new information can be obtained through neuroimaging data using machine learning classifiers. Feature selection has been an active research area and its key idea is to select a subset of input variables by removing features with little or no predictive data. Feature selection enhanced the clarity of the resulting classifier models and often dimensions a model that generalizes better to unseen points. In this work, fMRI classification technique is proposed using Multiple Parallel Feature Selection Algorithm. In case of parallel feature selection, the proposed algorithm’s computation is fully improved and parallelized based on data partitioning. Inputs for classifiers are used extensively through voxel-based features. The fMRI images are classified as normal or abnormal. From healthy persons normal fMRI images were obtained and from subjects with schizophrenia abnormal fMRI were obtained and a total of 140 normal fMRI and 67 abnormal fMRI were used. Multiple particle swarm optimization was used after independent component analysis phase and techniques varying by c1 and c2 were used through cluster computing techniques. From the obtained cluster the feature subset was selected and support vector machine is made use in classifying.


title: 5.75 to 44Gb/s quarter rate CDR with data rate selection in 90nm bulk CMOS

abstract: This paper presents a quarter rate clock/data recovery (CDR) circuit for plesiochronous serial I/O-links. This 2x-oversampled phase-tracking CDR, implemented in 90 nm bulk CMOS technology, covers the whole range of data rates from 5.75 to 44 Gb/s thanks to a data rate selection logic. A bit error rate <10-12 was verified up to 38 Gb/s using a 27-1 PRBS pattern. The CDR is able to track a maximum frequency deviation of plusmn615 ppm between incoming data and reference clock.


Randomized RANSAC


title: Relational reinforcement learning with guided demonstrations

abstract: Abstract Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics. However, in-depth exploration is usually required and the actions have to be known in advance. Thus, we propose a novel algorithm that integrates the option of requesting teacher demonstrations to learn new domains with fewer action executions and no previous knowledge. Demonstrations allow new actions to be learned and they greatly reduce the amount of exploration required, but they are only requested when they are expected to yield a significant improvement because the teacher's time is considered to be more valuable than the robot's time. Moreover, selecting the appropriate action to demonstrate is not an easy task, and thus some guidance is provided to the teacher. The rule-based model is analyzed to determine the parts of the state that may be incomplete, and to provide the teacher with a set of possible problems for which a demonstration is needed. Rule analysis is also used to find better alternative models and to complete subgoals before requesting help, thereby minimizing the number of requested demonstrations. These improvements were demonstrated in a set of experiments, which included domains from the international planning competition and a robotic task. Adding teacher demonstrations and rule analysis reduced the amount of exploration required by up to 60% in some domains, and improved the success ratio by 35% in other domains.


title: MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

abstract: We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DECPOMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multi-robot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.


title: Dependency-Based Rules for Concept-Level Sentiment Analysis

abstract: The Web is evolving through an era where the opinions of users are getting increasingly important and valuable. The distillation of knowledge from the huge amount of unstructured information on the Web can be a key factor for tasks such as social media marketing, branding, product positioning, and corporate reputation management. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions involves a deep understanding of natural language text by machines, from which we are still very far. To this end, concept-level sentiment analysis aims to go beyond a mere word-level analysis of text and provide novel approaches to opinion mining and sentiment analysis that enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data. A recent knowledge-based technology in this context is sentic computing, which relies on the ensemble application of commonsense computing and the psychology of emotions to infer the conceptual and affective information associated with natural language. Sentic computing, however, is limited by the richness of the knowledge base and by the fact that the bag-of-concepts model, despite more sophisticated than bag-of-words, misses out important discourse structure information that is key for properly detecting the polarity conveyed by natural language opinions. In this work, we introduce a novel paradigm to concept-level sentiment analysis that merges linguistics, common-sense computing, and machine learning for improving the accuracy of tasks such as polarity detection. By allowing sentiments to flow from concept to concept based on the dependency relation of the input sentence, in particular, we achieve a better understanding of the contextual role of each concept within the sentence and, hence, obtain a polarity detection engine that outperforms state-of-the-art statistical methods.


title: The Affordance Template ROS package for robot task programming

abstract: This paper introduces the Affordance Template ROS package for quickly programming, adjusting, and executing robot applications in the ROS RViz environment. This package extends the capabilities of RViz interactive markers [1] by allowing an operator to specify multiple end-effector waypoint locations and grasp poses in object-centric coordinate frames and to adjust these waypoints in order to meet the run-time demands of the task (specifically, object scale and location). The Affordance Template package stores task specifications in a robot-agnostic JSON description format such that it is trivial to apply a template to a new robot. As such, the Affordance Template package provides a robot-generic ROS tool appropriate for building semi-autonomous, manipulation-based applications. Affordance Templates were developed by the NASA-JSC DARPA Robotics Challenge (DRC) team and have since successfully been deployed on multiple platforms including the NASA Valkyrie and Robonaut 2 humanoids, the University of Texas Dreamer robot and the Willow Garage PR2. In this paper, the specification and implementation of the affordance template package is introduced and demonstrated through examples for wheel (valve) turning, pick-and-place, and drill grasping, evincing its utility and flexibility for a wide variety of robot applications.


title: Pilot Study of the Efficacy of 578 nm Copper Bromide Laser Combined with Intralesional Corticosteroid Injection for Treatment of Keloids and Hypertrophic Scars

abstract: Background: Treatments including intralesional corticosteroid injection, pressure therapy, cryotherapy, and various laser therapies have had limited success for keloids and hypertrophic scars. Objective: This trial evaluated the efficacy of a combination of 578 nm copper bromide laser and the more traditional intralesional corticosteroid injection for the treatment of keloids and hypertrophic scars with respect to scar color. Methods: Keloids or hypertrophic scars of 12 Korean patients were treated five times by the combined treatment at 4-week intervals. Clinical improvement was assessed by the physicians’ global assessment (PGA) comparing pre- and post-treatment photographs, as well as 4 weeks after the last treatment. Erythema intensity was quantified using a mexameter. Results: Most scars showed significant clinical improvement in PGA and decreased erythema intensity after 5 treatments. All patients showed improvements in symptoms like pruritus. Conclusion: The combined treatment is effective for keloids and hypertrophic scars, especially when the telangiectatic portion of the scars is prominent. The adjunctive use of 578 nm copper bromide laser decreased the telangiectatic side effects of an intralesional corticosteroid injection by reducing the vascular components of scars. (Ann Dermatol 26(2) 156∼161, 2014)


A Multi-View Fusion Neural Network for Answer Selection


title: Once daily, high dose versus divided, low dose gentamicin for open fractures.

abstract: Abstract A prospective, randomized study was performed on 75 Gustilo Grades II and III open fractures to determine the efficacy of once daily, high dose aminoglycoside therapy, compared with more conventional dosing, in reducing the infection rate when used in conjunction with an aggressive operative treatment protocol. All patients enrolled in the study were treated with immediate irrigation, debridement, operative stabilization of the fracture, and 1 g of cefazolin every 8 hours. At the time of admission patients were randomized to two groups. Patients in Group I received gentamicin 5 mg/kg divided into twice daily doses, and patients in Group II received gentamicin 6 mg/kg given once daily. All patients were monitored for renal toxicity and observed for radiographic and clinical signs of infection until fracture union. The results of the study revealed no statistically significant difference between once daily, high dose versus divided, low dose gentamicin in infection rates. Thus, daily dosing of gentamicin was found to be safe, effective, and cost efficient in the treatment of open fractures when combined with a cephalosporin and aggressive operative debridement and stabilization.


title: MoveMeant: Anonymously Building Community Through Shared Location Histories

abstract: Awareness of and connections to a local community are important for building social capital, sharing resources, and providing physical support, but have been elusive to create in dense urban environments. We describe the design and implementation of MoveMeant, a system aimed to increase local community awareness through shared location traces. MoveMeant securely uses anonymized location data generated automatically by mobile devices to display aggregate, community-level location data. We report findings from interviews with residents in the Bronx, New York City who participated in a deployment of MoveMeant over a 6-week period. Our findings show that people use the anonymous information to make judgments about the people and places in their community, while opting to reveal their identity for third places where there is an opportunity to connect socially.


title: Longitudinal wheel-slip control for four wheel independent steering and drive vehicles

abstract: In this paper, a longitudinal wheel-slip controller for four wheel independent steering and drive (4WISD) vehicles is proposed to suppress longitudinal wheel slip in varying road conditions. Different from conventional methods that consider single driving source and zero steering angle, the proposed controller considers all independent traction sources from each driving wheel and omnidirectional steering command so as to eliminate slip detection errors in 4WISD vehicles. The proposed controller requires low cost sensing equipment, including merely wheel speed sensor and accelerometer, which makes the system practical to be utilized. The proposed wheel-slip controller can be applied to vehicles with arbitrary quantity of driving wheels and different steering configurations such as traditional two-front-wheel steering and two-rear-wheel steering. Numerical simulation results are presented to demonstrate the efficiency of the proposed longitudinal wheel-slip controller.


title: Costs and benefits of integrating information between the cerebral hemispheres: a computational perspective.

abstract: Because interaction of the cerebral hemispheres has been found to aid task performance under demanding conditions, the present study examined how this effect is moderated by computational complexity, the degree of lateralization for a task, and individual differences in asymmetric hemispheric activation (AHA). Computational complexity was manipulated across tasks either by increasing the number of inputs to be processed or by increasing the number of steps to a decision. Comparison of within- and across-hemisphere trials indicated that the size of the between-hemisphere advantage increased as a function of task complexity, except for a highly lateralized rhyme decision task that can only be performed by the left hemisphere. Measures of individual differences in AHA revealed that when task demands and an individual's AHA both load on the same hemisphere, the ability to divide the processing between the hemispheres is limited. Thus, interhemispheric division of processing improves performance at higher levels of computational complexity only when the required operations can be divided between the hemispheres.


title: Blood , Sweat , and Fears The Autonomic Architecture of Emotion

abstract: Abstract: The autonomic nervous system (ANS) plays a critical role in emotion, providing metabolic support for adaptive action, generating appearance changes with high signal value for conspecifics, and producing visceral sensations that shape subjective emotional experience. In this chapter, I consider several of the most important ways that the ANS is involved in emotion, including: (a) peripheral activation of emotion; (b) autonomic influences on emotional language and the labeling of subjective emotional experience; (c) positive emotion and autonomic soothing; (d) expressive signs of autonomic origin; (e) autonomic substrates of emotional contagion and empathy; and (f) autonomic consequences of emotion regulation. For each, I describe relevant research from our laboratory and discuss implications for an evolutionary account of emotion. In these and many other ways the autonomic architecture of human emotion has evolved not only to move blood and tears in the service of fears, but also to provide us with a rich set of tools that help us communicate and signal the nature of our internal emotional experiences, understand the emotions of others, calm ourselves and others, and give us some modicum of control over harmful and unproductive emotions.


title: Revisiting Abstract Argumentation Frameworks

abstract: This paper argues that many extensions of Dung’s framework incorporating relations additional to binary attacks, are best viewed as abstractions of human rather than computational models of reasoning and debate. The paper then discusses how these additional relations may be reified into object level knowledge, thus enabling reconstruction of the extended framework as a Dung framework, and providing rational guidance for further reasoning and debate.


title: Drug Addiction as a Pathology of Staged Neuroplasticity

abstract: Using addictive drugs can evolve from controlled social use into the compulsive relapsing disorder that characterizes addiction. This transition to addiction results from genetic, developmental, and sociological vulnerabilities, combined with pharmacologically induced plasticity in brain circuitry that strengthens learned drug-associated behaviors at the expense of adaptive responding for natural rewards. Advances over the last decade have identified the brain circuits most vulnerable to drug-induced changes, as well as many associated molecular and morphological underpinnings. This growing knowledge has contributed to an expanded understanding of how drugs usurp normal learning circuitry to create the pathology of addiction, as evidenced by involuntary activation of reward circuits in response to drug-associated cues and simultaneous reports of drug craving. This new understanding provides unprecedented potential opportunities for novel pharmacotherapeutic targets in treating addiction. There appears to be plasticity associated with the addiction phenomenon in general as well as changes produced by addiction to a specific class of addicting drugs. These findings also provide the basis for the current understanding of addiction as a chronic, relapsing disease of the brain with changes that persist long after the last use of the drug. Here, we describe the neuroplasticity in brain circuits and cell function induced by addictive drugs that is thought to underlie the compulsions to resume drug-taking, and discuss how this knowledge is impelling exploration and testing of novel addiction therapies.


title: Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?

abstract: The growth of detection datasets and the multiple directions of object detection research provide both an unprecedented need and a great opportunity for a thorough evaluation of the current state of the field of categorical object detection. In this paper we strive to answer two key questions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors? Inspired by the recent work of Hoiem et al. on the standard PASCAL VOC detection dataset, we perform a large-scale study on the Image Net Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quantitatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent test bed for understanding the performance of detectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of image-level and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors.


title: How can human motion prediction increase transparency?

abstract: A major issue in the field of human-robot interaction for assistance to manipulation is transparency. This basic feature qualifies the capacity for a robot to follow human movements without any human-perceptible resistive forces. In this paper we address the issue of human motion prediction in order to increase the transparency of a robotic manipulator. Our aim is not to predict the motion itself, but to study how this prediction can be used to improve the robot transparency. For this purpose, we have designed a setup for performing basic planar manipulation tasks involving movements that are demanded to the subject and thus easily predictable. Moreover, we have developed a general controller which takes a predicted trajectory (recorded from offline free motion experiments) as an input and feeds the robot motors with a weighted sum of three controllers: torque feedforward, variable stiffness control and force feedback control. Subjects were then asked to perform the same task but with or without the robot assistance (which was not visible to the subject), and with several sets of gains for the controller tuning. First results seems to indicate that when a predictive controller with open loop torque feedforward is used, in conjunction with force- feedback control, the interaction forces are minimized. Therefore, the transparency is increased.


title: CLAMS: Bringing Quality to Data Lakes

abstract: With the increasing incentive of enterprises to ingest as much data as they can in what is commonly referred to as "data lakes", and with the recent development of multiple technologies to support this "load-first" paradigm, the new environment presents serious data management challenges. Among them, the assessment of data quality and cleaning large volumes of heterogeneous data sources become essential tasks in unveiling the value of big data. The coveted use of unstructured and semi-structured data in large volumes makes current data cleaning tools (primarily designed for relational data) not directly adoptable. We present CLAMS, a system to discover and enforce expressive integrity constraints from large amounts of lake data with very limited schema information (e.g., represented as RDF triples). This demonstration shows how CLAMS is able to discover the constraints and the schemas they are defined on simultaneously. CLAMS also introduces a scale-out solution to efficiently detect errors in the raw data. CLAMS interacts with human experts to both validate the discovered constraints and to suggest data repairs. CLAMS has been deployed in a real large-scale enterprise data lake and was experimented with a real data set of 1.2 billion triples. It has been able to spot multiple obscure data inconsistencies and errors early in the data processing stack, providing huge value to the enterprise.


title: HiSPEED: a system for mining performance appraisal data and text

abstract: Performance appraisal (PA) is a crucial HR process that enables an organization to periodically measure and evaluate every employee's performance and also to drive performance improvements. In this paper, we describe a novel system called HiSPEED to analyze PA data using automated statistical, datamining and text-mining techniques, to generate novel and actionable insights / patterns and to help in improving the quality and effectiveness of the PA process. The goal is to produce insights that can be used to answer (in part) the crucial "business questions" that HR executives and business leadership face in talent management. The business questions pertain to (i) improving the quality of the goal setting process, (ii) improving the quality of the self-appraisal comments and supervisor feedback comments, (iii) discovering high-quality supervisor suggestions for performance improvements (iv) discovering evidence provided by employees to support their self-assessments (v) measuring the quality of supervisor assessments (vi) understanding the root-causes of poor and exceptional performances (vii) detecting instances of personal and systemic biases and so forth. The paper discusses specially designed algorithms to answer these business questions and illustrates them by reporting the insights produced on a real-life PA dataset from a large multi-national IT services organization.


title: A Survey of Domain Adaptation for Neural Machine Translation

abstract: Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.


title: Cybersecurity Information Sharing Governance Structures: An Ecosystem of Diversity, Trust, and Tradeoffs

abstract: In recent years the cybersecurity policy debate in Washington has been dominated by calls for greater information sharing within the private sector, and between the private sector and the federal government. The passage of the Cybersecurity Information Sharing Act (CISA) (signed into law under the Cybersecurity Act of 2015) underscored federal efforts to collect information from the private sector, and assuaged some concerns regarding private sector liability in sharing activities. However, the law lacked specificity on how continued federal efforts would work with existing information sharing networks, and failed to address other challenges associated with sharing including trust building, privacy and propriety interests, reciprocation, and quality control. This paper aims to bring granularity to implementations of information sharing initiatives by creating a taxonomy of the governance and policy models within each of these organizations. The research shows how this diverse ecosystem of sharing models work together and separately, and the impact governance and policy have on key components critical to sharing infrastructure.


title: A New Deep Learning Application Based on Movidius NCS for Embedded Object Detection and Recognition

abstract: Nowadays, real-time detection and recognition of objects is a vital task in image processing and computer vision. This study presents an embedded powerful technique for real-time object detection and recognition that runs at high frames per second (FPS) on an embedded platform with movidius neural compute stick (NCS). This can be done by applying a deep learning for computer vision. We recommended an object detection and recognition for real-time video by using deep learning technique and OpenCV libraries. It includes the single shot detector (SSD) algorithm with a MobileNet architecture that are trained with caffe framework. In this paper, Raspberry Pi 3 was utilized to implement this system. So, it helps to monitor and captures the frames and detect and recognize the objects. Also, we used movidius neural compute stick that can be utilized with the Raspberry Pi 3 to achieve high FPS. The proposed method applies a few enhancements such as default boxes, multi scale features and depthwise separable convolution. These enhancements permit the proposed system to get a high accuracy in detection and recognition of objects. Engineering


title: Differences in negativity bias underlie variations in political ideology

abstract: Disputes between those holding differing political views are ubiquitous and deep-seated, and they often follow common, recognizable lines. The supporters of tradition and stability, sometimes referred to as conservatives, do battle with the supporters of innovation and reform, sometimes referred to as liberals. Understanding the correlates of those distinct political orientations is probably a prerequisite for managing political disputes, which are a source of social conflict that can lead to frustration and even bloodshed. A rapidly growing body of empirical evidence documents a multitude of ways in which liberals and conservatives differ from each other in purviews of life with little direct connection to politics, from tastes in art to desire for closure and from disgust sensitivity to the tendency to pursue new information, but the central theme of the differences is a matter of debate. In this article, we argue that one organizing element of the many differences between liberals and conservatives is the nature of their physiological and psychological responses to features of the environment that are negative. Compared with liberals, conservatives tend to register greater physiological responses to such stimuli and also to devote more psychological resources to them. Operating from this point of departure, we suggest approaches for refining understanding of the broad relationship between political views and response to the negative. We conclude with a discussion of normative implications, stressing that identifying differences across ideological groups is not tantamount to declaring one ideology superior to another.


title: Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment

abstract: Using emotion detection technologies from biophysical signals, this study explored how emotion evolves during learning process and how emotion feedback could be used to improve learning experiences. This article also described a cutting-edge pervasive e-Learning platform used in a Shanghai online college and proposed an affective e-Learning model, which combined learners’ emotions with the Shanghai e-Learning platform. The study was guided by Russell’s circumplex model of affect and Kort’s learning spiral model. The results about emotion recognition from physiological signals achieved a best-case accuracy (86.3%) for four types of learning emotions. And results from emotion revolution study showed that engagement and confusion were the most important and frequently occurred emotions in learning, which is consistent with the findings from AutoTutor project. No evidence from this study validated Kort’s learning spiral model. An experimental prototype of the affective e-Learning model was built to help improve students’ learning experience by customizing learning material delivery based on students’ emotional state. Experiments indicated the superiority of emotion aware over non-emotion-aware with a performance increase of 91%.


title: DangSan: Scalable Use-after-free Detection

abstract: Use-after-free vulnerabilities due to dangling pointers are an important and growing threat to systems security. While various solutions exist to address this problem, none of them is sufficiently practical for real-world adoption. Some can be bypassed by attackers, others cannot support complex multithreaded applications prone to dangling pointers, and the remainder have prohibitively high overhead. One major source of overhead is the need to synchronize threads on every pointer write due to pointer tracking. In this paper, we present DangSan, a use-after-free detection system that scales efficiently to large numbers of pointer writes as well as to many concurrent threads. To significantly reduce the overhead of existing solutions, we observe that pointer tracking is write-intensive but requires very few reads. Moreover, there is no need for strong consistency guarantees as inconsistencies can be reconciled at read (i.e., object deallocation) time. Building on these intuitions, DangSan's design mimics that of log-structured file systems, which are ideally suited for similar workloads. Our results show that DangSan can run heavily multithreaded applications, while introducing only half the overhead of previous multithreaded use-after-free detectors.


title: Real time Vehicle tracking system based on ARM7 GPS and GSM technology

abstract: This Paper presents GPS based tracking system has many application in today's world. For example Vehicle tracking, children tracking, any equipment tracking, fleet management etc. An efficient vehicle tracking system is implemented for monitoring the movement of any equipped vehicle from any location at any time. With the help of Global Positioning System (GPS), Global System for Mobile communication (GSM) modem and microcontroller are embedded with the aim of enabling users to locate their vehicles with ease and in a convenient manner. This system provides the facility to the user to track their vehicle remotely through the mobile network. This paper present the development of vehicle tracking systems hardware prototype and GUI application for displaying the actual position of vehicle.


Bankruptcy Prediction with Missing Data


title: Learning the Lie Groups of Visual Invariance

abstract: A fundamental problem in biological and machine vision is visual invariance: How are objects perceived to be the same despite transformations such as translations, rotations, and scaling? In this letter, we describe a new, unsupervised approach to learning invariances based on Lie group theory. Unlike traditional approaches that sacrifice information about transformations to achieve invariance, the Lie group approach explicitly models the effects of transformations in images. As a result, estimates of transformations are available for other purposes, such as pose estimation and visuomotor control. Previous approaches based on first-order Taylor series expansions of images can be regarded as special cases of the Lie group approach, which utilizes a matrix-exponential-based generative model of images and can handle arbitrarily large transformations. We present an unsupervised expectation-maximization algorithm for learning Lie transformation operators directly from image data containing examples of transformations. Our experimental results show that the Lie operators learned by the algorithm from an artificial data set containing six types of affine transformations closely match the analytically predicted affine operators. We then demonstrate that the algorithm can also recover novel transformation operators from natural image sequences. We conclude by showing that the learned operators can be used to both generate and estimate transformations in images, thereby providing a basis for achieving visual invariance.


title: Noninvasive blood glucose detection using near infrared sensor

abstract: The development of noninvasive blood glucose detector is desired to replace invasive method which is costly and uncomfortable. Near Infrared (NIR) spectroscopy is used to detect blood glucose noninvasively since glucose has specific absorbance spectrum in NIR range, 850nm–2500nm. LED is utilized as incoherent infrared source to irradiate body surface in wavelength 1550 nm. Penetrated light is then detected by InGaAs photodiode, sensitive for wavelength 850–1700 nm. As the measurement sites, earlobe is chosen since it is thin. Photodiode's current is converted into voltage using transimpedance amplifier circuit. In order to minimize high frequency noise, low pass filter is applied consecutively. Early testing is conducted by measuring penetrated light in various concentration of glucose solution with wavelength 1300nm, 1450nm, and 1550nm. Measurement indicates correlation between voltage and glucose concentration. In earlobe testing, light can penetrate tissue with detected voltage, 300mV–4V depend on the wavelength. Linear approximation and two point equation is applied to the data. This two approach extracts formula that can estimate blood glucose concentration with maximum 30% error. By using two point equation, a specific formula for each person can be obtained.


Lacrosse Players, Not Terrorists: The Effects of the Western Hemisphere Travel Initiative on Native American International Travel and Sovereignty


title: Multiclass MinMax rank aggregation

abstract: We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall τ and the Spearman footrule. As the problems are NP-hard, we proceed to describe a number of constant-approximation algorithms for solving them. We conclude with illustrative applications of the aggregation methods on the Mallows model and genomic data.


title: On the Spectral Efficiency of Space-Constrained Massive MIMO with Linear Receivers

abstract: In this paper, we investigate the spectral efficiency (SE) of massive multiple-input multiple-output (MIMO) systems with a large number of antennas at the base station (BS) accounting for physical space constraints. In contrast to the vast body of related literature, which considers fixed inter-element spacing, we elaborate on a practical topology in which an increase in the number of antennas in a fixed total space induces an inversely proportional decrease in the inter-antenna distance. For this scenario, we derive exact and approximate expressions, as well as simplified upper/lower bounds, for the SE of maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-squared error receivers (MMSE) receivers. In particular, our analysis shows that the MRC receiver is non-optimal for space-constrained massive MIMO topologies. On the other hand, ZF and MMSE receivers can still deliver an increasing SE as the number of BS antennas grows large. Numerical results corroborate our analysis and show the effect of the number of antennas, the number of users, and the total antenna array space on the sum SE performance.


title: Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations

abstract: Abstract In this work we focus on liver metastases detection in computed tomography (CT) examinations, using both a global context with a fully convolutional network (FCN), and a local patch level analysis with superpixel sparse based classification. The task of detecting metastases in the liver, in particular the small metastases, is important for early detection of liver cancer. Using a combined global and local approach, we present a system that can enhance detection capabilities. Our data contains a development set with CT examinations from 20 patients with a total of 68 lesions and a testing set with CT examinations from 14 patients with overall 55 lesions, out of which 35% were considered small lesions (longest diameter ≤ 1.5 cm). Experiments using 3-fold cross-validation resulted in a true positive rate of 94.6% with 2.9 false positives per case. These results are clinically promising, and should lead to better detection capabilities, including of small lesions, which is critical in cancer diagnosis.


title: Robust Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics and Bounded Uncertainties Based on Neural Networks

abstract: This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.


title: "Localization space": a framework for localization and planning, for systems using a sensor/landmarks module

abstract: One of the common ways of localization in robotics is the triangulation using a system composed of a sensor and some landmarks (which can be artificial or natural). This paper presents a framework, namely the localization space, in order to deal with problems such as the landmark placement and motion planning including the localization constraint. Based on this framework, we present general approaches to the optimal distribution of the landmarks or to the computation of reliable trajectories. The case of a mobile robot equipped with an orientable sensor (such as a pan vision system) is presented to illustrate the formal concepts and to show the practical relevance of the proposed tools.


title: Multihop Attention Networks for Question Answer Matching

abstract: Attention based neural network models have been successfully applied in answer selection, which is an important subtask of question answering (QA). These models often represent a question by a single vector and find its corresponding matches by attending to candidate answers. However, questions and answers might be related to each other in complicated ways which cannot be captured by single-vector representations. In this paper, we propose Multihop Attention Networks (MAN) which aim to uncover these complex relations for ranking question and answer pairs. Unlike previous models, we do not collapse the question into a single vector, instead we use multiple vectors which focus on different parts of the question for its overall semantic representation and apply multiple steps of attention to learn representations for the candidate answers. For each attention step, in addition to common attention mechanisms, we adopt sequential attention which utilizes context information for computing context-aware attention weights. Via extensive experiments, we show that MAN outperforms state-of-the-art approaches on popular benchmark QA datasets. Empirical studies confirm the effectiveness of sequential attention over other attention mechanisms.


title: Investigation in wireless power transmission for UAV charging

abstract: This paper proposes application of wireless power transfer for charging of electric-powered Unmanned Air Vehicles (UAV)s. Multi-rotor systems, such as quadrotors, are light-weight and easy to operate. They are available in different sizes and with the wide range of capabilities. The main limitation of electric-powered UAVs is their range and endurance, due the limited battery capacity. Increasing battery system size is not a viable solution as its weight becomes a limiting factor. Supercapacitors are not an option, because of their low energy density. An alternative is to recharge UAV on the job, using wireless energy transfer (WET). WET was originally investigated by Nikola Tesla in the beginning of the 20th century. His patents are now common ground for any power transmission technology research, both wired and wireless. Investigations in resonance-based wireless energy transfer promise efficient wireless power transmission over several meters. This offers an ability to recharge moving vehicles, such as cars, trains and UAVs, wirelessly. For example, this technology can be applied to extend the range of UAVs used for the inspection of power transmission lines and towers. Presented project investigate capabilities and limitations of the wireless power transmission, for particular UAV application, i.e. for the infrastructure inspections.


title: RFM-SLAM: Exploiting relative feature measurements to separate orientation and position estimation in SLAM

abstract: The SLAM problem is known to have a special property that when robot orientation is known, estimating the history of robot poses and feature locations can be posed as a standard linear least squares problem. In this work, we develop a SLAM framework that uses relative feature-to-feature measurements to exploit this structural property of SLAM. Relative feature measurements are used to pose a linear estimation problem for pose-to-pose orientation constraints. This is followed by solving an iterative non-linear on-manifold optimization problem to compute the maximum likelihood estimate for robot orientation given relative rotation constraints. Once the robot orientation is computed, we solve a linear problem for robot position and map estimation. Our approach reduces the computational burden of non-linear optimization by posing a smaller optimization problem as compared to standard graph-based methods for feature-based SLAM. Further, empirical results show our method avoids catastrophic failures that arise in existing methods due to using odometery as an initial guess for non-linear optimization, while its accuracy degrades gracefully as sensor noise is increased. We demonstrate our method through extensive simulations and comparisons with an existing state-of-the-art solver.


title: The Pharmacokinetics of Cimetidine and Metabolites in a Neonate

abstract: Limited pharmacokinetic data have been reported concerning the use of cimetidine in the neonate for the management of gastrointestinal hemorrhage. After receiving cimetidine at a dose of 10 mg/kg/d in a full-term infant, the steady-state peak and four-hour concentrations were 3.5 and 1.8 μg/ml, respectively. A mean half-life of 3.6 h for cimetidine and 2.2 h for cimetidine sulfoxide were determined. The cimetidine half-life was prolonged, and the total body clearance decreased as compared with values reported in critically ill children and adults.


title: Cooperative task planning of multi-robot systems with temporal constraints

abstract: This paper discusses a design methodology of cooperative trajectory generation for multi-robot systems. The trajectory of achieving cooperative tasks, i.e., with temporal constraints, is constructed by a nonlinear trajectory generation (NTG) algorithm. Three scenarios of multi-robot tasking are proposed at the cooperative task planning framework. The NTG algorithm is, then, used to generate real-time trajectory for desired robot activities. Given robot dynamics and constraints, the NTG algorithm first finds trajectory curves in a lower dimensional space and parameterizes the curves by a set of B-spline representations. The coefficients of the B-splines are further solved by sequential quadratic programming to satisfy the optimization objectives and constraints. The NTG algorithm has been implemented to generate real-time trajectories for a group of cooperative robots in the presence of spatial and temporal constraints. Finally, an illustrated example of cooperative task planning with temporal constraints is presented.


title: Impression Network for Video Object Detection

abstract: Video object detection is more challenging compared to image object detection. Previous works proved that applying object detector frame by frame is not only slow but also inaccurate. Visual clues get weakened by defocus and motion blur, causing failure on corresponding frames. Multi-frame feature fusion methods proved effective in improving the accuracy, but they dramatically sacrifice the speed. Feature propagation based methods proved effective in improving the speed, but they sacrifice the accuracy. So is it possible to improve speed and performance simultaneously? ::: Inspired by how human utilize impression to recognize objects from blurry frames, we propose Impression Network that embodies a natural and efficient feature aggregation mechanism. In our framework, an impression feature is established by iteratively absorbing sparsely extracted frame features. The impression feature is propagated all the way down the video, helping enhance features of low-quality frames. This impression mechanism makes it possible to perform long-range multi-frame feature fusion among sparse keyframes with minimal overhead. It significantly improves per-frame detection baseline on ImageNet VID while being 3 times faster (20 fps). We hope Impression Network can provide a new perspective on video feature enhancement. Code will be made available.


Statistical Learning Algorithms Applied to Automobile Insurance


title: Performance Evaluation of Concurrent Lock-free Data Structures on GPUs

abstract: Graphics processing units (GPUs) have emerged as a strong candidate for high-performance computing. While regular data-parallel computations with little or no synchronization are easy to map on the GPU architectures, it is a challenge to scale up computations on dynamically changing pointer-linked data structures. The traditional lock-based implementations are known to offer poor scalability due to high lock contention in the presence of thousands of active threads, which is common in GPU architectures. In this paper, we present a performance evaluation of concurrent lock-free implementations of four popular data structures on GPUs. We implement a set using lock-free linked list, hash table, skip list, and priority queue. On the first three data structures, we evaluate the performance of different mixes of addition, deletion, and search operations. The priority queue is designed to support retrieval and deletion of the minimum element and addition operations to the set. We evaluate the performance of these lock-free data structures on a Tesla C2070 Fermi GPU and compare it with the performance of multi-threaded lock-free implementations for CPU running on a 24-core Intel Xeon server. The linked list, hash table, skip list, and priority queue implementations achieve speedup of up to 7.4, 11.3, 30.7, and 30.8, respectively on the GPU compared to the Xeon server.


Expectation-Regulated Neural Model for Event Mention Extraction


title: Optimizing data warehouse loading procedures for enabling useful-time data warehousing

abstract: The purpose of a data warehouse is to aid decision making. As the real-time enterprise evolves, synchronism between transactional data and data warehouses is redefined. To cope with real-time requirements, the data warehouses must be able to enable continuous data integration, in order to deal with the most recent business data. Traditional data warehouses are unable to support any dynamics in structure and content while they are available for OLAP. Their data is periodically updated because they are unprepared for continuous data integration. For real-time enterprises with needs in decision support while the transactions are occurring, (near) real-time data warehousing seem very promising. In this paper we present a survey on testing today's most used loading techniques and analyze which are the best data loading methods, presenting a methodology for efficiently supporting continuous data integration for data warehouses. To accomplish this, we use techniques such as table structure replication with minimum content and query predicate restrictions for selecting data, to enable loading data in the data warehouse continuously, with minimum impact in query execution time. We demonstrate the efficiency of the method using benchmark TPC-H and executing query workloads while simultaneously performing continuous data integration.


title: A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects

abstract: We conducted a quantitative meta-analysis of previous research on the technology acceptance model (TAM) in an attempt to make well-grounded statements on the role of subjective norm. Furthermore, we compared TAM results by taking into account moderating effects of one individual-related factor (type of respondents), one technology-related factor (type of technology), and one contingent factor (culture). Results indicated a significant influence of subjective norm on perceived usefulness and behavioral intention to use. Moderating effects were found for all three factors. The findings yielded managerial implications for both intra-company and market-based settings.


title: Domestic Violence against Women in Jordan: Evidence from Health Clinics

abstract: To explore women’s attitudes toward intimate partner violence (IPV) and their determinants, as well as to document the prevalence of various types of violence among women attending public health centers in the Balka region, Jordan. A cross-sectional study among ever-married women aged 18–49 who visited the public health clinics in the governorate of Balka, Jordan, was carried out in August 2006. A total of 356 women was successfully interviewed. Descriptive statistics and adjusted odds ratios from logistic regression were used to assess associations between attitudes towards IPV and selected background variables. The vast majority (87%) of women reported different types of IPV against them in the last 12 months. The most common types of reported violence were emotional abuse (47.5%), followed by wife beating (19.6%). Almost one-third of women justified wife beating by husbands. Older age, younger age at marriage, rural residence, and non-working status were significantly associated with supportive attitudes towards wife beating. The study shows a high prevalence of IPV against women during the past year, and a high rate of justifications for wife beating. Increasing women’s empowerment, particularly economic security through work outside the home, may protect women from violent behavior in this context.


title: Input and Weight Space Smoothing for Semi-Supervised Learning

abstract: We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We propose a method to perform such smoothing, which combines known input-space smoothing with a novel weight-space smoothing, based on a min-max (adversarial) optimization. The resulting Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent with a small learning rate for a random subset of the weights, and standard gradient descent on the remaining weights in the same mini-batch. It is simple to implement and achieves state-of-the-art performance.