title: Preliminary assessment of dietary supplementation of Sangrovit® on red tilapia (Oreochromis niloticus) growth performance and health

abstract: Abstract A preliminary study was conducted to evaluate the effect of graded dietary supplementation of Sangrovit ® , a commercial product containing the isoquinoline alkaloid sanguinarine, on red tilapia ( Oreochromis niloticus ) growth performance, feed utilisation, hepatic function, haematological parameters and gut microbiota. Compared to the control group (1.03 g fish − 1 day − 1 ), significant elevations in mean daily feed intake (1.19–1.25) were observed in fish fed Sangrovit ® diets during the 60-day feeding period. Consequently, the specific growth rate (3.94–4.05% day − 1 ) and weight gain (66.80–71.85 g fish − 1 ) were significantly higher in the Sangrovit ® -fed groups. With the exception of total leukocyte levels, which were elevated in fish fed Sangrovit ® -supplemented diets, haematological and immunological parameters remained unaffected. Hepatic alanine aminotransferase activity and hepatosomatic index also remained unaffected in all fish groups. Compared to the control group, the allochthonous lactic acid bacteria (LAB) populations were lower in fish fed diets containing Sangrovit ® at 75 and 100 mg kg − 1 . The present study demonstrates that Sangrovit ® had a positive effect on tilapia growth performance with no apparent effects on carcass composition, hepatic function and health status.


title: The ERP response to the amount of information conveyed by words in sentences

abstract: Reading times on words in a sentence depend on the amount of information the words convey, which can be estimated by probabilistic language models. We investigate whether event-related potentials (ERPs), too, are predicted by information measures. Three types of language models estimated four different information measures on each word of a sample of English sentences. Six different ERP deflections were extracted from the EEG signal of participants reading the same sentences. A comparison between the information measures and ERPs revealed a reliable correlation between N400 amplitude and word surprisal. Language models that make no use of syntactic structure fitted the data better than did a phrase-structure grammar, which did not account for unique variance in N400 amplitude. These findings suggest that different information measures quantify cognitively different processes and that readers do not make use of a sentence’s hierarchical structure for generating expectations about the upcoming word.


title: Authorship attribution with thousands of candidate authors

abstract: In this paper, we use a blog corpus to demonstrate that we can often identify the author of an anonymous text even where there are many thousands of candidate authors. Our approach combines standard information retrieval methods with a text categorization meta-learning scheme that determines when to even venture a guess.


title: My Computer Is an Honor Student — but How Intelligent Is It? Standardized Tests as a Measure of AI

abstract: Given the well-known limitations of the Turing Test, there is a need for objective tests to both focus attention on, and measure progress towards, the goals of AI. In this paper we argue that machine performance on standardized tests should be a key component of any new measure of AI, because attaining a high level of performance requires solving significant AI problems involving language understanding and world modeling - critical skills for any machine that lays claim to intelligence. In addition, standardized tests have all the basic requirements of a practical test: they are accessible, easily comprehensible, clearly measurable, and offer a graduated progression from simple tasks to those requiring deep understanding of the world. Here we propose this task as a challenge problem for the community, summarize our state-of-the-art results on math and science tests, and provide supporting datasets


title: Human Body Tracking by Monocular Vision

abstract: This article describes a tracking method of 3D articulated complex objects (for example, the human body), from a monocular sequence of perspective images. These objects and their associated articulations must be modelled. The principle of the method is based on the interpretation of image features as the 3D perspective projections points of the object model and an iterative Levenberg-Marquardt process to compute the model pose in accordance with the analysed image.


title: Advanced FOUP purge using diffusers for FOUP door-off application

abstract: In a number of key IC fabrication steps in-process wafers are sensitive to moisture, oxygen and other airborne molecular contaminants in the air. Nitrogen purge of closed Front Opening Unified Pods (FOUP) have been implemented in many fabs to minimize wafer's exposure to the contaminants (or CDA purge if oxygen is not of concern). As the technology node advances, the need for minimizing the exposure has become even more stringent and in some processes requires FOUP purge while the FOUP door is off on an EFEM loadport. This requirement brings unique challenges to FOUP purge, especially at the front locations near FOUP opening, where EFEM air constantly tries to enter the FOUP. In this paper we present Entegris' latest experimental study on understanding the unique challenges of FOUP door-off purge and the excellent test results of newly designed advanced FOUP with purge flow distribution manifolds (diffusers).


title: Divided attention: An undesirable difficulty in memory retention

abstract: How can we improve memory retention? A large body of research has suggested that difficulty encountered during learning, such as when practice sessions are distributed rather than massed, can enhance later memory performance (see R. A. Bjork & E. L. Bjork, 1992). Here, we investigated whether divided attention during retrieval practice can also constitute a desirable difficulty. Following two initial study phases and one test phase with Swahili–English word pairs (e.g., vuvi–snake), we manipulated whether items were tested again under full or divided attention. Two days later, participants were brought back for a final cued-recall test (e.g., vuvi–?). Across three experiments (combined N = 122), we found no evidence that dividing attention while practicing retrieval enhances memory retention. This finding raises the question of why many types of difficulty during practice do improve long-term retention, but dividing attention does not.


title: Automated Lip Reading Technique for Password Authentication

abstract: As technology is starting to conquer every strata of the society, the war for protecting confidential data from being intercepted is growing intense by the hour. Biometricsecurity stands out as the most secure form of authentication in high security zones such as defense, space missions and research head-quarters. Today, forms of password-protection range from facerecognition to retina -scan. Here, we develop a system for recognizing and converting lip movement of an individual into a recognized pattern which is set as a password for the system using image-processing. This system is also a break-through for providing people with motor-disabilities a robust and easy way of protecting their data. By capturing and tracing the successive movement of lips during speech, the corresponding word can be detected. The captured images are represented as points on a two-dimensional flat manifold that enables us to efficiently define the pronunciation of each word and thereby analyze or synthesize the motion of the lips. The motion of lips helps us track the word syllable-by-syllable. With multiple levels of image processing, it becomes possible to set the matching parameters to a very close value, hence not allowing any bruteforce or other infamous hacking techniques to break into the user’s system. This lip reading technique also serves applications in areas where communication via direct speech is not possible.


title: Magnetic Position and Orientation Tracking System

abstract: Three-axis generation and sensing of quasi-static magneticdipole fields provide information sufficient to determine both the position and orientation of the sensor relative to the source. Linear rotation transformations based upon the previous measurements are applied to both the source excitation and sensor output vectors, yielding quantities that are linearly propotional to small changes in the position and orientation. Changes are separated using linear combinations of sensor output vectors, transformed to the desired coordinate frame, and used to update the previous measurements. Practical considerations for a head-tracking application are discussed.


title: Job Shop Scheduling by Local Search

abstract: We present a computational performance analysis of local search algorithms for job shop scheduling. The algorithms under investigation are iterative improvement, simulated annealing, threshold accepting and genetic local search. Our study shows that simulated annealing performs best in the sense that it finds better solutions than the other algorithms within the same amount of running time. Compared to more tailored algorithms, simulated annealing still finds the best results but only under the assumption that running time is of no concern.


title: A review on VANET security attacks and their countermeasure

abstract: In the development of smart cities across the world VANET plays a vital role for optimized route between source and destination. The VANETs is based on infra-structure less network. It facilitates vehicles to give information about safety through vehicle to vehicle communication (V2V) or vehicle to infrastructure communication (V2I). In VANETs wireless communication between vehicles so attackers violate authenticity, confidentiality and privacy properties which further effect security. The VANET technology is encircled with security challenges these days. This paper presents overview on VANETs architecture, a related survey on VANET with major concern of the security issues. Further, prevention measures of those issues, and comparative analysis is done. From the survey, found out that encryption and authentication plays an important role in VANETS also some research direction defined for future work.


title: Exploiting Continuity/Discontinuity of Basis Vectors in Spectrogram Decomposition for Harmonic-Percussive Sound Separation

abstract: In this paper, we present a novel method for harmonic-percussive sound separation (HPSS) by exploiting continuity/discontinuity properties in a matrix decomposition framework. It is widely accepted in the HPSS research that the harmonic and percussive components have anisotropic characteristics: The spectrum of the harmonic components and the time activation of the percussive components are sparse, whereas the spectrum of the percussive components and the time activation of the harmonic components are smooth. However, conventional methods fail to fully utilize the characteristics leading to suboptimal performance. Based on the observations that not the degree of sparseness but the degree of fluctuation is an accurate measure for distinguishing the harmonic and percussive components, we propose a novel HPSS algorithm by incorporating the continuity control in the iterative update formula of the matrix decomposition algorithm. In doing so, we first utilize probabilistic latent component analysis with Dirichlet prior, and later reformulate the algorithm in the nonnegative matrix factorization framework to reduce the computational cost. The comparative evaluation results show that the proposed method outperforms conventional methods in terms of both objective and subjective evaluation.


title: Designing Puzzles for Collaborative Gaming Experience - CASE: eScape

abstract: This paper examines the issues of puzzle design in the context of collaborative gaming. The qualitative research approach involves both the conceptual analysis of key terminology and a case study of a collaborative game called eScape. The case study is a design experiment, involving both the process of designing a game environment and an empirical study, where data is collected using multiple methods. The findings and conclusions emerging from the analysis provide insight into the area of multiplayer puzzle design. The analysis and reflections answer questions on how to create meaningful puzzles requiring collaboration and how far game developers can go with collaboration design. The multiplayer puzzle design introduces a new challenge for game designers. Group dynamics, social roles and an increased level of interaction require changes in the traditional conceptual understanding of a single-player puzzle.


title: Sentence-level Arabic sentiment analysis

abstract: Arabic sentiment analysis research existing currently is very limited. While sentiment analysis has many applications in English, the Arabic language is still recognizing its early steps in this field. In this paper, we show an application on Arabic sentiment analysis by implementing a sentiment classification for Arabic tweets. The retrieved tweets are analyzed to provide their sentiments polarity (positive, or negative). Since, this data is collected from the social network Twitter; it has its importance for the Middle East region, which mostly speaks Arabic.


title: Tracking trends: incorporating term volume into temporal topic models

abstract: Text corpora with documents from a range of time epochs are natural and ubiquitous in many fields, such as research papers, newspaper articles and a variety of types of recently emerged social media. People not only would like to know what kind of topics can be found from these data sources but also wish to understand the temporal dynamics of these topics and predict certain properties of terms or documents in the future. Topic models are usually utilized to find latent topics from text collections, and recently have been applied to temporal text corpora. However, most proposed models are general purpose models to which no real tasks are explicitly associated. Therefore, current models may be difficult to apply in real-world applications, such as the problems of tracking trends and predicting popularity of keywords. In this paper, we introduce a real-world task, tracking trends of terms, to which temporal topic models can be applied. Rather than building a general-purpose model, we propose a new type of topic model that incorporates the volume of terms into the temporal dynamics of topics and optimizes estimates of term volumes. In existing models, trends are either latent variables or not considered at all which limits the potential for practical use of trend information. In contrast, we combine state-space models with term volumes with a supervised learning model, enabling us to effectively predict the volume in the future, even without new documents. In addition, it is straightforward to obtain the volume of latent topics as a by-product of our model, demonstrating the superiority of utilizing temporal topic models over traditional time-series tools (e.g., autoregressive models) to tackle this kind of problem. The proposed model can be further extended with arbitrary word-level features which are evolving over time. We present the results of applying the model to two datasets with long time periods and show its effectiveness over non-trivial baselines.


title: A critical review of 7 years of Mobile Device Forensics

abstract: Mobile Device Forensics (MF) is an interdisciplinary field consisting of techniques applied to a wide range of computing devices, including smartphones and satellite navigation systems. Over the last few years, a significant amount of research has been conducted, concerning various mobile device platforms, data acquisition schemes, and information extraction methods. This work provides a comprehensive overview of the field, by presenting a detailed assessment of the actions and methodologies taken throughout the last seven years. A multilevel chronological categorization of the most significant studies is given in order to provide a quick but complete way of observing the trends within the field. This categorization chart also serves as an analytic progress report, with regards to the evolution of MF. Moreover, since standardization efforts in this area are still in their infancy, this synopsis of research helps set the foundations for a common framework proposal. Furthermore, because technology related to mobile devices is evolving rapidly, disciplines in the MF ecosystem experience frequent changes. The rigorous and critical review of the state-of-the-art in this paper will serve as a resource to support efficient and effective reference and adaptation.


title: A Distributed Inter-Cell Interference Coordination Scheme in Downlink Multicell OFDMA Systems

abstract: In this paper, the inter-cell interference coordination (ICIC) is investigated for the downlink transmission in OFDMA-based multi-cellular systems. The utility function is introduced to balance efficiency and fairness. Firstly, the system model is built based on the resource allocation rules of soft frequency reuse (SFR) and the utility-based optimization problem is formulated. Secondly, the problem is simplified and analyzed in the 3-cell layout to obtain a local optimizing allocation rule. Thirdly, a distributed heuristic ICIC scheme is developed in which a part of the spectrum is defined to be the spectrum pool. Cells make occupation decisions of the spectrum pool according to locally available information of traffic load. The proposed scheme has little signaling overhead and low complexity. Simulation results validate it can improve the cell edge throughput performance effectively.


title: Extracting lines of curvature from noisy point clouds

abstract: We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.


title: Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry

abstract: The presence of peers increases risk taking among adolescents but not adults. We posited that the presence of peers may promote adolescent risk taking by sensitizing brain regions associated with the anticipation of potential rewards. Using fMRI, we measured brain activity in adolescents, young adults, and adults as they made decisions in a simulated driving task. Participants completed one task block while alone, and one block while their performance was observed by peers in an adjacent room. During peer observation blocks, adolescents selectively demonstrated greater activation in reward-related brain regions, including the ventral striatum and orbitofrontal cortex, and activity in these regions predicted subsequent risk taking. Brain areas associated with cognitive control were less strongly recruited by adolescents than adults, but activity in the cognitive control system did not vary with social context. Results suggest that the presence of peers increases adolescent risk taking by heightening sensitivity to the potential reward value of risky decisions.


title: A behavioral study of bargaining in social networks

abstract: We report on a series of highly controlled human subject experiments in networked bargaining. The basic interaction between two players is the decision of how to share a mutual payment; we extend this to situate the players in a network. Various theories predict, to different levels of uniqueness, what the shares will be. We analyze our experimental results from three points of view: social efficiency, nodal differences, and human differences; and contrast our behavioral results with the theories.


title: A multimodal feature learning approach for sentiment analysis of social network multimedia

abstract: In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.


title: Transition-based RRT for path planning in continuous cost spaces

abstract: This paper presents a new method called Transition-based RRT (T-RRT) for path planning in continuous cost spaces. It combines the exploration strength of the RRT algorithm that rapidly grow random trees toward unexplored regions of the space, with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new potential state. This planner also relies on the notion of minimal work path that gives a quantitative way to compare path costs. The method also integrates self tuning of a parameter controlling its exploratory behavior. It yields to solution paths that efficiently follow low cost valleys and the saddle points of the cost space. Simulation results show that the method can be applied to a large set of applications including terrain costmap motions or planning low cost motions for free flying or articulated robots.


title: Modeling Driver Behavior in a Cognitive Architecture

abstract: OBJECTIVE: This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system. BACKGROUND: Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. METHOD: An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment. RESULTS: This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. CONCLUSION: The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks. APPLICATION: The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction. Keywords: Driver distraction Language: en


Bayesian analysis of GARCH and stochastic volatility : modeling leverage , jumps and heavy-tails for financial time series ∗


title: Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization

abstract: Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license.


title: Coupled snakelets for curled text-line segmentation from warped document images

abstract: Camera-captured, warped document images usually contain curled text-lines because of distortions caused by camera perspective view and page curl. Warped document images can be transformed into planar document images for improving optical character recognition accuracy and human readability using monocular dewarping techniques. Curled text-lines segmentation is a crucial initial step for most of the monocular dewarping techniques. Existing curled text-line segmentation approaches are sensitive to geometric and perspective distortions. In this paper, we introduce a novel curled text-line segmentation algorithm by adapting active contour (snake). Our algorithm performs text-line segmentation by estimating pairs of x-line and baseline. It estimates a local pair of x-line and baseline on each connected component by jointly tracing top and bottom points of neighboring connected components, and finally each group of overlapping pairs is considered as a segmented text-line. Our algorithm has achieved curled text-line segmentation accuracy of above 95% on the DFKI-I (CBDAR 2007 dewarping contest) dataset, which is significantly better than previously reported results on this dataset.


title: Prosthetic Hand Finger Control Using Fuzzy Sliding Modes

abstract: In order to improve the life quality of amputees, providing approximate manipulation ability of a human hand to that of a prosthetic hand is considered by many researchers. In this study, a biomechanical model of the index finger of the human hand is developed based on the human anatomy. Since the activation of finger bones are carried out by tendons, a tendon configuration of the index finger is introduced and used in the model to imitate the human hand characteristics and functionality. Then, fuzzy sliding mode control where the slope of the sliding surface is tuned by a fuzzy logic unit is proposed and applied to have the finger model to follow a certain trajectory. The trajectory of the finger model, which mimics the motion characteristics of the human hand, is pre-determined from the camera images of a real hand during closing and opening motion. Also, in order to check the robust behaviour of the controller, an unexpected joint friction is induced on the prosthetic finger on its way. Finally, the resultant prosthetic finger motion and the tendon forces produced are given and results are discussed.


title: 15-GHz fully integrated nMOS switches in a 0.13-/spl mu/m CMOS process

abstract: Two fully integrated nMOS switches have been demonstrated at 15 GHz in a 0.13-/spl mu/m CMOS foundry process. One incorporates on-chip LC impedance transformation networks (ITNs) while the second one does not. The switches with and without ITNs achieve the same 1.8-dB insertion loss at 15 GHz, but 21.5 and 15 dBm input P/sub 1dB/, respectively. The degradation of insertion loss due to use of ITNs is compensated by reducing the mismatch loss caused by the bond pad parasitics. The switch without ITNs is suitable for 3.1-10.6 GHz ultra-wide-band (UWB) applications. The switch with ITNs has /spl sim/5 dB worse isolation than the switch without. The difference is due to the larger transistor size of the switch with ITNs, which introduces lower parasitic impedance path between Tx/Rx ports and antenna port.


title: UP-Growth: an efficient algorithm for high utility itemset mining

abstract: Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose an efficient algorithm, namely UP-Growth (Utility Pattern Growth), for mining high utility itemsets with a set of techniques for pruning candidate itemsets. The information of high utility itemsets is maintained in a special data structure named UP-Tree (Utility Pattern Tree) such that the candidate itemsets can be generated efficiently with only two scans of the database. The performance of UP-Growth was evaluated in comparison with the state-of-the-art algorithms on different types of datasets. The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions.


title: Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms

abstract: Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. Offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modeling bias is usually unavoidably introduced. In this paper, we introduce a replay methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method.


title: A Novel Method for Tuning Configuration Parameters of Spark Based on Machine Learning

abstract: Apache Spark is an open source distributed data processing platform, which can use distributed memory abstraction to process large volume of data efficiently. With the application of Apache Spark more and more widely, some problems are exposed. One of the most important aspects is the performance problem. Apache Spark has more than 180 configuration parameters, which can be adjusted by users according to their own specific application so as to optimize the performance. Currently these parameters are tuned manually by trial and error, which is ineffective due to the large parameter space and the complex interactions among the parameters. In this paper, in order to make the parameter tuning process of Spark more effective, a novel method for tuning configuration of Spark based on machine learning is proposed, which is composed of binary classification and multi-classification. This method can be used to auto-tune the configuration parameters of Spark. Furthermore, several common machine learning algorithms based on the proposed method are explored, and experimental results show that decision tree model (C5.0) is the best model considering the accuracy and computational efficiency. Finally, the experimental results also show that the performance can get average 36% gain with the proposed method compared with the default configuration of Spark.


title: Medical WordNet: A New Methodology For The Construction And Validation Of Information Resources For Consumer Health

abstract: A consumer health information system must be able to comprehend both expert and nonexpert medical vocabulary and to map between the two. We describe an ongoing project to create a new lexical database called Medical WordNet (MWN), consisting of medically relevant terms used by and intelligible to non-expert subjects and supplemented by a corpus of natural-language sentences that is designed to provide medically validated contexts for MWN terms. The corpus derives primarily from online health information sources targeted to consumers, and involves two sub-corpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. The former consists of statements accredited as true on the basis of a rigorous process of validation, the latter of statements which non-experts believe to be true. We summarize the MWN / MFN / MBN project, and describe some of its applications.


title: Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices

abstract: Acoustic models based on long short-term memory recurrent neural networks (LSTM-RNNs) were applied to statistical parametric speech synthesis (SPSS) and showed significant improvements in naturalness and latency over those based on hidden Markov models (HMMs). This paper describes further optimizations of LSTM-RNN-based SPSS for deployment on mobile devices; weight quantization, multi-frame inference, and robust inference using an {\epsilon}-contaminated Gaussian loss function. Experimental results in subjective listening tests show that these optimizations can make LSTM-RNN-based SPSS comparable to HMM-based SPSS in runtime speed while maintaining naturalness. Evaluations between LSTM-RNN- based SPSS and HMM-driven unit selection speech synthesis are also presented.


title: Comparing Elliptic Curve Cryptography and RSA on 8-bit CPUs

abstract: Strong public-key cryptography is often considered to be too computationally expensive for small devices if not accelerated by cryptographic hardware. We revisited this statement and implemented elliptic curve point multiplication for 160-bit, 192-bit, and 224-bit NIST/SECG curves over GF(p) and RSA-1024 and RSA-2048 on two 8-bit microcontrollers. To accelerate multiple-precision multiplication, we propose a new algorithm to reduce the number of memory accesses.


title: Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization

abstract: We propose `Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden. Our approach only needs to modify the input image and can work with any network designed for object localization. During testing, we do not need to hide any patches. Our Hide-and-Seek approach obtains superior performance compared to previous methods for weakly-supervised object localization on the ILSVRC dataset. We also demonstrate that our framework can be easily extended to weakly-supervised action localization.


title: Teaching Effectiveness Research in the Past Decade: The Role of Theory and Research Design in Disentangling Meta-Analysis Results

abstract: This meta-analysis summarizes teaching effectiveness studies of the past decade and investigates the role of theory and research design in disentangling results. Compared to past analyses based on the process–product model, a framework based on cognitive models of teaching and learning proved useful in analyzing studies and accounting for variations in effect sizes. Although the effects of teaching on student learning were diverse and complex, they were fairly systematic. The authors found the largest effects for domainspecific components of teaching—teaching most proximal to executive processes of learning. By taking into account research design, the authors further disentangled meta-analytic findings. For example, domain-specific teaching components were mainly studied with quasi-experimental or experimental designs. Finally, correlational survey studies dominated teaching effectiveness studies in the past decade but proved to be more distal from the teaching–learning process.


title: 28 GHz millimeter wave cellular communication measurements for reflection and penetration loss in and around buildings in New York city

abstract: In this paper, we present reflection coefficients and penetration losses for common building materials at 28 GHz for the design and deployment of future millimeter wave mobile communication networks. Reflections from walls and buildings and penetration losses were measured for indoor and outdoor materials, such as tinted glass, clear glass, brick, concrete, and drywall at 28 GHz in New York City. A 400 Mega-chip-per-second sliding correlator channel sounder and 24.5 dBi steerable horn antennas were used to emulate future mobile devices with adaptive antennas that will likely be used in future millimeter wave cellular systems [1]. Measurements in and around buildings show that outdoor building materials are excellent reflectors with the largest measured reflection coefficient of 0.896 for tinted glass as compared to indoor building materials that are less reflective. We also found that penetration loss is dependent not only on the number of obstructions and distance between transmitter and receiver, but also on the surrounding environment. The greatest penetration loss containing three interior walls of an office building was found to be 45.1 dB, with 11.39 m separation between the transmitter and receiver.


title: Expressivity versus efficiency of graph kernels

abstract: Recently, kernel methods have become a popular tool for machine learning and data mining. As most ‘real-world’ data is structured, research in kernel methods has begun investigating kernels for various kinds of structured data. One of the most widely used tools for modeling structured data are graphs. In this paper we study the trade-off between expressivity and efficiency of graph kernels. First, we motivate the need for this discussion by showing that fully general graph kernels can not even be approximated efficiently. We also discuss generalizations of graph kernels defined in literature and show that they are either not positive definite or not very useful. Finally, we propose a new graph kernel based on subtree patterns. We argue that while a little more computationally expensive, this kernel is more expressive than kernels based on walks.


title: An autopilot system based on ROS distributed architecture and deep learning

abstract: An autopilot system includes several modules, and the software architecture has a variety of programs. As we all know, it is necessary that there exists one brand with a compatible sensor system till now, owing to complexity and variety of sensors before. In this paper, we apply (Robot Operating System) ROS-based distributed architecture. Deep learning methods also adopted by perception modules. Experimental results demonstrate that the system can reduce the dependence on the hardware effectively, and the sensor involved is convenient to achieve well the expected functionalities. The system adapts well to some specific driving scenes, relatively fixed and simple driving environment, such as the inner factories, bus lines, parks, highways, etc. This paper presents the case study of autopilot system based on ROS and deep learning, especially convolution neural network (CNN), from the perspective of system implementation. And we also introduce the algorithm and realization process including the core module of perception, decision, control and system management emphatically.


title: Fraud Detection of Credit Card Payment System by Genetic Algorithm

abstract: With an increase usage of credit cards for online purchases as well as regular purchases, causes a credit card fraud. In the mode of electronic payment system, fraud transactions are rising on the regular basis. The Modern techniques based on the Data Min- ing, Genetic Programming etc. has used in detecting fraudulent transactions. The technique of finding optimal solution for the problem and implicitly generate the results using genetic algorithm. The aim is to develop a method of generating test data and to detect fraudu- lent transaction with this algorithm. This algorithm is an optimization technique and evolutionary search based on the principles of genetic and natural selection, heuristic used to solve high complexity computational problems. This paper presents to find the detec- tion of credit card fraud mechanism and examines the result based on the principles of this algorithm. The benefit of detecting fraud is to clear for both credit card companies and their clients. The fraudulent transactions are not prevented from being cleared; the compa- ny must accept the financial cost of that transaction. This reduces the cost associated with higher interest rates, and its charges.


title: Real-time bidirectional path tracing via rasterization

abstract: Global illumination drastically improves visual realism of interactive applications. Although many interactive techniques are available, they have some limitations or employ coarse approximations. For example, general instant radiosity often has numerical error, because the sampling strategy fails in some cases. This problem can be reduced by a bidirectional sampling strategy that is often used in off-line rendering. However, it has been complicated to implement in real-time applications. This paper presents a simple real-time global illumination system based on bidirectional path tracing. The proposed system approximates bidirectional path tracing by using rasterization on a commodity DirectX® 11 capable GPU. Moreover, for glossy surfaces, a simple and efficient artifact suppression technique is also introduced.


title: Protection by apple peel polyphenols against indometacin-induced oxidative stress, mitochondrial damage and cytotoxicity in Caco-2 cells.

abstract: OBJECTIVES ::: Exposure of Caco-2 cells to indometacin can be a useful model to assess some of the cytotoxic events that appear to underlie the gastrointestinal lesions associated with the use of this anti-inflammatory agent. Using such a cellular model, we addressed here the cytoprotective potential of a recently standardized apple peel polyphenol extract, APPE. ::: ::: ::: METHODS ::: We firstly characterized APPE in terms of its free radical scavenging and antioxidant properties, and subsequently investigated its potential to protect Caco-2 cells against the deleterious effects of indometacin on cellular oxidative status (redox state, malondialdehyde, glutathione (GSH) and oxidized glutathione (GSSG) levels), mitochondrial function (ATP and mitochondrial membrane potential) and cell viability (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) reduction and lactate dehydrogenase (LDH) leakage). For comparative purposes, the free radical scavenging properties and reducing capacity of quercetin, epicatechin and rutin were also estimated. ::: ::: ::: KEY FINDINGS ::: In the absence of APPE, indometacin induced mitochondrial perturbations (reducing ATP and the mitochondrial membrane potential), enhanced the oxidative status (decreasing the GSH/GSSG ratio and increasing dichlorofluorescein oxidation and malondialdehyde) and lowered the cell viability (decreasing MTT reduction and increasing LDH leakage). APPE, whether pre-added or co-incubated with indometacin, concentration-dependently prevented these mitochondrial, oxidative and cell viability alterations. Prompted by the recently recognized ability of indometacin to enhance the mitochondrial formation of reactive oxygen species, APPE was also characterized in terms of its free radical-scavenging capacity. APPE was found to actively scavenge O(2).(-), HO. and peroxyl radicals. Such free radical-scavenging activity of APPE suggests that its ability to protect mitochondria and prevent the oxidative and lytic damage induced by indometacin arises from its potent antioxidant capacity. ::: ::: ::: CONCLUSIONS ::: In Caco-2 cells APPE prevented mitochondrial oxidative and cell viability alterations induced by indometacin possibly through its ability to scavenge reactive oxygen species. These findings are of interest in view of the high prevalence of gastrointestinal side-effects associated with the use of conventional anti-inflammatory agents.


title: Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture

abstract: Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture—in cooperation with image processing technologies—for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis.


title: Parallel Fuzzy c-Means Clustering for Large Data Sets

abstract: The parallel fuzzy c-means (PFCM) algorithm for clustering large data sets is proposed in this paper. The proposed algorithm is designed to run on parallel computers of the Single Program Multiple Data (SPMD) model type with the Message Passing Interface (MPI). A comparison is made between PFCM and an existing parallel k-means (PKM) algorithm in terms of their parallelisation capability and scalability. In an implementation of PFCM to cluster a large data set from an insurance company, the proposed algorithm is demonstrated to have almost ideal speedups as well as an excellent scaleup with respect to the size of the data sets.


title: Semantic web and human computation: The status of an emerging field

abstract: This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation ::: research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of ::: its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did this research line evolve ::: during the last decade? How do the papers in this special issue align with the main lines of work of the community? In this ::: editorial we inspect and reflect on the evolution of research at the intersection of Semantic Web and Human Computation. We ::: use a methodology based on Systematic Mapping Studies to collect quantitative bibliographic data which we analyze through the ::: lens of research topics envisioned by the research manifesto to characterize the evolution of research in this area, thus providing a ::: context for introducing the papers of this special issue. We found evidences of a thriving research field; while steadily maturing, ::: the field offers a number of open research opportunities for work where Semantic Web best practices and techniques are applied ::: to support and improve the state-of-the-art in Human Computation, but also for work that exploits the strength of both areas to ::: address scientifically and societally relevant issues.


title: Measuring the Performance of Schedulability Tests

abstract: The high computational complexity required for performing an exact schedulability analysis of fixed priority systems has led the research community to investigate new feasibility tests which are less complex than exact tests, but still provide a reasonable performance in terms of acceptance ratio. The performance of a test is typically evaluated by generating a huge number of synthetic task sets and then computing the fraction of those that pass the test with respect to the total number of feasible ones. The resulting ratio, however, depends on the metrics used for evaluating the performance and on the method for generating random task parameters. In particular, an important factor that affects the overall result of the simulation is the probability density function of the random variables used to generate the task set parameters. In this paper we discuss and compare three different metrics that can be used for evaluating the performance of schedulability tests. Then, we investigate how the random generation procedure can bias the simulation results of some specific scheduling algorithm. Finally, we present an efficient method for generating task sets with uniform distribution in a given space, and show how some intuitive solutions typically used for task set generation can bias the simulation results.


title: Facial thermal variations: A new marker of emotional arousal

abstract: Functional infrared thermal imaging (fITI) is considered a promising method to measure emotional autonomic responses through facial cutaneous thermal variations. However, the facial thermal response to emotions still needs to be investigated within the framework of the dimensional approach to emotions. The main aim of this study was to assess how the facial thermal variations index the emotional arousal and valence dimensions of visual stimuli. Twenty-four participants were presented with three groups of standardized emotional pictures (unpleasant, neutral and pleasant) from the International Affective Picture System. Facial temperature was recorded at the nose tip, an important region of interest for facial thermal variations, and compared to electrodermal responses, a robust index of emotional arousal. Both types of responses were also compared to subjective ratings of pictures. An emotional arousal effect was found on the amplitude and latency of thermal responses and on the amplitude and frequency of electrodermal responses. The participants showed greater thermal and dermal responses to emotional than to neutral pictures with no difference between pleasant and unpleasant ones. Thermal responses correlated and the dermal ones tended to correlate with subjective ratings. Finally, in the emotional conditions compared to the neutral one, the frequency of simultaneous thermal and dermal responses increased while both thermal or dermal isolated responses decreased. Overall, this study brings convergent arguments to consider fITI as a promising method reflecting the arousal dimension of emotional stimulation and, consequently, as a credible alternative to the classical recording of electrodermal activity. The present research provides an original way to unveil autonomic implication in emotional processes and opens new perspectives to measure them in touchless conditions.


title: Vision-based Real Estate Price Estimation

abstract: Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow's estimates.


title: Particle Markov chain Monte Carlo methods (with discussion

abstract: Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods. This allows us not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Levy-driven stochastic volatility model. Copyright (c) 2010 Royal Statistical Society.


title: Adaptive synthesis of indoor scenes via activity-associated object relation graphs

abstract: We present a system for adaptive synthesis of indoor scenes given an empty room and only a few object categories. Automatically suggesting indoor objects and proper layouts to convert an empty room to a 3D scene is challenging, since it requires interior design knowledge to balance the factors like space, path distance, illumination and object relations, in order to insure the functional plausibility of the synthesized scenes. We exploit a database of 2D floor plans to extract object relations and provide layout examples for scene synthesis. With the labeled human positions and directions in each plan, we detect the activity relations and compute the coexistence frequency of object pairs to construct activity-associated object relation graphs. Given the input room and user-specified object categories, our system first leverages the object relation graphs and the database floor plans to suggest more potential object categories beyond the specified ones to make resulting scenes functionally complete, and then uses the similar plan references to create the layout of synthesized scenes. We show various synthesis results to demonstrate the practicability of our system, and validate its usability via a user study. We also compare our system with the state-of-the-art furniture layout and activity-centric scene representation methods, in terms of functional plausibility and user friendliness.


title: Visualizing 3D Terrain, Geo-Spatial Data, and Uncertainty

abstract: Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question of how to visually communicate all three aspects: the 3D terrain, the geo-spatial data, and the data-associated uncertainty. We argue that visualizing all aspects with a high degree of detail will likely exceed the visual budget. Therefore, we propose a visualization strategy based on prioritizing a selected aspect and presenting the remaining two with less detail. We discuss various design options that allow us to obtain differently prioritized visual representations. Our approach has been implemented as a tool for rapid visualization prototyping in the context of avionics applications. Practical solutions are described for a use case related to the visualization of 3D terrain and uncertain weather data.


title: Optimal and suboptimal resource allocation techniques in cloud computing data centers

abstract: Cloud service providers are under constant pressure to improve performance, offer more diverse resource deployment options, and enhance application portability. To achieve these performance and cost objectives, providers need a comprehensive resource allocation system that handles both computational and network resources. A novel methodology is introduced to tackle the problem of allocating sufficient data center resources to client Virtual Machine (VM) reservation requests and connection scheduling requests. This needs to be done while achieving the providers' objectives and minimizing the need for VM migration. In this work, the problem of resource allocation in cloud computing data centers is formulated as an optimization problem and solved. Moreover, a set of heuristic solutions are introduced and used as VM reservation and connection scheduling policies. A relaxed suboptimal solution based on decomposing the original problem is also presented. The experimentation results for a diverse set of network loads show that the relaxed solution has achieved promising levels for connection request average tardiness. The proposed solution is able to reach better performance levels than heuristic solutions without the burden of long hours of running time. This makes it a feasible candidate for solving problems with a much higher number of requests and wider data ranges compared to the optimal solution.


title: POSTER: Intrusion Detection System for In-vehicle Networks using Sensor Correlation and Integration

abstract: The increasing utilization of Electronic Control Units (ECUs) and wireless connectivity in modern vehicles has favored the emergence of security issues. Recently, several attacks have been demonstrated against in-vehicle networks therefore drawing significant attention. This paper presents an Intrusion Detection System (IDS) based on a regression learning approach which estimates certain parameters by using correlated/redundant data. The estimated values are compared to observed ones to identify abnormal contexts that would indicate intrusion. Experiments performed with real-world vehicular data have shown that more than 90% of vehicle speed data can be precisely estimated within the error bound of 3 kph. The proposed IDS is capable of detecting and localizing attacks in real-time, which is fundamental to achieve automotive security.


Using the column oriented NoSQL model for implementing big data warehouses


title: A Semantic Publish-Subscribe Architecture for the Internet of Things

abstract: This paper presents a publish-subscribe architecture designed to support information level interoperability in smart space applications in the Internet of Things (IoT). The architecture is built on top of a generic SPARQL endpoint where publishers and subscribers use standard SPARQL Updates and Queries. Notifications about events [i.e., changes in the resource description framework (RDF) knowledge base] are expressed in terms of added and removed SPARQL binding results since the previous notification, limiting the network overhead and facilitating notification processing at subscriber side. A novel event detection algorithm, tailored on the IoT specificities (i.e., heterogeneous events need to be detected and continuous updates of few RDF triples dominate with respect to more complex updates), is presented along with the envisioned application design pattern and performance evaluation model. Eventually, a reference implementation is evaluated against a benchmark inspired by a smart city lighting case. The performance evaluation results show the capability to process up to 68k subscriptions/s triggered by simple single-lamp updates and up to 3.8k subscriptions/s triggered by more complex updates (i.e., 10 to 100 lamps).


title: Automatic car number plate recognition

abstract: A traffic surveillance system includes detection of vehicles which involves the detection and identification of license plate numbers. This paper proposes an intelligent approach of detecting vehicular number plates automatically using three efficient algorithms namely Ant colony optimization (ACO) used in plate localization for identifying the edges, a character segmentation and extraction algorithm and a hierarchical combined classification method based on inductive learning and SVM for individual character recognition. Initially the performance of the Ant Colony Optimization algorithm is compared with the existing algorithms for edge detection namely Canny, Prewitt, Roberts, Mexican Hat and Sobel operators. The Ant Colony Optimization used in communication systems has certain limitations when used in edge detection like random initial ant position in the image and the heuristic information being highly dictated by transition probabilities. In this paper, modifications like assigning a well-defined initial ant position and making use of weights to calculate heuristic value which will provide additional information about transition probabilities are used to overcome the limitations. Further a character extraction and segmentation algorithm which uses the concept of Kohonen neural network to identify the position and dimensions of characters is presented along with a comparison with the existing Histogram and Connected Pixels approach. Finally an inductive learning based classification method is compared with the Support Vector Machine based classification method and a combined classification method which uses both inductive learning and Support Vector Machine based approach for character recognition is proposed. The proposed character recognition algorithm may be more efficient than the other two.


title: Dendritic spine anomalies and PTEN alterations in a mouse model of VPA‐induced autism spectrum disorder

abstract: Mounting evidence suggests that the etiology of autism spectrum disorders (ASDs) is profoundly influenced by exposure to environmental factors, although the precise molecular and cellular links remain ill-defined. In this study, we examined how exposure to valproic acid (VPA) during pregnancy is associated with an increased incidence of ASD. A mouse model was established by injecting VPA at embryonic day 13, and its behavioral phenotypes including impaired social interaction, increased repetitive behaviors and decreased nociception were observed at postnatal days 21-42. VPA-treated mice showed dysregulation of synaptic structure in cortical neurons, including a reduced proportion of filopodium-type and stubby spines and increased proportions of thin and mushroom-type spines, along with a decreased spine head size. We also found that VPA-treatment led to decreased expression of phosphate and tensin homolog (PTEN) and increased levels of p-AKT protein in the hippocampus and cortex. Our data suggest that there is a correlation between VPA exposure and dysregulation of PTEN with ASD-like behavioral and neuroanatomical changes, and this may be a potential mechanism of VPA-induced ASD.


title: STRUCTURAL MONITORING OF TUNNELS USING TERRESTRIAL LASER SCANNING

abstract: In recent years terrestrial laser scanning is rapidly evolving as a surveying technique for the monitoring of engineering objects like roof constructions, mines, dams, viaducts and tunnels. The advantage of laser scanning above traditional surveying methods is that it allows for the rapid acquisition of millions of scan points representing the whole surface of the object considered. Still it is a big challenge to obtain accuracies and precisions in the millimeter level when quantifying deformation of an object between epochs. This work presents two major steps towards obtaining sub noise level accuracies in surveying applications using terrestrial laser scan data. The first step aims at obtaining a point cloud of optimal quality for each epoch. The second steps consists of an adjustment and testing procedure that identifies deformation by gaining benefit from both data redundancy and individual point quality. The discussion of both steps is illustrated using several examples from mainly tunnel monitoring projects in the Rotterdam area.


title: Standoff Detection of Weapons and Contraband in the 100 GHz to 1 THz Region

abstract: The techniques and technologies currently being investigated to detect weapons and contraband concealed on persons under clothing are reviewed. The basic phenomenology of the atmosphere and materials that must be understood in order to realize such a system are discussed. The component issues and architectural designs needed to realize systems are outlined. Some conclusions with respect to further technology developments are presented.


How to Make a Semantic Network Probabilistic


title: The iMPAcT Tool: Testing UI Patterns on Mobile Applications

abstract: This paper presents the iMPAcT tool that tests recurring behaviour, i.e., UI Patterns, on mobile applications. This tool is implemented in Java and makes use of Android's APIs UI Automator and UiAutomation. The tool automatically explores a mobile application in order to automatically identify and test UI Patterns. Each UI Pattern has a test strategy, Test Patterns, associated, which is applied when an UI Pattern is found. The approach works on top of a catalogue of UI Patterns, which determines which UI Patterns are to be tested, and what should their correct behaviour be, and may be used for any application.


title: Enhanced combined eye gaze direction classification and head flexion detection system

abstract: In the past years, the detection and tracking of eye-gaze has been considered an active research field due to the convenience it adds to a variety of applications. In addition, head movement detection has received similar attention. Both of these technologies can be used as alternative methods for interfacing devices. Several approaches have been presented in the literature and used in the implementation of many algorithms for these technologies. An enhancement to an eye gaze direction classification system is presented in this paper. Viola-Jones face detector is used in the eye gaze direction classification system for face detection and to define the eye region. However, Viola-Jones face detector has a limitation when detecting rotated faces. In the proposed approach, the Circular Hough Transform is used for locating the eye iris. Low level features are then computed and support vector machine (SVM), which was found to achieve the highest accuracy in the system, is used to classify the eye gaze direction. The proposed enhancement suggests calculating the head flexion angle using flex sensors and a microcontroller. The calculated flexion angle can then be used to rotate the face back to 0° angle. This overcomes Viola-Jones limitation regarding detecting rotated faces. It also increases the system accuracy when used with rotated face images. In addition to increasing system effectiveness in classifying eye gaze direction, determining the head direction is useful to be used as a control method. This combination of eye gaze direction classification and head direction detection provides various control signals that can be used in many applications such as assistive technology.


title: Deciphering malware’s use of TLS (without decryption)

abstract: The use of TLS by malware poses new challenges to network threat detection because traditional pattern-matching techniques can no longer be applied to its messages. However, TLS also introduces a complex set of observable data features that allow many inferences to be made about both the client and the server. We show that these features can be used to detect and understand malware communication, while at the same time preserving the privacy of benign uses of encryption. These data features also allow for accurate malware family attribution of network communication, even when restricted to a single, encrypted flow. ::: To demonstrate this, we performed a detailed study of how TLS is used by malware and enterprise applications. We provide a general analysis on millions of TLS encrypted flows, and a targeted study on 18 malware families composed of thousands of unique malware samples and ten-of-thousands of malicious TLS flows. Importantly, we identify and accommodate the bias introduced by the use of a malware sandbox. The performance of a malware classifier is correlated with a malware family's use of TLS, i.e., malware families that actively evolve their use of cryptography are more difficult to classify. ::: We conclude that malware's usage of TLS is distinct from benign usage in an enterprise setting, and that these differences can be effectively used in rules and machine learning classifiers.


title: Novel approach for stress recognition using EEG signal by SVM classifier

abstract: In this work, real time stress level recognition from Electroencephalogram (EEG) signal is proposed. Stress is a mental condition that effects the brain electrical activity to be different from the normal state. A system is designed to classify three levels of stress; Low Stress, Moderate Stress and High Stress. Validation of algorithm is carried out using by Stroop color-word test as stimuli to induce various levels of stress. EEG signals are collected using EEG reusable electrodes, which are placed on forehead scalp during the experiment. Measure of stress is taken by means of questionnaires method. Discrete Wavelet Transform (DWT) is used for pre-processing and using Support Vector Machine (SVM) as the classifier. Further analysis of stress is done by using trained algorithm as reference. Signals collected are continually given to algorithm to verify stress, three levels of stress can be recognized. Stress levels of the person under test are seen on screen using graph.


title: 3:1-Bandwidth millimeter-wave PUMA array

abstract: A planar-printed PUMA array at 4mm scale is presented that operates over roughly 12GHz to 38GHz (approximately 3:1 bandwidth), demonstrating PUMA technology manufactured at millimeter-wave scales. The simple design consists of a single layer of Rogers 3003 material plated front and back with through vias, sandwiched between a structural metal backing and a superstrate radome layer. The aperture is less than 0.4 wavelengths thick at the highest frequency of operation. Scan performance is excellent, with VSWR < 2 for 45-degree scans in all planes, VSWR < 3 for 60-degree scans in all planes and over all frequencies. Measured results are presented.


title: Joint semantic similarity assessment with raw corpus and structured ontology for semantic-oriented service discovery

abstract: Semantic-oriented service matching is one of the challenges in automatic Web service discovery. Service users may search for Web services using keywords and receive the matching services in terms of their functional profiles. A number of approaches to computing the semantic similarity between words have been developed to enhance the precision of matchmaking, which can be classified into ontology-based and corpus-based approaches. The ontology-based approaches commonly use the differentiated concept information provided by a large ontology for measuring lexical similarity with word sense disambiguation. Nevertheless, most of the ontologies are domain-special and limited to lexical coverage, which have a limited applicability. On the other hand, corpus-based approaches rely on the distributional statistics of context to represent per word as a vector and measure the distance of word vectors. However, the polysemous problem may lead to a low computational accuracy. In this paper, in order to augment the semantic information content in word vectors, we propose a multiple semantic fusion (MSF) model to generate sense-specific vector per word. In this model, various semantic properties of the general-purpose ontology WordNet are integrated to fine-tune the distributed word representations learned from corpus, in terms of vector combination strategies. The retrofitted word vectors are modeled as semantic vectors for estimating semantic similarity. The MSF model-based similarity measure is validated against other similarity measures on multiple benchmark datasets. Experimental results of word similarity evaluation indicate that our computational method can obtain higher correlation coefficient with human judgment in most cases. Moreover, the proposed similarity measure is demonstrated to improve the performance of Web service matchmaking based on a single semantic resource. Accordingly, our findings provide a new method and perspective to understand and represent lexical semantics.


title: Fast solving maximum weight clique problem in massive graphs

abstract: This paper explores techniques for fast solving the maximum weight clique problem (MWCP) in very large scale real-world graphs. Because of the size of such graphs and the intractability of MWCP, previously developed algorithms may not be applicable. Although recent heuristic algorithms make progress in solving MWCP in massive graphs, they still need considerable time to get a good solution. In this work, we propose a new method for MWCP which interleaves between clique construction and graph reduction. We also propose three novel ideas to make it efficient, and develop an algorithm called FastWClq. Experiments on massive graphs from various applications show that, Fast-WClq finds better solutions than state of the art algorithms while the run time is much less. Further, FastWClq proves the optimal solution for about half of the graphs in an averaged time less than one second.


title: Effect of Intensive Voice Treatment on Tone-Language Speakers with Parkinson's Disease

abstract: The aim of this study was to investigate the effect of intensive voice therapy on Cantonese speakers with Parkinson's disease. The effect of the treatment on lexical tone was of particular interest. Four Cantonese speakers with idiopathic Parkinson's disease received treatment based on the principles of Lee Silverman Voice Treatment (LSVT). Outcome measures included perceptual measures and acoustic correlates of loudness, pitch, intonation, and tone (only intonation and tone are detailed in this study). All four participants demonstrated an increase in loudness, an increase in pitch and pitch range, and improved intonation during connected speech, as measured perceptually and acoustically. However, there were no obvious changes in the accuracy of lexical tone production, based on either phonetic transcription or acoustic analysis. The lack of improvement in lexical tones may support previous claims of a dissociation in fundamental frequency control for intonation and lexical tone production.


title: Local force cues for strength and stability in a distributed robotic construction system

abstract: Construction of spatially extended, self-supporting structures requires a consideration of structural stability throughout the building sequence. For collective construction systems, where independent agents act with variable order and timing under decentralized control, ensuring stability is a particularly pronounced challenge. Previous research in this area has largely neglected considering stability during the building process. Physical forces present throughout a structure may be usable as a cue to inform agent actions as well as an indirect communication mechanism (stigmergy) to coordinate their behavior, as adding material leads to redistribution of forces which then informs the addition of further material. Here we consider in simulation a system of decentralized climbing robots capable of traversing and extending a two-dimensional truss structure, and explore the use of feedback based on force sensing as a way for the swarm to anticipate and prevent structural failures. We consider a scenario in which robots are tasked with building an unsupported cantilever across a gap, as for a bridge, where the goal is for the swarm to build any stable spanning structure rather than to construct a specific predetermined blueprint. We show that access to local force measurements enables robots to build cantilevers that span significantly farther than those built by robots without access to such information. This improvement is achieved by taking measures to maintain both strength and stability, where strength is ensured by paying attention to forces during locomotion to prevent joints from breaking, and stability is maintained by looking at how loads transfer to the ground to ensure against toppling. We show that swarms that take both kinds of forces into account have improved building performance, in both structured settings with flat ground and unpredictable environments with rough terrain.


title: A Wideband Dual-Band Magneto-Electric Dipole Antenna With Improved Feeding Structure

abstract: In this letter, a wideband dual-band magneto-electric dipole antenna with improved feeding structure is presented. A U-shaped electric dipole antenna is employed to generate the dual resonant frequencies. A folded shorted patch that works as a magnetic dipole antenna is assembled vertically to the ground plane. The antenna is excited by a novel feeding line with a polygonal structure that is designed to improve the impedance matching. Simulated and measured results show that the dual operation bands with bandwidths of 72% from 1.48 to 3.15 GHz and 21% from 4.67 to 5.78 GHz for $\vert{ S}_{11}\vert were achieved. Stable and symmetric unidirectional radiation patterns, low cross-polarization level, low back-radiation, and an antenna gain ranging from 6.5 to 9.1 dBi were obtained over the dual operating bands.


title: TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

abstract: Machine learning methods are widely used for a variety of prediction problems. Prediction as a service is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services, unless measures to keep client data private (even from the service provider) are designed. Equally important is to minimize the nature of computation and amount of communication required between client and server. Fully homomorphic encryption offers a way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The one drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine several ideas from the machine learning literature, particularly work on quantization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.


title: 3D Image-based Indoor Localization Joint With WiFi Positioning

abstract: We realize a system that utilizes WiFi to facilitate the image-based localization system, which avoids the confusion caused by the similar decoration inside the buildings. While WiFi-based localization thread obtains the rough location information, the image-based localization thread retrieves the best matching images and clusters the camera poses associated with the images into different location candidates. The image cluster closest to the WiFi localization outcome is selected for the exact camera pose estimation. The usage of WiFi significantly reduces the search scope, avoiding the extensive search of millions of descriptors in a 3D model. In the image-based localization stage, we also propose a novel 2D-to-2D-to-3D localization framework which follows a coarse-to-fine strategy to quickly locate the query image in several location candidates and performs the local feature matching and camera pose estimation after choosing the correct image location by WiFi positioning. The entire system demonstrates significant benefits in combining both images and WiFi signals in localization tasks and great potential to be deployed in real applications.


title: Casual game design values

abstract: In this article, I examine the phenomenon of casual games and the underlying transformation of digital play which the rise of this particular segment of the games industry is exposing. I argue that instead of a genuinely new phenomenon, we are facing a change that can be more accurately characterized as "the normalization of digital play". The expansion of the player basis as well as the instrumental and functional approaches to games concurs with the transformations of other digital environments. As digital environments have become more accessible to large populations and are increasingly part of everyday lives, the ways and means of using them are becoming multifaceted and part of wider experiences. In order to respond to the changes, I propose a framework of casual games design values. Transformation in the games cultures requires transformation in design approaches.


title: Stane: synthesized surfaces for tactile input

abstract: Stane is a hand-held interaction device controlled by tactile input: scratching or rubbing textured surfaces and tapping. The system has a range of sensors, including contact microphones, capacitive sensing and inertial sensing, and provides audio and vibrotactile feedback. The surface textures vary around the device, providing perceivably different textures to the user. We demonstrate that the vibration signals generated by stroking and scratching these surfaces can be reliably classified, and can be used as a very cheaply manufacturable way to control different aspects of interaction. The system is demonstrated as a control for a music player.


title: Cookies and Web browser design: toward realizing informed consent online

abstract: We first provide criteria for assessing informed consent online. Then we examine how cookie technology and Web browser designs have responded to concerns about informed consent. Specifically, we document relevant design changes in Netscape Navigator and Internet Explorer over a 5-year period, starting in 1995. Our retrospective analyses leads us to conclude that while cookie technology has improved over time regarding informed consent, some startling problems remain. We specify six of these problems and offer design remedies. This work fits within the emerging field of Value-Sensitive Design.


abstract: A novel broadband magic-T based on stripline Y-junction and stripline-slotline transition is proposed in this paper. The magic-T based on stripline Y-junction and stripline-slotline transition has many advantages such as wide bandwidth, easy fabrication, excellent phase and amplitude balance. The working principle of the magic-T is analyzed, and the physical parameters of the magic-T with excellent performance are presented. The magic-T is simulated with a 3D FEM electromagnetic simulation software and the results are presented. The frequency bandwidth is 40%, when the simulated VSWR of the port of the magic-T is less than 2, and the simulated amplitude imbalance is less than ±0.5dB, and the simulated phase imbalance is less than ±0.1°, and the simulated isolation between the sum port and the difference port is higher than 50dB.


title: Discriminative Multi-instance Multitask Learning for 3D Action Recognition

abstract: As the prosperity of low-cost and easy-operating depth cameras, skeleton-based human action recognition has been extensively studied recently. However, most of the existing methods partially consider that all 3D joints of a human skeleton are identical. Actually, these 3D joints exhibit diverse responses to different action classes, and some joint configurations are more discriminative to distinguish a certain action. In this paper, we propose a discriminative multi-instance multitask learning (MIMTL) framework to discover the intrinsic relationship between joint configurations and action classes. First, a set of discriminative and informative joint configurations for the corresponding action class is captured in multi-instance learning model by regarding the action and the joint configurations as a bag and its instances, respectively. Then, a multitask learning model with group structure constraints is exploited to further reveal the intrinsic relationship between the joint configurations and different action classes. We conduct extensive evaluations of MIMTL using three benchmark 3D action recognition datasets. Experimental results show that our proposed MIMTL framework performs favorably compared with several state-of-the-art approaches.


title: Surveys on attitudes towards legalisation of euthanasia: importance of question phrasing

abstract: Methods: Results were compared from two different surveys in populations with similar characteristics. The alternatives "positive", "negative", and "don't know" (first questionnaire) were replaced with an explanatory text, "no legal sanction", four types of legal sanctions, and no possibility to answer "don't know" (second questionnaire). Four undergraduate student groups (engineering, law, medicine, and nursing) answered. ::: ::: Results: In the first questionnaire (n = 684) 43% accepted euthanasia (range 28–50%), 14% (8–33%) did not, and 43% (39–59%) answered "don't know". Two per cent of the respondents declined to answer. In comparison with previous surveys on attitudes to euthanasia the proportion of "don't know" was large. The results of the second questionnaire (n = 639), showed that 38% favoured "no legal prosecution" (26–50%). However, 62% (50–74%) opted for different kinds of legal sanctions, and two of four groups expressed significantly different views in the two surveys. A proportion of 10% declined to answer the second questionnaire. ::: ::: Conclusion: An introduction of an explanatory text and a wider range of response alternatives produced differences between the results of the two surveys conducted.


title: Impact of probability estimation on frequency of urine culture requests in ambulatory settings.

abstract: OBJECTIVE ::: To determine the perceptions of the medical community about urine culture in diagnosing urinary tract infections. ::: ::: ::: METHODS ::: The cross-sectional survey based of consecutive sampling was conducted at Shifa International Hospital, Islamabad, on 200 doctors, including medical students of the Shifa College of Medicine, from April to October 2010. A questionnaire with three common clinical scenarios of low, intermediate and high pre-test probability for urinary tract infection was used to assess the behaviour of the respondents to make a decision for urine culture test. The differences between the reference estimates and the respondents' estimates of pre- and post-test probability were assessed. The association of estimated probabilities with the number of tests ordered was also evaluated. The respondents were also asked about the cost effectiveness and safety of urine culture and sensitivity. Data was analysed using SPSS version 15. ::: ::: ::: RESULTS ::: In low pre-test probability settings, the disease probability was over-estimated, suggesting the participants' inability to rule out the disease. The post-test probabilities were, however, under-estimated by the doctors as compared to the students. In intermediate and high pre-test probability settings, both over- and underestimation of probabilities were noticed. Doctors were more likely to consider ordering the test as the disease probability increased. Most of the respondents were of the opinion that urine culture was a cost-effective test and there was no associated potential harm. ::: ::: ::: CONCLUSIONS ::: The wide variation in the clinical use of urine culture necessitates the formulation of appropriate guidelines for the diagnostic use of urine culture, and application of Bayesian probabilistic thinking to real clinical situations.


title: TransNets: Learning to Transform for Recommendation

abstract: Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.


title: Building an ontology catalogue for smart cities

abstract: Apart from providing semantics and reasoning power to data, ontologies enable and facilitate interoperability across heterogeneous systems or environments. A good practice when developing ontologies is to reuse as much knowledge as possible in order to increase interoperability by reducing heterogeneity across models and to reduce development effort. Ontology registries, indexes and catalogues facilitate the task of finding, exploring and reusing ontologies by collecting them from different sources. This paper presents an ontology catalogue for the smart cities and related domains. This catalogue is based on curated metadata and incorporates ontology evaluation features. Such catalogue represents the first approach within this community and it would be highly useful for new ontology developments or for describing and annotating existing ontologies.


title: ObliviStore: High Performance Oblivious Cloud Storage

abstract: We design and build ObliviStore, a high performance, distributed ORAM-based cloud data store secure in the malicious model. To the best of our knowledge, ObliviStore is the fastest ORAM implementation known to date, and is faster by 10X or more in comparison with the best known ORAM implementation. ObliviStore achieves high throughput by making I/O operations asynchronous. Asynchrony introduces security challenges, i.e., we must prevent information leakage not only through access patterns, but also through timing of I/O events. We propose various practical optimizations which are key to achieving high performance, as well as techniques for a data center to dynamically scale up a distributed ORAM. We show that with 11 trusted machines (each with a modern CPU), and 20 Solid State Drives, ObliviStore achieves a throughput of 31.5MB/s with a block size of 4KB.


title: Following My Head and My Heart: Integrating Preschoolers' Empathy, Theory of Mind, and Moral Judgments

abstract: Associations among hypothetical, prototypic moral, and conventional judgments; theory of mind (ToM); empathy; and personal distress were examined in 108 socioeconomically diverse preschoolers (Mage = 42.94 months, SD = 1.42). Repeated measures analysis of covariance with empathy, false beliefs, and their interaction as covariates indicated that empathy was significantly associated with judgments of greater moral but not conventional transgression severity, particularly for psychological harm, and with deserved punishment for unfairness. False beliefs were associated with (combined) moral criterion judgments of rule and authority independence and inalterability. Empathy also was positively associated with criterion judgments but only for children low in ToM. Personal distress was unrelated to judgments. Results demonstrate the importance of both affective and cognitive processes in preschoolers' moral judgments.


"What Is Your Evidence?" A Study of Controversial Topics on Social Media


title: NEMOx: scalable network MIMO for wireless networks

abstract: Network MIMO (netMIMO) has potential for significantly enhancing the capacity of wireless networks with tight coordination of access points (APs) to serve multiple users concurrently. Existing schemes realize netMIMO by integrating distributed APs into one ``giant'' MIMO but do not scale well owing to their global synchronization requirement and overhead in sharing data between APs. To remedy this limitation, we propose a novel system, NEMOx, that realizes netMIMO downlink transmission for large-scale wireless networks. NEMOx organizes a network into practical-size clusters, each containing multiple distributed APs (dAPs) that opportunistically synchronize with each other for netMIMO downlink transmission. Inter-cluster interference is managed with a decentralized channel-access algorithm, which is designed to balance between the dAPs' cooperation gain and spatial reuse---a unique tradeoff in netMIMO. Within each cluster, NEMOx optimizes the power budgeting among dAPs and the set of users to serve, ensuring fairness and effective cancellation of cross-talk interference. We have implemented and evaluated a prototype of NEMOx in a software radio testbed, demonstrating its throughput scalability and multiple folds of performance gain over current wireless LAN architecture and alternative netMIMO schemes.


title: Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection

abstract: Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR models the portfolio vector as a Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. CWMR’s closed-form updates clearly reflect the mean reversion trading idea. We also present several variants of CWMR algorithms, including a CWMR mixture algorithm that is theoretical universal. Empirically, CWMR strategy is able to effectively exploit the power of mean reversion for online portfolio selection. Extensive experiments on various real markets show that the proposed strategy is superior to the state-of-the-art techniques. The experimental testbed including source codes and data sets is available online.


title: WiFi-Based Passive ISAR for High-Resolution Cross-Range Profiling of Moving Targets

abstract: The application of Inverse Synthetic Aperture Radar (ISAR) techniques to WiFi-based passive radar is investigated in this paper with the purpose of obtaining high resolution cross-range profiling of moving targets. It is firstly shown that the ISAR processing must follow the typical disturbance cancellation stage required in passive radar to remove undesired contributions from the stationary scene which are likely to mask the target returns. Thus the attention is focused on the analysis of the impact of the disturbance cancellation stage on both the autofocus (i.e. the target motion parameters estimation) and the target profiling stages. An effective cancellation strategy is introduced to cope with the target signal distortion at the output of the cancellation stage which might be responsible of severe target profile degradation. The reported experimental results show that the proposed technique allows an effective removal of the undesired contributions while preserving the quality of the target crossrange profile.