title: MEGA 5 : Molecular Evolutionary Genetics Analysis Using Maximum Likelihood , Evolutionary Distance , and Maximum Parsimony Methods

abstract: Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.


title: Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching

abstract: We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human/nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets.


title: Analysis of the tongue fur and tongue features by naive Bayesian classifier

abstract: Tongue diagnosis is one of the core diagnostic methods in traditional Chinese Medicine (TCM), and greasy tongue fur/ coating (Nitai) NB did a search and saw “coating” used for which is I feel is more comprehensible to the western mind then fur is an important classification in tongue diagnosis. We employed naive Bayesian classifier to differentiate the three typical categories of fur/coating (Nitai) i.e. greasy fur/coating (Nitai), sub-greasy fur/coating (predominantly Nitai) and normal fur/coating , and to verify the sensitivity of the different tongue indicators for classification purpose.


title: Enterprise Database Applications and the Cloud: A Difficult Road Ahead

abstract: There is considerable interest in moving DBMS applications from inside enterprise data centers to the cloud, both to reduce cost and to increase flexibility and elasticity. Some of these applications are "green field" projects (i.e., new applications), others are existing legacy systems that must be migrated to the cloud. In another dimension, some are decision support applications while others are update-oriented. In this paper, we discuss the technical and political challenges that these various enterprise applications face when considering cloud deployment. In addition, a requirement for quality-of-service (QoS) guarantees will generate additional disruptive issues. In some circumstances, achieving good DBMS performance on current cloud architectures and future hardware technologies will be non-trivial. In summary, there is a difficult road ahead for enterprise database applications.


title: Vision system and depth processing for DRC-HUBO+

abstract: This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of a scene and objects robust to challenging environment conditions. We also propose a depth-map upsampling method that produces an outliers-free depth map by explicitly handling depth outliers. Our system is suitable for an interactive robot with real-world that requires accurate object detection and pose estimation. We evaluate our depth processing algorithm over state-of-the-art algorithms on several synthetic and real-world datasets.


title: Text Classification from Positive and Unlabeled Data using Misclassified Data Correction

abstract: This paper addresses the problem of dealing with a collection of labeled training documents, especially annotating negative training documents and presents a method of text classification from positive and unlabeled data. We applied an error detection and correction technique to the results of positive and negative documents classified by the Support Vector Machines (SVM). The results using Reuters documents showed that the method was comparable to the current state-of-the-art biasedSVM method as the F-score obtained by our method was 0.627 and biased-SVM was 0.614.


title: The economics of mobile payments : Understanding stakeholder issues for an emerging financial technology application

abstract: Economic theory provides a unique vantage point from which to examine issues with respect to emerging technologies, where standards and adoption, business process changes and implementation outcomes, information security, investments and business value, and industry impact require care and consideration on the part of senior management strategists and financial services leaders. In this article, we examine a new technology application which is coming into its own around the world, in association with the revolution in wireless connectivity: mobile payments. Although there are likely to be nuances and surprises with this technology application, we caution the reader to recognize that many of the same economic forces will be at work as were with other financial services and related technology applications in the past. We apply a robust evaluative framework that permits identification of the relevant stakeholders and applicable theory in the analysis of consumer, firm, business process, market, industrial and social issues. Our findings are intended to guide senior managers in dealing with the economic aspects of mobile payments, and to help identify some important directions for the research.


ASQFor: Automatic SPARQL query formulation for the non-expert


11-XII-7 Aeneas-- Colony I Meets Three-Axis Pointing


title: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

abstract: A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.


title: Pedestrian detection using HOG, LUV and optical flow as features with AdaBoost as classifier

abstract: Pedestrian detection has been used in applications such as car safety, video surveillance, and intelligent vehicles. In this paper, we present a pedestrian detection scheme using HOG, LUV and optical flow features with AdaBoost Decision Stump classifier. Our experiments on Caltech-USA pedestrian dataset show that the proposed scheme achieves promising results of about 16.7% log-average miss rate.


title: Big Data Meet Green Challenges: Greening Big Data

abstract: Nowadays, there are two significant tendencies, how to process the enormous amount of data, big data, and how to deal with the green issues related to sustainability and environmental concerns. An interesting question is whether there are inherent correlations between the two tendencies in general. To answer this question, this paper firstly makes a comprehensive literature survey on how to green big data systems in terms of the whole life cycle of big data processing, and then this paper studies the relevance between big data and green metrics and proposes two new metrics, effective energy efficiency and effective resource efficiency in order to bring new views and potentials of green metrics for the future times of big data.


title: BEGAN: Boundary Equilibrium Generative Adversarial Networks

abstract: We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks. This method balances the generator and discriminator during training. Additionally, it provides a new approximate convergence measure, fast and stable training and high visual quality. We also derive a way of controlling the trade-off between image diversity and visual quality. We focus on the image generation task, setting a new milestone in visual quality, even at higher resolutions. This is achieved while using a relatively simple model architecture and a standard training procedure.


title: Microblogging Queries on Graph Databases: An Introspection

abstract: Microblogging data is growing at a rapid pace. This poses new challenges to the data management systems, such as graph databases, that are typically suitable for analyzing such data. In this paper, we share our experience on executing a wide variety of micro-blogging queries on two popular graph databases: Neo4j and Sparksee. Our queries are designed to be relevant to popular applications of micro-blogging data. The queries are executed on a large real graph data set comprising of nearly 50 million nodes and 326 million edges.


title: Evaluation of mechanical properties across micro alloyed HSLA steel weld joints using Automated Ball Indentation

abstract: Abstract The object of the present investigations is to evaluate the mechanical properties across the SMAW, SAW, FCAW and A-GTAW weld joints of micro alloyed HSLA steel. The correlation between microstructure, micro-hardness and tensile properties obtained using automated ball indent (ABI) has been undertaken. The variation of yield strength (YS), ultimate tensile strength (UTS) and strain hardening component (n) values across base metal, heat affected zone and weld metal were obtained from the ABI results and validated with the standard conventional tensile test results. The cross-weld joints were found to have minor increase in yield strength with corresponding decrease ductility as compared to the base steel. The high strength of weld metals was attributed to the presence of high percentage of bainitic and acicular ferritic microphases. The transformation of base metal to bainitic structure was found to be the significant factor contributing to the strength of HAZ in the weld joints. The tensile strength was observed to vary significantly across the weld joints. The strength values (YS and UTS) decreased systematically across the weld joint from weld metal to base metal. The strain hardening exponent was found to be comparable across the weld joints with marginal higher values for HAZ. The comparable values of strain hardening exponent for weld metal and base metal showed the balanced strength and ductility of the welds. The FCAW and SAW were found to be better welding techniques that provide sufficient strength and least variation in mechanical properties across the weld joints.


title: The Robust Manifold Defense: Adversarial Training using Generative Models

abstract: We propose a new type of attack for finding adversarial examples for image classifiers. Our method exploits spanners, i.e. deep neural networks whose input space is low-dimensional and whose output range approximates the set of images of interest. Spanners may be generators of GANs or decoders of VAEs. The key idea in our attack is to search over latent code pairs to find ones that generate nearby images with different classifier outputs. We argue that our attack is stronger than searching over perturbations of real images. Moreover, we show that our stronger attack can be used to reduce the accuracy of Defense-GAN to 3\%, resolving an open problem from the well-known paper by Athalye et al. We combine our attack with normal adversarial training to obtain the most robust known MNIST classifier, significantly improving the state of the art against PGD attacks. Our formulation involves solving a min-max problem, where the min player sets the parameters of the classifier and the max player is running our attack, and is thus searching for adversarial examples in the {\em low-dimensional} input space of the spanner. ::: All code and models are available at \url{this https URL}


title: The Appeal of MOBA Games: What Makes People Start, Stay, and Stop

abstract: Online multiplayer games are often rich sources of complex social interactions. In this paper, we focus on the unique player experiences (PX) created by Multiplayer Online Battle Arena (MOBA) games. We examine key phases of players' engagement with the genre and investigate why players start, stay, and stop playing MOBAs. Our study identifies how team interactions during play with friends or strangers affect PX during these phases. Results indicate the ability to play with friends is salient when beginning play and during periods of engagement. Teams that include friends support a wider range of play possibilities - socially and competitively -- than teams of strangers. However, social factors appear less relevant to those choosing to stop playing, who do so for a variety of reasons. This study contributes to the field by identifying a strategy to improve the wellbeing of players.


title: Chinese Poetry Generation with Planning based Neural Network

abstract: Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user's writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning-based method can ensure that the generated poem is coherent and semantically consistent with the user's intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets.


Mass production of decomposing fungus for biodegradation process of sugarcane by-products.


title: Cumulative Attribute Space for Age and Crowd Density Estimation

abstract: A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalar-valued output. Such a learning problem is made difficult due to sparse and imbalanced training data and large feature variations caused by both uncertain viewing conditions and intrinsic ambiguities between observable visual features and the scalar values to be estimated. Encouraged by the recent success in using attributes for solving classification problems with sparse training data, this paper introduces a novel cumulative attribute concept for learning a regression model when only sparse and imbalanced data are available. More precisely, low-level visual features extracted from sparse and imbalanced image samples are mapped onto a cumulative attribute space where each dimension has clearly defined semantic interpretation (a label) that captures how the scalar output value (e.g. age, people count) changes continuously and cumulatively. Extensive experiments show that our cumulative attribute framework gains notable advantage on accuracy for both age estimation and crowd counting when compared against conventional regression models, especially when the labelled training data is sparse with imbalanced sampling.


Comparative Analysis of Mobile App Reverse Engineering Methods on Dalvik and ART


title: Multiple Tuple Spaces in Linda

abstract: Multiple tuple spaces have been envisioned for Linda since the system's first comprehensive description; they are intended for two purposes. First, by allowing tuples to be organized into a hierarchy of separate spaces, they should make it possible to construct large Linda programs out of modules, to realize Linda's long-standing potential to be a model for persistent storage, to enforce separation between the system and users in a Linda-based operating system, and to support abstraction. Second, if we allow tuple spaces to be included among the fields of ordinary tuples, the Linda tuple-manipulation operators will allow us to operate not only on single data objects but on whole computations.


title: Predicting poaching for wildlife Protection

abstract: Wildlife species such as tigers and elephants are under the threat of poaching. To combat poaching, conservation agencies (“defenders”) need to 1) anticipate where the poachers are likely to poach and 2) plan effective patrols. We propose an anti-poaching tool CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning), which helps the defenders achieve both goals. CAPTURE builds a novel hierarchical model for poacher-patroller interaction. It considers the patroller's imperfect detection of signs of poaching, the complex temporal dependencies in the poacher's behaviors, and the defender's lack of knowledge of the number of poachers. Further, CAPTURE uses a new game-theoretic algorithm to compute the optimal patrolling strategies and plan effective patrols. This paper investigates the computational challenges that CAPTURE faces. First, we present a detailed analysis of parameter separation and cell abstraction, two novel approaches used by CAPTURE to efficiently learn the parameters in the hierarchical model. Second, we propose two heuristics—piecewise linear approximation and greedy planning—to speed up the computation of the optimal patrolling strategies. In this paper, we discuss the lessons learned from using CAPTURE to analyze real-world poaching data collected over 12 years in Queen Elizabeth National Park in Uganda.


title: Measuring interprofessional team collaboration in expanded school mental health: Model refinement and scale development.

abstract: Expanded school mental health (ESMH) utilizes interprofessional collaboration to implement learning support and mental health promotion strategies in schools. This study reports on the early development and initial psychometric examination of a new scale, the Index of Interprofessional Team Collaboration for Expanded School Mental Health (IITC-ESMH), for measuring the functioning of interprofessional teams. Exploratory factor analysis results, using data collected from 436 members of interprofessional teams in schools, yielded a 26-item scale with a four-factor model (a) Reflection on Process, (b) Professional Flexibility, (c) Newly Created Professional Activities, and (d) Role Interdependence. Cronbach's alphas for the four factors were .91, .91, .84, and .80 respectively. The findings from this study provide evidence to support the IITC-ESMH as a reliable instrument for measuring interprofessional collaboration.


title: Existence of triple positive periodic solutions of a functional differential equation depending on a parameter

abstract: We establish the existence of three positive periodic solutions ::: for a class of delay functional differential equations depending ::: on a parameter by the Leggett-Williams fixed point theorem.


title: Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses

abstract: Community colleges continue to experience tremendous growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging the huge volumes of existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables.


title: Introducing BigMag — A novel system for 3D magnetic actuation of flexible surgical manipulators

abstract: Magnetic interaction can be utilized for remote actuation of surgical manipulators. However, platforms currently available for that purpose have limited workspaces, inadequate field strength or very low bandwidth of the electrical subsystem. In this paper, we present BigMag, a novel platform capable of magnetic steering of continuum manipulators for medical purposes. BigMag comprises of 6 mobile coils and is capable of generating the fields of at least 40 mT in any direction at every point of its workspace. Moreover, we introduce a mathematical model for 3D mobile coil arrays. Each coil is modelled using finite element data adjusted by measurement-based correction, (a maximum observed mean error between the model and the prediction of 3.36 ± 5.62%). The model for a full system is validated in two tasks. In the first task, the system executes a prescribed rotating field (mean error between the model and measurement of 7.51% and minimum R2 of 0.964). The second task tests the estimation of the field for known 3D trajectories (minimum R2 of 0.967). The investigation concludes with a demonstration of BigMag capabilities in actuation of magnetic catheters in confined spaces usinguser-controlled steering.


title: Sub-structural niching in estimation of distribution algorithms

abstract: We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)---a niching method used in hierarchical Bayesian optimization algorithm---with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS.


title: A privacy-aware framework for targeted advertising

abstract: Much of today's Internet ecosystem relies on online advertising for financial support. Since the effectiveness of advertising heavily depends on the relevance of the advertisements (ads) to user's interests, many online advertisers turn to targeted advertising through an ad broker, who is responsible for personalized ad delivery that caters to user's preference and interest. Most of existing targeted advertising systems need to access the users' profiles to learn their traits, which, however, has raised severe privacy concerns and make users unwilling to involve in the advertising systems. Spurred by the growing privacy concerns, this paper proposes a privacy-aware framework to promote targeted advertising. In our framework, the ad broker sits between advertisers and users for targeted advertising and provides certain amount of compensation to incentivize users to click ads that are interesting yet sensitive to them. The users determine their clicking behaviors based on their interests and potential privacy leakage, and the advertisers pay the ad broker for ad clicking. Under this framework, the optimal strategies of the advertisers, the ad broker and the users are analyzed by formulating the problem as a three-stage game, in which a unique Nash Equilibrium is achieved. In particular, we analyze the players' behaviors for the scenarios of independent advertisers and competing advertisers. Extensive simulations have been conducted and the results validate the effectiveness of the proposed framework by showing that the utilities of all entities are significantly improved compared with traditional systems.


title: Design optimization and system integration of robotic hummingbird

abstract: Flying animals with flapping wings may best exemplify the astonishing ability of natural selection on design optimization by excelling both stability and maneuverability at insect/hummingbird scale. Flapping Wing Micro Air Vehicle (FWMAV) holds great promise in bridging the performance gap between engineering system and their natural counterparts. Designing and constructing such a system is a challenging problem under stringent size, weight and power (SWaP) constraints. In this work, we presented a systematic approach for design optimization and integration for a hummingbird inspired FWMAV. Our formulation covers aspects of actuation, dynamics, flight stability and control, which was validated by experimental data for both rigid and flexible wings, ranging from low to high wing loading. The optimization yields prototypes with onboard sensors, electronics, and computation units. The prototype flaps at 30Hz to 40Hz, with 7.5 to 12 grams of system weight and 12 to 20 grams of maximum lift. Liftoff was demonstrated with added payloads. Flapping wing platforms with different requirements and scales can now be designed and optimized with minor modifications of proposed formulation.


title: The Role of Flow Experience in Cyber-Game Addiction

abstract: Consumer habit, an important key to repetitive consumption, is an interesting yet puzzling phenomenon. Sometimes this consumption becomes obsessive—consumers will continue to act a certain way even when they feel it is not in their best interests. However, not all consumers develop such addictions. This study uses cyber-game addiction syndrome as an analogue to trace the possible causes of consumer addiction. Results from structure equation modeling show that repetition of favorite activities has a moderate effect upon addiction, which is in line with the assertion of rational addiction theory. However, flow experience—the emotional state embracing perceptional distortion and enjoyment—shows a much stronger impact on addiction. This suggests that consumers who have experienced flow are more likely to be addicted.


An Analysis of the Online Banking Security Issues Reported by Hole , Moen , and Tjostheim


title: Using UML class diagrams for a comparative analysis of relational, object-oriented, and object-relational database mappings

abstract: This paper illustrates the manner in which UML can be used to study mappings to different types of database systems. After introducing UML through a comparison to the EER model, UML diagrams are used to teach different approaches for mapping conceptual designs to the relational model. As we cover object-oriented and object-relational database systems, different features of UML are used over the same enterprise example to help students understand mapping alternatives for each model. Students are required to compare and contrast the mappings in each model as part of the learning process. For object-oriented and object-relational database systems, we address mappings to the ODMG and SQL99 standards in addition to specific commercial implementations.


Modeling Scientific Influence for Research Trending Topic Prediction


title: MicroRNA Expression Changes during Interferon-Beta Treatment in the Peripheral Blood of Multiple Sclerosis Patients

abstract: MicroRNAs (miRNAs) are small non-coding RNA molecules acting as post-transcriptional regulators of gene expression. They are involved in many biological processes, and their dysregulation is implicated in various diseases, including multiple sclerosis (MS). Interferon-beta (IFN-beta) is widely used as a first-line immunomodulatory treatment of MS patients. Here, we present the first longitudinal study on the miRNA expression changes in response to IFN-beta therapy. Peripheral blood mononuclear cells (PBMC) were obtained before treatment initiation as well as after two days, four days, and one month, from patients with clinically isolated syndrome (CIS) and patients with relapsing-remitting MS (RRMS). We measured the expression of 651 mature miRNAs and about 19,000 mRNAs in parallel using real-time PCR arrays and Affymetrix microarrays. We observed that the up-regulation of IFN-beta-responsive genes is accompanied by a down-regulation of several miRNAs, including members of the mir-29 family. These differentially expressed miRNAs were found to be associated with apoptotic processes and IFN feedback loops. A network of miRNA-mRNA target interactions was constructed by integrating the information from different databases. Our results suggest that miRNA-mediated regulation plays an important role in the mechanisms of action of IFN-beta, not only in the treatment of MS but also in normal immune responses. miRNA expression levels in the blood may serve as a biomarker of the biological effects of IFN-beta therapy that may predict individual disease activity and progression.


title: Deep learning applications and challenges in big data analytics

abstract: Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.


title: original article The Product Ecology: Understanding Social Product Use and Supporting Design Culture

abstract: The field of interaction design has broadened its focus from issues surrounding one person interacting with one system to how systems are socially and culturally situated among groups of people. To understand the situations surrounding product use interaction design researchers have turned to qualitative, ethnographic research methods. However, stripped from underlying theory, these methods can be prescriptive at best. This paper introduces Product Ecology as a theoretical design framework to describe how products evoke social behavior, to provide a roadmap for choosing appropriate qualitative research methods and to extend design culture within HCI by allowing for flexible, design-centered research planning and opportunity-seeking. This product-centered framework is illustrated as a method for selecting a set of design research methods and for working with other research approaches that study people in naturalistic settings.


title: CoDetect: Financial Fraud Detection With Anomaly Feature Detection

abstract: Financial fraud, such as money laundering, is known to be a serious process of crime that makes illegitimately obtained funds go to terrorism or other criminal activity. This kind of illegal activities involve complex networks of trade and financial transactions, which makes it difficult to detect the fraud entities and discover the features of fraud. Fortunately, trading/transaction network and features of entities in the network can be constructed from the complex networks of the trade and financial transactions. The trading/transaction network reveals the interaction between entities, and thus anomaly detection on trading networks can reveal the entities involved in the fraud activity; while features of entities are the description of entities, and anomaly detection on features can reflect details of the fraud activities. Thus, network and features provide complementary information for fraud detection, which has potential to improve fraud detection performance. However, the majority of existing methods focus on networks or features information separately, which does not utilize both information. In this paper, we propose a novel fraud detection framework, CoDetect, which can leverage both network information and feature information for financial fraud detection. In addition, the CoDetect can simultaneously detecting financial fraud activities and the feature patterns associated with the fraud activities. Extensive experiments on both synthetic data and real-world data demonstrate the efficiency and the effectiveness of the proposed framework in combating financial fraud, especially for money laundering.


title: Learning Knowledge Embeddings by Combining Limit-based Scoring Loss

abstract: In knowledge graph embedding models, the margin-based ranking loss as the common loss function is usually used to encourage discrimination between golden triplets and incorrect triplets, which has proved effective in many translation-based models for knowledge graph embedding. However, we find that the loss function cannot ensure the fact that the scoring of correct triplets must be low enough to fulfill the translation. In this paper, we present a limit-based scoring loss to provide lower scoring of a golden triplet, and then to extend two basic translation models TransE and TransH, separately to TransE-RS and TransH-RS by combining limit-based scoring loss with margin-based ranking loss. Both the presented models have low complexities of parameters benefiting for application on large scale graphs. In experiments, we evaluate our models on two typical tasks including triplet classification and link prediction, and also analyze the scoring distributions of positive and negative triplets by different models. Experimental results show that the introduced limit-based scoring loss is effective to improve the capacities of knowledge graph embedding.


title: Lymphomatoid papulosis and associated lymphomas: a retrospective case series of 84 patients.

abstract: AIMS ::: To determine incidence and risk factors for developing lymphoma in patients with lymphomatoid papulosis (LyP), and to identify putative triggers amenable to treatment. ::: ::: ::: METHODS ::: The prognostic effect of severity of LyP, viral infection by history or serology, beta-2-microglobulin level, lactic dehydrogenase (LDH) level, and CD4 : CD8 ratio were evaluated using logistic regression models. Responses to prophylactic or palliative treatment were assessed. ::: ::: ::: RESULTS ::: In total, 84 patients (38 men, 46 women, median age at diagnosis 48.5 years) were identified. Of these, 34 (40%) were also diagnosed with one or more lymphomas: 16 (19%) had mycosis fungoides, 15 (17%) had primary cutaneous anaplastic large-cell lymphoma (pcALCL), 2 had both MF and pcALCL, and 1 had MF with large cell transformation and 1 had peripheral T-cell lymphoma. Of the 61 people presenting with LyP alone, only 11 (18%) subsequently developed lymphoma, with a median onset of 17.6 years (95% CI: 4, not obtained). Men were 2.5 times more likely than women to develop lymphoma (P = 0.04). Exposure to Epstein-Barr virus (EBV) was associated with an increase in incidence of 4.8 times (P = 0.16). Treatment for a putative infectious trigger resulted in improvement for 15 of 24 patients (63%). ::: ::: ::: CONCLUSION ::: Referral bias may explain the higher (40%) incidence of lymphoma in this population of LyP patients, compared with the 10-20% incidence commonly cited in the literature. In the subset of patients presenting with LyP alone, only 18% later developed lymphoma. Male patients or patients with prior EBV infection may have a higher risk for developing lymphoma, and some patients improved with treatment of putative infectious triggers.


title: High-Resolution Underwater Mapping Using Side-Scan Sonar

abstract: The goal of this study is to generate high-resolution sea floor maps using a Side-Scan Sonar(SSS). This is achieved by explicitly taking into account the SSS operation as follows. First, the raw sensor data is corrected by means of a physics-based SSS model. Second, the data is projected to the sea-floor. The errors involved in this projection are thoroughfully analysed. Third, a probabilistic SSS model is defined and used to estimate the probability of each sea-floor region to be observed. This probabilistic information is then used to weight the contribution of each SSS measurement to the map. Because of these models, arbitrary map resolutions can be achieved, even beyond the sensor resolution. Finally, a geometric map building method is presented and combined with the probabilistic approach. The resulting map is composed of two layers. The echo intensity layer holds the most likely echo intensities at each point in the sea-floor. The probabilistic layer contains information about how confident can the user or the higher control layers be about the echo intensity layer data. Experimental results have been conducted in a large subsea region.


title: Learning from various labeling strategies for suicide-related messages on social media: An experimental study

abstract: Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media. Due in large part to the fuzzy nature of what constitutes suicidal risks, most supervised approaches for learning to automatically detect suicide-related activity in social media require a great deal of human labor to train. However, humans themselves have diverse or conflicting views on what constitutes suicidal thoughts. So how to obtain reliable gold standard labels is fundamentally challenging and, we hypothesize, depends largely on what is asked of the annotators and what slice of the data they label. We conducted multiple rounds of data labeling and collected annotations from crowdsourcing workers and domain experts. We aggregated the resulting labels in various ways to train a series of supervised models. Our preliminary evaluations show that using unanimously agreed labels from multiple annotators is helpful to achieve robust machine models.


title: Dressings for treating foot ulcers in people with diabetes: an overview of systematic reviews

abstract: Background ::: Foot ulcers in people with diabetes mellitus are a common and serious global health issue. Dressings form a key part of ulcer treatment, with clinicians and patients having many different types to choose from. A clear and current overview of current evidence is required to facilitate decision-making regarding dressing use. ::: ::: Objectives ::: To summarize data from systematic reviews of randomised controlled trial evidence on the effectiveness of dressings for healing foot ulcers in people with diabetes mellitus (DM). ::: ::: Methods ::: We searched the following databases for relevant systematic reviews and associated analyses: the Cochrane Central Register of Controlled Trials (CENTRAL; The Cochrane Library 2015, Issue 2); Database of Abstracts of Reviews of Effects (DARE; The Cochrane Library 2015, Issue 1); Ovid MEDLINE (In-Process & Other Non-Indexed Citations, 14 April 2015); Ovid EMBASE (1980 to 14 April 2015). We also handsearched the Cochrane Wounds Group list of reviews. Two review authors independently performed study selection, risk of bias assessment and data extraction. Complete wound healing was the primary outcome assessed; secondary outcomes included health-related quality of life, adverse events, resource use and dressing performance. ::: ::: Main results ::: We found 13 eligible systematic reviews relevant to this overview that contained a total of 17 relevant RCTs. One review reported the results of a network meta-analysis and so presented information on indirect, as well as direct, treatment effects. Collectively the reviews reported findings for 11 different comparisons supported by direct data and 26 comparisons supported by indirect data only. Only four comparisons informed by direct data found evidence of a difference in wound healing between dressing types, but the evidence was assessed as being of low or very low quality (in one case data could not be located and checked). There was also no robust evidence of a difference between dressing types for any secondary outcomes assessed. ::: ::: Authors' conclusions ::: There is currently no robust evidence for differences between wound dressings for any outcome in foot ulcers in people with diabetes (treated in any setting). Practitioners may want to consider the unit cost of dressings, their management properties and patient preference when choosing dressings.


Evaluating Information Security Investments from Attackers Perspective: the Return-On-Attack (ROA)


title: Predicting Program Properties from "Big Code"

abstract: We present a new approach for predicting program properties from massive codebases (aka "Big Code"). Our approach first learns a probabilistic model from existing data and then uses this model to predict properties of new, unseen programs. The key idea of our work is to transform the input program into a representation which allows us to phrase the problem of inferring program properties as structured prediction in machine learning. This formulation enables us to leverage powerful probabilistic graphical models such as conditional random fields (CRFs) in order to perform joint prediction of program properties. As an example of our approach, we built a scalable prediction engine called JSNice for solving two kinds of problems in the context of JavaScript: predicting (syntactic) names of identifiers and predicting (semantic) type annotations of variables. Experimentally, JSNice predicts correct names for 63% of name identifiers and its type annotation predictions are correct in 81% of the cases. In the first week since its release, JSNice was used by more than 30,000 developers and in only few months has become a popular tool in the JavaScript developer community. By formulating the problem of inferring program properties as structured prediction and showing how to perform both learning and inference in this context, our work opens up new possibilities for attacking a wide range of difficult problems in the context of "Big Code" including invariant generation, decompilation, synthesis and others.


No-Reference Image Quality Assessment algorithm for Contrast-Distorted Images based on local statistics features


title: NR-grep: A Fast and Flexible Pattern Matching Tool

abstract: We present nrgrep (‘non-deterministic reverse grep’), a new pattern-matching tool designed for efficient search of complex patterns. Unlike previous tools of the grep family, such as agrep and Gnu grep, nrgrep is based on a single and uniform concept: the bit-parallel simulation of a non-deterministic suffix automaton. As a result, nrgrep can find from simple patterns to regular expressions, exactly or allowing errors in the matches, with an efficiency that degrades smoothly as the complexity of the searched pattern increases. Another concept that is fully integrated into nrgrep and that contributes to this smoothness is the selection of adequate subpatterns for fast scanning, which is also absent in many current tools. We show that the efficiency of nrgrep is similar to that of the fastest existing string-matching tools for the simplest patterns, and is by far unmatched for more complex patterns. Copyright © 2001 John Wiley & Sons, Ltd.


title: Calibrating Deep Convolutional Gaussian Processes

abstract: The wide adoption of Convolutional Neural Networks CNNs in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions. Previous work on combining CNNs with Gaussian processes GPs has been developed under the assumption that the predictive probabilities of these models are well-calibrated. In this paper we show that, in fact, current combinations of CNNs and GPs are miscalibrated. We proposes a novel combination that considerably outperforms previous approaches on this aspect, while achieving state-of-the-art performance on image classification tasks.


title: Long Term Annual Electricity Demand Forecasting in Sri Lanka by Artificial Neural Networks

abstract: Electricity has become a major form of end use energy in present complex society. The influence of electricity is tremendous and has been recognized as a basic human need. It is an important element of infrastructure on which the socio economic development of the country heavily depends. Electricity demand forecasting is very important and crucial for a utility, in order to make right decisions regarding future power plant and network development. Accurate electricity demand forecasting is one of the challenges and several techniques are used in forecasting demand based on the availability of data in each country. Electricity utility of Sri Lanka in their long term generation expansion planning studies use three long term demand forecasting methodologies namely econometric approach, time trend approach and end user approach. New application for long term demand forecasting based on Artificial Intelligence has identified as important due to its ability in mapping complex non-linear relationships. Therefore under this study, the use AI method based on Artificial Neural Networks for long term annual electricity demand forecasting in Sri Lanka is discussed and modeled including Socio-Economic Indicators and Climatic Conditions.


title: Platform-tolerant PIFA-type UHF RFID tag antenna

abstract: Platform-tolerant tag antennas are desired for ubiquitous RFID systems. Metal-mountable or wideband tag antennas can not guarantee platform-tolerance. This paper presents the design approaches of platform-tolerant tag antennas. A compact PIFA-type UHF tag antenna is proposed accordingly. Simulation and measurement results are provided to demonstrate the platform-tolerance feature of the proposed antenna and to validate the design approaches presented.


title: Research: The Emergency Contraception Collaborative Prescribing Experience in Washington State

abstract: Objective To describe how prescribers and pharmacists view the Emergency Contraceptive Pills (ECP) program, and to evaluate pharmacists' performance through the use of a consumer survey. Design Self-administered provider satisfaction surveys were mailed 6 months after the program's inception. Consumer satisfaction surveys were distributed at the point of ECP service for return by mail. Setting The program encouraged pharmacists and prescribers in western Washington to enter into collaborative prescribing agreements, increasing consumers' access to ECP. Patients or Other Participants Pharmacists who had attended ECP training sessions, prescribers who had authorized pharmacists to prescribe ECP, and women who had been prescribed ECP by pharmacists. Main Outcome Measures Providers' reasons for participating, attitudes toward the ECP program, and experiences with ECP as a result of the program; feedback from women receiving ECP from pharmacists. Results 309 pharmacist surveys and 55 prescriber surveys were sent, of which 159 (51%) and 27 (49%), respectively, were returned. Meeting patient needs and having a professional responsibility to participate were commonly reported reasons for ECP program involvement. Both pharmacists and prescribers (92%) reported being "satisfied" or "very satisfied" with their prescribing agreements. On the 470 consumer surveys returned out of 7,000 distributed (6.5%), pharmacists were rated highly satisfactory for their interactions with patients and the quality of information about ECP use given, but less satisfactory for information about adverse effects, recognition and follow-up of ECP failure, and regular contraceptive methods. Conclusion All participants expressed satisfaction with the ECP program. This example should support the initiation of similar programs in other states.


title: Network access authentication infrastructure using EAP-TTLS on diameter EAP application

abstract: In our universal AAA (Authentication, Authorization, and Accounting) infrastructure project, we have already developed the implementations of Diameter Base Protocol and Diameter EAP Application. As part of this project, we developed the first open-source of an EAP-TTLS server on Diameter EAP Application for network access control. EAP-TTLS is one of the authentication methods in EAP. EAP-TTLS has two phases. In phase 1, the user authenticates the EAP-TTLS server by the certificate of the EAP-TTLS server. In phase 2, the EAP-TTLS server authenticates the user by user's password transmitted through the secure tunnel established in phase 1. Our implementation supports several authentication methods in phase 2 such as PAP, CHAP, MS-CHAP, and MS-CHAPv2. It was made sure that the EAP-TTLS server worked correctly for several types of user terminals such as Windows, Linux, iPad, and Android. The evaluation results show that the processing time of the EAP-TTLS server is short enough for practical use.


title: Developing an augmented reality racing game

abstract: Augmented reality (AR) makes it possible to create games in which virtual objects are overlaid on the real world, and real objects are tracked and used to control virtual ones. We describe the development of an AR racing game created by modifying an existing racing game, using an AR infrastructure that we developed for use with the XNA game development platform. In our game, the driver wears a tracked video see-through head-worn display, and controls the car with a passive tangible controller. Other players can participate by manipulating waypoints that the car must pass and obstacles with which the car can collide. We discuss our AR infrastructure, which supports the creation of AR applications and games in a managed code environment, the user interface we developed for the AR racing game, the game's software and hardware architecture, and feedback and observations from early demonstrations.


title: Adversarial training for multi-context joint entity and relation extraction

abstract: Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).


title: Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts

abstract: This paper proposes several recurrent neural network-based models for recognizing requisite and effectuation (RE) parts in Legal Texts. Firstly, we propose a modification of BiLSTM-CRF model that allows the use of external features to improve the performance of deep learning models in case large annotated corpora are not available. However, this model can only recognize RE parts which are not overlapped. Secondly, we propose two approaches for recognizing overlapping RE parts including the cascading approach which uses the sequence of BiLSTM-CRF models and the unified model approach with the multilayer BiLSTM-CRF model and the multilayer BiLSTM-MLP-CRF model. Experimental results on two Japan law RRE datasets demonstrated advantages of our proposed models. For the Japanese National Pension Law dataset, our approaches obtained an $$F_{1}$$ F1 score of 93.27% and exhibited a significant improvement compared to previous approaches. For the Japan Civil Code RRE dataset which is written in English, our approaches produced an $$F_{1}$$ F1 score of 78.24% in recognizing RE parts that exhibited a significant improvement over strong baselines. In addition, using external features and in-domain pre-trained word embeddings also improved the performance of RRE systems.


title: CCR: Clustering and Collaborative Representation for Fast Single Image Super-Resolution

abstract: Clustering and collaborative representation (CCR) have recently been used in fast single image super-resolution (SR). In this paper, we propose an effective and fast single image super-resolution (SR) algorithm by combining clustering and collaborative representation. In particular, we first cluster the feature space of low-resolution (LR) images into multiple LR feature subspaces and group the corresponding high-resolution (HR) feature subspaces. The local geometry property learned from the clustering process is used to collect numerous neighbor LR and HR feature subsets from the whole feature spaces for each cluster center. Multiple projection matrices are then computed via collaborative representation to map LR feature subspaces to HR subspaces. For an arbitrary input LR feature, the desired HR output can be estimated according to the projection matrix, whose corresponding LR cluster center is nearest to the input. Moreover, by learning statistical priors from the clustering process, our clustering-based SR algorithm would further decrease the computational time in the reconstruction phase. Extensive experimental results on commonly used datasets indicate that our proposed SR algorithm obtains compelling SR images quantitatively and qualitatively against many state-of-the-art methods.


title: Semantic Representation of Cloud Patterns and Services with Automated Reasoning to Support Cloud Application Portability

abstract: During the past years the Cloud Computing offer has exponentially grown, with new Cloud providers, platforms and services being introduced in the IT market. The extreme variety of services, often providing non uniform and incompatible interfaces, makes it hard for customers to decide how to develop, or even worse to migrate, their own application into the Cloud. This situation can only get worse when customers want to exploit services from different providers, because of the portability and interoperability issues that often arise. In this paper we propose a uniform, integrated, machine-readable, semantic representation of cloud services, patterns, appliances and their compositions. Our approach aims at supporting the development of new applications for the Cloud environment, using semantic models and automatic reasoning to enhance potability and interoperability when multiple platforms are involved. In particular, the proposed reasoning procedure allows to: perform automatic discovery of Cloud services and Appliances; map between agnostic and vendor dependent Cloud Patterns and Services; automatically enrich the semantic knowledge base.


title: Personalized PageRank Based Multi-document Summarization

abstract: This paper presents a novel multi-document summarization approach based on personalized pagerank (PPRSum). In this algorithm, we uniformly integrate various kinds of information in the corpus. At first, we train a salience model of sentence global features based on Naive Bayes Model. Secondly, we generate a relevance model for each corpus utilizing the query of it. Then, we compute the personalized prior probability for each sentence in the corpus utilizing the salience model and the relevance model both. With the help of personalized prior probability, a Personalized PageRank ranking process is performed depending on the relationships among all sentences in the corpus. Additionally, the redundancy penalty is imposed on each sentence. The summary is produced by choosing the sentences with both high query-focused information richness and high information novelty. Experiments on DUC2007 are performed and the ROUGE evaluation results show that PPRSum ranks between the 1st and the 2nd systems on DUC2007 main task.


title: The Role of Renewable Energy in the Promotion of Circular Urban Metabolism

abstract: Cities are human creations requiring large amounts of materials and energy. Constant consumption of resources exerts pressure on the environment not only due to its exploitation, but also because once processed, the resources produce waste, emissions or effluents. Cities are responsible for more than three quarters of the emissions of greenhouse gases. It is anticipated that the urban population will increase by up to 80% by the mid-21st century, which will make the current energy model unsustainable, as it is based on the intensive use of fossil resources. A change in urban planning is required to meet the energy requirements of cities. Several studies mention that renewable energy must be used in cities, but they do not identify the resources and technologies that can be used to promote circular urban metabolism. A review of the literature establishes that there are eleven renewable technologies with different degrees of maturity that could reduce the import of energy resources, which would contribute to changing the metabolic linear model into a circular model. However, the applicability of the different possibilities is conditional upon the availability of resources, costs, policies and community acceptance.


title: Incorporating Lexical Priors into Topic Models

abstract: Topic models have great potential for helping users understand document corpora. This potential is stymied by their purely unsupervised nature, which often leads to topics that are neither entirely meaningful nor effective in extrinsic tasks (Chang et al., 2009). We propose a simple and effective way to guide topic models to learn topics of specific interest to a user. We achieve this by providing sets of seed words that a user believes are representative of the underlying topics in a corpus. Our model uses these seeds to improve both topic-word distributions (by biasing topics to produce appropriate seed words) and to improve document-topic distributions (by biasing documents to select topics related to the seed words they contain). Extrinsic evaluation on a document clustering task reveals a significant improvement when using seed information, even over other models that use seed information naively.


title: Learning to Rank Answers to Non-Factoid Questions from Web Collections

abstract: This work investigates the use of linguistically motivated features to improve search, in particular for ranking answers to non-factoid questions. We show that it is possible to exploit existing large collections of question-answer pairs (from online social Question Answering sites) to extract such features and train ranking models which combine them effectively. We investigate a wide range of feature types, some exploiting natural language processing such as coarse word sense disambiguation, named-entity identification, syntactic parsing, and semantic role labeling. Our experiments demonstrate that linguistic features, in combination, yield considerable improvements in accuracy. Depending on the system settings we measure relative improvements of 14% to 21% in Mean Reciprocal Rank and [email protected], providing one of the most compelling evidence to date that complex linguistic features such as word senses and semantic roles can have a significant impact on large-scale information retrieval tasks.


title: GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

abstract: Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical tool to handle these likelihood free problems, but they require a very large number of simulations. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior inference. Both algorithms use confidence estimates for the accept probability in the Metropolis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algorithm stores the information obtained from every simulation in a Gaussian process which acts as a surrogate function for the simulated statistics. Experiments on a challenging realistic biological problem illustrate the potential of these algorithms.


Exploring Diachronic Lexical Semantics with JeSemE


title: One More Tag Enables Fine-Grained RFID Localization and Tracking

abstract: Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-tag localization, in which each object is attached with one tag. In this paper, we propose a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader’s antenna spacing and placement; 2) supporting accurate calibration of the reader’s antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader’s antenna geometry does have impact on tag positioning performance.


title: Membrane Biological Reactors

abstract: Combining membrane technology with biological reactors for the treatment of municipal and industrial wastewaters has led to the development of three generic membrane processes within bioreactors: for separation and recycle of solids, for bubble-less aeration of the bioreactor, and for extraction of priority organic pollutants from hostile industrial wastewaters. Biosolid separation is, however, the most widely studied and has found full-scale applications in many countries. This chapter elaborates on the history, fundamentals, research and development challenges, as well as the commercial application of the biosolid-separation membrane biological reactor, which is commonly known as membrane bioreactor.


title: Brain Tumor Detection Using Histogram Thresholding to Get the Threshold point

abstract: In this paper, a computer-based method for defining tumor region in the brain using MRI images is presented. Today's modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Brain tumor, a mass of tissue that grows out of control is one of the major causes for the increase in mortality among children and adults. Segmenting the regions of brain is the major challenge in tumor detection. A large number of effective segmentation algorithms have been used for segmentation in grey scale images ranging from simple edge-based methods to composite high-level approaches using modern and advanced pattern recognition approaches. Brain tumor detection is one of the challenging tasks in medical image processing. Four MRI images were collected and the experiments were conducted for statistical analysis also. A comparative study is made and the results are of great interest and practical utility.


title: The differences in motivations of online game players and offline game players: A combined analysis of three studies at higher education level

abstract: Computer games have become a highly popular form of entertainment and have had a large impact on how University students spend their leisure time. Due to their highly motivating properties computer games have come to the attention of educationalists who wish to exploit these highly desirable properties for educational purposes. Several studies have been performed looking at motivations for playing computer games in a general context and in a Higher Education (HE) context. These studies did not focus on the differences in motivations between online and offline game players. Equally the studies did not look at the differences in motivations of people who prefer single player games and people who prefer multiplayer games. If games-based learning is to become a recognised teaching approach then such motivations for playing computer games must be better understood. This paper presents the combined analysis of three studies at HE level, performed over a four year period from 2005 to 2009. The paper focuses on differences of motivations in relation to single player/multiplayer preference and online/offline game participation. The study found that challenge is the top ranking motivation and recognition is the lowest ranking motivation for playing computer games in general. Challenge is also the top ranking motivation for playing games in HE while fantasy and recognition are the lowest ranking motivations for playing games in HE. Multiplayer gamers derive more competition, cooperation, recognition, fantasy and curiosity from playing games and online gamers derive more challenge, cooperation, recognition and control from playing games. Multiplayer gamers and online gamers ranked competition, cooperation and recognition significantly more important for playing games in HE than single players and offline participants.


title: Familiarization, Attention, and Recognition Memory in Infancy: An Event-Related Potential and Cortical Source Localization Study.

abstract: This study investigated the effects of familiarization and attention on event-related potential (ERP) correlates of recognition memory in infants. Infants 4.5, 6, or 7.5 months of age were either familiarized with 2 stimuli that were used during later testing or presented 2 stimuli that were not used later. Then, infants were presented with a recording of Sesame Street to elicit attention or inattention and presented with familiar and novel stimuli. A negative ERP component over the frontal and central electrodes (Nc) was larger in the preexposure familiarization group for novel- than for familiar-stimulus presentations, whereas the Nc did not differ for the group not receiving a familiarization exposure. Spatial independent components analysis of the electroencephelogram and “equivalent current dipole” analysis were used to examine putative cortical sources of the ERP components. The cortical source of Nc was located in areas of prefrontal cortex and anterior cingulate cortex. Visual attention and recognition memory in infants are closely related. For example, infants demonstrate greater memory for events they were exposed to while in an attentive state than for events they were exposed to in an inattentive state (Frick & Richards, 2001; Richards, 1997). Events that have been partially encoded into memory or events that are novel elicit larger attention responses than those events that have been fully encoded (Bornstein, 1985; Fantz, 1961, 1963). Infants show larger orienting responses to novel events than to those events that are familiar to them. Studies examining visual attention and recognition memory simultaneously can provide insight into the overall cognitive activity involved in an organism’s adaptive responses to environmental information. The present study shows that attention-elicited event-related potential (ERP) responses differ as a function of the infant’s familiarity with the stimuli and suggests that these effects are mediated by the prefrontal cortex of the brain.


title: Just-In-Time GPU Compilation for Interpreted Languages with Partial Evaluation

abstract: Computer systems are increasingly featuring powerful parallel devices with the advent of many-core CPUs and GPUs. This offers the opportunity to solve computationally-intensive problems at a fraction of the time traditional CPUs need. However, exploiting heterogeneous hardware requires the use of low-level programming language approaches such as OpenCL, which is incredibly challenging, even for advanced programmers. On the application side, interpreted dynamic languages are increasingly becoming popular in many domains due to their simplicity, expressiveness and flexibility. However, this creates a wide gap between the high-level abstractions offered to programmers and the low-level hardware-specific interface. Currently, programmers must rely on high performance libraries or they are forced to write parts of their application in a low-level language like OpenCL. Ideally, nonexpert programmers should be able to exploit heterogeneous hardware directly from their interpreted dynamic languages. In this paper, we present a technique to transparently and automatically offload computations from interpreted dynamic languages to heterogeneous devices. Using just-in-time compilation, we automatically generate OpenCL code at runtime which is specialized to the actual observed data types using profiling information. We demonstrate our technique using R, which is a popular interpreted dynamic language predominately used in big data analytic. Our experimental results show the execution on a GPU yields speedups of over 150x compared to the sequential FastR implementation and the obtained performance is competitive with manually written GPU code. We also show that when taking into account start-up time, large speedups are achievable, even when the applications run for as little as a few seconds.


title: MoveableMaker: Facilitating the Design, Generation, and Assembly of Moveable Papercraft

abstract: In this work, we explore moveables, i.e., interactive papercraft that harness user interaction to generate visual effects. First, we present a survey of children's books that captured the state of the art of moveables. The results of this survey were synthesized into a moveable taxonomy and informed MoveableMaker, a new tool to assist users in designing, generating, and assembling moveable papercraft. MoveableMaker supports the creation and customization of a number of moveable effects and employs moveable-specific features including animated tooltips, automatic instruction generation, constraint-based rendering, techniques to reduce material waste, and so on. To understand how MoveableMaker encourages creativity and enhances the workflow when creating moveables, a series of exploratory workshops were conducted. The results of these explorations, including the content participants created and their impressions, are discussed, along with avenues for future research involving moveables.


title: Analisys of hospital bed capacity via queuing theory and simulation

abstract: Eighty percent of Brazilian population is assisted by the public health system (SUS), while the rest uses private health insurance. The existing number of beds do not meet the entire demand and Brazilian government has made efforts to improve the system. The Health Ministry has analyzed a new proposal for planning the bed capacity. This proposal uses a queuing model and a new categorization of hospitalization and beds, taking into account specialties, age of the patients, and appropriate occupancy rates. In this paper, we use the proposed technique to estimate the required beds for Belo Horizonte, a mid-sized city in Brazil. We compare number of beds required by centralized and non-centralized administration. A simulation model was developed to analyze the dynamic behavior of the system and searching for the best configuration. This model was used to evaluate the results obtained by queuing model and to check its usability.


title: Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions

abstract: Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.


title: An New Method for Multi-Object Tracking Using Energy Minimization-Based Data Association

abstract: multiple object tracking is an active and important research topic. It faces many challenging problems. Object extraction and data association are two most key steps in multiple object tracking. To improve tracking performance, this paper proposed a tracking method which combines Kalman filter and energy minimization-based data association. Moving objects are segmented through frame difference. Its can be consider as the vertex. All detections in adjacent frames are be used to construct a graph. The energy is finally minimized with a graph cuts optimization. Data association can be consider as multiple labeling problems. Object corresponding can be obtained through energy minimization. Experiment results demonstrate this method can be accurately tracking two moving objects.


title: ElasticSwitch: practical work-conserving bandwidth guarantees for cloud computing

abstract: While cloud computing providers offer guaranteed allocations for resources such as CPU and memory, they do not offer any guarantees for network resources. The lack of network guarantees prevents tenants from predicting lower bounds on the performance of their applications. The research community has recognized this limitation but, unfortunately, prior solutions have significant limitations: either they are inefficient, because they are not work-conserving, or they are impractical, because they require expensive switch support or congestion-free network cores. In this paper, we propose ElasticSwitch, an efficient and practical approach for providing bandwidth guarantees. ElasticSwitch is efficient because it utilizes the spare bandwidth from unreserved capacity or underutilized reservations. ElasticSwitch is practical because it can be fully implemented in hypervisors, without requiring a specific topology or any support from switches. Because hypervisors operate mostly independently, there is no need for complex coordination between them or with a central controller. Our experiments, with a prototype implementation on a 100-server testbed, demonstrate that ElasticSwitch provides bandwidth guarantees and is work-conserving, even in challenging situations.


title: Online state of health estimation on NMC cells based on predictive analytics

abstract: Abstract Accurate on board state of health estimation is a key battery management system function to provide optimal management of the battery system under control. In this regard, this paper presents an extensive study and comparison of three of commonly used supervised learning methods for state of health estimation in Graphite/Nickel Manganese Cobalt oxide cells. The three methods were based from the study of both incremental capacity and differential voltage curves. According to the ageing evolution of both curves, features were extracted and used as inputs for the estimation techniques. Ordinary Least Squares, Multilayer Perceptron and Support Vector Machine were used as the estimation techniques and accurate results were obtained while requiring a low computational effort. Moreover, this work allows a deep comparison of the different estimation techniques in terms of accuracy, online estimation and BMS applicability. In addition, estimation can be developed by partial charging and/or partial discharging, reducing the required maintenance time.


title: Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation

abstract: Understanding visual relationships involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the (subj,obj) pair (both semantically and spatially) to predict the predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships, but complicates learning since the semantic space of visual relationships is huge and the training data is limited, especially for the long-tail relationships that have few instances. To overcome this, we use knowledge of linguistic statistics to regularize visual model learning. We obtain linguistic knowledge by mining from both training annotations (internal knowledge) and publicly available text, e.g., Wikipedia (external knowledge), computing the conditional probability distribution of a predicate given a (subj,obj) pair. Then, we distill the knowledge into a deep model to achieve better generalization. Our experimental results on the Visual Relationship Detection (VRD) and Visual Genome datasets suggest that with this linguistic knowledge distillation, our model outperforms the state-of-the-art methods significantly, especially when predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on VRD zero-shot testing set).


title: Anomaly network traffic detection algorithm based on information entropy measurement under the cloud computing environment

abstract: In recent years, there are more and more abnormal activities in the network, which greatly threaten network security. Hence, it is of great importance to collect the data which indicate the running statement of the network, and distinguish the anomaly phenomena of the network in time. In this paper, we propose a novel anomaly network traffic detection algorithm under the cloud computing environment. Firstly, the framework of the anomaly network traffic detection system is illustrated, and six type of network traffic features are consider in this work, that is, (1) number of source IP address, (2) number of source port number, (3) number of destination IP address, (4) number of destination port number, (5) Number of packet type, and (6) number of network packets. Secondly, we propose a novel hybrid information entropy and SVM model to tackle the proposed problem by normalizing values of network features and exploiting SVM detect anomaly network behaviors. Finally, experimental results demonstrate that the proposed algorithm can detect anomaly network traffic with high accuracy and it can also be used in the large scale dataset.


title: Wide Residual Networks for Semantic Segmentation

abstract: In the task of object recognition, convolutional neural networks (CNNs) have achieved high performance. In addition, these CNNs are also applied to the field of semantic image segmentation. However, applying the classification models to semantic segmentation tasks has a problem, lack of global context and reduction in resolution. In this work, we propose global context module and high resolution path in order to solve above problems. By simply combining them with an existing classification model (wide residual networks), our methods yield high-accuracy segmentation models. Our proposed approaches produce competitive results, the mean intersection over union (IoU) 67.6% and global accuracy 91.1%, on CamVid test set.


title: Fuzzy Logic and Deep Steering Control based Recommendation System for Self-Driving Car

abstract: Automatic cruise control system such as steering and velocity control are complex tasks that work as fundamental to all automatic systems in self-driving cars. The challenging task is to deal with steering signals at high speeds and to manage the vehicle’s velocity with significant changes in steering signals. In order to reach a destination with optimized time and stable trajectory, an autonomous vehicle system should utilize steering control and velocity signal in constraints. In this article, we propose a recommendation system based on Fuzzy logic and Deep Steering Neural Networks. Convolution neural networks(CNNs) works as a front-end stage for predicting steering control, and fuzzy logic in a back-end stage works as natural inferences for recommending velocity and adapting new steering control. The front-end stage uses CNNs with the input from raw sensory data, and the output extracts steering control prediction that feeds forward to the back-end stage. Key functions of back-end stage combine a number of dynamic vehicle information including, steering prediction to extract more effective steering control, and velocity for the autonomous car. The convolution neural network was trained on the Udacity driving datasheet with a number of hours of training and testing data, and the all system is built on MATLAB with GPU coder.


title: Non-uniform recursive subdivision surfaces

abstract: Doo-Sabin and Catmull-Clark subdivision surfaces are based on the notion of repeated knot insertion of uniform tensor product B-spline surfaces. This paper develops rules for non-uniform Doo-Sabin and Catmull-Clark surfaces that generalize non-uniform tensor product Bspline surfaces to arbitrary topologies. This added flexibility allows, among other things, the natural introduction of features such as cusps, creases, and darts, while elsewhere maintaining the same order of continuity as their uniform counterparts.


title: Standardization and Its Effects on K-Means Clustering Algorithm

abstract: Data clustering is an important data exploration technique with many applications in data mining. K- means is one of the most well known methods of data mining that partitions a dataset into groups of patterns, many methods have been proposed to improve the performance of the K-means algorithm. Standardization is the central preprocessing step in data mining, to standardize values of features or attributes from different dynamic range into a specific range. In this paper, we have analyzed the performances of the three standardization methods on conventional K-means algorithm. By comparing the results on infectious diseases datasets, it was found that the result obtained by the z-score standardization method is more effective and efficient than min-max and decimal scaling standardization methods.


title: Abortion and subsequent depressive symptoms: an analysis of the National Longitudinal Study of Adolescent Health

abstract: BACKGROUND ::: Twenty states currently require that women seeking abortion be counseled on possible psychological responses, with six states stressing negative responses. The majority of research finds that women whose unwanted pregnancies end in abortion do not subsequently have adverse mental health outcomes; scant research examines this relationship for young women. ::: ::: ::: METHODS ::: Four waves of data from the National Longitudinal Study of Adolescent Health were analyzed. Population-averaged lagged logistic and linear regression models were employed to test the relationship between pregnancy resolution outcome and subsequent depressive symptoms, adjusting for prior depressive symptoms, history of traumatic experiences, and sociodemographic covariates. Depressive symptoms were measured using a nine-item version of the Center for Epidemiologic Studies Depression scale. Analyses were conducted among two subsamples of women whose unwanted first pregnancies were resolved in either abortion or live birth: (1) 856 women with an unwanted first pregnancy between Waves 2 and 3; and (2) 438 women with an unwanted first pregnancy between Waves 3 and 4 (unweighted n's). ::: ::: ::: RESULTS ::: In unadjusted and adjusted linear and logistic regression analyses for both subsamples, there was no association between having an abortion after an unwanted first pregnancy and subsequent depressive symptoms. In fully adjusted models, the most recent measure of prior depressive symptoms was consistently associated with subsequent depressive symptoms. ::: ::: ::: CONCLUSIONS ::: In a nationally representative, longitudinal dataset, there was no evidence that young women who had abortions were at increased risk of subsequent depressive symptoms compared with those who give birth after an unwanted first pregnancy.


title: End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification

abstract: Autoregressive decoding is the only part of sequence-to-sequence models that prevents them from massive parallelization at inference time. Non-autoregressive models enable the decoder to generate all output symbols independently in parallel. We present a novel non-autoregressive architecture based on connectionist temporal classification and evaluate it on the task of neural machine translation. Unlike other non-autoregressive methods which operate in several steps, our model can be trained end-to-end. We conduct experiments on the WMT English-Romanian and English-German datasets. Our models achieve a significant speedup over the autoregressive models, keeping the translation quality comparable to other non-autoregressive models.


title: Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach

abstract: Most applications of hyperspectral imagery require processing techniques which achieve two fundamental goals: 1) detect and classify the constituent materials for each pixel in the scene; 2) reduce the data volume/dimensionality, without loss of critical information, so that it can be processed efficiently and assimilated by a human analyst. The authors describe a technique which simultaneously reduces the data dimensionality, suppresses undesired or interfering spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel vector onto a subspace which is orthogonal to the undesired signatures. This operation is an optimal interference suppression process in the least squares sense. Once the interfering signatures have been nulled, projecting the residual onto the signature of interest maximizes the signal-to-noise ratio and results in a single component image that represents a classification for the signature of interest. The orthogonal subspace projection (OSP) operator can be extended to k-signatures of interest, thus reducing the dimensionality of k and classifying the hyperspectral image simultaneously. The approach is applicable to both spectrally pure as well as mixed pixels. >


title: For micro-expression recognition: Database and suggestions

abstract: Micro-expression is gaining more attention in both the scientific field and the mass media. It represents genuine emotions that people try to conceal, thus making it a promising cue for lie detection. Since micro-expressions are considered almost imperceptible to naked eyes, researchers have sought to automatically detect and recognize these fleeting facial expressions to help people make use of such deception cues. However, the lack of well-established micro-expression databases might be the biggest obstacle. Although several databases have been developed, there may exist some problems either in the approach of eliciting micro-expression or the labeling. We built a spontaneous micro-expression database with rigorous frame spotting, AU coding and micro-expression labeling. This paper introduces how the micro-expressions were elicited in a laboratory situation and how the database was built with the guide of psychology. In addition, this paper proposes issues that may help researchers effectively use micro-expression databases and improve micro-expression recognition. (C) 2014 Elsevier B.V. All rights reserved.


title: Dig1 protects against cell death provoked by glyphosate-based herbicides in human liver cell lines

abstract: Background ::: Worldwide used pesticides containing different adjuvants like Roundup formulations, which are glyphosate-based herbicides, can provoke some in vivo toxicity and in human cells. These pesticides are commonly found in the environment, surface waters and as food residues of Roundup tolerant genetically modified plants. In order to know their effects on cells from liver, a major detoxification organ, we have studied their mechanism of action and possible protection by precise medicinal plant extracts called Dig1.


title: New Methods for Quantification of Visual Photosensitivity Threshold and Symptoms

abstract: PURPOSE ::: Visual photosensitivity is a common symptom difficult to measure and diagnose, and is found in many ocular and neurological disorders. We developed two novel reproducible quantitative assessments of visual photosensitivity. ::: ::: ::: METHODS ::: We designed and built the ocular photosensitivity analyzer (OPA), an automated instrument to determine light intensity visual photosensitivity threshold (VPT), and developed the Visual Light Sensitivity Questionnaire-8 (VLSQ-8), an eight-question survey to assess the presence and severity of photosensitivity symptoms. We evaluated the test-retest variability and obtained normative values of these two approaches in 35 healthy normal subjects, distributed evenly over five age groups from eight to 60 years. Each subject underwent two test sessions, each with VLSQ-8, eye examination, and OPA, four weeks apart, between April 2015 and June 2016. ::: ::: ::: RESULTS ::: Log-transformed VPTs (log10lux) and VLSQ-8 results were highly reproducible between the two sessions (VPT intraclass correlation coefficient [ICC] = 0.86; 95% confidence interval [CI] = 0.71-0.93; binocular testing, VLSQ-items ICC range = 0.53-0.87). No consistent significant differences in VPTs were found with monocular (P = 0.053, session 1) or binocular (P = 0.26) testing. Subjects in age group >30 to 40 years had significantly higher VPTs than those in other age groups (P ≤ 0.011) except the >40 to 50 years age group (P = 0.11). Photosensitivity symptoms assessed by the VLSQ-8 generally were low and highly reproducible with ≥88% of responses between the 2 sessions being within one category of each other. ::: ::: ::: CONCLUSIONS ::: Our results provide reliability data and normative results toward validation of two novel approaches to quantify visual photosensitivity and provide support for their potential use in ocular and neurologic conditions as well as in clinical trials. ::: ::: ::: TRANSLATIONAL RELEVANCE ::: The new quantitative photosensitivity approaches are potential measures to characterize disease severity, monitor disease progression, and evaluate treatment efficacy.


title: Deep Online Video Stabilization With Multi-Grid Warping Transformation Learning

abstract: Video stabilization techniques are essential for most hand-held captured videos due to high-frequency shakes. Several 2D-, 2.5D-, and 3D-based stabilization techniques have been presented previously, but to the best of our knowledge, no solutions based on deep neural networks had been proposed to date. The main reason for this omission is shortage in training data as well as the challenge of modeling the problem using neural networks. In this paper, we present a video stabilization technique using a convolutional neural network. Previous works usually propose an off-line algorithm that smoothes a holistic camera path based on feature matching. Instead, we focus on low-latency, real-time camera path smoothing that does not explicitly represent the camera path and does not use future frames. Our neural network model, called StabNet , learns a set of mesh-grid transformations progressively for each input frame from the previous set of stabilized camera frames and creates stable corresponding latent camera paths implicitly. To train the network, we collect a dataset of synchronized steady and unsteady video pairs via a specially designed hand-held hardware. Experimental results show that our proposed online method performs comparatively to the traditional off-line video stabilization methods without using future frames while running about 10 times faster. More importantly, our proposed StabNet is able to handle low-quality videos, such as night-scene videos, watermarked videos, blurry videos, and noisy videos, where the existing methods fail in feature extraction or matching.


title: Botnet in DDoS Attacks: Trends and Challenges

abstract: Threats of distributed denial of service (DDoS) attacks have been increasing day-by-day due to rapid development of computer networks and associated infrastructure, and millions of software applications, large and small, addressing all varieties of tasks. Botnets pose a major threat to network security as they are widely used for many Internet crimes such as DDoS attacks, identity theft, email spamming, and click fraud. Botnet based DDoS attacks are catastrophic to the victim network as they can exhaust both network bandwidth and resources of the victim machine. This survey presents a comprehensive overview of DDoS attacks, their causes, types with a taxonomy, and technical details of various attack launching tools. A detailed discussion of several botnet architectures, tools developed using botnet architectures, and pros and cons analysis are also included. Furthermore, a list of important issues and research challenges is also reported.


title: Protocols for Public Key Cryptosystems

abstract: New Cryptographic protocols which take full advantage of the unique properties of public key cryptosystems are now evolving. Several protocols for public key distribution and for digital signatures are briefly compared with each other and with the conventional alternative.


title: Inferring User Routes and Locations Using Zero-Permission Mobile Sensors

abstract: Leakage of user location and traffic patterns is a serious security threat with significant implications on privacy as reported by recent surveys and identified by the US Congress Location Privacy Protection Act of 2014. While mobile phones can restrict the explicit access to location information to applications authorized by the user, they are ill-equipped to protect against side-channel attacks. In this paper, we show that a zero-permissions Android app can infer vehicular users' location and traveled routes, with high accuracy and without the users' knowledge, using gyroscope, accelerometer, and magnetometer information. We modeled this problem as a maximum likelihood route identification on a graph. The graph is generated from the OpenStreetMap publicly available database of roads. Our route identification algorithms output both a ranked list of potential routes as well a ranked list of route-clusters. Through extensive simulations over 11 cities, we show that for most cities with probability higher than 50% it is possible to output a short list of 10 routes containing the traveled route. In real driving experiments (over 980 Km) in the cities of Boston (resp. Waltham), Massachusetts, we report a probability of 30% (resp. 60%) of inferring a list of 10 routes containing the true route.