title: Unsupervised Event Tracking by Integrating Twitter and Instagram

abstract: This paper proposes an unsupervised framework for tracking real world events from their traces on Twitter and Instagram. Empirical data suggests that event detection from Instagram streams errs on the false-negative side due to the relative sparsity of Instagram data (compared to Twitter data), whereas event detection from Twitter can suffer from false-positives, at least if not paired with careful analysis of tweet content. To tackle both problems simultaneously, we design a unified unsupervised algorithm that fuses events detected originally on Instagram (called I-events) and events detected originally on Twitter (called T-events), that occur in adjacent periods, in an attempt to combine the benefits of both sources while eliminating their individual disadvantages. We evaluate the proposed framework with real data crawled from Twitter and Instagram. The results indicate that our algorithm significantly improves tracking accuracy compared to baselines.


title: Dynamic resource management in SDN-based virtualized networks

abstract: Network virtualization allows for an abstraction between user and physical resources by letting a given physical infrastructure to be shared by multiple service providers. However, network virtualization presents some challenges, such as, efficient resource management, fast provisioning and scalability. By separating a network's control logic from the underlying routers and switches, software defined networking (SDN) promises an unprecedented simplification in network programmability, management and innovation by service providers, and hence, its control model presents itself as a candidate solution to the challenges in network virtualization. In this paper, we use the SDN control plane to efficiently manage resources in virtualized networks by dynamically adjusting the virtual network (VN) to substrate network (SN) mappings based on network status. We extend an SDN controller to monitor the resource utilisation of VNs, as well as the average loading of SN links and switches, and use this information to proactively add or remove flow rules from the switches. Simulations show that, compared with three state-of-art approaches, our proposal improves the VN acceptance ratio by about 40% and reduces VN resource costs by over 10%.


title: Discovering Data Mining: From Concept to Implementation

abstract: This book teaches newcomers all they need to know to profit from today's powerful data mining technologies.Through extensive case studies and examples, you'll learn how companies are using data mining right now to achieve powerful results -- and how you can do it, too. Learn how data mining can help you build intimate relationships with customers that haven't existed for generations. Discover which areas of your business can benefit from data mining; the business, social and technical issues involved; how to prevent fraud and abuse; and how to make the most of outside consultants. The book also demonstrates IBM's powerful new Intelligent Miner tool and shows how it can be applied.Business and systems analysts; strategic planners; marketing executives; and other managers who can benefit from data mining.


title: Snakeboard motion planning with viscous friction and skidding

abstract: The snakeboard is a well-studied example for mechanical systems analysis, largely because of its simultaneous richness in behavior and simplicity in design. However, few snakeboard models incorporate dissipative friction in the traveling direction and skidding as a violation of the rigid nonholonomic constraints. In this paper we investigate these effects on trajectory planning by evaluating a previously proposed friction model as well as a novel skidding model based on the addition of Rayleigh dissipation functions. We show how these additions change the usual behavior of gaits in the forward planning problem, and incorporate the changes into the solutions of the inverse planning problem by utilizing body coordinates along with a curvature parameterization for trajectories.


title: Depression and quality of sleep in maintenance hemodialysis patients.

abstract: INTRODUCTION ::: Sleep disorders and psychological disturbances are common in end-stage renal disease (ESRD) patients. However, despite their frequency and importance, such conditions often go unnoticed, since all patients do not clearly manifest fully expressed symptoms. ::: ::: ::: OBJECTIVE ::: This study aimed to determine the prevalence of depression and poor sleep quality and to examine the association between these disorders and demographic, clinical and treatment-related characteristics of ESRD patients on hemodialysis (HD). ::: ::: ::: METHODS ::: The study included 222 patients (132 men and 90 women), mean age 57.3 +/- 11.9 years, from 3 HD centers in Central Serbia, which provided us with biochemical parameters and demographic data. Sleep quality and depression were assessed using the Pittsburgh Sleep Quality Index (PSQI) and Beck Depression Inventory (BDI), respectively. ::: ::: ::: RESULTS ::: The average BDI was 16.1 +/- 11.3. Depressed patients were significantly older (p=0.041), had a significantly lower dialysis adequacy (p=0.027) and a significantly worse quality of sleep (p < 0.001), while they did not show significant difference as regarding sex, employment, marital status, comorbidities, dialysis type, dialysis vintage, shift and laboratory parameters.The average PSQI was 7.8 +/- 4.5 and 64.2% of patients were poor sleepers. Poor sleepers were significantly older (p = 0.002), they were more often females (p = 0.027) and had a significantly higher BDI (p < 0.001), while other investigated variables were. not correlated with sleep quality. A statistically significant positive correlation was found between BDI and PSQI (r = 0.604; p < 0.001). ::: ::: ::: CONCLUSION ::: Depression and poor sleep quality are frequent and interrelated among HD patients.


title: Visual Analysis of Time-Series Similarities for Anomaly Detection in Sensor Networks

abstract: We present a system to analyze time-series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo-referenced sensor data, in particular for anomaly detection. We split the recordings into fixed-length patterns and show them in order to compare them over time and space using two linked views. Apart from geo-based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities.


title: Exploring the link between self-compassion and body image in university women

abstract: Abstract The purpose of the present research was to examine the relationships between self-compassion and women's body image. In Study 1, female undergraduates ( N = 142) completed three measures of body image and measures of self-esteem and self-compassion. Results showed that high self-compassion predicted fewer body concerns independently of self-esteem. Moreover, when both self-compassion and self-esteem were included as predictors, self-compassion accounted for unique variance in body preoccupation and weight concerns whereas self-esteem did not. In Study 2, this finding was partially replicated with one component (self-judgment) of self-compassion uniquely predicting body preoccupation in undergraduate women ( N = 187). High scores on self-compassion also predicted less eating guilt independent of self-esteem. Additionally, self-compassion was shown to partially mediate the relationship between body preoccupation and depressive symptoms. The findings highlight the possibility that a consideration of self-compassion for body image may contribute to identifying who is most at risk for body/shape concerns.


The Acquisition of Reading Comprehension Skill


title: The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review

abstract: Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.


title: A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

abstract: RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation >= 0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation >= 0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.


Semantic-based Model Matching with EMFCompare


title: Analyzing Information Flow in JavaScript-Based Browser Extensions

abstract: JavaScript-based browser extensions (JSEs) enhance the core functionality of web browsers by improving their look and feel, and are widely available for commodity browsers. To enable a rich set of functionalities, browsers typically execute JSEs with elevated privileges. For example, unlike JavaScript code in a web application, code in a JSE is not constrained by the same-origin policy. Malicious JSEs can misuse these privileges to compromise confidentiality and integrity, e.g., by stealing sensitive information, such as cookies and saved passwords, or executing arbitrary code on the host system. Even if a JSE is not overtly malicious, vulnerabilities in the JSE and the browser may allow a remote attacker to compromise browser security. We present Sabre (Security Architecture for Browser Extensions), a system that uses in-browser information-flow tracking to analyze JSEs. Sabre associates a label with each in-memory JavaScript object in the browser, which determines whether the object contains sensitive information. Sabre propagates labels as objects are modified by the JSE and passed between browser subsystems. Sabre raises an alert if an object containing sensitive information is accessed in an unsafe way, e.g., if a JSE attempts to send the object over the network or write it to a file. We implemented Sabre by modifying the Firefox browser and evaluated it using both malicious JSEs as well as benign ones that contained exploitable vulnerabilities. Our experiments show that Sabre can precisely identify potential information flow violations by JSEs.


title: Articles Algorithms for Constraint- Satisfaction Problems: A

abstract: A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This article overviews many of these approaches in a tutorial fashion.


title: Comparing the performance of natural, semi-natural, and non-natural locomotion techniques in virtual reality

abstract: One of the goals of much virtual reality (VR) research is to increase realism. In particular, many techniques for locomotion in VR attempt to approximate real-world walking. However, it is not yet fully understood how the design of more realistic locomotion techniques affects user task performance. We performed an experiment to compare a semi-natural locomotion technique (based on the Virtusphere device) with a traditional, non-natural technique (based on a game controller) and a fully natural technique (real walking). We found that the Virtusphere technique was significantly slower and less accurate than both of the other techniques. Based on this result and others in the literature, we speculate that locomotion techniques with moderate interaction fidelity will often have performance inferior to both high-fidelity techniques and well-designed low-fidelity techniques. We argue that our experimental results are an effect of interaction fidelity, and perform a detailed analysis of the fidelity of the three locomotion techniques to support this argument.


title: A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments

abstract: Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.


title: Design of integrated rectangular SIW filter and microstrip patch antenna

abstract: This paper presents a new class of integrated rectangular Substrate Integrated Waveguide (SIW) filter and microstrip patch antenna for RF/microwave front-end subsystems. To validate this concept, the integrated microwave filter and antenna based upon a cascaded approach at center frequency of 2 GHz is demonstrated and verified through simulation and measurement. Experimental results show good agreement with simulated results. This study also proposes a new technique to integrate the rectangular SIW filter and the microstrip patch antenna based upon the multilayered approach. The simulation is implemented using FR-4 on a 1.6 mm dielectric substrate thick with dielectric constant = 4.6. The simulation performance shows promising results that could be further proved during the experimental works. This study is useful for any microwave system design where the reduction of overall physical volume and weight as well as cost is very important.


title: Data Cleaning: Problems and Current Approaches

abstract: We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning.


title: Mindfulness and Meditation Practice as Moderators of the Relationship between Age and Subjective Wellbeing among Working Adults

abstract: Promoting the health and wellbeing of an aging and age-diverse workforce is a timely and growing concern to organizations and to society. To help address this issue, we investigated the relationship between age and subjective wellbeing by examining the moderating role of mindfulness in two independent studies. In study 1, trait mindfulness was examined as a moderator of the relationship between age and vitality and between age and work-family balance in a sample of 240 participants. In study 2, data from the second phase of the Midlife Development in the USA (MIDUS II) project was used to investigate mindful-practice (i.e., meditation) as a moderator of the relationships between age and multiple measures of subjective wellbeing (life satisfaction, psychological health, physical health) in a sample of 2477 adults. Results revealed that mindfulness moderates the relationship between age and multiple indicators of subjective wellbeing. In addition, study 2 results indicated that individuals who reported that they mediated often combined with those who reported they meditated a lot reported better physical health than those who reported that they never meditate. The findings suggest that cultivating mindfulness can be a proactive tool for fostering health and subjective wellbeing in an aging and age-diverse workforce.


title: Semi-Supervised Hashing for Large-Scale Search

abstract: Hashing-based approximate nearest neighbor (ANN) search in huge databases has become popular due to its computational and memory efficiency. The popular hashing methods, e.g., Locality Sensitive Hashing and Spectral Hashing, construct hash functions based on random or principal projections. The resulting hashes are either not very accurate or are inefficient. Moreover, these methods are designed for a given metric similarity. On the contrary, semantic similarity is usually given in terms of pairwise labels of samples. There exist supervised hashing methods that can handle such semantic similarity, but they are prone to overfitting when labeled data are small or noisy. In this work, we propose a semi-supervised hashing (SSH) framework that minimizes empirical error over the labeled set and an information theoretic regularizer over both labeled and unlabeled sets. Based on this framework, we present three different semi-supervised hashing methods, including orthogonal hashing, nonorthogonal hashing, and sequential hashing. Particularly, the sequential hashing method generates robust codes in which each hash function is designed to correct the errors made by the previous ones. We further show that the sequential learning paradigm can be extended to unsupervised domains where no labeled pairs are available. Extensive experiments on four large datasets (up to 80 million samples) demonstrate the superior performance of the proposed SSH methods over state-of-the-art supervised and unsupervised hashing techniques.


Schema Profiling of Document Stores


title: Empirical Support for a Causal Relationship Between Gamification and Learning Outcomes

abstract: Preparing for exams is an important yet stressful time for many students. Self-testing is known to be an effective preparation strategy, yet some students lack motivation to engage or persist in self-testing activities. Adding game elements to a platform supporting self-testing may increase engagement and, by extension, exam performance. We conduct a randomized controlled experiment (n=701) comparing the effect of two game elements -- a points system and a badge system -- used individually and in combination. We find that the badge system elicits significantly higher levels of voluntary self-testing activity and this effect is particularly pronounced amongst a relatively small cohort. Importantly, this increased activity translates to a significant improvement in exam scores. Our data supports a causal relationship between gamification and learning outcomes, mediated by self-testing behavior. This provides empirical support for Landers' theory of gamified learning when the gamified activity is conducted prior to measuring learning outcomes.


title: Speaker dependent speech emotion recognition using MFCC and Support Vector Machine

abstract: In human computer interaction, speech emotion recognition is playing a pivotal part in the field of research. Human emotions consist of being angry, happy, sad, disgust, neutral. In this paper the features are extracted with hybrid of pitch, formants, zero crossing, MFCC and its statistical parameters. The pitch detection is done by cepstral algorithm after comparing it with autocorrelation and AMDF. The training and testing part of the SVM classifier is compared with different kernel function like linear, polynomial, quadratic and RBF. The polish database is used for the classification. The comparison between the different kernels is obtained for the corresponding feature vector.


title: The AES Implantation Based on OpenCL for Multi/many Core Architecture

abstract: In this article we present a study on an implementation, named clAES, of the symmetric key cryptography algorithm Advanced Encryption Standard (AES) using the Open Computing Language (OpenCL) emerging standard. We will show a comparison of the results obtained benchmarking clAES on various multi/many core architectures. We will also introduce the basic concepts of AES and OpenCL in order to describe the details of clAES implementation. This study represents a first step in a broader project which final goal is to develop a full OpenSSL library implementation on heterogeneous computing devices such as multi-core CPUs and GPUs.


title: An Analytical Approach for Dimensioning Mixed Traffic Networks

abstract: We present an analytical method for analyzing and dimensioning a network based communication architecture. The method is based on the classic (sigma, p) network calculus. We use a TDMA approach for creating logically separated networks which makes statistical methods possible for calculations on best effort traffic, and supports implementation of guaranteed bandwidth services by using virtual circuits with looped containers


title: Iterative Residual CNNs for Burst Photography Applications

abstract: Modern inexpensive imaging sensors suffer from inherent hardware constraints which often result in captured images of poor quality. Among the most common ways to deal with such limitations is to rely on burst photography, which nowadays acts as the backbone of all modern smartphone imaging applications. In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model. This, in turn, allows us to restore a single image of higher quality from a sequence of low-quality images as the solution of an optimization problem. Inspired by an extension of the gradient descent method that can handle non-smooth functions, namely the proximal gradient descent, and modern deep learning techniques, we propose a convolutional iterative network with a transparent architecture. Our network uses a burst of low-quality image frames and is able to produce an output of higher image quality recovering fine details which are not distinguishable in any of the original burst frames. We focus both on the burst photography pipeline as a whole, i.e., burst demosaicking and denoising, as well as on the traditional Gaussian denoising task. The developed method demonstrates consistent state-of-the art performance across the two tasks and as opposed to other recent deep learning approaches does not have any inherent restrictions either to the number of frames or their ordering.


title: Distributed Augmented Reality for Collaborative Design Applications

abstract: This paper presents a system for constructing collaborative design applications based on distributed augmented reality. Augmented reality interfaces are a natural method for presenting computer-based design by merging graphics with a view of the real world. Distribution enables users at remote sites to collaborate on design tasks. The users interactively control their local view, try out design options, and communicate design proposals. They share virtual graphical objects that substitute for real objects which are not yet physically created or are not yet placed into the real design environment.


title: Analysis of Programs with Exception-Handling Constructs

abstract: Analysis techniques, such as control flow, data flow, and control dependence, are used for a variety of software engineering tasks, including structural and regression testing, dynamic execution profiling, static and dynamic slicing, and program understanding. To be applicable to programs in languages such as Java and C++, these analysis techniques must account for the effects of exception occurrences and exception handling constructs; failure to do so can cause the analysis techniques to compute incorrect results and, thus, limit the usefulness of the applications that use them. This paper discusses the effects of exception handling constructs on several analysis techniques. The paper presents techniques to construct representations for programs with explicit exception occurrences-exceptions that are raised explicitly through throw statements-and exception handling constructs. The paper presents algorithms that use these representations to perform the desired analyses. The paper also discusses several software engineering applications that use these analyses. Finally, the paper describes empirical results pertaining to the occurrence of exception handling constructs in Java programs and their effect on some analysis tasks.


title: Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications

abstract: Objective. Recent studies have provided evidence that temporal envelope driven speech decoding from high-density electroencephalography (EEG) and magnetoencephalography recordings can identify the attended speech stream in a multi-speaker scenario. The present work replicated the previous high density EEG study and investigated the necessary technical requirements for practical attended speech decoding with EEG. Approach. Twelve normal hearing participants attended to one out of two simultaneously presented audiobook stories, while high density EEG was recorded. An offline iterative procedure eliminating those channels contributing the least to decoding provided insight into the necessary channel number and optimal cross-subject channel configuration. Aiming towards the future goal of near real-time classification with an individually trained decoder, the minimum duration of training data necessary for successful classification was determined by using a chronological cross-validation approach. Main results. Close replication of the previously reported results confirmed the method robustness. Decoder performance remained stable from 96 channels down to 25. Furthermore, for less than 15 min of training data, the subject-independent (pre-trained) decoder performed better than an individually trained decoder did. Significance. Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.


title: Estimator initialization in vision-aided inertial navigation with unknown camera-IMU calibration

abstract: This paper focuses on motion estimation using inertial measurements and observations of naturally occurring point features. To date, this task has primarily been addressed using filtering methods, which track the system state starting from known initial conditions. However, when no prior knowledge of the initial system state is available, (e.g., at the onset of the system's operation), the existing approaches are not applicable. To address this problem, in this work we present algorithms for computing the system's observable quantities (platform attitude and velocity, feature positions, and IMU-camera calibration) directly from the sensor measurements, without any prior knowledge. A key contribution of this work is a convex-optimization based algorithm for computing the rotation matrix between the camera and IMU. We show that once this rotation matrix has been computed, all remaining quantities can be determined by solving a quadratically constrained least-squares problem. To increase their accuracy, the initial estimates are refined by an iterative maximum-likelihood estimator.


title: Solving the moving obstacle path planning problem using embedded variational methods

abstract: An analytically derived algorithm to solve a simple two-dimensional robot planning problem subject to moving obstacle constraints is presented. Normally a variational formulation is intractable since an obstacle-cluttered environment will present multiple trajectories that are locally optimal solutions. To derive an algorithm which produces a unique solution, an embedding method commonly found in homotopic methods is used. A fictitious third dimension is added to the two-dimensional formulation; local (but nonglobal solutions) in the original problem become saddle-point trajectories in the embedded formulation, allowing for convergence of a numerical algorithm to continue along a descent direction. The computational algorithm becomes globally convergent, i.e., convergence to the global solution is achieved regardless of the choice of initial trajectory used to start the algorithm. Simulation results demonstrate the effectiveness of the algorithm. >


title: Investigation of user behavior on social networking sites

abstract: Social networking sites (SNS) are used for social and professional interaction with people. SNS popularity has encouraged researchers to analyze the relationship of activities performed on SNS with user behavior. In doing so, the term “user behavior” is rather used ambiguously with different interpretations, which makes it difficult to identify studies on user behavior in relation to SNS. This phenomenon has encouraged this thorough research on the characteristics of user behavior being discussed in the literature. Therefore, in this study, we aim to identify, analyze, and classify the characteristics associated with user behavior to answer the research questions designed to conduct this research. A mapping study (also called scoping study), which is a type of systematic literature review, is employed to identify potential studies from digital databases through a developed protocol. Thematic analysis is carried out for the classification of user behavior. We identified 116 primary studies for full analysis. This study found seven characteristics associated with behavior that have direct influence on SNS use and nine factors that have an indirect effect. All studies were conducted largely under seven areas that set the context of these studies. Findings show that the research on SNS is still in its early stage. The range of topics covered in the analyzed studies is quite expansive, although the depth in terms of number of studies under each topic is quite limited. This study reports that activities performed on SNS are either associated with user behavior or reflect personality characteristics. The findings of this study could be used by practitioners to evaluate their SNS platforms and develop more user-centered applications. These studies can also help organizations to understand better the needs of their employees.


MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection


title: Decision Support System For Safety Warning Of Bridge – A Case Study In Central Taiwan

abstract: This study aims at developing the decision support system (DSS) for safety warning of bridge. In the DSS, real-time and forecasted radar rainfalls are used to predict flood stage, velocity and scouring depth around bridge piers for one to three hours ahead. The techniques adopted in the DSS include (1) measurement and correction models of radar rainfall, (2) a grid-based distributed rainfall-runoff model for simulating reservoir inflows, (3) models for predicting flood stages, velocities and scouring depths around bridge piers, and (4) ultimate analysis approaches for evaluating safety of pier foundation. The DSS can support the management department to decide whether they should close bridges or not during floods. The proposed DSS gave a test-run during Typhoon Morakot in 2009 in Dajia River Basin, central Taiwan. The results show the DSS has reasonable performances during floods. INTRODUCTION Most of the rivers in Taiwan are short and slope steeply towards the sea, which usually causes floods upstream of the basins when typhoons hit. These floods caused by typhoons seriously threaten the safety of the bridges across the rivers. How to prevent the disasters for protecting people’s life and property in the future is seriously considered by the bridge administrative department. This study aims at developing the safety warning of bridge (SWOB) DSS. This paper is organized in two main topics. The first topic describes how SWOB DSS is developed, including a general overview of its main procedures, structure, components and model used in it. The second topic is the application (preliminary test-run) of SWOB DSS in a case study area in central Taiwan to show how it supports decision-making of safety warning of bridge. SWOB DSS: THE PROPOSED APPROACH SWOB DSS is the proposed approach to address the issue of safety warning of bridge during floods. It constitutes a structured and instructive tool to help decision makers to issue “Safety!”, “Warning!”, or “No thoroughfare!” for bridges during floods. In order to describe the process of developing this approach, it’s helpful to understand the main steps of the decision making process related to the exercise of judging the safety of bridges during floods. The process for supporting judging the safety of bridges and issuing the warning during floods is shown in Figure 1. Figure 1. Process for developing and adjusting strategies. In this process, it’s organized into 5 basic steps. Step 1 is to obtain the real-time and forecasted radar rainfalls (one to three hours ahead) and correct their errors. Step 2 uses the corrected radar rainfalls as the input of a grid-based distributed rainfall-runoff model (GDRRM) (Yu and Jeng, 1997[1]; Wang et al., 2006[2]; and Wang et al., 2008[3]) for estimating the inflows of the control dam (Shihkang Dam). Based on the estimated inflows of the dam, Step 3 adopts the operating rules of the dam to calculate its outflows. Then, Step 4 performs the river hydraulic routing for the downstream of the dam to predict the flood stages and velocities of the downstream bridges, and analyzes the sediment transport for predicting the possible scouring depths around bridge piers during floods. Based on the aforementioned information of scouring depths around bridge piers, Step 5 is to analyze the safety of bridges and decide whether the bridges should be closed or not. The main components of SWOB DSS are the database, the 5 modules, and the web-GIS platform with a user-friendly interface. Database: The database provides the input data including the physiographic data upstream of the dam, raingauge rainfalls, radar rainfalls, inflows/outflows of the dam, downstream flow discharge, etc. Module 1: Error correction for radar rainfalls. The module receives the real-time and forecasted radar rainfalls (one to three hours ahead) and performs the support vector machine regression (SVR) model for constructing the error correction model. For enhancing performances of the SVR, the SVR coupled with the Kriging method was adopted in the DSS. Module 2: Calculating inflows of the dam. The module receives the corrected real-time and forecasted radar rainfalls (one to three hours ahead) from Module I as the input of the GDRRM for estimating the inflows of the dam. Module 3: Calculating outflows of the dam. This module uses the operating rules of the dam to calculate its outflows based on the inflow from Module II. The operating rules of the dam can be gained from its management authority and are programmed into “if-then” statements to relate the inflow and the outflow. Module 4: Predicting stage, velocity and scours. This module performs the river hydraulic routing (CCHE1D model) for the downstream of the dam to predict the flood stages and velocities of the downstream bridges, and performs the sediment transport model for getting possible scouring depths around the bridge piers during floods. Module 5: Analyzing safety of bridges. The ultimate analysis approaches were used to evaluate the safety of pier foundation for providing failure envelops considered scouring depth, river Obtain and correct real-time and forecasted radar rainfalls. Estimate the inflows of the dam. Adopt operating rules of dam to calculate outflows. Perform hydraulic routing and analyze sediment transport. Issue “Safe !”, “Warning !” or “No thoroughfare!” Step 1


title: Framework for Evaluation of Sound Event Detection in Web Videos

abstract: The largest source of sound events is web videos. Most videos lack sound event labels at segment level, however, a significant number of them do respond to text queries, from a match found to their metadata by the search engine. In this paper we explore the extent to which a search query could be used as the true label for the presence of sound events in the videos. For this, we developed a framework for large-scale sound event recognition on web videos. The framework crawls videos using search queries corresponding to 78 sound event labels drawn from three datasets. The datasets are used to train three classifiers, which were then run on 3.7 million video segments. We evaluated performance using the search query as the true label and compare it (on a subset) with human labeling. Both types exhibited close performance, to within 10%, and similar performance trends as the number of evaluated segments increased. Hence, our experiments show potential for using search query as a preliminary true label for sound events in web videos.


title: Why and How Java Developers Break APIs

abstract: Modern software development depends on APIs to reuse code and increase productivity. As most software systems, these libraries and frameworks also evolve, which may break existing clients. However, the main reasons to introduce breaking changes in APIs are unclear. Therefore, in this paper, we report the results of an almost 4-month long field study with the developers of 400 popular Java libraries and frameworks. We configured an infrastructure to observe all changes in these libraries and to detect breaking changes shortly after their introduction in the code. After identifying breaking changes, we asked the developers to explain the reasons behind their decision to change the APIs. During the study, we identified 59 breaking changes, confirmed by the developers of 19 projects. By analyzing the developers' answers, we report that breaking changes are mostly motivated by the need to implement new features, by the desire to make the APIs simpler and with fewer elements, and to improve maintainability. We conclude by providing suggestions to language designers, tool builders, software engineering researchers and API developers.


title: Coverage problems in sensor networks: A survey

abstract: Sensor networks, which consist of sensor nodes each capable of sensing environment and transmitting data, have lots of applications in battlefield surveillance, environmental monitoring, industrial diagnostics, etc. Coverage which is one of the most important performance metrics for sensor networks reflects how well a sensor field is monitored. Individual sensor coverage models are dependent on the sensing functions of different types of sensors, while network-wide sensing coverage is a collective performance measure for geographically distributed sensor nodes. This article surveys research progress made to address various coverage problems in sensor networks. We first provide discussions on sensor coverage models and design issues. The coverage problems in sensor networks can be classified into three categories according to the subject to be covered. We state the basic coverage problems in each category, and review representative solution approaches in the literature. We also provide comments and discussions on some extensions and variants of these basic coverage problems.


title: RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices

abstract: Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.


title: Indirect measures of gene flow and migration: FST≠1/(4Nm+1)

abstract: The difficulty of directly measuring gene flow has lead to the common use of indirect measures extrapolated from genetic frequency data. These measures are variants of FST, a standardized measure of the genetic variance among populations, and are used to solve for Nm, the number of migrants successfully entering a population per generation. Unfortunately, the mathematical model underlying this translation makes many biologically unrealistic assumptions; real populations are very likely to violate these assumptions, such that there is often limited quantitative information to be gained about dispersal from using gene frequency data. While studies of genetic structure per se are often worthwhile, and FST is an excellent measure of the extent of this population structure, it is rare that FST can be translated into an accurate estimate of Nm.


title: Lane detection by orientation and length discrimination

abstract: This paper describes a novel lane detection algorithm for visual traffic surveillance applications under the auspice of intelligent transportation systems. Traditional lane detection methods for vehicle navigation typically use spatial masks to isolate instantaneous lane information from on-vehicle camera images. When surveillance is concerned, complete lane and multiple lane information is essential for tracking vehicles and monitoring lane change frequency from overhead cameras, where traditional methods become inadequate. The algorithm presented in this paper extracts complete multiple lane information by utilizing prominent orientation and length features of lane markings and curb structures to discriminate against other minor features. Essentially, edges are first extracted from the background of a traffic sequence, then thinned and approximated by straight lines. From the resulting set of straight lines, orientation and length discriminations are carried out three-dimensionally with the aid of two-dimensional (2-D) to three-dimensional (3-D) coordinate transformation and K-means clustering. By doing so, edges with strong orientation and length affinity are retained and clustered, while short and isolated edges are eliminated. Overall, the merits of this algorithm are as follows. First, it works well under practical visual surveillance conditions. Second, using K-means for clustering offers a robust approach. Third, the algorithm is efficient as it only requires one image frame to determine the road center lines. Fourth, it computes multiple lane information simultaneously. Fifth, the center lines determined are accurate enough for the intended application.


title: Digital Color Imaging

abstract: This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.


Research on Fair Trading Mechanism of Surplus Power Based on Blockchain


title: DataInk: Direct and Creative Data-Oriented Drawing

abstract: Creating whimsical, personal data visualizations remains a challenge due to a lack of tools that enable for creative visual expression while providing support to bind graphical content to data. Many data analysis and visualization creation tools target the quick generation of visual representations, but lack the functionality necessary for graphics design. Toolkits and charting libraries offer more expressive power, but require expert programming skills to achieve custom designs. In contrast, sketching affords fluid experimentation with visual shapes and layouts in a free-form manner, but requires one to manually draw every single data point. We aim to bridge the gap between these extremes. We propose DataInk, a system supports the creation of expressive data visualizations with rigorous direct manipulation via direct pen and touch input. Leveraging our commonly held skills, coupled with a novel graphical user interface, DataInk enables direct, fluid, and flexible authoring of creative data visualizations.


title: Implementation and experimental investigation of sensorless speed control with initial rotor position estimation for interior permanent magnet synchronous motor drive

abstract: In this paper, a new approach to sensorless speed control and initial rotor position estimation for interior permanent magnet synchronous motor (IPMSM) drive is presented. In rotating condition, speed and rotor position estimation of IPMSM drive are obtained through an extended Kalman filter (EKF) algorithm simply by measurement of the stator line voltages and currents. The main difficulty in developing an EKF for IPMSM is the complexity of the dynamic model expressed in the stationary coordinate system. This model is more complex than that of the surface PMSM, because of the asymmetry of the magnetic circuit. The starting procedure is a problem under sensorless drives, because no information is available before starting. The initial rotor position is estimated by a suitable sequence of voltage pulses intermittently applied to the stator windings at standstill and the measurement of the peak current values of the current leads to the rotor position. Magnetic saturation effect on the saliency is used to distinguish the north magnetic pole from the south. To illustrate our work, we present experimental results for an IPMSM obtained on a floating point digital signal processor (DSP) TMS320C31/40 MHz based control system.


title: Automatic fault detection and diagnosis in complex software systems by information-theoretic monitoring

abstract: Management metrics of complex software systems exhibit stable correlations which can enable fault detection and diagnosis. Current approaches use specific analytic forms, typically linear, for modeling correlations. In this paper we use Normalized Mutual Information as a similarity measure to identify clusters of correlated metrics, without knowing the specific form. We show how we can apply the Wilcoxon Rank-Sum test to identify anomalous behaviour. We present two diagnosis algorithms to locate faulty components: RatioScore, based on the Jaccard Coefficient, and SigScore, which incorporates knowledge of component dependencies. We evaluate our mechanisms in the context of a complex enterprise application. Through fault-injection experiments, we show that we can detect 17 out of 22 faults without any false positives. We diagnose the faulty component in the top five anomaly scores 7 times out of 17 using SigScore, which is 40% better than when system structure is ignored.


title: The reactable: Exploring the synergy between live music performance and tabletop tangible interfaces

abstract: In recent years we have seen a proliferation of musical tables. Believing that this is not just the result of a tabletop trend, in this paper we first discuss several of the reasons for which live music performance and HCI in general, and musical instruments and tabletop interfaces in particular, can lead to a fertile two-way cross-pollination that can equally benefit both fields. After that, we present the reac Table, a musical instrument based on a tabletop interface that exemplifies several of these potential achievements.


title: Distributed-Actuation Mechanism for a Finger-Type Manipulator: Theory and Experiments

abstract: In this paper, a distributed-actuation method has been newly proposed based on a simple sliding-actuation mechanism for a finger-type manipulator design. Based on the spatially distributed force on the proposed sliding-actuation mechanism, it has been shown that the distributed actuation provides additional design freedom to optimize the manipulator performance. To verify the effectiveness of the proposed method, we developed a finger-type manipulator, which consists of four links with three joints, and performed experiments. The experimental results show that the fingertip force of the developed manipulator can be effectively increased and easily managed by the distributed-actuation method.


title: Advanced synthesis technique for extracted pole and NRN filters

abstract: This paper proposes a new synthesis technique for extracted pole and non-resonating nodes (NRN) filter. The circuit elements can be set to the values extracted from the simulation of its electromagnetic (EM) model during the synthesis procedure. Accurate mapping relationship between the physical dimensions and prototype circuit can be achieved with the fully analytical approach. A three pole waveguide filter with one finite transmission zero is designed to illustrate the new procedure. A four pole waveguide filter with two finite transmission zeroes from literature is re-designed and analyzed to show the validity of the proposed approach, accuracy and key differences.


eHealth WBAN : Energy-Efficient and Priority-Based Enhanced IEEE 802 . 15 . 6 CSMA / CA MAC Protocol Wireless Body Area Network


title: Demand-Weighted Completeness Prediction for a Knowledge Base

abstract: In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.


title: Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks

abstract: As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of “block mining” by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network, which is constrained by the computing resource and deployed as an infrastructure for decentralized data management applications. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.


title: dhSegment: A generic deep-learning approach for document segmentation

abstract: In recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. We argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in order to handle the variability of historical series. In this paper, we address multiple tasks simultaneously such as page extraction, baseline extraction, layout analysis or multiple typologies of illustrations and photograph extraction. We propose an open-source implementation of a CNN-based pixel-wise predictor coupled with task dependent post-processing blocks. We show that a single CNN-architecture can be used across tasks with competitive results. Moreover most of the task-specific post-precessing steps can be decomposed in a small number of simple and standard reusable operations, adding to the flexibility of our approach.


title: Author Impact Factor: tracking the dynamics of individual scientific impact

abstract: The impact factor (IF) of scientific journals has acquired a major role in the evaluations of the output of scholars, departments and whole institutions. Typically papers appearing in journals with large values of the IF receive a high weight in such evaluations. However, at the end of the day one is interested in assessing the impact of individuals, rather than papers. Here we introduce Author Impact Factor (AIF), which is the extension of the IF to authors. The AIF of an author A in year t is the average number of citations given by papers published in year t to papers published by A in a period of Δt years before year t. Due to its intrinsic dynamic character, AIF is capable to capture trends and variations of the impact of the scientific output of scholars in time, unlike the h-index, which is a growing measure taking into account the whole career path.


title: Improving speaker recognition performance in the domain adaptation challenge using deep neural networks

abstract: Traditional i-vector speaker recognition systems use a Gaussian mixture model (GMM) to collect sufficient statistics (SS). Recently, replacing this GMM with a deep neural network (DNN) has shown promising results. In this paper, we explore the use of DNNs to collect SS for the unsupervised domain adaptation task of the Domain Adaptation Challenge (DAC).We show that collecting SS with a DNN trained on out-of-domain data boosts the speaker recognition performance of an out-of-domain system by more than 25%. Moreover, we integrate the DNN in an unsupervised adaptation framework, that uses agglomerative hierarchical clustering with a stopping criterion based on unsupervised calibration, and show that the initial gains of the out-of-domain system carry over to the final adapted system. Despite the fact that the DNN is trained on the out-of-domain data, the final adapted system produces a relative improvement of more than 30% with respect to the best published results on this task.


title: Multivariate Statistical Process Control Applied to Sulfate Determination by Sequential Injection Analysis

abstract: A methodology was developed for determining sulfates in water at levels ::: up to 500 mg l ::: -1 ::: using a sequential injection analysis ::: system. The multivariate calibration model avoids the need for the ::: separation of interferents and sample pre-treatment. The trueness of the ::: multivariate calibration model was assessed by comparing the predictions ::: of the model with reference concentrations determined by a reference ::: method using the joint interval test for the slope and intercept of the ::: regression line with errors on both axes. The accuracy, evaluated by the ::: root mean square error of prediction, reached 6.9%. Multivariate ::: statistical process control techniques were used to check the ::: system’s stability before developing the model and the validity of ::: the model when it is used to predict the concentrations of unknown ::: samples.


title: Fabricating BRDFs at high spatial resolution using wave optics

abstract: Recent attempts to fabricate surfaces with custom reflectance functions boast impressive angular resolution, yet their spatial resolution is limited. In this paper we present a method to construct spatially varying reflectance at a high resolution of up to 220dpi, orders of magnitude greater than previous attempts, albeit with a lower angular resolution. The resolution of previous approaches is limited by the machining, but more fundamentally, by the geometric optics model on which they are built. Beyond a certain scale geometric optics models break down and wave effects must be taken into account. We present an analysis of incoherent reflectance based on wave optics and gain important insights into reflectance design. We further suggest and demonstrate a practical method, which takes into account the limitations of existing micro-fabrication techniques such as photolithography to design and fabricate a range of reflection effects, based on wave interference.


title: SourceMiner Evolution: A Tool for Supporting Feature Evolution Comprehension

abstract: Program comprehension is an essential activity to perform software maintenance and evolution. Comprehensibility often encompasses the analysis of individual logical units, called features, which are often scattered through many program modules. Understanding how the feature code is implemented along the software evolution history is essential, for instance, to perform refactoring activities. However, existing tools do not provide means to comprehend the feature code evolution. To overcome this shortcoming, this paper presents a tool called Source Miner Evolution (SME) that provides multiple interactive and coordinated views to comprehend feature code evolution. SME implements a feature-sensitive comparison of multiple program versions. Our usability assessment with experienced developers indicated that SME allows them to efficiently perform recurring comprehension tasks on evolving feature code. The developers' performance was influenced by the combination of visual SME mechanisms, such as colors, tool tips and menu-popup interactions over the features' code elements.


title: Dynamic Word Embeddings

abstract: We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2vec [Mikolov et al., 2013]. These embedding vectors are connected in time through a latent diffusion process. We describe two scalable variational inference algorithms--skip-gram smoothing and skip-gram filtering--that allow us to train the model jointly over all times; thus learning on all data while simultaneously allowing word and context vectors to drift. Experimental results on three different corpora demonstrate that our dynamic model infers word embedding trajectories that are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices.


title: Comparing continuous and intermittent assistance controllers for assistive devices

abstract: This paper introduces a novel assistive device (AD) control strategy that provides intermittent assistance and is capable of encouraging user effort during AD use. The assistance regulation controller (ARC) presented here is designed to regulate joint torque and can be used for arbitrary joint and limb motions without requiring any a priori information about the user's intended motion. Data from eight healthy subjects who performed a forearm tracking experiment using a single degree-of-freedom powered orthosis demonstrate that the ARC is capable of encouraging user effort. On average, subjects generated 40% more joint torque when performing the tracking experiment with the ARC instead of a conventional torque-amplifying controller. The results also demonstrate that the ARC is safe to use for dynamic orthosis-assisted joint motions which require several transitions into and out of the assistance and no assistance states of the controller. As such, the results also confirm that the ARC is an effective assist-as-needed control strategy suitable for use with ADs.


title: Social importance enhances prospective memory: evidence from an event-based task

abstract: ABSTRACTProspective memory performance can be enhanced by task importance, for example by promising a reward. Typically, this comes at costs in the ongoing task. However, previous research has suggested that social importance (e.g., providing a social motive) can enhance prospective memory performance without additional monitoring costs in activity-based and time-based tasks. The aim of the present study was to investigate the influence of social importance in an event-based task. We compared four conditions: social importance, promising a reward, both social importance and promising a reward, and standard prospective memory instructions (control condition). The results showed enhanced prospective memory performance for all importance conditions compared to the control condition. Although ongoing task performance was slowed in all conditions with a prospective memory task when compared to a baseline condition with no prospective memory task, additional costs occurred only when both the social importance a...


title: Articles Computer Models of Creativity

abstract: Creativity isn’t magical. It’s an aspect of normal human intelligence, not a special faculty granted to a tiny elite. There are three forms: combinational, exploratory, and transformational. All three can be modeled by AI—in some cases, with impressive results. AI techniques underlie various types of computer art. Whether computers could “really” be creative isn’t a scientific question but a philosophical one, to which there’s no clear answer. But we do have the beginnings of a scientific understanding of creativity.


title: Lost Shopping! Monocular Localization in Large Indoor Spaces

abstract: In this paper we propose a novel approach to localization in very large indoor spaces (i.e., 200+ store shopping malls) that takes a single image and a floor plan of the environment as input. We formulate the localization problem as inference in a Markov random field, which jointly reasons about text detection (localizing shop's names in the image with precise bounding boxes), shop facade segmentation, as well as camera's rotation and translation within the entire shopping mall. The power of our approach is that it does not use any prior information about appearance and instead exploits text detections corresponding to the shop names. This makes our method applicable to a variety of domains and robust to store appearance variation across countries, seasons, and illumination conditions. We demonstrate the performance of our approach in a new dataset we collected of two very large shopping malls, and show the power of holistic reasoning.


title: Tweeting biomedicine: an analysis of tweets and citations in the biomedical literature

abstract: Data collected by social media platforms have been introduced as new sources for indicators to help measure the impact of scholarly research in ways that are complementary to traditional citation analysis. Data generated from social media activities can be used to reflect broad types of impact. This article aims to provide systematic evidence about how often Twitter is used to disseminate information about journal articles in the biomedical sciences. The analysis is based on 1.4 million documents covered by both PubMed and Web of Science and published between 2010 and 2012. The number of tweets containing links to these documents was analyzed and compared to citations to evaluate the degree to which certain journals, disciplines, and specialties were represented on Twitter and how far tweets correlate with citation impact. With less than 10% of PubMed articles mentioned on Twitter, its uptake is low in general but differs between journals and specialties. Correlations between tweets and citations are low, implying that impact metrics based on tweets are different from those based on citations. A framework using the coverage of articles and the correlation between Twitter mentions and citations is proposed to facilitate the evaluation of novel social-media-based metrics.


title: Support vector machines for speaker and language recognition

abstract: Abstract Support vector machines (SVMs) have proven to be a powerful technique for pattern classification. SVMs map inputs into a high-dimensional space and then separate classes with a hyperplane. A critical aspect of using SVMs successfully is the design of the inner product, the kernel, induced by the high dimensional mapping. We consider the application of SVMs to speaker and language recognition. A key part of our approach is the use of a kernel that compares sequences of feature vectors and produces a measure of similarity. Our sequence kernel is based upon generalized linear discriminants. We show that this strategy has several important properties. First, the kernel uses an explicit expansion into SVM feature space—this property makes it possible to collapse all support vectors into a single model vector and have low computational complexity. Second, the SVM builds upon a simpler mean-squared error classifier to produce a more accurate system. Finally, the system is competitive and complimentary to other approaches, such as Gaussian mixture models (GMMs). We give results for the 2003 NIST speaker and language evaluations of the system and also show fusion with the traditional GMM approach.


title: Learning SURF Cascade for Fast and Accurate Object Detection

abstract: This paper presents a novel learning framework for training boosting cascade based object detector from large scale dataset. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by three key differences. First, the proposed framework adopts multi-dimensional SURF features instead of single dimensional Haar features to describe local patches. In this way, the number of used local patches can be reduced from hundreds of thousands to several hundreds. Second, it adopts logistic regression as weak classifier for each local patch instead of decision trees in the VJ framework. Third, we adopt AUC as a single criterion for the convergence test during cascade training rather than the two trade-off criteria (false-positive-rate and hit-rate) in the VJ framework. The benefit is that the false-positive-rate can be adaptive among different cascade stages, and thus yields much faster convergence speed of SURF cascade. Combining these points together, the proposed approach has three good properties. First, the boosting cascade can be trained very efficiently. Experiments show that the proposed approach can train object detectors from billions of negative samples within one hour even on personal computers. Second, the built detector is comparable to the state-of-the-art algorithm not only on the accuracy but also on the processing speed. Third, the built detector is small in model-size due to short cascade stages.


title: Efficient Broadcast in Structured P 2 P Networks

abstract: In this position paper, we present an efficient algorithm for performing a broadcast operation with minimal cost in structured DHT-based P2P networks. In a system of N nodes, a broadcast message originating at an arbitrary node reaches all other nodes after exactly N − 1 messages. We emphasize the perception of a class of DHT systems as a form of distributed k-ary search and we take advantage of that perception in constructing a spanning tree that is utilized for efficient broadcasting. We consider broadcasting as a basic service that adds to existing DHTs the ability to search using arbitrary queries as well as dissiminate/collect global information.


title: RES: Regularized Stochastic BFGS Algorithm

abstract: RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second order methods, on the other hand, is impracticable because computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both, the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian egeinvalues of the sample functions are sufficient to guarantee convergence to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.


Deep Convolution Neural Networks for Pulmonary Nodule Detection in CT imaging


An Overview of the Developmental Process for the Modular Prosthetic Limb


title: Piano AR: A Markerless Augmented Reality Based Piano Teaching System

abstract: This paper presents a novel markerless augmented reality based piano teaching system, which tracks the real keyboard of piano naturally. Following the virtual hands augmented on the keyboard, beginners of piano can practice playing piano. Instead of the marker in traditional AR system, the geometrical parameters of the piano keyboard are detected to calculate the transformation matrix from keyboard coordinate to camera coordinate. We first extract all the contours of the image, from which possible contours of keyboard are found. Then the area of keyboard is identified by the structure of white and black keys predefined. We evaluated the system with speed and accuracy. As a result, the system operates at 15fps, and the average error worked out by our method is about 1.97 pixels. The usefulness of our approach is reflected in an example that a user learns to play piano.


Weakness in Slovenia, Croatia and Serbia Mapping the Paths of the Yugoslav Model: Labour Strength and


title: BitDeposit: Deterring Attacks and Abuses of Cloud Computing Services through Economic Measures

abstract: Dependability in cloud computing applications can be negatively affected by various attacks or service abuses. To come ahead of this threat, we propose an economic measure to deter attacks and various service abuses in cloud computing applications. Our proposed defense is based on requiring a service user to pay a small deposit, using digital currency, before invoking the service. Once they are done using the service, and there has been no detected abuse or attack, the deposit is paid back by the service provider to the service user. If an attack or an abuse is detected, the service user is not paid back and the service provider gets to keep the deposit. We propose the use of micro payments with a decentralized nature and small transaction fees, such as the Bit coin digital currency. Moreover, thanks to the existence of money exchanges which convert the Bit coin currency to real world currency, service providers can recoup loses when they exchange the confiscated deposits for real world currency.


title: Face Liveness Detection Based on Skin Blood Flow Analysis

abstract: Face recognition systems have been widely adopted for user authentication in security systems due to their simplicity and effectiveness. However, spoofing attacks, including printed photos, displayed photos, and replayed video attacks, are critical challenges to authentication, and these spoofing attacks allow malicious invaders to gain access to the system. This paper proposes two novel features for face liveness detection systems to protect against printed photo attacks and replayed attacks for biometric authentication systems. The first feature obtains the texture difference between red and green channels of face images inspired by the observation that skin blood flow in the face has properties that enable distinction between live and spoofing face images. The second feature estimates the color distribution in the local regions of face images, instead of whole images, because image quality might be more discriminative in small areas of face images. These two features are concatenated together, along with a multi-scale local binary pattern feature, and a support vector machine classifier is trained to discriminate between live and spoofing face images. The experimental results show that the performance of the proposed method for face spoof detection is promising when compared with that of previously published methods. Furthermore, the proposed system can be implemented in real time, which is valuable for mobile applications.


title: MT-spike: A multilayer time-based spiking neuromorphic architecture with temporal error backpropagation

abstract: Modern deep learning enabled artificial neural networks, such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN), have achieved a series of breaking records on a broad spectrum of recognition applications. However, the enormous computation and storage requirements associated with such deep and complex neural network models greatly challenge their implementations on resource-limited platforms. Time-based spiking neural network has recently emerged as a promising solution in Neuromorphic Computing System designs for achieving remarkable computing and power efficiency within a single chip. However, the relevant research activities have been narrowly concentrated on the biological plausibility and theoretical learning approaches, causing inefficient neural processing and impracticable multilayer extension thus significantly limitations on speed and accuracy when handling the realistic cognitive tasks. In this work, a practical multilayer time-based spiking neuromorphic architecture, namely "MT-Spike", is developed to fill this gap. With the proposed practical time-coding scheme, average delay response model, temporal error backpropagation algorithm, and heuristic loss function, "MT-Spike" achieves more efficient neural processing through flexible neural model size reduction while offering very competitive classification accuracy for realistic recognition tasks. Simulation results well validated that the algorithmic power of deep multi-layer learning can be seamlessly merged with the efficiency of time-based spiking neuromorphic architecture, demonstrating great potentials of "MT-Spike" in resource and power constrained embedded platforms.


title: Study of a 5.8 GHz frequency band patch antenna integrated into a vehicle for automotive DSRC applications

abstract: This paper presents a simulation study of a 5.8 GHz patch antenna integrated into a vehicle for Dedicated Short Range Communication applications (DSRC). The DSRC deals with car-to-car and car-to-infrastructure communications and will permit to improve road transport by using information and communication technologies. This study provides simulation results validated by measurements that enable to assess the effects of car structures on radiation patterns.


title: Polynomial Optics: A Construction Kit for Efficient Ray-Tracing of Lens Systems

abstract: Simulation of light transport through lens systems plays an important role in graphics. While basic imaging properties can be conveniently derived from linear models (like ABCD matrices), these approximations fail to describe nonlinear effects and aberrations that arise in real optics. Such effects can be computed by proper ray tracing, for which, however, finding suitable sampling and filtering strategies is often not a trivial task. Inspired by aberration theory, which describes the deviation from the linear ray transfer in terms of wavefront distortions, we propose a ray-space formulation for nonlinear effects. In particular, we approximate the analytical solution to the ray tracing problem by means of a Taylor expansion in the ray parameters. This representation enables a construction-kit approach to complex optical systems in the spirit of matrix optics. It is also very simple to evaluate, which allows for efficient execution on CPU and GPU alike, including the computation of mixed derivatives of any order. We evaluate fidelity and performance of our polynomial model, and show applications in high-quality offline rendering and at interactive frame rates.


title: Iguana: a practical data-dependent parsing framework

abstract: Data-dependent grammars extend context-free grammars with arbitrary computation, variable binding, and constraints. These features provide the user with the freedom and power to express syntactic constructs outside the realm of context-free grammars, e.g., indentation rules in Haskell and type definitions in C. Data-dependent grammars have been recently presented by Jim et al. as a grammar formalism that enables construction of parsers from a rich format specification. Although some features of data-dependent grammars are available in current parsing tools, e.g., semantic predicates in ANTLR, data-dependent grammars have not yet fully found their way into practice. In this paper we present Iguana, a data-dependent parsing framework, implemented on top of the GLL parsing algorithm. In addition to basic features of data-dependent grammars, Iguana also provides high-level syntactic constructs, e.g., for operator precedence and indentation rules, which are implemented as desugaring to data-dependent grammars. These high-level constructs enable a concise and declarative way to define the syntax of programming languages. Moreover, Iguana's extensible data-dependent grammar API allows the user to easily add new high-level constructs or modify existing ones. We have used Iguana to parse various real programming languages, such as OCaml, Haskell, Java, and C#. In this paper we describe the architecture and features of Iguana, and report on its current implementation status.


title: Design of Intelligent Ambulance and Traffic Control

abstract: This paper represents the unique feature which is very useful to ambulance drivers to take an alternate route in case of congestion. The various performance evaluation criteria are average waiting time, average distance traveled by vehicles, switching frequency of green light at a junction, efficient emergency mode operation and satisfactory operation of SMS using GSM Mobile. The performance of the Intelligent Traffic Light Controller is compared with the Fixed Mode Traffic Light Controller. It is observed that the proposed Intelligent Traffic Light Controller is more efficient than the conventional controller in respect of less waiting time, more distance traveled by average vehicles and efficient operation during emergency mode and GSM interface. Moreover, the designed system has simple architecture, fast response time, user friendliness and scope for further expansion.


title: Pose and illumination invariant face recognition using binocular stereo 3D reconstruction

abstract: Traditional 2D face recognition systems drastically fails with pose variance and poor illuminations. Many techniques but with limited success has been introduced. Expensive 3D setup can be used to deal with this problem. In this work a low cost, low computation and quick good quality 3D reconstruction helping 2D face recognition systems is proposed. The proposed system is a fast automatic 3D face reconstruction approach from rectified stereo images. An automatic synthesis of training images of various face poses is proposed. Three enhancements adaptive histogram equalization (AHE) to improve contrast of face images, horizontal gradient ordinal relationship pattern(HGORP) to handle poor illumination and steerable filter(SF) for noise reduction and illumination invariance are used to improve the system performance. Later SURF based matching is done with score level fusion of all three enhancements. A database of 107 subjects has been collected to evaluate the system performance. It is observed that the proposed system can handle large pose variations and poor illumination very well.


abstract: In this paper, we propose a tool, named Static UML Model Generator from Analysis of Requirements (SUGAR), which generates both use-case and class models by emphasizing on natural language requirements. SUGAR aims at integrating both requirement analysis and design phases by identifying use-cases, actors, classes along with its attributes and methods with proper association among classes. This tool extends the idea of previously existing tools and implemented with the help of efficient natural language processing tools of Stanford NLP Group, WordNet and JavaRAP using the modified approach of Rational Unified Process with better accuracy. SUGAR has added new features and also able to incorporate solution for those problems existed in previous tools by developing both analysis and design class models. SUGAR generates all static UML models in Java in conjunction with Rational Rose and provides all functionalities of the system even though the developer is having less domain knowledge.


title: A General Framework for Regularized, Similarity-Based Image Restoration

abstract: Any image can be represented as a function defined on a weighted graph, in which the underlying structure of the image is encoded in kernel similarity and associated Laplacian matrices. In this paper, we develop an iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian. We propose a cost function, which consists of a new data fidelity term and regularization term derived from the specific definition of the normalized graph Laplacian. The normalizing coefficients used in the definition of the Laplacian and associated regularization term are obtained using fast symmetry preserving matrix balancing. This results in some desired spectral properties for the normalized Laplacian such as being symmetric, positive semidefinite, and returning zero vector when applied to a constant image. Our algorithm comprises of outer and inner iterations, where in each outer iteration, the similarity weights are recomputed using the previous estimate and the updated objective function is minimized using inner conjugate gradient iterations. This procedure improves the performance of the algorithm for image deblurring, where we do not have access to a good initial estimate of the underlying image. In addition, the specific form of the cost function allows us to render the spectral analysis for the solutions of the corresponding linear equations. In addition, the proposed approach is general in the sense that we have shown its effectiveness for different restoration problems, including deblurring, denoising, and sharpening. Experimental results verify the effectiveness of the proposed algorithm on both synthetic and real examples.


title: Factorized adaptation for deep neural network

abstract: In this paper, we propose a novel method to adapt contextdependent deep neural network hidden Markov model (CD-DNNHMM) with only limited number of parameters by taking into account the underlying factors that contribute to the distorted speech signal. We derive this factorized adaptation method from the perspectives of joint factor analysis and vector Taylor series expansion, respectively. Evaluated on Aurora 4, the proposed method can get 19.0% and 10.6% relative word error rate reduction on test set B and D with only 20 adaptation utterances, and can have decent improvement with as few as two adaptation utterances. We also show that the proposed method is better than feature discriminative linear regression (fDLR), an existing DNN adaptation method. Its small number of parameters and short training time offer an attractive solution to low-footprint speech applications.


title: The Sharing Economy Meets the Semantic Web: An Ontology for the Matchmaking of Peers

abstract: The Sharing Economy with the central theme of “access over ownership” gains popularity and is receiving a lot of coverage by the media. Companies like UBER, AIRBNB and BLABLACAR are valued in the billions and enable individuals to act as private taxi drivers, rent out their apartments or participate in ridesharing. Besides those prominent companies, there are platforms that facilitate the sharing of tools, miscellaneous types of equipment, 3D printers and even pets. A peer searching for a specific resource to borrow needs to at first identify relevant websites and then look through various offers to find an appropriate one. This laborious process can be eased by making use of Semantic Web technologies. Using the Design Science Research Methodology (DSRM), we synthesize the characteristics of various exemplary Sharing Economy websites with regard to the description of resources and services that can be shared including the transaction details and other relevant components. Following up, core concepts serving as a foundation for the conceptualization and the design of a description language in the form of an ontology are derived. The description language is based on Semantic Web technologies to enable efficient semantic search and matchmaking using established standards like RDF(S), OWL, and SPARQL. The underlying idea is to abstract from proprietary implementations in order to provide a common ground for service description in the domain of the Sharing Economy. Thus, a search against multiple websites could be facilitated to improve the discoverability and comparability of individual offers on different websites.


title: Treatment of chronic hepatitis C: another therapeutic option

abstract: Ribavirin is a nucleoside analogue with antiviral activity against a number of DNA and RNA viruses. That molecule is administered per os and its most frequent adverse effect is haemolysis, moderate in most cases. Used alone, ribavirin normalized liver enzyme concentrations in 45% of patients while viraemia is not significantly modified. Histological improvement is observed in responder patients. The most interesting results have been obtained by associating ribavirin with interferon. In patients who have never been treated, a six-month course of ribavirin-interferon association produced a lasting and total response in 47% of patients versus 25% in patients treated with interferon alone. In relapsers, this bi-therapy produced total and prolonged response in more than 40% of cases and in 20% of non-responder patients. Lastly, that association appears promising in treating hepatitis C reinfection after liver transplantation.


title: Technology trends of access control in IoT and requirements analysis

abstract: Since IoT devices can cause problems, such as invasion of privacy and threat to our safety, security in IoT is the most important element. IoT is an environment in which various devices to communicate an environment in which various devices communicate with one another without user intervention or with minimal user intervention. Therefore, authentication and access control technology between IoT devices are important element in the IoT security. In this paper, we describe our survey of access control technique in IoT environment and requirements of it.


title: The Configurable SAT Solver Challenge (CSSC)

abstract: It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014.


THE STRATEGIC DEPLOYMENT OF THE CHILD PSYCHIATRIST IN PREVENTIVE PSYCHIATRY


title: Big data privacy issues in public social media

abstract: Big Data is a new label given to a diverse field of data intensive informatics in which the datasets are so large that they become hard to work with effectively. The term has been mainly used in two contexts, firstly as a technological challenge when dealing with dataintensive domains such as high energy physics, astronomy or internet search, and secondly as a sociological problem when data about us is collected and mined by companies such as Facebook, Google, mobile phone companies, retail chains and governments. In this paper we look at this second issue from a new perspective, namely how can the user gain awareness of the personally relevant part Big Data that is publicly available in the social web. The amount of user-generated media uploaded to the web is expanding rapidly and it is beyond the capabilities of any human to sift through it all to see which media impacts our privacy. Based on an analysis of social media in Flickr, Locr, Facebook and Google+, we discuss privacy implications and potential of the emerging trend of geo-tagged social media. We then present a concept with which users can stay informed about which parts of the social Big Data deluge is relevant to them.


title: Survey of Model-Based Reinforcement Learning: Applications on Robotics

abstract: Reinforcement learning is an appealing approach for allowing robots to learn new tasks. Relevant literature reveals a plethora of methods, but at the same time makes clear the lack of implementations for dealing with real life challenges. Current expectations raise the demand for adaptable robots. We argue that, by employing model-based reinforcement learning, the—now limited—adaptability characteristics of robotic systems can be expanded. Also, model-based reinforcement learning exhibits advantages that makes it more applicable to real life use-cases compared to model-free methods. Thus, in this survey, model-based methods that have been applied in robotics are covered. We categorize them based on the derivation of an optimal policy, the definition of the returns function, the type of the transition model and the learned task. Finally, we discuss the applicability of model-based reinforcement learning approaches in new applications, taking into consideration the state of the art in both algorithms and hardware.


title: 3D attention-driven depth acquisition for object identification

abstract: We address the problem of autonomously exploring unknown objects in a scene by consecutive depth acquisitions. The goal is to reconstruct the scene while online identifying the objects from among a large collection of 3D shapes. Fine-grained shape identification demands a meticulous series of observations attending to varying views and parts of the object of interest. Inspired by the recent success of attention-based models for 2D recognition, we develop a 3D Attention Model that selects the best views to scan from, as well as the most informative regions in each view to focus on, to achieve efficient object recognition. The region-level attention leads to focus-driven features which are quite robust against object occlusion. The attention model, trained with the 3D shape collection, encodes the temporal dependencies among consecutive views with deep recurrent networks. This facilitates order-aware view planning accounting for robot movement cost. In achieving instance identification, the shape collection is organized into a hierarchy, associated with pre-trained hierarchical classifiers. The effectiveness of our method is demonstrated on an autonomous robot (PR) that explores a scene and identifies the objects to construct a 3D scene model.


FFT-based Terrain Segmentation for Underwater Mapping