title: An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

abstract: An adaptive and fast constant false alarm rate (CFAR) algorithm based on automatic censoring (AC) is proposed for target detection in high-resolution synthetic aperture radar (SAR) images. First, an adaptive global threshold is selected to obtain an index matrix which labels whether each pixel of the image is a potential target pixel or not. Second, by using the index matrix, the clutter environment can be determined adaptively to prescreen the clutter pixels in the sliding window used for detecting. The G 0 distribution, which can model multilook SAR images within an extensive range of degree of homogeneity, is adopted as the statistical model of clutter in this paper. With the introduction of AC, the proposed algorithm gains good CFAR detection performance for homogeneous regions, clutter edge, and multitarget situations. Meanwhile, the corresponding fast algorithm greatly reduces the computational load. Finally, target clustering is implemented to obtain more accurate target regions. According to the theoretical performance analysis and the experiment results of typical real SAR images, the proposed algorithm is shown to be of good performance and strong practicability.


title: Financial time series forecasting using support vector machines

abstract: Abstract Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.


title: The Cityscapes Dataset

abstract: Semantic understanding of urban street scenes through visual perception has been widely studied due to many possible practical applications. Key challenges arise from the high visual complexity of such scenes. In this paper, we present ongoing work on a new large-scale dataset for (1) assessing the performance of vision algorithms for different tasks of semantic urban scene understanding, including scene labeling, instance-level scene labeling, and object detection; (2) supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks. We aim to provide a large and diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5000 frames in addition to a larger set of weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Several aspects are still up for discussion, and timely feedback from the community would be greatly appreciated. Details on annotated classes and examples will be available at www. cityscapes-dataset.net. Moreover, we will use this website to collect remarks and suggestions.


title: Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

abstract: Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.


title: Deep-Person: Learning Discriminative Deep Features for Person Re-Identification

abstract: Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context between these parts is ignored for the independent extractor to each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of body parts from head to foot. Integrating the contextual information strengthens the discriminative ability of local representation. We also leverage the complementary information between local and global feature. Furthermore, we integrate both identification task and ranking task in one network, where a discriminative embedding and a similarity measurement are learned concurrently. This results in a novel three-branch framework named Deep-Person, which learns highly discriminative features for person Re-ID. Experimental results demonstrate that Deep-Person outperforms the state-of-the-art methods by a large margin on three challenging datasets including Market-1501, CUHK03, and DukeMTMC-reID. Specifically, combining with a re-ranking approach, we achieve a 90.84% mAP on Market-1501 under single query setting.


title: Global Burden of Cardiovascular Diseases: Part II: Variations in Cardiovascular Disease by Specific Ethnic Groups and Geographic Regions and Prevention Strategies

abstract: This two-part article provides an overview of the global burden of atherothrombotic cardiovascular disease. Part I initially discusses the epidemiological transition which has resulted in a decrease in deaths in childhood due to infections, with a concomitant increase in cardiovascular and other chronic diseases; and then provides estimates of the burden of cardiovascular (CV) diseases with specific focus on the developing countries. Next, we summarize key information on risk factors for cardiovascular disease (CVD) and indicate that their importance may have been underestimated. Then, we describe overarching factors influencing variations in CVD by ethnicity and region and the influence of urbanization. Part II of this article describes the burden of CV disease by specific region or ethnic group, the risk factors of importance, and possible strategies for prevention.


title: Sensitivity of Normality Tests to Non-normal Data (Kepekaan Ujian Kenormalan Terhadap Data Tidak Normal)

abstract: In many statistical analyses, data need to be approximately normal or normally distributed. The Kolmogorov-Smirnov test, Anderson-Darling test, Cramer-von Mises test, and Shapiro-Wilk test are four statistical tests that are widely used for checking normality. One of the factors that influence these tests is the sample size. Given any test of normality mentioned, this study determined the sample sizes at which the tests would indicate that the data is not normal. The performance of the tests was evaluated under various spectrums of non-normal distributions and different sample sizes. The results showed that the Shapiro-Wilk test is the best normality test because this test rejects the null hypothesis of normality test at the smallest sample size compared to the other tests, for all levels of skewness and kurtosis of these distributions.


title: SUITOR: an attentive information system

abstract: Attentive systems pay attention to what users do so that they can attend to what users need. Such systems track user behavior, model user interests, and anticipate user desires and actions. Because the general class of attentive systems is broad — ranging from human butlers to web sites that profile users — we have focused specifically on attentive information systems , which observe user actions with information resources, model user information states, and suggest information that might be helpful to users. In particular, we describe an implemented system, Simple User Interest Tracker (Suitor), that tracks computer users through multiple channels — gaze, web browsing, application focus — to determine their interests and to satisfy their information needs. By observing behavior and modeling users, Suitor finds and displays potentially relevant information that is both timely and non-disruptive to the users' ongoing activities.


title: Designing an effective vibration-based notification interface for mobile phones

abstract: We conducted an experiment to understand how mobile phone users perceive the urgency of ten simple vibration alerts that were created from four basic signals: short on, short off, long on, and long off. The short and long signals correspond to 200 ms and 600 ms, respectively. To convey the level of urgency of notifications and help users prioritize them, the design of mobile phone vibration alerts should consider that the gap length preceding or succeeding a signal, the number of gaps in the vibration pattern, and the vibration's duration affect an alert's perceived level of urgency. Our study specifically shows that shorter gap lengths between vibrations (200 ms vs. 600 ms), a vibration pattern with one gap instead of two, and shorter vibration all contribute to making the user perceive the alert as more urgent.


title: Approximate Queries and Representations for Large Data Sequences

abstract: Many new database application domains such as experimental sciences and medicine are characterized by large sequences as their main form of data. Using approximate representation can significantly reduce the required storage and search space. A good choice of representation, can support a broad new class of approximate queries, needed in there domains. These queries are concerned with application dependent features of the data as opposed to the actual sampled points. We introduce a new notion of generalized approximate queries and a general divide and conquer approach that supports them. This approach uses families of real-valued functions as an approximate representation. We present an algorithm for realizing our technique, and the results of applying it to medical cardiology data.


title: A Kinect-based system for cognitive rehabilitation exercises monitoring

abstract: In this paper, a 3D computer vision system for cognitive assessment and rehabilitation based on the Kinect device is presented. It is intended for individuals with body scheme dysfunctions and left-right confusion. The system processes depth information to overcome the shortcomings of a previously presented 2D vision system for the same application. It achieves left and right-hand tracking, and face and facial feature detection (eye, nose, and ears) detection. The system is easily implemented with a consumer-grade computer and an affordable Kinect device and is robust to drastic background and illumination changes. The system was tested and achieved a successful monitoring percentage of 96.28%. The automation of the human body parts motion monitoring, its analysis in relation to the psychomotor exercise indicated to the patient, and the storage of the result of the realization of a set of exercises free the rehabilitation experts of doing such demanding tasks. The vision-based system is potentially applicable to other tasks with minor changes.


title: Image Analysis Using Mathematical Morphology

abstract: For the purposes of object or defect identification required in industrial vision applications, the operations of mathematical morphology are more useful than the convolution operations employed in signal processing because the morphological operators relate directly to shape. The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations. Examples are given for each morphological concept and explanations are given for many of their interrelationships.


title: A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future

abstract: This paper aims to describe the role of advanced sensing systems in the electric grid of the future. In detail, the project, development, and experimental validation of a smart power meter are described in the following. The authors provide an outline of the potentialities of the sensing systems and IoT to monitor efficiently the energy flow among nodes of an electric network. The described power meter uses the metrics proposed in the IEEE Standard 1459–2010 to analyze and process voltage and current signals. Information concerning the power consumption and power quality could allow the power grid to route efficiently the energy by means of more suitable decision criteria. The new scenario has changed the way to exchange energy in the grid. Now, energy flow must be able to change its direction according to needs. Energy cannot be now routed by considering just only the criterion based on the simple shortening of transmission path. So, even energy coming from a far node should be preferred, if it has higher quality standards. In this view, the proposed smart power meter intends to support the smart power grid to monitor electricity among different nodes in an efficient and effective way.


Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques: A Review


Scalable and Distributed Key-Value Store-based Data Management Using RDMA-Memcached


title: Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning

abstract: Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a deep transfer learning scheme, called selective joint fine-tuning, for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. However, the source learning task does not use all existing training data. Our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. Specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. Experiments demonstrate that our deep transfer learning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. Such tasks include Caltech 256, MIT Indoor 67, and fine-grained classification problems (Oxford Flowers 102 and Stanford Dogs 120). In comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model. Codes and models are available at https://github.com/ZYYSzj/Selective-Joint-Fine-tuning.


title: Conditional image generation using feature-matching GAN

abstract: Generative Adversarial Net is a frontier method of generative models for images, audios and videos. In this paper, we focus on conditional image generation and introduce conditional Feature-Matching Generative Adversarial Net to generate images from category labels. By visualizing state-of-art discriminative conditional generative models, we find these networks do not gain clear semantic concepts. Thus we design the loss function in the light of metric learning to measure semantic distance. The proposed model is evaluated on several well-known datasets. It is shown to be of higher perceptual quality and better diversity then existing generative models.


title: Approximate Maximin Shares for Groups of Agents

abstract: We investigate the problem of fairly allocating indivisible goods among interested agents using the concept of maximin share. Procaccia and Wang showed that while an allocation that gives every agent at least her maximin share does not necessarily exist, one that gives every agent at least $2/3$ of her share always does. In this paper, we consider the more general setting where we allocate the goods to groups of agents. The agents in each group share the same set of goods even though they may have conflicting preferences. For two groups, we characterize the cardinality of the groups for which a constant factor approximation of the maximin share is possible regardless of the number of goods. We also show settings where an approximation is possible or impossible when there are several groups.


title: Different Implementations of AES Cryptographic Algorithm

abstract: Currently, AES is regarded as the most popular symmetric cryptographic algorithm. It is very significant to develop high performance AES to further broaden its widespread applications. And in this paper, it is mainly about the different optimized designs and implementations of AES algorithm. Firstly, it tests the fast implementation of AES algorithm and the performance has been improved by about 50 times when compared to the standard AES algorithm, Secondly, using the Intel AES-NI extended instruction sets, and the performance has been improved by about 50 times compared with the fast implementation of AES algorithm, Finally, using CUDA and GPU to execute the AES in parallel, and it can improve the performance by about 18 times compared with the fast implementation of AES algorithm.


title: Recruiting Hard-to-Reach Populations for Survey Research: Using Facebook and Instagram Advertisements and In-Person Intercept in LGBT Bars and Nightclubs to Recruit LGBT Young Adults

abstract: BACKGROUND ::: Tobacco public education campaigns focus increasingly on hard-to-reach populations at higher risk for smoking, prompting campaign creators and evaluators to develop strategies to reach hard-to-reach populations in virtual and physical spaces where they spend time. ::: ::: ::: OBJECTIVE ::: The aim of this study was to describe two novel recruitment strategies (in-person intercept interviews in lesbian, gay, bisexual, and transgender [LGBT] social venues and targeted social media ads) and compares characteristics of participants recruited via these strategies for the US Food and Drug Administration's This Free Life campaign evaluation targeting LGBT young adults who smoke cigarettes occasionally. ::: ::: ::: METHODS ::: We recruited LGBT adults aged 18-24 years in the United States via Facebook and Instagram ads (N=1709, mean age 20.94, SD 1.94) or intercept in LGBT social venues (N=2348, mean age 21.98, SD 1.69) for the baseline evaluation survey. Covariates related to recruitment strategy were age; race or ethnicity; LGBT identity; education; pride event attendance; and alcohol, cigarette, and social media use. ::: ::: ::: RESULTS ::: Lesbian or gay women (adjusted odds ratio, AOR 1.88, 95% CI 1.54-2.29, P<.001), bisexual men and women (AOR 1.46, 95% CI 1.17-1.82, P=.001), gender minorities (AOR 1.68, 95% CI 1.26-2.25, P<.001), and other sexual minorities (AOR 2.48, 95% CI 1.62-3.80, P<.001) were more likely than gay men to be recruited via social media (than intercept). Hispanic (AOR 0.73, 95% CI 0.61-0.89, P=.001) and other or multiracial, non-Hispanic participants (AOR 0.70, 95% CI 0.54-0.90, P=.006) were less likely than white, non-Hispanic participants to be recruited via social media. As age increased, odds of recruitment via social media decreased (AOR 0.76, 95% CI 0.72-0.80, P<.001). Participants with some college education (AOR 1.27, 95% CI 1.03-1.56, P=.03) were more likely than those with a college degree to be recruited via social media. Participants reporting past 30-day alcohol use were less likely to be recruited via social media (AOR 0.33, 95% CI 0.24-0.44, P<.001). Participants who reported past-year pride event attendance were more likely to be recruited via social media (AOR 1.31, 95% CI 1.06-1.64, P=.02), as well as those who used Facebook at least once daily (AOR 1.43, 95% CI 1.14-1.80, P=.002). Participants who reported using Instagram at least once daily were less likely to be recruited via social media (AOR 0.73, 95% CI 0.62-0.86, P<.001). Social media recruitment was faster (incidence rate ratio, IRR=3.31, 95% CI 3.11-3.52, P<.001) and less expensive (2.2% of combined social media and intercept recruitment cost) but had greater data quality issues-a larger percentage of social media respondents were lost because of duplicate and low-quality responses (374/4446, 8.41%) compared with intercept respondents lost to interviewer misrepresentation (15/4446, 0.34%; P<.001). ::: ::: ::: CONCLUSIONS ::: Social media combined with intercept provided access to important LGBT subpopulations (eg, gender and other sexual minorities) and a more diverse sample. Social media methods have more data quality issues but are faster and less expensive than intercept. Recruiting hard-to-reach populations via audience-tailored strategies enabled recruitment of one of the largest LGBT young adult samples, suggesting these methods' promise for accessing hard-to-reach populations.


title: Virtual Valcamonica: Collaborative Exploration of Prehistoric Petroglyphs and Their Surrounding Environment in Multi-User Virtual Reality

abstract: In this article, we present a novel, multi-user, virtual reality environment for the interactive, collaborative 3D analysis of large 3D scans and the technical advancements that were necessary to build it: a multi-view rendering system for large 3D point clouds, a suitable display infrastructure, and a suite of collaborative 3D interaction techniques. The cultural heritage site of Valcamonica in Italy with its large collection of prehistoric rock-art served as an exemplary use case for evaluation. The results show that our output-sensitive level-of-detail rendering system is capable of visualizing a 3D dataset with an aggregate size of more than 14 billion points at interactive frame rates. The system design in this exemplar application results from close exchange with a small group of potential users: archaeologists with expertise in rockart. The system allows them to explore the prehistoric art and its spatial context with highly realistic appearance. A set of dedicated interaction techniques was developed to facilitate collaborative visual analysis. A multi-display workspace supports the immediate comparison of geographically distributed artifacts. An expert review of the final demonstrator confirmed the potential for added value in rock-art research and the usability of our collaborative interaction techniques.


An Alternating Direction Algorithm for Nonnegative Matrix Factorization


title: Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be

abstract: Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify over 3000 IoT-related articles. Further, by clustering such collected data using machine learning technologies, we are able to compare academic views with the findings from industry and other sources, in an attempt to understand the gaps between them, the trend of the IoT security risks and new problems that need further attention. We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas. We use this taxonomy as a beacon to assess each IoT work across a number of properties we define. Our assessment reveals that relevant security and privacy problems are far from solved. We discuss how each proposed solution can be applied to a problem area and highlight their strengths, assumptions and constraints. We stress the need for a security framework for IoT vendors and discuss the trend of shifting security liability to external or centralized entities. We also identify open research problems and provide suggestions towards a secure IoT ecosystem.


title: Resource Allocation and Power Control for Mobile Relay in TDD-OFDMA Downlink

abstract: This paper investigates the system performances of the IEEE 802.16j system with mobile relays such as cell throughput and service coverage. It also focuses on resource management problems such as overlapped subchannel allocation (OVSA) and orthogonal subchannel allocation (ORSA) and relay power control such as simplified on-off mobile relay control (SOMRC). The performance of the proposed schemes is evaluated with both proportional fair (PF) and mobility assisted opportunistic scheduling (MAOS) by SH. Ali and V.C.M. Leung (2005) schedulers. Simulation results show that the cell throughputs of the proposed schemes are greater than one without relay, and the cell throughput of OVSA scheme is improved with increasing the number of mobile relay stations (MRSs). OVSA with SOMRC also provides larger cell throughput than OVSA without MRS power control. The service coverage of ORSA scheme is greater than one of OVSA scheme, and OVSA with SOMRC offers expanded service coverage than OVSA without MRS power control.


title: Dynamic highway-node routing

abstract: We introduce a dynamic technique for fast route planning in large road networks. For the first time, it is possible to handle the practically relevant scenarios that arise in present-day navigation systems: When an edge weight changes (e.g., due to a traffic jam), we can update the preprocessed information in 2-40ms allowing subsequent fast queries in about one millisecond on average. When we want to perform only a single query, we can skip the comparatively expensive update step and directly perform a prudent query that automatically takes the changed situation into account. If the overall cost function changes (e.g., due to a different vehicle type), recomputing the preprocessed information takes typically less than two minutes. ::: ::: The foundation of our dynamic method is a new static approach that generalises and combines several previous speedup techniques. It has outstandingly low memory requirements of only a few bytes per node.


title: Maximizing children's opportunities with inclusive play: considerations for interactive technology design

abstract: Inclusive play, defined as play among children with and without disabilities, provides learning opportunities that challenge stereotypes, foster strong friendships, and help children develop empathy and other social and emotional skills. Designing technologies to support inclusive play are understudied in Human-Computer Interaction. We synthesized literature, conducted design ethnography in an inclusive classroom, and interviewed and surveyed parents and teachers to explore this problem. Our research contributes an empirical understanding of the current state of inclusive play and a characterization of the design space for interactive technologies that can support children and adults with inclusive play. We identify key facilitators of inclusive play: direct and embedded supports, transparency, adjustability, emphasis on children's interests and strengths, and current technology use. We also describe significant barriers to inclusive play: effort required to facilitate inclusive play, children's preferences, parental inexperience, and inappropriate technology. Through our discussion, we conclude that interactive technologies should be designed to harness the facilitators and help overcome the barriers in order to maximize children's opportunities with inclusive play.


title: EEG feature extraction using common spatial pattern with spectral graph decomposition

abstract: This paper investigates the application of spectral graph decomposition method in improving the discriminative quality of spectral components of the common spatial pattern for seizure classification. The aim is to improve the variance between the normal and abnormal EEG patterns in feature extraction process. The affinity matrix of the graph spectral decomposition that derive the Laplacian matrix encodes the dataset of the CSP covariance matrix as a correlation instead of Euclidean (or Minkowski) distance on Gaussian kernel function. The feature vector containing pattern of abnormality is sorted in order of the magnitude of their simple statistical mean values. The results obtained show that the CSP-Spectral Graph Decompsotion approach seems to provide a better discriminative features than CSP feature extraction process.


title: Secure Strategyproof Ascending-Price Spectrum Auction

abstract: Spectrum auction, as an ideal solution for the dynamic channel allocation problem, has become a research hotspot in recent years. Nevertheless, many spectrum auction schemes only focus on economic issues. They lack either a formal security guarantee of bidder privacy or certain auction properties, which may dampen potential bidders’ enthusiasm. In this paper, we first point out that a recent ascendingprice auction scheme GAVEL (MobiHoc ’16) failed to provide privacy since the bidder valuation for channels could be inferred by observing when the bidder quits the auction. In response, we design a privacy-preserving scheme which respects essential auction properties (strategyproofness, efficiency, and adaptive bidding) and spectrum properties (reusable and finegrained channels). Our formal security analysis proves that it is secure against semi-honest adversaries and thus protects the bidders’ sensitive information including demand curves and true valuations. It is the first provably secure ascendingprice auction scheme for spectrum allocation to the best of our knowledge.


Glycerol — Just a Moisturizer? Biological and Biophysical Effects


title: Teams in Organizations: From Input-Process-Output Models to IMOI Models

abstract: This review examines research and theory relevant to work groups and teams typically embedded in organizations and existing over time, although many studies reviewed were conducted in other settings, including the laboratory. Research was organized around a two-dimensional system based on time and the nature of explanatory mechanisms that mediated between team inputs and outcomes. These mechanisms were affective, behavioral, cognitive, or some combination of the three. Recent theoretical and methodological work is discussed that has advanced our understanding of teams as complex, multilevel systems that function over time, tasks, and contexts. The state of both the empirical and theoretical work is compared as to its impact on present knowledge and future directions.


title: What would users change in my app? summarizing app reviews for recommending software changes

abstract: Mobile app developers constantly monitor feedback in user reviews with the goal of improving their mobile apps and better meeting user expectations. Thus, automated approaches have been proposed in literature with the aim of reducing the effort required for analyzing feedback contained in user reviews via automatic classification/prioritization according to specific topics. In this paper, we introduce SURF (Summarizer of User Reviews Feedback), a novel approach to condense the enormous amount of information that developers of popular apps have to manage due to user feedback received on a daily basis. SURF relies on a conceptual model for capturing user needs useful for developers performing maintenance and evolution tasks. Then it uses sophisticated summarisation techniques for summarizing thousands of reviews and generating an interactive, structured and condensed agenda of recommended software changes. We performed an end-to-end evaluation of SURF on user reviews of 17 mobile apps (5 of them developed by Sony Mobile), involving 23 developers and researchers in total. Results demonstrate high accuracy of SURF in summarizing reviews and the usefulness of the recommended changes. In evaluating our approach we found that SURF helps developers in better understanding user needs, substantially reducing the time required by developers compared to manually analyzing user (change) requests and planning future software changes.


title: GUI ripping: reverse engineering of graphical user interfaces for testing

abstract: Graphical user interfaces (GUIs) are important parts oftoday's software and their correct execution is required toensure the correctness of the overall software. A populartechnique to detect defects in GUIs is to test them by executingtest cases and checking the execution results. Testcases may either be created manually or generated automaticallyfrom a model of the GUI. While manual testingis unacceptably slow for many applications, our experiencewith GUI testing has shown that creating a model that canbe used for automated test case generation is difficult.We describe a new approach to reverse engineer a modelrepresented as structures called a GUI forest, event-flowgraphs and an integration tree directly from the executableGUI. We describe "GUI Ripping", a dynamic process inwhich the software's GUI is automatically "traversed" byopening all its windows and extracting all their widgets(GUI objects), properties, and values. The extracted informationis then verified by the test designer and used to automaticallygenerate test cases. We present algorithms for theripping process and describe their implementation in a toolsuite that operates on Java and Microsoft Windows' GUIs.We present results of case studies which show that ourapproach requires very little human intervention and is especiallyuseful for regression testing of software that is modifiedfrequently. We have successfully used the "GUI Ripper"in several large experiments and have made it availableas a downloadable tool.


title: A strong nonrandom pattern in Matlab default random number generator

abstract: The default random number generator in Matlab versions between 5 and at least 7.3 (R2006b) has a strong dependence between the numbers zi+1,zi+16,zi+28 in the generated sequence. In particular, there is no index i such that the inequalities zi+1 < 1/4, 1/4 zi+16 < 1/2, and 1/2 zi+28 are satisfied simultaneously. This fact is proved as a consequence of the recurrence relation defining the generator. A random sequence satisfies the inequalities with probability 1/32. Another example demonstrating the dependence is a simple function f with values 1 and 1, such that the correlation between f(zi+1,zi+16) and sign(zi+28 1/2) is at least 0.416, while it should be zero. A simple distribution on three variables that closely approximates the joint distribution of zi+1,zi+16,zi+28 is described. The region of zero density in the approximating distribution has volume 4/21 in the three dimensional unit cube. For every integer 1 k 10, there is a parallelepiped with edges 1/2 k+1 , 1/2 k and 1/2 k+1 , where the density of the distribution is 2 k . Numerical simulation confirms that the distribution of the original generator matches the approximation within small random error corresponding to the sample size.


title: Computerized Progressive Attentional Training (CPAT) Program: Effective Direct Intervention for Children with ADHD

abstract: We tested the efficacy of a pioneering intervention program grounded in a contemporary theoretical framework of attention and designed to directly improve the various attentional functions of children with ADHD. The computerized progressive attentional training (CPAT) program is composed of four sets of structured tasks that uniquely activate sustained attention, selective attention, orienting of attention, and executive attention. Performance was driven by tight schedules of feedback and participants automatically advanced in ordered levels of difficulty contingent upon performance. Twenty 6- to 13-year-old children with ADHD were assigned to the experimental group and received the CPAT sessions twice a week over an 8-week period. Sixteen age-matched control children with ADHD were assigned to the control group and participated in sessions of the same frequency, length, and format except that instead of performing the training tasks they played various computer games during the session. The experimental ...


title: Do Neural Nets Learn Statistical Laws behind Natural Language?

abstract: The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf’s law and Heaps’ law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf’s law and Heaps’ law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.


title: Trading strategies to exploit blog and news sentiment

abstract: We use quantitative media (blogs, and news as a comparison) data generated by a large-scale natural language processing (NLP) text analysis system to perform a comprehensive and comparative study on how company related news variables anticipates or reflects the company's stock trading volumes and financial returns. Building on our findings, we give a sentiment-based market-neutral trading strategy which gives consistently favorable returns with low volatility over a long period. Our results are significant in confirming the performance of general blog and news sentiment analysis methods over broad domains and sources. Moreover, several remarkable differences between news and blogs are also identified.


Applying the Waek Learning Framework to Understand and Improve C4.5


title: QRSF: QoS-aware resource scheduling framework in cloud computing

abstract: Cloud computing harmonizes and delivers the ability of resource sharing over different geographical sites. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair. The efficient management of dynamic nature of resource can be done with the help of cloud workloads. Till cloud workload is deliberated as a central capability, the resources cannot be utilized in an effective way. In literature, very few efficient resource scheduling policies for energy, cost and time constraint cloud workloads are reported. This paper presents an efficient cloud workload management framework in which cloud workloads have been identified, analyzed and clustered through K-means on the basis of weights assigned and their QoS requirements. Further scheduling has been done based on different scheduling policies and their corresponding algorithms. The performance of the proposed algorithms has been evaluated with existing scheduling policies through CloudSim toolkit. The experimental results show that the proposed framework gives better results in terms of energy consumption, execution cost and time of different cloud workloads as compared to existing algorithms.


title: Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

abstract: We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.


title: Exploiting Saliency for Object Segmentation from Image Level Labels

abstract: There have been remarkable improvements in the semantic labelling task in the recent years. However, the state of the art methods rely on large-scale pixel-level annotations. This paper studies the problem of training a pixel-wise semantic labeller network from image-level annotations of the present object classes. Recently, it has been shown that high quality seeds indicating discriminative object regions can be obtained from image-level labels. Without additional information, obtaining the full extent of the object is an inherently ill-posed problem due to co-occurrences. We propose using a saliency model as additional information and hereby exploit prior knowledge on the object extent and image statistics. We show how to combine both information sources in order to recover 80% of the fully supervised performance – which is the new state of the art in weakly supervised training for pixel-wise semantic labelling.


title: Android permissions demystified

abstract: Android provides third-party applications with an extensive API that includes access to phone hardware, settings, and user data. Access to privacy- and security-relevant parts of the API is controlled with an install-time application permission system. We study Android applications to determine whether Android developers follow least privilege with their permission requests. We built Stowaway, a tool that detects overprivilege in compiled Android applications. Stowaway determines the set of API calls that an application uses and then maps those API calls to permissions. We used automated testing tools on the Android API in order to build the permission map that is necessary for detecting overprivilege. We apply Stowaway to a set of 940 applications and find that about one-third are overprivileged. We investigate the causes of overprivilege and find evidence that developers are trying to follow least privilege but sometimes fail due to insufficient API documentation.


title: Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption

abstract: This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.


title: Dynamic Locomotion Gaits of a Compliantly Actuated Quadruped With SLIP-Like Articulated Legs Embodied in the Mechanical Design

abstract: The spring loaded inverted pendulum (SLIP) model has been extensively shown to be fundamental for legged locomotion. However, the way this low-order template model dynamics is anchored in high-dimensional articulated multibody systems describing compliantly actuated robots (and animals) is not obvious and has not been shown so far. In this letter, an articulated leg mechanism and a corresponding quadrupedal robot design are introduced, for which the natural oscillation dynamics is structurally equivalent to the SLIP. On the basis of this property, computationally simple and robust control methods are proposed, which implement the gaits of pronking, trotting, and dynamic walking in the real robotic system. Experiments with a compliantly actuated quadruped featuring only low-performance electrical drives validate the effectiveness of the proposed approach.


title: SciMATE: A Novel MapReduce-Like Framework for Multiple Scientific Data Formats

abstract: Despite the popularity of MapReduce, there are several obstacles to applying it for developing scientific data analysis applications. Current MapReduce implementations require that data be loaded into specialized file systems, like the Hadoop Distributed File System (HDFS), whereas with rapidly growing size of scientific datasets, reloading data in another file system or format is not feasible. We present a framework that allows scientific data in different formats to be processed with a MapReduce like API. Our system is referred to as SciMATE, and is based on the MATE system developed at Ohio State. SciMATE is developed as a customizable system, which can be adapted to support processing on any of the scientific data formats. We have demonstrated the functionality of our system by creating instances that can be processing NetCDF and HDF5 formats as well as flat-files. We have also implemented three popular data mining applications and have evaluated their execution with each of the three instances of our system.


title: Return On Security Investment (ROSI) - A Practical Quantitative Modell

abstract: Organizations need practical security benchmarking tools in order to plan effective security strategies. This paper explores a number of techniques that can be used to measure security within an organization. It proposes a new benchmarking methodology that produces results that are of strategic importance to both decision makers and technology implementers. The approach taken reflects a work-in-progress that is a combination of practical experience and direct research.


title: Cyber Deception: Overview and the Road Ahead

abstract: Since the concept of deception for cybersecurity was introduced decades ago, several primitive systems, such as honeypots, have been attempted. More recently, research on adaptive cyber defense techniques has gained momentum. The new research interests in this area motivate us to provide a high-level overview of cyber deception. We analyze potential strategies of cyber deception and its unique aspects. We discuss the research challenges of creating effective cyber deception–based techniques and identify future research directions.


title: Collaborative Security: A Survey and Taxonomy

abstract: Security is oftentimes centrally managed. An alternative trend of using collaboration in order to improve security has gained momentum over the past few years. Collaborative security is an abstract concept that applies to a wide variety of systems and has been used to solve security issues inherent in distributed environments. Thus far, collaboration has been used in many domains such as intrusion detection, spam filtering, botnet resistance, and vulnerability detection. In this survey, we focus on different mechanisms of collaboration and defense in collaborative security. We systematically investigate numerous use cases of collaborative security by covering six types of security systems. Aspects of these systems are thoroughly studied, including their technologies, standards, frameworks, strengths and weaknesses. We then present a comprehensive study with respect to their analysis target, timeliness of analysis, architecture, network infrastructure, initiative, shared information and interoperability. We highlight five important topics in collaborative security, and identify challenges and possible directions for future research. Our work contributes the following to the existing research on collaborative security with the goal of helping to make collaborative security systems more resilient and efficient. This study (1) clarifies the scope of collaborative security, (2) identifies the essential components of collaborative security, (3) analyzes the multiple mechanisms of collaborative security, and (4) identifies challenges in the design of collaborative security.


title: CTP: An efficient, robust, and reliable collection tree protocol for wireless sensor networks

abstract: We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.


title: Practical condition synchronization for transactional memory

abstract: Few transactional memory implementations allow for condition synchronization among transactions. The problems are many, most notably the lack of consensus about a single appropriate linguistic construct, and the lack of mechanisms that are compatible with hardware transactional memory. In this paper, we introduce a broadly useful mechanism for supporting condition synchronization among transactions. Our mechanism supports a number of linguistic constructs for coordinating transactions, and does so without introducing overhead on in-flight hardware transactions. Experiments show that our mechanisms work well, and that the diversity of linguistic constructs allows programmers to chose the technique that is best suited to a particular application.


title: Encryption algorithm using Programmable Cellular Automata

abstract: In this paper is developed a high-performance encryption system that works according with the Programmable Cellular Automata (PCA) theory. The essence of the theoretical and practical efforts which are done in this new field is represented by the idea that bio-inspired based cryptosystems are capable to have similar performances regarding the classic methods based on computational techniques. The proposed encryption and decryption modules are identically and the cryptosystem is featured by its large key space and high speed due to cellular automata's parallel information processing. The PCA block cipher has been implemented in software using C# programming language and is used, as a dynamic library, in order to assure the encryption of medical data sent over the internet.


title: Fast 3D Geometric Proximity Queries between Rigid and Deformable Models Using Graphics Hardware Acceleration

abstract: We present an approach for computing generalized proximity information between arbitrary polygonal models using graphics hardware acceleration. Our algorithm combines object-space localization, multi-pass rendering techniques, and accelerated distance field computation to perform complex proximity queries at interactive rates. It is applicable to any closed, possibly non-convex, polygonal object and requires no precomputation, making it suitable for both rigid and dynamically deformable geometry of relatively high complexity. The proximity queries include, not only collision detection, but also the computation of intersections, minimum separation distance, closest points, penetration depth and direction, and contact points and normals. The load is balanced between CPU and graphics subsystems through a hybrid geometry and image-based approach. Geometric object-space techniques coarsely localize potential interactions between two objects, and image-space techniques accelerated with graphics hardware provide the low-level proximity information. We have implemented our system using the OpenGL graphics library and have tested it on various hardware configurations with a wide range of object complexities and contact scenarios. In all cases, interactive frame rates are achieved. In addition, our algorithm’s performance is heavily based on the graphics hardware computational power growth curve which has exceeded the expectations of Moore’s Law for general CPU power growth.


Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Noisy Images


title: The Symptoms of Autism Spectrum Disorders in Adolescence and Adulthood

abstract: This article describes the symptoms of autism spectrum disorders (ASD) manifested by 405 individuals between the ages of 10 and 53 years, all of whom had an ASD diagnosis. Data were collected using the Autism Diagnostic Interview–Revised (ADI-R) to assess the pattern of autism symptoms in adolescence and adulthood. Findings include that although virtually all sample members met the criteria for Autistic Disorder earlier in their childhood, just over half (54.8%) would have met autism criteria if current scores were used to complete the diagnostic algorithm; that adolescents were more likely to improve in the Reciprocal Social Interaction domain than the adults, whereas the adults were more likely to improve in the Restricted, Repetitive Behaviors and Interests domain, and there were no differences in severity of symptoms between cohorts in the Communication domain; and that individual symptoms showed unique trajectories, with greatest symptom abatement between lifetime and current ADI-R ratings for speaking in at least three-word phrases and the least symptom improvement for having friendships. Findings were interpreted in the context of life course development, reformulations of diagnostic criteria, and changing service contexts for individuals with autism spectrum disorders.


title: Neural Network-based Indoor Localization in WSN Environments

abstract: With the advancement of wireless technology even more wireless sensor network (WSN) applications are gaining ground. Their field of application is increasingly widening. This paper examines the WSN application which allows indoor localization based on the Fingerprint (FP) method. The communication between the modules was monitored during the experiment whereby the received radio signal strength indicator (RSSI) values from 5 modules were recorded by a mobile sensor. The received data was used for training of the feed-forward type of neural network. Through use of the trained neural network and the measured RSSI values an indoor localization was realized in a real environment. The neural network-based localization method is analyzed applying the cumulative distribution function (CDF). For the reference model the well-known weighted k-nearest neighbour (WkNN) method was used.


title: Integration of genomics and histology revises diagnosis and enables effective therapy of refractory cancer of unknown primary with PDL1 amplification

abstract: Identification of the tissue of origin in cancer of unknown primary (CUP) poses a diagnostic challenge and is critical for directing site-specific therapy. Currently, clinical decision-making in patients with CUP primarily relies on histopathology and clinical features. Comprehensive molecular profiling has the potential to contribute to diagnostic categorization and, most importantly, guide CUP therapy through identification of actionable lesions. We here report the case of an advanced-stage malignancy initially mimicking poorly differentiated soft-tissue sarcoma that did not respond to multiagent chemotherapy. Molecular profiling within a clinical whole-exome and transcriptome sequencing program revealed a heterozygous, highly amplified KRAS G12S mutation, compound-heterozygous TP53 mutation/deletion, high mutational load, and focal high-level amplification of Chromosomes 9p (including PDL1 [CD274] and JAK2) and 10p (including GATA3). Integrated analysis of molecular data and histopathology provided a rationale for immune checkpoint inhibitor (ICI) therapy with pembrolizumab, which resulted in rapid clinical improvement and a lasting partial remission. Histopathological analyses ruled out sarcoma and established the diagnosis of a poorly differentiated adenocarcinoma. Although neither histopathology nor molecular data were able to pinpoint the tissue of origin, our analyses established several differential diagnoses including triple-negative breast cancer (TNBC). We analyzed 157 TNBC samples from The Cancer Genome Atlas, revealing PDL1 copy number gains coinciding with excessive PDL1 mRNA expression in 24% of cases. Collectively, these results illustrate the impact of multidimensional tumor profiling in cases with nondescript histology and immunophenotype, show the predictive potential of PDL1 amplification for immune checkpoint inhibitors (ICIs), and suggest a targeted therapeutic strategy in Chromosome 9p24.1/PDL1-amplified cancers.


title: GAMIFYING INFORMATION SYSTEMS - A SYNTHESIS OF GAMIFICATION MECHANICS AND DYNAMICS

abstract: Trying to take advantage of the growing passion for games, Gamification is a trending phenomenon that aims at motivating people by applying elements common to games in different contexts. While first applications of Gamification exist in finance, health, education and entertainment, utility of Gamification in various contexts has not been sufficiently explored. Aiming to understand how Gamification can be applied to information systems to increase end-user motivation, we performed a systematic literature review to identify game elements used in Gamification. Our study yields a synthesis of mechanics and dynamics in five clusters – system design, challenges, rewards, social influences and user specifics. While our study reveals that Gamification has potential for motivating information system end-users, we also discuss the potential risks. Future research should analyse concrete implementations of Gamification in information systems contexts and investigate related long-term effects. From a practical perspective, we present Gamification as an innovative approach to enhancing end-user motivation to utilise information systems and illustrate the various options that can be applied to design Gamification applications.


title: Language model adaptation for video lectures transcription

abstract: Videolectures are currently being digitised all over the world for its enormous value as reference resource. Many of these lectures are accompanied with slides. The slides offer a great opportunity for improving ASR systems performance. We propose a simple yet powerful extension to the linear interpolation of language models for adapting language models with slide information. Two types of slides are considered, correct slides, and slides automatic extracted from the videos with OCR. Furthermore, we compare both time aligned and unaligned slides. Results report an improvement of up to 3.8 % absolute WER points when using correct slides. Surprisingly, when using automatic slides obtained with poor OCR quality, the ASR system still improves up to 2.2 absolute WER points.


title: Designs of fully on-chip antennas in (Bi)CMOS technology

abstract: The paper presents several feasible millimeter wave on-chip antenna designs suitable to be fabricated in CMOS technology without any additional process. The results are listed and compared with state-of-the-art designs in the literature. The difficulties in designing high efficiency antenna on CMOS chip are discussed.


title: Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction

abstract: Three different algorithms for mobile robot vision-based obstacle detection are presented in this paper each based on different assumptions The first two algorithms are qualitative in that they return only yes/no answers regarding the presence of obstacles in the field of view; no 3D reconstruction is performed. They have the advantage of fast determination of the existence of obstacles in a scene based on the solvability of a linear system. The first algorithm uses information about the ground plane, while the second only assumes that the ground is planar. The third algorithm is quantitative in that it continuously estimates the ground plane and reconstructs partial 3D structures by determining the height above the ground plane of each point in the scene. Experimental results are presented for real and simulated data, and the performance of the three algorithms under different noise levels is compared in simulation. We conclude that in terms of the robustness of performance, the third algorithm is superior to the other two.


title: Integration of a Wireless I/O Interface for PROFIBUS and PROFINET for Factory Automation

abstract: Cost related to the wiring of sensors and actuators in the manufacturing industry is rapidly increasing with the number of field devices. Complex engineering as well as cable wear and tear are important factors. For over a decade, fieldbuses have been the dominating approach to simplify installation, reduce engineering efforts, and increase reliability at the field level. The next step in this process is to introduce wireless communication in certain problematic parts of the field network. In order to do this, a reliable real-time communication system, designed to cope with the harsh environments where factory automation systems are employed, is required. The wireless interface for sensors and actuators (WISA), with its two subconcepts WISA-power and WISA-com, is such a system. This paper discusses how the WISA concept can be efficiently integrated into wired field networks for factory automation-both state-of-the-art [process fieldbus decentral peripherals (PROFIBUS DP)] and new (PROFINET IO) networks. We propose amendments to WISA, which will improve the 802.11b/g coexistence and harmonize the integration of WISA for sensor/actuator communication in, e.g., PROFINET IO systems. The suggested improvements relate to both the radio protocol in order to improve coexistence with other wireless technologies as well as concepts for the integration of WISA into the field network of PROFINET/PROFIBUS.


title: Mean Square Convergence of Consensus Algorithms in Random WSNs

abstract: Distributed consensus algorithms for estimation of parameters or detection of events in wireless sensor networks have attracted considerable attention in recent years. A necessary condition to achieve a consensus on the average of the initial values is that the topology of the underlying graph is balanced or symmetric at every time instant. However, communication impairments can make the topology vary randomly in time, and instantaneous link symmetry between pairs of nodes is not guaranteed unless an acknowledgment protocol or an equivalent approach is implemented. In this paper, we evaluate the convergence of the consensus algorithm in the mean square sense in wireless sensor networks with random asymmetric topologies. For the case of links with equal probability of connection, a closed form expression for the mean square error of the state along with the dynamical range and the optimum value of the link weights that guarantee convergence are derived. For the case of links with different probabilities of connection, an upper bound for the mean square error of the state is derived. This upper bound can be computed for any time instant and can be employed to compute a link weight that reduces the convergence time of the algorithm.


title: Reinforcement learning of traffic light controllers adapting to traffic congestion

abstract: Due to the increasing amounts of traffic in and around urban areas there is a growing need for intelligent traffic lights that optimize traffic flow. In this paper we describe the optimization of traffic light controllers using a multi-agent, model-based reinforcement learning or approximate real-time dynamic programming approach. Our methods optimize individual traffic lights locally, but the optimization takes into account traffic congestion at neighboring traffic lights, such that there is implicit cooperation between them. We show, using experiments performed with a traffic simulator, that this approach outperforms existing methods.


The Semantic Web - ISWC 2003


Enhancing collaborative human-robot interaction through physiological-signal based communication


title: A Copula-based Joint Deficit Index for Droughts

abstract: Summary Current drought information is based on indices that do not capture the joint behaviors of hydrologic variables. To address this limitation, the potential of copulas in characterizing droughts from multiple variables is explored in this study. Starting from the standardized index (SI) algorithm, a modified index accounting for seasonality is proposed for precipitation and streamflow marginals. Utilizing Indiana stations with long-term observations (a minimum of 80 years for precipitation and 50 years for streamflow), the dependence structures of precipitation and streamflow marginals with various window sizes from 1- to 12-months are constructed from empirical copulas. A joint deficit index (JDI) is defined by using the distribution function of copulas. This index provides a probability-based description of the overall drought status. Not only is the proposed JDI able to reflect both emerging and prolonged droughts in a timely manner, it also allows a month-by-month drought assessment such that the required amount of precipitation for achieving normal conditions in future can be computed. The use of JDI is generalizable to other hydrologic variables as evidenced by similar drought severities gleaned from JDIs constructed separately from precipitation and streamflow data. JDI further allows the construction of an inter-variable drought index, where the entire dependence structure of precipitation and streamflow marginals is preserved.


title: How anthropomorphism affects empathy toward robots

abstract: A long-standing question within the robotics community is about the degree of human-likeness robots ought to have when interacting with humans. We explore an unexamined aspect of this problem: how people empathize with robots along the anthropomorphic spectrum. We conducted an experiment that measured how people empathized with robots shown to be experiencing mistreatment by humans. Our results indicate that people empathize more strongly with more human-looking robots and less with mechanical-looking robots.


title: YALE: rapid prototyping for complex data mining tasks

abstract: KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring the development of new methods or processing schemes. Like in software development, the development of such solutions demands for careful analysis, specification, implementation, and testing. Rapid prototyping is an approach which allows crucial design decisions as early as possible. A rapid prototyping system should support maximal re-use and innovative combinations of existing methods, as well as simple and quick integration of new ones.This paper describes Y ale , a free open-source environment forKDD and machine learning. Y ale provides a rich variety of methods whichallows rapid prototyping for new applications and makes costlyre-implementations unnecessary. Additionally, Y ale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool. Following the paradigm of visual programming eases the design of processing schemes. While the graphical user interface supports interactive design, the underlying XML representation enables automated applications after the prototyping phase.After a discussion of the key concepts of Y ale , we illustrate the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization. These case studies cover tasks like feature engineering, text mining, data stream mining and tracking drifting concepts, ensemble methods and distributed data mining. This variety of applications is also reflected in a broad user base, we counted more than 40,000 downloads during the last twelve months.


title: Health of the world's adolescents: a synthesis of internationally comparable data

abstract: Summary Adolescence and young adulthood offer opportunities for health gains both through prevention and early clinical intervention. Yet development of health information systems to support this work has been weak and so far lagged behind those for early childhood and adulthood. With falls in the number of deaths in earlier childhood in many countries and a shifting emphasis to non-communicable disease risks, injuries, and mental health, there are good reasons to assess the present sources of health information for young people. We derive indicators from the conceptual framework for the Series on adolescent health and assess the available data to describe them. We selected indicators for their public health importance and their coverage of major health outcomes in young people, health risk behaviours and states, risk and protective factors, social role transitions relevant to health, and health service inputs. We then specify definitions that maximise international comparability. Even with this optimisation of data usage, only seven of the 25 indicators, covered at least 50% of the world's adolescents. The worst adolescent health profiles are in sub-Saharan Africa, with persisting high mortality from maternal and infectious causes. Risks for non-communicable diseases are spreading rapidly, with the highest rates of tobacco use and overweight, and lowest rates of physical activity, predominantly in adolescents living in low-income and middle-income countries. Even for present global health agendas, such as HIV infection and maternal mortality, data sources are incomplete for adolescents. We propose a series of steps that include better coordination and use of data collected across countries, greater harmonisation of school-based surveys, further development of strategies for socially marginalised youth, targeted research into the validity and use of these health indicators, advocating for adolescent-health information within new global health initiatives, and a recommendation that every country produce a regular report on the health of its adolescents.


title: Conceptions of the transition to adulthood: Perspectives from adolescence through midlife.

abstract: Conceptions of the transition to adulthood were examined among adolescents (age 13–19, N = 171), emerging adults (age 20–29, N = 179), and young-to-midlife adults (age 30–55, N = 165). The focus was on whether conceptions of the transition to adulthood would be different among young-to-midlife adults compared to the younger age groups. In all age groups, individualistic criteria were the most likely to be considered important markers of the transition to adulthood, specifically accepting responsibility for one's actions, deciding on one's beliefs and values, establishing an equal relationship with parents, and becoming financially independent. However, young-to-midlife adults were less likely than adolescents to consider biological transitions to be important, and more likely than adolescents or emerging adults to view norm compliance (such as avoiding drunk driving) as a necessary part of the transition to adulthood. In all three groups, role transitions (e.g., marriage) ranked lowest in importance.


Reflections through the looking glass


title: Longitudinal Study of Child Face Recognition

abstract: We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects, in the age group [2, 18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3, 18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction.


title: High-Precision Rotor Position Detection for High-Speed Surface PMSM Drive Based on Linear Hall-Effect Sensors

abstract: Accurate rotor position detection is essential for the surface permanent magnet synchronous motor (SPMSM) drive system to realize the stable and reliable operation. In this paper, two linear hall-effect sensors are utilized to detect the rotor position. However, the estimated errors in the measured signals, especially the harmonic components, impact on the accuracy of the rotor angle estimation significantly. For this reason, a high-precision rotor position detection method, which is based on the synchronous frequency extractors (SFEs) is presented. Two SFEs are used to filter out the high-order harmonics contained in the hall-effect signals. Further, the estimation of the rotor speed is used for the field-oriented control scheme of the PMSM. The proposed method is advantageous since it is simple, low cost, and high estimation accuracy. Finally, an experimental driveline system used for testing the electrical performance of developed high-speed magnetically suspended motor is built. The experimental results validate the feasibility and effectiveness of the proposed method in whole speed range.


title: Object-Based Convolutional Neural Network for High-Resolution Imagery Classification

abstract: Timely and accurate classification and interpretation of high-resolution images are very important for urban planning and disaster rescue. However, as spatial resolution gets finer, it is increasingly difficult to recognize complex patterns in high-resolution remote sensing images. Deep learning offers an efficient strategy to fill the gap between complex image patterns and their semantic labels. However, due to the hierarchical abstract nature of deep learning methods, it is difficult to capture the precise outline of different objects at the pixel level. To further reduce this problem, we propose an object-based deep learning method to accurately classify the high-resolution imagery without intensive human involvement. In this study, high-resolution images were used to accurately classify three different urban scenes: Beijing (China), Pavia (Italy), and Vaihingen (Germany). The proposed method is built on a combination of a deep feature learning strategy and an object-based classification for the interpretation of high-resolution images. Specifically, high-level feature representations extracted through the convolutional neural networks framework have been systematically investigated over five different layer configurations. Furthermore, to improve the classification accuracy, an object-based classification method also has been integrated with the deep learning strategy for more efficient image classification. Experimental results indicate that with the combination of deep learning and object-based classification, it is possible to discriminate different building types in Beijing Scene, such as commercial buildings and residential buildings with classification accuracies above 90%.


title: A Complete Recipe for Stochastic Gradient MCMC

abstract: Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic simulations. However, such stochastic gradient MCMC samplers have lagged behind their full-data counterparts in terms of the complexity of dynamics considered since proving convergence in the presence of the stochastic gradient noise is non-trivial. Even with simple dynamics, significant physical intuition is often required to modify the dynamical system to account for the stochastic gradient noise. In this paper, we provide a general recipe for constructing MCMC samplers--including stochastic gradient versions--based on continuous Markov processes specified via two matrices. We constructively prove that the framework is complete. That is, any continuous Markov process that provides samples from the target distribution can be written in our framework. We show how previous continuous-dynamic samplers can be trivially"reinvented"in our framework, avoiding the complicated sampler-specific proofs. We likewise use our recipe to straightforwardly propose a new state-adaptive sampler: stochastic gradient Riemann Hamiltonian Monte Carlo (SGRHMC). Our experiments on simulated data and a streaming Wikipedia analysis demonstrate that the proposed SGRHMC sampler inherits the benefits of Riemann HMC, with the scalability of stochastic gradient methods.


title: Polymer–Ceramic Composites for Microwave Applications: Fabrication and Performance Assessment

abstract: We present a novel technique to fabricate conformal and pliable substrates for microwave applications including systems-on-package. The produced materials are fabricated by combining ceramic powders with polymers to generate a high-contrast substrate that is concurrently pliable (bendable). Several such polymer-ceramic substrates are fabricated and used to examine the performance of a patch antenna and a coupled line filter. This paper presents the substrate mixing method while measurements are given to evaluate the loss performance of the substrates. Overall, the fabricated composites lead to flexible substrates with a permittivity of up to epsivr=20 and sufficiently low loss


title: Log-Free Concurrent Data Structures

abstract: Non-volatile RAM (NVRAM) makes it possible for data structures to tolerate transient failures, assuming however programmers have designed these structures in a way to preserve their consistency upon recovery. Previous approaches, typically transactional, inherently used logging, resulting in implementations that are significantly slower than their DRAM counterparts. In this paper, we introduce a set of techniques that, in the common case, remove the need for logging (and costly durable store instructions) both in the data structure algorithm as well as in the associated memory management scheme. Together, these generic techniques enable us to design what we call log-free concurrent data structures, which, as we illustrate on linked lists, hash tables, skip lists, and BSTs, can provide several-fold performance improvements over previous, transaction-based implementations, with overheads of the order of milliseconds for recovery after a failure. We also highlight how our techniques can be integrated into practical systems, by introducing a durable version of Memcached that is able to maintain the performance of its volatile counterpart.


title: A 10-Year Retrospective of Research in Health Mass Media Campaigns: Where Do We Go From Here?

abstract: Mass media campaigns have long been a tool for promoting public health. How effective are such campaigns in changing health-related attitudes and behaviors, however, and how has the literature in this area progressed over the past decade? The purpose of the current article is threefold. First, I discuss the importance of health mass media campaigns and raise the question of whether they are capable of effectively impacting public health. Second, I review the literature and discuss what we have learned about the effectiveness of campaigns over the past 10 years. Finally, I conclude with a discussion of possible avenues for the health campaign literature over the next 10 years. The overriding conclusion is the following: The literature is beginning to amass evidence that targeted, well-executed health mass media campaigns can have small-to-moderate effects not only on health knowledge, beliefs, and attitudes, but on behaviors as well, which can translate into major public health impact given the wide reach ...


title: A survey on vehicle detection based on convolution neural networks

abstract: Vehicle detection has attracted a lot of research attention and has been employed in many areas of life including public safety and security. Accurately monitoring and detecting vehicles on the road in real time is one of the challenging tasks in this area of research. Towards this purpose, computer vision techniques are commonly used, where Convolution Neural Networks (CNN) got special focus. This paper presents an overview of CNN based vehicle detection methods in the context of monocular vision, where a camera is placed at the back of a vehicle to monitor the road situation, as well as an overview of CNN based Deep Convolution Neural Network (DNN) vehicle detection methods in the context of satellite imagery. The recent developments of these CNN based methods are discussed.


title: Machine-to-Machine Communications in Ultra-Dense Networks—A Survey

abstract: To achieve 1000-fold capacity increase in 5G wireless communications, ultradense network (UDN) is believed to be one of the key enabling technologies. Most of the previous research activities on UDNs were based very much on human-to-human communications. However, to provide ubiquitous Internet of Things services, machine-to-machine (M2M) communications will play a critical role in 5G systems. As the number of machine-oriented connections increases, it is expected that supporting M2M communications is an essential requirement in all future UDNs. In this paper, we aim to bridge the gaps between M2M communications and UDNs, which were commonly considered as two separate issues in the literature. The paper begins with a brief introduction on M2M communications and UDNs, and then will discuss the issues on the roles of M2M communications in future UDNs. We will identify different ways to implement M2M communications in the UDNs from the perspectives of layered architecture, including physical, media access control, network, and application layers. Other two important issues, i.e., security and network virtualization, will also be addressed. Before the end of this paper, we will give a summary on identified research topics for future studies.


title: Cutaneous reflexes in small muscles of the hand

abstract: A study has been made of the responses of motoneurones innervating small muscles of the hand to electrical and mechanical stimulation of the skin. Both excitatory and inhibitory effects could be observed in the same muscle after a single stimulus to a given area of skin. The earliest excitatory and inhibitory responses are probably mediated by group III and the smaller group II afferent nerve fibres. A later inhibition results from activity in the larger group II fibres which are connected to cutaneous mechanoreceptors, especially those in the tips of the fingers and thumb. This late inhibitory reflex may operate through the fusimotor system. The possible roles of these reflexes are discussed in relation to previous investigations in man and the cat.


title: Deep convolution neural network with stacks of multi-scale convolutional layer block using triplet of faces for face recognition in the wild

abstract: Recently, deep convolutional neural networks have set a new trend in fields of face recognition by improving the state-of-the-art performance. By using deep neural networks, much more sophisticated and high level abstracted features can be learned automatically. In this paper, we propose a method for face recognition using multi-scale convolution layer blocks and triplets of faces in unconstrained environments. We use the ensemble of deep convolution neural networks trained on differently scaled and aligned face images. This extracts low dimensional but high-level abstraction and discriminative features for face recognition. With these features, we employ the jointly Bayesian model and transfer learning which adapts the knowledge trained from the source domain to target domain. Experiment shows that our proposed method achieves 98.33% pair-wise verification accuracy on the LFW dataset.


title: A Hybrid Word-Character Approach to Abstractive Summarization

abstract: Abstractive summarization is the popular research topic nowadays. Due to the difference in language property, Chinese summarization also gains lots of attention. Most of studies use character-based representation instead of word-based to keep out the error introduced by word segmentation and OOV problem. However, we believe that word-based representation can capture the semantics of the articles more accurately. We proposed a hybrid word-character model preserves the advantage of both word-based and character-based representations. Our method also enables us to use larger word vocabulary size than anyone else. We call this new method HWC (Hybrid Word-Character). We conduct the experiments on LCSTS Chinese summarization dataset, and out-perform the current state-of-the-art by at least 8 ROUGE points.


title: A 1.2–1.6-GHz Substrate-Integrated-Waveguide RF MEMS Tunable Filter

abstract: This paper presents a high-performance substrate-integrated-waveguide RF microelectromechanical systems (MEMS) tunable filter for 1.2-1.6-GHz frequency range. The proposed filter is developed using packaged RF MEMS switches and utilizes a two-layer structure that effectively isolates the cavity filter from the RF MEMS switch circuitry. The two-pole filter implemented on RT/Duroid 6010LM exhibits an insertion loss of 2.2-4.1 dB and a return loss better than 15 dB for all tuning states. The relative bandwidth of the filter is 3.7 ± 0.5% over the tuning range. The measured Qu of the filter is 93-132 over the tuning range, which is the best reported Q in filters using off-the-shelf RF MEMS switches on conventional printed circuit board substrates. In addition, an upper stopband rejection better than 28 dB is obtained up to 4.0 GHz by employing low-pass filters at the bandpass filter terminals at the cost of 0.7-1.0-dB increase in the insertion loss.


title: The Making of a 3D-Printed, Cable-Driven, Single-Model, Lightweight Humanoid Robotic Hand

abstract: Dexterity robotic hands can (Cummings, 1996) greatly enhance the functionality of humanoid robots, but the making of such hands with not only human-like appearance but also the capability of performing the natural movement of social robots is a challenging problem. The first challenge is to create the hand’s articulated structure and the second challenge is to actuate it to move like a human hand. A robotic hand for humanoid robot should look and behave human-like. At the same time, it also needs to be light and cheap for widely-used purposes. We start with studying the biomechanical features of a human hand and propose a simplified mechanical model of robotic hands, which can achieve the important local motions of the hand. Then we use 3D modelling techniques to create a single interlocked hand model that integrates pin and ball joints to our hand model. Compared to other robotic hands, our design saves the time required for assembling and adjusting, which makes our robotic hand ready-to-use right after the 3D printing is completed. Finally, the actuation of the hand is realised by cables and motors. Based on this approach, we have designed a cost-effective, 3D printable, compact and lightweight robotic hand. Our robotic hand weighs 150 grams, has 15 joints which are similar to a real human hand and six Degree of Freedoms (DOFs). It is actuated by only six small size actuators. The wrist connecting part is also integrated into the hand model and could be customized for different robots such as Nadine robot(Magnenat Thalmann, Tian, & Yao, 2017). The compact servo bed can be hidden inside the Nadine robot’s sleeve and the whole robotic hand platform will not cause extra load to her arm as the total weight (150 grams robotic hand and 162 grams artificial skin) is almost the same as her previous unarticulated robotic hand which is 348 grams. The paper also shows our test results with and without silicon artificial hand skin, and on Nadine robot.


title: Fast Document Segmentation Using Contourand X-Y Cut Technique

abstract: This paper describes fast and efficient method for page segmentation of document containing nonrectangular block. The segmentation is based on edge following algorithm using small window of 16 by 32 pixels. This segmentation is very fast since only border pixels of paragraph are used without scanning the whole page. Still, the segmentation may contain error if the space between them is smaller than the window used in edge following. Consequently, this paper reduce this error by first identify the missed segmentation point using direction information in edge following then, using X-Y cut at the missed segmentation point to separate the connected columns. The advantage of the proposed method is the fast identification of missed segmentation point. This methodology is faster with fewer overheads than other algorithms that need to access much more pixel of a document. KeywordsContour Direction Technique, Missed Segmentation


title: Message Passing Algorithms for Compressed Sensing

abstract: Compressed sensing aims to undersample certain high-dimensional signals, yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a known basis. Currently, the best known sparsity-undersampling tradeoff is achieved when reconstructing by convex optimization -- which is expensive in important large-scale applications. Fast iterative thresholding algorithms have been intensively studied as alternatives to convex optimization for large-scale problems. Unfortunately known fast algorithms offer substantially worse sparsity-undersampling tradeoffs than convex optimization. We introduce a simple costless modification to iterative thresholding making the sparsity-undersampling tradeoff of the new algorithms equivalent to that of the corresponding convex optimization procedures. The new iterative-thresholding algorithms are inspired by belief propagation in graphical models. Our empirical measurements of the sparsity-undersampling tradeoff for the new algorithms agree with theoretical calculations. We show that a state evolution formalism correctly derives the true sparsity-undersampling tradeoff. There is a surprising agreement between earlier calculations based on random convex polytopes and this new, apparently very different theoretical formalism.


title: Potential Role of LPS in the Outcome of Helicobacter pylori Related Diseases

abstract: In this study we asked a question whether H. pylori LPS with or without LewisXY (Le) determinants as well as LBP (lipopolysaccharide binding protein) and sCD14 molecules recognizing bacterial LPS may be involved in atherogenesis. Sera from 57 patients with coronary heart disease (CHD), 27 H. pylori infected dyspeptic patients-H.p.(+) and 49 healthy controls (HC) were tested for IgM and IgG to H. pylori LPS expressing LeX (LPS LeX) or LeXY (LPS LeXY) determinants and to a glycine acid extract (GE). Immune complexes (ICs) of Lewis antigens and specific IgM or IgG were also determined. The prevalence of anti-GE IgG and IgA was significantly higher in CHD as compared to HC and the same as in the H.p.(+) group. The highest levels of anti-GE IgG were detected only for CHD group. CHD patients showed upregulation of IgG to LPS LeX and LeXY. In contrast, an upregulation of IgM to such LPSs was found for healthy subjects. The levels of LeY-anti-LeY IgG ICs were higher in CHD patients than in healthy controls similarly to the levels of LBP. There was no difference in sCD14 concentration between CHD and HC groups. The results obtained in this study indicate that H. pylori infections may be the risk factors of atherosclerosis due to: 1) an enhanced humoral response to H. pylori surface antigens, 2) a host predisposition to respond to Lewis determinants present in H. pylori LPS by IgG, 3) increased levels of serum LBP.


ZoomNet: Deep Aggregation Learning for High-Performance Small Pedestrian Detection


Thread Lift: Classification, Technique, and How to Approach to the Patient


title: SIW-Based Array Antennas With Sequential Feeding for X-Band Satellite Communication

abstract: Novel phased array antennas suitable for X-band satellite communication are introduced and investigated for right-handed circular polarization (RHCP) in a multi-layered fabrication. The primary function of the proposed antennas is to transmit high quality data obtained from satellites and the ground station systems with the lowest amount of loss. For accurate data transmission, the important parameters regarding antenna specifications and electrical performance are the antenna gain and the high-purity polarization. Hence, this paper describes novel procedures for gain enhancements of the array antennas working at X-band (from 8.0 to 8.5 GHz) with an RHCP characteristic, including a single element, 2×2 array, and 2×4 array with four-way and eight-way power dividers, respectively. Taking into consideration the long-distance communication between a satellite and ground station, a substrate-integrated waveguide structure has been proposed, in particular because it is known to have low radiation loss and a low weight comparable to that of a microstrip, as well as low material loss similar to that of a metal-surrounded waveguide.


title: Assessing the performance of schools: limits and league tables

abstract: Examination results from GCSE and A levels, National Curriculum test scores, and findings from international studies are all used to make judgements about schools and teachers. Annual ’ tables’ of examination results for all schools and colleges in England and Wales are published and are intended to be used, by parents and others, as criteria for choosing among schools. In this article I shall argue that such uses have little justification. I shall argue that league tables may have a role as screening devices: that they can help to identify the relatively few institutions which (by definition) appear to have highly untypical patterns of performance and I shall discuss how this, limited, role is best realised in practice. When discussing the use of test or examination results we should, of course, remember that educational institutions have a responsibility for encouraging children’s learning and development across a much wider range of areas than reasonably can be tested School league tables The systematic publication of ‘performance tables’ for public examination results, begun in 1992, is now an established feature of the educational system in England and Wales. At GCSE the most prominent feature is the presentation, for each school, of the percentage who achieve 5 or more passes at grades A-C: at A-level an average grade point score is produced for each school. The national and local press are encouraged to present school and college results ranked in terms of these percentages and averages, and the Parents’ Charter encourages people to use these tables in choosing schools and colleges. While the Tory government has the responsibility for introducing this system, the Labour party has provided scant criticism of it and has given little indication that it would introduce substantial amendments. The principal argument against examination league tables is that the performance of a school is determined largely by the pre-existing achievements of the students when they enter it. Since schools differ markedly in this respect, for example some schools are highly selective, it is impossible to judge the quality of the education within a school solely in terms of such outputs. More recently the Government has accepted the inadequacy of using such crude rankings and has accepted the argument that what is required are so called ‘value added’ tables in which there is a proper allowance for pre-existing achievements (DFE, 1995): inconsistently, it