title: Road traffic congestion monitoring and measurement using active RFID and GSM technology

abstract: In this paper, we present an intelligent Traffic Congestion Monitoring & Measurement System called TrafficMonitor to monitor and measure the road traffic congestions using probe vehicle. The concept of probe vehicle has come up in recent times for collecting real time traffic data. Our system provides an easy platform to analyze the traffic movement and congestion pattern. TrafficMonitor is a rapidly deployable, cost-effective and easily maintainable traffic congestion monitoring & measurement system that combines active RFID (based on IEEE 802.15.4 protocol, 2.4 GHz ISM band) and GSM technologies. The congestion detection algorithm is based upon calculation of vehicular speed over a stretch of road and the average waiting time of vehicles at road-crossing. Besides providing a complete description of our system and the concepts developed, the paper also provides a comprehensive description of on-road test results to support our concepts. We also provide a detailed description of all the field trials conducted, various traffic data gathered and finally the conclusions derived from such data. Government agencies, especially traffic control department, may use this system for realtime congestion monitoring by installing the system with probe vehicles. Road research organizations and NGOs may use this for studying and analyzing traffic mobility and congestion patterns.


title: Efficient Location Prediction in Mobile Cellular Networks

abstract: Mobile context-aware applications are capable of predicting the context of the user in order to operate pro-actively and provide advanced services. We propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. We introduce different variants of the considered location predictor dealing with location (cell) identifiers and directions. Symbolic location classification is treated as a supervised learning problem. We evaluate the prediction efficiency and accuracy of the proposed predictors through synthetic and real-world traces and compare our solution with existing algorithms for location prediction. Our findings are very promising for the location prediction problem and the adoption of proactive context-aware applications and services.


title: Evolutionary mining of relaxed dependencies from big data collections

abstract: Many modern application contexts, especially those related to the semantic Web, advocate for automatic techniques capable of extracting relationships between semi-structured data, for several purposes, such as the identification of inconsistencies or patterns of semantically related data, query rewriting, and so forth. One way to represent such relationships is to use relaxed functional dependencies ( rfd s), since they can embed approximate matching paradigms to compare unstructured data, and admit the possibility of exceptions for them. To this end, thresholds might need to be specified in order to limit the similarity degree in approximate comparisons or the occurrence of exceptions. Thanks to the availability of huge amount of data, including unstructured data available on the Web, nowadays it is possible to automatically discover rfd s from data. However, due to the many different combinations of similarity and exception thresholds, the discovery process has an exponential complexity. Thus, it is vital devising proper optimization strategies, in order to make the discovery process feasible. To this end, in this paper, we propose a genetic algorithm to discover rfd s from data, also providing an empirical evaluation demonstrating its effectiveness.


title: A review of data mining using big data in health informatics

abstract: The amount of data produced within Health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. In addition, this information can improve the quality of healthcare offered to patients. However, there are a number of issues that arise when dealing with these vast quantities of data, especially how to analyze this data in a reliable manner. The basic goal of Health Informatics is to take in real world medical data from all levels of human existence to help advance our understanding of medicine and medical practice. This paper will present recent research using Big Data tools and approaches for the analysis of Health Informatics data gathered at multiple levels, including the molecular, tissue, patient, and population levels. In addition to gathering data at multiple levels, multiple levels of questions are addressed: human-scale biology, clinical-scale, and epidemic-scale. We will also analyze and examine possible future work for each of these areas, as well as how combining data from each level may provide the most promising approach to gain the most knowledge in Health Informatics.


title: Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings

abstract: We propose a new unified framework for monolingual (MoIR) and cross-lingual information retrieval (CLIR) which relies on the induction of dense real-valued word vectors known as word embeddings (WE) from comparable data. To this end, we make several important contributions: (1) We present a novel word representation learning model called Bilingual Word Embeddings Skip-Gram (BWESG) which is the first model able to learn bilingual word embeddings solely on the basis of document-aligned comparable data; (2) We demonstrate a simple yet effective approach to building document embeddings from single word embeddings by utilizing models from compositional distributional semantics. BWESG induces a shared cross-lingual embedding vector space in which both words, queries, and documents may be presented as dense real-valued vectors; (3) We build novel ad-hoc MoIR and CLIR models which rely on the induced word and document embeddings and the shared cross-lingual embedding space; (4) Experiments for English and Dutch MoIR, as well as for English-to-Dutch and Dutch-to-English CLIR using benchmarking CLEF 2001-2003 collections and queries demonstrate the utility of our WE-based MoIR and CLIR models. The best results on the CLEF collections are obtained by the combination of the WE-based approach and a unigram language model. We also report on significant improvements in ad-hoc IR tasks of our WE-based framework over the state-of-the-art framework for learning text representations from comparable data based on latent Dirichlet allocation (LDA).


title: Learning geometric concepts with nasty noise

abstract: We study the efficient learnability of geometric concept classes - specifically, low-degree polynomial threshold functions (PTFs) and intersections of halfspaces - when a fraction of the data is adversarially corrupted. We give the first polynomial-time PAC learning algorithms for these concept classes with dimension-independent error guarantees in the presence of nasty noise under the Gaussian distribution. In the nasty noise model, an omniscient adversary can arbitrarily corrupt a small fraction of both the unlabeled data points and their labels. This model generalizes well-studied noise models, including the malicious noise model and the agnostic (adversarial label noise) model. Prior to our work, the only concept class for which efficient malicious learning algorithms were known was the class of origin-centered halfspaces. ::: Specifically, our robust learning algorithm for low-degree PTFs succeeds under a number of tame distributions -- including the Gaussian distribution and, more generally, any log-concave distribution with (approximately) known low-degree moments. For LTFs under the Gaussian distribution, we give a polynomial-time algorithm that achieves error $O(\epsilon)$, where $\epsilon$ is the noise rate. At the core of our PAC learning results is an efficient algorithm to approximate the low-degree Chow-parameters of any bounded function in the presence of nasty noise. To achieve this, we employ an iterative spectral method for outlier detection and removal, inspired by recent work in robust unsupervised learning. Our aforementioned algorithm succeeds for a range of distributions satisfying mild concentration bounds and moment assumptions. The correctness of our robust learning algorithm for intersections of halfspaces makes essential use of a novel robust inverse independence lemma that may be of broader interest.


title: The effects of sensorimotor-based intervention versus therapeutic practice on improving handwriting performance in 6- to 11-year-old children

abstract: OBJECTIVE. The aim of this study was to investigate the effects of two interventions (sensorimotor and therapeutic practice) on handwriting and selected sensorimotor components in elementary-age children. METHOD. Thirty-eight children 6 to 11 years of age with handwriting dysfunction but no identified educational need were randomly assigned to one of the two intervention groups or a control group. Intervention groups met four times per week over 5 weeks. Handwriting was measured pre- and postintervention using the Test of Handwriting Skills. Visual perception (motor-reduced), visual-motor integration, proprioception, and in-hand manipulation were also measured. RESULTS. Children receiving therapeutic practice moderately improved handwriting whereas children receiving sensorimotor intervention declined in handwriting performance. The control group did not change significantly. Sensorimotor impairment was noted at pretest in three or four components and selected sensorimotor component function improved with intervention.


title: SHOT BOUNDARY DETECTION AT TRECVID 2006

abstract: The first step for video-content analysis, content-based video browsing and retrieval is the partitioning of a video sequence into shots. A shot is the fundamental unit of a video, it captures a continuous action from a single camera and represents a spatio-temporally coherent sequence of frames. Thus, shots are considered as the primitives for higher level content analysis, indexing and classification. Although many video shot boundary detection algorithms have been proposed in the literature, in most approaches, several parameters and thresholds have to be set in order to achieve good results. In this paper, we present a robust learning detector of shot boundaries without any threshold to set nor any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. Our experiments provide very good results dealing with a large amount of features thanks to our kernel-based SVM classifier method.


title: SecDevOps: Is It a Marketing Buzzword? - Mapping Research on Security in DevOps

abstract: DevOps is changing the way organizations develop and deploy applications and service customers. Many organizations want to apply DevOps, but they are concerned by the security aspects of the produced software. This has triggered the creation of the terms SecDevOps and DevSecOps. These terms refer to incorporating security practices in a DevOps environment by promoting the collaboration between the development teams, the operations teams, and the security teams. This paper surveys the literature from academia and industry to identify the main aspects of this trend. The main aspects that we found are: definition, security best practices, compliance, process automation, tools for SecDevOps, software configuration, team collaboration, availability of activity data and information secrecy. Although the number of relevant publications is low, we believe that the terms are not buzzwords, they imply important challenges that the security and software communities shall address to help organizations develop secure software while applying DevOps processes.


title: Monitoring Vital Signs: Development of a Modified Early Warning Scoring (Mews) System for General Wards in a Developing Country

abstract: OBJECTIVE ::: The aim of the study was to develop and validate, by consensus, the construct and content of an observations chart for nurses incorporating a modified early warning scoring (MEWS) system for physiological parameters to be used for bedside monitoring on general wards in a public hospital in South Africa. ::: ::: ::: METHODS ::: Delphi and modified face-to-face nominal group consensus methods were used to develop and validate a prototype observations chart that incorporated an existing UK MEWS. This informed the development of the Cape Town ward MEWS chart. ::: ::: ::: PARTICIPANTS ::: One specialist anaesthesiologist, one emergency medicine specialist, two critical care nurses and eight senior ward nurses with expertise in bedside monitoring (N = 12) were purposively sampled for consensus development of the MEWS. One general surgeon declined and one neurosurgeon replaced the emergency medicine specialist in the final round. ::: ::: ::: RESULTS ::: Five consensus rounds achieved ≥70% agreement for cut points in five of seven physiological parameters respiratory and heart rates, systolic BP, temperature and urine output. For conscious level and oxygen saturation a relaxed rule of <70% agreement was applied. A reporting algorithm was established and incorporated in the MEWS chart representing decision rules determining the degree of urgency. Parameters and cut points differed from those in MEWS used in developed countries. ::: ::: ::: CONCLUSIONS ::: A MEWS for developing countries should record at least seven parameters. Experts from developing countries are best placed to stipulate cut points in physiological parameters. Further research is needed to explore the ability of the MEWS chart to identify physiological and clinical deterioration.


title: A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

abstract: Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in this area to predict the stock market price, including regression algorithms which can be useful tools to provide good performance of financial time series prediction. Support Vector Regression is one of the most powerful algorithms in machine learning. There have been countless successes in utilizing SVR algorithm for stock market prediction. In this paper, we propose a novel hybrid approach based on machine learning and filtering techniques. Our proposed approach combines Support Vector Regression and Hodrick–Prescott filter in order to optimize the prediction of stock price. To assess the performance of this proposed approach, we have conducted several experiments using real world datasets. The principle objective of this paper is to demonstrate the improvement in predictive performance of stock market and verify the works of our proposed model in comparison with other optimized models. The experimental results confirm that the proposed algorithm constitutes a powerful model for predicting stock market prices.


title: Optimal common-centroid-based unit capacitor placements for yield enhancement of switched-capacitor circuits

abstract: Yield is defined as the probability that the circuit under consideration meets with the design specification within the tolerance. Placement with higher correlation coefficients has fewer mismatches and lower variation of capacitor ratio, thus achieving higher yield performance. This study presents a new optimization criterion that quickly determines if the placement is optimal. The optimization criterion leads to the development of the concepts of C-entries and partitioned subarrays which can significantly reduce the searching space for finding the optimal/near-optimal placements on a sufficiently large array size.


title: Interactive bookshelf surface for in situ book searching and storing support

abstract: We propose an interactive bookshelf surface to augment a human ability for in situ book searching and storing. In book searching support, when a user touches the edge of the bookshelf, the cover image of a stored book located above the touched position is projected directly onto the book spine. As a result, the user can search for a desired book by sliding his (or her) finger across the shelf edge. In book storing support, when a user brings a book close to the bookshelf, the place where the book should be stored is visually highlighted by a projection light. This paper also presents sensing technologies to achieve the above mentioned interactive techniques. In addition, by considering the properties of the human visual system, we propose a simple visual effect to reduce the legibility degradation of the projected image contents by the complex textures and geometric irregularities of the spines. We confirmed the feasibility of the system and the effectiveness of the proposed interaction techniques through user studies.


title: CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

abstract: Abstract The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a computer numerical control (CNC) tool machine and the estimation of its remaining useful life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.


Learning Attention from Multi-Modal Imaging and Text: Application for Lesion Localisation in DWI


title: Detection of pornographic content in internet images

abstract: Pornographic image detection is an important and challenging problem. Detection of pornography on the Internet is even more challenging because of the scale (billions of images) and diversity (small to very large images, graphic, grey scale images, etc.) of image content. The performance requirements (precision, recall, and speed) are also very stringent. Because of this, no single technique provides the required performance. In this paper, we describe a framework for detecting images with pornographic content. The framework combines various techniques based on object-level and pixel-level analysis of image content. To enable high-precision, we detect body parts (including faces) in images. For high-recall, low-level techniques like color and texture features are used. For adaptation to new datasets, we also support learning of appropriate color models from weakly-labeled datasets. In addition to image-based analysis, both text-based and site-level analysis are performed. Unlike many adult detection techniques, we explicitly leverage techniques like texture analysis and face detection for non-adult content identification. The multiple cues are combined in a systematic manner using ROC analysis and boosting. Evaluations on real world web data indicate that the system has the best performance among the systems compared.


title: Comparison of Green Light Optimal Speed Advisory approaches

abstract: Green Light Optimal Speed Advisory (GLOSA) systems provide drivers with speed advices that allow them to pass traffic lights during green interval. In this article we compare performance of single- and multi-segment GLOSA approaches. In a single-segment GLOSA traffic signals are considered independently, i.e. the system provides vehicles with the optimal speed for the segment ahead of the nearest traffic signals. In a multi-segment GLOSA several signals in a sequence on a vehicle's route are taken into account, that is, vehicles receive speed advices for a set of segments ahead of the vehicle. Traveling time and fuel efficiency are used as performance measures. We demonstrate that as long as traffic conditions allow drivers selecting a wide range of speed (e.g. during free-flow) a multi-segment GLOSA results in much better performance when compared with a single-segment approach.


title: Contract driven development = test driven development - writing test cases

abstract: Although unit tests are recognized as an important tool in software development, programmers prefer to write code, rather than unit tests. Despite the emergence of tools like JUnit which automate part of the process, unit testing remains a time-consuming, resource-intensive, and not particularly appealing activity.This paper introduces a new development method, called Contract Driven Development. This development method is based on a novel mechanism that extracts test cases from failure-producing runs that the programmers trigger. It exploits actions that developers perform anyway as part of their normal process of writing code. Thus, it takes the task of writing unit tests off the developers' shoulders, while still taking advantage of their knowledge of the intended semantics and structure of the code. The approach is based on the presence of contracts in code, which act as the oracle of the test cases. The test cases are extracted completely automatically, are run in the background, and can easily be maintained over versions. The tool implementing this methodology is called Cdd and is available both in binary and in source form.


title: Putting Things in Context: Community-specific Embedding Projections for Sentiment Analysis.

abstract: Variation in language is ubiquitous, and is particularly evident in newer forms of writing such as social media. Fortunately, variation is not random, but is usually linked to social factors. By exploiting linguistic homophily --- the tendency of socially linked individuals to use language similarly --- it is possible to build models that are more robust to variation. In this paper, we focus on social network communities, which make it possible to generalize sociolinguistic properties from authors in the training set to authors in the test sets, without requiring demographic author metadata. We detect communities via standard graph clustering algorithms, and then exploit these communities by learning community-specific projections of word embeddings. These projections capture shifts in word meaning in different social groups; by modeling them, we are able to improve the overall accuracy of Twitter sentiment analysis by a significant margin over competitive prior work.


title: Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

abstract: Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here we explore the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). Our approach is two-stage. In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 hours of driving. We first show that training with attention does not degrade the performance of the end-to-end network. Then we show that the network causally cues on a variety of features that are used by humans while driving.


title: Cultural Representations of Gender and Science: Portrayals of Female Scientists and Engineers in Popular Films

abstract: Images of female scientists and engineers in popular films convey cultural and social assumptions about the role of women in science, engineering, and technology (SET).This study analyzed cultural representations of gender conveyed through images of female scientists and engineers in popular films from 1991 to 2001. While many of these depictions of female scientists and engineers emphasized their appearance and focused on romance, most depictions also presented female scientists and engineers in professional positions of high status. Other images that showed the female scientists and engineers’ interactions with male colleagues, however, reinforced traditional social and cultural assumptions about the role of women in SET through overt and subtle forms of stereotyping. This article explores the significance of these findings for developing programs to change girls’ perceptions of scientists and engineers and attitudes toward SET careers.


title: Accurate Models for Microstrip Computer-Aided Design

abstract: Very accurate and simple equations are presented for both single and coupled microstrip lines' electrical parameters, i.e. impedances, effective dielectric constants, and attenuation including the effect of anisotropy in the substrate. For the single microstrip the effects of dispersion and non-zero strip thickness are also included.


title: NiagaraCQ: a scalable continuous query system for Internet databases

abstract: Continuous queries are persistent queries that allow users to receive new results when they become available. While continuous query systems can transform a passive web into an active environment, they need to be able to support millions of queries due to the scale of the Internet. No existing systems have achieved this level of scalability. NiagaraCQ addresses this problem by grouping continuous queries based on the observation that many web queries share similar structures. Grouped queries can share the common computation, tend to fit in memory and can reduce the I/O cost significantly. Furthermore, grouping on selection predicates can eliminate a large number of unnecessary query invocations. Our grouping technique is distinguished from previous group optimization approaches in the following ways. First, we use an incremental group optimization strategy with dynamic re-grouping. New queries are added to existing query groups, without having to regroup already installed queries. Second, we use a query-split scheme that requires minimal changes to a general-purpose query engine. Third, NiagaraCQ groups both change-based and timer-based queries in a uniform way. To insure that NiagaraCQ is scalable, we have also employed other techniques including incremental evaluation of continuous queries, use of both pull and push models for detecting heterogeneous data source changes, and memory caching. This paper presents the design of NiagaraCQ system and gives some experimental results on the system's performance and scalability.


title: Research on Three-dimensional Missile Trajectory Modeling Based on Proportion Guide

abstract: Based on the intrinsic rule of proportion guide trajectory, utilizing the method of coordinates transform, the model of missile intercepting air-raid targets is established by selecting a suitable reference frame. Then three-dimensional missile trajectory simulation based on proportion guide can be realized with kinds of errors added into the course of information processing, such as navigation errors, control errors of missile, observation errors of missile and target. The results show that the model can reveal characteristics of proportion guide trajectory intuitively.


title: Pixelwise View Selection for Unstructured Multi-View Stereo

abstract: This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information, pixelwise view selection using photometric and geometric priors, and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate state-of-the-art performance in terms of accuracy, completeness, and efficiency.


title: A novel antenna array design for breast cancer detection

abstract: Early detection of breast cancer in women is critical for proper and effective treatment. This, however, requires to precisely locate the position and size of the tumor in breast since tumor is very small at its early stage. Microwave imaging techniques provide an attractive and effective alternate to achieve these objectives compared to their conventional counterparts such as mammography. In this paper, we present a novel Multi-Ring Slots Ultra-Wide-Band (MRS-UWB) patch antenna that can be used for breast tumor detection. After confirming the measured and simulated results of a single antenna element, we extend our design to an antenna array with 18 antenna elements. We combine the previously introduced Confocal Microwave Imaging (CMI) algorithm with our proposed antenna array to locate the position and size of the breast tumor. In the proposed scheme, we use the back scattered data received on the antenna array along with the frequency-time domain transformation, i.e. Inverse Fast Fourier Transform (IFFT), to reconstruct the tumor image. The results show that the proposed antenna array with CMI is capable to detect a smaller size tumor even of the size of 1 mm with high resolution. Furthermore, the proposed antenna array design has smaller dimensions that make the design more practical and cost effective for the use.


On the Evolution of Random Graphs


title: Software Crash Analysis for Automatic Exploit Generation on Binary Programs

abstract: This paper presents a new method, capable of automatically generating attacks on binary programs from software crashes. We analyze software crashes with a symbolic failure model by performing concolic executions following the failure directed paths, using a whole system environment model and concrete address mapped symbolic memory in S2 E. We propose a new selective symbolic input method and lazy evaluation on pseudo symbolic variables to handle symbolic pointers and speed up the process. This is an end-to-end approach able to create exploits from crash inputs or existing exploits for various applications, including most of the existing benchmark programs, and several large scale applications, such as a word processor (Microsoft office word), a media player (mpalyer), an archiver (unrar), or a pdf reader (foxit). We can deal with vulnerability types including stack and heap overflows, format string, and the use of uninitialized variables. Notably, these applications have become software fuzz testing targets, but still require a manual process with security knowledge to produce mitigation-hardened exploits. Using this method to generate exploits is an automated process for software failures without source code. The proposed method is simpler, more general, faster, and can be scaled to larger programs than existing systems. We produce the exploits within one minute for most of the benchmark programs, including mplayer. We also transform existing exploits of Microsoft office word into new exploits within four minutes. The best speedup is 7,211 times faster than the initial attempt. For heap overflow vulnerability, we can automatically exploit the unlink() macro of glibc, which formerly requires sophisticated hacking efforts.


title: Information-Based Compact Pose SLAM

abstract: Pose SLAM is the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of Pose SLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loop-closure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed Pose-SLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets.


title: Feminine transformations: gender reassignment surgical tourism in Thailand.

abstract: Every year, hundreds of transgendered people from the United States, Europe, Asia, Canada, and Australia travel to Thailand to undergo cosmetic and gender reassignment surgeries (GRS). Many GRS clinics market themselves almost exclusively to non-Thai trans women (people assigned a male sex at birth who later identify as female). This article draws on ethnographic research with patients visiting Thailand for GRS to explore how trans women patients related their experience of medical care in Thailand to Thai cultural traditions, in particular "traditional" Thai femininity and Theravada Buddhist rituals and beliefs. Foreign patients in Thai hospital settings engage not only with medical practices but also with their perceptions of Thai cultural traditions--which inflect their feminine identifications. I draw on two patients' accounts of creating personal rituals to mark their gender reassignment surgery, placing these accounts within the context of biomedical globalization and debates about the touristic appropriation of non-"Western" cultural practices.


title: A Comparative Study of Violence Risk Assessment Tools: A Systematic Review and Metaregression Analysis of 68 Studies Involving 25,980 Participants

abstract: There are a large number of structured instruments that assist in the assessment of antisocial, violent and sexual risk, and their use appears to be increasing in mental health and criminal justice settings. However, little is known about which commonly used instruments produce the highest rates of predictive validity, and whether overall rates of predictive validity differ by gender, ethnicity, outcome, and other study characteristics. We undertook a systematic review and meta-analysis of nine commonly used risk assessment instruments following PRISMA guidelines. We collected data from 68 studies based on 25,980 participants in 88 independent samples. For 54 of the samples, new tabular data was provided directly by authors. We used four outcome statistics to assess rates of predictive validity, and analyzed sources of heterogeneity using subgroup analysis and metaregression. A tool designed to detect violence risk in juveniles, the Structured Assessment of Violence Risk in Youth (SAVRY), produced the highest rates of predictive validity, while an instrument used to identify adults at risk for general offending, the Level of Service Inventory – Revised (LSIR), and a personality scale commonly used for the purposes of risk assessment, the Psychopathy Checklist – Revised (PCL-R), produced the lowest. Instruments produced higher rates of predictive validity in older and in predominantly White samples. Risk assessment procedures and guidelines by mental health services and criminal justice systems may need review in light of these findings.


title: CONTEXTUAL CLASSIFICATION OF POINT CLOUD DATA BY EXPLOITING INDIVIDUAL 3D NEIGBOURHOODS

abstract: Abstract. The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.


title: Artificial Neural Networks in the Detection of Known and Unknown DDoS Attacks: Proof-of-Concept

abstract: A Distributed Denial of Service attack (DDoS) is designed to overload a target device and its networks with packets to damage its resources or services. This paper proposes an Artificial Neural Network (ANN) detection engine to flag known and unknown attacks from genuine traffic. Based on experiments and data analysis, specific patterns are selected to separate genuine from DDoS packets, thus allowing normal traffic to reach its destination. The mitigation process is triggered when the detection system identifies attacks based on the known characteristic features (patterns) that were fed to the ANN during the training process. Such characteristic patterns separate attacks from normal traffic. We have evaluated our solution against related work based on accuracy, sensitivity, specificity and precision.


title: Syntax description synthesis using gradient boosted trees

abstract: The article considers partially formalized text documents. For such documents, it is not possible to construct a formal grammar. Therefore, an external syntax description is used to build the syntax tree. The problem is the high labor intensity and the high professional requirements for manual preparation of such descriptions. It is proposed to use machine learning methods to automate this process. The training set is composed using the documents with known syntax description. Each document is represented as a syntax tree using the TEXnous parser. Each node of these trees represents a syntax element, and the set of nodes forms the training set. A way of a single syntax element description is proposed so that a formal description of the syntax elements constitutes the space of classes. In the article, this space is limited to the set of parser modes used during the documents analysis. A set of scientific articles is used for the experiments. XGBoost implementation of gradient boosted trees is chosen for result classification problem.


title: Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation

abstract: BACKGROUND:Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment. OBJECTIVE:The main challenges for skin lesion segmentation are numerous variations in viewpoint and scale of skin lesion region. METHODS:To handle these challenges, we propose a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images. Specifically, the deep dense layer and generic multi-path Deep RefineNet are combined to improve the segmentation performance. The deep representation of all available layers is aggregated to form the global feature maps using skip connection. Also, the dense deconvolution layer is leveraged to capture diverse appearance features via the contextual information. Finally, we apply the dense deconvolution layer to smooth segmentation maps and obtain final high-resolution output. RESULTS:Our proposed method shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesion challenge dataset and achieves the accuracy of 96.0% and 93.9%, which obtained a 6.0% and 1.2% increase over the traditional method, respectively. CONCLUSIONS:By utilizing Dense Deconvolution Net, the average time for processing one testing images with our proposed framework was 0.253 s.


title: A Family of Dual-Phase-Combined Zero-Current Switching Switched-Capacitor Converters

abstract: A family of dual-phase-combined resonant switched-capacitor (SC) converters with a similar structure is presented in this paper. Comparing with their conventional single-phase version, each of the proposed dual-phase converters is more than just a switched capacitor but also provides the service of two phases operated in the fully complementary manner. This design provides a more stable output voltage with low ripple and significantly reduces the quantity of switches. These converters therefore have the common features of smaller size, lower cost, and higher power density than the conventional multiphase SC converters. With the zero-current switching technique, the proposed converters also possess high-power conversion efficiency. The detailed analysis of the circuit operation and voltage ripples is presented. Experimental results are also provided to confirm the performance of the new family of converters.


title: Characteristic brain magnetic resonance imaging pattern in patients with macrocephaly and PTEN mutations.

abstract: We describe an MRI phenotype seen in a series of patients with mutations in PTEN who have clinical features consistent with PTEN hamartoma tumor syndrome (PHTS). Retrospective review of clinical data and MRI was performed in 23 subjects evaluated in four different tertiary care centers with clinical programs in inherited disorders of the white matter. Patients were referred due to abnormal MRI features and abnormal PTEN sequencing was identified. All subjects had significant macrocephaly (on average >4 SD above the mean), developmental delay with or without autism spectrum disorder and uniform MRI features of enlarged perivascular spaces and multifocal periventricular white matter abnormalities. The phenotype of PHTS may include MRI abnormalities such as multifocal periventricular white matter abnormalities and enlarged perivascular spaces. These neuroimaging findings, in association with macrocephaly and developmental delay, should prompt consideration of PTEN as a diagnostic possibility.


title: Prospective of Fifth Generation Mobile Communications

abstract: This paper explores future mobile systems with emphasis on re-configurability based on cognitive and software defined radios. 5G (Fifth Generation) network architecture consisting of reconfigurable multitechnology core and a single fully reconfigurable terminal able to autonomously operate in different heterogeneous access networks is proposed. The proposed network is enforced by nanotechnology, cloud computing and based on All IP Platform. The paper highlights 5G main development challenges and illustrates why there is a need for 5G. It also reviews in brief the evolution of wireless and cellular systems focusing on four main key factors: radio access, data rates, bandwidth and switching schemes in addition to change in network architecture. The 3G transitional cellular and wireless systems toward 4G and the true 4G IMT-advanced systems are thoroughly presented.


title: Differentially Private Exponential Random Graphs

abstract: We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, and thus offer rigorous privacy guarantees. More specifically, we use the randomized response mechanism to release networks under $\epsilon$-edge differential privacy. To maintain utility for statistical inference, treating the original graph as missing, we propose a way to use likelihood based inference and Markov chain Monte Carlo (MCMC) techniques to fit ERGMs to the produced synthetic networks. We demonstrate the usefulness of the proposed techniques on a real data example.


title: Heterogeneity-aware Distributed Parameter Servers

abstract: We study distributed machine learning in heterogeneous environments in this work. We first conduct a systematic study of existing systems running distributed stochastic gradient descent; we find that, although these systems work well in homogeneous environments, they can suffer performance degradation, sometimes up to 10x, in heterogeneous environments where stragglers are common because their synchronization protocols cannot fit a heterogeneous setting. Our first contribution is a heterogeneity-aware algorithm that uses a constant learning rate schedule for updates before adding them to the global parameter. This allows us to suppress stragglers' harm on robust convergence. As a further improvement, our second contribution is a more sophisticated learning rate schedule that takes into consideration the delayed information of each update. We theoretically prove the valid convergence of both approaches and implement a prototype system in the production cluster of our industrial partner Tencent Inc. We validate the performance of this prototype using a range of machine-learning workloads. Our prototype is 2-12x faster than other state-of-the-art systems, such as Spark, Petuum, and TensorFlow; and our proposed algorithm takes up to 6x fewer iterations to converge.


title: Optimizing cluster-head selection in Wireless Sensor Networks using Genetic Algorithm and Harmony Search Algorithm

abstract: Being an important factor in designing Wireless Sensor Networks, network lifetime depends on the energy of the sensor nodes which is limited by the battery of the node. Clustering is considered to be an energy management strategy in wireless sensor networks, and Leach is one of the most well-known clustering mechanisms. Random cluster selection in this method has lead to its inefficiency. In this paper we proposed two algorithms, GP-Leach and HS-Leach. We improved the energy consumption by partitioning the network and using the evolutionary algorithms for optimized cluster head selection considering WSN nodes' position information and residual energy. The simulation results performed in MATLAB shows that our proposed algorithms are more efficient and they increased the lifetime of the network.


An Automated Bengali Sign Language Recognition System Based on Fingertip Finder Algorithm


title: Modular Marx generator for dV/dt testing of power semiconductor devices

abstract: A solid-state modular Marx Generator was designed for the purpose of testing the dV/dt capability of Power Semiconductor devices. The Marx Generator is capable of producing up to 1 kV voltage transients with rise times on the order of 10-nanoseconds. This capability to do variable voltage amplitude leads to customizable dV/dt tests. The solid-state modular design will be covered in detail within the paper. The solid-state construction allows for this adjustable dV/dt rating through the utilization of multiple modular stages of Power MOSFETs. These Power MOSFETs have a lower voltage blocking ability, but faster switch closing times. The different modules are controlled through the use of fiber-optic links. These links signal the floating Gate Driver circuitry to synchronize their switch closing time. The floating Gate Driver utilizes isolated switch-mode DC-DC converters to supply the power needed to charge the MOSFET gates, from one ground-referenced power source. The Marx Generator uses high voltage Silicon Carbide Schottky Diodes for the voltage blocking elements when the individual modular stages trigger. The lack of reverse recovery for these high voltage SiC Diodes enables the fast voltage transients that are requisite for dV/dt ranges needed to test Power Semiconductor devices.


title: Recent Advances In Piezoelectric Nano generators In Energy Harvesting Applications

abstract: Recently, the nanogenerators which can convert the mechanical energy into electricity by using piezoelectric materials have exhibited great potential in microscale power supply and sensor systems. In this paper, i provide a comprehensive review of the research progress in piezoelectric nanogenerators with different material. The fundamental piezoelectric theory and typical piezoelectric materials are firstly reviewed. After that, the working mechanism, modeling, and structure design of different types of piezoelectric nanogenerators were discussed. Then the recent progress of piezoelectric nanogenerators was also reviewed. Finally, i also discussed the potential application and future development of the piezoelectric nanogenerators.


title: Automatically Recommending Peer Reviewers in Modern Code Review

abstract: Code review is an important part of the software development process. Recently, many open source projects have begun practicing code review through “modern” tools such as GitHub pull-requests and Gerrit. Many commercial software companies use similar tools for code review internally. These tools enable the owner of a source code change to request individuals to participate in the review, i.e., reviewers. However, this task comes with a challenge. Prior work has shown that the benefits of code review are dependent upon the expertise of the reviewers involved. Thus, a common problem faced by authors of source code changes is that of identifying the best reviewers for their source code change. To address this problem, we present an approach, namely cHRev , to automatically recommend reviewers who are best suited to participate in a given review, based on their historical contributions as demonstrated in their prior reviews. We evaluate the effectiveness of cHRev on three open source systems as well as a commercial codebase at Microsoft and compare it to the state of the art in reviewer recommendation. We show that by leveraging the specific information in previously completed reviews (i.e.,quantification of review comments and their recency), we are able to improve dramatically on the performance of prior approaches, which (limitedly) operate on generic review information (i.e., reviewers of similar source code file and path names) or source coderepository data. We also present the insights into why our approach cHRev outperforms the existing approaches.


title: Image reconstruction using electron micrographs of insect flight muscle. Use of thick transverse sections to supplement data from tilted thin longitudinal sections.

abstract: Three-dimensional reconstruction using electron micrographs of thin sections is a powerful technique for determining cross-bridge structure. Tilt restrictions in the electron microscope prevent data collection beyond tilt angles of 60 degrees, giving rise to a "missing cone" of transform data. We show here how much of this data can be obtained using micrographs of thick transverse sections, and the effect this data has on reconstructed images of the insect flight muscle MYAC layer. As a byproduct, the analysis showed that section thinning resulting from prolonged electron irradiation had occurred in the thin longitudinal section used for the previously published MYAC layer reconstruction (Taylor et al., 1984). Comparison of projection density maps calculated from the thin longitudinal section reconstruction and the thick section data show that the data within the missing cone that is not accessible by tilting sharpens the boundaries of the components, flattens the density profile across the thick filament, and enlarges the molecular envelope of the thin filament. We conclude that the reconstructed images of the MYAC layer provide a picture of the structural principles underlying the system but that transform data within the missing cone are necessary to describe accurately the envelopes and profiles of these structural elements.


title: Soft Gripper Dynamics Using a Line-Segment Model With an Optimization-Based Parameter Identification Method

abstract: Soft robotics is an emerging field that focuses on the development and application of soft robots. Due to their highly deformable features, it is difficult to model and control such robots. In this paper, we proposed a simplified model to simulate a fluidic elastomer actuator (FEA). The model consists of a series of line segments connected by viscoelastic joints. Pneumatic inputs were modeled as active torques acting at each joint. The Lagrangian dynamic equations were derived. An optimization-based method was proposed to identify the unknown model parameters. Experiments were conducted using three-dimensional (3D) printed FEAs. Calibration results of a single FEA showed the repeatability of the pressure actuated bending angles, and the proposed dynamic model can precisely reproduce the deformation behavior of the FEA. Grasping experiments showed that the proposed dynamic model can predict the grasping forces, which was validated by a separate experiment of grasping force measurement. The presented methods can be extended to model other soft robots.


title: Combinatorial control of gene expression

abstract: Revealing the molecular principles of eukaryotic transcription factor assembly on specific DNA sites is pivotal to understanding how genes are differentially expressed. By analyzing structures of transcription factor complexes bound to specific DNA elements we demonstrate how protein and DNA regulators manage gene expression in a combinatorial fashion.


title: A biaxial piezoelectric MEMS scanning mirror and its application to pico-projectors

abstract: In this paper, we report a two-dimensional MEMS optical scanner that actuate and detect the angular motion through the piezoelectric effect of thin film PZT. Typical scan ranges were ±12° at 25 kHz for the horizontal resonance and ±8° at 60 Hz for the vertical non-resonance, and the overall power consumption was 100 mW or less. A newly designed mechanical rib structure on the backside of the mirror was found to suppress the dynamic deformation of the mirror to the level compatible with a high definition of 720 pixels or more. A scanning laser type pico-projector was developed for demonstration.


title: Grounding language in action

abstract: We report a new phenomenon associated with language comprehension: theaction—sentence compatibility effect (ACE). Participants judged whether sentences were sensible by making a response that required moving toward or away from their bodies. When a sentence implied action in one direction (e.g., “Close the drawer” implies action away from the body), the participants had difficulty making a sensibility judgment requiring a response in the opposite direction. The ACE was demonstrated for three sentences types: imperative sentences, sentences describing the transfer of concrete objects, and sentences describing the transfer of abstract entities, such as “Liz told you the story.” These data are inconsistent with theories of language comprehension in which meaning is represented as a set of relations among nodes. Instead, the data support an embodied theory of meaning that relates the meaning of sentences to human action.


title: Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds

abstract: Third-party cloud computing represents the promise of outsourcing as applied to computation. Services, such as Microsoft's Azure and Amazon's EC2, allow users to instantiate virtual machines (VMs) on demand and thus purchase precisely the capacity they require when they require it. In turn, the use of virtualization allows third-party cloud providers to maximize the utilization of their sunk capital costs by multiplexing many customer VMs across a shared physical infrastructure. However, in this paper, we show that this approach can also introduce new vulnerabilities. Using the Amazon EC2 service as a case study, we show that it is possible to map the internal cloud infrastructure, identify where a particular target VM is likely to reside, and then instantiate new VMs until one is placed co-resident with the target. We explore how such placement can then be used to mount cross-VM side-channel attacks to extract information from a target VM on the same machine.


title: External Bridging and Internal Bonding:: Unlocking the Generative Resources of Member Time and Attention Spent in Online Communities

abstract: Sharing members with other online communities can be problematic for community responsiveness, as less member time and attention become available for the focal community. However, member time and attention spent externally have the potential to generate knowledge that bolsters the focal community’s responsiveness. Little is known about when member time and attention spent externally become such generative resources. Building on and extending the knowledge collaboration theory of online communities, we examine the network conditions that enable the generative effect of member time and attention spent externally on community responsiveness. We propose a contingent model wherein a focal community’s external bridging (the extent of bridging nonredundant communities) and internal bonding (the extent of cohesion among members) jointly moderate the effects of member time and attention spent externally on community responsiveness. We theorize that external bridging provides the opportunity to access novel external knowledge and that internal bonding improves the motivation to share knowledge to assist others. Our results show that member time and attention spent externally have a positive effect on community responsiveness only in the presence of both external bridging and internal bonding. The study has important implications for advancing the boundary conditions regarding generative resources and online knowledge collaboration.


title: Software defined radio for small satellites

abstract: Cubesats offer relatively lost-cost access to space. They are actively used for a wide range of scientific studies and technology development efforts. Small size, relatively low available power, and highly constrained cost have typically limited the radios on these satellites to low data rates. In this paper we present our first AeroCube 915 MHz software defined radio based on a Zynq processor and using Lime Micro transceiver developed to address the low-cost constraint while at the same time offering a state-of-the-art communications link. The radio supports a wide range of modulations and powerful error correction code capable of 10 Mbps. The coding and modulation can be adapted in real-time as the range from the ground station to the satellite changes to maximize downlink throughput.


title: Design of axial flux permanent magnet generators using various magnetic materials in locally manufactured small wind turbines

abstract: Locally manufactured small wind turbines are most frequently used in off-grid battery based renewable energy systems and typically utilize coreless axial flux permanent magnet (AFPM) generators. Due to reports of neodymium magnet corrosion, the performance of coreless AFPM generators is investigated with the use of both neodymium and ferrite magnetic materials. Generator designs are conducted for rotor diameters between 2.4 and 4.2m, applying a Particle Swarm Optimization (PSO) in the generator design process, while the final designs are verified and fine-tuned with Finite Element Analysis (FEA) simulations. Optimal magnet dimensions are derived for both magnetic materials and for each rotor diameter, while a ‘universal’ magnet design is compared with commercially available magnets. Experimental comparison of a ferrite and a neodymium magnet generator design for a 2.4m rotor is conducted, proving that ferrite magnet AFPM generators are suitable for locally manufactured small wind turbines, making them less prone to corrosion, but also heavier in weight.


title: Power Consumption Breakdown on a Modern Laptop

abstract: The purpose of this work was to obtain a component-wise breakdown of the power consumption a modern laptop. We measured the power usage of the key components in an IBM ThinkPad R40 laptop using an Agilent Oscilloscope and current probes. We obtained the power consumption for the CPU, optical drive, hard disk, display, graphics card, memory, and wireless card subsystems-either through direct measurement or subtractive measurement and calculation. Moreover, we measured the power consumption of each component for a variety of workloads. We found that total system power consumption varies a lot (8 W to 30 W) depending on the workload, and moreover that the distribution of power consumption among the components varies even more widely. We also found that though power saving techniques such as DVS can reduce CPU power considerably, the total system power is still dominated by CPU power in the case of CPU intensive workloads. The display is the other main source of power consumption in a laptop; it dominates when the CPU is idle. We also found that reducing the backlight brightness can reduce the system power significantly, more than any other display power saving techniques. Finally, we observed OS differences in the power consumption.


title: Tree-structured survival analysis.

abstract: In this note, tree-structured recursive partitioning schemes for classification, probability class estimation, and regression are adapted to cover censored survival analysis. The only assumptions required are those which guarantee identifiability of conditional distributions of lifetime given covariates. Thus, the techniques are applicable to more general situations than are those of the famous semi-parametric model of Cox.


title: Hybrid key: An automatic tool for real-time high quality chroma keying

abstract: We present Hybrid Key, an automatic tool using chromatic information as the key signal for compositing multiple video layers together. This tool is based on our recently proposed chroma-keying mechanism which simulates human perception by utilizing a hybrid color space and reaches the minimum criteria of real-time high-definition video processing [1]. In this paper, we improve the quality of our perception-based algorithm, and optimized the implementation with a hybrid GPU/CPU architecture. Our new system introduces the task prioritizing and asynchronous processing without jeopardizing the quality. It efficiently distributes computational burdens between different processing units and therefore leaving more room for final alpha and foreground estimation as well as the newly added alpha optimization. In addition, our implementation can make use of GPU specified optimizations to further improve its efficiency. These advantages not only ensure the best overall quality in comparison to other state-of-the-art chroma keyers, but also provide a cost-efficient real-time 4K high-definition chroma keying solution and create the first iPad/iPhone application for high definition chroma keying on the go.


title: The relationship between nature relatedness and anxiety.

abstract: This study investigated the relationship between anxiety and feelings of being connected to nature. Two standardised self-report scales, the Nature Relatedness Scale and the State Trait Inventory for Cognitive and Somatic Anxiety, were used in tandem with a qualitative question. Quantitative results indicated that connection to nature was significantly related to lower levels of overall, state cognitive and trait cognitive anxiety. Qualitative results revealed seven themes: relaxation, time out, enjoyment, connection, expanse, sensory engagement and a healthy perspective. Taken together, these results suggest that opportunities that enhance experiences of being connected to nature may reduce unhelpful anxiety.


title: Synthesis, Crystal Structures, Thermal and Luminescent Properties of Rare Earth Metal Complexes with 1,2,4,5-Benzenetetracarboxylic Acid

abstract: Pr(Hbtca)(H2O)] (1), [Eu(Hbtca)(H2O)] (2), [Gd(Hbtca)(H2O)] (3) (H4btca: 1, 2, 4, 5-benzenetetracarboxlyic acid) have been synthesized and characterized by single crystal X-ray diffraction studies, elemental analysis and FT-IR. Structural determination reveals that these three complexes are isostructural. In compounds, center atoms coordinate to one ligand (Hbtca) and one water molecule with nine-coordinated distorted monocapped anti-square prism geometry. Four carboxyl groups of (Hbtca) adopt l2–g :g and l1–g :g two different coordination modes. The whole ligand (Hbtca) acts as a l6-bridge to connect six different rare earth M(III) ions to generate a 3D network. Thermal stability of three complexes and luminescent property of 2 are investigated.


title: StoreSim: Optimizing Information Leakage in Multicloud Storage Services

abstract: Many schemes have been recently advanced for storing data on multiple clouds. Distributing data over different cloud storage providers (CSPs) automatically provides users with a certain degree of information leakage control, as no single point of attack can leak all user's information. However, unplanned distribution of data chunks can lead to high information disclosure even while using multiple clouds. In this paper, to address this problem we present StoreSim, an information leakage aware storage system in multicloud. StoreSim aims to store syntactically similar data on the same cloud, thus minimizing the user's information leakage across multiple clouds. We design an approximate algorithm to efficiently generate similarity-preserving signatures for data chunks based on MinHash and Bloom filter, and also design a function to compute the information leakage based on these signatures. Next, we present an effective storage plan generation algorithm based on clustering for distributing data chunks with minimal information leakage across multiple clouds. Finally, we evaluate our scheme using two real datasets from Wikipedia and GitHub. We show that our scheme can reduce the information leakage by up to 60% compared to unplanned placement.


title: An overview of embedding models of entities and relationships for knowledge base completion

abstract: Knowledge bases (KBs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform knowledge base completion or link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This article serves as a brief overview of embedding models of entities and relationships for knowledge base completion, summarizing up-to-date experimental results on standard benchmark datasets FB15k, WN18, FB15k-237, WN18RR, FB13 and WN11.


title: High-resolution transmission electron microscopy of inorganic materials in cellular and topological random systems

abstract: The structure of amorphous Se films, in the topological random system, has been studied by the computer-simulation, electron diffraction (ED), and high-resolution transmission electron microscopy (HRTEM). As an example of HRTEM of the cellular random system, our recent investigation on Ba-ferrites is reviewed beforehand. In HRTEM images of spin-glass BaTi2-xSnxFe4O11 (x less than 0.6), magnetic clusters or clusters of FeO6-octahedra surrounded by TiO6-octahedra have been found. The structure of BaSn2Fe4O11 (x = 2) has been determined. The ordering of Sn (Ti) and Fe ions increases with increasing x, which interprets the change from the spin-glass state to nonspin-glass state at x = 0.6. It is shown that a cluster of polarized lattice ions is detectable in images of polar-glass BaxK2-xFexTi6-xO13 (x greater than or equal to 1.2). The structure models of amorphous Se films forming on a substrate have been constructed on a computer, and their radial distribution function (RDF) and HRTEM images have been calculated. Experimental RDF and HRTEM images have been obtained from vacuum-deposited amorphous Se films and are compared with the calculated ones. It is concluded that in the as-deposited films most of Se molecules may be composed of Se atoms as small as three and that by the electron-beam irradiation the molecules link to form spiral chains in amorphous state and then arrange to have the hexagonal crystal structure.


title: EARLY INTERVENTION AND EARLY EXPERIENCE

abstract: For 4 decades, vigorous efforts have been based on the premise that early intervention for children of poverty and, more recently, for children with developmental disabilities can yield significant improvements in cognitive, academic, and social outcomes. The history of these efforts is briefly summarized and a conceptual framework presented to understand the design, research, and policy relevance of these early interventions. This framework, biosocial developmental contextualism, derives from social ecology, developmental systems theory, developmental epidemiology, and developmental neurobiology. This integrative perspective predicts that fragmented, weak efforts in early intervention are not likely to succeed, whereas intensive, high-quality, ecologically pervasive interventions can and do. Relevant evidence is summarized in 6 principles about efficacy of early intervention. The public policy challenge in early intervention is to contain costs by more precisely targeting early interventions to those who most need and benefit from these interventions. The empirical evidence on biobehavioral effects of early experience and early intervention has direct relevance to federal and state policy development and resource allocation.


title: Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks

abstract: Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry and pose the challenges of not having adequate computing resources and of high costs involved in human labeling efforts. Training data subset selection and active learning techniques have been proposed as possible solutions to these challenges respectively. A special class of subset selection functions naturally model notions of diversity, coverage and representation and they can be used to eliminate redundancy and thus lend themselves well for training data subset selection. They can also help improve the efficiency of active learning in further reducing human labeling efforts by selecting a subset of the examples obtained using the conventional uncertainty sampling based techniques. In this work we empirically demonstrate the effectiveness of two diversity models, namely the Facility-Location and Disparity-Min models for training-data subset selection and reducing labeling effort. We do this for a variety of computer vision tasks including Gender Recognition, Scene Recognition and Object Recognition. Our results show that subset selection done in the right way can add 2-3% in accuracy on existing baselines, particularly in the case of less training data. This allows the training of complex machine learning models (like Convolutional Neural Networks) with much less training data while incurring minimal performance loss.


title: Exploring a Multidimensional Representation of Documents and Queries (extended version)

abstract: In Information Retrieval (IR), whether implicitly or explicitly, queries and documents are often represented as vectors. However, it may be more beneficial to consider documents and/or queries as multidimensional objects. Our belief is this would allow building "truly" interactive IR systems, i.e., where interaction is fully incorporated in the IR framework. ::: ::: The probabilistic formalism of quantum physics represents events and densities as multidimensional objects. This paper presents our first step towards building an interactive IR framework upon this formalism, by stating how the first interaction of the retrieval process, when the user types a query, can be formalised.


title: VirtualTable: a projection augmented reality game

abstract: VirtualTable is a projection augmented reality installation where users are engaged in an interactive tower defense game. The installation runs continuously and is designed to attract people to a table, which the game is projected onto. Any number of players can join the game for an optional period of time. The goal is to prevent the virtual stylized soot balls, spawning on one side of the table, from reaching the cheese. To stop them, the players can place any kind of object on the table, that then will become part of the game. Depending on the object, it will become either a wall, an obstacle for the soot balls, or a tower, that eliminates them within a physical range. The number of enemies is dependent on the number of objects in the field, forcing the players to use strategy and collaboration and not the sheer number of objects to win the game.


title: Optimizing seed selection for fuzzing

abstract: Randomly mutating well-formed program inputs or simply fuzzing, is a highly effective and widely used strategy to find bugs in software. Other than showing fuzzers find bugs, there has been little systematic effort in understanding the science of how to fuzz properly. In this paper, we focus on how to mathematically formulate and reason about one critical aspect in fuzzing: how best to pick seed files to maximize the total number of bugs found during a fuzz campaign. We design and evaluate six different algorithms using over 650 CPU days on Amazon Elastic Compute Cloud (EC2) to provide ground truth data. Overall, we find 240 bugs in 8 applications and show that the choice of algorithm can greatly increase the number of bugs found. We also show that current seed selection strategies as found in Peach may fare no better than picking seeds at random. We make our data set and code publicly available.


MINING TECHNIQUES : A SURVEY PAPER


title: Accuracy of outpatient endometrial biopsy in the diagnosis of endometrial cancer: a systematic quantitative review.

abstract: OBJECTIVE ::: To determine the accuracy of outpatient endometrial biopsy in diagnosing endometrial cancer in women with abnormal uterine bleeding. ::: ::: ::: DESIGN ::: A systematic quantitative review of published research. ::: ::: ::: METHODS ::: Studies were selected if accuracy of outpatient endometrial biopsy was estimated compared with a reference standard. Diagnostic accuracy was determined by pooled likelihood ratios for positive and negative test results. There were 1013 subjects in 13 diagnostic evaluations reported in 11 primary studies. ::: ::: ::: RESULTS ::: A positive test result on outpatient biopsy diagnosed endometrial cancer with a pooled likelihood ratio of 66.48 (95% CI 30.04-147.13) while a negative test result had a pooled likelihood ratio of 0.14 (95% CI 0.08-0.27). The post test probability of endometrial cancer was 81.7% (95% CI 59.7%-92.9%) for a positive test and 0.9% (95% CI 0.4%-2.4%) for a negative test. ::: ::: ::: CONCLUSION ::: Outpatient endometrial biopsy has a high overall accuracy in diagnosing endometrial cancer when an adequate specimen is obtained. A positive test result is more accurate for ruling in disease than a negative test result is for ruling it out. Therefore, in cases of abnormal uterine bleeding where symptoms persist despite negative biopsy, further evaluation will be warranted.


title: An Overview of Recent Application Trends at the AAMAS Conference: Security, Sustainability and Safety

abstract: A key feature of the AAMAS conference is its emphasis on ties to real-world applications. The focus of this article is to provide a broad overview of application-focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends.


title: READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents

abstract: Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.


title: Effects of plyometric training followed by a reduced training programme on physical performance in prepubescent soccer players.

abstract: Background. In adult population, stretch-shortening cycle exercise (plyometric exercise) is often used to improve leg muscle power and vertical jump performance. In children, limited information regarding this type of exercise is available. The purpose of this study was to examine the effectiveness of plyometric training and maintenance training on physical performances in prepubescent soccer players. Methods. Twenty boys aged 12-13 years was divided in two groups (10 in each): jump group (JG) and control group (CG). JG trained 3 days/week during 10 weeks, and performed various plyometric exercises including jumping, hurdling and skipping. The subsequent reduced training period lasted 8 weeks. However, all subjects continued their soccer training. Maximal cycling power (Pmax) was calculated using a force-velocity cycling test. Jumping power was assessed by using the following tests: countermovement jump (CMJ), squat jump (SJ), drop jump (DJ), multiple 5 bounds (MB5) and repeated rebound jump for 15 seconds (RRJ15). Running velocities included: 20, 30 and 40 m (V 20 , V 30 , V 40 m). Body fat percentage (BF%) and lean leg volume were estimated by anthropometry. Results. Before training, except for BF%, all baseline anthropometric characteristics were similar between JG and CG. After the training programme, Pmax (p<0.01), CMJ (p<0.01), SJ (p<0.05), MB5 (p<0.01), RRJ15 (p<0.01) and V 20 m (p<0.05), performances increased in the JG. During this period no significant performance increase was obtained in the CG. After the 8-week of reduced training, except Pmax (p<0.05) for CG, any increase was observed in both groups. Conclusions. These results demonstrate that short-term plyometric training programmes increase athletic performances in prepubescent boys. These improvements were maintained after a period of reduced training.


title: The nature of the epidermal barrier : biochemical aspects

abstract: Abstract As epidermal keratinocytes differentiate, they synthesize lipids, much of which are packaged into small organelles called lamellar granules. Acetate from the circulation serves as the principal carbon source for epidermal lipid synthesis, and the required energy is produced primarily by glycolysis with reduction of the resulting pyruvate to lactate. Late in the differentiation program, the contents of the lamellar granules are extruded into the extracellular spaces. Lipases, also delivered to the intercellular spaces via the lamellar granules, completely break down the remaining phospholipids and deglycosylate the glycolipids. Ceramides, cholesterol and fatty acids remain as the principal lipids in the stratum corneum. The modification of the lipid initially extruded from the lamellar granules is accompanied by a transformation from small, flattened vesicles to broad multilamellar lipid sheets which fill most of the intercellular spaces of the stratum corneum, and it is this intercellular lipid that provides the epidermal permeability barrier. Knowledge of the molecular events underlying formation and function of the permeability barrier can both improve our understanding of the normal protective role of the barrier and could be useful in design of transdermal drug delivery systems.


title: Reconstruction of cheek defects: a review of current techniques.

abstract: Abstract The article discusses the fundamental principles of cheek reconstruction and summarizes recently published techniques and reviews in the field. Reconstruction of the cheek is a complex endeavour. Patient, defect, and potential donor site factors must be carefully considered in the restoration of cheek form and function. Fortunately the surgeon, each with his or her own personal preferences and experience, has a wealth of techniques available from which to choose. New flaps and techniques are described including recent reviews of traditional techniques. Examples include a modification to the Mustarde flap, an axial pedicled flap from the radix nasi region, a technique of superficial musculoaponeurotic system plication to achieve primary closure of large defects, and reviews of the submental island flap, the subcutaneous cervicofacial flap, the medial sural artery perforator flap, and the anterolateral thigh flap. Surgical innovation and conscientious assessments of traditional techniques continue to advance the field of cheek reconstruction towards improved aesthetic and functional outcomes.


title: A Study into the Cybersecurity Awareness Initiatives for School Learners in South Africa and the UK

abstract: This research reports on a study into the cybersecurity awareness initiatives for school learners in South Africa and the UK, which are supported by government, industry and academia. Furthermore, this article provides an overview of similarities and differences between initiatives across countries, and posits as to the reasons why they may exist. The research concludes by presenting recommendations for both countries to improve school cybersecurity initiatives.


title: Targeted and depth-first exploration for systematic testing of android apps

abstract: Systematic exploration of Android apps is an enabler for a variety of app analysis and testing tasks. Performing the exploration while apps run on actual phones is essential for exploring the full range of app capabilities. However, exploring real-world apps on real phones is challenging due to non-determinism, non-standard control flow, scalability and overhead constraints. Relying on end-users to conduct the exploration might not be very effective: we performed a 7-use study on popular Android apps, and found that the combined 7-use coverage was 30.08% of the app screens and 6.46% of the app methods. Prior approaches for automated exploration of Android apps have run apps in an emulator or focused on small apps whose source code was available. To address these problems, we present A3E, an approach and tool that allows substantial Android apps to be explored systematically while running on actual phones, yet without requiring access to the app's source code. The key insight of our approach is to use a static, taint-style, dataflow analysis on the app bytecode in a novel way, to construct a high-level control flow graph that captures legal transitions among activities (app screens). We then use this graph to develop an exploration strategy named Targeted Exploration that permits fast, direct exploration of activities, including activities that would be difficult to reach during normal use. We also developed a strategy named Depth-first Exploration that mimics user actions for exploring activities and their constituents in a slower, but more systematic way. To measure the effectiveness of our techniques, we use two metrics: activity coverage (number of screens explored) and method coverage. Experiments with using our approach on 25 popular Android apps including BBC News, Gas Buddy, Amazon Mobile, YouTube, Shazam Encore, and CNN, show that our exploration techniques achieve 59.39--64.11% activity coverage and 29.53--36.46% method coverage.


title: SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization

abstract: Extractive multi-document summarization systems usually rank sentences in a document set with some ranking strategy and then select a few highly ranked sentences into the summary. One of the most popular ranking algorithms is the graph-based ranking algorithm. In this paper, we investigate making use of semantic role information to enhance the graph-based ranking algorithm for multi-document summarization. We first parse the sentences and obtain the semantic roles, and then propose a novel SRRank algorithm and two extensions to make better use of the semantic role information. Our proposed algorithms can simultaneously rank the sentences, semantic roles and words in a heterogeneous ranking process. Experimental results on two DUC datasets demonstrate that our proposed algorithms significantly outperform a few baselines, and the semantic role information is validated to be very helpful for multidocument summarization.


title: Interactive projection system based on a two-dimensional position sensitive detector

abstract: Interactive projection systems based on CCD/CMOS have been greatly developed in recent years for their non-contact sensing type. However, the location accuracy and tracing speed are limited by the resolution and frame rate of the CCD/CMOS. In this paper, we introduced a new type of non-contact interactive projection system based on a two-dimensional position sensitive detector (PSD). By using the PSD, both high positioning accuracy and high tracing speed can be achieved. Experimental results show that the positioning error is about 2mm over a 1200mm×1000mm projection screen, and the sampling rate is at least 200Hz.


title: [Treatment of the keratinizing skin disorder, psoriasis, by topical vitamin D3].

abstract: The active vitamin D(3) regulates proliferation and differentiation of epidermal keratinocytes. Although topical application of steroid has been principal therapy for psoriasis, there occurs some side effects such as skin atropy, telangiectasis, and purpura. Recently topical vitamin D(3), tacalcitol, calcipotriol, and maxacalcitol, which avoid these side effects, are widely used for psoriasis. In this review, we discussed the mechanism, effect, and side effect of topical vitamin D(3) for psoriasis.


title: On-load tap changer diagnosis-an off-line method for detecting degradation and defects: part 2

abstract: The following conclusions may be drawn from the material presented above: 1. Dynamic resistance measurements on OLTCs provide early warning of developing faults. They may be used to assign condition indices based on dynamic resistance boundary values. 2. The boundary values can be derived from field measurements, laboratory experiments, and failure investigations. 3. Using a flowchart in conjunction with the dynamic resistance data greatly simplifies the condition index assignment.


title: Ethical guidelines for a superintelligence

abstract: Nick Bostrom, in his new book SuperIntelligence, argues that the creation of an artificial intelligence with human-level intelligence will be followed fairly soon by the existence of an almost omnipotent superintelligence, with consequences that may well be disastrous for humanity. He considers that it is therefore a top priority for mankind to figure out how to imbue such a superintelligence with a sense of morality; however, he considers that this task is very difficult. I discuss a number of flaws in his analysis, particularly the viewpoint that implementing ethical behavior is an especially difficult problem in AI research.


title: Creating Effective Collaborative Learning Groups in an Online Environment

abstract: Collaborative learning in an online classroom can take the form of discussion among the whole class or within smaller groups. This paper addresses the latter, examining first whether assessment makes a difference to the level of learner participation and then considering other factors involved in creating effective collaborative learning groups. Data collected over a three year period (15 cohorts) from the Foundations course in the Master of Distance Education (MDE) program offered jointly by University of Maryland University College (UMUC) and the University of Oldenburg does not support the authors‟ original hypothesis that assessment makes a significant difference to learner participation levels in small group learning projects and leads them to question how much emphasis should be placed on grading work completed in study groups to the exclusion of other strategies. Drawing on observations of two MDE courses, including the Foundations course, their extensive online teaching experience, and a review of the literature, the authors identify factors other than grading that contribute positively to the effectiveness of small collaborative learning groups in the online environment. In particular, the paper focuses on specific instructional strategies that facilitate learner participation in small group projects, which result in an enhanced sense of community, increased skill acquisition, and better learning outcomes.


Modeling Lithium-ion Battery Chargers in PLECS ®


title: Real-time emotion recognition for gaming using deep convolutional network features

abstract: The goal of the present study is to explore the application of deep convolutional network features to emotion recognition. Results indicate that they perform similarly to other published models at a best recognition rate of 94.4%, and do so with a single still image rather than a video stream. An implementation of an affective feedback game is also described, where a classifier using these features tracks the facial expressions of a player in real-time.


Homography-Based Extrinsic Self-Calibration for Cameras in Automotive Applications


title: Supervised and projected sparse coding for image classification

abstract: Classic sparse representation for classification (SRC) method fails to incorporate the label information of training images, and meanwhile has a poor scalability due to the expensive computation for l1 norm. In this paper, we propose a novel subspace sparse coding method with utilizing label information to effectively classify the images in the subspace. Our new approach unifies the tasks of dimension reduction and supervised sparse vector learning, by simultaneously preserving the data sparse structure and meanwhile seeking the optimal projection direction in the training stage, therefore accelerates the classification process in the test stage. Our method achieves both flat and structured sparsity for the vector representations, therefore making our framework more discriminative during the subspace learning and subsequent classification. The empirical results on 4 benchmark data sets demonstrate the effectiveness of our method.


title: A novel planar inverted-F antenna for 2G/3G/LTE systems

abstract: In this paper, a novel planar inverted-F antenna (PIFA) operating at six bands (GSM850, GSM900, DCS1800, PCS1900, UMTS2100, LTE2300) is proposed. By adding a parasitic element and two slots in the patch of basic PIFA, the bandwidth on the upper frequency band can be improved to cover more communication systems. All the simulated and measured results show that the proposed PIFA possesses −6dB bandwidth 185 MHz (805 MHz to 990 MHz) at the lower frequency band and 815 MHz (1705 MHz to 2520 MHz) at the upper frequency band. Moreover, the proposed antenna exhibits good performance of total radiated power (TRP), (18 ∼28 dBm) and total isotropic sensitivity (TIS), (−91 ∼−107 dBm), under the free space environment. Thus, the novel PIFA is suitable for 2G/3G/LTE mobile communication systems.


title: Tutorial on Metrics of User Engagement: Applications to News, Search and E-Commerce

abstract: User engagement plays a central role in companies operating online services, such as search engines, news portals, e-commerce sites, and social networks. A main challenge is to leverage collected knowledge about the daily online behavior of millions of users to understand what engage them short-term and more importantly long-term. The most common way that engagement is measured is through various online metrics, acting as proxy measures of user engagement. This tutorial will review these metrics, their advantages and drawbacks, and their appropriateness to various types of online services. As case studies, we will focus on three types of services, news, search and e-commerce. We will also briefly discuss how to develop better machine learning models to optimize online metrics, and design experiments to test these models.


title: A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

abstract: BackgroundProtein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.ResultsIn this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods.ConclusionThe method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.


title: Unsupervised Cross-Domain Image Generation

abstract: We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.


title: Action capture with accelerometers

abstract: We create a performance animation system that leverages the power of low-cost accelerometers, readily available motion capture databases, and construction techniques from e-textiles. Our system, built with only off-the-shelf parts, consists of five accelerometers sewn into a comfortable shirt that streams data to a computer. The accelerometer readings are continuously matched against accelerations computed from existing motion capture data, and an avatar is animated with the closest match. We evaluate our system visually and using simultaneous motion and accelerometer capture.