title: Real-Time Semantic Clothing Segmentation

abstract: Clothing segmentation is a challenging field of research which is rapidly gaining attention. This paper presents a system for semantic segmentation of primarily monochromatic clothing and printed/stitched textures in single images or live video. This is especially appealing to emerging augmented reality applications such as retexturing sports players’ shirts with localized adverts or statistics in TV/internet broadcasting. We initialise points on the upper body clothing by body fiducials rather than by applying distance metrics to a detected face. This helps prevent segmentation of the skin rather than clothing. We take advantage of hue and intensity histograms incorporating spatial priors to develop an efficient segmentation method. Evaluated against ground truth on a dataset of 100 people, mostly in groups, the accuracy has an average F-score of 0.97 with an approach which can be over 88% more efficient than the state of the art.


Enhancing an Interactive Recommendation System with Review-based Information Filtering


title: An Entropy-Based Network Anomaly Detection Method

abstract: Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i) preparation of a concept of original entropy-based network anomaly detection method, (ii) implementation of the method, (iii) preparation of original dataset, (iv) evaluation of the method.


title: Smart Traffic Lights: A First Parallel Computing Approach

abstract: Optimal traffic light scheduling is a fundamental problem in modern urban areas. It has severe impact on traffic flow management, energy consumption and vehicular emissions, as well as on urban noise. The vast number of traffic lights in modern cities increases the complexity of the scheduling problem and, at the same time, urgently needs for efficient algorithms that optimize the light cycle programs. In this work, we propose a solution for the traffic light scheduling problem by using Differential Evolution, and investigate the benefits of parallelism on this complex problem. For understanding the impact in the city, the popular micro-simulator SUMO is used. We evaluate our approach on close-to-reality problem scenarios consisting of two large urban areas located in the cities of Malaga, Spain, and Paris, France. Our results are promising and encourage further investigation of parallel approaches to enable scalability.


title: Cost-effective on-demand associative author name disambiguation

abstract: Authorship disambiguation is an urgent issue that affects the quality of digital library services and for which supervised solutions have been proposed, delivering state-of-the-art effectiveness. However, particular challenges such as the prohibitive cost of labeling vast amounts of examples (there are many ambiguous authors), the huge hypothesis space (there are several features and authors from which many different disambiguation functions may be derived), and the skewed author popularity distribution (few authors are very prolific, while most appear in only few citations), may prevent the full potential of such techniques. In this article, we introduce an associative author name disambiguation approach that identifies authorship by extracting, from training examples, rules associating citation features (e.g., coauthor names, work title, publication venue) to specific authors. As our main contribution we propose three associative author name disambiguators: (1) EAND (Eager Associative Name Disambiguation), our basic method that explores association rules for name disambiguation; (2) LAND (Lazy Associative Name Disambiguation), that extracts rules on a demand-driven basis at disambiguation time, reducing the hypothesis space by focusing on examples that are most suitable for the task; and (3) SLAND (Self-Training LAND), that extends LAND with self-training capabilities, thus drastically reducing the amount of examples required for building effective disambiguation functions, besides being able to detect novel/unseen authors in the test set. Experiments demonstrate that all our disambigutators are effective and that, in particular, SLAND is able to outperform state-of-the-art supervised disambiguators, providing gains that range from 12% to more than 400%, being extremely effective and practical.


title: Cultural Competency, Race, and Skin Tone Bias Among Pharmacy, Nursing, and Medical Students

abstract: The Institute of Medicine report, Unequal Treatment, asserts that conscious and unconscious bias of providers may affect treatments delivered and contribute to health disparities. The primary study objective is to measure, compare, and contrast objective and subjective cognitive processes among pharmacy, nursing, and medical students to discern potential implications for health disparities. Data were collected using a cultural competency questionnaire and two implicit association tests (IATs). Race and skin tone IATs measure unconscious bias. Cultural competency scores were significantly higher for non-Hispanic Blacks and Hispanics in medicine and pharmacy compared with non-Hispanic Whites. Multiracial nursing students also had significantly higher cultural competency scores than non-Hispanic Whites. The IAT results indicate that these health care preprofessionals exhibit implicit race and skin tone biases: preferences for Whites versus Blacks and light skin versus dark skin. Cultural competency curricula...


title: Multi-source learning with block-wise missing data for Alzheimer's disease prediction

abstract: With the advances and increasing sophistication in data collection techniques, we are facing with large amounts of data collected from multiple heterogeneous sources in many applications. For example, in the study of Alzheimer's Disease (AD), different types of measurements such as neuroimages, gene/protein expression data, genetic data etc. are often collected and analyzed together for improved predictive power. It is believed that a joint learning of multiple data sources is beneficial as different data sources may contain complementary information, and feature-pruning and data source selection are critical for learning interpretable models from high-dimensional data. Very often the collected data comes with block-wise missing entries; for example, a patient without the MRI scan will have no information in the MRI data block, making his/her overall record incomplete. There has been a growing interest in the data mining community on expanding traditional techniques for single-source complete data analysis to the study of multi-source incomplete data. The key challenge is how to effectively integrate information from multiple heterogeneous sources in the presence of block-wise missing data. In this paper we first investigate the situation of complete data and present a unified ``bi-level" learning model for multi-source data. Then we give a natural extension of this model to the more challenging case with incomplete data. Our major contributions are threefold: (1) the proposed models handle both feature-level and source-level analysis in a unified formulation and include several existing feature learning approaches as special cases; (2) the model for incomplete data avoids direct imputation of the missing elements and thus provides superior performances. Moreover, it can be easily generalized to other applications with block-wise missing data sources; (3) efficient optimization algorithms are presented for both the complete and incomplete models. We have performed comprehensive evaluations of the proposed models on the application of AD diagnosis. Our proposed models compare favorably against existing approaches.


Dashboard for Actionable Feedback on Learning Skills: How Learner Profile Affects Use


title: Working with Young Night Drifters in Hong Kong

abstract: As societies develop, the life patterns of people change. Late night life has become a pattern in most developed and developing places; and the phenomenon of young night drifters (YNDs) occurs. The disturbances, nuisance and problems created by and related to the YNDs have aroused much public concern. With reference to the increase concern over the phenomenon and related problems – e.g. drugs taking, prostituting, bullying, thefting, creating public nuisances, personal safety, etc. – of YNDs in the territory, and supported by some studies, the Social Welfare Department of the Government funded the set up of 18 YND teams in September 2001 to serve the YNDs in priority communities in the territory. About 10 years ago, it had been estimated that there were about 10,000 to 20,000 YNDs, who drifted at night, in the whole territory. But a more recent figure estimated that there are about 50,000 to 60,000 YNDs in the territory each night. By reviewing and comparing some significant studies, both governmental and non-governmental, of the phenomenon, the profile of the YNDs, their nocturnal activities, their reasons for engaging in the activities, their service needs and demand, and the services provided to them are presented in this paper. What different systems – e.g. family, school, police, welfare service, and district – can do to reduce the negative impacts of YNDs are discussed at the end of the paper.


title: Counteracting geometrical attacks on robust image watermarking by constructing a deformable . . .

abstract: Counteracting geometrical attacks remains one of the most challenging problems in robust watermarking. In this paper, we resist rotation, scaling, and translation (RST) by constructing a kind of deformable pyramid transform (DPT) that is shift-invariant, steerable, and scalable. The DPT is extended from a closed-form polar-separable steerable pyramid transform (SPT). The radial component of the SPT's basis filters is taken as the kernel of the scalable basis filters, and the angular component is used for the steerable basis filters. The shift-invariance is inherited from the SPT by retaining undecimated high-pass and band-pass subbands. Based on the designed DPT, we theoretically derive interpolation functions for steerability and scalability and synchronization mechanisms for translation, rotation, and scaling. By exploiting the preferable characteristics of DPT, we develop a new template-based robust image watermarking scheme that is resilient to RST. Translation invariance is achieved by taking the Fourier magnitude of the cover image as the DPT's input. The resilience to rotation and scaling is obtained using the synchronization mechanisms for rotation and scaling, for which an efficient template-matching algorithm has been devised. Extensive simulations show that the proposed scheme is highly robust to geometrical attacks, such as RST, cropping, and row/column line removal, as well as common signal processing attacks such as JPEG compression, additive white Gaussian noise, and median filtering.


title: Efficient circuit clustering for area and power reduction in FPGAs

abstract: We present a routability-driven bottom-up clustering technique for area and power reduction in clustered FPGAs. This technique uses a cell connectivity metric to identify seeds for efficient clustering. Effective seed selection, coupled with an interconnect-resource aware clustering and placement, can have a favorable impact on circuit routability. It leads to better device utilization, savings in area, and reduction in power consumption. Routing area reduction of 35% is achieved over previously published results. Power dissipation simulations using a buffered pass-transistor-based FPGA interconnect model are presented. They show that our clustering technique can reduce the overall device power dissipation by an average of 13%.


title: Continuous Beam Steering Through Broadside Using Asymmetrically Modulated Goubau Line Leaky-Wave Antennas

abstract: Goubau line is a single-conductor transmission line, featuring easy integration and low-loss transmission properties. Here, we propose a periodic leaky-wave antenna (LWA) based on planar Goubau transmission line on a thin dielectric substrate. The leaky-wave radiations are generated by introducing periodic modulations along the Goubau line. In this way, the surface wave, which is slow-wave mode supported by the Goubau line, achieves an additional momentum and hence enters the fast-wave region for radiations. By employing the periodic modulations, the proposed Goubau line LWAs are able to continuously steer the main beam from backward to forward within the operational frequency range. However, the LWAs usually suffer from a low radiation efficiency at the broadside direction. To overcome this drawback, we explore both transversally and longitudinally asymmetrical modulations to the Goubau line. Theoretical analysis, numerical simulations and experimental results are given in comparison with the symmetrical LWAs. It is demonstrated that the asymmetrical modulations significantly improve the radiation efficiency of LWAs at the broadside. Furthermore, the measurement results agree well with the numerical ones, which experimentally validates the proposed LWA structures. These novel Goubau line LWAs, experimentally demonstrated and validated at microwave frequencies, show also great potential for millimeter-wave and terahertz systems.


title: Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review

abstract: Abstract This paper presents a comprehensive review of the work done, during the 1968–2005, in the application of statistical and intelligent techniques to solve the bankruptcy prediction problem faced by banks and firms. The review is categorized by taking the type of technique applied to solve this problem as an important dimension. Accordingly, the papers are grouped in the following families of techniques: (i) statistical techniques, (ii) neural networks, (iii) case-based reasoning, (iv) decision trees, (iv) operational research, (v) evolutionary approaches, (vi) rough set based techniques, (vii) other techniques subsuming fuzzy logic, support vector machine and isotonic separation and (viii) soft computing subsuming seamless hybridization of all the above-mentioned techniques. Of particular significance is that in each paper, the review highlights the source of data sets, financial ratios used, country of origin, time line of study and the comparative performance of techniques in terms of prediction accuracy wherever available. The review also lists some important directions for future research.


title: Persian Text Normalization using Classification Tree and Support Vector Machine

abstract: Text normalization is one of the most important tasks in text processing and text to speech conversion. In this paper, we propose a machine learning method to determine the type of Farsi language non-standard words (NSWs) by only using the structure of these words. Two methods including support vector machines (SVM) and classification and regression trees (CART) were used and evaluated on different training and test sets for NSW type classification in Farsi. The experimental results show that, NSW type classification in Farsi can be efficiently done by using only the structural form of Farsi non-standard words. In addition, the results is compared with a previous work done on normalization using multi-layer perceptron (MLP) neural network and shows that SVM outperforms MLP in both number of efforts and total performance


title: Hard Negative Mining for Metric Learning Based Zero-Shot Classification

abstract: Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.


title: Machine learning in tracking associations with stereo vision and lidar observations for an autonomous vehicle

abstract: Obstacles detection is used nowdays for a number of road safety applications, increasing the drivers awareness in potential dangerous situations. A reliable and robust obstacles detection continues to be largely investigated and still remains an open challenge, especially for difficult scenarios and in general cases, with loosened constraints and multiple simultaneous use-cases. This work presents an obstacles detection, tracking and fusion algorithm which allows to reconstruct the environment surrounding the vehicle. While the techniques used for the detection are well-known in literature, the improvements introduced by this paper regard the data association and tracking approach of heterogeneous sensors observations. An innovative multi-dimensional structure based on association costs originating from a classifier provides an optimal solution to the association problem with respect to the total association cost. An Unscented Kalman Filter (UKF) managing a variable number of observations, arbitrarily composable, allows to correctly address the combined tracking and fusion challenge. The results, obtained on a public benchmark, show improvements with respect to state of the art systems.


title: Modeling and vision-based control of large-dimension cable-driven parallel robots using a multiple-camera setup

abstract: Abstract This paper deals with the modeling and vision-based control of large-dimension cable-driven parallel robots. Inverse kinematics and instantaneous inverse kinematics models are derived from the elastic catenary cable modeling. These models turn out to be dependent on the pose of the mobile platform (end-effector), on the cable tangent directions and on the cable tensions. In order to control the motion of the robot, a position-based visual servo control is used, where the mobile platform pose is measured by vision and used for regulation. A multi-camera setup and load cells provide the aforementioned desired measurements, i.e., the mobile platform pose, the directions of the tangents to the cables, and the cable tensions. The proposed approach was validated in experiments on the large-dimension cable-driven parallel robot prototype CoGiRo of global dimensions 15 m × 11 m × 6 m (L × l × h). A maximum error of less than 1 cm in position and 0.5∘ in orientation was achieved. Moreover, in the case of cable-driven parallel robots larger than the prototype CoGiRo, simulations were conducted in order to assess the influence on the vision-based control of four instantaneous inverse kinematics models.


title: Motion saliency outweighs other low-level features while watching videos

abstract: The importance of motion in attracting attention is well known. While watching videos, where motion is ::: prevalent, how do we quantify the regions that are motion salient? In this paper, we investigate the role of ::: motion in attention and compare it with the influence of other low-level features like image orientation and ::: intensity. We propose a framework for motion saliency. In particular, we integrate motion vector information ::: with spatial and temporal coherency to generate a motion attention map. The results show that our model ::: achieves good performance in identifying regions that are moving and salient. We also find motion to have ::: greater influence on saliency than other low-level features when watching videos.


title: Software defect prediction using machine learning on test and source code metrics

abstract: Context. Software testing is the process of finding faults in software while executing it. The results of the testing are used to find and correct faults. Software defect prediction estimates where faults are likely to occur in source code. The results from the defect prediction can be used to opti- mize testing and ultimately improve software quality. Machine learning, that concerns computer programs learning from data, is used to build pre- diction models which then can be used to classify data. Objectives. In this study we, in collaboration with Ericsson, investigated whether software metrics from source code files combined with metrics from their respective tests predicts faults with better prediction perfor- mance compared to using only metrics from the source code files. Methods. A literature review was conducted to identify inputs for an ex- periment. The experiment was applied on one repository from Ericsson to identify the best performing set of metrics. Results. The prediction performance results of three metric sets are pre- sented and compared with each other. Wilcoxon’s signed rank tests are performed on four different performance measures for each metric set and each machine learning algorithm to demonstrate significant differences of the results. Conclusions. We conclude that metrics from tests can be used to predict faults. However, the combination of source code metrics and test metrics do not outperform using only source code metrics. Moreover, we conclude that models built with metrics from the test metric set with minimal infor- mation of the source code can in fact predict faults in the source code.


title: IT Governance Mechanisms in Multisourcing--A Business Group Perspective

abstract: Many international corporations are adopting multisourcing strategies. Multisourcing is defined as the blending of services from multiple company-internal and external suppliers. To date, performance measurement and governance-related aspects have been scarcely covered in the academic literature of IT outsourcing. With this study, we intend to broaden understanding of how business groups utilize governance mechanisms in a multi-sourced IT environment. This article contributes to the body of knowledge in three ways. First, it describes established IT governance mechanisms, second, it presents insights into a real-life example of IT governance mechanisms at a leading financial services provider, and third, it extends current literature toward multisourcing in a group-context by proposing a framework of IT governance mechanisms targeting the different relationships of multisourcing in business groups. In addition, this research is useful for corporations facing similar challenges.


title: Blockchain solutions for big data challenges: A literature review

abstract: The popularity of Blockchain technology and the huge extent of its application, results with much ongoing research in different practical and scientific areas. Although still new and in experimenting phase, the Blockchain is being seen as a revolutionary solution, addressing modern technology concerns like decentralization, trust, identity, data ownership and data-driven decisions. At the same time, the world is facing an expansion in quantity and diversity of digital data that are generated by both users and machines. While actively searching for the best way to store, organize and process Big Data, the Blockchain technology comes in providing significant input. Its proposed solutions about decentralized management of private data, digital property resolution, IoT communication and public institutions' reforms are having significant impact on how Big Data may evolve. This paper presents the novel solutions associated with some of the Big Data areas that can be empowered by the Blockchain technology.


title: Multiple string LED driver with flexible and high performance PWM dimming control

abstract: The main objectives in driving multiple LED strings include achieving uniform current control and high performance PWM dimming for all strings. This work proposes a new multiple string LED driver to achieve not only current balance, but also flexible and wide range PWM dimming ratio for each string. A compact single-inductor multiple-output topology is adopted in the driver, accompanied by synchronous integrators and variable dimming frequency, to achieve both high efficiency and high performance dimming. By using the proposed variable dimming frequency scheme, high dimming frequency is applied to a string with high dimming ratio, which helps to maintain the deviation of LED string current in an acceptable range, while low dimming frequency is applied to a string with low dimming ratio, which helps to achieve rectangular LED current waveform. Meanwhile, the new time multiplexing control scheme automatically optimizes the LED string's bus voltages, thus minimizes each string's power loss. A three string LED driver prototype is constructed to validate the effectiveness of the peoposed control scheme, where the three strings can have different dimming ratios between 4% and 100%.


title: Design of compact coaxial waveguide TE21 mode coupler

abstract: This paper proposes a compact Ku band TE21 mode coupler based on coaxial waveguide. This compact coupler takes only half the length of conventional circular waveguide coupler while maintains at least 55dB rejection of unwanted modes and reaches -6.3dB coupling to each of the rectangular waveguide arms in a bandwidth of 700MHz. The coupler can miniaturise the total length of monopulse system which utilizing TE21 mode for self-tracking.


title: StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection

abstract: One-stage object detectors such as SSD or YOLO already have shown promising accuracy with small memory footprint and fast speed. However, it is widely recognized that one-stage detectors have difficulty in detecting small objects while they are competitive with two-stage methods on large objects. In this paper, we investigate how to alleviate this problem starting from the SSD framework. Due to their pyramidal design, the lower layer that is responsible for small objects lacks strong semantics(e.g contextual information). We address this problem by introducing a feature combining module that spreads out the strong semantics in a top-down manner. Our final model StairNet detector unifies the multi-scale representations and semantic distribution effectively. Experiments on PASCAL VOC 2007 and PASCAL VOC 2012 datasets demonstrate that StairNet significantly improves the weakness of SSD and outperforms the other state-of-the-art one-stage detectors.


title: Detecting the Presence of Cyberbullying Using Computer Software

abstract: Cyberbullying is willful and repeated harm inflicted through the ::: medium of electronic text. Computer software was developed to ::: detect the presence of cyberbullying in online chat conversations. ::: Rules based on a dictionary of key words are used to classify a ::: window of posts. A truth set of MySpace threads was created. The ::: software was found to correctly identify windows containing ::: cyberbullying 85.30% of the time, and it identifies innocent ::: windows 51.91% of the time. The overall accuracy is 58.63%. ::: This suggests that our coding rules must be refined to not falsely ::: flag so much innocent conversation.


title: Website fingerprinting as a cybercrime investigation model: Role and challenges

abstract: The mass-production of online websites is one of the most confrontations of detecting cybercriminal activities. Monitoring the construction or visitation of such websites is somewhat unsophisticated process if it relies on filtering the URL addresses alone, like Proxy. However, user online interactions can be concealed if passed through security protocols or anonymity networks, like Tor. Nowadays, Tor has been widely used to conceal website addresses, web page contents, user actions, and user anonymity. On the other hand, website traffic analysis and fingerprinting techniques endeavor to break such privacy by revealing user actions and anonymity. Basically, this is considered as a negative behavior. However, in this paper, we study how fingerprinting techniques can positively be adapted by Internet Service Providers to be used against Cybercrime activities. In other words, fingerprinting techniques can play a vital role for investigating and mitigating cybercrimes. We present several fingerprinting techniques and countermeasures from a cybercrime point of view. Then, we (1) illustrate how fingerprinting techniques can be applied as cybercrime investigation models, and (2) discuss the expected challenges of such application.


title: Perceived Space in the HTC Vive

abstract: Underperception of egocentric distance in virtual reality has been a persistent concern for almost 20 years. Modern head-mounted displays (HMDs) appear to have begun to ameliorate underperception. The current study examined several aspects of perceived space in the HTC Vive. Blind-walking distance judgments, verbal distance judgments, and size judgments were measured in two distinct virtual environments (VEs)—a high-quality replica of a real classroom and an empty grass field—as well as the real classroom upon which the classroom VE was modeled. A brief walking interaction was also examined as an intervention for improving anticipated underperception in the VEs. Results from the Vive were compared to existing data using two older HMDs (nVisor SX111 and ST50). Blind-walking judgments were more accurate in the Vive compared to the older displays, and did not differ substantially from the real world nor across VEs. Size judgments were more accurate in the classroom VE than the grass VE and in the Vive compared to the older displays. Verbal judgments were significantly smaller in the classroom VE compared to the real classroom and did not significantly differ across VEs. Blind-walking and size judgments were more accurate after walking interaction, but verbal judgments were unaffected. The results indicate that underperception of distance in the HTC Vive is less than in older displays but has not yet been completely resolved. With more accurate space perception afforded by modern HMDs, alternative methods for improving judgments of perceived space—such as walking interaction—may no longer be necessary.


title: Frequency-dependent resistance in Litz-wire planar windings for domestic induction heating appliances

abstract: In this paper, the frequency-dependent resistance in Litz-wire planar windings for domestic induction heating appliances is analyzed. For these inductors, in which the size is not an essential constraint, an analytical model is developed based on the superposition of different loss effects in the wire. Eddy current losses, including conduction losses and proximity-effect losses, both internal and external, were considered and modeled. The magnetic field necessary to evaluate the external proximity losses is as well analytically calculated considering the complete winding and load properties. To verify this model and its limitations, several inductors with different wires and numbers of turns were constructed and results with both non-loaded and loaded inductors are compared with theoretical predictions.


title: Breakaway: an ambient display designed to change human behavior

abstract: We present Breakaway, an ambient display that encourages people, whose job requires them to sit for long periods of time, to take breaks more frequently. Breakaway uses the information from sensors placed on an office chair to communicate in a non-obtrusive manner how long the user has been sitting. Breakaway is a small sculpture placed on the desk. Its design is inspired by animation arts and theater, which rely heavily on body language to express emotions. Its shape and movement reflect the form of the human body; an upright position reflecting the body's refreshed pose, and a slouching position reflecting the body's pose after sitting for a long time. An initial evaluation shows a correlation between the movement of the sculpture and when participants took breaks, suggesting that ambient displays that make use of aesthetic and lifelike form might be promising for making positive changes in human behavior.


title: Multiuser Precoding With Limited Cooperation for Large-Scale MIMO Multicell Downlink

abstract: Multiple base stations (BSs) equipped with large antenna arrays can perform cooperative downlink transmissions using beamforming for cell-edge users to provide high data rate services. However, due to an excessive overhead for the channel state information (CSI) exchange between BSs, the cooperation with full CSI may not be practical for large antenna arrays. Thus, based on an existing precoding approach, two different precoding approaches with partial cooperation, where no CSI exchange is required, are proposed. One of them (called Type III precoding) has a better performance, and its performance can be further improved by user allocation, which becomes possible by deriving a closed-form expression for transmission power. For low-complexity implementations, an algorithm is derived for the user allocation based on the greedy algorithm. Under certain channel conditions, closed-form expressions for average transmission powers are also derived to see asymptotic performance. Based on them, we can show that the average transmission power becomes $Q$ times higher than that with full cooperation if precoding is carried out with local CSI only, where $Q$ is the number of BSs in cooperation, which would be considered as a penalty due to partial cooperation. Through simulations, it is shown that this penalty (i.e., the increase of transmission power) can be reduced by user allocation.


MontyLingua: An end-to-end natural language processor with common sense


title: Video-based 3D reconstruction, laparoscope localization and deformation recovery for abdominal minimally invasive surgery: a survey

abstract: Background ::: ::: The intra-operative three-dimensional (3D) structure of tissue organs and laparoscope motion are the basis for many tasks in computer-assisted surgery (CAS), such as safe surgical navigation and registration of pre-operative and intra-operative data for soft tissues. ::: ::: ::: ::: Methods ::: ::: This article provides a literature review on laparoscopic video-based intra-operative techniques of 3D surface reconstruction, laparoscope localization and tissue deformation recovery for abdominal minimally invasive surgery (MIS). ::: ::: ::: ::: Results ::: ::: This article introduces a classification scheme based on the motions of a laparoscope and the motions of tissues. In each category, comprehensive discussion is provided on the evolution of both classic and state-of-the-art methods. ::: ::: ::: ::: Conclusions ::: ::: Video-based approaches have many advantages, such as providing intra-operative information without introducing extra hardware to the current surgical platform. However, an extensive discussion on this important topic is still lacking. This survey paper is therefore beneficial for researchers in this field. Copyright © 2015 John Wiley & Sons, Ltd.


title: Extracting Records from the Web Using a Signal Processing Approach

abstract: Extracting records from web pages enables a number of important applications and has immense value due to the amount and diversity of available information that can be extracted. This problem, although vastly studied, remains open because it is not a trivial one. Due to the scale of data, a feasible approach must be both automatic and efficient (and of course effective). We present here a novel approach, fully automatic and computationally efficient, using signal processing techniques to detect regularities and patterns in the structure of web pages. Our approach segments the web page, detects the data regions within it, identifies the records boundaries and aligns the records. Results show high f-score and linearithmic time complexity behaviour.


title: Accelerate deep Q-network learning by n-step backup

abstract: Reinforcement learning is notorious for being difficult to train and for its prolonged training time. In this paper, we propose a new n-step backup methods to accelerate deep Q-network learning, which results in faster and better convergence. Although it is proven that n-step backup does not perform well in off-policy experience replay, we obtain improved performance by discovering and solving a number of problems. Specifically, we reduce the bias through n -step backup while avoiding the accompanied larger variance and higher computation overhead. Secondly, we attack the deteriorated overestimation problem in n-step backup, while enjoying faster propagation. We evaluate the performance of our approach in the Arcade Learning Environment.


title: Fractional Order Generalization of Anomalous Diffusion as a Multidimensional Extension of the Transmission Line Equation

abstract: In this paper, a new fractional order generalization of the diffusion equation is developed to describe the anisotropy of anomalous diffusion that is often observed in brain tissues using magnetic resonance imaging (MRI). The new model embeds three different fractional order exponents-corresponding to the principal directions of water diffusion-into the governing Bloch-Torrey equation. The model was used to analyze diffusion weighted MRI data acquired from a normal human brain using a 3T clinical MRI scanner. Analysis of the data revealed normal Gaussian diffusion in the cerebral spinal fluid (isotropic fractional order exponent of (0.90 ±0.1), and anomalous diffusion in both the white (0.67 ±0.1) and the gray (0.77 ±0.1) matter. In addition, we observed anisotropy in the fractional exponent values for white mater (0.59 ±0.1) along the fibers versus 0.68 ±0.1 across the fibers), but not for gray matter. This model introduces new parameters to describe the complexity of the tissue microenvironment that may be sensitive biomarkers of the structural changes arising in neural tissues with the onset of disease.


title: Towards Data-Driven Autonomics in Data Centers

abstract: Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.


An Automatic Database Generation and Ontology Mapping from OWL File


title: SMART 6: recent updates and new developments

abstract: Simple modular architecture research tool (SMART) is an online tool (http://smart.embl.de/) for the identification and annotation of protein domains. It provides a user-friendly platform for the exploration and comparative study of domain architectures in both proteins and genes. The current release of SMART contains manually curated models for 784 protein domains. Recent developments were focused on further data integration and improving user friendliness. The underlying protein database based on completely sequenced genomes was greatly expanded and now includes 630 species, compared to 191 in the previous release. As an initial step towards integrating information on biological pathways into SMART, our domain annotations were extended with data on metabolic pathways and links to several pathways resources. The interaction network view was completely redesigned and is now available for more than 2 million proteins. In addition to the standard web access to the database, users can now query SMART using distributed annotation system (DAS) or through a simple object access protocol (SOAP) based web service.


title: Moderators of nonverbal indicators of deception: A meta-analytic synthesis.

abstract: In many legal proceedings, fact finders scrutinize the demeanor of a defendant or witness, particularly his or her nonverbal behavior, for indicators of deception. This meta-analysis investigated directly observable nonverbal correlates of deception as a function of different moderator variables. Although lay people and professionals alike assume that many nonverbal behaviors are displayed more frequently while lying, of 11 different behaviors observable in the head and body area, only 3 were reliably associated with deception. Nodding, foot and leg movements, and hand movements were negatively related to deception in the overall analyses weighted by sample size. Most people assume that nonverbal behaviors increase while lying; however, these behaviors decreased, whereas others showed no change. There was no evidence that people avoid eye contact while lying, although around the world, gaze aversion is deemed the most important signal of deception. Most effect sizes were found to be heterogeneous. Analyses of moderator variables revealed that many of the observed relationships varied as a function of content, motivation, preparation, sanctioning of the lie, experimental design, and operationalization. Existing theories cannot readily account for the heterogeneity in findings. Thus, practitioners are cautioned against using these indicators in assessing the truthfulness of oral reports.


title: Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

abstract: As a basic task in computer vision, semantic segmentation can provide fundamental information for object detection and instance segmentation to help the artificial intelligence better understand real world. Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization. In this paper, we apply several famous FCNN to brain tumor segmentation, making comparisons and adjusting network architectures to achieve better performance measured by metrics such as precision, recall, mean of intersection of union (mIoU) and dice score coefficient (DSC). The adjustments to the classic FCNN include adding more connections between convolutional layers, enlarging decoders after up sample layers and changing the way shallower layers' information is reused. Besides the structure modification, we also propose a new classifier with a hierarchical dice loss. Inspired by the containing relationship between classes, the loss function converts multiple classification to multiple binary classification in order to counteract the negative effect caused by imbalance data set. Massive experiments have been done on the training set and testing set in order to assess our refined fully convolutional neural networks and new types of loss function. Competitive figures prove they are more effective than their predecessors.


Ontological based interoperability and integration framework for heterogeneous weather systems


title: A Survey of the Trajectories Conceptual Framework: Investigating Theory Use in HCI

abstract: We present a case study of how Human-Computer Interaction (HCI) theory is reused within the field. We analyze the HCI literature in order to reveal the impact of one particular theory, the trajectories framework that has been cited as an example of both contemporary HCI theory and a strong concept that sits between theory and design practice. Our analysis of 60 papers that seriously engaged with trajectories reveals the purposes that the framework served and which parts of it they used. We compare our findings to the originally stated goals of trajectories and to subsequent claims of its status as both theory and strong concept. The results shed new light on what we mean by theory in HCI, including its relationship to practice and to other disciplines.


title: CPAchecker: A Tool for Configurable Software Verification

abstract: Configurable software verification is a recent concept for expressing different program analysis and model checking approaches in one single formalism. This paper presents CPAchecker, a tool and framework that aims at easy integration of new verification components. Every abstract domain, together with the corresponding operations, implements the interface of configurable program analysis (CPA). The main algorithm is configurable to perform a reachability analysis on arbitrary combinations of existing CPAs. In software verification, it takes a considerable amount of effort to convert a verification idea into actual experimental results -- we aim at accelerating this process. We hope that researchers find it convenient and productive to implement new verification ideas and algorithms using this flexible and easy-to-extend platform, and that it advances the field by making it easier to perform practical experiments. The tool is implemented in Java and runs as command-line tool or as Eclipse plug-in. CPAchecker implements CPAs for several abstract domains. We evaluate the efficiency of the current version of our tool on software-verification benchmarks from the literature, and compare it with other state-of-the-art model checkers. CPAchecker is an open-source toolkit and publicly available.


title: Predictors of Math Anxiety and Its Influence on Young Adolescents' Course Enrollment Intentions and Performance in Mathematics.

abstract: We used structural modeling procedures to assess the influence of past math grades, math ability perceptions, performance expectancies, and value perceptions on the level of math anxiety reported in a sample of 7th- through 9th-grade students (N = 250). A second set of analyses examined the relative influence of these performance, self-perception, and affect variables on students' subsequent grades and course enrollment intentions in mathematics. The findings indicated that math anxiety was most directly related to students' math ability perceptions, performance expectancies, and value perceptions. Students' performance expectancies predicted subsequent math grades, whereas their value perceptions predicted course enrollment intentions. Math anxiety did not have significant direct effects on either grades or intentions. The findings also suggested that the pattern of relations are similar for boys and girls. The results are discussed in relation to expectancy-value and self-efficacy theories of academic achievement. A strong background in mathematics is critical for many career and job opportunities in today's increasingly technological society. However, many academically capable students prematurely restrict their educational and career options by discontinuing their mathematical training early in high school. Several recent surveys (National Assessment of Educational Progress [NAEP], 1988; National Center for Educational Statistics [NCES], 1984) indicate that only half of all high school graduates enroll in mathematics courses beyond the 10th grade. These reports also indicate that fewer women than men enroll in the more advanced courses in high school mathematics (NAEP, 1988; NCES, 1984), although the "gender gap" is beginning to narrow (Chipman & Thomas, 1985; Eccles, 1987). Furthermore, students of both sexes, but particularly women, do not attain a high level of mathematical competency, even if they have completed 4 years of high school math (NAEP, 1988).


title: External Insider Threat: A Real Security Challenge in Enterprise Value Webs

abstract: Increasingly, organizations collaborate with other organizations in value webs with various arrangements, such as outsourcing, partnering, joint ventures, or subcontracting. As the Jericho Forum (an industry consortium of the Open Group) observed, in all these forms of collaboration, the boundaries between organizations become permeable and, as a consequence, insiders and outsiders can no longer be neatly separated using the notion of a perimeter. Such organizational arrangements have security implications because individuals from the value web are neither outsiders nor completely insiders. To address this phenomenon this paper proposes a third set of individuals, called External Insiders. External insiders add challenges to the already known insider threat problem because, unlike outsiders, external insiders have granted access and are trusted; and, unlike traditional insiders, external insiders are not subjected to as many internal controls enforced by the organization for which they are external insiders. In fact, external insiders are part of two or more organizational control structures, and business-to-business contracts are often insufficiently detailed to establish security requirements at the level of granularity needed to counter the threat they pose.


title: Computational performance of a single bandpass electro-optic delay oscillator

abstract: Reservoir computing has recently been put forward as the generic name of a new research line in machine learning [1]. This field combines Echo State Networks, introduced by Jaeger [2], and Liquid State Machines, introduced by Maass [3]. These systems, that can be used to solve complex classification problems in a computationally efficient way, were proposed as an alternative to the recurrent neural networks that depend on very intensive and time-consuming training algorithms. Reservoir computing systems have two distinct parts: the reservoir, and an output layer. Traditionally, the reservoir consists of a vast number of randomly interconnected nodes, receiving input signals. The output layer is a linear weighted sum of the node states with the weights optimised by learning. In practice, networks of several hundred nonlinear nodes often have a good performance.


title: Brief questions to identify patients with inadequate health literacy.

abstract: BACKGROUND AND OBJECTIVES ::: No practical method for identifying patients with low heath literacy exists. We sought to develop screening questions for identifying patients with inadequate or marginal health literacy. ::: ::: ::: METHODS ::: Patients (n=332) at a VA preoperative clinic completed in-person interviews that included 16 health literacy screening questions on a 5-point Likert scale, followed by a validated health literacy measure, the Short Test of Functional Health Literacy in Adults (STOHFLA). Based on the STOFHLA, patients were classified as having either inadequate, marginal, or adequate health literacy. Each of the 16 screening questions was evaluated and compared to two comparison standards: (1) inadequate health literacy and (2) inadequate or marginal health literacy on the STOHFLA. ::: ::: ::: RESULTS ::: Fifteen participants (4.5%) had inadequate health literacy and 25 (7.5%) had marginal health literacy on the STOHFLA. Three of the screening questions, "How often do you have someone help you read hospital materials?" "How confident are you filling out medical forms by yourself?" and "How often do you have problems learning about your medical condition because of difficulty understanding written information?" were effective in detecting inadequate health literacy (area under the receiver operating characteristic curve of 0.87, 0.80, and 0.76, respectively). These questions were weaker for identifying patients with marginal health literacy. ::: ::: ::: CONCLUSIONS ::: Three questions were each effective screening tests for inadequate health literacy in this population.


title: Temperature dependence of the charge carrier mobility in disordered organic semiconductors at large carrier concentrations

abstract: Temperature-activated charge transport in disordered organic semiconductors at large carrier concentrations, especially relevant in organic field-effect transistors OFETs, has been thoroughly considered using a recently developed analytical formalism assuming a Gaussian density-of-states DOS distribution and MillerAbrahams jump rates. We demonstrate that the apparent Meyer-Neldel compensation rule MNR is recovered regarding the temperature dependences of the charge carrier mobility upon varying the carrier concentration but not regarding varying the width of the DOS. We show that establishment of the MNR is a characteristic signature of hopping transport in a random system with variable carrier concentration. The polaron formation was not involved to rationalize this phenomenon. The MNR effect has been studied in a OFET based on C60 films, a material with negligible electron-phonon coupling, and successfully described by the present model. We show that this phenomenon is entirely due to the evolution of the occupational DOS profile upon increasing carrier concentration and this mechanism is specific to materials with Gaussian-shaped DOS. The suggested model provides compact analytical relations which can be readily used for the evaluation of important material parameters from experimentally accessible data on temperature dependence of the mobility in organic electronic devices. Experimental results on temperature-dependent charge mobility reported before for organic semiconductors by other authors can be well interpreted by using the model presented in this paper. In addition, the presented analytical formalism predicts a transition to a Mott-type charge carrier hopping regime at very low temperatures, which also manifests a MNR effect.


title: Julienne: A Framework for Parallel Graph Algorithms using Work-efficient Bucketing

abstract: Existing graph-processing frameworks let users develop efficient implementations for many graph problems, but none of them support efficiently bucketing vertices, which is needed for bucketing-based graph algorithms such as \Delta-stepping and approximate set-cover. Motivated by the lack of simple, scalable, and efficient implementations of bucketing-based algorithms, we develop the Julienne framework, which extends a recent shared-memory graph processing framework called Ligra with an interface for maintaining a collection of buckets under vertex insertions and bucket deletions. We provide a theoretically efficient parallel implementation of our bucketing interface and study several bucketing-based algorithms that make use of it (either bucketing by remaining degree or by distance) to improve performance: the peeling algorithm for k-core (coreness), \Delta-stepping, weighted breadth-first search, and approximate set cover. The implementations are all simple and concise (under 100 lines of code). Using our interface, we develop the first work-efficient parallel algorithm for k-core in the literature with nontrivial parallelism. We experimentally show that our bucketing implementation scales well and achieves high throughput on both synthetic and real-world workloads. Furthermore, the bucketing-based algorithms written in Julienne achieve up to 43x speedup on 72 cores with hyper-threading over well-tuned sequential baselines, significantly outperform existing work-inefficient implementations in Ligra, and either outperform or are competitive with existing special-purpose parallel codes for the same problem. We experimentally study our implementations on the largest publicly available graphs and show that they scale well in practice, processing real-world graphs with billions of edges in seconds, and hundreds of billions of edges in a few minutes. As far as we know, this is the first time that graphs at this scale have been analyzed in the main memory of a single multicore machine.


title: Policy-sealed data: A new abstraction for building trusted cloud services

abstract: Accidental or intentional mismanagement of cloud software by administrators poses a serious threat to the integrity and confidentiality of customer data hosted by cloud services. Trusted computing provides an important foundation for designing cloud services that are more resilient to these threats. However, current trusted computing technology is ill-suited to the cloud as it exposes too many internal details of the cloud infrastructure, hinders fault tolerance and load-balancing flexibility, and performs poorly. We present Excalibur, a system that addresses these limitations by enabling the design of trusted cloud services. Excalibur provides a new trusted computing abstraction, called policy-sealed data, that lets data be sealed (i.e., encrypted to a customer-defined policy) and then unsealed (i.e., decrypted) only by nodes whose configurations match the policy. To provide this abstraction, Excalibur uses attribute-based encryption, which reduces the overhead of key management and improves the performance of the distributed protocols employed. To demonstrate that Excalibur is practical, we incorporated it in the Eucalyptus open-source cloud platform. Policy-sealed data can provide greater confidence to Eucalyptus customers that their data is not being mismanaged.


title: Committee-Based Color Constancy

abstract: We show how to achieve better illumination estimates for color constancy by combining the results of several existing algorithms. We consider committee methods based on both linear and non–linear ways of combining the illumination estimates from the original set of color constancy algorithms. Committees of grayworld, white patch and neural net methods are tested. The committee results are always more accurate than the estimates of any of the other algorithms taken in isolation.


Gr upSM Schistosoma Prevalence World-Wide


title: Epidemiology of post-neonatal bacterial meningitis in Cape Town children.

abstract: Bacterial meningitis is a major cause of childhood morbidity and mortality in South Africa. However, comprehensive regional or national epidemiological data, essential for rational public health interventions, are lacking. The purpose of this 1-year prospective study, from 1 August 1991 to 31 July 1992, was to define the magnitude of the problem of childhood bacterial meningitis in Cape Town. The study group consisted of all children, aged > 1 month to < 14 years, who presented with proven bacterial meningitis at all the hospitals in the Cape Town metropolitan area. During the year 201 cases were identified: 101 (50.2%) were due to Neisseria meningitidis, 74 (36.8%) were due to Haemophilus influenzae and 26 (12.9%) were due to Streptococcus pneumoniae. The overall incidence rate (95% confidence interval) for children less than 14 years, 5 years and 1 year was 34 (30-40), 76 (65-88) and 257 (213-309) per 100,000 children, respectively. The rate was highest in black infants, 416 (316-545)/100,000. This was 2 times greater than the rate in coloured infants and about 4.5 times greater than the rate in white infants. The median age of all the children was 10 months. The ages of children with haemophilus and pneumococcal meningitis were similar, 9 and 7.5 months respectively (P = 0.43), while children with meningococcal meningitis were significantly older (22 months) than the others (P < 0.01). The overall case fatality rate was 5%, and 12.9% of survivors had significant neurological sequelae (disability) on discharge.


title: Uplink Scheduling and Link Adaptation for Narrowband Internet of Things Systems

abstract: Narrowband Internet of Things (NB-IoT) is a new narrow-band radio technology introduced in the Third Generation Partnership Project release 13 to the 5th generation evolution for providing low-power wide-area IoT. In NB-IoT systems, repeating transmission data or control signals has been considered as a promising approach for enhancing coverage. Considering the new feature of repetition, link adaptation for NB-IoT systems needs to be performed in 2-D, i.e., the modulation and coding scheme (MCS) and the repetition number. Therefore, existing link adaptation schemes without consideration of the repetition number are no longer applicable. In this paper, a novel uplink link adaptation scheme with the repetition number determination is proposed, which is composed of the inner loop link adaptation and the outer loop link adaptation, to guarantee transmission reliability and improve throughput of NB-IoT systems. In particular, the inner loop link adaptation is designed to cope with block error ratio variation by periodically adjusting the repetition number. The outer loop link adaptation coordinates the MCS level selection and the repetition number determination. Besides, key technologies of uplink scheduling, such as power control and transmission gap, are analyzed, and a simple single-tone scheduling scheme is proposed. Link-level simulations are performed to validate the performance of the proposed uplink link adaptation scheme. The results show that our proposed uplink link adaptation scheme for NB-IoT systems outperforms the repetition-dominated method and the straightforward method, particularly for good channel conditions and larger packet sizes. Specifically, it can save more than 14% of the active time and resource consumption compared with the repetition-dominated method and save more than 46% of the active time and resource consumption compared with the straightforward method.


title: On the analysis of big data indexing execution strategies

abstract: Efficient response to search queries is very crucial for data analysts to obtain timely results from big data spanned over heterogeneous machines. Currently, a number of big-data processing frameworks are available in which search operations are performed in distributed and parallel manner. However, implementation of indexing mechanism results in noticeable reduction of overall query processing time. There is an urge to assess the feasibility and impact of indexing towards query execution performance. This paper investigates the performance of state-of-the-art clustered indexing approaches over Hadoop framework which is de facto standard for big data processing. Moreover, this study leverages a comparative analysis of non-clustered indexing overhead in terms of time and space taken by indexing process for varying volume data sets with increasing Index Hit Ratio. Furthermore, the experiments evaluate performance of search operations in terms of data access and retrieval time for queries that use indexes. We then validated the obtained results using Petri net mathematical modeling. We used multiple data sets in our experiments to manifest the impact of growing volume of data on indexing and data search and retrieval performance. The results and highlighted challenges favorably lead researchers towards improved implication of indexing mechanism in perspective of data retrieval from big data. Additionally, this study advocates selection of a non-clustered indexing solution so that optimized search performance over big data is obtained.


title: An enhanced computational feature selection method for medical synonym identification via bilingualism and multi-corpus training

abstract: Medical synonym identification has been an important part of medical natural language processing (NLP). However, in the field of Chinese medical synonym identification, there are problems like low precision and low recall rate. To solve the problem, in this paper, we propose a method for identifying Chinese medical synonyms. We first selected 13 features including Chinese and English features. Then we studied the synonym identification results of each feature alone and different combinations of the features. Through the comparison among identification results, we present an optimal combination of features for Chinese medical synonym identification. Experiments show that our selected features have achieved 97.37% precision rate, 96.00% recall rate and 97.33% F1 score.


title: Grid-based indoor localization using smartphones

abstract: We investigate properties and the accuracy of grid-based Bayesian filters for indoor localization. We use only the compass and a (hardware or software) step detector which are available in commodity smartphones. In the case when a hardware step detector is not present, we propose a reliable step detection algorithm using the accelerometer. Our main goal is to evaluate how the accuracy of a grid-based Bayesian filter is influenced by the discretization of environment, the structure of a grid, the accuracy of the expected step length, and the way a building is modeled. As the result of evaluation, we propose two correction algorithms. The first algorithm is designed to eliminate the influence of discretization. Since the accuracy of the expected step length influences the accuracy of estimated positions computed by a grid-based Bayesian filter, we propose an algorithm that analyzes compass data and changes of estimated positions in order to generate hints to increase or decrease the current value of the expected step length. Evaluation results confirm an improvement when the proposed algorithms are applied.


title: What-If Reasoning with Counterfactual Gaussian Processes

abstract: Answering "What if?" questions is important in many domains. For example, would a patient's disease progression slow down if I were to give them a dose of drug A? Ideally, we answer our question using an experiment, but this is not always possible (e.g., it may be unethical). As an alternative, we can use non-experimental data to learn models that make counterfactual predictions of what we would observe had we run an experiment. In this paper, we propose the counterfactual GP, a counterfactual model of continuous-time trajectories (time series) under sequences of actions taken in continuous-time. We develop our model within the potential outcomes framework of Neyman and Rubin. The counterfactual GP is trained using a joint maximum likelihood objective that adjusts for dependencies between observed actions and outcomes in the training data. We report two sets of experimental results using the counterfactual GP. The first shows that it can be used to learn the natural progression (i.e. untreated progression) of biomarker trajectories from observational data. In the second, we show how the CGP can be used for medical decision support by learning counterfactual models of renal health under different types of dialysis.


title: Cyclic electron flow around photosystem I is essential for photosynthesis

abstract: Photosynthesis provides at least two routes through which light energy can be used to generate a proton gradient across the thylakoid membrane of chloroplasts, which is subsequently used to synthesize ATP. In the first route, electrons released from water in photosystem II (PSII) are eventually transferred to NADP+ by way of photosystem I (PSI)1. This linear electron flow is driven by two photochemical reactions that function in series. The cytochrome b6f complex mediates electron transport between the two photosystems and generates the proton gradient (ΔpH). In the second route, driven solely by PSI, electrons can be recycled from either reduced ferredoxin or NADPH to plastoquinone, and subsequently to the cytochrome b6f complex2,3,4,5. Such cyclic flow generates ΔpH and thus ATP without the accumulation of reduced species. Whereas linear flow from water to NADP+ is commonly used to explain the function of the light-dependent reactions of photosynthesis, the role of cyclic flow is less clear. In higher plants cyclic flow consists of two partially redundant pathways. Here we have constructed mutants in Arabidopsis thaliana in which both PSI cyclic pathways are impaired, and present evidence that cyclic flow is essential for efficient photosynthesis.


title: Survival signalling by Akt and eIF4E in oncogenesis and cancer therapy

abstract: Evading apoptosis is considered to be a hallmark of cancer, because mutations in apoptotic regulators invariably accompany tumorigenesis. Many chemotherapeutic agents induce apoptosis, and so disruption of apoptosis during tumour evolution can promote drug resistance. For example, Akt is an apoptotic regulator that is activated in many cancers and may promote drug resistance in vitro. Nevertheless, how Akt disables apoptosis and its contribution to clinical drug resistance are unclear. Using a murine lymphoma model, we show that Akt promotes tumorigenesis and drug resistance by disrupting apoptosis, and that disruption of Akt signalling using the mTOR inhibitor rapamycin reverses chemoresistance in lymphomas expressing Akt, but not in those with other apoptotic defects. eIF4E, a translational regulator that acts downstream of Akt and mTOR, recapitulates Akt's action in tumorigenesis and drug resistance, but is unable to confer sensitivity to rapamycin and chemotherapy. These results establish Akt signalling through mTOR and eIF4E as an important mechanism of oncogenesis and drug resistance in vivo, and reveal how targeting apoptotic programmes can restore drug sensitivity in a genotype-dependent manner.


title: Multi-Task Learning for Spatio-Temporal Event Forecasting

abstract: Spatial event forecasting from social media is an important problem but encounters critical challenges, such as dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different populations in different locations). Most existing approaches (e.g., LASSO regression, dynamic query expansion, and burst detection) are designed to address some of these challenges, but not all of them. This paper proposes a novel multi-task learning framework which aims to concurrently address all the challenges. Specifically, given a collection of locations (e.g., cities), we propose to build forecasting models for all locations simultaneously by extracting and utilizing appropriate shared information that effectively increases the sample size for each location, thus improving the forecasting performance. We combine both static features derived from a predefined vocabulary by domain experts and dynamic features generated from dynamic query expansion in a multi-task feature learning framework; we investigate different strategies to balance homogeneity and diversity between static and dynamic terms. Efficient algorithms based on Iterative Group Hard Thresholding are developed to achieve efficient and effective model training and prediction. Extensive experimental evaluations on Twitter data from four different countries in Latin America demonstrated the effectiveness of our proposed approach.


title: AU ball on plate balancing robot

abstract: The ball on plate system is the extension of traditional ball on beam balancing problem in control theory. In this paper the implementation of a proportional-integral-derivative controller (PID controller) to balance a ball on a plate has been demonstrated. To increase the system response time and accuracy multiple controllers are piped through a simple custom serial protocol to boost the processing power, and overall performance. A single HD camera module is used as a sensor to detect the ball's position and two RC servo motors are used to tilt the plate to balance the ball. The result shows that by implementing multiple PUs (Processing Units) redundancy and high resolution can be achieved in real-time control systems.


title: A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

abstract: Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as budget-per-time-period vs. budget-per-campaign and binary-utility vs. distance-based-utility. We study the hardness of the task assignment problem in the offline setting and propose online heuristics which exploits the spatial and temporal knowledge acquired over time. Our experiments are conducted with spatial crowdsourcing workloads generated by the SCAWG tool and extensive results show the effectiveness and efficiency of our proposed solutions.


title: Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review

abstract: Software code review is an inspection of a code change by an independent third-party developer in order to identify and fix defects before an integration. Effectively performing code review can improve the overall software quality. In recent years, Modern Code Review (MCR), a lightweight and tool-based code inspection, has been widely adopted in both proprietary and open-source software systems. Finding appropriate code-reviewers in MCR is a necessary step of reviewing a code change. However, little research is known the difficulty of finding code-reviewers in a distributed software development and its impact on reviewing time. In this paper, we investigate the impact of reviews with code-reviewer assignment problem has on reviewing time. We find that reviews with code-reviewer assignment problem take 12 days longer to approve a code change. To help developers find appropriate code-reviewers, we propose RevFinder, a file location-based code-reviewer recommendation approach. We leverage a similarity of previously reviewed file path to recommend an appropriate code-reviewer. The intuition is that files that are located in similar file paths would be managed and reviewed by similar experienced code-reviewers. Through an empirical evaluation on a case study of 42,045 reviews of Android Open Source Project (AOSP), OpenStack, Qt and LibreOffice projects, we find that RevFinder accurately recommended 79% of reviews with a top 10 recommendation. RevFinder also correctly recommended the code-reviewers with a median rank of 4. The overall ranking of RevFinder is 3 times better than that of a baseline approach. We believe that RevFinder could be applied to MCR in order to help developers find appropriate code-reviewers and speed up the overall code review process.


title: Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources

abstract: Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2 - eps for any given eps greater than 0. We also show that no non-adaptive algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ride-sharing systems. We also present heuristics that perform well in practice.


Leveraging multiple FAA radars for NWS operation


An Effective Quality Evaluation Protocol For Speech Enhancement Algorithms


title: Assessing the value of coding standards: An empirical study

abstract: In spite of the widespread use of coding standards and tools enforcing their rules, there is little empirical evidence supporting the intuition that they prevent the introduction of faults in software. Not only can compliance with a set of rules having little impact on the number of faults be considered wasted effort, but it can actually result in an increase in faults, as any modification has a non-zero probability of introducing a fault or triggering a previously concealed one. Therefore, it is important to build a body of empirical knowledge, helping us understand which rules are worthwhile enforcing, and which ones should be ignored in the context of fault reduction. In this paper, we describe two approaches to quantify the relation between rule violations and actual faults, and present empirical data on this relation for the MISRA C 2004 standard on an industrial case study.


title: Communication between Brain Areas Based on Nested Oscillations

abstract: Abstract Unraveling how brain regions communicate is crucial for understanding how the brain processes external and internal information. Neuronal oscillations within and across brain regions have been proposed to play a crucial role in this process. Two main hypotheses have been suggested for routing of information based on oscillations, namely communication through coherence and gating by inhibition. Here, we propose a framework unifying these two hypotheses that is based on recent empirical findings. We discuss a theory in which communication between two regions is established by phase synchronization of oscillations at lower frequencies ( 40 Hz). Our framework, consistent with numerous recent empirical findings, posits that cross-frequency interactions are essential for understanding how large-scale cognitive and perceptual networks operate.


title: Predicting Corporate Failure: Empirical Evidence for the UK

abstract: The main purpose of this paper is the development and validation of a failure classification model for UK public industrial companies using current techniques: logit analysis and Neural Networks. Our dataset consists of 51 matched-pairs of failed and nonfailed UK public industrial firms over the period 1988-1997. Prediction models are developed for up to three years prior to the failure event.


title: Nine year old boy with chromosome 1q23.3-q25.1 deletion

abstract: Interstitial deletions of the long arm of chromosome 1 are rare, and recent reports of individuals with molecularly characterized deletions have resulted in advances in genotype–phenotype correlation. The recognizable phenotype associated with 1q23.3-q25.1 includes pre- and post-natal growth retardation, microcephaly, intellectual disability, delayed language acquisition, small hands and feet with brachydactyly and single palmar crease, and distinctive facial features including short bulbous nose, micrognathia, and ear malformations. We report a patient with an 11.35 Mb deletion from 1q23.3-q25.1 who has these features in addition to a rarely reported complication—profound sensorineural hearing loss. He has both pre- and post-natal growth deficiency and growth hormone deficiency that was diagnosed at age 2 years. However, unlike other individuals with this deletion and growth hormone deficiency, this boy has responded to treatment with human growth hormone. © 2016 Wiley Periodicals, Inc.


title: Tooth anatomy risk factors influencing root canal working length accessibility

abstract: The aim of this study was to analyze the specific influence of root canal anatomy on the accessibility of working length during root canal therapy. Four hundred seventy-six root canal therapy cases (amounting to a total of 1 005 root canals) were examined. The anatomy risk factors assessed in each case included: tooth type (tooth location), root canal curvature, and root canal calcification, as well as endodontic retreatment. The investigation examined the correlation between each of these anatomic factors and the working length, with statistical analysis consisting of Chi-square tests and multiple logistic regression analysis. In an independent factor analysis, tooth type (tooth location), root canal curvature, canal calcification, and endodontic retreat ment were determined to be the primary risk factors. In a multiple-factor regression model, root curvature and canal calcification were found to most significantly influence root canal working length accessibility (P<0.05). Root canal anatomy increases the difficulty of root canal preparation. Appropriate consideration of tooth anatomy will assist in accurate determination of preparation difficulty before instrumentation. This study alerts clinical therapists to anatomical factors influencing the working length accessibility, and allows for a direct estimate of success rate given in situ measurements of tooth factors during the root canal treatment procedure.


title: MobSpiro: Mobile based spirometry for detecting COPD

abstract: Chronic respiratory diseases are diseases of the airways and other structures of the lung, usually resulting in difficulty in breathing and other symptoms. Chronic obstructive pulmonary disease (COPD) is considered to be one of the most common of respiratory diseases. Nowadays, spirometry remains the golden standard for diagnosing and staging COPD. By taking into consideration the possibility of this disease worsening over time and its negative impact on patient's life, sufferers should conduct regular spirometry checks at medical centers or buy expensive and portable devices to monitor and manage the disease. Therefore, COPD spirometry is costly in terms of both, money and time. In this work and due to the pervasiveness and advancement of smartphones, we attempt to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile based spirometer capable of diagnosing and managing COPD in a reliable and cost effective manner. We develop a model that allows the computation of the two critical parameters FVC and FEV1 by establishing a relationship between the frequency response of human exhalation recorded by mobile microphone and the actual flow rate. These two parameters are critical in diagnosing COPD. Preliminary results show that the mean percent error between FVC, FEV1, and FEV1/FVC ratio data as computed using MobSpiro application and the clinical spirometer is 4.6%, 3.1%, and 3.5% respectively. These results prove the effectiveness of the proposed system when compared to the clinical spirometer, and confirm that smartphones can play an important role in healthcare in the coming future.


title: A Miniaturized Multilayered Marchand Balun Using Coupled Artificial Transmission Lines

abstract: A miniaturized Marchand balun is investigated in this letter. The Marchand balun is realized on a six-layered printed circuit board by integration of two coupled artificial transmission line sections. An additional artificial line is inserted in-between the coupled lines for compensating the asymmetry. With the help of the even odd mode analysis, the electrical characteristics of the coupled lines are investigated in detail. The proposed balun has the smallest occupied size among the previous designs on printed circuit boards. It features excellent amplitude and phase imbalance, and a comparable return loss bandwidth as well. The design methodology, simulation, and experimental results are introduced thoroughly in the letter.


title: Five Degrees of Happiness: Effective Smiley Face Likert Scales for Evaluating with Children

abstract: This paper focuses on achieving optimal responses through supporting children's judgements, using Smiley Face Likert scales as a rating scale for quantitative questions in evaluations. It highlights the need to provide appropriate methods for children to communicate judgements, highlighting that the traditional Smiley Face Likert scale does not provide an appropriate method. The paper outlines a range of studies, identifying that to achieve differentiated data and full use of rating scales by children that faces with positive emotions should be used within Smiley Face Likert scales. The proposed rating method, the Five Degrees of Happiness Smiley Face Likert scale, was used in a large-scale summative evaluation of a Serious Game resulting in variance within and between children, with all points of the scale used.


title: Dual-Polarized Microstrip Patch Planar Array Antennas With Improved Port-to-Port Isolation

abstract: A method of preventing the degradation in the isolation between the orthogonal polarization ports caused by beamforming network routing in combined edge/aperture fed dual-polarized microstrip-patch planar array antennas is described. The simulated and measured performance of such planar arrays is demonstrated. Measured port isolations of 50 dB at center frequency, and more than 40 dB over a 4% bandwidth, are achieved. In addition, insight into the physical reasons for the improved port-to-port isolation levels, of the proposed element geometry and beamforming network layout, is obtained through prudent use of the electromagnetic modelling.


title: Memory abilities in action video game players

abstract: Abstract Action video game players (AVGPs) have been shown to outperform non-action video game players (NVGPs) on tasks of perception and attention. Here we set out to investigate if these benefits also extended to another cognitive domain, memory. In particular, while there is some previous evidence suggesting AVGPs demonstrate better visual short-term memory, it is unclear whether this extends to long-term memory processes or indeed, whether these enhancements are due to memory per se or are instead reflective of changes in speed of processing or strategy. Using four tasks that tap distinct areas of memory processing we found evidence for greater speed of processing and enhanced visual short-term memory in AVGPs and compared to NVGPs. However, we found either equivalent or possibly decreased performance in AVGPs in tasks related to long-term memory access. Furthermore, differences in strategy were noted across tasks, in particular differences in the tradeoff between speed and accuracy, which calls for a closer investigation of how task instructions bias performance in future studies.


title: Video Games in Education

abstract: Computer and video games are a maturing medium and industry and have caught the attention of scholars across a variety of disciplines. By and large, computer and video games have been ignored by educators. When educators have discussed games, they have focused on the social consequences of game play, ignoring important educational potentials of gaming. This paper examines the history of games in educational research, and argues that the cognitive potential of games have been largely ignored by educators. Contemporary developments in gaming, particularly interactive stories, digital authoring tools, and collaborative worlds, suggest powerful new opportunities for educational media.


title: Choosing a Twice More Accurate Dot Product Implementation

abstract: The fused multiply and add (FMA) operation computes a floating point multiplication followed by an addition or a subtraction as a single floating point operation. Intel IA-64, IBM RS/6000 and PowerPC architectures implement this FMA operation. The aim of this talk is to study how the FMA improves the computation of dot product with classical and compensated algorithms. The latters double the accuracy of the former at the same working precision. Six algorithms are considered. We present associated theoretical error bounds. Numerical experiments illustrate the actual efficiency in terms of accuracy and running time. We show that the FMA does not improve in a significant way the accuracy of the result whereas it increases significantly the actual speed of the algorithms.


Target detection and localization method for distributed monopulse arrays in the presence of mainlobe jamming.


title: Foreign Direct Investment, Financial Development and Economic Growth: Empirical Evidence from North African Countries

abstract: The present paper examines the causal linkage between foreign direct investment (FDI), financial development, and economic growth in a panel of 4 countries of North Africa (Tunisia, Morocco, Algeria and Egypt) over the period 1980-2011. The study moves away from the traditional cross-sectional analysis, and focuses on more direct evidence of the channels through which FDI inflows can promote economic growth of the host country. Using Generalized Method of Moment (GMM) panel data analysis, we find strong evidence of a positive relationship between FDI and economic growth. We also find evidence that the development of the domestic financial system is an important prerequisite for FDI to have a positive effect on economic growth. The policy implications of this study appeared clear. Improvement efforts need to be driven by local-level reforms to ensure the development of domestic financial system in order to maximize the benefits of the presence of FDI.


title: An overview on control algorithms for automated highway systems

abstract: This paper surveys lateral and longitudinal vehicle control algorithms in automated highway systems. In the lateral control, an onboard sensing system detects or captures a reference on a roadway indicating the path of an automated vehicle, and PID control and state variable feedback control based on the modern control theory with deviation from a planned path are mainly used to drive the vehicle along the path. In the longitudinal control, an inter-vehicle gap and relative speed to a preceding vehicle are measured, and feedback control with state variables including deviations in the gap, relative speed, and relative acceleration, some of which are obtained by the transmission over inter-vehicle communication rather than measurement, is used to maintain a predetermined gap in a platoon. Lateral and longitudinal vehicle control algorithms are explained with references to some systems developed since 1960s.


title: Inkjet Printing of Multilayer Millimeter-Wave Yagi-Uda Antennas on Flexible Substrates

abstract: This letter presents two high-gain, multidirector Yagi-Uda antennas for use within the 24.5-GHz ISM band, realized through a multilayer, purely additive inkjet printing fabrication process on a flexible substrate. Multilayer material deposition is used to realize these 3-D antenna structures, including a fully printed 120- μm-thick dielectric substrate for microstrip-to-slotline feeding conversion. The antennas are fabricated, measured, and compared to simulated results showing good agreement and highlighting the reliable predictability of the printing process. An endfire realized gain of 8 dBi is achieved within the 24.5-GHz ISM band, presenting the highest-gain inkjet-printed antenna at this end of the millimeter-wave regime. The results of this work further demonstrate the feasibility of utilizing inkjet printing for low-cost, vertically integrated antenna structures for on-chip and on-package integration throughout the emerging field of high-frequency wireless electronics.


title: A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events

abstract: State-of-the-art event encoding approaches rely on sentence or phrase level labeling, which are both time consuming and infeasible to extend to large scale text corpora and emerging domains. Using a multiple instance learning approach, we take advantage of the fact that while labels at the sentence level are difficult to obtain, they are relatively easy to gather at the document level. This enables us to view the problems of event detection and extraction in a unified manner. Using distributed representations of text, we develop a multiple instance formulation that simultaneously classifies news articles and extracts sentences indicative of events without any engineered features. We evaluate our model in its ability to detect news articles about civil unrest events (from Spanish text) across ten Latin American countries and identify the key sentences pertaining to these events. Our model, trained without annotated sentence labels, yields performance that is competitive with selected state-of-the-art models for event detection and sentence identification. Additionally, qualitative experimental results show that the extracted event-related sentences are informative and enhance various downstream applications such as article summarization, visualization, and event encoding.


title: User Interface Personalization in News Apps

abstract: News is increasingly being accessed on smartphones and tablets, establishing mobile news reading as one of the most popular activities on mobile devices. News reading is also a very individual activity with marked differences in the way people read and access the news, however, news apps have limited personalization. In this paper, we approach news personalization as a two-dimensional problem. We discuss news personalization in terms of 'what' content is delivered to the user and 'how' that content is consumed. We present our approach towards user interface personalization in news apps and we conclude that news content recommendation and user interface personalization should co-exist in news apps.


title: Road safety legislation in the Americas

abstract: Legislating five of the main risk factors for road traffic injuries (RTIs), as much as enforcing the law, is essential in forging an integral culture of road safety. Analysis of the level of progression in law enforcement allows for an evaluation of the state of world regions. A secondary analysis of the 2009 Global status report on road safety: time for action survey was undertaken to evaluate legislation on five risk factors (speed management, drinking and driving, motorcycle helmet use, seatbelt use, and use of child restraints) in the Americas. Laws were classified depending on their level of progression: the existence of legislation, whether the legislation is adequate, a level of law enforcement > 6 (on a scale of 0-10), and whether the law is considered comprehensive. A descriptive analysis was performed. The totality of the countries has national or subnational legislation for at least one of the five risk factors. However, 63% have laws on the five risk factors studied, and none of them has comprehensive laws for all five. Seatbelt use appears to be the most extended enforced legislation, while speeding laws appear to be the least enforced. There are positive efforts that should be recognized in the region. However, the region stands in different stages of progression. Law enforcement remains the main issue to be tackled. Laws should be based on evidence about what is already known to be effective.


title: TUMindoor: An extensive image and point cloud dataset for visual indoor localization and mapping

abstract: Recent advances in the field of content-based image retrieval (CBIR) have made it possible to quickly search large image databases using photographs or video sequences as a query. With appropriately tagged images of places, this technique can be applied to the problem of visual location recognition. While this task has attracted large interest in the community, most existing approaches focus on outdoor environments only. This is mainly due to the fact that the generation of an indoor dataset is elaborate and complex. In order to allow researchers to advance their approaches towards the challenging field of CBIR-based indoor localization and to facilitate an objective comparison of different algorithms, we provide an extensive, high resolution indoor dataset. The free for use dataset includes realistic query sequences with ground truth as well as point cloud data, enabling a localization system to perform 6-DOF pose estimation.


title: Prevention of violence against women and girls: what does the evidence say?

abstract: In this Series paper, we review evidence for interventions to reduce the prevalence and incidence of violence against women and girls. Our reviewed studies cover a broad range of intervention models, and many forms of violence--ie, intimate partner violence, non-partner sexual assault, female genital mutilation, and child marriage. Evidence is highly skewed towards that from studies from high-income countries, with these evaluations mainly focusing on responses to violence. This evidence suggests that women-centred, advocacy, and home-visitation programmes can reduce a woman's risk of further victimisation, with less conclusive evidence for the preventive effect of programmes for perpetrators. In low-income and middle-income countries, there is a greater research focus on violence prevention, with promising evidence on the effect of group training for women and men, community mobilisation interventions, and combined livelihood and training interventions for women. Despite shortcomings in the evidence base, several studies show large effects in programmatic timeframes. Across different forms of violence, effective programmes are commonly participatory, engage multiple stakeholders, support critical discussion about gender relationships and the acceptability of violence, and support greater communication and shared decision making among family members, as well as non-violent behaviour. Further investment in intervention design and assessment is needed to address evidence gaps.