title: Can Beach Cleans Do More Than Clean-Up Litter? Comparing Beach Cleans to Other Coastal Activities

abstract: Coastal visits not only provide psychological benefits but can also contribute to the accumulation of rubbish. Volunteer beach cleans help address this issue, but may only have limited, local impact. Consequently, it is important to study any broader benefits associated with beach cleans. This article examines the well-being and educational value of beach cleans, as well as their impacts on individuals’ behavioral intentions. We conducted an experimental study that allocated students (n = 90) to a beach cleaning, rock pooling, or walking activity. All three coastal activities were associated with positive mood and pro-environmental intentions. Beach cleaning and rock pooling were associated with higher marine awareness. The unique impacts of beach cleaning were that they were rated as most meaningful but linked to lower restorativeness ratings of the environment compared with the other activities. This research highlights the interplay between environment and activities, raising questions for future resea...


title: On the Challenges of Physical Implementations of RBMs

abstract: Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the cost of sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are designed to reproduce aspects of the D-Wave quantum computer, but the issues we investigate arise in most forms of physical computation.


title: A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks

abstract: Purpose: Neural networks have received recent interest for reconstruction of undersampled MR acquisitions. Ideally network performance should be optimized by drawing the training and testing data from the same domain. In practice, however, large datasets comprising hundreds of subjects scanned under a common protocol are rare. The goal of this study is to introduce a transfer-learning approach to address the problem of data scarcity in training deep networks for accelerated MRI. ::: Methods: Neural networks were trained on thousands of samples from public datasets of either natural images or brain MR images. The networks were then fine-tuned using only few tens of brain MR images in a distinct testing domain. Domain-transferred networks were compared to networks trained directly in the testing domain. Network performance was evaluated for varying acceleration factors (2-10), number of training samples (0.5-4k) and number of fine-tuning samples (0-100). ::: Results: The proposed approach achieves successful domain transfer between MR images acquired with different contrasts (T1- and T2-weighted images), and between natural and MR images (ImageNet and T1- or T2-weighted images). Networks obtained via transfer-learning using only tens of images in the testing domain achieve nearly identical performance to networks trained directly in the testing domain using thousands of images. ::: Conclusion: The proposed approach might facilitate the use of neural networks for MRI reconstruction without the need for collection of extensive imaging datasets.


Biological Computing


title: Information-Maximization Clustering Based on Squared-Loss Mutual Information

abstract: Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it involves only continuous optimization of model parameters, which is substantially simpler than discrete optimization of cluster assignments. However, existing methods still involve nonconvex optimization problems, and therefore finding a good local optimal solution is not straightforward in practice. In this letter, we propose an alternative information-maximization clustering method based on a squared-loss variant of mutual information. This novel approach gives a clustering solution analytically in a computationally efficient way via kernel eigenvalue decomposition. Furthermore, we provide a practical model selection procedure that allows us to objectively optimize tuning parameters included in the kernel function. Through experiments, we demonstrate the usefulness of the proposed approach.


title: Extensions of recurrent neural network language model

abstract: We present several modifications of the original recurrent neural network language model (RNN LM).While this model has been shown to significantly outperform many competitive language modeling techniques in terms of accuracy, the remaining problem is the computational complexity. In this work, we show approaches that lead to more than 15 times speedup for both training and testing phases. Next, we show importance of using a backpropagation through time algorithm. An empirical comparison with feedforward networks is also provided. In the end, we discuss possibilities how to reduce the amount of parameters in the model. The resulting RNN model can thus be smaller, faster both during training and testing, and more accurate than the basic one.


title: Deriving Conceptual Schema from XML Databases

abstract: In this paper, two concepts from different research areas are addressed together, namely functional dependency (FD) and multidimensional association rule (MAR). FD is a class of integrity constraints that have gained fundamental importance in relational database design. MAR is a class of patterns which has been studied rigorously in data mining. We employ MAR to mine the interesting rules from XML Databases. The mined interesting rules are considered as candidate FDs whose all confidence itemsets are 100%. To prune the weak rules, we pay attention to support and correlation itemsets. The final strong rules are used to generate an Object-Role Model conceptual schema diagram.


title: fMRI Resting Slow Fluctuations Correlate with the Activity of Fast Cortico-Cortical Physiological Connections

abstract: Recording of slow spontaneous fluctuations at rest using functional magnetic resonance imaging (fMRI) allows distinct long-range cortical networks to be identified. The neuronal basis of connectivity as assessed by resting-state fMRI still needs to be fully clarified, considering that these signals are an indirect measure of neuronal activity, reflecting slow local variations in de-oxyhaemoglobin concentration. Here, we combined fMRI with multifocal transcranial magnetic stimulation (TMS), a technique that allows the investigation of the causal neurophysiological interactions occurring in specific cortico-cortical connections. We investigated whether the physiological properties of parieto-frontal circuits mapped with short-latency multifocal TMS at rest may have some relationship with the resting-state fMRI measures of specific resting-state functional networks (RSNs). Results showed that the activity of fast cortico-cortical physiological interactions occurring in the millisecond range correlated selectively with the coupling of fMRI slow oscillations within the same cortical areas that form part of the dorsal attention network, i.e., the attention system believed to be involved in reorientation of attention. We conclude that resting-state fMRI ongoing slow fluctuations likely reflect the interaction of underlying physiological cortico-cortical connections.


title: Sensing as a Service: Challenges, Solutions and Future Directions

abstract: Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains, such as environmental monitoring, social networking, healthcare, transportation, etc. We introduce a new concept, sensing as a service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S2aaS cloud needs to meet the following requirements: 1) it must be able to support various mobile phone sensing applications on different smartphone platforms; 2) it must be energy-efficient; and 3) it must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this vision paper, we identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions.


title: Psychological Stress and Mitochondria: A Conceptual Framework

abstract: ABSTRACTBackgroundThe integration of biological, psychological, and social factors in medicine has benefited from increasingly precise stress response biomarkers. Mitochondria, a subcellular organelle with its own genome, produce the energy required for life and generate signals that enable stress a


title: Analysis of YouTube’s traffic adaptation to dynamic environments

abstract: The popular Internet service, YouTube, has adopted by default the HyperText Markup Language version 5 (HTML5). With this adoption, YouTube has moved to Dynamic Adaptive Streaming over HTTP (DASH) as Adaptive BitRate (ABR) video streaming technology. Furthermore, rate adaptation in DASH is solely receiver-driven. This issue motivates this work to make a deep analysis of YouTube’s particular DASH implementation. Firstly, this article provides a state of the art about DASH and adaptive streaming technology, and also YouTube traffic characterization related work. Secondly, this paper describes a new methodology and test-bed for YouTube’s DASH implementation traffic characterization and performance measurement. This methodology and test-bed do not make use of proxies and, moreover, they are able to cope with YouTube traffic redirections. Finally, a set of experimental results are provided, involving a dataset of 310 YouTube’s videos. The depicted results show a YouTube’s traffic pattern characterization and a discussion about allowed download bandwidth, YouTube’s consumed bitrate and quality of the video. Moreover, the obtained results are cross-validated with the analysis of HTTP requests performed by YouTube’s video player. The outcomes of this article are applicable in the field of Quality of Service (QoS) and Quality of Experience (QoE) management. This is valuable information for Internet Service Providers (ISPs), because QoS management based on assured download bandwidth can be used in order to provide a target end-user’s QoE when YouTube service is being consumed.


title: Design and development of the wearable hand exoskeleton system for rehabilitation of hand impaired patients

abstract: In the past decade, the population of disability grew rapidly and became one of main problems in our society. The significant amounts of impaired patients include not only lower limb but also upper limb impairment such as the motor function of arm and hand. However, physical therapy requires high personal expenses and takes long time to complete the rehabilitation. In order to solve the problem mentioned above, a wearable hand exoskeleton system was developed in this paper. The hand exoskeleton system typically designed to accomplish the requirements for rehabilitation. Figure 1 shows the prototype of hand exoskeleton system which can be easily worn on human hand. The developed exoskeleton finger can provide bi-directional movements in bending and extension motion for all joints of the finger through cable transmission. The kinematic relations between the fingertip and metacarpal was derived and verified. Moreover, the construction of control system is presented in this paper. The preliminary experiment results for finger's position control have demonstrated that the proposed device is capable of accommodating to the aforementioned variables.


Factors Influencing Job Satisfaction Among Nurses of Pafos General Hospital in Cyprus


title: Norms with feeling: towards a psychological account of moral judgment

abstract: There is a large tradition of work in moral psychology that explores the capacity for moral judgment by focusing on the basic capacity to distinguish moral violations (e.g. hitting another person) from conventional violations (e.g. playing with your food). However, only recently have there been attempts to characterize the cognitive mechanisms underlying moral judgment (e.g. Cognition 57 (1995) 1; Ethics 103 (1993) 337). Recent evidence indicates that affect plays a crucial role in mediating the capacity to draw the moral/conventional distinction. However, the prevailing account of the role of affect in moral judgment is problematic. This paper argues that the capacity to draw the moral/conventional distinction depends on both a body of information about which actions are prohibited (a Normative Theory) and an affective mechanism. This account leads to the prediction that other normative prohibitions that are connected to an affective mechanism might be treated as non-conventional. An experiment is presented that indicates that “disgust” violations (e.g. spitting at the table), are distinguished from conventional violations along the same dimensions as moral violations. q 2002 Elsevier Science B.V. All rights reserved.


title: Performance Analysis of Uplink Multi-User OFDMA in IEEE 802.11ax

abstract: IEEE 802.11ax is the upcoming standard of the IEEE 802.11 wireless local area networks (WLAN) family. Until its most recent standard, i.e. 802.11ac, the primary focus of the 802.11 Working Group has been to increase the overall throughput of the physical (PHY) layer using innovative mechanisms such as multi-user multiple input multiple output (MU- MIMO), higher order modulation and coding schemes etc. However, these PHY layer gains often fail to translate to high throughput at the medium access control (MAC) layer, particularly in dense deployment scenarios. To address this limitation, IEEE 802.11ax introduces new features, most notably the use of Orthogonal Frequency Division Multiple Access (OFDMA), thereby enabling concurrent MU transmissions. In this paper, we first provide an overview of the uplink MU OFDMA in IEEE 802.11ax. Second, we provide an analytical model for characterizing the performance of the 802.11ax MAC layer. We investigate the trade-off between providing high network throughput and supporting new users using a metric - namely, BSR delivery rate. Finally, we validate our analyses using extensive NS-3 simulations, and present the resulting findings.


title: Visualizing information on mobile devices

abstract: Visualization can make a wide range of mobile applications more intuitive and productive. The mobility context and technical limitations such as small screen size make it impossible to simply port visualization applications from desktop computers to mobile devices, but researchers are starting to address these challenges. From a purely technical point of view, building more sophisticated mobile visualizations become easier due to new, possibly standard, software APIs such as OpenGLES and increasingly powerful devices. Although ongoing improvements would not eliminate most device limitations or alter the mobility context, they make it easier to create and experiment with alternative approaches.


title: Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

abstract: Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian techniques lack scalability to large dataset and network sizes. In this work we present a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP). Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show that PBP provides accurate estimates of the posterior variance on the network weights.


title: RETRACTED ARTICLE: Detecting anomalous emotion through big data from social networks based on a deep learning method

abstract: Anomaly detection in social media refers to the detection of users’ abnormal opinions, sentiment patterns, or special temporal aspects of such patterns. Social media platforms, such as Sina Weibo or Twitter, provide a Big-data platform for information retrieval, which include user feedbacks, opinions, and information on most issues. This paper proposes a hybrid neural network model called Convolutional Neural Network-Long-Short Term Memory(CNN-LSTM), we successfully applies the model to sentiment analysis on a microblog Big-data platform and obtains significant improvements that enhance the generalization ability. Based on the sentiment of a single post in Weibo, this study also adopted the multivariate Gaussian model and the power law distribution to analyze the users’ emotion and detect abnormal emotion on microblog, the multivariate Gaussian method automatically captures the correlation between different features of the emotions and saves a certain amount of time through the batch calculation of the joint probability density of data sets. Through the measure of a joint probability density value and validation of the corpus from social network, anomaly detection accuracy of an individual user is 83.49% and that for a different month is 87.84%. The results of the distribution test show that individual user’s neutral, happy, and sad emotions obey the normal distribution but the surprised and angry emotions do not. In addition, the group-based emotions on microblogs obey the power law distribution but individual emotions do not.


title: Asynchronous Multi-Sensor Fusion for 3D Mapping and Localization

abstract: In this paper, we address the problem of optimally fusing multiple heterogeneous and asynchronous sensors for use in 3D mapping and localization of autonomous vehicles. To this end, based on the factor graph-based optimization framework, we design a modular sensor-fusion system that allows for efficient and accurate incorporation of multiple navigation sensors operating at different sampling rates. In particular, we develop a general method of out-of-sequence (asynchronous) measurement alignment to incorporate heterogeneous sensors into a factor graph for mapping and localization in 3D, without requiring the addition of new graph nodes, thus allowing the graph to have an overall reduced complexity. The proposed sensor-fusion system is validated on a real-world experimental dataset, in which the asynchronous-measurement alignment is shown to have an improved performance when compared to a naive approach without alignment.


title: Calibration of linear array antenna using restoration technique with near-field compressed sensing

abstract: An idea of correcting the excitation coefficients which tend to perturb due to various reasons in a phased array antenna is proposed by a method where the concepts of dithering and restoration are applied for the magnitude and phase of the excitation of individual elements with the help of near field detection and calculations. This concept of adaptive correction through the use of multiple near field sensors for calibration of linear array antennas is presented. Results explaining the necessity of minimum number of measurements through the compressed sensing are also discussed.


title: Pattern and frequency reconfigurable annular slot antenna using PIN diodes

abstract: This paper presents the use of pin diodes to reconfigure the impedance match and modify the radiation pattern of an annular slot antenna (ASA). The planar antenna is fabricated on one side of a Duroid substrate and the microstrip feeding line with the matching network is fabricated on the opposite side of the board. The central frequency is 5.8 GHz and, by reconfiguring the matching circuit, the antenna was also designed to operate at 5.2 and 6.4 GHz. Pin diodes are also used to short the ASA in preselected positions along the circumference, thereby changing the direction of the null in the plane defined by the circular slot changes. As a proof of concept, two pin diodes are placed 45/spl deg/ on both sides of the feeding line along the ASA and the direction of the null is shown to align with the direction defined by the circular slot center and the diode. Consequently, a design that is reconfigurable in both frequency and radiation pattern is accomplished. Return loss and radiation pattern measurements and simulations are presented, which are in very good agreement.


title: Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models

abstract: We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation.


title: OMPC: an Open-Source MATLAB®-to-Python Compiler

abstract: Free access to scientific information facilitates scientific progress. Open-access scientific journals are a first step in this direction; a further step is to make auxiliary and supplementary materials that accompany scientific publications, such as methodological procedures and data-analysis tools, open and accessible to the scientific community. To this purpose it is instrumental to establish a software base, which will grow toward a comprehensive free and open-source language of technical and scientific computing. Endeavors in this direction are met with an important obstacle. MATLAB®, the predominant computation tool in many fields of research, is a closed-source commercial product. To facilitate the transition to an open computation platform, we introduce an Open-source MATLAB®-to-Python Compiler (OMPC), a platform that uses syntax adaptation and emulation to allow transparent import of existing MATLAB® functions into Python programs. The imported MATLAB® modules run independent of MATLAB®, relying on Python's numerical and scientific libraries. Python offers a stable and mature open source platform that, in many respects, surpasses commonly used, expensive commercial closed source packages. The proposed software will therefore facilitate the transparent transition towards a free and general open-source lingua franca for scientific computation, while enabling access to the existing methods and algorithms of technical computing already available in MATLAB®. OMPC is available at http://ompc.juricap.com.


title: Feature Selection Using Principal Component Analysis

abstract: Principal component analysis (PCA) has been widely applied in the area of computer science. It is well-known that PCA is a popular transform method and the transform result is not directly related to a sole feature component of the original sample. However, in this paper, we try to apply principal components analysis (PCA) to feature selection. The proposed method well addresses the feature selection issue, from a viewpoint of numerical analysis. The analysis clearly shows that PCA has the potential to perform feature selection and is able to select a number of important individuals from all the feature components. Our method assumes that different feature components of original samples have different effects on feature extraction result and exploits the eigenvectors of the covariance matrix of PCA to evaluate the significance of each feature component of the original sample. When evaluating the significance of the feature components, the proposed method takes a number of eigenvectors into account. Then it uses a reasonable scheme to perform feature selection. The devised algorithm is not only subject to the nature of PCA but also computationally efficient. The experimental results on face recognition show that when the proposed method is able to greatly reduce the dimensionality of the original samples, it also does not bring the decrease in the recognition accuracy.


title: Using probe vehicle trajectories in stop-and-go waves for inferring unobserved vehicles

abstract: Data from vehicles instrumented with GPS or other localization technologies are increasingly becoming widely available due to the investments in Connected and Automated Vehicles (CAVs) and the prevalence of personal mobile devices such as smartphones. Tracking or trajectory data from these probe vehicles are already being used in practice for travel time or speed estimation and for monitoring network conditions. However, there has been limited work on extracting other critical traffic flow variables, in particular density and flow, from probe data. This paper presents a microscopic approach (akin to car-following) for inferring the number of unobserved vehicles in between a set of probe vehicles in the traffic stream. In particular, we develop algorithms to extract and exploit the somewhat regular patterns in the trajectories when the probe vehicles travel through stop-and-go waves in congested traffic. Using certain critical points of trajectories as the input, the number of unobserved vehicles between consecutive probes are then estimated through a Naive Bayes model. The parameters needed for the Naive Bayes include means and standard deviations for the probability density functions (pdfs) for the distance headways between vehicles. These parameters are estimated through supervised as well as unsupervised learning methods. The proposed ideas are tested based on the trajectory data collected from US 101 and I-80 in California for the FHWA's NGSIM (next generation simulation) project. Under the dense traffic conditions analyzed, the results show that the number of unobserved vehicles between two probes can be predicted with an accuracy of ±1 vehicle almost always.


title: Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique

abstract: To develop a recommender system, the collaborative filtering is the best known approach, which considers the ratings of users who have similar rating profiles or rating patterns. Consistently, it is able to compute the similarity of users when there are enough ratings expressed by users. Therefore, a major challenge of the collaborative filtering approach can be how to make recommendations for a new user, that is called cold-start user problem. To solve this problem, there have been proposed a few efficient methods based on ask-to-rate technique in which the profile of a new user is made by integrating information gained from a quick interview. This paper is a review of these proposed methods and how to use the ask-to-rate technique. Consequently, they are categorized into non-adaptive and adaptive methods. Then, each category is analyzed and their methods are compared.


title: Relation Annotation for Understanding Research Papers

abstract: We describe a new annotation scheme for formalizing relation structures in research papers. The scheme has been developed through the investigation of computer science papers. Using the scheme, we are building a Japanese corpus to help develop information extraction systems for digital libraries. We report on the outline of the annotation scheme and on annotation experiments conducted on research abstracts from the IPSJ Journal.


title: Segmentation of multiple organs in non-contrast 3D abdominal CT images

abstract: Objective ::: We propose a simultaneous extraction method for 12 organs from non-contrast three-dimensional abdominal CT images.


title: Application of Bitcoin Data-Structures&Design Principles to Supply Chain Management

abstract: Heretofore the concept of "blockchain" has not been precisely defined. Accordingly the potential useful applications of this technology have been largely inflated. This work sidesteps the question of what constitutes a blockchain as such and focuses on the architectural components of the Bitcoin cryptocurrency, insofar as possible, in isolation. We consider common problems inherent in the design of effective supply chain management systems. With each identified problem we propose a solution that utilizes one or more component aspects of Bitcoin. This culminates in five design principles for increased efficiency in supply chain management systems through the application of incentive mechanisms and data structures native to the Bitcoin cryptocurrency protocol.


title: Constrained Clustering Algorithms: Practical Issues and Applications

abstract: Recently a new fashion of semi-supervised clustering algorithms, coined as Constrained Clustering, has emerged. These algorithms can incorporate some a priori domain knowledge to the clustering process, allowing the user to guide the method and improve the quality of the partitions. Up to this date, the research on this topic has been focused on developing new algorithms, mostly overlooking certain practical questions whose importance is capital in realworld problems. In this thesis we identify and study two of these issues, constraint extraction and robustness to noise. In this thesis we perform an analysis of the robustness of some Constrained Clustering algorithms to noisy sets of constraints, designing an experiment in which their behaviour is tested with synthetic sets of inaccurate constraints created with two noise models, one of them based on intuitions about the nature of real errors in the constraints. The strengths and weaknesses of each algorithm are discussed and used to conclude the scenarios in which using it is the best decision. Moreover, we likewise propose in this work two schemes to automatically extract constraints in two important domains: web pages and text in general. In the former we use information external to the web pages (their social tags), whereas in the later we use information which is not taken into account by the usual text representation schemes (the order of the words). Both methods are tested in thorough experiments over reference collections and compared with suitable baselines. Lastly, and keeping with the practical focus of this thesis, we have as well analysed how to apply Constrained Clustering to tackle an existing real-world problem, the Avoiding Bias task, proposing a scheme that uses constraints to codify the partition to be avoided. We study as well how to improve the quality of the alternative partitions, proposing two approaches which use spectral clustering techniques.


On Bias , Variance , 0 / 1-Loss , and the Curse-of-Dimensionality


Trust and Helpfulness in Amazon Reviews : Final Report


title: Software integration in global software development: Success factors for GSD vendors

abstract: Global software development (GSD) is in a booming stage from the last decade with the advances in information and communication technologies (ICTs). Despite of the benefits gained from GSD, the developer organizations still face a lot of difficulties in integrating the software components, developed by various GSD teams, into a final product. The objective of the study presented in this paper is to find out critical success factors (CSFs) for vendors that can play a positive role at any stage of the software integration process. To achieve the purpose we conducted a systematic literature review (SLR) for the identifications of the factors that can be adapted to assist the integration process at various stages i.e. before integration, during integration and after the integration. We have found a total of fourteen success factors among which nine factors are ranked as CSFs. Some of the top ranked CSFs are “Consistency in Requirements and Architecture Design”, “Intra and inter team Communication and Coordination” and “Component/Unit Testing prior to integration”.


title: Off-Line Arabic Character Recognition – A Review

abstract: Off-line recognition requires transferring the text under consideration into an image file. This represents the only available solution to bring the printed materials to the electronic media. However, the transferring process causes the system to lose the temporal information of that text. Other complexities that an off-line recognition system has to deal with are the lower resolution of the document and the poor binarisation, which can contribute to readability when essential features of the characters are deleted or obscured. Recognising Arabic script presents two additional challenges: orthography is cursive and letter shape is context sensitive. Certain character combinations form new ligature shapes, which are often font-dependent. Some ligatures involve vertical stacking of characters. Since not all letters connect, word boundary location becomes an interesting problem, as spacing may separate not only words, but also certain characters within a word. Various techniques have been implemented to achieve high recognition rates. These techniques have tackled different aspects of the recognition system. This review is organised into five major sections, covering a general overview, Arabic writing characteristics, Arabic text recognition system, Arabic OCR software and conclusions.


title: Data-Aided Channel Estimation in Large Antenna Systems

abstract: This paper is concerned with the uplink in a multi-cell large antenna system. We study a channel estimation scheme where partially decoded data is used to estimate the channel. We show that there are two types of interference components in this scheme that do not vanish even when the number of antennas grows to infinity: cross-contamination and self-contamination. Cross contamination is in principle similar to pilot contamination in a conventional pilot-based channel estimation scheme, while self-contamination is unique for the data-aided scheme. The data-aided scheme can effectively suppress the contamination effect by increasing the data frame length without causing rate loss. This is confirmed by both analysis and simulation results.


title: On the limiting performance of turbo-Hadamard codes

abstract: In this letter, we employ the extrinsic information transfer (EXIT) chart technique to assess the limiting performance of turbo-Hadamard codes and identify the optimized design parameters for such codes. It is shown that for a sufficiently long code length and a sufficiently large number of iterations, a carefully designed low rate turbo-Hadamard code can potentially achieve successful decoding at E/sub b//N/sub 0//spl ap/-1.3 dB, which is about 0.29 dB from the ultimate Shannon limit.


title: Catheter and Material Selection for Transarterial Embolization: Technical Considerations

abstract: In this second part of the report,2 the authors discuss the advantages and disadvantages of several embolization agents. These include Gelfoam, silicone spheres, polyvinyl alcohol foam (PVA), isobutyl-2-cyanoacrylate (IBCA), silicone fluid mixtures, and tantalum powder. The techniques employed and conditions under which these materials should be used are discussed.


title: Osteology of Ischikauia steenackeri (Teleostei: Cypriniformes) with comments on its systematic position

abstract: The lakeweed chub Ischikauia steenackeri is a medium-sized, herbivorous fish and the sole extant member of the genus Ischikauia, which is endemic to Lake Biwa and the Yodo River drainage, Japan. In order to clarify its systematic position, the skeletal anatomy of I. steenackeri is described and its relationships with related genera are discussed. The present data suggest the monophyly of Ischikauia and seven cultrine genera (Culter, Chanodichthys, Megalobrama, Sinibrama, Hemiculter, Toxabramis, and Anabarilius) based on a unique character, the metapterygoid elongated dorsally. Additionally, our data suggest that Ischikauia is closely related to Culter, Chanodichthys, Megalobrama, and Sinibrama. This relationship is supported by three synapomorphies that are common to them: a narrow third infraorbital, dorsal extension of the third supraneural, and a large quadrate foramen.


title: Runway operations planning: a two-stage solution methodology

abstract: The airport runway is a scarce resource that must be shared by different runway operations (arrivals, departures and runway crossings). Given the possible sequences of runway events, careful runway operations planning (ROP) is required if runway utilization is to be maximized. Thus, runway operations planning (ROP) is a critical component of airport operations planning in general and surface operations planning in particular. From the perspective of departures, ROP solutions are aircraft departure schedules developed by optimally allocating runway time for departures given the time required for arrivals and crossings. In addition to the obvious objective of maximizing throughput, other objectives, such as guaranteeing fairness and minimizing environmental impact, may be incorporated into the ROP solution subject to constraints introduced by air traffic control (ATC) procedures. Generating optimal runway operations plans was approached in the paper by Anagnostakis et al. (2001) with a "one-stage" optimization routine that considered all the desired objectives and constraints, and the characteristics of each aircraft (weight class, destination, air traffic control (ATC) constraints) at the same time. Since, however, at any given point in time, there is less uncertainty in the predicted demand for departure resources in terms of weight class than in terms of specific aircraft, the ROP problem can be parsed into two stages. In the context of the departure planner (DP) research project, this paper introduces runway operations planning (ROP) as part of the wider surface operations optimization (SOO) and describes a proposed "two stage" heuristic algorithm for solving the runway operations planning (ROP) problem. Focus is specifically given on including runway crossings in the planning process of runway operations. In the first stage, sequences of departure class slots and runway crossings slots are generated and ranked based on departure runway throughput under stochastic conditions. In the second stage, the departure class slots are populated with specific flights from the pool of available aircraft, by solving an integer program. Preliminary results from the algorithm implementation on real-world traffic data are included in (Anagnostakis and Clarke, 2002).


title: Event detection from flickr data through wavelet-based spatial analysis

abstract: Detecting events from web resources has attracted increasing research interests in recent years. Our focus in this paper is to detect events from photos on Flickr, an Internet image community website. The results can be used to facilitate user searching and browsing photos by events. The problem is challenging considering: (1) Flickr data is noisy, because there are photos unrelated to real-world events; (2) It is not easy to capture the content of photos. This paper presents our effort in detecting events from Flickr photos by exploiting the tags supplied by users to annotate photos. In particular, the temporal and locational distributions of tag usage are analyzed in the first place, where a wavelet transform is employed to suppress noise. Then, we identify tags related with events, and further distinguish between tags of aperiodic events and those of periodic events. Afterwards, event-related tags are clustered such that each cluster, representing an event, consists of tags with similar temporal and locational distribution patterns as well as with similar associated photos. Finally, for each tag cluster, photos corresponding to the represented event are extracted. We evaluate the performance of our approach using a set of real data collected from Flickr. The experimental results demonstrate that our approach is effective in detecting events from the Flickr photo collection.


title: Overlapping Communities Explain Core–Periphery Organization of Networks

abstract: Networks provide a powerful way to study complex systems of interacting objects. Detecting network communities—groups of objects that often correspond to functional modules—is crucial to understanding social, technological, and biological systems. Revealing communities allows for analysis of system properties that are invisible when considering only individual objects or the entire system, such as the identification of module boundaries and relationships or the classification of objects according to their functional roles. However, in networks where objects can simultaneously belong to multiple modules at once, the decomposition of a network into overlapping communities remains a challenge. Here we present a new paradigm for uncovering the modular structure of complex networks, based on a decomposition of a network into any combination of overlapping, nonoverlapping, and hierarchically organized communities. We demonstrate on a diverse set of networks coming from a wide range of domains that our approach leads to more accurate communities and improved identification of community boundaries. We also unify two fundamental organizing principles of complex networks: the modularity of communities and the commonly observed core–periphery structure. We show that dense network cores form as an intersection of many overlapping communities. We discover that communities in social, information, and food web networks have a single central dominant core while communities in protein–protein interaction (PPI) as well as product copurchasing networks have small overlaps and form many local cores.


title: Bidirectional Long-Short Term Memory for Video Description

abstract: Video captioning has been attracting broad research attention in multimedia community. However, most existing approaches either ignore temporal information among video frames or just employ local contextual temporal knowledge. In this work, we propose a novel video captioning framework, termed as \emph{Bidirectional Long-Short Term Memory} (BiLSTM), which deeply captures bidirectional global temporal structure in video. Specifically, we first devise a joint visual modelling approach to encode video data by combining a forward LSTM pass, a backward LSTM pass, together with visual features from Convolutional Neural Networks (CNNs). Then, we inject the derived video representation into the subsequent language model for initialization. The benefits are in two folds: 1) comprehensively preserving sequential and visual information; and 2) adaptively learning dense visual features and sparse semantic representations for videos and sentences, respectively. We verify the effectiveness of our proposed video captioning framework on a commonly-used benchmark, i.e., Microsoft Video Description (MSVD) corpus, and the experimental results demonstrate that the superiority of the proposed approach as compared to several state-of-the-art methods.


A NEURAL NETWORK CLASSIFICATION APPROACH FOR OIL SPILL DETECTION ON SAR IMAGES


title: Analysis of Windings in Variable Reluctance Resolver

abstract: In this paper, the relationship between the numbers of stator slots, winding polarities, and rotor poles for variable reluctance resolvers is derived and verified, which makes it possible for the same stator and winding to be shared by the rotors with different poles. Based on the established relationship, a simple factor is introduced to evaluate the level of voltage harmonics as an index for choosing appropriate stator slot and rotor pole combinations. With due account for easy manufacturing, alternate windings are proposed without apparent deterioration in voltage harmonics of a resolver. In particular, alternate windings with nonoverlapping uniform coils are proved to be possible for output windings in some stator slot and rotor pole combinations, which further simplify the manufacture process. Finite element method is adopted to verify the proposed design, together with experiments on the prototypes.


title: RIS-Based Compact Circularly Polarized Microstrip Antennas

abstract: Compact, asymmetric\symmetric-slotted\slit-microstrip patch antennas on reactive impedance surface (RIS) are proposed and studied for circularly polarized (CP) radiation. The antennas consist of a slotted-slit-microstrip patch on a RIS substrate. The CP radiation with compact size is achieved by asymmetric\symmetric-slot-slit cut along the orthogonal\diagonal directions of the patch radiator. The asymmetric\symmetric-slotted\slit microstrip patches on the RIS structure are used for further miniaturization of the antenna with improvement in CP radiation. The measured results of the compact asymmetric-cross slotted square patch antenna are 1.6% (2.51-2.55 GHz) for 3-dB axial ratio bandwidth, 5.2% (2.47-2.60 GHz) for 10-dB return loss bandwidth, and 3.41 dBic for gain over 3-dB axial ratio bandwidth. The overall antenna volume is 0.292λo × 0.292λo × 0.0308λo on a low cost FR4 substrate at 2.5 GHz.


title: Prediction-Based Dynamic Resource Allocation for Video Transcoding in Cloud Computing

abstract: This paper presents prediction-based dynamic resource allocation algorithms to scale video transcoding service on a given Infrastructure as a Service cloud. The proposed algorithms provide mechanisms for allocation and deallocation of virtual machines (VMs) to a cluster of video transcoding servers in a horizontal fashion. We use a two-step load prediction method, which allows proactive resource allocation with high prediction accuracy under real-time constraints. For cost-efficiency, our work supports transcoding of multiple on-demand video streams concurrently on a single VM, resulting in a reduced number of required VMs. We use video segmentation at group of pictures level, which splits video streams into smaller segments that can be transcoded independently of one another. The approach is demonstrated in a discrete-event simulation and an experimental evaluation involving two different load patterns.


title: Fast and robust fixed-point algorithms for independent component analysis

abstract: Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. We use a combination of two different approaches for linear ICA: Comon's information theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions.


title: Oregano: Overview of the Literature on Health Benefits

abstract: Oregano is an herb that has been cultivated for centuries in the Mediterranean area, although it now can be found on most continents. Actually, there is not simply one "oregano," but rather several species that may contribute to the oregano used for culinary purposes. Origanum vulgare (also referred


title: Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives

abstract: State-of-the-art word embeddings, which are often trained on bag-of-words (BOW) contexts, provide a high quality representation of aspects of the semantics of nouns. However, their quality decreases substantially for the task of verb similarity prediction. In this paper we show that using symmetric pattern contexts (SPs, e.g., “X and Y”) improves word2vec verb similarity performance by up to 15% and is also instrumental in adjective similarity prediction. The unsupervised SP contexts are even superior to a variety of dependency contexts extracted using a supervised dependency parser. Moreover, we observe that SPs and dependency coordination contexts (Coor) capture a similar type of information, and demonstrate that Coor contexts are superior to other dependency contexts including the set of all dependency contexts, although they are still inferior to SPs. Finally, there are substantially fewer SP contexts compared to alternative representations, leading to a massive reduction in training time. On an 8G words corpus and a 32 core machine, the SP model trains in 11 minutes, compared to 5 and 11 hours with BOW and all dependency contexts, respectively.


title: Texture Feature based Automated Seeded Region Growing in Abdominal MRI Segmentation

abstract: A new texture feature based seeded region growing algorithm is proposed for the automated segmentation of organs in Abdominal MR image. Co-occurrence texture feature and semi-variogram texture feature are extracted from the image and the seeded region growing algorithm is run on these feature spaces. With a given Region of Interest(ROI), a seed point is automatically picked up based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing 'explosion'. This algorithm is tested on 12 series of 3D abdominal MR images.


title: A Series Stacked IGBT Switch With Robustness Against Short-Circuit Fault for Pulsed Power Applications

abstract: The limited value of the insolated-gate bipolar transistors (IGBTs) blocking voltage is an important issue for applying these devices to the high-voltage power converters. An effective solution is to use a series configuration of these devices in order to achieve higher voltage ratings in addition to save the interesting features such as fast rising time. To reach this goal, acceptable equal voltage sharing for the IGBTs are provided by the series stacking schemes in the normal operation. However, a considerable difference in their voltage level will be occurred in the short-circuit condition. Although the IGBT can withstand the short-circuit current in a defined time, the occurred high voltage stress for the IGBT is fatal in such condition. To solve the mentioned problem, this paper proposes a short-circuit protection system suitable for the series stacked IGBTs. Using this proposal, the safe voltage level for IGBTs as well as the adequate time for the performance of the detection and elimination of the short-circuit fault will be provided. Each IGBT can have a different short-circuit current in the active region since the proposed scheme makes the IGBTs independent in terms of the current in the series structure. An external resistor added to the IGBTs emitter improves the equality of the IGBTs short-circuit currents and consequently their voltage sharing. This resistor controls the system short-circuit current by changing the operating point of IGBTs in active region in the short-circuit fault. The experimental and simulation results show that the safe condition is provided for the IGBTs under the short-circuit fault.


IDRA: An ontology driven Cognitive Computing System


title: Dust Accumulation on the Surface of Photovoltaic Panels: Introducing the Photovoltaic Soiling Index (PVSI)

abstract: This article presents an empirical review of research concerning the impact of dust accumulation on the performance of photovoltaic (PV) panels. After examining the articles published in international scientific journals, many differences between the studies were found within the context of the PV technologies used, the contribution to this type of study from different countries, and the variety in the representation of the results where each study has its unique parameters, testing equipment, and relevant standards. Due to those variations and differences, it has been found that it is very difficult and impractical to compare between results of the corresponding studies. Hence, in addition to the valuable reviews found in literature, this article demonstrates another new perspective that highlights the gaps in the studies related to the impact of dust accumulation on PV panels. The conclusions of this study are considered to be the seed for establishing a new initiative—The Photovoltaic Soiling Index (PVSI)—which would be an indicator for the performance of PV panels under exposure to dust at the Standard Test Conditions (STC), as well as at other operating conditions in different locations around the globe.


title: Semantic Impact and Faults in Source Code Changes: An Empirical Study

abstract: Changes to source code have become a critical factor in fault predictions. Text or syntactic approaches have been widely used. Textual analysis focuses on changed text fragments while syntactic analysis focuses on changed syntactic entities. Although both of them have demonstrated their advantages in experimental results, they only study code fragments modified during changes. Because of semantic dependencies within programs, we believe that code fragments impacted by changes are also helpful. Given a source code change, we identify its impact by program slicing along the variable def-use chains. To evaluate the effectiveness of change impacts in fault detection and prediction, we compare impacted code with changed code according to size and fault density. Our experiment on the change history of a successful industrial project shows that: fault density in changed and impacted fragments are higher than other areas; for large changes, their impacts have higher fault density than changes themselves; interferences within change impact contribute to the high fault density in large changes. Our study suggests that, like change itself, change impact is also a high priority indicator in fault prediction, especially for changes of large scales.


title: Designing planning and control interfaces to support user collaboration with flying robots

abstract: Robots are becoming increasingly prevalent and are already providing assistance in a variety of activities, ranging from space exploration to domestic housework. Recent advances in the design of sensors, motors, and microelectromechanical systems have enabled the development of a new class of small aerial robots. These free-flying robots hold great promise in assisting humans by acting as mobile sensor platforms to collect data in areas that are difficult to access or infeasible to instrument. In this work, we explored the design of interfaces that support users in working with free-flying robots to accomplish tasks including inventory logistics and management, environmental data collection, and visual inspection. Extending prior work in control interfaces for ground robots, we conducted a formative study in order to identify key design requirements for free-flyer interfaces. We designed several realistic tasks for use in evaluating human–robot interaction within the context of indoor free-flyer operation...


title: Competing Risks and Time-Dependent Covariates

abstract: Time-dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time-fixed covariates. This study briefly recalls the different types of time-dependent covariates, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time-dependent covariates are to be included in the modeling process, then it is still possible to estimate cause-specific hazards but prediction of the cumulative incidences and survival probabilities based on these is no longer feasible. This article aims at providing some possible strategies for dealing with these prediction problems. In a multi-state framework, a first approach uses internal covariates to define additional (intermediate) transient states in the competing risks model. Another approach is to apply the landmark analysis as described by van Houwelingen [Scandinavian Journal of Statistics 2007, 34, 70-85] in order to study cumulative incidences at different subintervals of the entire study period. The final strategy is to extend the competing risks model by considering all the possible combinations between internal covariate levels and cause-specific events as final states. In all of those proposals, it is possible to estimate the changes/differences of the cumulative risks associated with simple internal covariates. An illustrative example based on bone marrow transplant data is presented in order to compare the different methods.


title: A Robotic Fitness Coach for the Elderly

abstract: The ultimate goal of ambient assisted living is to help elderly people live a healthy life in the convenience of their homes by making more intelligent technology bring them a set of required assistive tools. In this paper we describe a robotic fitness coach that learns a set of physical exercises from a professional trainer, and assists elderly subjects in performing these gestures. The gestures were selected from an actual training programme at an elderly care home. When demonstrating gestures, the robot performs the learned gestures to the best of its abilities, and while monitoring the elderly subject with an RGB-D camera, provides verbal guidance to complement the visual display, correcting gestures on the fly. We provide a detailed description of the training programme, the gesture acquisition, replication and evaluation algorithms, our solution to the robot stability problem, and a set of preliminary user tests to validate our approach.


title: Cooperative Learning in Neural Networks using Particle Swarm Optimizers

abstract: This paper presents a method to employ particle swarms optimizers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each which is optimized cooperatively in its own swarm. the application of this technique to neural network training is investigated, with promising results.


title: Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy

abstract: Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems - the models (often deep networks or wide networks or both) are compute and memory intensive. Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models. In this paper, we study the combination of these two techniques and show that the performance of low-precision networks can be significantly improved by using knowledge distillation techniques. Our approach, Apprentice, achieves state-of-the-art accuracies using ternary precision and 4-bit precision for variants of ResNet architecture on ImageNet dataset. We present three schemes using which one can apply knowledge distillation techniques to various stages of the train-and-deploy pipeline.


title: StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

abstract: Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.


title: Folding and unfolding origami tessellation by reusing folding path

abstract: Recent advances in robotics engineering have enabled the realization of self-folding machines. Rigid origami is usually used as the underlying model for the self-folding machines whose surface remains rigid during folding except at joints. A key issue in designing rigid origami is foldability that concerns about finding folding steps from a flat sheet of crease pattern to a desired folded state. Although recent computational methods allow rapid simulation of folding process of certain rigid origamis, these methods can fail even when the input crease pattern is extremely simple. In this paper, we take on the challenge of planning folding and unfolding motion of origami tessellations, which are composed of repetitive crease patterns. The number of crease lines of a tessellation is usually large, thus searching in such a high dimensional configuration space with the requirement of maintaining rigidity is nontrivial. We propose a motion planner that takes symmetry into consideration and reuses folding path found on the essential crease pattern. Both of these strategies enable us to fold large origami tessellation much more efficiently than existing methods. Our experimental results show that the proposed method successfully folds several types of rigid origami tessellations that existing methods fail to fold.


title: Statistical Decision-Tree Models For Parsing

abstract: Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor performance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to text-processing in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too complex and detailed to develop manually for most interesting domains; (2) parsing models must rely heavily on lexical and contextual information to analyze sentences accurately; and (3) existing n-gram modeling techniques are inadequate for parsing models. In experiments comparing SPATTER with IBM's computer manuals parser, SPATTER significantly outperforms the grammar-based parser. Evaluating SPATTER against the Penn Treebank Wall Street Journal corpus using the PARSEVAL measures, SPATTER achieves 86% precision, 86% recall, and 1.3 crossing brackets per sentence for sentences of 40 words or less, and 91% precision, 90% recall, and 0.5 crossing brackets for sentences between 10 and 20 words in length.


title: Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis

abstract: Current cancer diagnosis employs various nuclear morphometric measures. While these have allowed accurate late-stage prognosis, early diagnosis is still a major challenge. Recent evidence highlights the importance of alterations in mechanical properties of single cells and their nuclei as critical drivers for the onset of cancer. We here present a method to detect subtle changes in nuclear morphometrics at single-cell resolution by combining fluorescence imaging and deep learning. This assay includes a convolutional neural net pipeline and allows us to discriminate between normal and human breast cancer cell lines (fibrocystic and metastatic states) as well as normal and cancer cells in tissue slices with high accuracy. Further, we establish the sensitivity of our pipeline by detecting subtle alterations in normal cells when subjected to small mechano-chemical perturbations that mimic tumor microenvironments. In addition, our assay provides interpretable features that could aid pathological inspections. This pipeline opens new avenues for early disease diagnostics and drug discovery.


title: Graph Queries in a Next-Generation Datalog System

abstract: Recent theoretical advances have enabled the use of special monotonic aggregates in recursion. These special aggregates make possible the concise expression and efficient implementation of a rich new set of advanced applications. Among these applications, graph queries are particularly important because of their pervasiveness in data intensive application areas. In this demonstration, we present our Deductive Application Language (DeAL) System, the first of a new generation of Deductive Database Systems that support applications that could not be expressed using regular stratification, or could be expressed using XY-stratification (also supported in DeAL) but suffer from inefficient execution. Using example queries, we will (i) show how complex graph queries can be concisely expressed using DeAL and (ii) illustrate the formal semantics and efficient implementation of these powerful new monotonic constructs.


title: A Bridging Framework for Model Optimization and Deep Propagation

abstract: Optimizing designed mathematical models is the most fundamental methodology in statistic and learning areas. However, the generally designed schematic models may hard to extract complex data distributions in real-world scenarios. Recently, training deep propagations (i.e., networks) has gained promising performance in some particular applications. Unfortunately, existing network structures are often built in heuristic way, thus lack of principled interpretation and solid theoretical support. In this work, we provide a new framework, named Propagative Convergent Network (PCN), to bridge the gaps between these two different methodologies (i.e., model optimization and deep propagation) in a collaborative manner. On the one hand, we demonstrate how to utilize PCN as a deeply-trained solver for nonconvex model optimization. Different from other network-based iterations, which often lack theoretical investigations, we can successfully prove the strict convergence and estimate the convergence rate of PCN. On the other hand, by relaxing the constraints and performing end-to-end training, we also develop a PCN-based strategy to integrate domain knowledge (formulated as models) and data distributions (learned by networks), resulting in a generic ensemble learning framework for challenging applications. Extensive experiments verify our theoretical results and show the superiority of PCN against state-of-the-art methods.


Abdominal Trauma : Nonoperative Management and Postoperative Considerations


title: DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling

abstract: Most existing Android malware detection and categorization techniques are static approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program behaviors, dynamic approaches are potentially more resilient against these attacks. Yet existing dynamic approaches mostly rely on characterizing system calls which are subject to system-call obfuscation. This paper presents DroidCat, a novel dynamic app classification technique, to complement existing approaches. By using a diverse set of dynamic features based on method calls and inter-component communication (ICC) Intents without involving permission, app resources, or system calls while fully handling reflection, DroidCat achieves superior robustness than static approaches as well as dynamic approaches relying on system calls. The features were distilled from a behavioral characterization study of benign versus malicious apps. Through three complementary evaluation studies with 34 343 apps from various sources and spanning the past nine years, we demonstrated the stability of DroidCat in achieving high classification performance and superior accuracy compared with the two state-of-the-art peer techniques that represent both static and dynamic approaches. Overall, DroidCat achieved 97% F1-measure accuracy consistently for classifying apps evolving over the nine years, detecting or categorizing malware, 16%–27% higher than any of the two baselines compared. Furthermore, our experiments with obfuscated benchmarks confirmed higher robustness of DroidCat over these baseline techniques. We also investigated the effects of various design decisions on DroidCat’s effectiveness and the most important features for our dynamic classification. We found that features capturing app execution structure such as the distribution of method calls over user code and libraries are much more important than typical security features such as sensitive flows.


title: Monaural speech separation and recognition challenge

abstract: Robust speech recognition in everyday conditions requires the solution to a number of challenging problems, not least the ability to handle multiple sound sources. The specific case of speech recognition in the presence of a competing talker has been studied for several decades, resulting in a number of quite distinct algorithmic solutions whose focus ranges from modeling both target and competing speech to speech separation using auditory grouping principles. The purpose of the monaural speech separation and recognition challenge was to permit a large-scale comparison of techniques for the competing talker problem. The task was to identify keywords in sentences spoken by a target talker when mixed into a single channel with a background talker speaking similar sentences. Ten independent sets of results were contributed, alongside a baseline recognition system. Performance was evaluated using common training and test data and common metrics. Listeners' performance in the same task was also measured. This paper describes the challenge problem, compares the performance of the contributed algorithms, and discusses the factors which distinguish the systems. One highlight of the comparison was the finding that several systems achieved near-human performance in some conditions, and one out-performed listeners overall.


title: Incorporating Structured Commonsense Knowledge in Story Completion

abstract: The ability to select an appropriate story ending is the first step towards perfect narrative comprehension. Story ending prediction requires not only the explicit clues within the context, but also the implicit knowledge (such as commonsense) to construct a reasonable and consistent story. However, most previous approaches do not explicitly use background commonsense knowledge. We present a neural story ending selection model that integrates three types of information: narrative sequence, sentiment evolution and commonsense knowledge. Experiments show that our model outperforms state-ofthe-art approaches on a public dataset, ROCStory Cloze Task (Mostafazadeh et al. 2017), and the performance gain from adding the additional commonsense knowledge is significant.


title: AF-Softmax for Face Recognition

abstract: In order to make facial features more discriminative, some loss functions have been proposed recently. However, these loss functions are not very robust when categories are imbalanced or many easy examples dominate training losses. To overcome these shortcomings, we propose angular focus softmax (AF-Softmax) loss by introducing a focusing factor to lessen the losses attached to those well-classified examples. We refer to our model trained with AF-Softmax loss as AF-Face. To test our approach, we conduct extensive experiments on Youtube Faces (YTF) [8] and Labeled Face in the Wild (LFW) [13] and achieve better performance than SphereFace [9]. These experiments confirm the effectiveness of AF-Softmax on face recognition task.


title: Functional echo state network for time series classification

abstract: Abstract Echo state networks (ESNs) are a new approach to recurrent neural networks (RNNs) that have been successfully applied in many domains. Nevertheless, an ESN is a predictive model rather than a classifier, and methods to employ ESNs in time series classification (TSC) tasks have not yet been fully explored. In this paper, we propose a novel ESN approach named functional echo state network (FESN) for time series classification. The basic idea behind FESN is to replace the numeric variable output weights of an ESN with time-varying output-weight functions and introduce a temporal aggregation operator to the output layer that can project temporal signals into discrete class labels, thereby transforming the ESN from a predictive model into a true classifier. Subsequently, to learn the output-weight functions, a spatio-temporal aggregation learning algorithm is proposed based on orthogonal function basis expansion. By leveraging the nonlinear mapping capacity of a reservoir and the accumulation of temporal information in the time domain, FESN can not only enhance the separability of different classes in a high-dimensional functional space but can also consider the relative importance of temporal data at different time steps according to dynamic output-weight functions. Theoretical analyses and experiments on an extensive set of UCR data were conducted on FESN. The results show that FESN yields better performance than single-algorithm methods, has comparable accuracy with ensemble-based methods and exhibits acceptable computational complexity. Interestingly, for some time series datasets, we visualized some interpretable features extracted by FESN via specific patterns within the output-weight functions.


title: Aspect-based sentiment analysis for Indonesian restaurant reviews

abstract: Aspect-based sentiment analysis summarizes what people like and dislike from reviews of products or services. In this paper, we adapt the first rank research at SemEval 2016 to improve the performance of aspect-based sentiment analysis for Indonesian restaurant reviews. We use six steps for aspect-based sentiment analysis i.e.: preprocess the reviews, aspect extraction, aspect categorization, sentiment classification, opinion structure generation, and rating calculation. We collect 992 sentences for experiment and 383 sentences for evaluation. We conduct experiment to find best feature combination for aspect extraction, aspect categorization, and sentiment classification. The aspect extraction, aspect categorization, and sentiment classification have F1-measure value of 0.793, 0.823, and 0.642 respectively.


title: Boosting algorithms for uplift modeling

abstract: Uplift modeling is an area of machine learning which aims at predicting the causal effect of some action on a given individual. The action may be a medical procedure, marketing campaign, or any other circumstance controlled by the experimenter. Building an uplift model requires two training sets: the treatment group, where individuals have been subject to the action, and the control group, where no action has been performed. An uplift model allows then to assess the gain resulting from taking the action on a given individual, such as the increase in probability of patient recovery or of a product being purchased. This paper describes an adaptation of the well-known boosting techniques to the uplift modeling case. We formulate three desirable properties which an uplift boosting algorithm should have. Since all three properties cannot be satisfied simultaneously, we propose three uplift boosting algorithms, each satisfying two of them. Experiments demonstrate the usefulness of the proposed methods, which often dramatically improve performance of the base models and are thus new and powerful tools for uplift modeling.


title: A two-stage flow-based intrusion detection model for next-generation networks

abstract: The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.


title: Topology Preserved Regular Superpixel

abstract: Most existing super pixel algorithms ignore the topology and regularities, which results in undesirable sizes and location relationships for subsequent processing. In this paper, we introduce a new method to compute the regular super pixels while preserving the topology. Start from regular seeds, our method relocates them to the pixel with locally maximal edge magnitudes. Then, we find the local optimal path between each relocated seed and its four neighbors using Dijkstra algorithm. Thanks to the local constraints, our method obtains homogeneous super pixels with explicit adjacency in low-texture and uniform regions and, simultaneously, maintains the edge cues in the high contrast and salient contents. Quantitative and qualitative experimental results on Berkeley Segmentation Database Benchmark demonstrate that our proposed algorithm outperforms the existing regular super pixel methods.


title: Guest Editor's Introduction: Model-Driven Engineering

abstract: Model-driven engineering technologies offer a promising approach to address the inability of third-generation languages to alleviate the complexity of platforms and express domain concepts effectively.


Theoretical Issues in Dakota Phonology and Morphology


title: Non-Size Exclusion Effects During Gel Permeation Chromatography of Milk Protein Hydrolysates on an FPLC Superose 12 Column

abstract: Abstract Hydrophobic peptides and aromatic amino acids adsorbed to the matrix of a Superose 12 FPLC column during gel permeation chromatography (GPC) of milk protein hydrolysates. The adsorption phenomenon was most obvious when hydrolysates were prepared using enzyme mixtures which contained exopeptidases. The elution areas of peaks for tryptophan and tyrosine from a Superose 12 column was linear with concentration in the range 0 to 1 μmol. ml-1. Amino acid analysis confirmed that a strongly adsorbed peak in the elution profile, measured by absorbance at 280 nm, of an extensively hydrolysed rennet casein contained tryptophan. Individual hydrophobic peptides, used as molecular weight markers, also interacted with the column, eluting later than expected. Nitrate in a general water supply used for the production of a whey protein hydrolysate on a pilot-scale was responsible for the appearance of an additional peak in the elution profiles from a Superose 12 column, as measured by absorbance at 214 nm.


title: An Efficient Cryptographic Hash Algorithm (BSA)

abstract: Recent cryptanalytic attacks have exposed the vulnerabilities of some widely used cryptographic hash functions like MD5 and SHA-1. Attacks in the line of differential attacks have been used to expose the weaknesses of several other hash functions like RIPEMD, HAVAL. In this paper we propose a new efficient hash algorithm that provides a near random hash output and overcomes some of the earlier weaknesses. Extensive simulations and comparisons with some existing hash functions have been done to prove the effectiveness of the BSA, which is an acronym for the name of the 3 authors.


title: Modeling Document Novelty with Neural Tensor Network for Search Result Diversification

abstract: Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to measure the novelty of a candidate document with respect to other documents. In the heuristic approaches, the predefined document similarity functions are directly utilized for defining the novelty. In the learning approaches, the novelty is characterized based on a set of handcrafted features. Both the similarity functions and the features are difficult to manually design in real world due to the complexity of modeling the document novelty. In this paper, we propose to model the novelty of a document with a neural tensor network. Instead of manually defining the similarity functions or features, the new method automatically learns a nonlinear novelty function based on the preliminary representation of the candidate document and other documents. New diverse learning to rank models can be derived under the relational learning to rank framework. To determine the model parameters, loss functions are constructed and optimized with stochastic gradient descent. Extensive experiments on three public TREC datasets show that the new derived algorithms can significantly outperform the baselines, including the state-of-the-art relational learning to rank models.


title: Short survey: Dual arm manipulation-A survey

abstract: Recent advances in both anthropomorphic robots and bimanual industrial manipulators had led to an increased interest in the specific problems pertaining to dual arm manipulation. For the future, we foresee robots performing human-like tasks in both domestic and industrial settings. It is therefore natural to study specifics of dual arm manipulation in humans and methods for using the resulting knowledge in robot control. The related scientific problems range from low-level control to high level task planning and execution. This review aims to summarize the current state of the art from the heterogenous range of fields that study the different aspects of these problems specifically in dual arm manipulation.


title: Two factor authentication using EEG augmented passwords

abstract: The current research with EEG devices in the user authentication context has some deficiencies that address expensive equipment, the requirement of laboratory conditions and applicability. In this paper we address this issue by using widely available and inexpensive EEG device to verify its capability for authentication. As a part of this research, we developed two phase authentication that enables users to enhance their password with the mental state by breaking the password into smaller, marry them with mental state, and generate one time pad for a secure session.


title: An unobtrusive and multimodal approach for behavioral engagement detection of students

abstract: We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various experiments were conducted on a student dataset collected in an authentic classroom.


title: Performance Evaluation of IEEE 802.15.4 OQPSK and CSS PHY in the Presence of Interference

abstract: Industrial environments often exhibit harsh propagation conditions for wireless transmissions. This paper investigates the influence of different interference characteristics on the performance of IEEE 802.15.4 using the OQPSK and Chirp Spread Spectrum (CSS) PHY. Bit error rates are determined for AWGN, Rayleigh fading and self-interference scenarios. GNU Radio, a Software Defined Radio framework, serves as simulation environment. In addition to the implementation of the CSS transmitter and receiver, a low-complexity synchronization algorithm, which only consists of a correlator and a second order Phase Locked Loop (PLL), and its limitations are presented. Furthermore, MAC layer simulations are performed for beacon-enabled (slotted) as well as nonbeacon- enabled (unslotted) mode to make a statement about achievable throughput and latency in the presence of interference. It shows that the CSS PHY clearly surpasses the OQPSK PHY in throughput. Moreover, the simulation results are confirmed by the derivation of the theoretical maximum throughput for the investigated PHY layers.


Schinzel-Giedion Syndrome with Congenital Megacalycosis in a Turkish Patient: Report of SETBP1 Mutation and Literature Review of the Clinical Features


title: Characterization of the Metal-Semiconductor Interface of Pt-GaN Diode Hydrogen Sensors

abstract: In this paper, interaction mechanism of hydrogen with GaN metal-insulator-semiconductor (MIS) diodes has been investigated, focusing on the metal/semiconductor interfaces. As a result, the following three points are revealed: First, MIS Pt-SiO2-GaN diodes show a marked improvement in detection sensitivity, suggesting that the device interface plays a critical role in sensing. Second, exposure of the diodes to hydrogen is found to change the conduction mechanisms from Fowler-Nordheim tunneling to Pool-Frenkel emission. Third, interface trap level density of the diodes is found to be reduced by hydrogen exposure even at room temperature. These results support the validity of the hydrogen-induced dipole layer model.


title: Transient Electromagnetic Field Coupling to Long Shielded Cables

abstract: A model is presented for determining the transient currents and voltages induced within long shielded coaxial cables by an impinging electromagnetic pulse (EMP). The transmission-line theory is applied to establish the differential equations describing the behavior of the cables in the presence of a uniform plane traveling wave. At first, induced sheath currents are calculated taking into account different coupling modes. Then, internal voltages and currents are computed via the surface transfer impedance of cable shields for one and multilayer outer conductors. Comparison with measurements show the validity of the proposed method. The computer codes developed for this purpose have been found to be a very useful tool for the optimization of shielded transmission cables.


title: Thyroid hormone and male gonadal function.

abstract: I. Introduction WHILE the effects of hyper- and hypothyroidism1 in female gonadal functions are well established (1, 2), controversy exists regarding the impact of thyroid diseases on male reproduction. This is due to various reasons: 1) the apparent clinical irrelevance of signs and symptoms related to male gonadal function, compared with the systemic effects of hyper- and hypothyroidism; 2) the paucity of well controlled clinical studies, due to the fact that thyroid diseases are more common in females than in males; and 3) the demonstration in the 1950s that the adult male gonad of experimental animals is metabolically unresponsive to thyroid hormone (3). Since then, the concept that the testis is unaffected by iodothyronines became widely accepted. For this reason, the potential of thyroid hormone in the modulation of male reproductive functions was not determined for several years. Indeed, in experimental studies on the effect of thyroid hormone in various tissues, the testis has been used as a negat...


title: Cartoon image segmentation based on improved SLIC superpixels and adaptive region propagation merging

abstract: This paper present a novel algorithm for cartoon image segmentation based on the simple linear iterative clustering (SLIC) superpixels and adaptive region propagation merging. To break the limitation of the original SLIC algorithm in confirming to image boundaries, this paper proposed to improve the quality of the superpixels generation based on the connectivity constraint. To achieve efficient segmentation from the superpixels, this paper employed an adaptive region propagation merging algorithm to obtain independent segmented object. Compared with the pixel-based segmentation algorithms and other superpixel-based segmentation methods, the method proposed in this paper is more effective and more efficient by determining the propagation center adaptively. Experiments on abundant cartoon images showed that our algorithm outperforms classical segmentation algorithms with the boundary-based and region-based criteria. Furthermore, the final cartoon image segmentation results are also well consistent with the human visual perception.


title: ROSA: R Optimizations with Static Analysis

abstract: R is a popular language and programming environment for data scientists. It is increasingly co-packaged with both relational and Hadoop-based data platforms and can often be the most dominant computational component in data analytics pipelines. Recent work has highlighted inefficiencies in executing R programs, both in terms of execution time and memory requirements, which in practice limit the size of data that can be analyzed by R. This paper presents ROSA, a static analysis framework to improve the performance and space efficiency of R programs. ROSA analyzes input programs to determine program properties such as reaching definitions, live variables, aliased variables, and types of variables. These inferred properties enable program transformations such as C++ code translation, strength reduction, vectorization, code motion, in addition to interpretive optimizations such as avoiding redundant object copies and performing in-place evaluations. An empirical evaluation shows substantial reductions by ROSA in execution time and memory consumption over both CRAN R and Microsoft R Open.


title: Mantoo - A Set of Management Tools for Controlling SDN Experiments

abstract: OSHI -- Open Source Hybrid IP/SDN networking is a hybrid approach allowing the coexistence of traditional IP routing with SDN based forwarding within the same provider domain. In this demo, we will show a set of Open Source management tools for the emulation of the proposed solution over the Mininet emulator and over distributed test beds. We refer to this suite of tools as Mantoo (Management tools). Mantoo includes an extensible web-based graphical topology designer providing different layered network "views" (e.g. From physical links to service relationships among nodes). The framework is able to validate a topology, to automatically deploy it over a Mininet emulator or a distributed SDN test bed, to access nodes by opening consoles directly via the web GUI.


title: Why are deep nets reversible: A simple theory, with implications for training

abstract: Generative models for deep learning are promising both to improve understanding of the model, and yield training methods requiring fewer labeled samples. ::: Recent works use generative model approaches to produce the deep net's input given the value of a hidden layer several levels above. However, there is no accompanying "proof of correctness" for the generative model, showing that the feedforward deep net is the correct inference method for recovering the hidden layer given the input. Furthermore, these models are complicated. ::: The current paper takes a more theoretical tack. It presents a very simple generative model for RELU deep nets, with the following characteristics: (i) The generative model is just the reverse of the feedforward net: if the forward transformation at a layer is $A$ then the reverse transformation is $A^T$. (This can be seen as an explanation of the old weight tying idea for denoising autoencoders.) (ii) Its correctness can be proven under a clean theoretical assumption: the edge weights in real-life deep nets behave like random numbers. Under this assumption ---which is experimentally tested on real-life nets like AlexNet--- it is formally proved that feed forward net is a correct inference method for recovering the hidden layer. ::: The generative model suggests a simple modification for training: use the generative model to produce synthetic data with labels and include it in the training set. Experiments are shown to support this theory of random-like deep nets; and that it helps the training.


title: Automatic synthetic background defocus for a single portrait image

abstract: Capturing image with defocused background by using a large aperture is a widely used technique in digital single-lens reflex (DSLR) camera photography. It is also desired to provide this function to smart phones. In this paper, a new algorithm is proposed to synthesize such an effect for a single portrait image. The foreground portrait is detected using a face prior based salient object detection algorithm. Then with an improved gradient domain guided image filter, the details in the foreground are enhanced while the background pixels are blurred. In this way, the background objects are defocused and thus the foreground objects are emphasized. The resultant image looks similar to image captured using a camera with a large aperture. The proposed algorithm can be adopted in smart phones, especially for the front cameras of smart phones.


title: Model Shrinking for Embedded Keyword Spotting

abstract: In this paper we present two approaches to improve computational efficiency of a keyword spotting system running on a resource constrained device. This embedded keyword spotting system detects a pre-specified keyword in real time at low cost of CPU and memory. Our system is a two stage cascade. The first stage extracts keyword hypotheses from input audio streams. After the first stage is triggered, hand-crafted features are extracted from the keyword hypothesis and fed to a support vector machine (SVM) classifier on the second stage. This paper focuses on improving the computational efficiency of the second stage SVM classifier. More specifically, select a subset of feature dimensions and merge the SVM classifier to a smaller size, while maintaining the keyword spotting performance. Experimental results indicate that we can remove more than 36% of the non-discriminative SVM features, and reduce the number of support vectors by more than 60% without significant performance degradation. This results in more than 15% relative reduction in CPU utilization.


title: Generating multi-style paper pop-up designs using 3D primitive fitting

abstract: Paper pop-ups are fascinating three-dimensional books that impart stories and information more vividly to their readers. The design and construction of these pop-up books, however, are generally done by hand, and given the lack of expertise in this area, have necessitated the need for automated or computer-assisted design of paper pop-ups. This paper presents an automatic algorithm and the underlying theory for producing paper pop-up designs from 3D models. Existing studies on paper pop-ups have mainly focused on individual styles and proposed formulations for only a limited set of mechanisms. In our work, we design multi-style pop-ups by combining the formulations of previously studied styles with our derived validity conditions for box-style pop-ups. We use a mesh abstraction technique that fits volumetric primitives unto a 3D mesh, which are later automatically mapped to selected mechanisms. We also preserve important texture and shape contours using a hybrid object- and image-space approach. Finally, we generate printable design layouts and the corresponding assembly instructions to facilitate the actual production, which verifies the feasibility of our pop-ups.


Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media


title: Prioritized Experience Replay

abstract: Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari games. DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 41 out of 49 games.