title: Studying the factors affecting the risk of forest fire occurrence and applying neural networks for prediction

abstract: Lebanon is considered the gate to east and west, with an area of 10452 km2. Its location has made of it a main destination for tourists from all over the world. Besides, it is rich in caves and its mountains are covered with different kinds of trees. Cedars and Pine trees are known in Lebanon since ancient times. Yet Lebanon has been facing a critical threat of losing its green mountains and fields. Forest fire has caused the loss of many green acres in the past years. This paper studies the effects of climatological data: temperature, relative humidity, wind speed and daily precipitation on the risk of forests fire occurrence in Lebanon. These impacts impose the adaptation of certain techniques that could help to predict fires and thus avoid their happening. Artificial Neural Networks have been utilized for the purpose. The weather data of the year 2012 collected from North Lebanon, Kfarchakhna station are taken for study. We have studied the effects of both the number of neurons in the hidden layer and the training technique on the network's performance and the mean squared error. This is an indication of the good performance of such network in adopting its predicting decision.


title: Improved SIFT-features matching for object recognition

abstract: The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm's robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains. ::: ::: To evaluate the performance of the proposed approach, it was applied to real images acquired with the stereo camera system of the rehabilitation robotic system FRIEND II. The experimental results show an increase in the number of correct features matched and, at the same time, a decrease in the number of outliers in comparison with the original SIFT algorithm. Compared with the original SIFT algorithm, a 40% reduction in processing time was achieved for the matching of the stereo images.


title: Reducing shoulder-surfing by using gaze-based password entry

abstract: Shoulder-surfing -- using direct observation techniques, such as looking over someone's shoulder, to get passwords, PINs and other sensitive personal information -- is a problem that has been difficult to overcome. When a user enters information using a keyboard, mouse, touch screen or any traditional input device, a malicious observer may be able to acquire the user's password credentials. We present EyePassword, a system that mitigates the issues of shoulder surfing via a novel approach to user input. With EyePassword, a user enters sensitive input (password, PIN, etc.) by selecting from an on-screen keyboard using only the orientation of their pupils (i.e. the position of their gaze on screen), making eavesdropping by a malicious observer largely impractical. We present a number of design choices and discuss their effect on usability and security. We conducted user studies to evaluate the speed, accuracy and user acceptance of our approach. Our results demonstrate that gaze-based password entry requires marginal additional time over using a keyboard, error rates are similar to those of using a keyboard and subjects preferred the gaze-based password entry approach over traditional methods.


title: Friendships through IM: Examining the Relationship between Instant Messaging and Intimacy

abstract: This study explores the relationship between the amount of Instant Messenger (IM) use and the level of perceived intimacy between friends. Results showed the amount of IM use was positively associated not only with verbal intimacy, but also with affective and social intimacy. Findings are consistent with the relationship liberated perspective of computer-mediated communication, and suggest that IM promotes rather than hinders intimacy. Moreover, frequent conversation via IM actually encourages the desire to meet face-to-face. Theoretical as well as practical implications of the results for geographically remote friends and families are discussed.


title: From extant to extinct: locomotor ontogeny and the evolution of avian flight.

abstract: Evolutionary transformations are recorded by fossils with transitional morphologies, and are key to understanding the history of life. Reconstructing these transformations requires interpreting functional attributes of extinct forms by exploring how similar features function in extant organisms. However, extinct-extant comparisons are often difficult, because extant adult forms frequently differ substantially from fossil material. Here, we illustrate how postnatal developmental transitions in extant birds can provide rich and novel insights into evolutionary transformations in theropod dinosaurs. Although juveniles have not been a focus of extinct-extant comparisons, developing juveniles in many groups transition through intermediate morphological, functional and behavioral stages that anatomically and conceptually parallel evolutionary transformations. Exploring developmental transitions may thus disclose observable, ecologically relevant answers to long puzzling evolutionary questions.


Coordination Boundary Identification with Similarity and Replaceability


title: Rate-compatible puncturing of low-density parity-check codes

abstract: In this correspondence, we consider puncturing of low-density parity-check (LDPC) codes for additive white Gaussian noise (AWGN) channels. We show that good puncturing patterns exist and that the puncturing can be performed in a rate-compatible fashion. Furthermore, rate-compatible puncturing results in a small loss of performance with respect to threshold, namely, the punctured code is good (in terms of threshold) across a range of rates when compared with the optimal codes for each rate. This allows one to implement a single "mother" encoder and decoder that is good across a wide range of rates.


title: Evolution of planning for wireless communication systems

abstract: In this paper we provide a detailed and comprehensive survey of proposed approaches for network design, charting the evolution of models and techniques for the automatic planning of cellular wireless services. These problems present themselves as a trade-off between commitment to infrastructure and quality of service, and have become increasingly complex with the advent of more sophisticated protocols and wireless architectures. Consequently these problems are receiving increased attention from researchers in a variety of fields who adopt a wide range of models, assumptions and methodologies for problem solution. We seek to unify this dispersed and fragmented literature by charting the evolution of centralised planning for cellular systems.


title: Multinomial logistic regression for breast thermogram classification

abstract: The use of thermal imaging in breast cancer diagnosis is a state of art in many oncology diagnosis centers. This research is fueled by the growing popularity of Thermograms. Breast Cancer is the most common and lethal disease these days. In order to help radiologist examine different stages of Breast Cancer, Logistic Regression Classifier helps them to interpret images. The dataset of 400 images have been collected from HCG Hospital Bangalore. First images are de noised using median filters. Removal of noise will help in increasing the accuracy of classifier, hence detecting cancer at the early stage. Secondly, Histogram equalization and color segmentation is done to differentiate the pixels of same intensity and further determine the region of interest (ROI). Thirdly, Texture Analysis Using the Gray-Level Co-Occurrence Matrix is done where GLCM features like entropy, energy, auto correlation etc are extracted. At last these Texture Features serve as inputs to proposed classifier Multinomial Logistic Regression to detect the normal, early and late stages of breast cancer. The proposed Classifier is also compared with other classifiers like SVM, ANN and KNN for accuracy, precision, reliability and Compression ratio.


title: E-ETL Framework: ETL Process Reparation Algorithms Using Case-Based Reasoning

abstract: External data sources (EDSs) being integrated in a data warehouse (DW) frequently change their structures/schemas. As a consequence, in many cases, an already deployed ETL workflow stops its execution, yielding errors. Since structural changes of EDSs are frequent, an automatic reparation of an ETL workflow after such a change is of high importance. In this paper we present a framework, called E-ETL, for handling the evolution of an ETL layer. In the framework, an ETL workflow is semi-automatically or automatically (depending on a case) repaired as the result of structural changes in data sources, so that it works with the changed data sources. E-ETL supports three different reparation methods, but in this paper we discuss the one that is based on case-based reasoning. The proposed framework is being developed as a module external to an ETL engine, so that it can work with any engine that supports API for manipulating ETL workloads.


title: Abnormal event detection and localization in crowded scenes based on PCANet

abstract: Detecting and localizing abnormal events in crowded scenes still remains a challenging task among computer vision community. An unsupervised framework is proposed in this paper to address the problem. Low-level features and optical flows (OF) of video sequences are extracted to represent motion information in the temporal domain. Moreover, abnormal events usually occur in local regions and are closely linked to their surrounding areas in the spatial domain. To extract high-level information from local regions and model the relationship in spatial domain, the first step is to calculate optical flow maps and divide them into a set of non-overlapping sub-maps. Next, corresponding PCANet models are trained using the sub-maps at same spatial location in the optical flow maps. Based on the block-wise histograms extracted by PCANet models, a set of one-class classifiers are trained to predict the anomaly scores of test frames. The framework is completely unsupervised because it utilizes only normal videos. Experiments were carried out on UCSD Ped2 and UMN datasets, and the results show competitive performance of this framework when compared with other state-of-the-art methods.


title: Liposome Immobilization on Polymer Gel Particles by in situ Formation of Covalent Linkages

abstract: In situ formation of disulfide linkages between liposomes and Sephacryl gel particles both of that were bearing mercapto moieties was demonstrated. The reaction led to immobilization of the liposomes on the gel. The immobilized liposomes could be detached by reductive treatment of the liposome-gel conjugate, and the resulting gel was still capable of further immobilizing liposomes.


title: Sampling from large graphs

abstract: Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of them become impractical for large graphs. Thus graph sampling is essential.The natural questions to ask are (a) which sampling method to use, (b) how small can the sample size be, and (c) how to scale up the measurements of the sample (e.g., the diameter), to get estimates for the large graph. The deeper, underlying question is subtle: how do we measure success?.We answer the above questions, and test our answers by thorough experiments on several, diverse datasets, spanning thousands nodes and edges. We consider several sampling methods, propose novel methods to check the goodness of sampling, and develop a set of scaling laws that describe relations between the properties of the original and the sample.In addition to the theoretical contributions, the practical conclusions from our work are: Sampling strategies based on edge selection do not perform well; simple uniform random node selection performs surprisingly well. Overall, best performing methods are the ones based on random-walks and "forest fire"; they match very accurately both static as well as evolutionary graph patterns, with sample sizes down to about 15% of the original graph.


title: SupportNet: solving catastrophic forgetting in class incremental learning with support data

abstract: A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting. Here we propose a novel method, SupportNet, to efficiently and effectively solve the catastrophic forgetting problem in the class incremental learning scenario. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to stabilize the learned representation and ensure the robustness of the learned model. We validate our method with comprehensive experiments on various tasks, which show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and even reaches similar performance as the deep learning model trained from scratch on both old and new data. Our program is accessible at: this https URL


Automatic Closed Loop Speed Control of DC Motor


title: A Simple Method for Commonsense Reasoning

abstract: Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset~\cite{levesque2011winograd}. In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.


CounterFactual Regression with Importance Sampling Weights


title: Sentiment Strength Prediction Using Auxiliary Features

abstract: With an increasingly large amount of sentimental information embedded in online documents, sentiment analysis is quite valuable to product recommendation, opinion summarization, and so forth. Different from most works on identifying documents' qualitative affective information, this research focuses on the measurement of users' intensity over each sentimental category. Affect indicates positive or negative sentiment, while cognition includes certainty and tentative. Thus, our research can help bridge the cognitive and affective gaps between users and documents. The contributions of this study are twofold: (i) we proposed a neural network-based framework to sentiment strength prediction by convolving hybrid vectors, and (ii) we considered words jointly with a set of linguistic features for enhancing model robustness and adaptiveness. By exploiting the auxiliary features of sentiments from the corpus, the proposed model did not rely on well-established lexicons, and showed its robustness over sparse words. Experiments on six corpora validated the effectiveness of our sentiment strength prediction method.


title: Induction of simultaneous and sequential malolactic fermentation in durian wine.

abstract: This study represented for the first time the impact of malolactic fermentation (MLF) induced by Oenococcus oeni and its inoculation strategies (simultaneous vs. sequential) on the fermentation performance as well as aroma compound profile of durian wine. There was no negative impact of simultaneous inoculation of O. oeni and Saccharomyces cerevisiae on the growth and fermentation kinetics of S. cerevisiae as compared to sequential fermentation. Simultaneous MLF did not lead to an excessive increase in volatile acidity as compared to sequential MLF. The kinetic changes of organic acids (i.e. malic, lactic, succinic, acetic and α-ketoglutaric acids) varied with simultaneous and sequential MLF relative to yeast alone. MLF, regardless of inoculation mode, resulted in higher production of fermentation-derived volatiles as compared to control (alcoholic fermentation only), including esters, volatile fatty acids, and terpenes, except for higher alcohols. Most indigenous volatile sulphur compounds in durian were decreased to trace levels with little differences among the control, simultaneous and sequential MLF. Among the different wines, the wine with simultaneous MLF had higher concentrations of terpenes and acetate esters while sequential MLF had increased concentrations of medium- and long-chain ethyl esters. Relative to alcoholic fermentation only, both simultaneous and sequential MLF reduced acetaldehyde substantially with sequential MLF being more effective. These findings illustrate that MLF is an effective and novel way of modulating the volatile and aroma compound profile of durian wine.


title: TagAssist: Automatic Tag Suggestion for Blog Posts

abstract: In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion. Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.


Transparent and Specular Object Reconstruction


title: Detecting Data Errors: Where are we and what needs to be done?

abstract: Data cleaning has played a critical role in ensuring data quality for enterprise applications. Naturally, there has been extensive research in this area, and many data cleaning algorithms have been translated into tools to detect and to possibly repair certain classes of errors such as outliers, duplicates, missing values, and violations of integrity constraints. Since different types of errors may coexist in the same data set, we often need to run more than one kind of tool. In this paper, we investigate two pragmatic questions: (1) are these tools robust enough to capture most errors in real-world data sets? and (2) what is the best strategy to holistically run multiple tools to optimize the detection effort? To answer these two questions, we obtained multiple data cleaning tools that utilize a variety of error detection techniques. We also collected five real-world data sets, for which we could obtain both the raw data and the ground truth on existing errors. In this paper, we report our experimental findings on the errors detected by the tools we tested. First, we show that the coverage of each tool is well below 100%. Second, we show that the order in which multiple tools are run makes a big difference. Hence, we propose a holistic multi-tool strategy that orders the invocations of the available tools to maximize their benefit, while minimizing human effort in verifying results. Third, since this holistic approach still does not lead to acceptable error coverage, we discuss two simple strategies that have the potential to improve the situation, namely domain specific tools and data enrichment. We close this paper by reasoning about the errors that are not detectable by any of the tools we tested.


title: Modeling learner affect with theoretically grounded dynamic bayesian networks

abstract: Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are based on general models of affect without a specific focus on learner emotions. This paper presents work that investigates the benefits of using theoretical models of learner emotions to guide the development of Bayesian networks for prediction of student affect. Predictive models are empirically learned from data acquired from 260 students interacting with the game-based learning environment, CRYSTAL ISLAND. Results indicate the benefits of using theoretical models of learner emotions to inform predictive models. The most successful model, a dynamic Bayesian network, also highlights the importance of temporal information in predicting learner emotions. This work demonstrates the benefits of basing predictive models of learner emotions on theoretical foundations and has implications for how these models may be used to validate theoretical models of emotion.


title: Explaining the relation between thin ideal internalization and body dissatisfaction among college women: The roles of social comparison and body surveillance

abstract: Abstract Sociocultural models of disordered eating lack comprehensive explanations as to how thin ideal internalization leads to body dissatisfaction. This study examined two social psychological theories as explanations of this relation, namely social comparison and objectification theories, in a sample of 265 women attending a Southeastern university. Social comparison (both general and appearance-related) and body surveillance (the indicator of objectification) were tested as mediators of the relation between thin ideal internalization and body dissatisfaction using bootstrapping analyses. Results indicated that body surveillance was a significant specific mediator of this relation; however, neither operationalization of social comparison emerged as such. Results serve to elaborate upon the sociocultural model of disordered eating by providing a more comprehensive understanding of the processes by which thin ideal internalization manifests itself in body dissatisfaction. The current findings also highlight the importance of targeting body surveillance in clinical settings.


title: From an Unknown Other to an Attachment Figure: How Do Mental Representations Change as Attachments Form?

abstract: Mental representations are the building blocks that guide affect, thought, and behaviors within adult attachment relationships. To date, research has focused on how individual differences in mental representations meaningfully relate to a person’s chronic attachment style and the attachment dynamics of a specific relationship, as well as their effects on perception, behavior, physiology, well-being, and other consequential outcomes. In the present chapter, we address a fundamentally different question: How do mental representations change as two individuals develop an attachment bond in adulthood? We propose a social cognitive framework for understanding how mental representations of others and self change over time as a function of various relationship turning points and lead to the formation of a full-fledged attachment bond. We propose that the positive psychological and physical outcomes, as well as the emotion regulatory effects, observed in pair bonds occur as a result of the two people forming over time a linked or “coupled” cognitive system. This “coupled” or “interlocked” system reflects enduring changes at the level of mental representations in the minds of both individuals, including elaboration and increased chronic accessibility of the partner mental representation, increased interconnectedness between self and partner representation, and automaticity of behavioral scripts. We review literature from diverse fields, including social and cognitive psychology and neuroscience, and integrate it within this social cognitive approach. We end by identifying questions and avenues for future research.


title: Effect of dopants on electron localization length in polyaniline

abstract: Abstract The electronic behavior of chemically synthesized polyaniline depends on the amount of disorder—whether homogeneous or inhomogeneous—created during synthesis and doping. Its physical properties are significantly affected by a number of parameters such as catalyst type, solution pH, oxidant/monomer ratio, solvent type, secondary doping, dopant type, dopant size and synthesis temperature. With the view of studying the dependence of electron localization behavior on the dopant type, all other factors remaining the same, we report in this paper a systematic study of NMR, EPR, magnetic susceptibility, and dc conductivity measurements for seven doping acids. The EPR lineshape in all these cases is close to a Lorentzian, but the linewidth is sensitively dependent on the type of the dopant. In spite of differences in the magnitude and the temperature dependences of dc conductivity and magnetic susceptibility data, the experiments reveal a localization length of approximately 30 A for all the samples. It is concluded that, unlike drastically different charge transport behaviors caused by the presence of substituent groups on phenyl rings of the polymer chains, dopant molecules belonging to a given family have virtually no effect on the electron localization length.


title: LabVIEW based fuzzy logic controller for haptic interface

abstract: LabVIEW is an application software used in virtual instrumentation (VI). It offers better data acquisition and interfacing with hardware. Haptic technology has vast applications in various fields such as medical, military and other day-to-day life's products, for example, smart phones, video games. In the haptic system, stability and transparency are the key performance issues which are affected by various factors such as uncertainty and delay. In this paper, to maintain the stability and transparency of haptic system in the presence of said factors, an interface controller is designed using conventional Ziegler-Nichols technique. Moreover, a novel technique, self-tuning Fuzzy logic based PI is also proposed to improve the performance of haptic system using LabVIEW.


title: Use of Fourier Series Analysis for Motion Artifact Reduction and Data Compression of Photoplethysmographic Signals

abstract: Pulse oximeters require artifact-free clean photoplethysmograph (PPG) signals obtained at red and infrared (IR) wavelengths for the estimation of the level of oxygen saturation ( SpO2) in the arterial blood of a patient. Movement of a patient corrupts a PPG signal with motion artifacts and introduces large errors in the computation of SpO2. A novel method for removing motion artifacts from corrupted PPG signals by applying Fourier series analysis on a cycle-by-cycle basis is presented in this paper. Aside from artifact reduction, the proposed method also provides data compression. Experimental results indicate that the proposed method is insensitive to heart rate variation, introduces negligible error in the processed PPG signals due to the additional processing, preserves all the morphological features of the PPG, provides 35 dB reduction in motion artifacts, and achieves a data compression factor of 12.


title: Reviewing the Big Questions Literature;: or, Should HCI Have Big Questions?

abstract: What are big questions? Why do scholars propose them? How are they generated? Could they be valuable and useful in HCI research? In this paper we conduct a thorough review of "big questions" literature, which draws on scholarship from a variety of fields and disciplines. Our intended contribution is twofold. First, we provide a substantive review of big questions scholarship, which to our knowledge has never been done before. Second, we leverage this summary as a means of examining the value and utility of big questions in HCI as a research discipline. Whether HCI decides that generating and having big questions would be a desirable path forward, we believe that examining the potential for big questions is a useful way of becoming more reflective about HCI research.


title: Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval

abstract: Constructing a joint representation invariant across different modalities (e.g., video, language) is of significant importance in many multimedia applications. While there are a number of recent successes in developing effective image-text retrieval methods by learning joint representations, the video-text retrieval task, however, has not been explored to its fullest extent. In this paper, we study how to effectively utilize available multimodal cues from videos for the cross-modal video-text retrieval task. Based on our analysis, we propose a novel framework that simultaneously utilizes multi-modal features (different visual characteristics, audio inputs, and text) by a fusion strategy for efficient retrieval. Furthermore, we explore several loss functions in training the embedding and propose a modified pairwise ranking loss for the task. Experiments on MSVD and MSR-VTT datasets demonstrate that our method achieves significant performance gain compared to the state-of-the-art approaches.


title: Cerebral small vessel disease: from a focal to a global perspective

abstract: Cerebral small vessel disease (SVD) is commonly observed on neuroimaging among elderly individuals and is recognized as a major vascular contributor to dementia, cognitive decline, gait impairment, mood disturbance and stroke. However, clinical symptoms are often highly inconsistent in nature and severity among patients with similar degrees of SVD on brain imaging. Here, we provide a new framework based on new advances in structural and functional neuroimaging that aims to explain the remarkable clinical variation in SVD. First, we discuss the heterogeneous pathology present in SVD lesions despite an identical appearance on imaging and the perilesional and remote effects of these lesions. We review effects of SVD on structural and functional connectivity in the brain, and we discuss how network disruption by SVD can lead to clinical deficits. We address reserve and compensatory mechanisms in SVD and discuss the part played by other age-related pathologies. Finally, we conclude that SVD should be considered a global rather than a focal disease, as the classically recognized focal lesions affect remote brain structures and structural and functional network connections. The large variability in clinical symptoms among patients with SVD can probably be understood by taking into account the heterogeneity of SVD lesions, the effects of SVD beyond the focal lesions, the contribution of neurodegenerative pathologies other than SVD, and the interaction with reserve mechanisms and compensatory mechanisms.


title: Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems

abstract: Configuring the hybrid precoders and combiners in a millimeter wave multiuser multiple-input multiple-output system is challenging in frequency selective channels. In this paper, we develop a system that uses compressive estimation on the uplink to configure precoders and combiners for the downlink. In the first step, the base station (BS) simultaneously estimates the channels from all the mobile stations on each subcarrier. To reduce the number of measurements required, compressed sensing techniques are developed that exploit common support on the different subcarriers. In the second step, exploiting reciprocity and the channel estimates the BS designs hybrid precoders and combiners. Two algorithms are developed for this purpose, with different performance and complexity tradeoffs: First, a factorization of the purely digital solution; and second, an iterative hybrid design. Extensive numerical experiments evaluate the proposed solutions comparing to the state-of-the-art strategies, and illustrating design tradeoffs in overhead, complexity, and performance.


title: Reading Scene Text with Attention Convolutional Sequence Modeling

abstract: Reading text in the wild is a challenging task in the field of computer vision. Existing approaches mainly adopted Connectionist Temporal Classification (CTC) or Attention models based on Recurrent Neural Network (RNN), which is computationally expensive and hard to train. In this paper, we present an end-to-end Attention Convolutional Network for scene text recognition. Firstly, instead of RNN, we adopt the stacked convolutional layers to effectively capture the contextual dependencies of the input sequence, which is characterized by lower computational complexity and easier parallel computation. Compared to the chain structure of recurrent networks, the Convolutional Neural Network (CNN) provides a natural way to capture long-term dependencies between elements, which is 9 times faster than Bidirectional Long Short-Term Memory (BLSTM). Furthermore, in order to enhance the representation of foreground text and suppress the background noise, we incorporate the residual attention modules into a small densely connected network to improve the discriminability of CNN features. We validate the performance of our approach on the standard benchmarks, including the Street View Text, IIIT5K and ICDAR datasets. As a result, state-of-the-art or highly-competitive performance and efficiency show the superiority of the proposed approach.


title: Image Segmentation Based on Oscillatory Correlation

abstract: We study the image segmentation on the basis of locally excitatory, globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a lateral potential for each oscillators so that only oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of the lateral potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions but without affecting those corresponding to major regions. We show that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and we illustrate network properties by computer stimulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real gray-level images. A number of issues regarding biological plausibility and perceptual organization are discussed. We argue that LEGION provides a novel and effective framework for image segmentation and figure-ground segregation.


title: Investigating the energy consumption of a wireless network interface in an ad hoc networking environment

abstract: Energy-aware design and evaluation of network protocols requires knowledge of the energy consumption behavior of actual wireless interfaces. But little practical information is available about the energy consumption behavior of well-known wireless network interfaces and device specifications do not provide information in a form that is helpful to protocol developers. This paper describes a series of experiments which obtained detailed measurements of the energy consumption of an IEEE 802.11 wireless network interface operating in an ad hoc networking environment. The data is presented as a collection of linear equations for calculating the energy consumed in sending, receiving and discarding broadcast and point-to-point data packets of various sizes. Some implications for protocol design and evaluation in ad hoc networks are discussed.


title: Pressure-Activated Electrical Interconnection During Micro-Transfer-Printing

abstract: Sharp electrically conductive structures integrated into micro-transfer-print compatible components provide an approach to forming electrically interconnected systems during the assembly procedure. Silicon micromachining techniques are used to fabricate print-compatible components with integrated, electrically conductive, pressure-concentrating structures. The geometry of the structures allow them to penetrate a polymer receiving layer during the elastomer stamp printing operation, and reflow of the polymer following the transfer completes the electrical interconnection when capillary action forces the gold-coated pressure-concentrator into a metal landing site. Experimental results and finite element simulations support a discussion of the mechanics of the interconnection.


title: A development of Fractal PIFA (planar inverted F antenna) with bandwidth enhancement for mobile phone applications

abstract: A novel Fractal planar inverted F antenna (F-PIFA) based on the self affinity design is presented in this paper. The procedure for designing a Fractal Planar Inverted F Antenna is explained and the 2nd iteration of Sierpinski Carpet is chosen to use in mobile phones. The F-PIFA has a total dimension 27mm × 27mm are designed and optimized in order to receive GSM (Global System for Mobile Communication) and UMTS (Universal Mobile Telecommunication System) and HiperLAN (HigH Performance Radio LAN with the frequency range from 1710 MHz to 2170 MHz, 1885 to 2200 MHz for 3G and 4800MHz to 5800MHz for HiperLAN respectively. The antenna achieved the GSM, UMTS and HiperLan frequency with −6dB return loss and has almost omnidirectional radiation pattern. This antenna has been tested using mobile phone model and all the performance met the criteria for a mobile phone application.


Algorithm Trading using Q-Learning and Recurrent Reinforcement Learning


title: Geometry of Compositionality

abstract: This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and displays high accuracy in context-specific compositionality detection of a variety of natural language phenomena (idiomaticity, sarcasm, metaphor) for different datasets in multiple languages. The key insight is to connect compositionality to a curious geometric property of word embeddings, which is of independent interest.


title: Real-time techniques for 3D flow visualization

abstract: Visualization of three-dimensional steady flow has to overcome a lot of problems to be effective. Among them are occlusion of distant details, lack of directional and depth hints and occlusion. We present methods which address these problems for real-time graphic representations applicable in virtual environments. We use dashtubes, i.e., animated, opacity-mapped streamlines, as a visualization icon for 3D-flow visualization. We present a texture mapping technique to keep the level of texture detail along a streamline nearly constant even when the velocity of the flow varies considerably. An algorithm is described which distributes the dashtubes evenly in space. We apply magic lenses and magic boxes as interaction techniques for investigating densely filled areas without overwhelming the observer with visual detail. Implementation details of these methods and their integration in our virtual environment conclude the paper.


title: A recursive algorithm for the forced alignment of very long audio segments.

abstract: In this paper we address the problem of aligning very long (often more than one hour) audio files to their corresponding textual transcripts in an effective manner. We present an efficient recursive technique to solve this problem that works well even on noisy speech signals. The key idea of this algorithm is to turn the forced alignment problem into a recursive speech recognition problem with a gradually restricting dictionary and language model. The algorithm is tolerant to acoustic noise and errors or gaps in the text transcript or audio tracks. We report experimental results on a 3 hour audio file containing TV and radio broadcasts. We will show accurate alignments on speech under a variety of real acoustic conditions such as speech over music and speech over telephone lines. We also report results when the same audio stream has been corrupted with white additive noise or compressed using a popular web encoding format such as RealAudio. This algorithm has been used in our internal multimedia indexing project. It has processed more than 200 hours of audio from varied sources, such as WGBH NOVA documentaries and NPR web audio files. The system aligns speech media content in about one to five times realtime, depending on the acoustic conditions of the audio signal.


title: Effects of Push Notifications on Learner Engagement in a Mobile Learning App

abstract: In this study, we evaluated the effect of push notifications on mobile app engagement. Over the duration of a month, we collected data from 12,071 online users of an English learning app named 'English Practice' which can be freely installed from the Google Play store. Four criteria of app engagement used by us were as follows: session count, session length, time consumption and retention. In addition, we examined the frequency of sending notifications, how to display notifications and the use of notifications on wearable devices. The results show that push notifications with regards to incoming messages can increase learner engagement most. On the other hand, abusing push notifications (e.g. if they are too frequent) result in the opposite effect. Based on the results, we suggest that educators or developers focus on an increased integration of social communication tools inside apps.


title: A discriminative framework for clustering via similarity functions

abstract: Problems of clustering data from pairwise similarity information are ubiquitous in Computer Science. Theoretical treatments typically view the similarity information as ground-truth and then design algorithms to (approximately) optimize various graph-based objective functions. However, in most applications, this similarity information is merely based on some heuristic; the ground truth is really the unknown correct clustering of the data points and the real goal is to achieve low error on the data. In this work, we develop a theoretical approach to clustering from this perspective. In particular, motivated by recent work in learning theory that asks "what natural properties of a similarity (or kernel) function are sufficient to be able to learn well?" we ask "what natural properties of a similarity function are sufficient to be able to cluster well?" To study this question we develop a theoretical framework that can be viewed as an analog of the PAC learning model for clustering, where the object of study, rather than being a concept class, is a class of (concept, similarity function) pairs, or equivalently, a property the similarity function should satisfy with respect to the ground truth clustering. We then analyze both algorithmic and information theoretic issues in our model. While quite strong properties are needed if the goal is to produce a single approximately-correct clustering, we find that a number of reasonable properties are sufficient under two natural relaxations: (a) list clustering: analogous to the notion of list-decoding, the algorithm can produce a small list of clusterings (which a user can select from) and (b) hierarchical clustering: the algorithm's goal is to produce a hierarchy such that desired clustering is some pruning of this tree (which a user could navigate). We develop a notion of the clustering complexity of a given property (analogous to notions of capacity in learning theory), that characterizes its information-theoretic usefulness for clustering. We analyze this quantity for several natural game-theoretic and learning-theoretic properties, as well as design new efficient algorithms that are able to take advantage of them. Our algorithms for hierarchical clustering combine recent learning-theoretic approaches with linkage-style methods. We also show how our algorithms can be extended to the inductive case, i.e., by using just a constant-sized sample, as in property testing. The analysis here uses regularity-type results of [FK] and [AFKK].


Appears in Agent Construction and Emotions, 2006 EMA: A computational model of appraisal dynamics


title: Minding the body: Situating gender identity diagnoses in the ICD-11

abstract: The World Health Organization (WHO) is in the process of revising the International Statistical Classifi cation of Diseases and Related Health Problems (ICD) and ICD-11 has an anticipated publication date of 2015. The Working Group on the Classifi cation of Sexual Disorders and Sexual Health (WGSDSH) is charged with evaluating clinical and research data to inform the revision of diagnostic categories related to sexuality and gender identity that are currently included in the mental and behavioural disorders chapter of ICD-10, and making initial recommendations regarding whether and how these categories should be represented in the ICD-11. The diagnostic classifi cation of disorders related to (trans)gender identity is an area long characterized by lack of knowledge, misconceptions and controversy. The placement of these categories has shifted over time within both the ICD and the American Psychiatric Association ’ s Diagnostic and Statistical Manual (DSM), refl ecting developing views about what to call these diagnoses, what they mean and where to place them. This article reviews several controversies generated by gender identity diagnoses in recent years. In both the ICD-11 and DSM-5 development processes, one challenge has been to fia balance between concerns related to the stigmatization of mental disorders and the need for diagnostic categories that facilitate access to healthcare. In this connection, this article discusses several human rights issues related to gender identity diagnoses, and explores the question of whether affected populations are best served by placement of these categories within the mental disorders section of the classifi cation. The combined stigmatization of being transgender and of having a mental disorder diagnosis creates a doubly burdensome situation for this group, which may contribute adversely to health status and to the attainment and enjoyment of human rights. The ICD-11 Working Group on the Classifi cation of Sexual Disorders and Sexual Health believes it is now appropriate to abandon a psychopathological model of transgender people based on 1940s conceptualizations of sexual deviance and to move towards a model that is (1) more refl ective of current scientifi c evidence and best practices; (2) more responsive to the needs, experience, and human rights of this vulnerable population; and (3) more supportive of the provision of accessible and high-quality healthcare services.


title: Block clustering of contingency table and mixture model

abstract: Block clustering or simultaneous clustering has become an important challenge in data mining context. It has practical importance in a wide of variety of applications such as text, web-log and market basket data analysis. Typically, the data that arises in these applications is arranged as a two-way contingency or co-occurrence table. In this paper, we embed the block clustering problem in the mixture approach. We propose a Poisson block mixture model and adopting the classification maximum likelihood principle we perform a new algorithm. Simplicity, fast convergence and scalability are the major advantages of the proposed approach.


title: Sign language word translator using Neural Networks for the Aurally Impaired as a tool for communication

abstract: Kohonen Self Organizing Maps are a type of Neural Networks which learn to recognize patterns and classify data sets in unsupervised manner. This research aimed to develop a system which converts hand gestures into Filipino words using algorithm such as Kohonen Self-Organizing Map. The system uses a webcam to capture hand images which can be processed to serve as input for Self-Organizing Map. Image processing techniques such as: color segmentation, visual-hand tracking, pre-processing, and feature extraction are used to achieve the objective. The system was developed using neural network toolbox and graphical user interface in MATLAB. The results show that the system can achieve 97.6% of recognition rate for 5 persons. It can be concluded that the system can be used by different users while generating high recognition rate.


title: Enabling coordinated register allocation and thread-level parallelism optimization for GPUs

abstract: The key to high performance on GPUs lies in the massive threading to enable thread switching and hide the latency of function unit and memory access. However, running with the maximum thread-level parallelism (TLP) does not necessarily lead to the optimal performance due to the excessive thread contention for cache resource. As a result, thread throttling techniques are employed to limit the number of threads that concurrently execute to preserve the data locality. On the other hand, GPUs are equipped with a large register file to enable fast context switch between threads. However, thread throttling techniques that are designed to mitigate cache contention, lead to under utilization of registers. Register allocation is a significant factor for performance as it not just determines the single-thread performance, but indirectly affects the TLP. The design space of register allocation and TLP presents new opportunities for performance optimization. However, the complicated correlation between the two factors inevitably lead to many performance dynamics and uncertainties. In this paper, we propose Coordinated Register Allocation and Thread-level parallelism (CRAT), a compiler-based performance optimization framework. In order to achieve this goal, CRAT first enables effective register allocation. Given a register per-thread limit, CRAT allocates the registers by analyzing the lifetime of variables. To reduce the spilling cost, CRAT spills the registers to shared memory when possible. Then, CRAT explores the design space by first pruning the design points that cause serious Ll cache thrashing and register under utilization. After that, CRAT employs a prediction model to find the best tradeoff between the single-thread performance and TLP. We evaluate CRAT using a set of representative workloads on GPUs. Experimental results indicate that compared to the optimal thread throttling technique, our framework achieves performance improvement up to 1.79X (geometric mean 1.25X).


title: Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset

abstract: Data pre-processing for machine learning methods is key step for knowledge discovery process. Depending on nature of the data, pre-processing might take the majority time of data analysis. Correctly prepared data for processing guarantees precise and reliable results of data analysis. This paper analyses initial data pre-processing influence to attack detection accuracy by using Decision Trees, Naive Bayes and Rule-Based classifiers with NSL-KDD dataset. In addition, the results of detected attacks accuracy dependency by selecting different attacks grouping options and using ensembles of various classifiers are presented.


title: Feature selection and ensemble of classifiers for Android malware detection

abstract: In recent years the Android Operating System (OS) has become one of the major stakeholders in the smartphone market. The growing consumers' adoption of Android has also brought many security concerns as the number of malicious applications targeting this OS has dramatically increased. Current malware detection methods include static and dynamic analysis. In this work, a set of results obtained for malware classification through machine learning techniques are presented. Although, the presented approach analyzes data obtained through static analysis techniques as other approaches, it differs from previous works by presenting detailed descriptions of the data sets characteristics, feature extraction and selection processes, the size of the training sample set, cross validation and validation sets are specified, providing explicit evidence for classification improvement. Even more, a comparative analysis of various ensembles is presented, having as objective to determine the best combination of classifiers based on the evaluation of the classification results.


title: Collaborative workplaces for innovation in service companies: barriers and enablers for supporting new ways of working

abstract: The paper aims to provide a systematic overview of the barriers and enablers that contribute to the success or failure of collaborative workplaces initiatives aimed at fostering innovation in service companies. The study is based on semi-structured interviews with innovation managers, human resource managers and facility department executives from a sample of multi-national service companies. Its primary focus is on the workplace initiatives carried out in their Spanish subsidiaries. The paper contributes to the extant research by identifying a conceptual model for collaborative workplaces and by providing a systematic overview of the related barriers and enablers. From the interviews, these factors were organized within a framework usable by practitioners for analysis. This paper is of interest to companies that aim to design collaborative workplace strategies to justify associated investments. It can also provide their managers with guidelines to lead the company in the transition toward new ways of working based on higher employee collaboration and flexibility.


title: Low-Power Clock Branch Sharing Double-Edge Triggered Flip-Flop

abstract: In this paper, a new technique for implementing low-energy double-edge triggered flip-flops is introduced. The new technique employs a clock branch-sharing scheme to reduce the number of clocked transistors in the design. The newly proposed design also employs conditional discharge and split-path techniques to further reduce switching activity and short-circuit currents, respectively. As compared to the other state of the art double-edge triggered flip-flop designs, the newly proposed CBS_ip design has an improvement of up to 20% and 12.4% in view of power consumption and PDP, respectively


title: Quantum Games and Quantum Strategies

abstract: We investigate the quantization of non-zero sum games. For the particular case of the Prisoners' Dilemma we show that this game ceases to pose a dilemma if quantum strategies are allowed for. We also construct a particular quantum strategy which always gives reward if played against any classical strategy.


title: ABSTRACT Generalized Naive Bayes Classifiers

abstract: This paper presents a generalization of the Naive Bayes Classifier. The method is specifically designed for binary classification problems commonly found in credit scoring and marketing applications. The Generalized Naive Bayes Classifier turns out to be a powerful tool for both exploratory and predictive analysis. It can generate accurate predictions through a flexible, non-parametric fitting procedure, while being able to uncover hidden patterns in the data. In this paper, the Generalized Naive Bayes Classifier and the original Bayes Classifier will be demonstrated. Also, important ties to logistic regression, the Generalized Additive Model (GAM), and Weight Of Evidence will be discussed.


title: Dynamic semantic interoperability of control in IoT-based systems: Need for adaptive middleware

abstract: The Internet of Things (IoT) phenomenon is giving rise to large scale IoT deployments comprising thousands of IoT devices with large degree of heterogeneity. The heterogeneity is further aggravated by dynamism-new applications and requirements, changes in capabilities of devices during their life-cycle and mobility of devices and applications. This is leading to semantic interoperability issues among these devices, which is hindering the ability of system designers to draw the maximum value from these deployments. To address this problem, we present InteropAdapt, an adaptive middleware that can maintain seamless semantic interoperability across dynamic events. The central aim of this paper is to exhibit different architectural elements of InteropAdapt in some detail. We also illustrate InteropAdapt via a simple yet realistic example in the smart office automation domain.


One Parser, Many Languages


title: Bio-inspired Mineralization Using Hydrophilic Polymers

abstract: Biomineralization processes result in organic/inorganic hybrid materials with complex shape, hierarchical organization, and superior materials properties. Chemistry, which is inspired by these processes, aims to mimic biomineralization principles and to transfer them to the general control of crystallization processes using an environmentally benign route. In this chapter, the latest advances in hydrophilic polymer-controlled morphosynthesis and bio-inspired mineralization of crystals are summarized with focus on the various principles that can be used to generate inorganic and organic crystals with unusual structural specialty and complexity. For this, classical crystallization pathways using crystal face-selective polymer adsorption can be applied as well as non-classical nanoparticle-mediated crystallization routes, which are based on amorphous or crystalline precursor particles. Current developments emphasize that probably all inorganic and organic crystals will be amenable to morphosynthetic control by the described strategies using either flexible polymer additives or suitable self-assembly mechanisms. The resulting unique hierarchical materials with structural specialty and complexity, and a size range spanning from nanometers to micrometers, are expected to find potential applications in various fields. In addition, bio-inspired mineralization with hydrophilic copolymers offers the chance to understand basic principles of the complex and synergetic biomineralization processes.


title: A Survey of Downlink Non-orthogonal Multiple Access for 5G Wireless Communication Networks

abstract: Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique for the next generation cellular communication networks. In this paper, we first discuss a simple NOMA model with two users served by a single-carrier simultaneously to illustrate its basic principles. Then, a more general model with multicarrier serving an arbitrary number of users on each subcarrier is also discussed. An overview of existing works on performance analysis, resource allocation, and multiple-input multiple-output NOMA are summarized and discussed. Furthermore, we discuss the key features of NOMA and its potential research challenges.


title: Modeling global water use for the 21st century: the Water Futures and Solutions (WFaS) initiative and its approaches

abstract: Abstract. To sustain growing food demand and increasing standard of living, global water use increased by nearly 6 times during the last 100 years, and continues to grow. As water demands get closer and closer to the water availability in many regions, each drop of water becomes increasingly valuable and water must be managed more efficiently and intensively. However, soaring water use worsens water scarcity conditions already prevalent in semi-arid and arid regions, increasing uncertainty for sustainable food production and economic development. Planning for future development and investments requires that we prepare water projections for the future. However, estimations are complicated because the future of the world's waters will be influenced by a combination of environmental, social, economic, and political factors, and there is only limited knowledge and data available about freshwater resources and how they are being used. The Water Futures and Solutions (WFaS) initiative coordinates its work with other ongoing scenario efforts for the sake of establishing a consistent set of new global water scenarios based on the shared socio-economic pathways (SSPs) and the representative concentration pathways (RCPs). The WFaS "fast-track" assessment uses three global water models, namely H08, PCR-GLOBWB, and WaterGAP. This study assesses the state of the art for estimating and projecting water use regionally and globally in a consistent manner. It provides an overview of different approaches, the uncertainty, strengths and weaknesses of the various estimation methods, types of management and policy decisions for which the current estimation methods are useful. We also discuss additional information most needed to be able to improve water use estimates and be able to assess a greater range of management options across the water–energy–climate nexus.


title: eCommerceGAN : A Generative Adversarial Network for E-commerce

abstract: E-commerce companies such as Amazon, Alibaba and Flipkart process billions of orders every year. However, these orders represent only a small fraction of all plausible orders. Exploring the space of all plausible orders could help us better understand the relationships between the various entities in an e-commerce ecosystem, namely the customers and the products they purchase. In this paper, we propose a Generative Adversarial Network (GAN) for orders made in e-commerce websites. Once trained, the generator in the GAN could generate any number of plausible orders. Our contributions include: (a) creating a dense and low-dimensional representation of e-commerce orders, (b) train an ecommerceGAN (ecGAN) with real orders to show the feasibility of the proposed paradigm, and (c) train an ecommerce-conditional-GAN (ec^2GAN) to generate the plausible orders involving a particular product. We propose several qualitative methods to evaluate ecGAN and demonstrate its effectiveness. The ec^2GAN is used for various kinds of characterization of possible orders involving a product that has just been introduced into the e-commerce system. The proposed approach ec^2GAN performs significantly better than the baseline in most of the scenarios.


title: Making a good decision: Value from fit.

abstract: The classic answer to what makes a decision good concerns outcomes. A good decision has high outcome benefits (it is worthwhile) and low outcome costs (it is worth it). I propose that, independent of outcomes or value from worth, people experience a regulatory fit when they use goal pursuit means th...


title: Sequence Labeling with Reinforcement Learning and Ranking Algorithms

abstract: Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assign a label to each element in a sequence. Until now, this problem has mainly been addressed with Markov models and Dynamic Programming. ::: ::: We propose a new approach where the sequence labeling task is seen as a sequential decision process. This method is shown to be very fast with good generalization accuracy. Instead of searching for a globally optimal label sequence, we learn to construct this optimal sequence directly in a greedy fashion. First, we show that sequence labeling can be modelled using Markov Decision Processes, so that several Reinforcement Learning (RL) algorithms can be used for this task. Second, we introduce a new RL algorithm which is based on the ranking of local labeling decisions.


title: Software visualization in software maintenance , reverse engineering , and re-engineering : a research survey

abstract: Software visualization is concerned with the static visualization as well as the animation of software artifacts, such as source code, executable programs, and the data they manipulate, and their attributes, such as size, complexity, or dependencies. Software visualization techniques are widely used in the areas of software maintenance, reverse engineering, and re-engineering, where typically large amounts of complex data need to be understood and a high degree of interaction between software engineers and automatic analyses is required. This paper reports the results of a survey on the perspectives of 82 researchers in software maintenance, reverse engineering, and re-engineering on software visualization. It describes to which degree the researchers are involved in software visualization themselves, what is visualized and how, whether animation is frequently used, whether the researchers believe animation is useful at all, which automatic graph layouts are used if at all, whether the layout algorithms have deficiencies, and--last but not least--where the medium-term and long-term research in software visualization should be directed. The results of this survey help to ascertain the current role of software visualization in software engineering from the perspective of researchers in these domains and give hints on future research avenues.


title: Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition

abstract: We propose a new method for building fast and compact CNN model for large scale handwritten Chinese character recognition (HCCR).We propose a new technique, namely Adaptive Drop-weight (ADW), for effectively pruning CNN parameters.We proposed the Global Supervised Low Rank Expansions (GSLRE) method for accelerating CNN model.Comparing with the state-of-the-art CNN method for HCCR, our approach is about 30-times faster yet 10-times smaller. Like other problems in computer vision, offline handwritten Chinese character recognition (HCCR) has achieved impressive results using convolutional neural network (CNN)-based methods. However, larger and deeper networks are needed to deliver state-of-the-art results in this domain. Such networks intuitively appear to incur high computational cost, and require the storage of a large number of parameters, which render them unfeasible for deployment in portable devices. To solve this problem, we propose a Global Supervised Low-rank Expansion (GSLRE) method and an Adaptive Drop-weight (ADW) technique to solve the problems of speed and storage capacity. We design a nine-layer CNN for HCCR consisting of 3755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0.21% drop in accuracy. In tests, the proposed algorithm can still surpass the best single-network performance reported thus far in the literature while requiring only 2.3MB for storage. Furthermore, when integrated with our effective forward implementation, the recognition of an offline character image takes only 9.7ms on a CPU. Compared with the state-of-the-art CNN model for HCCR, our approach is approximately 30 times faster, yet 10 times more cost efficient.


title: Accident Impact Prediction.

abstract: For the first time, real-time high-fidelity spatiotemporal data on the transportation networks of major cities have become available. This gold mine of data can be utilized to learn about the behavior of traffic congestion at different times and locations, potentially resulting in major savings in time and fuel, the two important commodities of the twenty-first century. According to FASANA Motion report (Report 2012), approximately 50 % of the freeway congestions are caused by nonrecurring issues, such as traffic accidents, weather hazard, special events, and construction zone closures. Hence, it is fairly important to quantify and predict the impact of traffic incidents on the surrounding traffic. This quantification can alleviate the significant financial and time losses attributed to traffic incidents, for example, it can be used by city transportation agencies for providing evacuation plan to eliminate potential congested grid locks, for effective dispatching of emergency vehicles, or even for long-term policy-making. Moreover, the predictive information can be either used by a driver directly to avoid potential gridlocks or consumed by a predictive route-planning algorithm (e.g., Demiryurek et al. 2011) to ensure a driver to select the best route from the start. The McKinsey report (McK 2011) predicts a worldwide consumer saving of more than $600 billion annually by 2020 for location-based services, where the biggest single consumer benefit will be from time and fuel savings from navigation services tapping into real-time traffic data. Therefore, let us consider a navigation system utilizing predictive route-planning algorithm as a next-generation consumer navigation system (incar or on smartphone). We notate such systems as ClearPath, as a motivating application, which can help drivers to effectively plan their routes in real time by avoiding the incidents’ impact areas. That is, suppose an accident is reported in real time (by crowdsourcing (WAZE 2014) or through agency reports or SIGALERTS (2013)) in front of a driver, but the accident is 20 min away. If we can effectively predict the impact of the accident, ClearPath would know that this accident would be cleared in the next 10 min. Thereby, ClearPath would guide the driver directly toward the accident because it knows that by the time the driver arrives in the area, there would be no accident.


title: Emergent Sources of Prebiotics: Seaweeds and Microalgae

abstract: In recent years, scientists have become aware that human microbiota, in general, and gut microbiota, in particular, play a major role in human health and diseases, such as obesity and diabetes, among others. A large number of evidence has come to light regarding the beneficial effects, either for the host or the gut microbiota, of some foods and food ingredients or biochemical compounds. Among these, the most promising seem to be polysaccharides (PS) or their derivatives, and they include the dietary fibers. Some of these PS can be found in seaweeds and microalgae, some being soluble fibers, such as alginates, fucoidans, carrageenans and exopolysaccharides, that are not fermented, at least not completely, by colonic microbiota. This review gives an overview of the importance of the dietary fibers, as well as the benefits of prebiotics, to human health. The potential of the PS from marine macro- and microalgae to act as prebiotics is discussed, and the different techniques to obtain oligosaccharides from PS are presented. The mechanisms of the benefits of fiber, in general, and the types and benefits of algal fibers in human health are highlighted. The findings of some recent studies that present the potential effects of prebiotics on animal models of algal biomass and their extracts, as well as oligo- and polysaccharides, are presented. In the future, the possibility of using prebiotics to modulate the microbiome, and, consequently, prevent certain human diseases is foreseen.


title: A Platform for Robotics Research Based on the Remote-Brained Robot Approach

abstract: Robots of the future will act in the real world. To realize robots with the common sense to do this will, it is generally believed, require massively parallel processing. This is a problem for those of us who want to do experiments with robots in the real world today—it is hard to build active, limber, situated robots when they have to carry along heavy brains. Our answer is “remote-brained robots.” A remote-brained robot is designed to have the brain and body separate, both conceptually and physically. It allows us to tie artificial intelligence (AI) with massive parallelism directly to the real world, enabling the verification of high-level AI techniques, which could previously only be used in simulation. Once the brain is placed remotely from the body, it encourages us to investigate many important research topics of embodied agents working in the real world. This paper describes such platform for future robotics and principal experiments done on the platform.


title: Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies

abstract: Abstract Clinical, experimental and neuroimaging studies indicate that the cerebellum is involved in neural processes beyond the motor domain. Cerebellar somatotopy has been shown for motor control, but topographic organization of higher-order functions has not yet been established. To determine whether existing literature supports the hypothesis of functional topography in the human cerebellum, we conducted an activation likelihood estimate (ALE) meta-analysis of neuroimaging studies reporting cerebellar activation in selected task categories: motor (n = 7 studies), somatosensory (n = 2), language (n = 11), verbal working memory (n = 8), spatial (n = 8), executive function (n = 8) and emotional processing (n = 9). In agreement with previous investigations, sensorimotor tasks activated anterior lobe (lobule V) and adjacent lobule VI, with additional foci in lobule VIII. Motor activation was in VIIIA/B; somatosensory activation was confined to VIIIB. The posterior lobe was involved in higher-level tasks. ALE peaks were identified in lobule VI and Crus I for language and verbal working memory; lobule VI for spatial tasks; lobules VI, Crus I and VIIB for executive functions; and lobules VI, Crus I and medial VII for emotional processing. Language was heavily right-lateralized and spatial peaks left-lateralized, reflecting crossed cerebro-cerebellar projections. Language and executive tasks activated regions of Crus I and lobule VII proposed to be involved in prefrontal-cerebellar loops. Emotional processing involved vermal lobule VII, implicated in cerebellar-limbic circuitry. These data provide support for an anterior sensorimotor vs. posterior cognitive/emotional dichotomy in the human cerebellum. Prospective studies of multiple domains within single individuals are necessary to better elucidate neurobehavioral structure–function correlations in the cerebellar posterior lobe.


title: Adaptive real-time anomaly detection with incremental clustering

abstract: Anomaly detection in information (IP) networks, detection of deviations from what is considered normal, is an important complement to misuse detection based on known attack descriptions. Performing anomaly detection in real-time places hard requirements on the algorithms used. First, to deal with the massive data volumes one needs to have efficient data structures and indexing mechanisms. Secondly, the dynamic nature of today's information networks makes the characterisation of normal requests and services difficult. What is considered as normal during some time interval may be classified as abnormal in a new context, and vice versa. These factors make many proposed data mining techniques less suitable for real-time intrusion detection. In this paper we present ADWICE, Anomaly Detection With fast Incremental Clustering, and propose a new grid index that is shown to improve detection performance while preserving efficiency in search. Moreover, we propose two mechanisms for adaptive evolution of the normality model: incremental extension with new elements of normal behaviour, and a new feature that enables forgetting of outdated elements of normal behaviour. These address the needs of a dynamic network environment such as a telecom management network. We evaluate the technique for network-based intrusion detection, using the KDD data set as well as on data from a telecom IP test network. The experiments show good detection quality and act as proof of concept for adaptation of normality.


title: Privacy: Is There An App for That?

abstract: Users of social networking sites (SNSs) increasingly must learn to negotiate privacy online with multiple service providers. Facebook's third-party applications (apps) add an additional layer of complexity and confusion for users seeking to understand and manage their privacy. We conducted a novel exploratory survey (conducted on Facebook as a Platform app) to measure how Facebook app users interact with apps, what they understand about how apps access and exchange their profile information, and how these factors relate to their privacy concerns. In our analysis, we paid special attention to our most knowledgeable respondents: given their expertise, would they differ in behaviors or attitudes from less knowledgeable respondents? We found that misunderstandings and confusion abound about how apps function and how they manage profile data. Against our expectations, knowledge or behavior weren't consistent predictors of privacy concerns with third-party apps or on SNSs in general. Instead, whether or not the respondent experienced an adverse privacy event on a social networking site was a reliable predictor of privacy attitudes.


title: Santhanam & Hartono/Lini<ing iT Capability to Firm Performance RESEARCH NOTE ISSUES IN LINKING INFORMATION TECHNOLOGY CAPABILITY TO FIRM PERFORMANCE^

abstract: The resource-based view has been proposed to investigate the impact of information technology (IT) investments on firm performance. Researchers have shown that a firm's ability to effectively leverage its IT investments by developing a strong IT capability can result in improved firm performance. We test the robustness of this approach and examine several related issues. Our results indicate that firms with superior IT capability indeed exhibit superior current and sustained firm performance when compared to average industry performance, even after adjusting for effects of prior firm performance. However, the differences in the results from various analyses suggest that the impact of "halo effects" and prior financial performance of firms must be taken into consideration in future tests of IT capability. Further, it is critical to develop theoretically derived multidimensional measures of IT capability in order to continue to apply the RBV approach to assess the impact of IT investments on firm performance.


title: Real-time price discovery in global stock, bond, and foreign exchange markets

abstract: Using a unique high-frequency futures dataset, we characterize the response of U.S., German and British stock, bond and foreign exchange markets to real-time U.S. macroeconomic news. We find that news produces conditional mean jumps; hence high-frequency stock, bond and exchange rate dynamics are linked to fundamentals. Equity markets, moreover, react differently to news depending on the stage of the business cycle, which explains the low correlation between stock and bond returns when averaged over the cycle. Hence our results qualify earlier work suggesting that bond markets react most strongly to macroeconomic news; in particular, when conditioning on the state of the economy, the equity and foreign exchange markets appear equally responsive. Finally, we also document important contemporaneous links across all markets and countries, even after controlling for the effects of macroeconomic news.


title: User position measures in social networks

abstract: Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network profile. The most important and representative measures are presented in the paper. It includes indegree centrality, proximity prestige, rank prestige, node position, outdegree centrality, eccentrality, closeness centrality, and betweenes centrality. Both feature analysis and experimental comparative studies revealed the general profile of selected measures.


title: 3D reconstruction of freely moving persons for re-identification with a depth sensor

abstract: In this work, we describe a novel method for creating 3D models of persons freely moving in front of a consumer depth sensor and we show how they can be used for long-term person re-identification. For overcoming the problem of the different poses a person can assume, we exploit the information provided by skeletal tracking algorithms for warping every point cloud frame to a standard pose in real time. Then, the warped point clouds are merged together to compose the model. Re-identification is performed by matching body shapes in terms of whole point clouds warped to a standard pose with the described method. We compare this technique with a classification method based on a descriptor of skeleton features and with a mixed approach which exploits both skeleton and shape features. We report experiments on two datasets we acquired for RGB-D re-identification which use different skeletal tracking algorithms and which are made publicly available to foster research in this new research branch.


Genomic amplification of 9p24.1 targeting JAK2, PD-L1, and PD-L2 is enriched in high-risk triple negative breast cancer


title: The disorderly exfoliated LDHs/PMMA nanocomposite synthesized by in situ bulk polymerization

abstract: Abstract A new nanocomposite—disorderly exfoliated layered double hydroxides/poly(methyl methacrylate) (LDHs/PMMA)—was prepared by a two-stage process with an in situ bulk polymerization of methyl methacrylate (MMA) in the presence of 10-undecenoate intercalated LDH (LDH-U). The LDH-U was prepared using the co-precipitation method. The structural and compositional details of the LDH-U were determined by X-ray diffraction (XRD), Fourier transformed infrared spectroscopy (FT-IR), 27 Al and 13 C magic-angle spinning nuclear magnetic resonance ( 27 Al and 13 C MAS NMR), elemental analysis (EA), inductively coupled plasma-mass (ICP) and transmission electron microscopy (TEM). During the preparation of LDHs/PMMA nanocomposite, XRD and TEM were also employed to monitor the formation of the exfoliated LDHs/PMMA nanocomposite and the dispersion behavior of the LDH layers, respectively. The pre-polymerization process exfoliates LDH layers within pre-polymer, according to the XRD and TEM results. Additionally, the LDHs/PMMA nanocomposite contained 5 wt% LDH-U, indicating that the LDH layers were well exfoliated and dispersed in the PMMA matrix in a disordered fashion.


title: Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization

abstract: We address the task of single depth image inpainting. Without the corresponding color images, previous or next frames, depth image inpainting is quite challenging. One natural solution is to regard the image as a matrix and adopt the low rank regularization just as color image inpainting. However, the low rank assumption does not make full use of the properties of depth images. A shallow observation inspires us to penalize the nonzero gradients by sparse gradient regularization. However, statistics show that though most pixels have zero gradients, there is still a non-ignorable part of pixels, whose gradients are small but nonzero. Based on this property of depth images, we propose a low gradient regularization method in which we reduce the penalty for small gradients while penalizing the nonzero gradients to allow for gradual depth changes. The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting. We compare our proposed low gradient regularization with the sparse gradient regularization. The experimental results show the effectiveness of our proposed approach.


title: What is SCADA

abstract: SCADA systems are widely used in industry for Supervisory Control and Data Acquisition of industrial processes. Companies that are members of standardisation committees (e.g. OPC, OLE for Process Control) and are thus setting the trends in matters of IT technologies generally develop these systems. As a matter of fact, they are now also penetrating the experimental physics laboratories for the controls of ancillary systems such as cooling, ventilation, power distribution, etc. More recently they were also applied for the controls of smaller size particle detectors such as the L3 muon detector and the NA48 experiment, to name just two examples at CERN. SCADA systems have made substantial progress over the recent years in terms of functionality, scalability, performance and openness such that they are an alternative to in house development even for very demanding and complex control systems as those of physics experiments. This paper describes SCADA systems in terms of their architecture, their interface to the process hardware, the functionality and application development facilities they provide. Some attention is paid to the industrial standards to which they abide, their planned evolution as well as the potential benefits of their use.


High Recall Open IE for Relation Discovery


title: Measurement of low-frequency noise of modern low-noise junction field effect transistors

abstract: A description of the low-noise measurement system designed for experimental investigation of low-frequency noise of modern low-noise junction field effect transistors (JFETs) at a frequency range from 0.5 Hz to 50 kHz is presented. The noise floor of the system in terms of noise voltage spectral density is about 1.3 nV//spl radic/Hz at frequency 0.5 Hz, 0.6 nV//spl radic/Hz at frequency 1 Hz, 0.08 nV//spl radic/Hz at frequency 10 Hz, and /spl les/0.04 nV//spl radic/Hz at frequencies 100 Hz-50 kHz. Such a system makes it possible to measure 1/f noise and channel thermal noise of modern very low noise n-channel JFETs. Specifically, discrete samples of JFETs, IF9030, IF1801, IF3601, and 2N4338, were investigated at the frequency range mentioned above. The system provided negligible contribution of all noise sources except intrinsic noise of measured JFETs which was a 1/f noise at frequencies f < 10 Hz and a channel thermal noise at frequencies f /spl ges/ 10 Hz. The measured values of the equivalent input noise voltage spectral density e/sub n/ of the best samples from investigated JFETs IF9030, IF1801, IF3601, and 2N4338 were 2.7, 3.2, 3.4, and 22 nV//spl radic/Hz at frequency 0.5 Hz; 1.5, 2.1, 1.8, and 14 nV//spl radic/Hz at frequency 1 Hz; and 0.5, 0.4, 0.6, and 3.2 nV//spl radic/Hz at frequencies more than 1 kHz, correspondingly.


title: UAV, do you see me? Establishing mutual attention between an uninstrumented human and an outdoor UAV in flight

abstract: We present the first demonstration of establishing mutual attention between an outdoor UAV in autonomous normal flight and an uninstrumented human user. We use the familiar periodic waving gesture as a signal to attract the UAV's attention. The UAV can discriminate this gesture from human walking and running that appears similarly periodic. Once a signaling person is observed and tracked, the UAV acknowledges that the user has its attention by hovering and performing a “wobble” behavior. Both parties are now ready for further interaction. The system works on-board the UAV using a single camera for input and is demonstrated working reliably in real-robot trials.


title: A review of split-bolus single-pass CT in the assessment of trauma patients

abstract: Purpose ::: The purpose of this study was to review and compare the image quality and radiation dose of split-bolus single-pass computed tomography(CT) in the assessment of trauma patients in comparison to standard multi-phase CT techniques.


title: Synthesizing number transformations from input-output examples

abstract: Numbers are one of the most widely used data type in programming languages. Number transformations like formatting and rounding present a challenge even for experienced programmers as they find it difficult to remember different number format strings supported by different programming languages. These transformations present an even bigger challenge for end-users of spreadsheet systems like Microsoft Excel where providing such custom format strings is beyond their expertise. In our extensive case study of help forums of many programming languages and Excel, we found that both programmers and end-users struggle with these number transformations, but are able to easily express their intent using input-output examples. ::: ::: In this paper, we present a framework that can learn such number transformations from very few input-output examples. We first describe an expressive number transformation language that can model these transformations, and then present an inductive synthesis algorithm that can learn all expressions in this language that are consistent with a given set of examples. We also present a ranking scheme of these expressions that enables efficient learning of the desired transformation from very few examples. By combining our inductive synthesis algorithm for number transformations with an inductive synthesis algorithm for syntactic string transformations, we are able to obtain an inductive synthesis algorithm for manipulating data types that have numbers as a constituent sub-type such as date, unit, and time. We have implemented our algorithms as an Excel add-in and have evaluated it successfully over several benchmarks obtained from the help forums and the Excel product team.


title: Alarm Prediction in LTE Networks

abstract: Antenna line alarms are a significant indicator for failing components in LTE base stations. By predicting such alarms it is possible to prepare for system repairs or for system resets in advance, which reduces the maintenance downtime for LTE operators when a hardware failure actually occurs. In this paper we present results from an antenna line alarm prediction method in a real LTE network. The prediction model is based on the behavior of a large set of performance counters (PMs) collected in a timeseries in the LTE base stations. We create the alarm prediction model by using the random forest search method and we reach and accuracy of 67-72% From this, we pinpoint the most influential PMs for predicting different types of alarms and we discuss how to use these PMs in future research.


title: Policy Optimization via Importance Sampling

abstract: Policy optimization is an effective reinforcement learning approach to solve continuous control tasks. Recent achievements have shown that alternating on-line and off-line optimization is a successful choice for efficient trajectory reuse. However, deciding when to stop optimizing and collect new trajectories is non-trivial as it requires to account for the variance of the objective function estimate. In this paper, we propose a novel model-free policy search algorithm, POIS, applicable in both control-based and parameter-based settings. We first derive a high-confidence bound for importance sampling estimation and then we define a surrogate objective function which is optimized off-line using a batch of trajectories. Finally, the algorithm is tested on a selection of continuous control tasks, with both linear and deep policies, and compared with the state-of-the-art policy optimization methods.


title: Multi-Scale Deep Compressive Sensing Network

abstract: With joint learning of sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at low subrates. This happens similarly in the multi-scale sampling scheme which also samples more low-frequency components. In this paper, we propose a multi-scale DCS convolutional neural network (MS-DCSNet) in which we convert image signal using multiple scale-based wavelet transform, then capture it through convolution block by block across scales. The initial reconstructed image is directly recovered from multi-scale measurements. Multi-scale wavelet convolution is utilized to enhance the final reconstruction quality. The network is able to learn both multi-scale sampling and multi-scale reconstruction, thus results in better reconstruction quality.


title: Acanthamoeba keratitis: confirmation of the UK outbreak and a prospective case-control study identifying contributing risk factors

abstract: Background/aims Acanthamoeba keratitis (AK) is a chronic debilitating corneal infection principally affecting contact lens (CL) users. Studies were designed to test claims that the UK incidence may have increased in 2012–2014 and to evaluate potential causes. Methods Annualised incidence data were collected from January 1984 to December 2016. Case-control study subjects were recruited between 14 April 2011 and 05 June 2017. Reusable CL users with AK were recruited retrospectively and prospectively. Controls were reusable CL users, recruited prospectively, with any disorder other than AK. Multivariable analysis of questionnaire data measured independent risk factors for AK. Results The current outbreak of AK started in 2010–2011 with an incidence threefold higher than in 2004–2009. Risk factors for AK were: Oxipol disinfection, CLs made of group IV CL materials, poor CL hygiene, deficient hand hygiene, use of CLs while swimming or bathing, being white British, and for those in social classes 4–9. Conclusion AK is a largely preventable disease. The current outbreak is unlikely to be due to any one of the identified risk factors in isolation. Improving CL and hand hygiene, avoiding CLs contamination with water and use of effective CL disinfection solutions, or daily disposable CLs, will reduce the incidence of AK. In the longer-term, water avoidance publicity for CL users can be expected to reduce the incidence further. Ongoing surveillance of AK numbers will identify changes in incidence earlier. Evaluation of Acanthamoeba contamination in end-user drinking water would contribute to our understanding of regional variations in the risk of exposure.


title: A Unified Framework for Contactless Hand Verification

abstract: Two-dimensional (2-D) hand-geometry features carry limited discriminatory information and therefore yield moderate performance when utilized for personal identification. This paper investigates a new approach to achieve performance improvement by simultaneously acquiring and combining three-dimensional (3-D) and 2-D features from the human hand. The proposed approach utilizes a 3-D digitizer to simultaneously acquire intensity and range images of the presented hands of the users in a completely contact-free manner. Two new representations that effectively characterize the local finger surface features are extracted from the acquired range images and are matched using the proposed matching metrics. In addition, the characterization of 3-D palm surface using SurfaceCode is proposed for matching a pair of 3-D palms. The proposed approach is evaluated on a database of 177 users acquired in two sessions. The experimental results suggest that the proposed 3-D hand-geometry features have significant discriminatory information to reliably authenticate individuals. Our experimental results demonstrate that consolidating 3-D and 2-D hand-geometry features results in significantly improved performance that cannot be achieved with the traditional 2-D hand-geometry features alone. Furthermore, this paper also investigates the performance improvement that can be achieved by integrating five biometric features, i.e., 2-D palmprint, 3-D palmprint, finger texture, along with 3-D and 2-D hand-geometry features, that are simultaneously extracted from the user's hand presented for authentication.


Evaluating Ontology-Mapping Tools: Requirements and Experience


title: Open innovation: State of the art and future perspectives

abstract: Abstract Open innovation has become one of the hottest topics in innovation management. This article intends to explore the limits in our understanding of the open innovation concept. In doing so, I address the questions of what (the content of open innovation), when (the context dependency) and how (the process). Open innovation is a rich concept, that can be implemented in many different ways. The context dependency of open innovation is one of the least understood topics; more research is needed on the internal and external environment characteristics affecting performance. The open innovation process relates to both the transition towards open innovation, and the various open innovation practices. As with any new concept, initial studies focus on successful and early adopters, are based on case studies, and descriptive. However, not all lessons learned from the early adopters may be applicable to following firms. Case study research increases our understanding of how things work and enables us to identify important phenomena. They should be followed by quantitative studies involving large samples to determine the relative importance of factors, to build path models to understand chains of effects, and to formally test for context dependencies. However, the evidence shows that open innovation has been a valuable concept for so many firms and in so many contexts, that it is on its way to find its final place in innovation management.


title: Improved Butler Matrix configuration for smart beamforming operations

abstract: In this work the authors propose a Butler Matrix (BM) based beamforming network (BFN) that feeds a linear antenna array. A BM 8×8 is integrated with a specific Switching Network (SN) and a group of Switched-Line Phase Shifters (SLPS), in order to properly drive the excitation of the antenna elements. The resulting configuration provides enhanced beamforming flexibility compared to a Switched Beam System (SBS) fed by a typical BM. Thus, a smart beamforming structure can be designed that combines the simplicity of a SBS with some of the advantages of an Adaptive Array System (AAS).