title: Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution

abstract: Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-arts.


title: Matching Networks for One Shot Learning

abstract: Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.


title: Learning Analytics Dashboards to Support Adviser-Student Dialogue

abstract: This paper presents LISSA (“Learning dashboard for Insights and Support during Study Advice”), a learning analytics dashboard designed, developed, and evaluated in collaboration with study advisers. The overall objective is to facilitate communication between study advisers and students by visualizing grade data that is commonly available in any institution. More specifically, the dashboard attempts to support the dialogue between adviser and student through an overview of study progress, peer comparison, and by triggering insights based on facts as a starting point for discussion and argumentation. We report on the iterative design process and evaluation results of a deployment in 97 advising sessions. We have found that the dashboard supports the current adviser-student dialogue, helps them motivate students, triggers conversation, and provides tools to add personalization, depth, and nuance to the advising session. It provides insights at a factual, interpretative, and reflective level and allows both adviser and student to take an active role during the session.


Graph Convolutional Neural Networks for ADME Prediction in Drug Discovery


title: User interface design of voice controlled consumer electronics

abstract: Today speech recognition of a small vocabulary can be realized so cost-effectively that the technology can penetrate into consumer electronics. But, as first applications that failed on the market show, it is by no means obvious how to incorporate voice control in a user interface. This paper addresses the issue of how to design a voice control so that the user perceives it as a benefit. User interface guidelines that are adapted or specific to voice control are presented. Then the process of designing a voice control in the user-centred approach is described. By means of two examples, the car stereo and telephone answering machine, it is shown how this is turned into practice.


title: Performance Comparison and Evaluation of Web Development Technologies in PHP, Python, and Node.js

abstract: Large scale, high concurrency, and vast amount of data are important trends for the new generation of website. Node.js becomes popular and successful to build data-intensive web applications. To study and compare the performance of Node.js, Python-Web and PHP, we used benchmark tests and scenario tests. The experimental results yield some valuable performance data, showing that PHP and Python-Web handle much less requests than that of Node.js in a certain time. In conclusion, our results clearly demonstrate that Node.js is quite lightweight and efficient, which is an idea fit for I/O intensive websites among the three, while PHP is only suitable for small and middle scale applications, and Python-Web is developer friendly and good for large web architectures. To the best of our knowledge, this is the first paper to evaluate these Web programming technologies with both objective systematic tests (benchmark) and realistic user behavior tests (scenario), especially taking Node.js as the main topic to discuss.


title: A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery

abstract: Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ::: ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to ::: control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv ::: Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source ::: Arduino board) is able to control the hand through character input associated with the taught actions of ::: the suite. This system provides evidence of the feasibility of brain signals being a viable approach to ::: controlling the chosen prosthetic. Results are then presented in the second framework. This latter one ::: allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the ::: system, clear visual representations of the performance and accuracy are presented in the results using a ::: confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with ::: various acquisition datasets were carried out and with a critical evaluation of the results given. Finally ::: depending on the classification of the brain signal a Python script outputs the driving command to the ::: Arduino to control the prosthetic. The proposed architecture performs overall good results for the design ::: and implementation of economically convenient BCI and prosthesis.


title: A review of feature selection methods with applications

abstract: Feature selection (FS) methods can be used in data pre-processing to achieve efficient data reduction. This is useful for finding accurate data models. Since exhaustive search for optimal feature subset is infeasible in most cases, many search strategies have been proposed in literature. The usual applications of FS are in classification, clustering, and regression tasks. This review considers most of the commonly used FS techniques. Particular emphasis is on the application aspects. In addition to standard filter, wrapper, and embedded methods, we also provide insight into FS for recent hybrid approaches and other advanced topics.


title: Touch projector: mobile interaction through video

abstract: In 1992, Tani et al. proposed remotely operating machines in a factory by manipulating a live video image on a computer screen. In this paper we revisit this metaphor and investigate its suitability for mobile use. We present Touch Projector, a system that enables users to interact with remote screens through a live video image on their mobile device. The handheld device tracks itself with respect to the surrounding displays. Touch on the video image is "projected" onto the target display in view, as if it had occurred there. This literal adaptation of Tani's idea, however, fails because handheld video does not offer enough stability and control to enable precise manipulation. We address this with a series of improvements, including zooming and freezing the video image. In a user study, participants selected targets and dragged targets between displays using the literal and three improved versions. We found that participants achieved highest performance with automatic zooming and temporary image freezing.


title: Reliability evaluation of the auxiliary electrical systems of power stations

abstract: This paper presents a novel approach to the reliability assessment of different designs of power station auxiliary electrical systems. This assessment evaluates the impact of the auxiliary system on the availability of the unit in terms of derated states of the unit. This technique permits the reliability cost to be calculated easily for different system designs. All realistic failure modes were taken into account. An efficient computer program using these techniques has been developed and was used to obtain the results presented in this paper.


title: Control Lists on Linux

abstract: Printed textiles and a process for production of resist effects on textile materials of mixed fibers of polyester and cellulose comprising impregnating the materials with a dye liquor containing dischargeable disperse dyestuffs and dischargeable reactive dyestuffs which react with the resisting agents, drying or partially drying the materials and subsequently printing a resist paste on the materials, and heat-treating the materials at temperatures of 100 DEG to 190 DEG C., the improvement comprises said dischargeable disperse dyestuffs are of the formula p0 wherein n is a number of 1 to 9; said dischargeable reactive dyestuffs being those containing reactive moieties of the formula -SO2-CH2-CH2-hal, -SO2-CH2-CH2-O-SO3M, -NH-SO2-CH2-CH2-O-SO3M or -SO2-CH=CH2 wherein M is hydrogen or a metal cation and hal is halogen; and the resist paste contains an alkali metal sulphite or an alkali metal bisulphite in combination with an alkali metal carbonate of alkali metal bicarbonate.


title: Onset of climate change at Last Glacial-Holocene transition: Role of the tropical Pacific

abstract: We study palaeoclimatic records from various sites spread around the earth, focusing on the start of the last glacial-interglacial transition. The warming, as recorded in the $\delta^{18}O$ record started first in the tropics, then propagated to the Antarctic and then finally to the Arctic. Our analysis of the data suggests that it took about 7.6 ka for onset of climate change to propagate globally. We propose that the tropical Pacific played a major role in initiating the warming in the tropics. We discuss mechanisms that could have transported this heat from the tropics to Antarctica and then to the Arctic during transition to the interglacial.


title: Intelligent web-based learning system with personalized learning path guidance

abstract: Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.


title: Improving logic obfuscation via logic cone analysis

abstract: Logic obfuscation can protect designs from reverse engineering and IP piracy. In this paper, a new attack strategy based on applying brute force iteratively to each logic cone is described and shown to significantly reduce the number of brute force key combinations that need to be tried by an attacker. It is shown that inserting key gates based on MUXes is an effective approach to increase security against this type of attack. Experimental results are presented quantifying the threat posed by this type of attack along with the relative effectiveness of MUX key gates in countering it.


title: Symbolic Dynamic Programming for Discrete and Continuous State MDPs

abstract: Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addressed automated decision-theoretic planning for DC-MDPs, optimal solutions have only been defined so far for limited settings, e.g., DC-MDPs having hyper-rectangular piecewise linear value functions. In this work, we extend symbolic dynamic programming (SDP) techniques to provide optimal solutions for a vastly expanded class of DC-MDPs. To address the inherent combinatorial aspects of SDP, we introduce the XADD — a continuous variable extension of the algebraic decision diagram (ADD) — that maintains compact representations of the exact value function. Empirically, we demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the first optimal automated solutions to DC-MDPs with linear and nonlinear piecewise partitioned value functions and showing the advantages of constraint-based pruning for XADDs.


title: Role of nanotechnology in agriculture with special reference to management of insect pests

abstract: Nanotechnology is a promising field of interdisciplinary research. It opens up a wide array of opportunities in various fields like medicine, pharmaceuticals, electronics and agriculture. The potential uses and benefits of nanotechnology are enormous. These include insect pests management through the formulations of nanomaterials-based pesticides and insecticides, enhancement of agricultural productivity using bio-conjugated nanoparticles (encapsulation) for slow release of nutrients and water, nanoparticle-mediated gene or DNA transfer in plants for the development of insect pest-resistant varieties and use of nanomaterials for preparation of different kind of biosensors, which would be useful in remote sensing devices required for precision farming. Traditional strategies like integrated pest management used in agriculture are insufficient, and application of chemical pesticides like DDT have adverse effects on animals and human beings apart from the decline in soil fertility. Therefore, nanotechnology would provide green and efficient alternatives for the management of insect pests in agriculture without harming the nature. This review is focused on traditional strategies used for the management of insect pests, limitations of use of chemical pesticides and potential of nanomaterials in insect pest management as modern approaches of nanotechnology.


title: An Improved Pulse Width Modulation Method for Chopper-Cell-Based Modular Multilevel Converters

abstract: In this paper, an improved pulse width modulation (PWM) method for chopper-cell (or half-bridge)based modular multilevel converters (MMCs) is proposed. This method can generate an output voltage with maximally 2N+1 (where N is the number of submodules in the upper or lower arm of MMC) levels, which is as great as that of the carrier-phase-shifted PWM (CPSPWM) method. However, no phase-shifted carrier is needed. Compared with the existing submodule unified pulse width modulated (SUPWM) method, the level number of the output voltage is almost doubled and the height of the step in the staircase voltage is reduced by 50%. Meanwhile, the equivalent switching frequency in the output voltage is twice that of the conventional SUPWM method. All these features lead to much reduced harmonic content in the output voltage. What is more, the voltages of the submodule capacitors can be well balanced without any close-loop voltage balancing controllers which are mandatory in the CPSPWM schemes. Simulation and experimental results on a MMC-based inverter show validity of the proposed method.


title: Preventing SQL injection attacks in stored procedures

abstract: An SQL injection attack targets interactive Web applications that employ database services. These applications accept user inputs and use them to form SQL statements at runtime. During an SQL injection attack, an attacker might provide malicious SQL query segments as user input which could result in a different database request. By using SQL injection attacks, an attacker could thus obtain and/or modify confidential/sensitive information. An attacker could even use a SQL injection vulnerability as a rudimentary IP/Port scanner of the internal corporate network. Several papers in literature have proposed ways to prevent SQL injection attacks in the application layer by examining dynamic SQL query semantics at runtime. However, very little emphasis is laid on securing stored procedures in the database layer which could also suffer from SQL injection attacks. Some papers in literature even refer to stored procedures as a remedy against SQL injection attacks. As stored procedures reside on the database front, the methods proposed by them cannot be applied to secure stored procedures themselves. In this paper, we propose a novel technique to defend against the attacks targeted at stored procedures. This technique combines static application code analysis with runtime validation to eliminate the occurrence of such attacks. In the static part, we design a stored procedure parser, and for any SQL statement which depends on user inputs, we use this parser to instrument the necessary statements in order to compare the original SQL statement structure to that including user inputs. The deployment of this technique can be automated and used on a need-only basis. We also provide a preliminary evaluation of the results of the technique proposed, as performed on several stored procedures in the SQL Server 2005 database.


title: Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs

abstract: Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions. Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S . We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.


title: Antiplatelet Agents for the Secondary Prevention of Ischemic Stroke or Transient Ischemic Attack: A Network Meta-Analysis

abstract: Stroke can cause high morbidity and mortality, and ischemic stroke (IS) and transient ischemic attack (TIA) patients have a high stroke recurrence rate. Antiplatelet agents are the standard therapy for these patients, but it is often difficult for clinicians to select the best therapy from among the multiple treatment options. We therefore performed a network meta-analysis to estimate the efficacy of antiplatelet agents for secondary prevention of recurrent stroke. We systematically searched 3 databases (PubMed, Embase, and Cochrane) for relevant studies published through August 2015. The primary end points of this meta-analysis were overall stroke, hemorrhagic stroke, and fatal stroke. A total of 30 trials were included in our network meta-analysis and abstracted data. Among the therapies evaluated in the included trials, the estimates for overall stroke and hemorrhagic stroke for cilostazol (Cilo) were significantly better than those for aspirin (odds ratio [OR] = .64, 95% credibility interval [CrI], .45-.91; OR = .23, 95% CrI, .08-.58). The estimate for fatal stroke was highest for Cilo plus aspirin combination therapy, followed by Cilo therapy. The results of our meta-analysis indicate that Cilo significantly improves overall stroke and hemorrhagic stroke in IS or TIA patients and reduces fatal stroke, but with low statistical significance. Our results also show that Cilo was significantly more efficient than other therapies in Asian patients; therefore, future trials should focus on Cilo treatment for secondary prevention of recurrent stroke in non-Asian patients.


title: TICSync: Knowing when things happened

abstract: Modern robotic systems are composed of many distributed processes sharing a common communications infrastructure. High bandwidth sensor data is often collected on one computer and served to many consumers. It is vital that every device on the network agrees on how time is measured. If not, sensor data may be at best inconsistent and at worst useless. Typical clocks in consumer grade PCs are highly inaccurate and temperature sensitive. We argue that traditional approaches to clock synchronization, such as the use of NTP are inappropriate in the robotics context. We present an extremely efficient algorithm for learning the mapping between distributed clocks, which typically achieves better than millisecond accuracy within just a few seconds. We also give a probabilistic analysis providing an upper-bound error estimate.


title: Multiband UWB trapezoidal antenna using U and Pi-shaped slots

abstract: This paper presents the analysis of CPW-fed trapezoidal monopole antenna added with parasitic elements and slots. From previous work, the proposed antenna altered by adding parasitic elements to enhance the operation bandwidth and achieved frequencies from 3.2GHz to 11.5GHz which covered Ultra Wide Bandwidth. This antenna is fabricated on FR4 for real measurement. Next experiment continues with slots insertion to generate notch and reconfigure frequency. U and π slots applied on trapezoidal patch. The antenna's performance observes through adjustment of slot's dimension.


title: Perspectives on Sensory Processing Disorder: A Call for Translational Research

abstract: This article explores the convergence of two fields, which have similar theoretical origins: a clinical field originally known as sensory integration and a branch of neuroscience that conducts research in an area also called sensory integration. Clinically, the term was used to identify a pattern of dysfunction in children and adults, as well as a related theory, assessment, and treatment method for children who have atypical responses to ordinary sensory stimulation. Currently the term for the disorder is Sensory Processing Disorder (SPD). In neuroscience, the term sensory integration refers to converging information in the brain from one or more sensory domains. A recent subspecialty in neuroscience labeled multisensory integration (MSI) refers to the neural process that occurs when sensory input from two or more different sensory modalities converge. Understanding the specific meanings of the term sensory integration intended by the clinical and neuroscience fields and the term multisensory integration in neuroscience is critical. A translational research approach would improve exploration of crucial research questions in both the basic science and clinical science. Refinement of the conceptual model of the disorder and the related treatment approach would help prioritize which specific hypotheses should be studied in both the clinical and neuroscience fields. The issue is how we can facilitate a translational approach between researchers in the two fields. Multidisciplinary, collaborative studies would increase knowledge of brain function and could make a significant contribution to alleviating the impairments of individuals with SPD and their families.


title: Social media in crisis management - The iSAR+ project survey

abstract: Social media together with still growing social media communities has become a powerful and promising solution in crisis and emergency management. Previous crisis events have proved that social media and mobile technologies used by citizens (widely) and public services (to some extent) have contributed to the post-crisis relief efforts. The iSAR+ EU FP7 project aims at providing solutions empowering citizens and PPDR (Public Protection and Disaster Relief) organizations in online and mobile communications for the purpose of crisis management especially in search and rescue operations. This paper presents the results of survey aiming at identification of preliminary end-user requirements in the close interworking with end-users across Europe.


title: Gender classification by combining clothing, hair and facial component classifiers

abstract: In this paper, we propose a novel gender classification framework, which utilizes not only facial features, but also external information, i.e. hair and clothing. Instead of using the whole face, we consider five facial components: forehead, eyes, nose, mouth and chin. We also design feature extraction methods for hair and clothing; these features have seldom been used in previous work because of their large variability. For each type of feature, we train a single support vector machine classifier with probabilistic output. The outputs of these classifiers are combined using various strategies, namely fuzzy integral, maximal, sum, voting, and product rule. The major contributions of this paper are (1) investigating the gender discriminative ability of clothing information; (2) using facial components instead of the whole face to obtain higher robustness for occlusions and noise; (3) exploiting hair and clothing information to facilitate gender classification. Experimental results show that our proposed framework improves classification accuracy, even when images contain occlusions, noise, and illumination changes.


title: Correlation Alignment for Unsupervised Domain Adaptation

abstract: In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces. It is also much simpler than other distribution matching methods. CORAL performs remarkably well in extensive evaluations on standard benchmark datasets. We first describe a solution that applies a linear transformation to source features to align them with target features before classifier training. For linear classifiers, we propose to equivalently apply CORAL to the classifier weights, leading to added efficiency when the number of classifiers is small but the number and dimensionality of target examples are very high. The resulting CORAL Linear Discriminant Analysis (CORAL-LDA) outperforms LDA by a large margin on standard domain adaptation benchmarks. Finally, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (DNNs). The resulting Deep CORAL approach works seamlessly with DNNs and achieves state-of-the-art performance on standard benchmark datasets. Our code is available at: https://github.com/VisionLearningGroup/CORAL.


title: A Kernel Theory of Modern Data Augmentation

abstract: Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding modern data augmentation techniques. We start by showing that for kernel classifiers, data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. We connect this general approximation framework to prior work in invariant kernels, tangent propagation, and robust optimization. Next, we explicitly tackle the compositional aspect of modern data augmentation techniques, proposing a novel model of data augmentation as a Markov process. Under this model, we show that performing $k$-nearest neighbors with data augmentation is asymptotically equivalent to a kernel classifier. Finally, we illustrate ways in which our theoretical framework can be leveraged to accelerate machine learning workflows in practice, including reducing the amount of computation needed to train on augmented data, and predicting the utility of a transformation prior to training.


title: Towards deep anchor learning

abstract: Anchor learning is a promising framework for electronic health record phenotyping and for predicting clinical state variables. In particular, anchor learning alleviates the problem of having to manually annotate (label) a large dataset (time consuming and expensive) by transforming certain key clinical observations (anchors) into noisy labels. Thus far, the predictive models in anchor learning have utilized rather simple features for the analysis. We hypothesize that anchor learning may benefit hugely from more powerful feature extraction. Recent advances in deep learning have illustrated neural networks' exceptional ability to learn powerful feature representations from data. In this paper, we propose the deep anchor learning framework, which uses a deep autoencoder to obtain stronger features for use in anchor learning. A support vector machine trained on the autoencoder features achieves state-of-the art performance on the task of postoperative delirium prediction from anchors.


title: Design of wideband miniaturized cross dipole antennas with chamfers

abstract: An approach to design wideband miniaturized cross dipole antenna is proposed. By introducing chamfers on the cross dipole. The lower resonant mode can move lower and the higher resonant mode can move higher. Therefore, wide impedance bandwidth and compact radiator size can be achieved simultaneously. Stable radiation pattern and stable gain are also obtained over the operating band. Two wideband cross dipole antennas with chamfers are proposed in this letter to verify the new method.


title: Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

abstract: A divide-and-conquer method classifying sentence types before sentiment analysis.Classifying sentence types by the number of opinion targets a sentence contain.A data-driven approach automatically extract features from input sentences. Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.


title: An empirical examination of factors influencing the intention to use mobile payment

abstract: With recent advances in mobile technologies, mobile commerce is having an increasingly profound impact on our daily lives, and beginning to offer interesting and advantageous new services. In particular, the mobile payment (m-payment) system has emerged, enabling users to pay for goods and services using their mobile devices (especially mobile phones) wherever they go. Mobile payment is anticipated to enjoy a bright future. In this paper, we reviewed the relevant literature regarding mobile payment services, analyzed the impact of m-payment system characteristics and user-centric factors on m-payment usage across different types of mobile payment users, and suggested new directions for future research in this emerging field. To analyze the adoption behaviors of m-payment users, we proposed an m-payment research model which consists of two user-centric factors (personal innovativeness and m-payment knowledge) and four m-payment system characteristics (mobility, reachability, compatibility, and convenience). We evaluated the proposed model empirically, applying survey data collected from m-payment users regarding their perceptions on mobile payment. We also attempted to categorize m-payment users into early and late adopters and delineated the different factors for these two types of adoptors that affect their intention to use m-payment. The results indicate that the strong predictors of the intention to use m-payment are perceived ease of use and perceived usefulness. All respondents reported that the compatibility of m-payment was not the primary reason in their decision to adopt it. Interestingly, our findings indicate that early adopters value ease of use, confidently relying on their own m-payment knowledge, whereas late adopters respond very positively to the usefulness of m-payment, most notably reachability and convenience of usage. Moreover, late adopters' perceived ease of use is influenced by personal innovativeness, which can probably be best explained by the fact that innovative late adopters are tech-savvy and feel confident to use m-payment technologies for their needs. Our study will assist managers in implementing appropriate business models and service strategies for different m-payment user groups, allowing them to exert appropriate time, effort, and investment for m-payment system development. Our study also provides directions for future mobile payment-related studies.


title: Tabu Search: A Tutorial

abstract: Tabu search is a “higher level” heuristic procedure for solving optimization problems, designed to guide other methods (or their component processes) to escape the trap of local optimality. Tabu search has obtained optimal and near optimal solutions to a wide variety of classical and practical problems in applications ranging from scheduling to telecommunications and from character recognition to neural networks. It uses flexible structures memory (to permit search information to be exploited more thoroughly than by rigid memory systems or memoryless systems), conditions for strategically constraining and freeing the search process (embodied in tabu restrictions and aspiration criteria), and memory functions of varying time spans for intensifying and diversifying the search (reinforcing attributes historically found good and driving the search into new regions). Tabu search can be integrated with branch-and-bound and cutting plane procedures, and it has the ability to start with a simple implementation th...


title: Optical Flow Based Urban Road Vehicle Tracking

abstract: Vehicle tracking is an important part in intelligent transportation surveillance. But now vehicle tracking faces with the problems such as scale change, the interference of similar color, low resolution video data and so on. In this paper an improved Markov chain Monte Carlo(MCMC) named optical flow MCMC(OF-MCMC) sampling tracking algorithm is proposed for vehicle tracking. First, we use the optical flow method to get the moving direction of the vehicle in initial frames, which can solve the problem of scale change, what's more the optical flow method can get the moving speed of the vehicle which replaces the second-order autoregressive motion model owing to the non-parameter characteristic. Second, when calculating whether one particle is accepted or not, a distance factor is considered, which can relieve the interference of similar vehicle nearby. Finally, to deal with vehicle tracking in low resolution of the video data, we generate a more accurate feature template with different features weighted to get better tracking results. Experimental results show that the proposed tracking algorithm has better performance than some traditional ones.


title: Efficient SMQT features for snow-based classification on face detection and character recognition tasks

abstract: Face detection using local successive mean quantization transform (SMQT) features and the sparse network of winnows (SNoW) classifier has received interest in the computer vision community due to its success under varying illumination conditions. Recent work has also demonstrated the effectiveness of this classification technique for character recognition tasks. However, heavy storage requirements of the SNoW classifier necessitate the development of efficient techniques to reduce storage and computational requirements. This study shows that the SNoW classifier built with only a limited number of distinguishing SMQT features provides comparable performance to the original dense snow classifier. Initial results using the well-known CMU-MIT facial image database and a private character database are used to demonstrate the effectiveness of the proposed method.


Reptiles and amphibians : their care and behaviour


title: Subpixel rendering for the pentile display based on the human visual system

abstract: Recently, with the increased demand for low-power displays regarding portable devices, the RGBG PenTile display is popularly utilized. However, unlike the traditional RGB-stripe display with its three color channels, each pixel of the RGBG PenTile display comprises only two color channels, thereby causing the color leakage image distortion. To cope with this problem, most of the conventional methods employ a preprocessing filter for subpixel rendering; however, these filters cannot remove the color leakage completely, and they also result in the blurring artifacts. In this paper, a novel approach to obtain the preprocessing filter is presented. We formulate a filter design method as a minimum mean square error problem and derive an optimal preprocessing filter that is based on the human visual system (HVS) as follows; first, two perceived images indicating how human recognizes the images on the RGB and RGBG displays are generated and then the difference between the two perceived images is minimized to derive the optimal filter. In addition, in order to prevent the blurring artifact, the proposed filter is applied to the previously detected color-leakage region only. Experimental results demonstrate that the proposed method outperforms the conventional methods in terms of both the subjective and objective image quality.


title: Direct Remaining Useful Life Estimation Based on Support Vector Regression

abstract: Prognostics is a major activity in the field of prognostics and health management. It aims at increasing the reliability and safety of systems while reducing the maintenance cost by providing an estimate of the current health status and remaining useful life (RUL). Classical RUL estimation techniques are usually composed of different steps: estimations of a health indicator, degradation states, a failure threshold, and finally the RUL. In this work, a procedure that is able to estimate the RUL of equipment directly from sensor values without the need for estimating degradation states or a failure threshold is developed. A direct relation between sensor values or health indicators is modeled using a support vector regression. Using this procedure, the RUL can be estimated at any time instant of the degradation process. In addition, an offline wrapper variable selection is applied before training the prediction model. This step has a positive impact on the accuracy of the prediction while reducing the computational time compared to existing indirect RUL prediction methods. To assess the performance of the proposed approach, the Turbofan dataset, widely considered in the literature, is used. Experimental results show that the performance of the proposed method is competitive with other existing approaches.


title: Use of locality sensitive hashing (LSH) algorithm to match Web of Science and Scopus

abstract: A novel hashing algorithm is applied to match two prominent and important bibliographic databases at the paper level. In the literature, such tasks have been studied and conducted many times, but relying only on journal information due to massive volume of indexed publications. As a result of paper based match, missing or erroneous items can be completed from other source or the overlap can be measured more reliably. In this context, we focus on measuring the overlap between Clarivate Analytics Web of Science (WoS) and Elsevier’s Scopus at the paper level. Our focus is on detecting exact matches, that is, no false positives are tolerated at all. To this end, we follow a twofold matching procedure. First, a locality sensitive hashing algorithm is applied, which provides fast approximate nearest neighbours and similarities, in order to obtain WoS-Scopus pair suggestions. Second, for each suggested pair, different heuristics are applied to identify those pair of records that indeed refer to the same publication. We observe that at least 74% of WoS publications are also indexed by Scopus. The percentage increases to 92% when only the cited publications are retained. The overlapped WoS records are also presented based on Institute for Scientific Information subject categories (SC). Of those, three big SCs, whose overlap ratios are relatively low, are chosen and examined in detail. Last but not the least, it takes just about an hour to match 14.2 million versus 19.6 million publications from a publication year range of 2004–2013 in a high performance computer environment.


title: A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

abstract: A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities.


title: Robust principal component analysis based on low-rank and block-sparse matrix decomposition

abstract: In this paper, we propose a convex program for low-rank and block-sparse matrix decomposition. Potential applications include outlier detection when certain columns of the data matrix are outliers. We design an algorithm based on the augmented Lagrange multiplier method to solve the convex program. We solve the subproblems involved in the augmented Lagrange multiplier method using the Douglas/Peaceman-Rachford (DR) monotone operator splitting method. Numerical simulations demonstrate the accuracy of our method compared with the robust principal component analysis based on low-rank and sparse matrix decomposition.


title: Creating Smart Environments: Analysis of Improving Security on Smart Homes

abstract: The more we immerse ourselves in the 'Digital Transformation', social and business, the more the new technologies that are launched to the market arise. Thinking about the concept of 'housing of the future', 'smart environments' or 'autonomous cars' may seem somewhat distant or perhaps very few people enjoy, but it is not. Above all, it is a phenomenon that is happening thanks to the development of the Internet of Things (IoT). The development of intelligent devices has been changing the behavioral patterns of users, such as using smart devices for everything to collect information that we rarely knew. However, with the introduction of IoT, we will be experiencing giant challenges to secure and protect its advanced information services due to the significant increase of the cyber-attacks, and number of interconnected resources that can be a double-edged sword. The purpose of this paper is to present a solution to this problem implementing a smart hub, complemented with an analytical review of security in all the devices interconnected focused in smart homes. It also presents how vulnerable and exposed we are with the interconnection of all our devices, identifying limitations and research challenges.


12 Explaining Differences between Groups:


title: Compact Neighborhood Index for Subgraph Queries in Massive Graphs

abstract: Subgraph queries also known as subgraph isomorphism search is a fundamental problem in querying graph-like structured data. It consists to enumerate the subgraphs of a data graph that match a query graph. This problem arises in many real-world applications related to query processing or pattern recognition such as computer vision, social network analysis, bioinformatic and big data analytic. Subgraph isomorphism search knows a lot of investigations and solutions mainly because of its importance and use but also because of its NP-completeness. Existing solutions use filtering mechanisms and optimise the order within witch the query vertices are matched on the data vertices to obtain acceptable processing times. However, existing approaches are iterative and generate several intermediate results. They also require that the data graph is loaded in main memory and consequently are not adapted to large graphs that do not fit into memory or are accessed by streams. To tackle this problem, we propose a new approach based on concepts widely different from existing works. Our approach distills the semantic and topological information that surround a vertex into a simple integer. This simple vertex encoding that can be computed and updated incrementally reduces considerably intermediate results and avoid to load the entire data graph into main memory. We evaluate our approach on several real-word datasets. The experimental results show that our approach is efficient and scalable.


title: Autonomic mobile networks: The use of artificial intelligence in wireless communications

abstract: Recent years, the wireless mobile communication technology develops rapidly, results in wireless information explosion. The network infrastructure is more and more complicate. It is inefficient and expansive to use the traditional operation and management methods which mainly relies on humans in the future wireless networks. It is promising to make the network self-operation, self-configuration, self-management. Fortunately, the Artificial Intelligence (AI) technique is a promising way to realize the radio network automation. In this paper, we give a general description of the state-of-the-art of the wireless communication systems and the need for intelligence functionalities. Then, we investigate the recent advances on the application of artificial intelligence technique in wireless communication domains. We believe that AI-empowered future network will accelerate the development of the ICT industry.


title: A Two Concentric Slot Loop Based Connected Array MIMO Antenna System for 4G/5G Terminals

abstract: In this paper, an integrated design with a multiple-input multiple-output (MIMO) antenna system for fourth generation (4G) and fifth generation (5G) applications is presented. The proposed design contains a two-element slot-based MIMO antenna system for 4G and a connected antenna array (CAA)-based two-element MIMO antenna system for a potential 5G band. Two rectangular loops are etched on the periphery of the ground plane. The top and bottom portions of the thin loops act as the two 4G MIMO antennas, while parts of their sides are acting as 5G arrays. The antenna system is fabricated on a commercially available Roger 4350 substrate with $\epsilon _{r}$ equal to 3.5, while the dimensions of the board are $100\times 60 \times 0.76$ mm3 representing a typical smart phone back plane size. The integrated antenna system covers multibands at 4G with a combined bandwidth of 1.565 GHz (−6 dB BW) in addition to the band between 16.50 and 17.80 GHz for 5G. The design is planar, low profile, simple, and compact in structure making suitable for wireless handheld devices and mobile terminals. The measured gain at 3.46 GHz was at least 2.22 dBi and at 17 GHz was 8 dBi for the 4G and 5G MIMO antenna systems, respectively. The envelope correlation coefficient was also calculated from the measured 3-D patterns and showed good MIMO performance. This is the first integrated 4G/5G MIMO antenna system with below 6 GHz and above 10-GHz covered bands using CAA.


title: A New Intrusion Detection Mechanism in SELinux

abstract: A new intrusion detection mechanism in SELinux based on dual-Markov chain and triggered by critical system calls is put forward in this paper. The mechanism integrates access control with intrusion detection by Linux Security Module (LSM) in SELinux and considers both the global behaviour characteristics and local behaviour characteristics. Theoretical analysis and experimental results show that the mechanism we propose is able to integrate access control with intrusion detection effectively and gets a preferable efficiency of intrusion detection, thus enhancing embedded system security.


Part-invariant Model for Music Generation and Harmonization


title: Dual-loop 2-step ZQ calibration for dedicated power supply voltage in LPDDR4 SDRAM

abstract: This paper presents a dual-loop 2-step ZQ calibration scheme with 20nm DRAM process to support dedicated supply voltage (VDD, VDDQ). The proposed calibration scheme maintains a target value of on-die termination (ODT) in DQ/CA regardless of the supply-voltage variations which are caused by dynamic voltage frequency switching (DVFS) and alleviates the calibration time which is increased by insertion of additional CA calibration. The offset of a comparator is averaged out by fraction-referred input switching-then-averaging scheme (FISA). And code-referred periodic ZQ update (CPZU) scheme can track the VT variation while minimizing the interference.


title: Semantic video segmentation: Exploring inference efficiency

abstract: We explore the efficiency of the CRF inference beyond image level semantic segmentation and perform joint inference in video frames. The key idea is to combine best of two worlds: semantic co-labeling and more expressive models. Our formulation enables us to perform inference over ten thousand images within seconds and makes the system amenable to perform video semantic segmentation most effectively. On CamVid dataset, with TextonBoost unaries, our proposed method achieves up to 8% improvement in accuracy over individual semantic image segmentation without additional time overhead. The source code is available at this https URL


title: A Controllable Bidirectional Battery Charger for Electric Vehicles with Vehicle-to-Grid Capability

abstract: This paper proposes a comprehensive methodology for the design of a controllable electric vehicle charger capable of making the most of the interaction with an autonomous smart energy management system (EMS) in a residential setting. Autonomous EMSs aim achieving the potential benefits associated with energy exchanges between consumers and the grid, using bidirectional and power-controllable electric vehicle chargers. A suitable design for a controllable charger is presented, including the sizing of passive elements and controllers. This charger has been implemented using an experimental setup with a digital signal processor to validate its operation. The experimental results obtained foresee an adequate interaction between the proposed charger and a compatible autonomous EMS in a typical residential setting.


title: RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model

abstract: Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results.


title: How Good Are Query Optimizers, Really?

abstract: Finding a good join order is crucial for query performance. In this paper, we introduce the Join Order Benchmark (JOB) and experimentally revisit the main components in the classic query optimizer architecture using a complex, real-world data set and realistic multi-join queries. We investigate the quality of industrial-strength cardinality estimators and find that all estimators routinely produce large errors. We further show that while estimates are essential for finding a good join order, query performance is unsatisfactory if the query engine relies too heavily on these estimates. Using another set of experiments that measure the impact of the cost model, we find that it has much less influence on query performance than the cardinality estimates. Finally, we investigate plan enumeration techniques comparing exhaustive dynamic programming with heuristic algorithms and find that exhaustive enumeration improves performance despite the sub-optimal cardinality estimates.


title: An Empirical Study of Learning to Rank for Entity Search

abstract: This work investigates the effectiveness of learning to rank methods for entity search. Entities are represented by multi-field documents constructed from their RDF triples, and field-based text similarity features are extracted for query-entity pairs. State-of-the-art learning to rank methods learn models for ad-hoc entity search. Our experiments on an entity search test collection based on DBpedia confirm that learning to rank methods are as powerful for ranking entities as for ranking documents, and establish a new state-of-the-art for accuracy on this benchmark dataset.


title: Automatic question generation from text - an aid to independent study

abstract: This report describes an experimental computer-based educational system called automatic question generation (AUTOQUEST) for assisting independent study of written text. Studies of reading comprehension have shown that retention of material is enhanced if the student is periodically required to answer questions about what he has read (Anderson & Biddle, 1975; Anderson et al., 1974; Alessi et al., 1974; Anderson et al., 1975a, 1975b). This principle has been employed in computer-managed instruction, but it requires considerable human effort to prepare the questions.


A New Implementation of Deep Neural Networks for Optical Character Recognition and Face Recognition


title: Introduction to Optical Music Recognition: Overview and Practical Challenges

abstract: Music has been always an integral part of human culture. In our computer age, it is not surprising that there is a growing inter- est to store music in a digitized form. Optical music recognition (OMR) refers to a discipline that investigates music score recognition systems. This is similar to well-known optical character recognition systems, ex- cept OMR systems try to automatically transform scanned sheet music into a computer-readable format. In such a digital format, semantic in- formation is also stored (instrumentation, notes, pitches and duration, contextual information, etc.). This article introduces the OMR field and presents an overview of the relevant literature and basic techniques. Prac- tical challenges and questions arising from the automatic recognition of music notation and its semantic interpretation are discussed as well as the most important open issues.


title: Material and energy recovery in integrated waste management systems. An evaluation based on life cycle assessment.

abstract: This paper reports the environmental results, integrated with those arising from mass and energy balances, of a research project on the comparative analysis of strategies for material and energy recovery from waste, funded by the Italian Ministry of Education, University and Research. The project, involving the cooperation of five University research groups, was devoted to the optimisation of material and energy recovery activities within integrated municipal solid waste (MSW) management systems. Four scenarios of separate collection (overall value of 35%, 50% without the collection of food waste, 50% including the collection of food waste, 65%) were defined for the implementation of energetic, environmental and economic balances. Two sizes of integrated MSW management system (IWMS) were considered: a metropolitan area, with a gross MSW production of 750,000 t/year and an average province, with a gross MSW production of 150,000 t/year. The environmental analysis was conducted using Life Cycle Assessment methodology (LCA), for both material and energy recovery activities. In order to avoid allocation we have used the technique of the expansion of the system boundaries. This means taking into consideration the impact on the environment related to the waste management activities in comparison with the avoided impacts related to the saving of raw materials and primary energy. Under the hypotheses of the study, both for the large and for the small IWMS, the energetic and environmental benefits are higher than the energetic and environmental impacts for all the scenarios analysed in terms of all the indicators considered: the scenario with 50% separate collection in a drop-off scheme excluding food waste shows the most promising perspectives, mainly arising from the highest collection (and recycling) of all the packaging materials, which is the activity giving the biggest energetic and environmental benefits. Main conclusions of the study in the general field of the assessment of the environmental performance of any integrated waste management scheme address the importance of properly defining, beyond the design value assumed for the separate collection as a whole, also the yields of each material recovered; particular significance is finally related to the amount of residues deriving from material recovery activities, resulting on average in the order of 20% of the collected materials.


title: Improving Delivery Ratio with a Handoff-Based Deterministic Routing Protocol (HDRP) in DTN

abstract: In Delay and Disruption Tolerant Networking (DTN), Epidemic, PRoPHET and Spray and Wait protocols work by distributing one or more message copies (Bundles). They can naturally adapt to the mobile situation where the destination node moves from an old connection endpoint to a new connection endpoint because any Bundle copy may luckily be able to reach the new connection endpoint where the mobile node is newly connected. However they suffer from low delivery ratio because their routing decisions are based on the local knowledge given by the next hop node and the forwarding is done opportunistically. To solve this problem, we propose a Handoff-based Deterministic Routing Protocol (HDRP) where the handoff mechanism includes the registration of locations by mobile nodes and backward propagation and caching of these locations at each of the routers in the experienced route. Our simulation results indicate that HDRP outperforms other existing protocols providing a high delivery ratio.


title: Research for the Task Scheduling Algorithm Optimization based on Hybrid PSO and ACO for Cloud Computing

abstract: In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.


title: Learning Fair Representations

abstract: We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.


title: User Perceptions of Sharing, Advertising, and Tracking

abstract: Extending earlier work, we conducted an online user study to investigate users’ understanding of online behavioral advertising (OBA) and tracking prevention tools (TPT), and whether users’ willingness to share data with advertising companies varied depending on the type of first party website. We presented results of 368 participant responses across four types of websites an online banking site, an online shopping site, a search engine and a social networking site. In general, we identified that participants had positive responses for OBA and that they demonstrated clear preferences for which classes of information they would like to disclose online. Our results generalize over a variety of website categories containing data with different levels of sensitivity, as opposed to only the medical context as was shown in previous work by Leon et al. In our study, participants’ privacy attitudes significantly dominated their sharing willingness. Interestingly, participants appreciated the idea of user-customized targeted ads and some would be more willing to share data if given prior control mechanisms for tracking protection tools.


title: Flexible Tactile Sensing Based on Piezoresistive Composites: A Review

abstract: The large expansion of the robotic field in the last decades has created a growing interest in the research and development of tactile sensing solutions for robot hand and body integration. Piezoresistive composites are one of the most widely employed materials for this purpose, combining simple and low cost preparation with high flexibility and conformability to surfaces, low power consumption, and the use of simple read-out electronics. This work provides a review on the different type of composite materials, classified according to the conduction mechanism and analyzing the physics behind it. In particular piezoresistors, strain gauges, percolative and quantum tunnelling devices are reviewed here, with a perspective overview on the most used filler types and polymeric matrices. A description of the state-of-the-art of the tactile sensor solutions from the point of view of the architecture, the design and the performance is also reviewed, with a perspective outlook on the main promising applications.


title: Ultrafast response humidity sensor using supramolecular nanofibre and its application in monitoring breath humidity and flow

abstract: Measuring humidity in dynamic situations calls for highly sensitive fast response sensors. Here we report, a humidity sensor fabricated using solution processed supramolecular nanofibres as active resistive sensing material. The nanofibres are built via self- assembly of donor and acceptor molecules (coronene tetracarboxylate and dodecyl methyl viologen respectively) involved in charge transfer interactions. The conductivity of the nanofibre varied sensitively over a wide range of relative humidity (RH) with unprecedented fast response and recovery times. Based on UV-vis, XRD and AFM measurements, it is found that the stacking distance in the nanofibre decreases slightly while the charge transfer band intensity increases, all observations implying enhanced charge transfer interaction and hence the conductivity. It is demonstrated to be as a novel breath sensor which can monitor the respiration rate. Using two humidity sensors, a breath flow sensor was made which could simultaneously measure RH and flow rate of exhaled nasal breath. The integrated device was used for monitoring RH in the exhaled breath from volunteers undergoing exercise and alcohol induced dehydration.


title: Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data

abstract: Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database. However, speech synthesis and voice conversion paradigms that are not considered in the ASVspoof2015 database are appearing. Such examples include direct waveform modelling and generative adversarial networks. We also need to investigate the feasibility of training spoofing systems using only low-quality found data. For that purpose, we developed a generative adversarial network-based speech enhancement system that improves the quality of speech data found in publicly available sources. Using the enhanced data, we trained state-of-the-art text-to-speech and voice conversion models and evaluated them in terms of perceptual speech quality and speaker similarity. The results show that the enhancement models significantly improved the SNR of low-quality degraded data found in publicly available sources and that they significantly improved the perceptual cleanliness of the source speech without significantly degrading the naturalness of the voice. However, the results also show limitations when generating speech with the low-quality found data.


title: Explaining the “ Identifiable Victim Effect ”

abstract: It is widely believed that people are willing to expend greater resources to save the lives of identified victims than to save equal numbers of unidentified or statistical victims. There are many possible causes of this disparity which have not been enumerated previously or tested empirically. We discuss four possible causes of the “identifiable victim effect” and present the results of two studies which indicate that the most important cause of the disparity in treatment of identifiable and statistical lives is that, for identifiable victims, a high proportion of those at risk can be saved.


Multicolor and Erasable DNA Photolithography


A New Class of Parallel Two-level Nonlinear Schwarz Preconditioned Inexact Newton Algorithms 1


title: Boundary-Seeking Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not work for discrete data. We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator. The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs). We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation. In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training.


title: Data Security Using LSB Steganography and Vigenere Chiper in an Android Environment

abstract: LSB steganography and Vigenere chiper methods are integrated in used for data security validation in this study. This approach used Arithmetic Coding method for data compression and data decompression. To maintain the authenticity of the data file, a hash function (SHA 256) technique was added. This paper presents a prototype called Ste-Chy as a proof of concept of the combination of these techniques. This approach helps the user in terms of the exchange of confidential data through an online share in Android-based media. For the confidential authentication purpose, the confidential message is hidden togetherwith the target image. The quality of the original image and stego images in this work produce an image picture in an acceptable level for the user. The bigger the secret of the message, the compression will produce higher compression ratio. With this approach, security process of exchange of confidential message shared through the online share smartphone is considerably secured especially in an android environment.


title: Empathy and Judging Other's Pain: An fMRI Study of Alexithymia

abstract: Because awareness of emotional states in the self is a prerequisite to recognizing such states in others, alexithymia (ALEX), difficulty in identifying and expressing one’s own emotional states, should involve impairment in empathy. Using functional magnetic resonance imaging (fMRI), we compared an ALEX group (n 5 16) and a non-alexithymia (non-ALEX) group (n 5 14) for their regional hemodynamic responses to the visual perception of pictures depicting human hands and feet in painful situations. Subjective pain ratings of the pictures and empathy-related psychological scores were also compared between the 2 groups. The ALEX group showed less cerebral activation in the left dorsolateral prefrontal cortex (DLPFC), the dorsal pons, the cerebellum, and the left caudal anterior cingulate cortex (ACC) within the pain matrix. The ALEX group showed greater activation in the right insula and inferior frontal gyrus. Furthermore, alexithymic participants scored lower on the pain ratings and on the scores related to mature empathy. In conclusion, the hypofunction in the DLPFC, brain stem, cerebellum, and ACC and the lower pain-rating and empathy-related scores in ALEX are related to cognitive impairments, particularly executive and regulatory aspects, of emotional processing and support the importance of self-awareness in empathy.


title: Accurate quadruple-gamma-curve correction for line inversion-based mobile TFT-LCD driver ICS

abstract: A line inversion-based gamma-curve correction scheme is presented for use in mobile TFT-LCD driver ICs. Novel voltage-symmetric gray curve synthesis (VSGS) method using a polarity-inverting gray voltage generator and variable resistor-tap biasing (VRTB) scheme using multioutput drivers have been adopted for accurately correcting gray curves for quadruple gamma values of y1.0, y1.8, y2.2, and y2.5. A test chip fabricated in a 6 V/0.7-μm triple-well CMOS process indicated that a gray voltage error was less than +8 mV, a gray-to-gray error-voltage difference was under +3 mV, and a pin-to-pin variation of each source driver output was +5 mV1.


title: Intent-aware long-term prediction of pedestrian motion

abstract: We present a method to predict long-term motion of pedestrians, modeling their behavior as jump-Markov processes with their goal a hidden variable. Assuming approximately rational behavior, and incorporating environmental constraints and biases, including time-varying ones imposed by traffic lights, we model intent as a policy in a Markov decision process framework. We infer pedestrian state using a Rao-Blackwellized filter, and intent by planning according to a stochastic policy, reflecting individual preferences in aiming at the same goal.


title: Clinical significance of cytomegalovirus (CMV) antigenemia in the prediction and diagnosis of CMV gastrointestinal disease after allogeneic hematopoietic stem cell transplantation

abstract: To evaluate the clinical significance of a cytomegalovirus (CMV) antigenemia assay in the prediction and diagnosis of CMV gastrointestinal (CMV-GI) disease after hematopoietic stem cell transplantation (HSCT), 19 allogeneic HSCT recipients developing CMV-GI disease were retrospectively reviewed. All patients were monitored by a CMV antigenemia assay, at least once weekly after engraftment. The median onset of CMV-GI disease occurred 31 days post transplant (range: 19-62). Only four of 19 patients (21%) developed a positive CMV antigenemia test before developing CMV-GI diseases. Although all 19 patients subsequently developed positive CMV antigenemia tests during their clinical courses, the values remained at a low-level in nine (47%) patients. Among the 14 patients in whom results of real-time polymerase chain reaction (PCR) were available, seven (50%) yielded positive results of real-time PCR before developing CMV-GI disease. In contrast to the values of CMV antigenemia, all 14 patients exclusively yielded high viral loads (median: 2.8 x 10(4) copies/ml plasma). We conclude that CMV antigenemia testing has limited value in prediction or early diagnosis of CMV-GI disease, and that real-time PCR could have a more diagnostic significance.


title: SPIDER: stealthy binary program instrumentation and debugging via hardware virtualization

abstract: The ability to trap the execution of a binary program at desired instructions is essential in many security scenarios such as malware analysis and attack provenance. However, an increasing percent of both malicious and legitimate programs are equipped with anti-debugging and anti-instrumentation techniques, which render existing debuggers and instrumentation tools inadequate. In this paper, we present Spider, a stealthy program instrumentation framework which enables transparent, efficient and flexible instruction-level trapping based on hardware virtualization. Spider uses invisible breakpoint, a novel primitive we develop that inherits the efficiency and flexibility of software breakpoint, and utilizes hardware virtualization to hide its side-effects from the guest. We have implemented a prototype of Spider on KVM. Our evaluation shows that Spider succeeds in remaining transparent against state-of-the-art anti-debugging and anti-instrumentation techniques; the overhead of invisible breakpoint is comparable with traditional hardware breakpoint. We also demonstrate Spider's usage in various security applications.


title: Local computations with probabilities on graphical structures and their application to expert systems

abstract: The invention comprises pigments of the general Formula I in which each X is hydrogen, chlorine or bromine, A is (a) an aliphatic hydrocarbon radical, (b) the phenylene group or (c) the naphthylene group and R is identical with the 1: 2-phthaloylcarbazole-aminocarbonyl radical shown in the above formula when A has the meaning defined under (a), and R is alkoxy, alkylamino, alkoylamino, trifluoroalkyl, alkylsulphonyl or fluoro when A has the meaning defined under (b) or (c), and in which general formula the group R-A-CO-NH- is in the 3- to 9-position. The invention further comprises a process for making pigments of the above Formula I in which each X is hydrogen, chloride, bromine or alkyl and (i) A is an aliphatic hydrocarbon radical and R is the 1: 2-phthaloylcarbazole-aminocarbonyl radical shown in the formula or (ii) A is an aromatic hydrocarbon radical and R is hydrogen, alkyl, alkoxy, alkylamino, alkoylamino, trifluoralkyl, alkylsulphonyl, halogen or the 1: 2-phthaloylcarbazoleaminocarbonyl radical, R-A-CO-NH- being linked in the 3- or 9-position, wherein an amino-1: 2-phthaloylcarbazole of the above Formula I in which the group R-A-CO- is replaced by hydrogen, is acylated with an appropriate aliphatic or aromatic dicarboxylic acid or an appropriate substituted or unsubstituted aromatic mono-carboxylic acid. The products are pigments for lacquers, printing pastes and plastics such as polyvinyl chloride.


Translating Artificial Intelligence Into Clinical Care


Design and Analysis of a Bio-Inspired Wire-Driven Multi-Section Flexible Robot Regular Paper


title: doi:10.1057/palgrave.jors.2602132 AI and OR in Management of Operations: History and Trends

abstract: The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.


Brain Tumor Classification Using SVM and Histogram Based Image Segmentation


title: Learning the Structure of Dynamic Probabilistic Networks

abstract: Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifying dynamic behaviors, and learning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains.


title: Penile size and somatometric parameters among Iranian normal adult men

abstract: This study aimed to determine the penile size and its correlation with somatometric parameters in physically normal Iranian adult men. To do this, a random sample of 1500 normal men aged between 20 and 40 years underwent tape measurements of penile dimensions in the stretched state under the same condition. The mean total penile length was 11.58±1.45 cm, the mean glanular length was 3.04±0.33 cm and the mean girth was 8.66±1.01 cm. Multivariate regression analysis showed that penile dimensions are significantly correlated with age (P=0.018), height (P<0.001) and index finger length (P<0.001). This analysis provided no evidence for significant effect of waist/hip ratio and weight on penile dimensions. As the penile augmentation is still in its experimental stage and its indications have not yet been clearly established, providing standardized data on penile dimensions seems to be necessary to make convenient decisions in the counseling and/or treatment of people with short penis concerns.


Bayesian clustering ensembles


title: An effectiveness study on trajectory similarity measures

abstract: The last decade has witnessed the prevalence of sensor and GPS technologies that produce a sheer volume of trajectory data representing the motion history of moving objects. Measuring similarity between trajectories is undoubtedly one of the most important tasks in trajectory data management since it serves as the foundation of many advanced analyses such as similarity search, clustering, and classification. In this light, tremendous efforts have been spent on this topic, which results in a large number of trajectory similarity measures. Generally, each individual work introducing a new distance measure has made specific claims on the superiority of their proposal. However, for most works, the experimental study was focused on demonstrating the efficiency of the search algorithms, leaving the effectiveness aspect unverified empirically. In this paper, we conduct a comparative experimental study on the effectiveness of six widely used trajectory similarity measures based on a real taxi trajectory dataset. By applying a variety of transformations we designed for each original trajectory, our experimental observations demonstrate the advantages and drawbacks of these similarity measures in different circumstances.


title: Probabilistic Latent Semantic Indexing Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval

abstract: The main purpose of this paper is to propose a novel latent semantic indexing (LSI), statistical approach to simultaneously mapping documents and terms into a latent semantic space. This approach can index documents more effectively than the vector space model (VSM). Latent semantic indexing (LSI), which is based on singular value decomposition (SVD), and probabilistic latent semantic indexing (PLSI) have already been proposed to overcome problems in document indexing, but critical problems remain. In contrast to LSI and PLSI, our method uses a more meaningful, robust statistical model based on factor analysis and information theory. As a result, this model can solve the remaining critical problems in LSI and PLSI. Experimental results with a test collection showed that our method is superior to LSI and PLSI from the viewpoints of information retrieval and classification. We also propose a new term weighting method based on entropy.


title: Synaptogyrin-3 Mediates Presynaptic Dysfunction Induced by Tau

abstract: Synaptic dysfunction is an early pathological feature of neurodegenerative diseases associated with Tau, including Alzheimer's disease. Interfering with early synaptic dysfunction may be therapeutically beneficial to prevent cognitive decline and disease progression, but the mechanisms underlying synaptic defects associated with Tau are unclear. In disease conditions, Tau mislocalizes into pre- and postsynaptic compartments; here we show that, under pathological conditions, Tau binds to presynaptic vesicles in Alzheimer's disease patient brain. We define that the binding of Tau to synaptic vesicles is mediated by the transmembrane vesicle protein Synaptogyrin-3. In fly and mouse models of Tauopathy, reduction of Synaptogyrin-3 prevents the association of presynaptic Tau with vesicles, alleviates Tau-induced defects in vesicle mobility, and restores neurotransmitter release. This work therefore identifies Synaptogyrin-3 as the binding partner of Tau on synaptic vesicles, revealing a new presynapse-specific Tau interactor, which may contribute to early synaptic dysfunction in neurodegenerative diseases associated with Tau.


title: Understanding loyalty program effectiveness: managing target and bystander effects

abstract: Loyalty programs are a ubiquitous marketing tactic, yet many of them perform poorly and the reasons for loyalty program failure remain unclear to both marketing managers and researchers. This article presents three studies—two experiments and one survey—in support of the notion that a greater understanding of loyalty program performance demands an expanded theoretical framework. Specifically, researchers and managers must account for loyalty programs’ effects on both target and bystander customers in the firm’s portfolio, the simultaneous effects of three performance-relevant mediating mechanisms (gratitude, status, unfairness), and the contingent effects of program delivery (rule clarity, reward exclusivity, reward visibility) on specific mediating linkages. The results provide insights into why and when loyalty programs fail and into the complex trade-offs managers face. Loyalty programs have opposing effects on target and bystander customers’ loyalty and sales. While rule clarity suppresses both negative bystander as well as positive target effects, reward visibility enhances both types of effects. Exclusive rewards offer a means to alleviate negative bystander effects without affecting targets. The article both conceptually and empirically establishes a comprehensive analysis framework that can help marketing managers and researchers evaluate and improve loyalty program effectiveness.


title: Are Corruption and Taxation Really Harmful to Growth? Firm Level Evidence

abstract: Exploiting a unique data set containing information about the estimated bribe payments of Ugandan firms, the authors study the relationship between bribe payments, taxes, and firm growth in Uganda for the period 1995-97. Using industry-location averages to circumvent the potential problem of endogeneity, and to deal with issues of measurement error, they find that both the rate of taxation, and the rate of bribery are negatively correlated with firm growth. For the full data set, a one percentage point increase in the bribery rate is associated with three percentage point reduction in firm growth - an effect about three times that of taxation. Moreover, after excluding outliers, the authors find that bribery has a much greater negative impact on growth, and taxation a considerably smaller one. This provides some validation of firm-level theories of corruption, which posit that corruption retards development, even more than taxation does.


title: Blockchain-Based Decentralized Cloud/Fog Solutions: Challenges, Opportunities, and Standards

abstract: Smart contracts and blockchain have the potential to change the current shape of cloud markets by enabling the development of completely decentralized cloud/fog solutions, which lower costs and enforce predictable results without requiring any intermediary. In this article, we survey three of these solutions, namely Golem, iExec, and SONM, compare them, and identify some of the problems they leave unsolved. Moreover, we consider existing standards for the development of interoperable decentralized cloud solutions that would allow such systems to compete with large providers and would prevent vendor lock-in. We believe that our study contributes to the evolution of cloud systems not only by pointing out incompatibilities among projects and possible solutions for research problems in the area, but also by reviewing the existing standards and suggesting new standardization opportunities.


title: Prostate cancer: Care beyond prostate cancer—improving patient outcomes

abstract: Evaluation of patient outcomes does not encompass all survivorship issues after prostate cancer treatment. Addressing the effect that these issues have on men's lives remains a great challenge; therefore, the American Cancer Society Prostate Cancer Survivorship Care Guidelines are a welcome addition to the medical literature.


title: Context-Aware Local Binary Feature Learning for Face Recognition

abstract: In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.


title: Formal Reasoning Techniques for Goal Models

abstract: Over the past decade, goal models have been used in Computer Science in order to represent software requirements, business objectives and design qualities. Such models extend traditional AI planning techniques for representing goals by allowing for partially defined and possibly inconsistent goals. This paper presents a formal framework for reasoning with such goal models. In particular, the paper proposes a qualitative and a numerical axiomatization for goal modeling primitives and introduces label propagation algorithms that are shown to be sound and complete with respect to their respective axiomatizations. In addition, the paper reports on experimental results on the propagation algorithms applied to a goal model for a US car manufacturer.


title: A Problem Based Learning Meta Analysis: Differences Across Problem Types, Implementation Types, Disciplines, and Assessment Levels

abstract: Problem based learning (PBL) in its most current form originated in Medical Education but has since been used in a variety of disciplines (Savery & Duff y, 1995) at a variety of educational levels (Savery, 2006). Although recent meta analyses have been conducted (Dochy, Segers, Van den Bossche, & Gijbels, 2003; Gijbels, Dochy, Van den Bossche, & Segers, 2005) that attempted to go beyond medical education, they found only one study in economics and were unable to explain large portions of the variance across results. This work builds upon their eff orts as a meta-analysis that crosses disciplines as well as categorizes the types of problems used (Jonassen, 2000), the PBL approach employed (Barrows, 1986), and the level of assessment (Gijbels et al., 2005; Sugrue, 1993, 1995). Across 82 studies and 201 outcomes the fi ndings favor PBL (dw = 0.13, +/.025) with a lack of homogeneity (Q = 954.27) that warrants a closer examination of moderating factors.


title: A Multi-tenant Database Architecture Design for Software Applications

abstract: Multi-tenant database is a new cloud computing paradigm that has recently attracted attention to deliver database functionalities for multiple tenants to create, store, and access their databases over the internet. This multi-tenant database should be highly configurable and secure to meet tenants' expectations and their different business requirements. In this paper, we propose an architecture design to build an intermediate database layer to be used between software applications and Relational Database Management Systems (RDBMS) to store and access multiple tenants' data in the Elastic Extension Table (EET) multi-tenant database schema. This database layer combines multi-tenant relational tables and virtual relational tables and makes them work together to act as one database for each tenant. This architecture design is suitable for multi-tenant database environment that can run any business domain database by using a combination of a database schema, which contains shared physical structured tables and virtual structured tenant's tables. Further, this multi-tenant database architecture design can be used as a base to build software applications in general and Software as a Service (SaaS) applications in particular.


title: Skin disease recognition using texture analysis

abstract: This research describes skin disease recognition by using neural network which based on the texture analysis. There are many skin diseases which have a lot of similarities in their symptoms, such as Measles (rubeola), German measles (rubella), and Chickenpox etc. In general, these diseases have similarities in pattern of infection and symptoms such as redness and rash. Diagnosis and recognition of skin disease take a very long term process because it requires patient's history, physical examination and proper laboratory diagnostic tests. Not only that, it also requires large number of features clinical as well as histopathological for analysis and to provide further treatment. The disease diagnosis and recognition becomes difficult as the complexity and number of features of the disease increases. Hence, a computer aided diagnosis and recognition system is introduced. Computer algorithm which contains few steps that involves image processing, image feature extraction and classification of data have been implemented with the help of classifier such as artificial neural network (ANN). The ANN can learn patterns of symptoms of particular diseases and provides faster diagnosis and recognition than a human physician. Thus, the patients can do the treatment for the skin disease faced immediately based on the symptoms detected.


title: Determining the physical properties of EpoClad negative photoresist for use in MEMS applications

abstract: In this paper, the internal stress and Young modulus of EpoClad are investigated using different methods. The swelling behavior when submerged in common processing liquids is also measured and reported. Different on-wafer micro electromechanical structures are used, together with nanoindentation measurements. The experimental results yield an internal stress that is strongly dependent on the processing conditions, the ambient and the submersion liquid. Young's modulus is found to be 4.4 ± 0.2 GPa.


title: 3.8 mW electrocardiogram (ECG) filtered electrical impedance tomography IC using I/Q homodyne architecture for breast cancer diagnosis

abstract: A low-power electrical impedance tomography (EIT) IC proposed for breast cancer diagnosis is implemented in 180nm CMOS process. For the breast cancer diagnosis, low power and high accuracy is required. The proposed IC reduces the power consumption to 3.8mW using the homodyne conversion mixer to lower the sampling rate of ADC. To gain high accuracy, the adaptive filter using the miller capacitor perfectly filters out the ECG signal. I/Q dual path architecture measures conductivity and permittivity components separately, so distance errors are reduced about 86% in simulation.


High-Speed Wireless Indoor Communication via Visible Light