title: Objects as Context for Detecting Their Semantic Parts

abstract: We present a semantic part detection approach that effectively leverages object information.We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets.


title: Visual Sentiment Prediction by Merging Hand-Craft and CNN Features

abstract: Nowadays, more and more people are getting used to social media such as Instagram, Facebook, Twitter, and Flickr to post images and texts to express their sentiment and emotions on almost all events and subjects. In consequence, analyzing sentiment of the huge number of images and texts on social networks has become more indispensable. Most of current research has focused on analyzing sentiment of textual data, while only few research has focused on sentiment analysis of image data. Some of these research has considered handcraft image features, the others has utilized Convolutional Neural Network (CNN) features. However, no research to our knowledge has considered mixing both hand-craft and CNN features. In this paper, we attempt to merge CNN which has shown remarkable achievements in Computer Vision recently, with handcraft features such as Color Histogram (CH) and Bag-of-Visual Words (BoVW) with some local features such as SURF and SIFT to predict sentiment of images. Furthermore, because it is often the case that the large amount of training data may not be easily obtained in the area of visual sentiment, we employ both data augmentation and transfer learning from a pre-trained CNN such as VGG16 trained with ImageNet dataset. With the handshake of hand-craft and End-to-End features from CNN, we attempt to attain the improvement of the performance of the proposed visual sentiment prediction framework. We conducted experiments on an image dataset from Twitter with polarity labels ("positive" and "negative"). The results of experiments demonstrate that our proposed visual sentimental prediction framework outperforms the current state-of-the-art methods.


title: Adaptive Service Composition in Flexible Processes

abstract: In advanced service oriented systems, complex applications, described as abstract business processes, can be executed by invoking a number of available Web services. End users can specify different preferences and constraints and service selection can be performed dynamically identifying the best set of services available at runtime. In this paper, we introduce a new modeling approach to the Web service selection problem that is particularly effective for large processes and when QoS constraints are severe. In the model, the Web service selection problem is formalized as a mixed integer linear programming problem, loops peeling is adopted in the optimization, and constraints posed by stateful Web services are considered. Moreover, negotiation techniques are exploited to identify a feasible solution of the problem, if one does not exist. Experimental results compare our method with other solutions proposed in the literature and demonstrate the effectiveness of our approach toward the identification of an optimal solution to the QoS constrained Web service selection problem


title: Description of coupling between degenerate modes of a dual-mode microstrip loop resonator using a novel perturbation arrangement and its dual-mode bandpass filter applications

abstract: A novel dual-mode resonator with square-patch or corner-cut elements located at four corners of a conventional microstrip loop resonator is proposed. One of these patches or corner cuts is called the perturbation element, while the others are called reference elements. In the proposed design method, the transmission zeros are created or eliminated without sacrificing the passband response by changing the perturbation's size depending on the size of the reference elements. A simple transmission-line model is used to calculate the frequencies of the two transmission zeros. It is shown that the nature of the coupling between the degenerate modes determines the type of filter characteristic, whether it is Chebyshev or elliptic. Finally, two dual-mode microstrip bandpass filters are designed and realized using degenerate modes of the novel dual-mode resonator. The filters are evaluated by experiment and simulation with very good agreement.


title: Coherent-array imaging using phased subarrays. Part I: basic principles

abstract: The front-end hardware complexity of a coherent array imaging system scales with the number of active array elements that are simultaneously used for transmission or reception of signals. Different imaging methods use different numbers of active channels and data collection strategies. Conventional full phased array (FPA) imaging produces the best image quality using all elements for both transmission and reception, and it has high front-end hardware complexity. In contrast, classical synthetic aperture (CSA) imaging only transmits on and receives from a single element at a time, minimizing the hardware complexity but achieving poor image quality. We propose a new coherent array imaging method--phased subarray (PSA) imaging--that performs partial transmit and receive beam-forming using a subset of adjacent elements at each firing step. This method reduces the number of active channels to the number of subarray elements; these channels are multiplexed across the full array and a reduced number of beams are acquired from each subarray. The low-resolution subarray images are laterally upsampled, interpolated, weighted, and coherently summed to form the final high-resolution PSA image. The PSA imaging reduces the complexity of the front-end hardware while achieving image quality approaching that of FPA imaging.


title: On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

abstract: Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, not providing any information about what made them arrive at a particular decision. This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. These pixel contributions can be visualized as heatmaps and are provided to a human expert who can intuitively not only verify the validity of the classification decision, but also focus further analysis on regions of potential interest. We evaluate our method for classifiers trained on PASCAL VOC 2009 images, synthetic image data containing geometric shapes, the MNIST handwritten digits data set and for the pre-trained ImageNet model available as part of the Caffe open source package.


title: Fuzzy gain-scheduled active fault-tolerant control of a wind turbine

abstract: Abstract Advanced fault detection and accommodation schemes are required for ensuring efficient and reliable operation of modern wind turbines. This paper presents a novel approach in designing a fault detection and diagnosis (FDD) and fault-tolerant control (FTC) scheme for a wind turbine using fuzzy modeling, identification and control techniques. First, an improved gain-scheduled proportional-integral (PI) control system based on fuzzy gain scheduling (FGS) technique for multi-input and multi-output wind turbine system is designed. Then, to accommodate sensor faults and based on a signal correction algorithm, an active fault-tolerant control system (AFTCS) is developed as an extension of the gain-scheduled PI control system. The AFTCS exploits the fault information from a model-based FDD scheme developed using fuzzy modeling and identification method. The proposed schemes are evaluated by a series of simulations on a well-known large off-shore wind turbine benchmark in the presence of wind turbulences, measurement noises, and different realistic fault scenarios. All results indicate high effectiveness and robustness of the designed control systems in both fault-free and faulty operations of the wind turbine.


title: Programming by choice: urban youth learning programming with scratch

abstract: This paper describes Scratch, a visual, block-based programming language designed to facilitate media manipulation for novice programmers. We report on the Scratch programming experiences of urban youth ages 8-18 at a Computer Clubhouse 'an after school center' over an 18-month period. Our analyses of 536 Scratch projects collected during this time documents the learning of key programming concepts even in the absence of instructional interventions or experienced mentors. We discuss the motivations of urban youth who choose to program in Scratch rather than using one of the many other software packages available to them and the implications for introducing programming at after school settings in underserved communities.


title: A hybrid GaN/organic X-band transmitter module

abstract: The design and implementation of a compact, flexible and lightweight X-band transmitter (Tx) module based on high-power gallium nitride (GaN) transistor technology and a low-cost organic package made from liquid crystal polymer (LCP) is presented. In-package measurements of the power amplifier (PA) at 8 GHz show a P.A.E.max of >31%, P1dB of 20 dBm and gain of 11.42 dB. A 4×1 patch antenna array was also fabricated on the same platform. Though no thermal management was used, an effective isotropically radiated power (EIRP) in excess of 20 dBm at 10 GHz was measured for the transmitter module, consisting of only a single-stage PA and antenna array, thus demonstrating that even greater performance can be achieved in the future.


title: Using hover to compromise the confidentiality of user input on Android

abstract: We show that the new hover (floating touch) technology, available in a number of today's smartphone models, can be abused by any Android application running with a common SYSTEM_ALERT_WINDOW permission to record all touchscreen input into other applications. Leveraging this attack, a malicious application running on the system is therefore able to profile user's behavior, capture sensitive input such as passwords and PINs as well as record all user's social interactions. To evaluate our attack we implemented Hoover, a proof-of-concept malicious application that runs in the system background and records all input to foreground applications. We evaluated Hoover with 40 users, across two different Android devices and two input methods, stylus and finger. In the case of touchscreen input by finger, Hoover estimated the positions of users' clicks within an error of 100 pixels and keyboard input with an accuracy of 79%. Hoover captured users' input by stylus even more accurately, estimating users' clicks within 2 pixels and keyboard input with an accuracy of 98%. We discuss ways of mitigating this attack and show that this cannot be done by simply restricting access to permissions or imposing additional cognitive load on the users since this would significantly constrain the intended use of the hover technology.


title: Four-OAM-Mode Antenna With Traveling-Wave Ring-Slot Structure

abstract: Based on metallic traveling-wave ring-slot structure, an easily realized four-orbital angular momentum (OAM)-mode antenna capable of generating four coaxially propagating waves with OAM modes of $l$ = $\mathbf {-3}$ , $\mathbf {-2}$ , $\mathbf {+2,}$ and $\mathbf {+3}$ , respectively, is experimentally demonstrated at 10 GHz. The feeding network is simply implemented using two $\mathbf {90^{\circ }}$ hybrid couplers. A ring-focus double-reflector with a favorable focus-diameter-ratio is specially designed to gain a high directivity. The main characteristics of the antenna—such as $S$ -parameters, directivity, and near-field radiation—are presented both in simulation and experiment. Employing a pair of four-OAM-mode antennas, the experiment shows that the near-field OAM-based radio communication channels are naturally isolated.


title: A Wearable Computing Prototype for Supporting Training Activities in Automotive Production

abstract: This paper presents the results of the wearable computing prototype supporting training- and qualification activities at the SKODA production facilities in Czech Republic. The emerged prototype is based upon the first of the 2 main "Variant Production Showcases" (training and assembly-line) which are to be implemented in the WearIT@work project (EC IP 004216). As an introduction, the authors of this paper investigate current training processes at Skoda, and derive the potential benefits and risks of applying wearable computing technology. Accordingly, the approach of creating the wearable prototypes, via usability experiments at the Skoda production site, is explained in detail. As a preliminary result, the first functional prototypes, including a task recognition prototype, based upon the components of the European Wearable Computing Platform, are described. The paper is rounded up by providing a short outlook regarding the second envisaged test case, which is focussed upon selected assembly line operations of blue collar workers.


title: I/O Braid: Scalable Touch-Sensitive Lighted Cords Using Spiraling, Repeating Sensing Textiles and Fiber Optics

abstract: We introduce I/O Braid, an interactive textile cord with embedded sensing and visual feedback. I/O Braid senses proximity, touch, and twist through a spiraling, repeating braiding topology of touch matrices. This sensing topology is uniquely scalable, requiring only a few sensing lines to cover the whole length of a cord. The same topology allows us to embed fiber optic strands to integrate co-located visual feedback. We provide an overview of the enabling braiding techniques, design considerations, and approaches to gesture detection. These allow us to derive a set of interaction techniques, which we demonstrate with different form factors and capabilities. Our applications illustrate how I/O Braid can invisibly augment everyday objects, such as touch-sensitive headphones and interactive drawstrings on garments, while enabling discoverability and feedback through embedded light sources.


title: Instance Tumor Segmentation using Multitask Convolutional Neural Network

abstract: Automatic tumor segmentation is an important and challenging clinical task because tumors have different sizes, shapes, contrasts, and locations. In this paper, we present an automatic instance semantic segmentation method based on deep neural networks (DNNs). The proposed networks are tailored to picture tumors in magnetic resonance imaging (MRI) and computed tomography (CT) images. We present an end-to-end multitask learning architecture comprising three stages, namely detection, segmentation, and classification. This paper introduces a new technique for tumor detection based on high-level extracted features from convolutional neural networks (CNNs) using the Hough transform technique. The detected tumor(s), are segmented with a set of fully connected (FC) layers, and the segmented mask is classified through FCs. The proposed architecture gives promising results on the popular medical image benchmarks. Our framework is generalized in the sense that it can be used in different types of medical images in varied sizes, such as the Liver Tumor Segmentation (LiTS-2017) challenge, and the Brain Tumor Segmentation (BraTS-2016) benchmark.


title: Incremental Few-Shot Learning with Attention Attractor Networks

abstract: Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many applications, it is often desirable to have the flexibility of learning additional concepts, with limited data and without re-training on the full training set. This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Network, which regularizes the learning of novel classes. In each episode, we train a set of new weights to recognize novel classes until they converge, and we show that the technique of recurrent back-propagation can back-propagate through the optimization process and facilitate the learning of these parameters. We demonstrate that the learned attractor network can help recognize novel classes while remembering old classes without the need to review the original training set, outperforming various baselines.


title: Intelligent Security Measures for Smart Cyber Physical Systems

abstract: The exponential growth of cyber-physical systems (CPS), especially in safety-critical applications, has imposed several security threats (like manipulation of communication channels, hardware components, and associated software) due to complex cybernetics and the interaction among (independent) CPS domains. These security threats have led to the development of different static as well as adaptive detection and protection techniques on different layers of the CPS stack, e.g., cross-layer and intra-layer connectivity. This paper first presents a brief overview of various security threats at different CPS layers, their respective threat models and associated research challenges to develop robust security measures. Moreover, this paper provides a brief yet comprehensive survey of the state-of-the-art static and adaptive techniques for detection and prevention, and their inherent limitations, i.e., incapability to capture the dormant or uncertainty-based runtime security attacks. To address these challenges, this paper also discusses the intelligent security measures (using machine learning-based techniques) against several characterized attacks on different layers of the CPS stack. Furthermore, we identify the associated challenges and open research problems in developing intelligent security measures for CPS. Towards the end, we provide an overview of our project on security for smart CPS along with important analyses.


title: Towards reliable spike-train recordings from thousands of neurons with multielectrodes

abstract: The new generation of silicon-based multielectrodes comprising hundreds or more electrode contacts offers unprecedented possibilities for simultaneous recordings of spike trains from thousands of neurons. Such data will not only be invaluable for finding out how neural networks in the brain work, but will likely be important also for neural prosthesis applications. This opportunity can only be realized if efficient, accurate and validated methods for automatic spike sorting are provided. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms.


title: Learning a Deep Convolutional Network for Image Super-Resolution

abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.


title: Prototype for 5G new air interface technology SCMA and performance evaluation

abstract: Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme proposed to meet the challenging demand of the future 5G communications, especially in support of the massive connections. The coded bits from each data stream will be directly mapped as multi-dimensional SCMA codeword in complex domain and then spread onto the physical resource elements in a sparse manner. The number of codewords that can be non-orthogonally multiplexed in one SCMA block can be made much larger than the number of orthogonal resource elements therein, resulting in an overloaded system. The sparsity in the spreading pattern and the design in the multidimensional modulator jointly ensure the SCMA codewords can be robustly decoded with low complexity. In this paper, we focus on the low complexity receiver design and verified the superior of an SCMA system via simulations and real-time prototyping. Lab tests and field tests all show that SCMA is a promising candidate for 5G non-orthogonal multiple access which can provide up to 300% overloading that triples the whole system throughput while still enjoying the link performance close to orthogonal transmissions.


title: Twenty Security Considerations for Cloud-Supported Internet of Things

abstract: To realize the broad vision of pervasive computing, underpinned by the “Internet of Things” (IoT), it is essential to break down application and technology-based silos and support broad connectivity and data sharing; the cloud being a natural enabler. Work in IoT tends toward the subsystem, often focusing on particular technical concerns or application domains, before offloading data to the cloud. As such, there has been little regard given to the security, privacy, and personal safety risks that arise beyond these subsystems; i.e., from the wide-scale, cross-platform openness that cloud services bring to IoT. In this paper, we focus on security considerations for IoT from the perspectives of cloud tenants, end-users, and cloud providers, in the context of wide-scale IoT proliferation, working across the range of IoT technologies (be they things or entire IoT subsystems). Our contribution is to analyze the current state of cloud-supported IoT to make explicit the security considerations that require further work.


title: It was the training data pruning too!

abstract: We study the current best model (KDG) for question answering on tabular data evaluated over the WikiTableQuestions dataset. Previous ablation studies performed against this model attributed the model's performance to certain aspects of its architecture. In this paper, we find that the model's performance also crucially depends on a certain pruning of the data used to train the model. Disabling the pruning step drops the accuracy of the model from 43.3% to 36.3%. The large impact on the performance of the KDG model suggests that the pruning may be a useful pre-processing step in training other semantic parsers as well.


title: Beyond Equilibrium: Predicting Human Behaviour in Normal Form

abstract: It is standard in multiagent settings to assume that agents will adopt Nash equilibrium strategies. However, studies in experimental economics demonstrate that Nash equilibrium is a poor description of human players' initial behavior in normal-form games. In this paper, we consider a wide range of widely-studied models from behavioral game theory. For what we believe is the first time, we evaluate each of these models in a meta-analysis, taking as our data set large-scale and publicly-available experimental data from the literature. We then propose modifications to the best-performing model that we believe make it more suitable for practical prediction of initial play by humans in normal-form games.


title: Simple Electromagnetic Modeling Procedure: From Near-Field Measurements to Commercial Electromagnetic Simulation Tool

abstract: As the first part of a study that aims to propose tools to take into account some electromagnetic compatibility aspects, we have developed a model to predict the electric and magnetic fields emitted by a device. This model is based on a set of equivalent sources (electric and magnetic dipoles) obtained from the cartographies of the tangential components of electric and magnetic near fields. One of its features is to be suitable for a commercial electromagnetic simulation tool based on a finite element method. This paper presents the process of modeling and the measurement and calibration procedure to obtain electromagnetic fields necessary for the model; the validation and the integration of the model into a commercial electromagnetic simulator are then performed on a Wilkinson power divider.


title: Investigation of a fault-tolerant three-level T-type inverter system

abstract: In recent years, a three-level T-type inverter has attracted considerable attention due to its advantages, such as simple structure and higher efficiency. However, the reliability of a three-level T-type inverter is particularly important as increased power switches are used. Therefore, a fault-tolerant three-level T-type inverter is proposed in this paper. Fault diagnosis and fault-tolerant control strategies for power switches both in half-bridge and neutral-point bridge are investigated. Under fault-tolerant operation, redundant fourth-leg is used to balance neutral-point voltage and the low-frequency voltage oscillation is eliminated completely. The fault ride-through capability and high-quality output waveforms can be obtained. The effectiveness of the proposed inverter topology and control methods is validated by the simulation and experimental results.


title: Design and Performance of a Double-Layer Miniaturized-Element Frequency Selective Surface

abstract: In this letter, a novel bandpass frequency selective surface (FSS) is proposed. Unlike the traditional FSSs, our building block makes use of a periodic cell with much smaller size than the resonant wavelength. The proposed FSS is composed by two layers of metallic unit cells printed on the substrate. The coupling between parallel patches in different metallic layers can produce the significant distributed capacitance of the FSS; as a result, the size of the FSS unit cell can be decreased to λ/16. Our simulated result shows that the transmission characteristic of the miniaturized FSS is very stable since the relative change rate of resonant frequency is less than 2% with respect to different incident angles from 0° to 45° . The measured results show good agreements with the simulated ones. In addition, our result also implies that the structure has wide application space, based on analysis of three other variations on the periodic structure.


title: How robotic products become social products: an ethnographic study of cleaning in the home

abstract: Robots that work with people foster social relationships between people and systems. The home is an interesting place to study the adoption and use of these systems. The home provides challenges from both technical and interaction perspectives. In addition, the home is a seat for many specialized human behaviors and needs, and has a long history of what is collected and used to functionally, aesthetically, and symbolically fit the home. To understand the social impact of robotic technologies, this paper presents an ethnographic study of consumer robots in the home. Six families' experience of floor cleaning after receiving a new vacuum (a Roomba robotic vacuum or the Flair, a handheld upright) was studied. While the Flair had little impact, the Roomba changed people, cleaning activities, and other product use. In addition, people described the Roomba in aesthetic and social terms. The results of this study, while initial, generate implications for how robots should be designed for the home.


title: Token-based authentication for smartphones

abstract: Due to short — but frequent — sessions of smartphone usage, the fast and easy usability of authentication mechanisms in this special environment has a big impact on user acceptance. In this work we propose a user-friendly alternative to common authentication methods (like PINs and patterns). The advantages of the proposed method are its security, fastness, and easy usage, requiring minimal user interaction compared to other authentication techniques currently used on smartphones. The mechanism described uses the presence of a Bluetooth-connected hardware-token to authenticate the user and can easily be implemented on current smartphones. It is based on an authentication protocol which meets the requirements on energy efficiency and limited resources by optimizing the communication effort. A prototype was implemented on an Android smartphone and an MSP430 based MCU. The token allows fast authentication without the need for additional user action. The entire authentication process can be completed in less than one second, the developed software prototype requires no soft- or hardware modifications (like rooting) of the Android phone.


title: Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation

abstract: Deep convolutional neural networks (CNNs) have become one of the state-of-the-art methods for image classification in various domains. For biomedical image classification where the number of training images is generally limited, transfer learning using CNNs is often applied. Such technique extracts generic image features from nature image datasets and these features can be directly adopted for feature extraction in smaller datasets. In this paper, we propose a novel deep neural network architecture based on transfer learning for microscopic image classification. In our proposed network, we concatenate the features extracted from three pretrained deep CNNs. The concatenated features are then used to train two fully-connected layers to perform classification. In the experiments on both the 2D-Hela and the PAP-smear datasets, our proposed network architecture produces significant performance gains comparing to the neural network structure that uses only features extracted from single CNN and several traditional classification methods.


title: Handwriting instruction in elementary schools.

abstract: OBJECTIVE. Classroom teachers teach handwriting, but when problems arise, students are referred to occupational therapy for remediation. This study, conducted by occupational therapists, reviews handwriting instruction by classroom teachers in one school district. ::: ::: METHOD. Teachers from kindergarten through grade 6 were asked to complete an open-ended questionnaire regarding handwriting instruction. ::: ::: RESULTS. Teachers differed in their methods of instruction, including in the programs and paper used, and practice provided. Teachers of grades 5 and 6 had to continue to review handwriting instruction, because all students could not fluently use handwriting as a tool of expression. ::: ::: CONCLUSION. Elementary students need structured instruction to develop the motor skill of writing. School-based occupational therapists can support effective handwriting instruction by interpreting information from motor learning theory pertaining to instruction and practice, which supports acquisition, transfer, and retention of handwriting skills. They also need to be cognizant of prior handwriting instruction when addressing handwriting difficulties


title: TensorBeat: Tensor Decomposition for Monitoring Multiperson Breathing Beats with Commodity WiFi

abstract: Breathing signal monitoring can provide important clues for health problems. Compared to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this article, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensors. Then canonical polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to identify the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multiperson breathing rate monitoring.


title: ACTIVE OBJECT CATEGORIZATION ON A HUMANOID ROBOT

abstract: We present a Bag of Words-based active object categorization technique implemented and tested on a humanoid robot. The robot is trained to categorize objects that are handed to it by a human operator. The robot uses hand and head motions to actively acquire a number of different views. A view planning scheme using entropy minimization reduces the number of views needed to achieve a valid decision. Categorization results are significantly improved by active elimination of background features using robot arm motion. Our experiments cover both, categorization when the object is handed to the robot in a fixed pose at training and testing, and object pose independent categorization. Results on a 4-class object database demonstrate the classification efficiency, a significant gain from multi-view compared to single-view classification, and the advantage of view planning. We conclude that humanoid robotic systems can be successfully applied to actively categorize objects a task with many potential applications ranging from edutainment to active surveillance.


title: Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System

abstract: Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.


title: Avoiding Probabilistic Reasoning Fallacies in Legal Practice using Bayesian Networks

abstract: Probabilistic reasoning fallacies in legal practice have been widely documented. Yet these fallacies continue to occur. This paper considers how best to avoid them. Although most fallacies can be easily explained and avoided by applying Bayes’ Theorem, attempts to do so with lawyers using the normal formulaic approach seem doomed to failure. In our experience, for simple arguments it is possible to explain common fallacies using purely visual presentation alternatives to the formulaic version of Bayes in ways that are fully understandable to lay people. However, as the evidence (and dependence between different evidence) becomes more complex, these visual approaches become infeasible. We show how Bayesian networks can be used, in conjunction with visual presentations, to address the more complex arguments in such a way that it is not necessary to expose the underlying complex Bayesian computations. In this way Bayesian Networks work like an electronic calculator for complex Bayesian computations. We demonstrate this new approach in explaining well known fallacies and a new fallacy (called the Crimewatch fallacy) that arose in a recent major murder trial in which we were expert witnesses. We also address the barriers to more widespread take-up of these methods within the legal profession, including the need to ‘believe’ the correctness of Bayesian calculations and the inevitable reluctance to consider subjective prior probabilities. The paper provides a number of original contributions: a classification of fallacies that is conceptually simpler than previous approaches; the new fallacy; and a proposal, whose necessity is based on real case experience, for a radically new means of fully exploiting Bayes to enhance legal reasoning


title: Ambiguity Identification and Measurement in Natural Language Texts

abstract: Text ambiguity is one of the most interesting phenomenon in human communication and a difficult problem in Natural Language Processing (NLP). Identification of text ambiguities is an important task for evaluating the quality of text and uncovering its vulnerable points. There exist several types of ambiguity. In the present work we review and compare different approaches to ambiguity identification task. We also propose our own approach to this problem. Moreover, we present the prototype of a tool for ambiguity identification and measurement in natural language text. The tool is intended to support the process of writing high quality documents.


title: Realistic human action recognition: When deep learning meets VLAD

abstract: Human action recognition from realistic scenarios is extremely challenging due to large intra-class variation and complex background clutters. In this paper, by leveraging the strength of deep learning and vector of locally aggregated descriptors (VLAD), we propose a new methods for human action recognition from realistic datsets. We adopt stack convolu-tional independent subspace analysis (ISA) networks to learn 3D cuboid representation directly from spatio-temporal video data; we propose an improved VLAD by incorporating the spatio-temporal geometrical information to encode the deep learned local features. On two challenging realistic datasets: the YouTube action and HMDB51 datasets, the proposed method achieves state-of-the-art performance with an efficient linear SVM classifier, which is competitive with and even better than existing sophisticated algorithms.


title: Unsupervised Coreference Resolution by Utilizing the Most Informative Relations

abstract: In this paper we present a novel method for unsupervised coreference resolution. We introduce a precision-oriented inference method that scores a candidate entity of a mention based on the most informative mention pair relation between the given mention entity pair. We introduce an informativeness score for determining the most precise relation of a mention entity pair regarding the coreference decisions. The informativeness score is learned robustly during few iterations of the expectation maximization algorithm. The proposed unsupervised system outperforms existing unsupervised methods on all benchmark data sets.


title: Enterprise Social Networks: A Successful Implementation within a Telecommunication Company

abstract: Although enterprise social network (ESN) has been implemented within a number of organizations to facilitate communication, collaboration and engagement among employees, many organizations are struggling to realize the benefits of this emergent workplace social platform (due to employee underutilization). This paper aims to elucidate a successful ESN use case among employees within a leading Australian telecommunication company. The findings indicate that the employees’ ESN behavior is gravitated by utilitarian and hedonic values, and driven by socio-technical factors. The factors are generically categorized as technological (i.e. platform and content quality), organizational (e.g. community management and top management involvement), social (e.g. critical mass, and constructive communication climate), individual (e.g. knowledge self-efficacy) and task factors (i.e. equivocal tasks and integration to business processes). We suggest that a successful implementation of ESN within an organization involves the nexus between these five factors and recommendations are also made about how ESN usage can be enhanced.


title: Semantic Code Repair using Neuro-Symbolic Transformation Networks

abstract: We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code. The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated. In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program. Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs. Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete. Specifically, the architecture (1) generates a shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space. We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs. Our model is able to predict the exact correct repair 41\% of the time with a single guess, compared to 13\% accuracy for an attentional sequence-to-sequence model.


title: Theory and tradition in German archaeology

abstract: ‘All quiet on the western front’ (Harke 1991) for years on end, and now, suddenly two theoretical works out of Germany, one of them even of handbook-character – what's happening? Is the enmity towards theoretical discourse of mainstream German archaeology (Eggert and Veit 1998, 11) finally abating?


BIOINFORMATICS APPLICATIONS NOTE Data and text mining


title: An Efficient Bandit Algorithm for Realtime Multivariate Optimization

abstract: Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21% conversion increase compared to the median layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.


title: The kinks of financial journalism

abstract: This paper studies the content of financial news as a function of past market returns. As a proxy for media content we use positive and negative word counts from general financial news columns from the Wall Street Journal and the New York Times. Our empirical analysis allows us to discriminate between theories that predict hyping good stock performance to those that emphasize negative news. The evidence is conclusive: negative market returns taint the ink of typewriters, while positive returns barely do. JEL classification: G01, G14.


title: Double negative metamaterial design using open split ring resonator

abstract: Metamaterials are artificially designed materials having electromagnetic properties which are not generally found in nature. Various resonators exhibiting metamaterial characteristics are split ring resonator (SRR), spiral resonator (SR), omega and fractal resonator. This paper presents a new type of resonator called as an open split ring resonator (OSRR) exhibiting negative permeability. OSRR is modified structure of split ring resonator (SRR). A metamaterial cell is constructed by the OSRR and wire arrangement. The complex permittivity and permeability of the cell is retrieved from the scattering parameters. As compared to the split ring resonator, open split ring resonator is smaller in size for the same resonance frequency and hence this leads to a size miniaturization in various metamaterial applications.


title: Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

abstract: With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results.


title: Compositional Approaches for Representing Relations Between Words: A Comparative Study

abstract: Identifying the relations that exist between words (or entities) is important for various natural language processing tasks such as, relational search, noun-modifier classification and analogy detection. A popular approach to represent the relations between a pair of words is to extract the patterns in which the words co-occur with from a corpus, and assign each word-pair a vector of pattern frequencies. Despite the simplicity of this approach, it suffers from data sparseness, information scalability and linguistic creativity as the model is unable to handle previously unseen word pairs in a corpus. In contrast, a compositional approach for representing relations between words overcomes these issues by using the attributes of each individual word to indirectly compose a representation for the common relations that hold between the two words. This study aims to compare different operations for creating relation representations from word-level representations. We investigate the performance of the compositional methods by measuring the relational similarities using several benchmark datasets for word analogy. Moreover, we evaluate the different relation representations in a knowledge base completion task.


title: Bag of Surrogate Parts Feature for Visual Recognition

abstract: Convolutional neural networks (CNNs) have attracted significant attention in visual recognition. Several recent studies have shown that, in addition to the fully connected layers, the features derived from the convolutional layers of CNNs can also achieve promising performance in image classification tasks. In this paper, we propose a new feature from the convolutional layers, called Bag of Surrogate Parts (BoSP), and its spatial variant, Spatial-BoSP (S-BoSP). The main idea is, we assume the feature maps in the convolutional layers as surrogate parts, and densely sample and assign image regions to these surrogate parts by observing the activation values. Together with BoSP/S-BoSP, we further propose another two schemes to enhance the performance: scale pooling and global-part prediction. Scale pooling aims to handle the objects with different scales and deformations, and global-part prediction combines the predictions of global and part features. By conducting extensive experiments on generic object, fine-grained object and scene datasets, we find the proposed scheme can not only achieve superior performance to the fully connected feature, but also produces competitive or, in some cases, remarkably better performance than the state of the art.


Deep Low-Resolution Person Re-Identification


title: Self-oscillation flyback converter with lossless snubber for contactless power supply application

abstract: This paper presents the self-oscillating flyback converter with an integrated flyback-transformer whose auxiliary winding is utilized as the lossless snubber inductor and output-voltage sensing winding. In the conventional flyback converter, the turn-off switching loss caused by high switching frequency leads to low conversion efficiency. The proposed lossless snubber scheme not only reduces the voltage spikes at turn-off interval, but also recycles the energy stored in the leakage inductor to the input source for improving conversion efficiency. Considering further simplification, the lossless snubber inductor and the output-voltage sensing winding can be combined together as a single winding. A prototype circuit of the 15W self-oscillating flyback converter with lossless snubber circuit and output-voltage sensing circuit is built to verify the performances, such as the voltage stress on the switch, the conversion efficiency, and the load regulation.


title: Alkaloids analysis using off-line two-dimensional supercritical fluid chromatography × ultra-high performance liquid chromatography.

abstract: In this study, an off-line two-dimensional (2-D) supercritical fluid chromatography (SFC) × ultra-high performance liquid chromatography (UHPLC) method with high orthogonality was developed for the analysis of the practical amide alkaloids fraction from P. longum L. The effects of SFC parameters such as column type, organic modifier, temperature and back-pressure on separation were systematically evaluated. Different selectivity was observed for different columns (BEH, BEH 2-EP, XAmide and CSH FP). An investigation was then carried out of the orthogonality of different columns and systems following a geometric approach with a set of amide alkaloid samples. The orthogonality between a CSH FP column and a BEH column reached 50.79%, which was much higher than that for the other columns. While the orthogonality between SFC and UHPLC based on an XAmide column and an HSS T3 column reached 69.84%, which was the highest of all the combinations. At last, the practical amide alkaloids fraction was analyzed with an off-line 2-D chromatography SFC × UHPLC system. In total, at least 340 peaks were detected by this method. Rapid separation in these two dimensions and easy post treatment of SFC facilitated this 2-D system for the separation of complex samples.


title: Cross-Self-Bias Rail-to-Rail Column Buffer for 640-Channel 6-Bit TFT-LCD

abstract: A 640-channel 6-bit LCD column driver with a cross-self-bias push-pull buffer is presented. To design a column driver for thin-film transistor liquid-crystal displays (TFT-LCDs), both low power consumption and chip area are key problems that must be solved. Therefore, the proposed cross-self-bias buffer amplifier effectively reduces the circuit area and power consumption overheads of conventional class-AB biasing techniques. The 640-channel 6-bit column driver is implemented using 0.18-μm high-voltage CMOS technology. The proposed column driver is then used in an SXGA LCD panel to perform the experimental characterization. The measured performance of column driver exhibits maximum settling times of 1.4 μs and 1.2 μs for rising and falling edges, respectively, under one-dot inversion. The quiescent current consumption of the proposed rail-to-rail buffer is about 4 μA .


title: Deep learning cardiac motion analysis for human survival prediction

abstract: Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images. This dense motion model forms the input to a supervised system called 4Dsurvival that can efficiently predict human survival.


title: Using SentiWordNet for multilingual sentiment analysis

abstract: This paper introduces a methodology for determining polarity of text within a multilingual framework. The method leverages on lexical resources for sentiment analysis available in English (SentiWordNet). First, a document in a different language than English is translated into English using standard translation software. Then, the translated document is classified according to its sentiment into one of the classes "positive" and "negative". For sentiment classification, a document is searched for sentiment bearing words like adjectives. By means of SentiWordNet, scores for positivity and negativity are determined for these words. An interpretation of the scores then leads to the document polarity. The method is tested for German movie reviews selected from Amazon and is compared to a statistical polarity classifier based on n-grams. The results show that working with standard technology and existing sentiment analysis approaches is a viable approach to sentiment analysis within a multilingual framework.


title: Teamwork in Multiteam Systems.

abstract: The authors examined how networks of teams integrate their efforts to succeed collectively. They proposed that integration processes used to align efforts among multiple teams are important predictors of multiteam performance. The authors used a multiteam system (MTS) simulation to assess how both cross-team and within-team processes relate to MTS performance over multiple performance episodes that differed in terms of required interdependence levels. They found that cross-team processes predicted MTS performance beyond that accounted for by within-team processes. Further, cross-team processes were more important for MTS effectiveness when there were high cross-team interdependence demands as compared with situations in which teams could work more independently. Results are discussed in terms of extending theory and applications from teams to multiteam systems.


title: Trefoil Torus Knot Monopole Antenna

abstract: A wideband and simple torus knot monopole antenna is presented in this letter. The antenna is fabricated using additive manufacturing technology, commonly known as 3-D printing. The antenna is mechanically simple to fabricate and has stable radiation pattern as well as input reflection coefficient below -10 dB over a frequency range of 1-2 GHz. A comparison of measured and simulated performance of the antenna is also presented.


title: FPGA side-channel receivers

abstract: The popularity of FPGAs is rapidly growing due to the unique advantages that they offer. However, their distinctive features also raise new questions concerning the security and communication capabilities of an FPGA-based hardware platform. In this paper, we explore the some of the limits of FPGA side-channel communication. Specifically, we identify a previously unexplored capability that significantly increases both the potential benefits and risks associated with side-channel communication on an FPGA: an in-device receiver. We designed and implemented three new communication mechanisms: speed modulation, timing modulation and pin hijacking. These non-traditional interfacing techniques have the potential to provide reliable communication with an estimated maximum bandwidth of 3.3 bit/sec, 8 Kbits/sec, and 3.4 Mbits/sec, respectively.


title: Towards Automated Log Parsing for Large-Scale Log Data Analysis

abstract: Logs are widely used in system management for dependability assurance because they are often the only data available that record detailed system runtime behaviors in production. Because the size of logs is constantly increasing, developers (and operators) intend to automate their analysis by applying data mining methods, therefore structured input data (e.g., matrices) are required. This triggers a number of studies on log parsing that aims to transform free-text log messages into structured events. However, due to the lack of open-source implementations of these log parsers and benchmarks for performance comparison, developers are unlikely to be aware of the effectiveness of existing log parsers and their limitations when applying them into practice. They must often reimplement or redesign one, which is time-consuming and redundant. In this paper, we first present a characterization study of the current state of the art log parsers and evaluate their efficacy on five real-world datasets with over ten million log messages. We determine that, although the overall accuracy of these parsers is high, they are not robust across all datasets. When logs grow to a large scale (e.g., 200 million log messages), which is common in practice, these parsers are not efficient enough to handle such data on a single computer. To address the above limitations, we design and implement a parallel log parser (namely POP) on top of Spark, a large-scale data processing platform. Comprehensive experiments have been conducted to evaluate POP on both synthetic and real-world datasets. The evaluation results demonstrate the capability of POP in terms of accuracy, efficiency, and effectiveness on subsequent log mining tasks.


title: DIVE: A Graph-Based Visual-Analytics Framework for Big Data

abstract: The need for data-centric scientific tools is growing; domains such as biology, chemistry, and physics are increasingly adopting computational approaches. So, scientists must deal with the challenges of big data. To address these challenges, researchers built a visual-analytics platform named DIVE (Data Intensive Visualization Engine). DIVE is a data-agnostic, ontologically expressive software framework that can stream large datasets at interactive speeds. In particular, DIVE makes novel contributions to structured-data-model manipulation and high-throughput streaming of large, structured datasets.


title: High-performance PMASynRM with ferrite magnet for EV/HEV applications

abstract: Although motors that use rare-earth permanent magnets typically exhibit high performance, high costs and concerns about the stability of raw material supplies can limit production. This paper proposes a permanent-magnet-assisted synchronous reluctance motor (PMASynRM) with ferrite magnets that do not use rare earth materials. The performance of the proposed PMASynRM is evaluated based on the 2-D finite element method and an experiment using a prototype machine. The motor parameters, torque, output power, and efficiency characteristics are discussed. The analysis and experimental results reveal that the proposed PMASynRM has an efficiency equivalent to that of conventional rare-earth permanent magnet synchronous motors used in EV/HEVs.


title: Lactic acid fermentation of crude sorghum extract.

abstract: Crude extract from sweet sorghum supplemented with vetch juice was utilized as the carbohydrate source for fermentative production of lactic acid. Fermentation of media containing 7% (w/v) total sugar was completed in 60-80 hours by Lactobacillus plantarum, product yield averaging 85%. Maximum acid production rates were dependent on pH, initial substrate distribution, and concentration, the rates varying from 2 to 5 g/liter per hour. Under limited medium supplementation the lactic acid yield was lowered to 67%. The fermented ammoniated product contained over eight times as much equivalent crude protein (N x 6.25) as the original medium. Unstructured kinetic models were developed for cell growth, lactic acid formation, and substrate consumption in batch fermentation. With the provision of experimentally determined kinetic parameters, the proposed models accurately described the fermentation process. 15 references.


title: Improving the Accuracy and Speed of Support Vector Machines

abstract: Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for ill-posed problems. Against this very general backdrop, any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization performance (the "virtual support vector" method) does so by incorporating known invariances of the problem. This method achieves a drop in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine. The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance, achieving 1.1 % error. The virtual support vector method is applicable to any SVM problem with known invariances. The reduced set method is applicable to any support vector machine.


title: Stock trading rule discovery with an evolutionary trend following model

abstract: eTrend is a hybrid long-term and short-term evolutionary trend following model.eTrend generates high Sortino ratio and cumulative return after transaction cost.eTrend outperforms B&H strategy, as well as DT and ANN trading models.Concept drift analysis has identified adverse rules between bear and bull markets. Evolutionary learning is one of the most popular techniques for designing quantitative investment (QI) products. Trend following (TF) strategies, owing to their briefness and efficiency, are widely accepted by investors. Surprisingly, to the best of our knowledge, no related research has investigated TF investment strategies within an evolutionary learning model. This paper proposes a hybrid long-term and short-term evolutionary trend following algorithm (eTrend) that combines TF investment strategies with the eXtended Classifier Systems (XCS). The proposed eTrend algorithm has two advantages: (1) the combination of stock investment strategies (i.e., TF) and evolutionary learning (i.e., XCS) can significantly improve computation effectiveness and model practicability, and (2) XCS can automatically adapt to market directions and uncover reasonable and understandable trading rules for further analysis, which can help avoid the irrational trading behaviors of common investors. To evaluate eTrend, experiments are carried out using the daily trading data stream of three famous indexes in the Shanghai Stock Exchange. Experimental results indicate that eTrend outperforms the buy-and-hold strategy with high Sortino ratio after the transaction cost. Its performance is also superior to the decision tree and artificial neural network trading models. Furthermore, as the concept drift phenomenon is common in the stock market, an exploratory concept drift analysis is conducted on the trading rules discovered in bear and bull market phases. The analysis revealed interesting and rational results. In conclusion, this paper presents convincing evidence that the proposed hybrid trend following model can indeed generate effective trading guidance for investors.


title: Prediction of heart diseases using associative classification

abstract: Today's health-care services have come a long way to provide medical care to the patients and protect them from various diseases. This paper comprises the development of a framework based on associative classification techniques on heart dataset for early diagnosis of heart based diseases. It is hard to diagnose the heart diseases with just observation that arrives suddenly and may prove fatal when it's uncontrolled. The implementation of work is done on Cleveland heart diseases dataset from the University of California Irvine (UCI) machine learning repository to test on different data mining techniques. The various attributes related to cause of heart diseases are viz: gender, age, chest pain type, blood pressure, blood sugar etc that can predict early symptoms heart disease. Various data mining algorithms such as Aprior, FP-Growth, Naive bayes, ZeroR, OneR, J48 and k-nearest neighbor are applied in this study for prediction of heart diseases. On basis of best results the development of heart disease prediction system is done by using hybrid technique for classification associative rules (CARs) to achieve the prediction accuracy of 99.19%.


title: Spatiotemporal Modeling for Crowd Counting in Videos

abstract: Region of Interest (ROI) crowd counting can be formulated as a regression problem of learning a mapping from an image or a video frame to a crowd density map. Recently, convolutional neural network (CNN) models have achieved promising results for crowd counting. However, even when dealing with video data, CNN-based methods still consider each video frame independently, ignoring the strong temporal correlation between neighboring frames. To exploit the otherwise very useful temporal information in video sequences, we propose a variant of a recent deep learning model called convolutional LSTM (ConvLSTM) for crowd counting. Unlike the previous CNN-based methods, our method fully captures both spatial and temporal dependencies. Furthermore, we extend the ConvLSTM model to a bidirectional ConvLSTM model which can access long-range information in both directions. Extensive experiments using four publicly available datasets demonstrate the reliability of our approach and the effectiveness of incorporating temporal information to boost the accuracy of crowd counting. In addition, we also conduct some transfer learning experiments to show that once our model is trained on one dataset, its learning experience can be transferred easily to a new dataset which consists of only very few video frames for model adaptation.


title: Predicting levels of reading and writing achievement in typically developing, english-speaking 2nd and 5th graders

abstract: Abstract Human traits tend to fall along normal distributions. The aim of this research was to evaluate an evidence-based conceptual framework for predicting expected individual differences in reading and writing achievement outcomes for typically developing readers and writers in early and middle childhood from Verbal Reasoning with or without Working Memory Components (phonological, orthographic, and morphological word storage and processing units, phonological and orthographic loops, and rapid switching attention for cross-code integration). Verbal Reasoning (reconceptualized as Bidirectional Cognitive–Linguistic Translation) plus the Working Memory Components (reconceptualized as a language learning system) accounted for more variance than Verbal Reasoning alone, except for handwriting for which Working Memory Components alone were better predictors. Which predictors explained unique variance varied within and across reading (oral real word and pseudoword accuracy and rate, reading comprehension) and writing (handwriting, spelling, composing) skills and grade levels (second and fifth) in this longitudinal study. Educational applications are illustrated and theoretical and practical significance discussed.


The aNALoGuE Challenge: Non Aligned Language GEneration


title: An organizational theoretic review of green supply chain management literature

abstract: Green supply chain management (GSCM) has gained increasing attention within both academia and industry. As the literature grows, finding new directions by critically evaluating the research and identifying future directions becomes important in advancing knowledge for the field. Using organizational theories to help categorize the literature provides opportunities to address both the objectives of understanding where the field currently stands and identifying research opportunities and directions. After providing a background discussion on GSCM, we categorize and review recent GSCM literature under nine broad organizational theories, with a special emphasis on investigation of adoption, diffusion and outcomes of GSCM practices. Within this review framework, we also identify GSCM research questions that are worthy of investigation. Additional organizational theories which are considered valuable for future GSCM research are also identified with a conclusion for this review.


title: GALEOR: Leakage reduction for CMOS circuits

abstract: High performance and computational capability in the current generation processors are made possible by small feature sizes and high device density. To maintain the current drive strength and control the power dissipation in these processors, simultaneous scaling down of supply and threshold voltages is performed. High device density and low threshold voltages result in an increase in the leakage current dissipation. Large on chip caches are integrated onto the current generation processors which are becoming a major contributor to total leakage power. The proposed static power reduction technique, GALEOR (GAted LEakage TransistOR), reduces the leakage current flowing through the circuits. In the proposed leakage power technique, two gated leakage transistors are inserted between NMOS and PMOS circuitry of the existing circuit such that gates of the extra inserted transistors are connected to their respective drain regions. GALEOR technique was tested on standard cell gates and memory elements. Area overhead is minimized by eliminating the use of control logic to switch between the active and standby states. Performance overhead is increased due to the reduced output voltage swing.


title: UFO - Hidden Backdoor Discovery and Security Verification in IoT Device Firmware

abstract: Recently, the use of embedded devices such as WiFi APs, IP CAM, and drones in Internet of Things (IoT) applications has become more widespread. These embedded devices are connected to networks and are often used for critical services. Thus, they receive significant attention from hackers who attempt to find a major intrusion vector in IoT applications. Hackers focus on identifying hidden backdoors in embedded devices to gain full remote access; if they gain access, they can cause significant damage to critical infrastructures. Therefore, to improve embedded device security, this study introduces Universal Firmware vulnerability Observer (UFO); UFO is a firmware vulnerability discovery system, which can automatically perform tasks such as reversing firmware embedded filesystem, identifying vulnerability, and exploring password leaks to meet the IoT firmware security verification standards, including OWASP, UL-2900, and ICSA Labs. In addition, we design a Shell Script Dependency algorithm to help identify hidden backdoor problems by discovering suspicious shell script execution paths in the extracted firmware filesystem. We use 237 real-world embedded device firmware files to evaluate UFO. The results indicate that the effectiveness of reversing firmware binary is 96%, which is significantly higher than that of open source tools. Besides, we also conclude that 73% of firmware files contain Common Vulnerabilities and Exposures in their embedded Linux kernel, 22% of firmware files can leak login passwords, and 6% of firmware files contain hidden backdoors. Moreover, we reported hidden backdoor problems to two IoT device vendors in Taiwan and received their confirmation. UFO can be successfully used for verifying firmware security and discovering hidden backdoor threats in commercial IoT devices.


title: A deep convolutional neural network model for automated identification of abnormal EEG signals

abstract: Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.


A Variational Autoencoding Approach for Inducing Cross-lingual Word Embeddings


LKIM : The Linux Kernel Integrity


title: Homology Modeling, Molecular Docking and Molecular Dynamics Based Functional Insights into Rice Urease Bound to Urea

abstract: Urease (EC 3.5.1.5) is an important member of most popular amidohydrolases superfamily that is well known for catalyzes the hydrolysis of urea into ammonia and carbon dioxide. Urease protein exclusively found in a wide range of living organisms including plant, algae, bacteria, fungi and some invertebrates. In plants, urease play an important role of recapturing the nitrogen from urea. Despite its critical interplay in plants the structural and functional aspects of urease in O. sativa are still unresolved. In the present study, a three-dimensional structure of rice urease was deduced by using homology modelling based approach. Molecular dynamics simulations were performed to gain further insight into the molecular mechanism and mode of action of urease of rice. Further, the possible binding interactions of modeled structure of urease with urea were assessed by using a geometry-based molecular docking algorithm. The study reveals the role of Ser324, Ala329 and Val385 of rice urease enzyme in binding with the substrate urea. In conclusion, this study presents a 3D model of rice urease and helps understanding the molecular basis for the mechanism of urease interaction with substrate urea at atomic level.


title: Online public communications by police & fire services during the 2012 Hurricane Sandy

abstract: Social media and other online communication tools are a subject of great interest in mass emergency response. Members of the public are turning to these solutions to seek and offer emergency information. Emergency responders are working to determine what social media policies should be in terms of their "public information" functions. We report on the online communications from all the coastal fire and police departments within a 100 mile radius of Hurricane Sandy's US landfall. Across four types of online communication media, we collected data from 840 fire and police departments. Findings indicate that few departments used these online channels in their Sandy response efforts, and that communications differed between fire and police departments and across media type. However, among the highly engaged departments, there is evidence that they bend and adapt policies about what constitutes appropriate public communication in the face of emergency demands; therefore, we propose that flexibility is important in considering future emergency online communication policy. We conclude with design recommendations for making online communication media more "listenable" for both emergency managers and members of the public.


title: A UHF class E2 DC/DC converter using GaN HEMTs

abstract: In this paper, the design of a class E2 resonant DC/DC converter, operating at UHF band, is proposed. Combining the use of GaN HEMT devices, both for the inverter and the synchronous rectifier, with high Q lumped-element multi-harmonic matching networks, a peak efficiency value of 72% has been obtained at 780 MHz with a 10.3 W output power. By means of a Pulse Width Modulation (PWM) over the gate driving envelope, the output voltage may be controlled while keeping low switching losses, with an estimated small-signal bandwidth (BW) and a slew rate of 11 MHz and 630 V/µSeg, respectively.


title: The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response

abstract: Utilities face the challenge of responding to power outages due to storms and ice damage, but most power grids are not equipped with sensors to pinpoint the precise location of the faults causing the outage. Instead, utilities have to depend primarily on phone calls (trouble calls) from customers who have lost power to guide the dispatching of utility trucks. In this paper, we develop a policy that routes a utility truck to restore outages in the power grid as quickly as possible, using phone calls to create beliefs about outages, but also using utility trucks as a mechanism for collecting additional information. This means that routing decisions change not only the physical state of the truck (as it moves from one location to another) and the grid (as the truck performs repairs), but also our belief about the network, creating the first stochastic vehicle routing problem that explicitly models information collection and belief modeling. We address the problem of managing a single utility truck, which we start by formulating as a sequential stochastic optimization model which captures our belief about the state of the grid. We propose a stochastic lookahead policy, and use Monte Carlo tree search (MCTS) to produce a practical policy that is asymptotically optimal. Simulation results show that the developed policy restores the power grid much faster compared to standard industry heuristics.


title: Adaptive integration of feature matches into variational optical flow methods

abstract: Despite the significant progress in terms of accuracy achieved by recent variational optical flow methods, the correct handling of large displacements still poses a severe problem for many algorithms. In particular if the motion exceeds the size of an object, standard coarse-to-fine estimation schemes fail to produce meaningful results. While the integration of point correspondences may help to overcome this limitation, such strategies often deteriorate the performance for small displacements due to false or ambiguous matches. In this paper we address the aforementioned problem by proposing an adaptive integration strategy for feature matches. The key idea of our approach is to use the matching energy of the baseline method to carefully select those locations where feature matches may potentially improve the estimation. This adaptive selection does not only reduce the runtime compared to an exhaustive search, it also improves the reliability of the estimation by identifying unnecessary and unreliable features and thus by excluding spurious matches. Results for the Middlebury benchmark and several other image sequences demonstrate that our approach succeeds in handling large displacements in such a way that the performance for small displacements is not compromised. Moreover, experiments even indicate that image sequences with small displacements can benefit from carefully selected point correspondences.


title: Autocomplete Textures for 3D Printing

abstract: Texture is an essential property of physical objects that affects aesthetics, usability, and functionality. However, designing and applying textures to 3D objects with existing tools remains difficult and time-consuming; it requires proficient 3D modeling skills. To address this, we investigated an auto-completion approach for efficient texture creation that automates the tedious, repetitive process of applying texture while allowing flexible customization. We developed techniques for users to select a target surface, sketch and manipulate a texture with 2D drawings, and then generate 3D printable textures onto an arbitrary curved surface. In a controlled experiment our tool sped texture creation by 80% over conventional tools, a performance gain that is higher with more complex target surfaces. This result confirms that auto-completion is powerful for creating 3D textures.


title: Estimating micro-populations through social media analytics

abstract: Estimation of crowd sizes or the occupancy of buildings and skyscrapers can often be essential. However, traditional ways of estimation through manual counting, image processing or in the case of skyscrapers, through total water usage are awkward, inefficient and often inaccurate. Social media has developed rapidly in the last decade. In this work, we provide novel solutions to estimate the population of suburbs and skyscrapers—so-called micro-populations, through the use of social media. We develop a big data solution leveraging large-scale harvesting and analysis of Twitter data. By harvesting real-time tweets and clustering tweets within suburbs and skyscrapers, we show how micro-populations can be calculated. To validate this, we construct linear and spatial models for the suburbs in four cities of Australia using census data and geospatial data models (shapefiles). Our prediction of micro-population shows that Twitter can indeed be used for population prediction with a high degree of accuracy.


title: Abusive supervision and subordinates' organizational citizenship behavior.

abstract: The relationship between subordinates’ perceptions of abusive supervision and supervisors’ evaluations of subordinates’ organizational citizenship behavior (OCB) was explored among a sample of 373 Air National Guard members and their military supervisors. As predicted, the relationship between abusive supervision and subordinates’ OCB was stronger among subordinates who defined OCB as extra-role behavior (compared with those defining OCB as in-role behavior), and this effect was fully mediated by the interactive effect of procedural justice and OCB role definitions. The study’s implications for theory and research are discussed, its limitations are identified, and directions for future research are suggested.


title: The design of an output data collection framework for NS-3

abstract: An important design decision in the construction of a simulator is how to enable users to access the data generated in each run of a simulation experiment. As the simulator executes, the samples of performance metrics that are generated beg to be exposed either in their raw state or after having undergone mathematical processing. Also of concern is the particular format this data assumes when externalized to mass storage, since it determines the ease of processing by other applications or interpretation by the user. In this paper, we present a framework for the ns-3 network simulator for capturing data from inside an experiment, subjecting it to mathematical transformations, and ultimately marshaling it into various output formats. The application of this functionality is illustrated and analyzed via a study of common use cases. Although the implementation of our approach is specific to ns-3, this design presents lessons transferrable to other platforms.


title: To Apply Yourself Is Human, to Reapply Divine

abstract: Prefrontal cortex is involved in adapting our emotional response to setbacks. While we feel that some setbacks are controllable, others are not. Here, Bhanji and Delgado (2014) reveal the neural substrates of persistence in the face of controllable and uncontrollable setbacks.


title: Ten Problems in Experimental Mathematics

abstract: This article was stimulated by the recent SIAM ''100 DigitChallenge'' of Nick Trefethen, beautifully described in a recent book. Indeed, these ten numeric challenge problems are also listed in a recent book by two of present authors, where they are followed by the ten symbolic/numeric challenge problems that are discussed in this article. Our intent was to present ten problems that are characteristic of the sorts of problems that commonly arise in ''experimental mathematics''. The challenge in each case is to obtain a high precision numeric evaluation of the quantity, and then, if possible, to obtain a symbolic answer, ideally one with proof. Our goal in this article is to provide solutions to these ten problems, and in the process present a concise account of how one combines symbolic and numeric computation, which may be termed ''hybrid computation'', in the process of mathematical discovery.


title: CAVE2: a hybrid reality environment for immersive simulation and information analysis

abstract: Hybrid Reality Environments represent a new kind of visualization spaces that blur the line between virtual ::: environments and high resolution tiled display walls. This paper outlines the design and implementation of the ::: CAVE2TM Hybrid Reality Environment. CAVE2 is the world’s first near-seamless flat-panel-based, surround-screen immersive system. Unique to CAVE2 is that it will enable users to simultaneously view both 2D and 3D information, providing more flexibility for mixed media applications. CAVE2 is a cylindrical system of 24 feet in diameter and 8 feet tall, and consists of 72 near-seamless, off-axisoptimized passive stereo LCD panels, creating an approximately 320 degree panoramic environment for displaying information at 37 Megapixels (in stereoscopic 3D) or 74 Megapixels in 2D and at a horizontal visual acuity of 20/20. Custom LCD panels with shifted polarizers were built so the images in the top and bottom rows of LCDs are optimized for vertical off-center viewing- allowing viewers to come closer to the displays while minimizing ghosting. CAVE2 is designed to support multiple operating modes. In the Fully Immersive mode, the entire room can be dedicated to one virtual simulation. In 2D model, the room can operate like a traditional tiled display wall enabling users to work with large numbers of documents at the same time. In the Hybrid mode, a mixture of both 2D and 3D applications can be simultaneously supported. The ability to treat immersive work spaces in this Hybrid way has never been achieved before, and leverages the special abilities of CAVE2 to enable researchers to seamlessly interact with large collections of 2D and 3D data. To realize this hybrid ability, we merged the Scalable Adaptive Graphics Environment (SAGE) - a system for supporting 2D tiled displays, with Omegalib - a virtual reality middleware supporting OpenGL, OpenSceneGraph and Vtk applications.


title: Renal Function at Two Years in Liver Transplant Patients Receiving Everolimus: Results of a Randomized, Multicenter Study

abstract: In a 24-month prospective, randomized, multicenter, open-label study, de novo liver transplant patients were randomized at 30 days to everolimus (EVR) + Reduced tacrolimus (TAC; n = 245), TAC Control (n = 243) or TAC Elimination (n = 231). Randomization to TAC Elimination was stopped prematurely due to a significantly higher rate of treated biopsy-proven acute rejection (tBPAR). The incidence of the primary efficacy endpoint, composite efficacy failure rate of tBPAR, graft loss or death postrandomization was similar with EVR + Reduced TAC (10.3%) or TAC Control (12.5%) at month 24 (difference -2.2%, 97.5% confidence interval [CI] -8.8%, 4.4%). BPAR was less frequent in the EVR + Reduced TAC group (6.1% vs. 13.3% in TAC Control, p = 0.010). Adjusted change in estimated glomerular filtration rate (eGFR) from randomization to month 24 was superior with EVR + Reduced TAC versus TAC Control: difference 6.7 mL/min/1.73 m(2) (97.5% CI 1.9, 11.4 mL/min/1.73 m(2), p = 0.002). Among patients who remained on treatment, mean (SD) eGFR at month 24 was 77.6 (26.5) mL/min/1.73 m(2) in the EVR + Reduced TAC group and 66.1 (19.3) mL/min/1.73 m(2) in the TAC Control group (p < 0.001). Study medication was discontinued due to adverse events in 28.6% of EVR + Reduced TAC and 18.2% of TAC Control patients. Early introduction of everolimus with reduced-exposure tacrolimus at 1 month after liver transplantation provided a significant and clinically relevant benefit for renal function at 2 years posttransplant.


title: BRDF models for accurate and efficient rendering of glossy surfaces

abstract: Glossy surface reflectance is hard to model accuratley using traditional parametric BRDF models. An alternative is provided by data driven reflectance models, however these models offers less user control and generally results in lower efficency. In our work we propose two new lightweight parameteric BRDF models for accurate modeling of glossy surface refllectance, one inspired by Rayleigh-Rice theory for optically smooth surfaces and one inspired by microfacet-theory. We base our models on a thourough study of the scattering behaviour of measured reflectance data from the MERL database. The study focuses on two key aspects of BRDF models, parametrization and scatter distribution. We propose a new scattering distributuion for glossy BRDFs inspired by the ABC model for surface statistics of optically smooth surfaces. Based on the survey we consider two parameterizations, one based on micro-facet theory using the halfway vector and one inspired by the parametrization for the Rayleigh-Rice BRDF model considering the projected devaition vector. To enable efficent rendering we also show how the new models can be approximatley sampled for importance sampling the scattering integral.


title: An Overview on Clustering Methods

abstract: Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.


title: ROSy+: 3D Object Pose Normalization Based on PCA and Reflective Object Symmetry with Application in 3D Object Retrieval

abstract: A novel pose normalization method based on 3D object reflective symmetry is presented. It is a general purpose global pose normalization method; in this paper it is used to enhance the performance of a 3D object retrieval pipeline. Initially, the axis-aligned minimum bounding box of a rigid 3D object is modified by requiring that the 3D object is also in minimum angular difference with respect to the normals to the faces of its bounding box. To estimate the modified axis-aligned bounding box, a set of predefined planes of symmetry are used and a combined spatial and angular distance, between the 3D object and its symmetric object, is calculated. By minimizing the combined distance, the 3D object fits inside its modified axis-aligned bounding box and alignment with the coordinate system is achieved. The proposed method is incorporated in a hybrid scheme, that serves as the alignment method in a 3D object retrieval system. The effectiveness of the 3D object retrieval system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and demonstrated using both quantitative and qualitative measures via an extensive consistent evaluation on standard benchmarks. The results clearly show performance boost against current approaches.


title: A Comprehensive Analysis of AM–AM and AM–PM Conversion in an LDMOS RF Power Amplifier

abstract: In this paper, a Volterra analysis built on top of a normal harmonic balance simulation is used for a comprehensive analysis of the causes of AM-PM distortion in a LDMOS RF power amplifier (PA). The analysis shows that any nonlinear capacitors cause AM-PM. In addition, varying terminal impedances may pull the matching impedances and cause phase shift. The AM-PM is also affected by the distortion that is mixed down from the second harmonic. As a sample circuit, an internally matched 30-W LDMOS RF PA is used and the results are compared to measured AM-AM, AM-PM and large-signal S11.


title: Tribo-Aero-Electrostatic Separator for Ternary Mixtures of Granular Plastics

abstract: This work presents a comprehensive study of an electrostatic separation device for the recycling of granular plastic materials from waste electrical and electronic equipment (WEEE). The peculiarity of this system is the association of two charging techniques: (i) fluidized bed and (ii) sliding on inclined plates, and two superposed separation units, in order to achieve the selective sorting of a granular mixture composed of three different species of insulating materials. The tests were performed on a mixture of high density polyethylene (HDPE), polyvinyl chloride (PVC) and polycarbonate (PC). A set of tribocharging experiments was conducted for each material, so that to choose the nature of the walls of the static charger. The analysis of the purity of separated products was performed using an image processing program written in MATLAB. High purity separation (85%, 95% and 75% for the PVC, PC and HDPE products, respectively) was obtained at a recovery rate beyond 80%.


title: Deliberate Learning and the Evolution of Dynamic Capabilities

abstract: This paper investigates the mechanisms through which organizations develop dynamic capabilities, defined as routinized activities directed to the development and adaptation of operating routines. It addresses the role of (1) experience accumulation, (2) knowledge articulation, and (3) knowledge codifi- cation processes in the evolution of dynamic, as well as operational, routines. The argument is made that dynamic capabilities are shaped by the coevolution of these learning mechanisms. At any point in time, firms adopt a mix of learning behaviors constituted by a semiautomatic accumulation of experience and by deliberate investments in knowledge articulation and codification activities. The relative effectiveness of these capability-building mechanisms is analyzed here as contingent upon selected features of the task to be learned, such as its frequency, homogeneity, and degree of causal ambiguity. Testable hypotheses about these effects are derived. Somewhat counterintuitive implications of the analysis include the relatively superior effectiveness of highly deliberate learning processes such as knowledge codification at lower levels of frequency and homogeneity of the organizational task, in contrast with common managerial practice.