title: Wearable Glove-Type Driver Stress Detection Using a Motion Sensor

abstract: Increased driver stress is generally recognized as one of the major factors leading to road accidents and loss of life. Even though physiological signals are reported as the most reliable means to measure driver stresses, they often require the use of unique and expensive sensors, which produce dynamic and varying readings within individuals. This paper presents a novel means to predict a driver’s stress level by evaluating the movement pattern of the steering wheel. This is accomplished by using an inertial motion unit sensor, which is placed on a glove worn by the driver. The motion sensor selected for this paper was chosen because for its low cost and the fact that it is least affected by environmental factors as compared with a physiological signal. Experiments were conducted in three different environmental scenarios. The scenarios were classified as “urban,” “highway,” and “rural,” and they were chosen to simulate contrasting stress conditions experienced by the driver. In this paper, skin conductance and driver self-reports served as a reference stress to predict the driver’s stress level. Galvanic skin response, a well-known stress indicator, was captured along the driver’s palm and the readings were transmitted to a mobile device via low energy Bluetooth for further processing. The results revealed that indirect measurement of steering wheel movement with an inertial motion sensor could obtain accuracies up to an average rate of 94.78%. This demonstrates the opportunity for inclusion of motion sensors in wireless driver assistance systems for ambulatory monitoring of stress levels.


title: High-Efficiency Harmonically Terminated Diode and Transistor Rectifiers

abstract: This paper presents a theoretical analysis of harmonically terminated high-efficiency power rectifiers and experimental validation on a class-C single Schottky-diode rectifier and a class- F-1 GaN transistor rectifier. The theory is based on a Fourier analysis of current and voltage waveforms, which arise across the rectifying element when different harmonic terminations are presented at its terminals. An analogy to harmonically terminated power amplifier (PA) theory is discussed. From the analysis, one can obtain an optimal value for the dc load given the RF circuit design. An upper limit on rectifier efficiency is derived for each case as a function of the device on-resistance. Measured results from fundamental frequency source-pull measurement of a Schottky diode rectifier with short-circuit terminations at the second and third harmonics are presented. A maximal device rectification efficiency of 72.8% at 2.45 GHz matches the theoretical prediction. A 2.14-GHz GaN HEMT rectifier is designed based on a class-F-1 PA. The gate of the transistor is terminated in an optimal impedance for self-synchronous rectification. Measurements of conversion efficiency and output dc voltage for varying gate RF impedance, dc load, and gate bias are shown with varying input RF power at the drain. The rectifier demonstrates an efficiency of 85% for a 10-W input RF power at the transistor drain with a dc voltage of 30 V across a 98-Ω resistor.


title: 3D relative pose estimation from distance-only measurements

abstract: In this paper, we develop an algorithm for determining the relative position and attitude of two robots moving in 3D, using only dead-reckoning and inter-robot distance measurements. From the mechanical analogue of parallel manipulators, it is known that this problem has 40 solutions when six distance measurements are available. These general solutions are not known in closed-form, and existing closed-form solutions require additional bearing sensors, or impose strong constraints on the geometric structure of the manipulator (in our case, the robots' trajectories). This paper presents, for the first time, an efficient, algebraic algorithm to solve the relative pose using 10 distance measurements, without imposing any constraints on the robots' motion. We further present a weighted least-squares refinement step, and validate our algorithm in various simulations, demonstrating its efficiency and accuracy.


title: Shake-Shake regularization

abstract: The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%. Experiments on architectures without skip connections or Batch Normalization show encouraging results and open the door to a large set of applications. Code is available at this https URL


title: Emergence in Nascent Online Communities: An Affordance Perspective

abstract: Online communities like Wikipedia have the potential to transform our global society. Despite their growing importance, however, the extant literature has theorized only little about how member contribution behavior in a nascent online community emerges in interaction with the materiality of the technology platform. Studying this is critical for understanding the important early stage evolution of online communities. Without a rich history, the platform’s materiality is almost the only thing that members can use to make sense of the online community. We investigate this question by applying a mixedmethod approach to a longitudinal case study of AshokaHub, a nascent global online community of social entrepreneurs. We use an affordance perspective and our findings to date suggest that the materiality of the technology platform plays an important role in the emergence of member contribution behavior in the nascent online community. Moreover, they suggest extensions to the concepts of affordances and imbrication.


title: Consensus reaching model in the complex and dynamic MAGDM problem

abstract: In classical multiple attribute group decision making (MAGDM), decision makers evaluate predefined alternatives based on predefined attributes. In other words, the set of alternatives and the set of attributes are fixed throughout the decision process. However, real-world MAGDM problems (e.g., the decision processes of the United Nations Security Council) frequently have the following features. (1) Decision makers have different interests, and they thus use individual sets of attributes to evaluate the individual alternatives. In some situations, the individual sets of attributes may be heterogeneous. (2) In the decision process, decision makers do not have to reach a consensus regarding the use of the set of attributes. Instead, decision makers hope to find an alternative that is approved by all or most of them. (3) Finally, both the individual sets of attributes and the individual sets of alternatives can change dynamically in the decision process. By incorporating the above practical features into MAGDM, this study defines a complex and dynamic MAGDM problem, and proposes its resolution framework. In the resolution framework, a selection process in the context of heterogeneous attributes is proposed that obtains the ranking of individual alternatives and a collective solution. In addition, a consensus process is developed that generates adjustment suggestions for individual sets of attributes, individual sets of alternatives and individual preferences, thus helping decision makers reach consensus. Compared with existing MAGDM models, this study provides a flexible framework to form an approximate decision model to real-world MAGDM problems.


title: 360-Degree Video Quality Evaluation

abstract: 360-degree video is emerging as a new way of offering immersive visual experience. The quality evaluation of 360- degree video is more difficult compared to the quality evaluation of conventional video. However, to ensure successful development of 360-degree video coding technologies, it is essential to precisely measure both objective and subjective quality. In this paper, an overview of the 360-degree video quality evaluation framework established by the joint video exploration team (JVET) of ITU-T VCEG and ISO/IEC MPEG is provided. This framework aims at reproducing the different processes in the 360-degree video processing workflow that are related to coding. The results of different experiments conducted using the JVET framework are reported to illustrate the impact on objective and subjective quality with different projection formats and codecs.


title: Why the Net is not a Public Sphere

abstract: What is the relation between the idea of the public sphere and computer-mediated interaction? I argue that the notion of the public sphere is not only inapplicable to the Net, but also and more importantly, that it is damaging to practices of democracy under conditions of contemporary technoculture, conditions Manuel Castells theorizes as capitalism in the information mode of development and which I refer to as communicative capitalism.1 As an alternative to the public sphere, I consider the potential of a political architecture rooted in a notion of networks. To the extent that such an architecture can center democratic practice in conflict and contestation, so can it open up the democratic imagination in the networked societies of communicative capitalism.


title: A Measure for Objective Evaluation of Image Segmentation Algorithms

abstract: Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Little effort has been spent on the design of perceptually correct measures to compare an automatic segmentation of an image to a set of hand-segmented examples of the same image. This paper demonstrates how a modification of the Rand index, the Normalized Probabilistic Rand (NPR) index, meets the requirements of largescale performance evaluation of image segmentation. We show that the measure has a clear probabilistic interpretation as the maximum likelihood estimator of an underlying Gibbs model, can be correctly normalized to account for the inherent similarity in a set of ground truth images, and can be computed efficiently for large datasets. Results are presented on images from the publicly available Berkeley Segmentation dataset.


title: Cooperative Control Strategy of Energy Storage System and Microsources for Stabilizing the Microgrid during Islanded Operation

abstract: In this paper, the cooperative control strategy of microsources and the energy storage system (ESS) during islanded operation is presented and evaluated by a simulation and experiment. The ESS handles the frequency and the voltage as a primary control. And then, the secondary control in microgrid management system returns the current power output of the ESS into zero. The test results show that the proposed cooperative control strategy can regulate the frequency and the voltage, and the secondary control action can contribute to improve the control capability.


title: Semi-supervised transfer subspace for domain adaptation

abstract: Abstract Domain shift is defined as the mismatch between the marginal probability distributions of a source (training set) and a target domain (test set). A successful research line has been focusing on deriving new source and target feature representations to reduce the domain shift problem. This task can be modeled as a semi-supervised domain adaptation. However, without exploiting at the same time the knowledge available on the labeled source, labeled target, and unlabeled target data, semi-supervised methods are prone to fail. Here, we present a simple and effective Semi-Supervised Transfer Subspace (SSTS) method for domain adaptation. SSTS establishes pairwise constraints between the source and labeled target data, besides it exploits the global structure of the unlabeled data to build a domain invariant subspace. After reducing the domain shift by projecting both source and target domain onto this subspace, any classifier can be trained on the source and tested on target. Results on 49 cross-domain problems confirm that SSTS is a powerful mechanism to reduce domain shift. Furthermore, SSTS yields better classification accuracy than state-of-the-art domain adaptation methods.


title: The interplay of gender and social background: A longitudinal study of interaction effects in reading attitudes and behaviour

abstract: BACKGROUND ::: Researchers often report and discuss gender differences. However, recent research has drawn attention to interaction effects between gender and other social categories. ::: ::: ::: AIMS ::: This study analysed the development of disparities in students' reading-related self-concept, intrinsic motivation, and behaviour, as they relate to differences in gender and socio-economic family background. Drawing on expectancy-value theory, we regarded reading-related self-concept, motivation, and behaviour as key to explaining the growing differences between boys and girls in adolescence. Specifically, we focused on the interaction between gender and socio-economic background in children, which has been discussed in the context of moderating gender differences but not in the context of reading-related attitudes and behaviour. ::: ::: ::: SAMPLE ::: The investigation is based on a longitudinal sample of N = 717 German students between third and sixth grades. ::: ::: ::: METHOD ::: We used questionnaire data from both students and parents. To compare students' development across time, we applied multigroup latent growth curve models. ::: ::: ::: RESULTS ::: We found evidence of increasing gender differences, which were also moderated by the socio-economic status (SES) of parents: a gender gap either already existed (intrinsic motivation and reading behaviour) or intensified (reading self-concept and reading behaviour) between third and sixth grades. The interaction of gender and SES seemed particularly important for reading self-concept, with the gender gap growing less substantially for higher-SES children. Moreover, this pattern persisted for reading self-concept, even when controlling for achievement differences. ::: ::: ::: CONCLUSIONS ::: The results provide evidence that gender, social background, and the interaction of the two are relevant for development in the domain of reading, even in young children.


title: Optimal control of distributed energy resources using model predictive control

abstract: In an isolated power system (rural microgrid), distributed energy resources (DERs), such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage, and maximizing the ability to maintain real-time power balance during operations. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem under high penetration of wind. Simulation studies using different prediction horizons further demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in the system caused by wind and demand.


title: A Review on Security Evaluation for Pattern Classifier against Attack

abstract: The systems which can be used for pattern classification are used in adversarial application, for example spam filtering, network intrusion detection system, biometric authentication. This adversarial scenario’s exploitation may sometimes affect their performance and limit their practical utility. In case of pattern classification conception and contrive methods to adversarial environment is a novel and relevant research direction, which has not yet pursued in a systematic way. To address one main open issue: evaluating at contrive phase the security of pattern classifiers (for example the performance degradation under potential attacks which incurs during the operation). To propose a framework for evaluation of classifier security and also this framework can be applied to different classifiers on one of the application from the spam filtering,biometric authentication andnetwork intrusion detection.


Flower induction and pollen viability for western larch


title: Angiolymphoid Hyperplasia with Eosinophilia

abstract: A patient with multiple erythematous nodules on her posterior scalp presented to our dermatology clinic. Biopsy confirmed the diagnosis of angiolymphoid hyperplasia with eosinophilia. The etiology of this disorder is unclear. Several cases have been treated in the past with complete surgical excision, although the recurrence rate remains relatively high.


title: Supplier selection based on hierarchical potential support vector machine

abstract: Supplier selection is an important and widely studied topic since it has significant impact on purchasing management in supply chain. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, a new support vector machine technology, potential support vector machine, is introduced and then combined with decision tree to address issues on supplier selection including feature selection, multiclass classification and so on. So, hierarchical potential support vector machine and hierarchical system of features are put forward in the paper, and experiments show the proposed methodology has much better generalization performance and less computation consumptions than standard support vector machine.


title: Lexical pragmatics, ad hoc concepts and metaphor: A Relevance Theory perspective

abstract: Ostensive communication, the paradigm case of which is verbal communication, is the domain of a dedicated cognitive system, according to Relevance Theory (RT). This ‘pragmatics’ module is responsible for inferring the content or meaning that the communicator intends by his/her ostensive stimulus. An important sub-process within the system is the adjustment or modulation of lexically-encoded meaning, which makes it possible for speakers to communicate a vastly greater range of concepts than those that are stably encoded in their linguistic system. This includes the meaning communicated by at least some cases of metaphorically-used language. Taking a broadly Fodorian view that lexical concepts are atomic (unstructured), this paper looks at some issues raised by the idea that addressees infer ad hoc concepts as part of the on-line comprehension process. As a cognitive-scientific theory, RT is open to evidence from a range of sources, including native speaker-hearer intuitions, recorded instances of linguistic communication (corpus data) from both communicatively typical and atypical populations, results from relevant psychological and psycholinguistic experiments, and findings in cognitive neuroscience on brain activation during both utterance production and comprehension. At the end of the paper, some tentative suggestions are made of questions arising from work in theoretical lexical pragmatics which might be amenable to investigation at the neuropragmatic level.


title: Word sense disambiguation using WordNet and the Lesk algorithm

abstract: Word sense disambiguation is the process of automatically clarifying the meaning of a word in its context. It has drawn much interest in the last decade and much improved results are being obtained. In this paper we take the so-called Lesk approach. In our case, definitions of the senses of the words to be disambiguated, as well as of the ten surrounding nouns, adjectives and verbs, are derived and enriched using the WordNet lexical database. Two possible implications of this project could be that the results are dependent on the characteristics of a test document and on the characteristics of glosses, which needs to be further investigated. The average precision performed worse (0.45) than baseline precision (0.60) which was based on always selecting the most frequent sense. However, the presented approach has several limitations: a small sample, and a big number of fine senses in WordNet, many of which are not that distinguishable from each other. The future work would include experimenting with different variations of the approach.


Scene complexity affects lightness constancy with respect to changes in object slant and surround reflectance


title: Brief Screening for Family Psychiatric History

abstract: Background: Brief screens to collect lifetime family psychiatric history are useful in clinical practice and for identifying potential families for genetic studies. Methods: The Family History Screen (FHS) collects information on 15 psychiatric disorders and suicidal behavior in informants and their first-degree relatives. Since each question is posed only once about all family members as a group, the administrative time is 5 to 20 minutes, depending on family size and illness. Data on the validity against best-estimate (BE) diagnosis based on independent and blind direct interviews on 289 probands and 305 relatives and test-retest reliability across 15 months in 417 subjects are presented.


title: A robot's spatial perception communicated via human touch

abstract: A robot perceives space in order to navigate, map, search and investigate its surroundings. One application area where humans interact with robots is search and rescue. The robot may have unique capabilities such as seeing outside the visible spectrum and being able to navigate in tight and hazardous spaces. In such an operation, the human may also be an effective search agent. Thus, it would be beneficial if the robot's spatial perception was conveyed to the human via a secondary peripheral modality. We have worked on developing a visual to tactile substitution device for people who are visually impaired or blind. We propose to use this similar technique to the remote robot in order that it convey its spatial perception to the human operator or co-worker.


title: The Open Agent Architecture

abstract: SRI’s Open Agent Architecture (OAA), a research framework for constructing agent-based systems, makes it possible for software services to be provided through the cooperative efforts of distributed collections of autonomous agents. Communication and cooperation between agents are brokered by one or more facilitators, which are responsible for matching requests, from users and agents, with descriptions of the capabilities of other agents. Thus, it is not generally required that a requester (user or agent) know the identities, locations, or number of other agents involved in satisfying a request. Facilitators are not viewed as centralized controllers, however, but rather as coordinators, as they draw upon knowledge and advice from several different, potentially distributed, sources to guide their delegation choices. OAA is structured so as to minimize the effort involved in creating new agents and “wrapping” legacy applications, written in various languages and operating on various platforms; to encourage the reuse of existing agents; and to allow for dynamism and flexibility in the makeup of agent communities. Distinguishing features of OAA as compared with related work include extreme flexibility in using facilitator-based delegation of complex goals, triggers, and data management requests; agent-based provision of multimodal user interfaces; and built-in support for including the user as a privileged member of the agent community.


title: International sentiment analysis for news and blogs

abstract: There is a growing interest in mining opinions using sentiment analysis methods from sources such as news, blogs and product reviews. Most of these methods have been developed for English and are difficult to generalize to other languages. We explore an approach utilizing state-of-the-art machine translation technology and perform sentiment analysis on the English translation of a foreign language text. Our experiments indicate that (a) entity sentiment scores obtained by our method are statistically significantly correlated across nine languages of news sources and five languages of a parallel corpus; (b) the quality of our sentiment analysis method is largely translator independent; (c) after applying certain normalization techniques, our entity sentiment scores can be used to perform meaningful cross-cultural comparisons.


title: Similarity in multilegged locomotion: Bouncing like a monopode

abstract: Despite impressive variation in leg number, length, position and type of skeleton, similarities of legged, pedestrian locomotion exist in energetics, gait, stride frequency and ground-reaction force. Analysis of data available in the literature showed that a bouncing, spring-mass, monopode model can approximate the energetics and dynamics of trotting, running, and hopping in animals as diverse as cockroaches, quail and kangaroos. From an animal's mechanical-energy fluctuation and ground-reaction force, we calculated the compression of a virtual monopode's leg and its stiffness. Comparison of dimensionless parameters revealed that locomotor dynamics depend on gait and leg number and not on body mass. Relative stiffness per leg was similar for all animals and appears to be a very conservative quantity in the design of legged locomotor systems. Differences in the general dynamics of gait are based largely on the number of legs acting simultaneously to determine the total stiffness of the system. Four- and six-legged trotters had a greater whole body stiffness than two-legged runners operating their systems at about the same relative speed. The greater whole body stiffness in trotters resulted in a smaller compression of the virtual leg and a higher natural frequency and stride frequency.


title: Supervised diagnosis of induction motor faults: A proposed methodology for an improved performance evaluation

abstract: In the last years, numerous investigations have been made within the field of faults diagnosis in induction motors. Most of them use data obtained either from the time domain, through advanced techniques in the frequency domain or even by simulation tools. Some researchers have employed a considerable effort in designing sophisticated algorithms to achieve the best performance of the diagnosis system. In this paper, a novel methodology of evaluation is proposed to promote better evaluation practices in the field of diagnosis of induction motors based on supervised classification. A case study is presented where adequate scores are used for evaluating the performance of the proposed methodology, showing meaningful differences when evaluating several classifiers.


title: A roadmap towards personalized immunology

abstract: Big data generation and computational processing will enable medicine to evolve from a “one-size-fits-all” approach to precise patient stratification and treatment. Significant achievements using “Omics” data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and “social” interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in “Omics” data utilization and network analysis on patients’ samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.


title: Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

abstract: We propose ''supervised principal component analysis (supervised PCA)'', a generalization of PCA that is uniquely effective for regression and classification problems with high-dimensional input data. It works by estimating a sequence of principal components that have maximal dependence on the response variable. The proposed supervised PCA is solvable in closed-form, and has a dual formulation that significantly reduces the computational complexity of problems in which the number of predictors greatly exceeds the number of observations (such as DNA microarray experiments). Furthermore, we show how the algorithm can be kernelized, which makes it applicable to non-linear dimensionality reduction tasks. Experimental results on various visualization, classification and regression problems show significant improvement over other supervised approaches both in accuracy and computational efficiency.


title: Stereo vision images processing for real-time object distance and size measurements

abstract: Human has the ability to roughly estimate the distance and size of an object because of the stereo vision of human's eyes. In this project, we proposed to utilize stereo vision system to accurately measure the distance and size (height and width) of object in view. Object size identification is very useful in building systems or applications especially in autonomous system navigation. Many recent works have started to use multiple vision sensors or cameras for different type of application such as 3D image constructions, occlusion detection and etc. Multiple cameras system has becoming more popular since cameras are now very cheap and easy to deploy and utilize. The proposed measurement system consists of object detection on the stereo images and blob extraction and distance and size calculation and object identification. The system also employs a fast algorithm so that the measurement can be done in real-time. The object measurement using stereo camera is better than object detection using a single camera that was proposed in many previous research works. It is much easier to calibrate and can produce a more accurate results.


title: Human Swarms, a real-time method for collective intelligence.

abstract: Although substantial research has explored the emergence of collective intelligence in real-time human-based collaborative systems, much of this work has focused on rigid scenarios such as the Prisoner’s Dilemma (PD). (Pinheiro et al., 2012; Santos et al., 2012). While such work is of great research value, there’s a growing need for a flexible real-world platform that fosters collective intelligence in authentic decision-making situations. This paper introduces a new platform called UNUM that allows groups of online users to collectively answer questions, make decisions, and resolve dilemmas by working together in unified dynamic systems. Modeled after biological swarms, the UNUM platform enables online groups to work in real-time synchrony, collaboratively exploring a decision-space and converging on preferred solutions in a matter of seconds. We call the process “social swarming” and early real-world testing suggests it has great potential for harnessing collective intelligence.


title: More Than An Answer: Neural Pivot Network for Visual Qestion Answering

abstract: Most of existing works in visual question answering (VQA) are dedicated to improving the performance of answer predictions, while leaving the explanation of answering unexploited. We argue that, exploiting the explanations of question answering not only makes VQA explainable, but also quantitatively improves the prediction performance. In this paper, we propose a novel network architecture, termed Neural Pivot Network (NPN), towards simultaneous VQA and generating explanations in a multi-task learning architecture. NPN is trained by using both image-caption and image-question-answer pairs. In principle, CNN-based deep visual features are extracted and sent to both the VQA channel and the captioning module, the latter of which serves as a pivot to bridge the source image module to the target QA predictor. Such an innovative design enables us to introduce large-scale image-captioning training sets, e.g., MS-COCO Caption and Visual Genome Caption, together with cutting-edge image captioning models to benefit VQA learning. Quantitatively, the proposed NPN performs significantly better than alternatives and state-of-the-art schemes trained on VQA datasets only. Besides, by investigating the by-product of experiments, in-depth digests can be provided along with the answers.


title: Training recurrent networks using the extended Kalman filter

abstract: The author describes some relationships between the extended Kalman filter (EKF) as applied to recurrent net learning and some simpler techniques that are more widely used. In particular, making certain simplifications to the EKF gives rise to an algorithm essentially identical to the real-time recurrent learning (RTRL) algorithm. Since the EKF involves adjusting unit activity in the network, it also provides a principled generalization of the teacher forcing technique. Preliminary simulation experiments on simple finite-state Boolean tasks indicated that the EKF can provide substantial speed-up in number of time steps required for training on such problems when compared with simpler online gradient algorithms. The computational requirements of the EKF are steep, but scale with network size at the same rate as RTRL. >


title: BING++: A Fast High Quality Object Proposal Generator at 100fps

abstract: We are motivated by the need for an object proposal generation algorithm that achieves a good balance between proposal localization quality, object recall and computational efficiency. We propose a novel object proposal algorithm {\em BING++} which inherits the good computational efficiency of BING \cite{BingObj2014} but significantly improves its proposal localization quality. Central to our success is based on the observation that good bounding boxes are those that tightly cover objects. Edge features, which can be computed efficiently, play a critical role in this context. We propose a new algorithm that recursively improves BING's proposals by exploiting the fact that edges in images are typically associated with object boundaries. BING++ improves proposals recursively by incorporating nearest edge points (to proposal boundary pixels) to obtain a tighter bounding box. This operation has linear computational complexity in number of pixels and can be done efficiently using distance transform. Superpixel merging techniques are then employed as post-processing to further improve the proposal quality. Empirically on the VOC2007 dataset, using $10^3$ proposals and IoU threshold 0.5, our method achieves 95.3\% object detection recall (DR), 79.2\% mean average best overlap (MABO), and 68.7\% mean average precision (mAP) on object detection over 20 object classes within an average time of {\bf 0.009} seconds per image.


title: Architectural Design of a Cloud Robotic System for Upper-Limb Rehabilitation with Multimodal Interaction

abstract: The rise in the cases of motor impairing illnesses demands the research for improvements in rehabilitation therapy. Due to the current situation that the service of the professional therapists cannot meet the need of the motor-impaired subjects, a cloud robotic system is proposed to provide an Internet-based process for upper-limb rehabilitation with multimodal interaction. In this system, therapists and subjects are connected through the Internet using client/server architecture. At the client site, gradual virtual games are introduced so that the subjects can control and interact with virtual objects through the interaction devices such as robot arms. Computer graphics show the geometric results and interaction haptic/force is fed back during exercising. Both video/audio information and kinematical/physiological data are transferred to the therapist for monitoring and analysis. In this way, patients can be diagnosed and directed and therapists can manage therapy sessions remotely. The rehabilitation process can be monitored through the Internet. Expert libraries on the central server can serve as a supervisor and give advice based on the training data and the physiological data. The proposed solution is a convenient application that has several features taking advantage of the extensive technological utilization in the area of physical rehabilitation and multimodal interaction.


title: A few-shot deep learning approach for improved intrusion detection

abstract: Our generation has seen the boom and ubiquitous advent of Internet connectivity. Adversaries have been exploiting this omnipresent connectivity as an opportunity to launch cyber attacks. As a consequence, researchers around the globe devoted a big attention to data mining and machine learning with emphasis on improving the accuracy of intrusion detection system (IDS). In this paper, we present a few-shot deep learning approach for improved intrusion detection. We first trained a deep convolutional neural network (CNN) for intrusion detection. We then extracted outputs from different layers in the deep CNN and implemented a linear support vector machine (SVM) and 1-nearest neighbor (1-NN) classifier for few-shot intrusion detection. few-shot learning is a recently developed strategy to handle situation where training samples for a certain class are limited. We applied our proposed method to the two well-known datasets simulating intrusion in a military network: KDD 99 and NSL-KDD. These datasets are imbalanced, and some classes have much less training samples than others. Experimental results show that the proposed method achieved better performances than the state-of-the-art on those two datasets.


title: RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations

abstract: Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ubiquitous applications. Although existing studies have demonstrated the effectiveness and feasibility of running deep neural network inference operations on mobile and embedded devices, they overlooked the reliability of mobile computing models. Reliability measurements such as predictive uncertainty estimations are key factors for improving the decision accuracy and user experience. In this work, we propose RDeepSense, the first deep learning model that provides well-calibrated uncertainty estimations for resource-constrained mobile and embedded devices. RDeepSense enables the predictive uncertainty by adopting a tunable proper scoring rule as the training criterion and dropout as the implicit Bayesian approximation, which theoretically proves its correctness.To reduce the computational complexity, RDeepSense employs efficient dropout and predictive distribution estimation instead of model ensemble or sampling-based method for inference operations. We evaluate RDeepSense with four mobile sensing applications using Intel Edison devices. Results show that RDeepSense can reduce around 90% of the energy consumption while producing superior uncertainty estimations and preserving at least the same model accuracy compared with other state-of-the-art methods.


title: Isolated Micro-Grids With Renewable Hybrid Generation: The Case of Lençóis Island

abstract: In Brazil, around 2 000 000 families have not been connected to an electricity grid yet. Out of these, a significant number of villages may never be connected to the national grid due to their remoteness. For the people living in these communities, access to renewable energy sources is the only solution to meet their energy needs. In these communes, the electricity is mainly used for household purposes such as lighting. There is little scope for the productive use of energy. It is recognized that electric service contributes particularly to inclusive social development and to a lesser extent to pro-poor growth as well as to environmental sustainability. In this paper, we present the specification, design, and development of a standalone micro-grid supplied by a hybrid wind-solar generating source. The goal of the project was to provide a reliable, continuous, sustainable, and good-quality electricity service to users, as provided in bigger cities. As a consequence, several technical challenges arose and were overcome successfully as will be related in this paper, contributing to increase of confidence in renewable systems to isolated applications.


title: Classification of EMG Signals for Assessment of Neuromuscular Disorders

abstract: An accurate and computationally efficient means of feature extraction of electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the classification of neuromuscular disorders. The objective of this study is to discriminate between normal (NOR), myopathic (MYO) and neuropathic (NEURO) subjects. The experiment consisted of 22 pathogenic (11 MYO and 11 NEURO) and 12 healthy persons. The signals were recorded at 30% Maximum Voluntary Contraction (MVC) for 5 seconds. Features of MUAPs extracted in time have been quantitatively analysed. We have used binary SVM for classification. Separation of normal subjects from neuromuscular disease subjects has an accuracy of 83.45%, whereas separation of subjects from the two types of subjects (myopathic and neuropathic) has an accuracy of 68.29% which is again high. 


title: Enhanced human-machine interface in braking

abstract: Antilock brake system (ABS) technology with powerful electronic components has shown superior braking performance to conventional vehicles on test tracks. On real highways, however, the performance of the ABS-equipped car has been disappointing. The poor braking performance with ABS has resulted from the questionable design of the human-machine interface for the brake system. The goal of this study is to design brake systems that provide more intuitive brake control and proper braking-performance information for the driver. In this study automotive brake systems are modeled as a type of master-slave telemanipulator. Human force-displacement interaction at the brake pedal has a strong effect on braking performance. As a preliminary study in brake-system design, the characteristics of human leg motion and its underlying motor-control scheme are studied through experiments and simulations, and a model of braking motion by the driver's leg is developed. This paper proposes novel brake systems based on two new aspects. First, the mechanical impedance characteristics of the leg action of the driver are taken into consideration in designing the brake systems. Second, the brake systems provide the driver with kinesthetic feedback of braking conditions or performance. The effectiveness of the proposed designs in a combined driver-vehicle system is investigated using driving simulation.


Acutherapy for Knee Osteoarthritis Relief in the Elderly: A Systematic Review and Meta-Analysis


title: Fast Sampling for Bayesian Max-Margin Models

abstract: Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian modeling and predictive strengths of max-margin learning. However, Monte Carlo sampling for these models still remains challenging, especially for applications that involve large-scale datasets. In this paper, we present the stochastic subgradient Hamiltonian Monte Carlo (HMC) methods, which are easy to implement and computationally efficient. We show the approximate detailed balance property of subgradient HMC which reveals a natural and validated generalization of the ordinary HMC. Furthermore, we investigate the variants that use stochastic subsampling and thermostats for better scalability and mixing. Using stochastic subgradient Markov Chain Monte Carlo (MCMC), we efficiently solve the posterior inference task of various Bayesian max-margin models and extensive experimental results demonstrate the effectiveness of our approach.


title: Learning step size controllers for robust neural network training

abstract: This paper investigates algorithms to automatically adapt the learning rate of neural networks (NNs). Starting with stochastic gradient descent, a large variety of learning methods has been proposed for the NN setting. However, these methods are usually sensitive to the initial learning rate which has to be chosen by the experimenter. We investigate several features and show how an adaptive controller can adjust the learning rate without prior knowledge of the learning problem at hand.


title: Simple Schnorr multi-signatures with applications to Bitcoin

abstract: We describe a new Schnorr-based multi-signature scheme (i.e., a protocol which allows a group of signers to produce a short, joint signature on a common message) called $$\mathsf {MuSig}$$ ::: , provably secure under the Discrete Logarithm assumption and in the plain public-key model (meaning that signers are only required to have a public key, but do not have to prove knowledge of the private key corresponding to their public key to some certification authority or to other signers before engaging the protocol). $$\mathsf {MuSig}$$ ::: improves over the state-of-art scheme of Bellare and Neven (ACM Conference on Computer and Communications Security-CCS 2006) and its variants by Bagherzandi et al. (ACM Conference on Computer and Communications Security-CCS 2008) and Ma et al. (Des Codes Cryptogr 54(2):121–133, 2010) in two respects: (i) it is simple and efficient, having the same key and signature size as standard Schnorr signatures; (ii) it allows key aggregation, which informally means that the joint signature can be verified exactly as a standard Schnorr signature with respect to a single “aggregated” public key which can be computed from the individual public keys of the signers. To the best of our knowledge, this is the first multi-signature scheme provably secure under the Discrete Logarithm assumption in the plain public-key model which allows key aggregation. As an application, we explain how our new multi-signature scheme could improve both performance and user privacy in Bitcoin.


title: Detecting Fraud in the Real World

abstract: Finding telecommunications fraud in masses of call records is more difficult than finding a needle in a haystack. In the haystack problem, there is only one needle that does not look like hay, the pieces of hay all look similar, and neither the needle nor the hay changes much over time. Fraudulent calls may be rare like needles in haystacks, but they are much more challenging to find. Callers are dissimilar, so calls that look like fraud for one account look like expected behavior for another, while all needles look the same. Moreover, fraud has to be found repeatedly, as fast as fraud calls are placed, the nature of fraud changes over time, the extent of fraud is unknown in advance, and fraud may be spread over more than one type of service. For example, calls placed on a stolen wireless telephone may be charged to a stolen credit card. Finding fraud is like finding a needle in a haystack only in the sense of sifting through masses of data to find something rare. This chapter describes some issues involved in creating tools for building fraud systems that are accurate, able to adapt to changing legitimate and fraudulent behavior, and easy to use.


title: Injured bodies, damaged lives: experiences and narratives of Kenyan women with obstetric fistula and Female Genital Mutilation/Cutting

abstract: BACKGROUND ::: It is well acknowledged that Female Genital Mutilation/Cutting (FGM/C/C) leads to medical, psychological and sociocultural sequels. Over 200 million cases of FGM/C exist globally, and in Kenya alone, a total of 12,418,000 (28%) of women have undergone FGM/C, making the practice not only a significant national, but also a global health catastrophe. FGM/C is rooted in patriarchal and traditional cultures as a communal experience signifying a transition from girlhood to womanhood. The conversations surrounding FGM/C have been complicated by the involvement of women themselves in perpetuating the practice. ::: ::: ::: METHODS ::: A qualitative inquiry employing face-to-face, one-on-one, in-depth semi-structured interviews was used in a study that included 30 women living with obstetric fistulas in Kenya. Using the Social Network Framework and a feminist analysis we present stories of Kenyan women who had developed obstetric fistulas following prolonged and obstructed childbirth. ::: ::: ::: RESULTS ::: Of the 30 participants, three women reported that health care workers informed them that FGM/C was one of the contributing factors to their prolonged and obstructed childbirth. They reported serious obstetric complications including: the development of obstetric fistulas, lowered libido, poor quality of life and maternal and child health outcomes, including death. Fistula and subsequent loss of bodily functionalities such as uncontrollable leakage of body wastes, was reported by the women to result in rejection by spouses, families, friends and communities. Rejection further led to depression, loss of work, increased sense of apathy, lowered self-esteem and image, as well as loss of identity and communal sociocultural cohesion. ::: ::: ::: CONCLUSION ::: FGM/C is practised in traditional, patriarchal communities across Africa. Although the practice aims to bind community members and to celebrate a rite of passage; it may lead to harmful health and social consequences. Some women with fistula report their fistula was caused by FGM/C. Concerted efforts which embrace feminist understandings of society, as well as multi-sectoral, multidisciplinary and community development approaches need to be employed to address FGM/C, and to possibly reduce cases of obstetric fistulas in Kenya and beyond. Both government and non-government organisations need to be involved in making legislative, gender sensitive policies that protect women from FGM/C. In addition, the policy makers need to be in the front line to improve the lives of women who endured the consequences of FGM/C.


title: A Bipolar Pad in a 10-kHz 300-W Distributed IPT System for AGV Applications

abstract: This paper presents the magnetic analysis of a recently proposed multiple coil pad design, called the bipolar pad (BPP), in a distributed inductive power transfer (IPT) system for automatic guided vehicle (AGV) applications. The paper briefly discusses the magnetic design optimization procedure and then optimizes another recently proposed secondary pad design (generally used in lumped IPT systems), called the double-D quadrature pad (DDQP), for distributed AGV applications and compares its performance with the BPP. A four conductor single phase track topology is used here as primary. Both the pads discussed in this paper have already been shown to perform well in lumped IPT systems and hence the work presented in this paper highlights and validates the possibility of using these pads in mixed distributed/lumped IPT systems. A 3-D finite element modeling tool is used to evaluate and compare all the models presented in this paper. To validate these results, a prototype system is built with a BPP secondary driving a load through a controller circuit. A modification in the traditional control topology is proposed and shown to improve the overall system efficiency; before finally highlighting some of the practical design issues needed to be considered when building such a system.


title: Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

abstract: With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks.


Three Kinds of Network Security Situation Awareness Model Based on Big Data


title: Robust Fuzzy Observer-based Fault Detection for Nonlinear Systems

abstract: With the increasing demand for higher performance, safety and reliability of dynamic systems, fault diagnosis has received more and more attention. The observer-based strategy is one of the active research fields, which is widely used to construct model-based fault detection systems for technical processes which can be well modelled as linear time invariant systems. Fault diagnosis for nonlinear system is an active area of research. Observer-based fault detection includes two stages, residual generation and residual evaluation. The residual generation problems and residual evaluation problems for systems with ::: only deterministic disturbances or stochastic disturbances have been widely separately studied. Recently some efforts have been made in the integrated design of fault detection systems for systems with deterministic disturbances and stochastic disturbances. Recently, successful results of applying Takagi-Sugeno (TS) fuzzy model-based technique to solve fault detection and isolation problems met in the nonlinear system have been achieved. With TS model, a nonlinear dynamic system can be linearised around a number of operating points. Each linear model represents the local system behaviour around the ::: operating point. The global system behaviour is described by a fuzzy IF-THEN rules which represent local linear input/output relations of the nonlinear system. Applying the Takagi-Sugeno fuzzy model based technique to solve fault detection and isolation problems in the nonlinear systems is active area of research. The main contribution of this thesis is the design of robust fault detection systems based ::: on Takagi-Sugeno fuzzy filters. There are a number of schemes to achieve robustness problem in fault detection. One of them is to introduce a performance index. It is function of unknown input signal and fault signal. For continuous time system, first, robust fault detection system will be designed for nonlinear system with only deterministic disturbance as unknown inputs. Second, robust fault detection system will be designed for nonlinear system with deterministic disturbance as unknown inputs and parameter uncertainties. Finally, robust fault detection system will be designed for nonlinear system with deterministic disturbance as unknown inputs and stated delay. Sufficient conditions for solving robustness problem are given in terms of Linear Matrix Inequalities (LMIs). For ::: discrete time system, kalman filter design for nonlinear system is diffcult. In this thesis new fault detection approach will be presented for nonlinear system with only stochastic disturbance. Fault Detection (FD) system for each local subsystem is design by solving the corresponding Discrete-time Algebraic Riccati Equation (DARE). Optimisation algorithm based on minimizing the residual covariance matrix is used to obtain a robust FD system optimised for global system behaviour. The optimisation algorithm is established in terms ::: of LMIs. The different robust fault diagnosis system are developed to detect sensor faults of vehicle lateral dynamic control systems.


title: Uses of artificial intelligence in the Brazilian customs fraud detection system

abstract: There is an increasing concern about the control of customs operations. While globalization incentives the opening of the market, increasing amounts of imports and exports have been used to conceal several illicit activities, such as, tax evasion, smuggling, money laundry, and drug trafic. This fact makes it paramount for governments to find automatic or semi-automatic solutions to guide the customs' activities in order to minimize the number of manual inspections of goods. In this context, this paper presents an overview of some approaches developed in the HARPIA project that is a partnership between universities and the Brazilian Federal Revenue for the development of computational intelligence solutions to the management of customs risk.


title: Improving robot navigation in structured outdoor environments by identifying vegetation from laser data

abstract: This paper addresses the problem of vegetation detection from laser measurements. The ability to detect vegetation is important for robots operating outdoors, since it enables a robot to navigate more efficiently and safely in such environments. In this paper, we propose a novel approach for detecting low, grass-like vegetation using laser remission values. In our algorithm, the laser remission is modeled as a function of distance, incidence angle, and material. We classify surface terrain based on 3D scans of the surroundings of the robot. The model is learned in a self-supervised way using vibration-based terrain classification. In all real world experiments we carried out, our approach yields a classification accuracy of over 99%. We furthermore illustrate how the learned classifier can improve the autonomous navigation capabilities of mobile robots.


title: Tree Detection in Urban Regions Using Aerial Lidar and Image Data

abstract: In this letter, we present an approach to detecting trees in registered aerial image and range data obtained via lidar. The motivation for this problem comes from automated 3-D city modeling, in which such data are used to generate the models. Representing the trees in these models is problematic because the data are usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both lidar and aerial imagery. In this letter, we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and lidar, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using the weighted support vector machines, allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and the results presented as receiver operating characteristic curves are shown to validate our approach


title: Turkevich Method for Gold Nanoparticle Synthesis Revisited

abstract: The growth of gold nanoparticles by reduction by citrate and ascorbic acid has been examined in detail to explore the parameter space of reaction conditions. It is found that gold particles can be produced in a wide range of sizes, from 9 to 120 nm, with defined size distribution, following the earlier work of Turkevich and Frens. The reaction is initiated thermally or in comparison by UV irradiation, which results in similar final products. The kinetics of the extinction spectra show the multiple steps of primary and secondary clustering leading to polycrystallites.


MULTI-SENSOR 3 D DOCUMENTATION OF THE MAYA SITE OF COPAN


title: A novel microstrip line backward directional coupler with high directivity

abstract: A uni-planar backward directional couplers is analyzed and designed. Microstrip parallel coupled lines and asymmetrical delay lines make multi-sectioned coupler which enhances the directivity. In this paper, 20 dB multi-sectioned couplers with single and double delay lines are designed and measured to show the validation of analysis. The coupler with two delay lines has the directivity over 30 dB and the fractional bandwidth of 30% at the center frequency of 1.8 GHz.


title: Hierarchical Linearly-Solvable Markov Decision Problems

abstract: We present a hierarchical reinforcement learning framework that formulates each task in the hierarchy as a special type of Markov decision process for which the Bellman equation is linear and has analytical solution. Problems of this type, called linearly-solvable MDPs (LMDPs) have interesting properties that can be exploited in a hierarchical setting, such as efficient learning of the optimal value function or task compositionality. The proposed hierarchical approach can also be seen as a novel alternative to solving LMDPs with large state spaces. We derive a hierarchical version of the so-called Z-learning algorithm that learns different tasks simultaneously and show empirically that it significantly outperforms the state-of-the-art learning methods in two classical hierarchical reinforcement learning domains: the taxi domain and an autonomous guided vehicle task.


title: The role of information technology in supply chain integration

abstract: Firms face a new challenge in integrating the various activities of the supply chain by moving away from vertical integration and towards outsourcing. Supplier integration and logistics integration are two most important types of integration in Supply Chain Management (SCM). In this study, we evaluate the effects of the use of different Information Technologies (ITs) on these two types of the supply chain integration. A cross-sectional mail survey was used to collect empirical data. The results provide empirical evidence supporting the fact that IT facilitates the supplier as well as logistics integration.


title: A Marx-based power converter topology as a current source for pulsed magnets in particle accelerators

abstract: A modified Marx-based solid-state topology for supplying inductive loads is presented. The basic principle of charging capacitors in parallel and discharging them in series is conserved. Due to the negative di/dt phase the inductive load imposes a reversible voltage power converter. Furthermore, in some applications such as eddy current septum magnets a negative current is required as well. The presented topology allows the production of controlled half or full sine waves, with or without short or long flat-tops. The solution is able to regenerate back to the capacitor banks the energy from the magnet. To shape the desired current it is possible to time-shift the discharge of different Marx cells. Numerical simulations are used to illustrate the functionalities of this solution. This flexible converter can be easily used for all pulsed current converters in accelerator facilities with a high degree of modularity and flexibility.


High Level Synthesis of Canny Edge Detection Algorithm on Zynq Platform


title: Compact Broadband Circularly Polarized Antenna With Parasitic Patches

abstract: A broadband circularly polarized (CP) antenna with compact size is proposed. The antenna is composed of a loop feeding structure which provides sequential phase, four driven patches, and four parasitic patches. The driven patches, which are capacitively coupled by the feeding loop, generate one CP mode due to the sequentially rotated structure and four parasitic patches are introduced to produce additional CP mode. By combining with the CP mode of the feeding loop, the axial ratio (AR) bandwidth is greatly broadened. An antenna prototype is fabricated to validate the simulated results. Experimental results show that the antenna achieves a broad impedance bandwidth of 19.5% from 5.13 to 6.24 GHz and a 3-dB AR bandwidth of 12.9% (5.38–6.12 GHz). In addition, the proposed antenna also has a flat gain within the operating frequency band and a compact size of $0.92\lambda _{0}\times 0.92\lambda _{0}\times 0.028\lambda _{0}$ at 5.5 GHz.


title: Decoding Social Influence and the Wisdom of the Crowd in Financial Trading Network

abstract: In this paper, we study roles of social mechanisms in a financial system. Our data come from a novel on-line foreign exchange trading brokerage for individual investors, which also allows investors to form social network ties between each other and copy others' trades. From the dataset, we analyze the dynamics of this connected social influence systems in decision making processes. We discover that generally social trades outperform individual trades, but the social reputation of the top traders is not completely determined by their performance due to social feedback even when users are betting their own money. We also find that social influence plays a significant role in users' trades, especially decisions during periods of uncertainty. We report evidences suggesting that the dynamics of social influence contribute to market overreaction.


title: When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition

abstract: Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluates the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, a traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.


title: Nonlinear Control with Swing Damping of a Multirotor UAV with Suspended Load

abstract: In this paper, we consider the problem of trajectory tracking of a multirotor Unmanned Aerial Vehicle carrying a suspended payload. The movement of the suspended payload influences the dynamics of the multirotor, which must be appropriately handled by the controller to achieve satisfactory tracking results. We derive a mathematical model of the interconnected multi-body system using Kane’s equations, and develop a non-linear tracking controller based on the backstepping technique. In addition to suppressing the effects of the swinging payload, the controller also compensates for an unknown constant wind disturbance. The origin of the tracking error is proven UGAS (Uniformly Globally Asymptotically Stable) and ULES (Uniformly Locally Exponentially Stable) through Lyapunov analysis. To reduce the swing motion of the suspended load, a nominal swing-free path is generated through open loop shaping filters, then further perturbed through a delayed feedback approach from measured load deflection angles to achieve robustness. The proposed controller structure is verified by simulations and experiments.


Personalized Advice and Feedback for Diabetes Patients


title: Chronic Wound Healing: A Review of Current Management and Treatments

abstract: Wound healing is a complex, highly regulated process that is critical in maintaining the barrier function of skin. With numerous disease processes, the cascade of events involved in wound healing can be affected, resulting in chronic, non-healing wounds that subject the patient to significant discomfort and distress while draining the medical system of an enormous amount of resources. The healing of a superficial wound requires many factors to work in concert, and wound dressings and treatments have evolved considerably to address possible barriers to wound healing, ranging from infection to hypoxia. Even optimally, wound tissue never reaches its pre-injured strength and multiple aberrant healing states can result in chronic non-healing wounds. This article will review wound healing physiology and discuss current approaches for treating a wound.


title: Relationship of Corporal Punishment and Antisocial Behavior by Neighborhood

abstract: OBJECTIVES ::: To examine the relationship of corporal punishment with children's behavior problems while accounting for neighborhood context and while using stronger statistical methods than previous literature in this area, and to examine whether different levels of corporal punishment have different effects in different neighborhood contexts. ::: ::: ::: DESIGN ::: Longitudinal cohort study. ::: ::: ::: SETTING ::: General community. ::: ::: ::: PARTICIPANTS ::: 1943 mother-child pairs from the National Longitudinal Survey of Youth. ::: ::: ::: MAIN OUTCOME MEASURE ::: Internalizing and externalizing behavior problem scales of the Behavior Problems Index. ::: ::: ::: RESULTS AND CONCLUSIONS ::: Parental use of corporal punishment was associated with a 0.71 increase (P<.05) in children's externalizing behavior problems even when several parenting behaviors, neighborhood quality, and all time-invariant variables were accounted for. The association of corporal punishment and children's externalizing behavior problems was not dependent on neighborhood context. The research found no discernible relationship between corporal punishment and internalizing behavior problems.


title: Building better biomarkers: brain models in translational neuroimaging

abstract: Neuroimaging and pattern recognition are being combined to develop brain models of clinical disorders. Such models yield biomarkers that can be shared and validated across populations, narrowing the gap between neuroscience and clinical applications. The authors summarize 475 translational modeling studies, highlighting challenges and ways to improve biomarker development.


title: Texture Representation and Synthesis Using Correlation of Complex Wavelet Coefficient Magnitudes

abstract: We present a statistical characterization of texture images in the context of an overcomplete complex wavelet transform. The characterization is based on empirical observatio ns of statistical regularities in such images, and parameterized by (1) the local auto-correlatio n of the coefficients in each subband; (2) both the local auto-correlation and cross-correlation o f coefficient magnitudes at other orientations and spatial scales; and (3) the first few moments of th e image pixel histogram. We develop an efficient algorithm for synthesizing random im ages subject to these constraints using alternated projections, and demonstrate its effectivenes s on a wide range of synthetic and natural textures. We also show the flexibility of the repres entation, by applying to a variety of tasks which can be viewed as constrained image synthesis problems. Vision is arguably our most important sensory system, judging from both the ubiquity of visual forms of communication, and the large proportion of the human brain devoted to visual processing. Never


title: Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally

abstract: Finding regions for which there is higher controversy among different classifiers is insightful with regards to the domain and our models. Such evaluation can falsify assumptions, assert some, or also, bring to the attention unknown phenomena. The present work describes an algorithm, which is based on the Exceptional Model Mining framework, and enables that kind of investigations. We explore several public datasets and show the usefulness of this approach in classification tasks. We show in this paper a few interesting observations about those well explored datasets, some of which are general knowledge, and other that as far as we know, were not reported before.


title: Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications

abstract: In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.


Keeping the neural networks simple by minimizing the description length of the weights


title: Chunk-Based Resource Allocation in OFDMA Systems—Part II: Joint Chunk, Power and Bit Allocation

abstract: By grouping a number of adjacent subcarriers into a chunk, resource allocation can be carried out chunk by chunk in orthogonal frequency division multiple access (OFDMA) systems. Chunk-based resource allocation is an effective approach to reduce the complexity of resource allocation in OFDMA systems. In this paper, a chunk-based resource allocation scheme, i.e. joint chunk, power and bit allocation, is proposed and analyzed by maximizing the throughput under a total transmit power constraint. A scaling factor is introduced to achieve optimal allocation. Considering the digital nature of bits per symbol per subcarrier (bits/symbol/subcarrier), a digitization process is proposed to digitize the theoretically allocated bits/symbol/subcarrier to integer. System parameters, such as the power constraint, number of users, coherence bandwidth, number of subcarriers and number of chunks, are introduced and their impacts on the average throughput are studied. The performance of the dynamic power allocation scheme is compared with the fixed power allocation scheme. The numerical results show that the theoretical throughput of the fixed power allocation scheme is quite close to that of the dynamic power allocation scheme. However, when the digital nature of bits/symbol/subcarrier is considered, the average throughput of the dynamic power allocation outperforms the fixed power allocation scheme.


title: Ultrasonication and food technology: A review

abstract: AbstractWith increasing consumers demand and tightening of food and environmental regulations, traditional food-processing techniques have lost their optimum performance which gave rise to new and powerful technologies. Ultrasonic is a one of the fast, versatile, emerging, and promising non-destructive green technology used in the food industry from last few years. The ultrasound is being carried out in various areas of food technology namely crystallization, freezing, bleaching, degassing, extraction, drying, filtration, emulsification, sterilization, cutting, etc. Ultrasound is being applied as an effective preservation tool in many food-processing fields viz. vegetables and fruits, cereal products, honey, gels, proteins, enzymes, microbial inactivation, cereal technology, water treatment, diary technology, etc. This review summarizes the latest knowledge on impact and application of ultrasound in food technology.


title: Fault Detection, Isolation, and Control of Drive By Wire Systems

abstract: Fault tolerance is a property of a system that continues operating properly in the event of failure of some of its parts. It provides the ability of a system to provide a service complying with the specification in spite of faults. If operating quality decreases at all, the decrease is proportional to the severity of the failure, as compared to a naively-designed system in which even a small failure can cause total breakdown. Fault-tolerant design of a drive-by-wire (DBW) system provides key-method for achieving safe and reliable systems (Isermann et al, 2002; Shibahata, 2005). It is generally required in the design of all mechanical, electrical and software components of drive-by-wire systems. Extensive analysis is performed during the design and testing phase, combined with quality control methods during manufacturing. This design method reduces faulty behaviour dramatically and forms the basis for safe and reliable systems. The design method identify potential hazards and associated avoidance requirements, translates safety requirements into engineering requirements, provides design assessment and trade-off to support the ongoing design, assesses the relative compliance of the design to the requirements and document the findings, directs and monitors specialized safety testing, and monitors and reviews test and field issues for safety trends. It is noted that certain faults and failures cannot be avoided totally. Components in a system can fail even after with a fault tolerant design. These faults should be tolerated by additional measures and compensated in such a way that they do not cause the overall system (or certain critical functions) to fail. The most obvious way to reach this goal is to implement duplication in order to avoid single points of failure. Duplication can offer fault-tolerance in three different ways:  Replication: Providing multiple identical instances of the same system, directing tasks or requests to all of them in parallel, and choosing the correct result on the basis of a quorum.  Redundancy: Providing multiple identical instances of the same system and switching to one of the remaining instances in case of a failure (fall-back or backup). 11


title: Competencies of Exceptional and Non-Exceptional Software Engineers

abstract: Abstract The attributes of individual software engineers are perhaps the most important factors determining the success of software development. Our goal is to identify the professional competencies that are most essential. In particular, we seek to identify the attributes that differentiate exceptional and nonexceptional software engineers. Phase 1 of our research is a qualitative study designed to identify competencies to be used in the quantitative analysis performed in phase 2. In phase 1, we conduct an in-depth review of 10 exceptional and 10 nonexceptional software engineers working for a major computing firm. We use biographical data and Myers-Briggs Type Indicator test results to characterize our sample. We conduct Critical Incident Interviews focusing on the subjects' experience in software and identify 38 essential competencies of software engineers. Phase 2 of this study is a survey of 129 software engineers conducted to determine the competencies that differentiate exceptional and nonexceptional engineers. Years of experience in software is the only biographical predictor of performance. Analysis of the participants Q-Sort of the 38 competencies identified in phase 1 reveals that 9 of these competencies are differentially related to engineer performance by t test. A 10-variable canonical discrimination function consisting of 3 biographical variables and 7 competencies correctly classifies 81% of the cases. The statistical analyses indicate that exceptional engineers (at the company studied) can be distinguished by behaviors associated with an external focus: behaviors directed at people or objects outside the individual. Exceptional engineers are more likely than nonexceptional engineers to maintain a “big picture,” have a bias for action, be driven by a sense of mission, exhibit and articulate strong convictions, play a proactive role with management, and help other engineers.


The Dolev-Yao Intruder is the Most Powerful Attacker


title: As time goes by: discovering eras in evolving social networks

abstract: Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.


title: A Search-Based Approach for Cost-Effective Software Test Automation Decision Support and an Industrial Case Study

abstract: Test automation is a widely-used approach to reduce the cost of manual software testing. However, if it is not planned or conducted properly, automated testing would not necessarily be more cost effective than manual testing. Deciding what parts of a given System Under Test (SUT) should be tested in an automated fashion and what parts should remain manual is a frequently-asked and challenging question for practitioner testers. In this study, we propose a search-based approach for deciding what parts of a given SUT should be tested automatically to gain the highest Return On Investment (ROI). This work is the first systematic approach for this problem, and significance of our approach is that it considers automation in the entire testing process (i.e., from test-case design, to test scripting, to test execution, and test-result evaluation). The proposed approach has been applied in an industrial setting in the context of a software product used in the oil and gas industry in Canada. Among the results of the case study is that, when planned and conducted properly using our decision-support approach, test automation provides the highest ROI. In this study, we show that if automation decision is taken effectively, test-case design, test execution, and test evaluation can result in about 307%, 675%, and 41% ROI in 10 rounds of using automated test suites.


title: Simulator Sickness Questionnaire: Twenty Years Later

abstract: The present study used simulator sickness questionnaire data from nine different studies to validate and explore the work of the most widely used simulator sickness index. The ability to predict participant dropouts as a result of simulator sickness symptoms was also evaluated. Overall, participants experiencing nausea and nausea-related symptoms were the most likely to fail to complete simulations. Further, simulation specific factors that increase the discrepancy between visual and vestibular perceptions are also related to higher participant study dropout rates. As a result, it is suggested that simulations minimize turns, curves, stops, et cetera, if possible, in order to minimize participant simulation sickness symptoms. The present study highlights several factors to attend to in order to minimize elevated participant simulation sickness.


title: Recipe recommendation: accuracy and reasoning

abstract: Food and diet are complex domains for recommender technology, but the need for systems that assist users in embarking on and engaging with healthy living programs has never been more real. One key to sustaining long term engagement with eHealth services is the provision of tools, which assist and train users in planning correctly around the areas of diet and exercise. These tools require an understanding of user reasoning as well as user needs and are ideal application areas for recommender and personalization technologies. Here, we report on a large scale analysis of real user ratings on a set of recipes in order to judge the applicability and practicality of a number of personalization algorithms. Further to this, we report on apparent user reasoning patterns uncovered in rating data supplied for recipes and suggest ways to exploit this reasoning understanding in the recommendation process.


title: Evaluation for UV laser dicing process and its reliability for various designs of stack chip scale package

abstract: In the semiconductor market, the trend of packaging for die stacking technology moves to high density with thinner chips and higher capacity of memory devices. Moreover, the wafer sawing process is becoming more important for thin wafer, because its process speed tends to affect sawn quality and yield. ULK (Ultra low-k) device could require laser grooving application to reduce the stress during wafer sawing. Furthermore under 75um-thick thin low-k wafer is not easy to use the laser grooving application. So, UV laser dicing technology that is very useful tool for Si wafer was selected as full cut application, which has been being used on low-k wafer as laser grooving method.


title: A high IIP3 6.5 mW self-biased 0.3–3 GHz small area LNA

abstract: This paper presents a 300 MHz to 3 GHz Low-Noise Amplifier (LNA) with high HP3 and one of the smallest silicon area we could find. It is based on a single amplifier, where it is systematically optimized to achieve better results than more complex noise canceling topologies, thus, saving area and power consumption. A CMOS inverter with resistive feedback where transistors are self-biased in strong inversion is designed and optimized for low NF and high IIP3 and then the feedback resistor is calculated for input impedance matching. The post-layout simulation results in a 130 nm process show for the entire bandwidth a voltage gain around 12 to 15 dB, a NF < 3.6 dB, a Sii of −15.2 to −10.5 dB, HP3 of 4.7 to 5.3 dBm, total area of 40 um × 30 um and power consumption of 6.5 mW under 1.2 V supply. Monte Carlo simulations for 1000 samples show IIP3 of 3.3 to 6 dBm with σ of 0.48 dBm.


title: 3.3 A 0.5-to-32.75Gb/s flexible-reach wireline transceiver in 20nm CMOS

abstract: The introduction of high-speed backplane transceivers inside FPGAs has addressed critical issues such as the ease in scalability of performance, high availability, flexible architectures, the use of standards, and rapid time to market. These have been crucial to address the ever-increasing demand for bandwidth in communication and storage systems [1-3], requiring novel techniques in receiver (RX) and clocking circuits.


title: Q-learning based power control algorithm for D2D communication

abstract: In this paper, reinforcement learning (RL) based power control algorithm in underlay D2D communication is studied. The approach we use regards D2D communication as a multi-agents system, and power control is achieved by maximizing system capacity while maintaining the requirement of quality of service(QoS) from cellular users. We propose two RL based power control methods for D2D users, i.e., team-Q learning and distributed-Q learning. The former is a centralized method in which only one Q-value table needs to be maintained, while the latter enables D2D users to learn independently and reduces the complexity of Q-value table. Simulation results show the difference of the two Q-learning algorithm in terms of convergence and reward function. In addition, it is shown that through our distributed-Q learning, D2D users not only are able to learn their power in a self-organized way, but also achieve better system performance than that using traditional method in LTE(Long Term Evolution).


title: Hybrid-beamforming-based millimeter-wave cellular network optimization

abstract: Massive MIMO and millimeter-wave communication (mmWave) have recently emerged as two key technologies for building 5G wireless networks and beyond. To reconcile the conflict between the large antenna arrays and the limited amount of radio-frequency (RF) chains in mmWave systems, the so-called hybrid beamforming becomes a promising solution and has received a great deal of attention in recent years. However, existing research on hybrid beamforming focused mostly on the physical layer or signal processing aspects. So far, there is a lack of theoretical understanding on how hybrid beamforming could affect mmWave network optimization. In this paper, we consider the impacts of hybrid beamforming on utility-optimality and queueing delay in mmWave cellular network optimization. Our contributions in this paper are three-fold: i) we develop a joint hybrid beamforming and congestion control algorithmic framework for mmWave network utility maximization; ii) we reveal a pseudoconvexity structure in the hybrid beamforming scheduling problem, which leads to simplified analog beamforming protocol design; and iii) we theoretically characterize the scalings of utility-optimality and delay with respect to channel state information (CSI) accuracy in digital beamforming.


title: Mapping Large Spatial Flow Data with Hierarchical Clustering

abstract: It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolu- tion as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different dis- tance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clus- ters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.


title: Texture classification based on co-occurrence matrix and self-organizing map

abstract: This article presents a hybrid approach for texture-based image classification using the gray-level co-occurrence matrices (GLCM) and self-organizing map (SOM) methods. The GLCM is a matrix of how often different combinations of pixel brightness values (grey levels) occur in an image. The GLCM matrices extracted from an image database are processed to create the training data set for a SOM neural network. The SOM model organizes and extracts prototypes from processed GLCM matrices. This paper proposes a novel strategy to index match scores by searching through prototypes. A benchmark data set is used to demonstrate the usefulness of the proposed methodology. The evaluation of performance is based on accuracy in the framework of a Monte Carlo experience. This approach is compared with several classifiers in Li et al [1]. The experimental results on the Brodatz texture image database demonstrate that the proposed method is encouraging with an average successful rate of 97%.


title: Dual Bipolar Measures of Atanassov's Intuitionistic Fuzzy Sets

abstract: Measures of Atanassov's intuitionistic fuzzy sets (AIFSs), such as subsethood, cardinality, distance, similarity, correlation, and evaluation functions, are often used in application problems. This paper investigates such measures from various perspectives. First, based on the relative relations of an AIFS to other AIFSs, four functions, namely superiority, noninferiority, determinacy, and nonhesitancy, are constructed, which consist of dual bipolar scales, namely (superiority, noninferiority) and (determinacy, nonhesitancy). Then, the proposed measures of AIFSs are axiomatically defined using the dual bipolar scales. Geometrical demonstrations, in general, show consistency between the proposed measures and existing ones. However, unlike existing measures, the proposed measures can highlight the significant features of AIFSs. In addition, the attitude of decision makers is also implanted in measures to allocate the importance in the dual bipolar scales. Two numerical examples are used to demonstrate the applicability and distinctiveness of the proposed measures.


title: Neural Machine Translation with Supervised Attention

abstract: The attention mechanisim is appealing for neural machine translation, since it is able to dynam- ically encode a source sentence by generating a alignment between a target word and source words. Unfortunately, it has been proved to be worse than conventional alignment models in aligment accuracy. In this paper, we analyze and explain this issue from the point view of re- ordering, and propose a supervised attention which is learned with guidance from conventional alignment models. Experiments on two Chinese-to-English translation tasks show that the super- vised attention mechanism yields better alignments leading to substantial gains over the standard attention based NMT.


title: Identifying Crucial Parameter Correlations Maintaining Bursting Activity

abstract: Recent experimental and computational studies suggest that linearly correlated sets of parameters (intrinsic and synaptic properties of neurons) allow central pattern-generating networks to produce and maintain their rhythmic activity regardless of changing internal and external conditions. To determine the role of correlated conductances in the robust maintenance of functional bursting activity, we used our existing database of half-center oscillator (HCO) model instances of the leech heartbeat CPG. From the database, we identified functional activity groups of burster (isolated neuron) and half-center oscillator model instances and realistic subgroups of each that showed burst characteristics (principally period and spike frequency) similar to the animal. To find linear correlations among the conductance parameters maintaining functional leech bursting activity, we applied Principal Component Analysis (PCA) to each of these four groups. PCA identified a set of three maximal conductances (leak current, [Formula: see text]Leak; a persistent K current, [Formula: see text]K2; and of a persistent Na+ current, [Formula: see text]P) that correlate linearly for the two groups of burster instances but not for the HCO groups. Visualizations of HCO instances in a reduced space suggested that there might be non-linear relationships between these parameters for these instances. Experimental studies have shown that period is a key attribute influenced by modulatory inputs and temperature variations in heart interneurons. Thus, we explored the sensitivity of period to changes in maximal conductances of [Formula: see text]Leak, [Formula: see text]K2, and [Formula: see text]P, and we found that for our realistic bursters the effect of these parameters on period could not be assessed because when varied individually bursting activity was not maintained.


title: Psychometric properties of the Life Regard Index-Revised: a validation study of a measure of personal meaning.

abstract: A validation study of the English version of the 28-item Life Regard Index–Revised was undertaken with a sample of 91 participants from the general population. All previous studies of the Index have examined the Dutch version. The test-retest reliabilities at 8 wk. for the total Index (r = .87), Framework (r = .82), and Fulfillment (r = .81) subscales were very high. Cronbach alphas were .92, .83, and .87, respectively. A significant restriction of range was observed at the high-meaning end of the scale. Factor analysis only weakly supported the theorized two-factor structure. A very high disattenuated correlation between the Framework and Fulfillment subscales was observed (r = .94). The Index appeared to have adequate evidence supporting its concurrent and discriminant validity when compared with measures of hopelessness, spiritual well-being, and other measures of personal meaning. A significant positive association was found between the index and the Marlowe-Crowne Social Desirability Scale (r = .38)....