title: Video Person Re-identification by Temporal Residual Learning

abstract: In this paper, we propose a novel feature learning framework for video person re-identification (re-ID). The proposed framework largely aims to exploit the adequate temporal information of video sequences and tackle the poor spatial alignment of moving pedestrians. More specifically, for exploiting the temporal information, we design a temporal residual learning (TRL) module to simultaneously extract the generic and specific features of consecutive frames. The TRL module is equipped with two bi-directional LSTM (BiLSTM), which are, respectively, responsible to describe a moving person in different aspects, providing complementary information for better feature representations. To deal with the poor spatial alignment in video re-ID data sets, we propose a spatial-temporal transformer network (ST2N) module. Transformation parameters in the ST2N module are learned by leveraging the high-level semantic information of the current frame as well as the temporal context knowledge from other frames. The proposed ST2N module with less learnable parameters allows effective person alignments under significant appearance changes. Extensive experimental results on the large-scale MARS, PRID2011, ILIDS-VID, and SDU-VID data sets demonstrate that the proposed method achieves consistently superior performance and outperforms most of the very recent state-of-the-art methods.


title: Analysis of routing protocol for Low Power and Lossy Networks (RPL) using Cooja simulator

abstract: Recently, Low Power and Lossy Networks (LLNs) have become a subject of interest for different real time applications. It has become one of the most interesting research areas. These networks are often optimized to save energy and better utilization of limited resources available. Energy conservation while maintaining the performance of network is a major concern in such network. Internet Engineering Task Force (IETF) RoLL has developed a routing protocol for sensors networks, RPL (Routing Protocol For Low Power and Lossy Networks). It's a flexible protocol which allows large scale communicating objects with limited memory, energy and power constraints in Wireless Sensor Networks (WSN). RPL specifications are complex and existing implementation is still under study for better performance. It is important to note that RPL works in networks with lossy links and has ability to adapt various topologies. The aim of this paper is to study the working of RPL using Cooja simulator and performance analysis using different parameters. It will help researchers to contribute more towards RPL.


title: Copy-move forgery detection via texture description

abstract: Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images.


title: Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements †

abstract: Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed.


title: FPGA based traffic light controller

abstract: The main purpose of the traffic light control system is to control the congestion of vehicles at the junctions and also for safer pedestrian crossing. There have been many technologies used for implementing a traffic light controller all over the world. India being one of the densely populated countries, upgrading to a new control system and imposing it all over is a tedious process. This paper proposes the reconfigurable Traffic Light controller which can display the time of waiting in all the directions. It has been observed that the designed traffic light controller is working up to a maximum operating frequency of about 300 MHz. The coding has been done using the Verilog Hardware Descriptive Language.


title: Intelligent Glasses: A New Man-Machine Interface Concept Integrating Computer Vision and Human Tactile Perception

abstract: This paper introduces a new re-programmable interface concept, the Intelligent Glasses (IG): an association of physiological human inspired machine vision and tactile stimulation for 3D (static and dynamic) world representation. The IG interface concept is used to design a wearable system to facilitate navigation and assist mobility of the low vision/sightless in 3D non-cooperating environments at the Robotics Laboratory of Paris (LRP) and the French Atomic Energy Commission (CEA). Being a re-programmable concept, the IG can be easily transferred to any system used in situations of limited or absent vision and/or systems requiring concise and high speed information representation (such as intelligent mobile robotics systems, surgical robotics, virtual reality, highly reactive interfaces, physiological studies of human perception systems, ..).


title: The Museum Wearable: real-time sensor-driven understanding of visitors' interests for personalized visually-augmented museum experiences

abstract: This paper describes the museum wearable: a wearabl e computer which orchestrates an audiovisual narration as a function of the visitor's interests gathered from his/her physical path in the museum and length of stops. The wearable is made by a lightweight and small computer that people carry inside a shoulder pack. It offers an audiovisual augmentation of the surrounding environment using a small, lightweight eye-piece display (often called private-eye) attached to conventional headphones. Using custom built infrared location sensors distributed in the museum space, and statistical mathematical modeling, the museum wearable builds a progressively refined user model and uses it to deliver a personalized audiovisual narration to the visitor. This device will enrich and personalize the museum visit as a visual and auditory storyteller that is able to adapt its story to the audience's interests and guide the public through the path of the exhibit.


title: CommentWatcher: An Open Source Web-based platform for analyzing discussions on web forums

abstract: We present CommentWatcher, an open source tool aimed at analyzing discussions on web forums. Constructed as a web platform, CommentWatcher features automatic mass fetching of user posts from forum on multiple sites, extracting topics, visualizing the topics as an expression cloud and exploring their temporal evolution. The underlying social network of users is simultaneously constructed using the citation relations between users and visualized as a graph structure. Our platform addresses the issues of the diversity and dynamics of structures of webpages hosting the forums by implementing a parser architecture that is independent of the HTML structure of webpages. This allows easy on-the-fly adding of new websites. Two types of users are targeted: end users who seek to study the discussed topics and their temporal evolution, and researchers in need of establishing a forum benchmark dataset and comparing the performances of analysis tools.


title: A Statistical View on Bilingual Lexicon Extraction: From Parallel Corpora to Non-Parallel Corpora

abstract: We present two problems for statistically extracting bilingual lexicon: (1) How can noisy parallel corpora be used? (2) How can non-parallel yet comparable corpora be used? We describe our own work and contribution in relaxing the constraint of using only clean parallel corpora. DKvec is a method for extracting bilingual lexicons, from noisy parallel corpora based on arrival distances of words in noisy parallel corpora. Using DKvec on noisy parallel corpora in English/Japanese and English/Chinese, our evaluations show a 55.35% precision from a small corpus and 89.93% precision from a larger corpus. Our major contribution is in the extraction of bilingual lexicon from non-parallel corpora. We present a first such result in this area, from a new method-Convec. Convec is based on context information of a word to be translated. We show a 30% to 76% precision when top-one to top-20 translation candidates are considered. Most of the top-20 candidates are either collocations or words related to the correct translation. Since nonparallel corpora contain a lot more polysemous words, many-to-many translations, and different lexical items in the two languages, we conclude that the output from Convec is reasonable and useful.


title: Assessing the benefits of serious games to support sustainable decision-making for transboundary watershed governance

abstract: The overarching objective of this paper is to consider the ways that serious games could be developed in the future to maximise potential benefits in the context of governance of complex transbound...


title: Rituximab versus Cyclophosphamide in ANCA-Associated Renal Vasculitis

abstract: BACKGROUND ::: Cyclophosphamide induction regimens for antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis are effective in 70 to 90% of patients, but they are associated with high rates of death and adverse events. Treatment with rituximab has led to remission rates of 80 to 90% among patients with refractory ANCA-associated vasculitis and may be safer than cyclophosphamide regimens. ::: ::: ::: METHODS ::: We compared rituximab with cyclophosphamide as induction therapy in ANCA-associated vasculitis. We randomly assigned, in a 3:1 ratio, 44 patients with newly diagnosed ANCA-associated vasculitis and renal involvement to a standard glucocorticoid regimen plus either rituximab at a dose of 375 mg per square meter of body-surface area per week for 4 weeks, with two intravenous cyclophosphamide pulses (33 patients, the rituximab group), or intravenous cyclophosphamide for 3 to 6 months followed by azathioprine (11 patients, the control group). Primary end points were sustained remission rates at 12 months and severe adverse events. ::: ::: ::: RESULTS ::: The median age was 68 years, and the glomerular filtration rate (GFR) was 18 ml per minute per 1.73 m(2) of body-surface area. A total of 25 patients in the rituximab group (76%) and 9 patients in the control group (82%) had a sustained remission (P=0.68). Severe adverse events occurred in 14 patients in the rituximab group (42%) and 4 patients in the control group (36%) (P=0.77). Six of the 33 patients in the rituximab group (18%) and 2 of the 11 patients in the control group (18%) died (P=1.00). The median increase in the GFR between 0 and 12 months was 19 ml per minute in the rituximab group and 15 ml per minute in the control group (P=0.14). ::: ::: ::: CONCLUSIONS ::: A rituximab-based regimen was not superior to standard intravenous cyclophosphamide for severe ANCA-associated vasculitis. Sustained-remission rates were high in both groups, and the rituximab-based regimen was not associated with reductions in early severe adverse events. (Funded by Cambridge University Hospitals National Health Service Foundation Trust and F. Hoffmann-La Roche; Current Controlled Trials number, ISRCTN28528813.)


title: CPRI over Ethernet: Towards fronthaul/backhaul multiplexing

abstract: Ethernet has been proposed for the 5G fronthaul to transport the Common Public Radio Interface (CPRI) traffic between the radio equipment (RE) and the radio equipment control (REC). The advantages of adopting an Ethernet transport are threefold 1) the low cost of equipment, 2) the use of a shared infrastructure with statistical multiplexing, as well as 3) the ease of operations, administration and maintenance (OAM). In this paper, we introduce distributed timeslot scheduler for CPRI over Ethernet (DTSCoE) as a scheduling algorithm for IEEE 802.1Qbv to support CPRI traffic. DTSCoE is built upon the stream reservation protocol (SRP) IEEE 802.1Qcc to propagate timeslot information across the datapath without any centralized coordination. The simulation results demonstrate that DTSCoE reduces one-way delay to minimum and reduces the jitter to zero which satisfies the CPRI requirements.


' s personal copy Web robot detection : A probabilistic reasoning approach


title: Narcissism and the Use of Personal Pronouns

abstract: ABSTRACT In this study we explored the relationship between narcissism and the individual's use of personal pronouns during extemporaneous monologues The subjects, 24 males and 24 females, were asked to talk for approximately 5 minutes on any topic they chose Following the monologues the subjects were administered the Narcissistic Personality Inventory, the Eysenck Personality Questionnaire, and the Rotter Internal-External Locus of Control Scale The monologues were tape-recorded and later transcribed and analyzed for the subjects' use of personal pronouns As hypothesized, individuals who scored higher on narcissism tended to use more first person singular pronouns and fewer first person plural pronouns Discriminant validity for the relationship between narcissism and first person pronoun usage was exhibited in that narcissism did not show a relationship with subjects' use of second and third person pronouns, nor did the personality variables of extraversion, neuroticism, or locus of control exhibit any relationship with the subjects' personal pronoun usage


title: Ceph: a scalable, high-performance distributed file system

abstract: We have developed Ceph, a distributed file system that provides excellent performance, reliability, and scalability. Ceph maximizes the separation between data and metadata management by replacing allocation tables with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clusters of unreliable object storage devices (OSDs). We leverage device intelligence by distributing data replication, failure detection and recovery to semi-autonomous OSDs running a specialized local object file system. A dynamic distributed metadata cluster provides extremely efficient metadata management and seamlessly adapts to a wide range of general purpose and scientific computing file system workloads. Performance measurements under a variety of workloads show that Ceph has excellent I/O performance and scalable metadata management, supporting more than 250,000 metadata operations per second.


title: Controllable Invariance through Adversarial Feature Learning

abstract: Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance.


title: Powering the IoT through embedded machine learning and LoRa

abstract: The Internet of Things (IoT) technology is rapidly changing the way we live and the number of connected devices are increasing at an exponential pace. However, two key challenges are the battery life for off-grid IoT applications and the ability of edge devices to communicate over long range. Raw data transmission poses as a very power hungry activity for any device. The conventional cellular wide area networks are power-hungry and incompatible for battery-operated IoT devices. There is a need for low-power edge computing devices that reduce the transmission payload and integrate Low-Power Wide-Area Network (LPWAN) technologies, which offer a wide range connectivity while still providing a long battery life. One of the most promising LPWAN technologies today is LoRa. In this paper, we present a solution that employs machine learning on the edge device and performs low power transmission through LoRa. We demonstrate the use case of our solution through a field trial conducted in China for sow activity classification. By implementing embedded machine learning with LoRa, we could compress the transmitted data by 512 times and extend the battery life by three times. A very low energy expenditure of 5.1 mJ per classification result is achieved.


title: Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review

abstract: Virtualization plays a vital role in the construction of cloud computing. However, various vulnerabilities are existing in current virtualization implementations, and thus there are various security challenges at virtualization layer. In this paper, we investigate different vulnerabilities and attacks at virtualization layer of cloud computing. We examine the proposals of cloud intrusion detection system (IDS) and intrusion detection and prevention system frameworks. We recommend the cloud IDS requirements and research scope to achieve desired level of security at virtualization layer of cloud computing.


title: THE WRAPPER APPROACH

abstract: In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes. In addition, the feature subsets selected by the wrapper are significantly smaller than the original subsets used by the learning algorithms, thus producing more comprehensible models.


title: Active Inference, Evidence Accumulation, and the Urn Task

abstract: Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information often known as reflection impulsivity is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active Bayesian inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior-in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain at least at a heuristic level. Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology.


title: Signed networks in social media

abstract: Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social networks that has focused almost exclusively on positive interpretations of links between people, we study how the interplay between positive and negative relationships affects the structure of on-line social networks. We connect our analyses to theories of signed networks from social psychology. We find that the classical theory of structural balance tends to capture certain common patterns of interaction, but that it is also at odds with some of the fundamental phenomena we observe --- particularly related to the evolving, directed nature of these on-line networks. We then develop an alternate theory of status that better explains the observed edge signs and provides insights into the underlying social mechanisms. Our work provides one of the first large-scale evaluations of theories of signed networks using on-line datasets, as well as providing a perspective for reasoning about social media sites.


title: Fast and Guaranteed Tensor Decomposition via Sketching

abstract: Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent variable models and in data mining. In this paper, we propose fast and randomized tensor CP decomposition algorithms based on sketching. We build on the idea of count sketches, but introduce many novel ideas which are unique to tensors. We develop novel methods for randomized computation of tensor contractions via FFTs, without explicitly forming the tensors. Such tensor contractions are encountered in decomposition methods such as tensor power iterations and alternating least squares. We also design novel colliding hashes for symmetric tensors to further save time in computing the sketches. We then combine these sketching ideas with existing whitening and tensor power iterative techniques to obtain the fastest algorithm on both sparse and dense tensors. The quality of approximation under our method does not depend on properties such as sparsity, uniformity of elements, etc. We apply the method for topic modeling and obtain competitive results.


title: Aberration fields of a combination of plane symmetric systems.

abstract: By generalizing the wave aberration function to include plane symmetric systems, we describe the aberration fields for a combination of plane symmetric systems. The combined system aberration coefficients for the fields of spherical aberration, coma, astigmatism, defocus and distortion depend on the individual aberration coefficients and the orientations of the individual plane symmetric component systems. The aberration coefficients can be used to calculate the locations of the field nodes for the different types of aberration, including coma, astigmatism, defocus and distortion. This work provides an alternate view for combining aberrations in optical systems.


title: Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision

abstract: Training deep neural networks requires massive amounts of training data, but for many tasks only limited labeled data is available. This makes weak supervision attractive, using weak or noisy signals like the output of heuristic methods or user click-through data for training. In a semi-supervised setting, we can use a large set of data with weak labels to pretrain a neural network and then fine-tune the parameters with a small amount of data with true labels. This feels intuitively sub-optimal as these two independent stages leave the model unaware about the varying label quality. What if we could somehow inform the model about the label quality? In this paper, we propose a semi-supervised learning method where we train two neural networks in a multi-task fashion: a "target network" and a "confidence network". The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to weight the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model. We evaluate our learning strategy on two different tasks: document ranking and sentiment classification. The results demonstrate that our approach not only enhances the performance compared to the baselines but also speeds up the learning process from weak labels.


title: Hierarchical Reinforcement Learning for Adaptive Text Generation

abstract: We present a novel approach to natural language generation (NLG) that applies hierarchical reinforcement learning to text generation in the wayfinding domain. Our approach aims to optimise the integration of NLG tasks that are inherently different in nature, such as decisions of content selection, text structure, user modelling, referring expression generation (REG), and surface realisation. It also aims to capture existing interdependencies between these areas. We apply hierarchical reinforcement learning to learn a generation policy that captures these interdependencies, and that can be transferred to other NLG tasks. Our experimental results---in a simulated environment---show that the learnt wayfinding policy outperforms a baseline policy that takes reasonable actions but without optimization.


title: Stereo-based simultaneous localization, mapping and moving object tracking

abstract: Vision based simultaneous localization and mapping (SLAM) has recently received much research interest. However, vision based SLAM could be corrupted with the inclusion of moving entities, which makes it hard to operate in dynamic environments. Simultaneous localization, mapping and moving object tracking (SLAMMOT) serves as a solution to deal with moving objects while performing SLAM. The existing work has shown the feasibility of monocular SLAMMOT in dynamic environments. However, monocular SLAMMOT inherits the observability issue of bearings-only tracking in which moving entities would be unobservable according to motions of the camera and moving objects. In this paper, stereo-based SLAMMOT is proposed to solve the observability issue as well as increase the accuracy of localization, mapping and tracking. Simulation and experimental results demonstrate that the proposed stereo SLAMMOT is superior than monocular SLAMMOT in dynamic environments.


Serologic diagnosis of toxoplasmosis and prevalence of Toxoplasma gondii antibodies in selected feline, canine, and human populations.


title: Bleeding Frame and Region Detection in the Wireless Capsule Endoscopy Video

abstract: Wireless capsule endoscopy (WCE) enables noninvasive and painless direct visual inspection of a patient's whole digestive tract, but at the price of long time reviewing large amount of images by clinicians. Thus, an automatic computer-aided technique to reduce the burden of physicians is highly demanded. In this paper, we propose a novel color feature extraction method to discriminate the bleeding frames from the normal ones, with further localization of the bleeding regions. Our proposal is based on a twofold system. First, we make full use of the color information of WCE images and utilize K -means clustering method on the pixel represented images to obtain the cluster centers, with which we characterize WCE images as words-based color histograms. Then, we judge the status of a WCE frame by applying the support vector machine (SVM) and K -nearest neighbor methods. Comprehensive experimental results reveal that the best classification performance is obtained with YCbCr color space, cluster number 80 and the SVM. The achieved classification performance reaches 95.75% in accuracy, 0.9771 for AUC, validating that the proposed scheme provides an exciting performance for bleeding classification. Second, we propose a two-stage saliency map extraction method to highlight bleeding regions, where the first-stage saliency map is created by means of different color channels mixer and the second-stage saliency map is obtained from the visual contrast. Followed by an appropriate fusion strategy and threshold, we localize the bleeding areas. Quantitative as well as qualitative results show that our methods could differentiate the bleeding areas from neighborhoods correctly.


title: Meta-Lamarckian learning in memetic algorithms

abstract: Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the limited progress made on mitigating the effects of incorrect local search method choice, we present strategies for MA control that decide, at runtime, which local method is chosen to locally improve the next chromosome. The use of multiple local methods during a MA search in the spirit of Lamarckian learning is here termed Meta-Lamarckian learning. Two adaptive strategies for Meta-Lamarckian learning are proposed in this paper. Experimental studies with Meta-Lamarckian learning strategies on continuous parametric benchmark problems are also presented. Further, the best strategy proposed is applied to a real-world aerodynamic wing design problem and encouraging results are obtained. It is shown that the proposed approaches aid designers working on complex engineering problems by reducing the probability of employing inappropriate local search methods in a MA, while at the same time, yielding robust and improved design search performance.


title: Design of Home Appliances for a DC-Based Nanogrid System: An Induction Range Study Case

abstract: Efficiency has become a key design parameter when designing any electrical system. In recent years, significant attention has been paid to the design of optimized micro and nanogrids comprising residential area subsystems. Among the different proposed approaches, one of the most promising consists of a dc-based nanogrid optimized for the interoperation of electric loads, sources, and storage elements. Home appliances are one of the main loads in such dc-based nanogrids. In this paper, the design and implementation of an appliance for operation in a dc-based nanogrid is detailed. An induction heating range is considered as a design example, with some of the design considerations generalizable to any other appliances. The main design aspects, including the inductor system, power converter, and control, are considered. Finally, some simulation and experimental results of the expected converter performance are shown.


title: Attending to Attention: Detecting and Combating Mind Wandering during Computerized Reading

abstract: Mind wandering (MW) is a ubiquitous phenomenon that has a negative influence on performance and productivity in many contexts. We propose that intelligent interfaces should have some mechanism to detect and respond to MW in real-time. Towards this end, we developed an interface that automatically detects MW from eye-gaze during computerized reading. When MW is detected, the interface intervenes by asking just-in-time questions and encouraging re-reading as needed. After multiple rounds of iterative refinement, we summatively compared the interface to a yoked control condition in a randomized control trial with 104 participants. Preliminary results suggest that the system was successful in correcting comprehension deficits attributed to MW, thereby highlighting the potential for intelligent interfaces that improve performance by "attending to attention."


Novel Multi-Objective Artificial Bee Colony Optimization for Wrapper Based Feature Selection in Intrusion Detection


title: Effect of the trap density and distribution of the silicon nitride layer on the retention characteristics of charge trap flash memory devices

abstract: The trap distribution in the silicon-nitride layer, which was estimated by using experimental results, was used to clarify the retention characteristics of TaN-Al 2 O 3 -Si 3 N 4 -SiO 2 -Si (TANOS) memory devices. The dependence of the trap density and distribution of the silicon nitride layer on the retention characteristics in TANOS memory devices was investigated by using the two-coupled rate equations together with the Shockley- Reed statistics. Simulation results showed that the retention characteristics in TANOS memory devices increased with increasing trap density near and above the Fermi-level in the silicon-nitride layer. The simulation results for the retention characteristics of TANOS memory devices were in reasonable agreement with the experimental results. These observations can help improve understanding of the retention mechanisms and the reliability problems in charge trap flash (CTF) memory devices.


title: The role of emotion in clinical decision making: an integrative literature review

abstract: BackgroundTraditionally, clinical decision making has been perceived as a purely rational and cognitive process. Recently, a number of authors have linked emotional intelligence (EI) to clinical decision making (CDM) and calls have been made for an increased focus on EI skills for clinicians. The objective of this integrative literature review was to identify and synthesise the empirical evidence for a role of emotion in CDM.MethodsA systematic search of the bibliographic databases PubMed, PsychINFO, and CINAHL (EBSCO) was conducted to identify empirical studies of clinician populations. Search terms were focused to identify studies reporting clinician emotion OR clinician emotional intelligence OR emotional competence AND clinical decision making OR clinical reasoning.ResultsTwenty three papers were retained for synthesis. These represented empirical work from qualitative, quantitative, and mixed-methods approaches and comprised work with a focus on experienced emotion and on skills associated with emotional intelligence. The studies examined nurses (10), physicians (7), occupational therapists (1), physiotherapists (1), mixed clinician samples (3), and unspecified infectious disease experts (1). We identified two main themes in the context of clinical decision making: the subjective experience of emotion; and, the application of emotion and cognition in CDM. Sub-themes under the subjective experience of emotion were: emotional response to contextual pressures; emotional responses to others; and, intentional exclusion of emotion from CDM. Under the application of emotion and cognition in CDM, sub-themes were: compassionate emotional labour – responsiveness to patient emotion within CDM; interdisciplinary tension regarding the significance and meaning of emotion in CDM; and, emotion and moral judgement.ConclusionsClinicians’ experienced emotions can and do affect clinical decision making, although acknowledgement of that is far from universal. Importantly, this occurs in the in the absence of a clear theoretical framework and educational preparation may not reflect the importance of emotional competence to effective CDM.


title: Mobile assisted diabetic retinopathy detection using deep neural network

abstract: Diabetic retinopathy (DR) is a major microvascular complication resulting from diabetes and continues to have a serious impact on global health systems. Globally about 95 million people suffer from DR. This paper focuses on detection aspects of a mobile application developed to perform DR screening in real time. The application is powered by a tensorflow deep neural network architecture that is trained and tested on 16,798 fundus images. These images are preprocessed to remove noise and prepare them to be fed into neural network. Preprocessing steps involve averaging all the images using a 5×5 filter to improve the quality of images and then these images are resized to 256×256 pixels. After preprocessing the input dataset is fed into the neural network. The convolutional neural network model used in this project is MobileNets, which is used for mobile devices. The neural network has 28 convolutional layers and after each layer there is batchnorm and ReLU nonlinear function except at the final layer. The output from last layer is a class label either DR or no DR. The final accuracy of the model is 73.3%. This model is optimized to work on mobile devices and does not require Internet connection to run.


title: A novel bidirectional multilevel boost-buck dc-dc converter

abstract: A novel noninverting boost-buck dc-dc converter topology is presented, applicable when both sides of the converter need to have the same grounding. It is based on the back-to-back connection of two n-level diode-clamped converter legs, and allows bidirectional power flow. A simplified topology is proposed for unidirectional power flow applications. Two new pulse width modulation strategies with different advantages are proposed to operate the converter guaranteeing dc-link capacitor voltage balance in every switching cycle for all possible operating conditions and using small capacitance values. The semiconductor device losses are analytically compared to the losses in a conventional two-level boost-buck converter. The analysis yields a higher efficiency for the multilevel converter, especially as the number of levels increases. Experimental results are presented to validate the good converter performance in a five-level converter prototype.


title: Automatic tuberculosis detection using adaptive thresholding in chest radiographs

abstract: Tuberculosis is a global health threatening disease with mortality rates increasing day by day. As earlier TB detection methods do not ensure accurate results, there is an immediate action required to detect the disease with proper tools. To address this issue, a novel mechanism is proposed to detect tuberculosis in chest radiographs. The proposed method includes a three stage process of accurate detection of tuberculosis. In the first stage, lung segmentation using adaptive thresholding is utilized to segment the chest images which have different illumination. The second stage of feature extraction modifies the input image into a set of features which extracts relevant information contained in the chest image. In the third stage of process, k-Nearest Neighbor classifier (k-NN) outputs the decision that the input image is normal or abnormal. The detection process is evaluated using four performance analyzing criteria: accuracy, sensitivity, specificity and area under the ROC curve (AUC). Simulation result shows that the accuracy of our proposed system is 4% more than the graph-cut based TB detection system. A comparison task is also performed to measure the quality of classifier among three classifiers: k-NN, support vector machine and artificial neural network classifier. Results show that k-NN classifier outperforms the other two classifiers with much lower misclassification rate.


High gain spherical DRA operating on higher-order mode excited by microstrip patch


title: Joint Social and Content Recommendation for User-Generated Videos in Online Social Network

abstract: Online social network is emerging as a promising alternative for users to directly access video contents. By allowing users to import videos and re-share them through the social connections, a large number of videos are available to users in the online social network. The rapid growth of the user-generated videos provides enormous potential for users to find the ones that interest them; while the convergence of online social network service and online video sharing service makes it possible to perform recommendation using social factors and content factors jointly. In this paper, we design a joint social-content recommendation framework to suggest users which videos to import or re-share in the online social network. In this framework, we first propose a user-content matrix update approach which updates and fills in cold user-video entries to provide the foundations for the recommendation. Then, based on the updated user-content matrix, we construct a joint social-content space to measure the relevance between users and videos, which can provide a high accuracy for video importing and re-sharing recommendation. We conduct experiments using real traces from Tencent Weibo and Youku to verify our algorithm and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.


title: Automatic generation of library bindings using static analysis

abstract: High-level languages are growing in popularity. However, decades of C software development have produced large libraries of fast, time-tested, meritorious code that are impractical to recreate from scratch. Cross-language bindings can expose low-level C code to high-level languages. Unfortunately, writing bindings by hand is tedious and error-prone, while mainstream binding generators require extensive manual annotation or fail to offer the language features that users of modern languages have come to expect. We present an improved binding-generation strategy based on static analysis of unannotated library source code. We characterize three high-level idioms that are not uniquely expressible in C's low-level type system: array parameters, resource managers, and multiple return values. We describe a suite of interprocedural analyses that recover this high-level information, and we show how the results can be used in a binding generator for the Python programming language. In experiments with four large C libraries, we find that our approach avoids the mistakes characteristic of hand-written bindings while offering a level of Python integration unmatched by prior automated approaches. Among the thousands of functions in the public interfaces of these libraries, roughly 40% exhibit the behaviors detected by our static analyses.


title: Tampering Detection in Low-Power Smart Cameras

abstract: A desirable feature in smart cameras is the ability to autonomously detect any tampering event/attack that would prevent a clear view over the monitored scene. No matter whether tampering is due to atmospheric phenomena (e.g., few rain drops over the camera lens) or to malicious attacks (e.g., occlusions or device displacements), these have to be promptly detected to possibly activate countermeasures. Tampering detection is particularly challenging in battery-powered cameras, where it is not possible to acquire images at full-speed frame-rates, nor use sophisticated image-analysis algorithms.


title: Efficient Single-Precision Floating-Point Division Using Harmonized Parabolic Synthesis

abstract: This paper proposes a novel method for performing division on floating-point numbers represented in IEEE-754 single-precision (binary32) format. The method is based on an inverter, implemented as a combination of Parabolic Synthesis and second-degree interpolation, followed by a multiplier. It is implemented with and without pipeline stages individually and synthesized while targeting a Xilinx Ultrascale FPGA.The implementations show better resource usage and latency results when compared to other implementations based on different methods. In case of throughput, the proposed method outperforms most of the other works, however, some Altera FPGAs achieve higher clock rate due to the differences in the DSP slice multiplier design.Due to the small size, low latency and high throughput, the presented floating-point division unit is suitable for high performance embedded systems and can be integrated into accelerators or be used as a stand-alone accelerator.


title: 6um Pitch High Density Cu-Cu Bonding for 3D IC Stacking

abstract: For ultra-fine pitch and high density Cu pillar low temperature bonding (200°C), the surface contact between substrate and Cu pillar array is the key. Therefore, the fabrication quality of copper bump array affects severely the bonding results. The qualitative factors include (1) Cu pillar array height uniformity, (2) free of copper oxide layer, (3) Cu material property (e.g. elastic modulus, grain size), (4) certain levels of roughness and (5) even the Cu pillar with or without TSV in the wafer. In this paper, test vehicle with dia3um and pitch 6um TSV 20um thin wafer are design and fabricated. The test vehicle is used to study above major bonding contributors. Solid Cu-Cu interconnects are demonstrated with both Chip to chip (C2C) and Chip to wafer (C2W) process. The developed process is promising for high density I/O (


title: Visual-Inertial Combined Odometry System for Aerial Vehicles

abstract: The requirement to operate aircraft in GPS-denied environments can be met by using visual odometry. Aiming at a full-scale aircraft equipped with a high-accuracy inertial navigation system INS, the proposed method combines vision and the INS for odometry estimation. With such an INS, the aircraft orientation is accurate with low drift, but it contains high-frequency noise that can affect the vehicle motion estimation, causing position estimation to drift. Our method takes the INS orientation as input and estimates translation. During motion estimation, the method virtually rotates the camera by reparametrizing features with their depth direction perpendicular to the ground. This partially eliminates error accumulation in motion estimation caused by the INS high-frequency noise, resulting in a slow drift. We experiment on two hardware configurations in the acquisition of depth for the visual features: 1 the height of the aircraft above the ground is measured by an altimeter assuming that the imaged ground is a local planar patch, and 2 the depth map of the ground is registered with a two-dimensional laser in a push-broom configuration. The method is tested with data collected from a full-scale helicopter. The accumulative flying distance for the overall tests is approximately 78 km. We observe slightly better accuracy with the push-broom laser than the altimeter.


title: Neural Network Classifiers Estimate Bayesian a posteriori Probabilities

abstract: Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, sim...


title: An effect evaluation method for IMS SIP flooding attacks based on fuzzy comprehensive evaluation

abstract: To quantitatively understand the effects of SIP flooding attacks in IMS, the paper introduces an evaluation method for attack effect based on fuzzy comprehensive evaluation. The paper gets the evaluation indexes by analyzing the possible attack flow, and introduces analytic hierarchy process (AHP) to compute the indexes' weight, and makes fuzzy comprehensive evaluation for attacks. To reduce the bias brought by a single fuzzy operator's properties, the paper selects several operators to complement with each other. Finally, it is validated that the evaluation method is able to distinguish the attack effects resulted by different SIP flooding attacks effectively.


title: Inductive Compensation of High-Permittivity Coatings on Automobile Long-Range Radar Radomes

abstract: A theoretical and practical investigation of polymer composites including paint layers as radomes for automotive long-range radar systems operating at 76.5 GHz is carried out. The materials in focus of this research are metallic base paints with a high concentration of metal, and hence, a high dielectric constant. A multilayered free-space measurement technique is presented to derive the dielectric properties of coatings with a thickness of several micrometers. The impact of the paint layers on the radar performance are discussed in this paper, and a narrowband technique is presented to compensate the reflection effects of critical paints.


title: Joint learning of ontology and semantic parser from text

abstract: Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar and the semantic trees are used to learn the ontology on several levels -- classes, instances, taxonomic and non-taxonomic relations. The approach was evaluated on the first sentences of Wikipedia pages describing people.


title: A review of target decomposition theorems in radar polarimetry

abstract: In this paper, we provide a review of the different approaches used for target decomposition theory in radar polarimetry. We classify three main types of theorem; those based on the Mueller matrix and Stokes vector, those using an eigenvector analysis of the covariance or coherency matrix, and those employing coherent decomposition of the scattering matrix. We unify the formulation of these different approaches using transformation theory and an eigenvector analysis. We show how special forms of these decompositions apply for the important case of backscatter from terrain with generic symmetries.


title: Cytokinin‐dependent regulatory module underlies the maintenance of zinc nutrition in rice

abstract: Zinc (Zn) deficiency is a critical problem in human nutrition. Rice is the main source of calories for nearly half of world's population but has the shortcoming, from a nutritional perspective, of being low in Zn and other essential nutrients. Here we performed analyses with cytokinin-related mutants and transgenic lines to provide unequivocal evidence that cytokinins occupy a key position in controlling Zn status in plants. Transporters responsible for Zn uptake and chelators for the internal transport of Zn were strictly controlled by cytokinins. Moreover, cytokinin metabolism was regulated in a highly dynamic way in response to Zn status, which confers rice to adapt to heterogeneous Zn availability. Subsequently, fine-tuning of cytokinin metabolism by root-specific expression of a cytokinin degradation enzyme can improve both Zn nutrient and yield traits. Importantly, X-ray fluorescence imaging revealed that the increased Zn is broadly distributed from the aleurone layer to the inner endosperm. Our results indicate metabolic control of cytokinin could provide a key for breeding Zn-enriched rice. This article is protected by copyright. All rights reserved.


title: FIELD TESTING OF FLAT BOTTOMED STORAGE TANKS WITH ACOUSTIC EMISSION – A REVIEW ON THE GAINED EXPERIENCE

abstract: A new testing technique for corrosion and leakage testing of the flat bottoms of atmospheric, aboveground storage tanks (AST) with acoustic emission (AE) was developed under an EC funded project. Frequency-domain-based pattern recognition is used to identify the source mechanism of the found AE sources located at the tank floor. The basics of the applied method and the testing technique itself are outlined as well as results of performed tests are given together with statistical numbers regarding the grading of tested tank floors. Conventional nondestructive testing of tank floors relies mainly on visual testing and wall thickness measurements and therefore reflects the past service periods of the tank in total. On the other hand AE enables to monitor the active processes taking place at the time of test on the tank floor at conditions similar to normal tank service. The combination of both leads to a complementary view of the tank floor condition, taking advantage of both approaches: determination of the total degradation of the tank floor and monitoring of the actual degradation processes taking place under current service conditions.


title: Review of Charge Pump Topologies for Micro Energy Harvesting Systems

abstract: This paper reviews CMOS based charge pump topologies used within autonomous embedded micro-systems. These charge pump structures have evolved from its simplistic diode-tied, single-branches with major threshold drops to exponential type, dual-branches with sophisticated gate and substrate control for lower voltage operation. Published charge pumps are grouped based on architecture, operation principles and pump optimization techniques with their pros and cons compared and results contrasted. The various charge pump topologies and schemes used are considered based on pumping efficiency, power efficiency, charge transferability, circuit complexity, pumping capacitors, form factor and minimum supply voltages with an optimum load. This article concludes with an overview of suitable techniques and recommendations that will aid a designer in selecting the most suitable charge pump topology especially for low ambient micro energy harvesting applications.


title: Admissible Abstractions for Near-optimal Task and Motion Planning

abstract: We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving $10^{13}$ states without using a separate task planner.


title: Optimizing deep learning hyper-parameters through an evolutionary algorithm

abstract: There has been a recent surge of success in utilizing Deep Learning (DL) in imaging and speech applications for its relatively automatic feature generation and, in particular for convolutional neural networks (CNNs), high accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms.


title: A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks

abstract: Social engineering is used as an umbrella term for a broad spectrum of computer exploitations that employ a variety of attack vectors and strategies to psychologically manipulate a user. Semantic attacks are the specific type of social engineering attacks that bypass technical defences by actively manipulating object characteristics, such as platform or system applications, to deceive rather than directly attack the user. Commonly observed examples include obfuscated URLs, phishing emails, drive-by downloads, spoofed websites and scareware to name a few. This article presents a taxonomy of semantic attacks, as well as a survey of applicable defences. By contrasting the threat landscape and the associated mitigation techniques in a single comparative matrix, we identify the areas where further research can be particularly beneficial.


title: The Effects of Interpersonal Attitude of a Group of Agents on User’s Presence and Proxemics Behavior

abstract: In the everyday world people form small conversing groups where social interaction takes place, and much of the social behavior takes place through managing interpersonal space (i.e., proxemics) and group formation, signaling their attentio to others (i.e., through gaze behavior), and expressing certain attitudes, for example, friendliness, by smiling, getting close through increased engagement and intimacy, and welcoming newcomers. Many real-time interactive systems feature virtual anthropomorphic characters in order to simulate conversing groups and add plausibility and believability to the simulated environments. However, only a few have dealt with autonomous behavior generation, and in those cases, the agents’ exhibited behavior should be evaluated by users in terms of appropriateness, believability, and conveyed meaning (e.g., attitudes). In this article we present an integrated intelligent interactive system for generating believable nonverbal behavior exhibited by virtual agents in small simulated group conversations. The produced behavior supports group formation management and the expression of interpersonal attitudes (friendly vs. unfriendly) both among the agents in the group (i.e., in-group attitude) and towards an approaching user in an avatar-based interaction (out-group attitude). A user study investigating the effects of these attitudes on users’ social presence evaluation and proxemics behavior (with their avatar) in a three-dimensional virtual city environment is presented. We divided the study into two trials according to the task assigned to users, that is, joining a conversing group and reaching a target destination behind the group. Results showed that the out-group attitude had a major impact on social presence evaluations in both trials, whereby friendly groups were perceived as more socially rich. The user’s proxemics behavior depended on both out-group and in-group attitudes expressed by the agents. Implications of these results for the design and implementation of similar intelligent interactive systems for the autonomous generation of agents’ multimodal behavior are briefly discussed.


title: Convolutional Matrix Factorization for Document Context-Aware Recommendation

abstract: Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel context-aware recommendation model, convolutional matrix factorization (ConvMF) that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF). Consequently, ConvMF captures contextual information of documents and further enhances the rating prediction accuracy. Our extensive evaluations on three real-world datasets show that ConvMF significantly outperforms the state-of-the-art recommendation models even when the rating data is extremely sparse. We also demonstrate that ConvMF successfully captures subtle contextual difference of a word in a document. Our implementation and datasets are available at http://dm.postech.ac.kr/ConvMF.


AGILE DEVELOPMENT : LESSONS LEARNED FROM THE FIRST SCRUM


title: Development of meander line type antenna for the reception of high definition digital signals

abstract: This paper presents the development of an antenna for the reception of high definition television digital signals (HDTV). The proposed antenna is based on the half-wave dipole topology (λ/2) with geometry inspired by the meander line antenna (MLA). Simulations using software based on the finite element method were performed and the antenna reception efficiency was evaluated through measurements.


title: An Evolutionary Squeaky Wheel Optimisation Approach to Personnel Scheduling

abstract: The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation's effectiveness and execution speed by incorporating two extra steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repairs are carried out by using the Prioritization to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvement in the Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with interative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.


title: An application of Kano model to identify quality attributes of smart tourism

abstract: Tourism firms are realizing the significance of customer-centered philosophies and are turning to quality management approaches to help managing their businesses in the context of smart tourism. This paper started with the concept of smart tourism and applied Kano model to identify basic quality elements concerned by tourists in the context of smart tourism, which may help firms to understand better how to improve the service quality.


Automatic Stylistic Composition of Bach Chorales with Deep LSTM


title: A Riemannian Framework for Tensor Computing

abstract: Tensors are nowadays a common source of geometric information. In this paper, we propose to endow the tensor space with an affine-invariant Riemannian metric. We demonstrate that it leads to strong theoretical properties: the cone of positive definite symmetric matrices is replaced by a regular and complete manifold without boundaries (null eigenvalues are at the infinity), the geodesic between two tensors and the mean of a set of tensors are uniquely defined, etc. ::: ::: We have previously shown that the Riemannian metric provides a powerful framework for generalizing statistics to manifolds. In this paper, we show that it is also possible to generalize to tensor fields many important geometric data processing algorithms such as interpolation, filtering, diffusion and restoration of missing data. For instance, most interpolation and Gaussian filtering schemes can be tackled efficiently through a weighted mean computation. Linear and anisotropic diffusion schemes can be adapted to our Riemannian framework, through partial differential evolution equations, provided that the metric of the tensor space is taken into account. For that purpose, we provide intrinsic numerical schemes to compute the gradient and Laplace-Beltrami operators. Finally, to enforce the fidelity to the data (either sparsely distributed tensors or complete tensors fields) we propose least-squares criteria based on our invariant Riemannian distance which are particularly simple and efficient to solve.


title: Synthesizing highly expressive SQL queries from input-output examples

abstract: SQL is the de facto language for manipulating relational data. Though powerful, many users find it difficult to write SQL queries due to highly expressive constructs. While using the programming-by-example paradigm to help users write SQL queries is an attractive proposition, as evidenced by online help forums such as Stack Overflow, developing techniques for synthesizing SQL queries from given input-output (I/O) examples has been difficult, due to the large space of SQL queries as a result of its rich set of operators. In this paper, we present a new scalable and efficient algorithm for synthesizing SQL queries based on I/O examples. The key innovation of our algorithm is development of a language for abstract queries, i.e., queries with uninstantiated operators, that can be used to express a large space of SQL queries efficiently. Using abstract queries to represent the search space nicely decomposes the synthesis problem into two tasks: 1) searching for abstract queries that can potentially satisfy the given I/O examples, and 2) instantiating the found abstract queries and ranking the results. We have implemented this algorithm in a new tool called Scythe and evaluated it using 193 benchmarks collected from Stack Overflow. Our evaluation shows that Scythe can efficiently solve 74% of the benchmarks, most in just a few seconds, and the queries range from simple ones involving a single selection to complex queries with 6 nested subqueires.


title: A KIF Formalization for the IFF Category Theory Ontology

abstract: This paper begins the discussion of how the Information Flow Framework can be used to provide a principled foundation for the metalevel (or structural level) of the Standard Upper Ontology (SUO). This SUO structural level can be used as a logical framework for manipulating collections of ontologies in the object level of the SUO or other middle level or domain ontologies. From the Information Flow perspective, the SUO structural level resolves into several metalevel ontologies. This paper discusses a KIF formalization for one of those metalevel categories, the Category Theory Ontology. In particular, it discusses its category and colimit sub-namespaces.


title: Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy

abstract: Multivariate transfer entropy (TE) is a model-free approach to detect causalities in multivariate time series. It is able to distinguish direct from indirect causality and common drivers without assuming any underlying model. But despite these advantages it has mostly been applied in a bivariate setting as it is hard to estimate reliably in high dimensions since its definition involves infinite vectors. To overcome this limitation, we propose to embed TE into the framework of graphical models and present a formula that decomposes TE into a sum of finite-dimensional contributions that we call decomposed transfer entropy. Graphical models further provide a richer picture because they also yield the causal coupling delays. To estimate the graphical model we suggest an iterative algorithm, a modified version of the PC-algorithm with a very low estimation dimension. We present an appropriate significance test and demonstrate the method's performance using examples of nonlinear stochastic delay-differential equations and observational climate data (sea level pressure).


title: An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer

abstract: The aim of this investigation was to develop and test two artificial neural networks (ANN) to apply to physical activity data collected with a commonly used uniaxial accelerometer. The first ANN model estimated physical activity metabolic equivalents (METs), and the second ANN identified activity type. Subjects (n = 24 men and 24 women, mean age = 35 yr) completed a menu of activities that included sedentary, light, moderate, and vigorous intensities, and each activity was performed for 10 min. There were three different activity menus, and 20 participants completed each menu. Oxygen consumption (in ml x kg(-1) x min(-1)) was measured continuously, and the average of minutes 4-9 was used to represent the oxygen cost of each activity. To calculate METs, activity oxygen consumption was divided by 3.5 ml x kg(-1) x min(-1) (1 MET). Accelerometer data were collected second by second using the Actigraph model 7164. For the analysis, we used the distribution of counts (10th, 25th, 50th, 75th, and 90th percentiles of a minute's second-by-second counts) and temporal dynamics of counts (lag, one autocorrelation) as the accelerometer feature inputs to the ANN. To examine model performance, we used the leave-one-out cross-validation technique. The ANN prediction of METs root-mean-squared error was 1.22 METs (confidence interval: 1.14-1.30). For the prediction of activity type, the ANN correctly classified activity type 88.8% of the time (confidence interval: 86.4-91.2%). Activity types were low-level activities, locomotion, vigorous sports, and household activities/other activities. This novel approach of applying ANNs for processing Actigraph accelerometer data is promising and shows that we can successfully estimate activity METs and identify activity type using ANN analytic procedures.


title: Compact Dual-Polarized Printed Slot Antenna

abstract: A dual-polarized printed slot antenna is constructed by inserting the open-end slot into the coplanar waveguide center conductor of T-shaped slot. In order to verify the antenna performance, an antenna prototype was fabricated. The experimental results show that its working bandwidth is about 10.5%. In the operating frequency band, the isolation between two ports is higher than 27 dB. The proposed two-port antenna has bidirectional radiation characteristics, but the gain of the T-shaped slot element is higher than that of the open-end slot element. In the maximum radiation direction, the copolarization level of the T-shaped slot is 39 dB higher than the cross polarization and 30 dB for the open-end slot. The proposed dual-polarized antenna has a compact structure.


title: BioID: A Multimodal Biometric Identification System

abstract: Biometric identification systems, which use physical features to check a person's identity, ensure much greater security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single feature, however, sometimes fails to be exact enough for identification. Another disadvantage of using only one feature is that the chosen feature is not always readable. Dialog Communication Systems (DCS AG) developed BioID, a multimodal identification system that uses three different features-face, voice, and lip movement-to identify people. With its three modalities, BioID achieves much greater accuracy than single-feature systems. Even if one modality is somehow disturbed-for example, if a noisy environment drowns out the voice-the ether two modalities still lead to an accurate identification. This article goes into detail about the system functions, explaining the data acquisition and preprocessing techniques for voice, facial, and lip imagery data. The authors also explain the classification principles used for optical features and the sensor fusion options (the combinations of the three results-face, voice, lip movement-to obtain varying levels of security).


title: PatchBatch: A Batch Augmented Loss for Optical Flow

abstract: We propose a new pipeline for optical flow computation, based on Deep Learning techniques. We suggest using a Siamese CNN to independently, and in parallel, compute the descriptors of both images. The learned descriptors are then compared efficiently using the L2 norm and do not require network processing of patch pairs. The success of the method is based on an innovative loss function that computes higher moments of the loss distributions for each training batch. Combined with an Approximate Nearest Neighbor patch matching method and a flow interpolation technique, state of the art performance is obtained on the most challenging and competitive optical flow benchmarks.


title: A compact low-profile ultra wideband antenna for biomedical applications

abstract: This paper presents a compact planar ultra-wideband (UWB) microstrip antenna for microwave medical applications. The proposed antenna has a low profile structure, consisting of a modified radiating patch with stair steps and open slots, microstrip feed line, and T-like shape slots at the ground plane. The optimized antenna is capable of being operated in frequency range of 3.06–11.4 GHz band having good omnidirectional radiation pattern and high gain, which satisfies the requirements of UWB (3.1–10.6 GHz) applications. The antenna system has a compact size of 18×30×0.8mm3. These features make the proposed UWB antenna a good candidate for microwave medical imaging applications.


title: Heart Rate Variability Anatomy and Physiology

abstract: Heart rate variability (HRV) is generated by the interaction of multiple regulatory mechanisms that operate on different time scales. This article examines the regulation of the heart, the meaning of HRV, Thayer and Lane's neurovisceral integration model, the sources of HRV, HRV frequency and time domain measurements, Porges's polyvagal theory, and resonance frequency breathing. The medical implications of HRV biofeedback for cardiovascular rehabilitation and inflammatory disorders are considered.


title: Ambient intelligence: A survey

abstract: In this article we survey ambient intelligence (AmI), including its applications, some of the technologies it uses, and its social and ethical implications. The applications include AmI at home, care of the elderly, healthcare, commerce, and business, recommender systems, museums and tourist scenarios, and group decision making. Among technologies, we focus on ambient data management and artificial intelligence; for example planning, learning, event-condition-action rules, temporal reasoning, and agent-oriented technologies. The survey is not intended to be exhaustive, but to convey a broad range of applications, technologies, and technical, social, and ethical challenges.


title: Small-size circular polarized patch antenna

abstract: Designing of an microstrip patch antenna with circular polarization (CP) and reduced size is a challenging and complex task. Here such an antenna is proposed and experimentally studied. Deployment of meandering technique and shorting pins together at the four corners of patch gives less back radiation along with acheiving CP and fairly reduce the size of antenna. Also it can provide inductive and capacitive loading effect to the patch which in turn control the frequency of operation. In this paper, a study is conducted with different shorting strip structures such as rectangle, U-shape, and meandering. Simulations were carried out in HFSS and compared the simulated results with different structures. It is found that meandering technique gives better size reduction than other two because the strongest current strengths concentrates at the meandering shorting strips. In addition, meandering technique provides high front-to-back ratio than others.


title: Interaction of streptothricin and related antibiotics with nucleic acids

abstract: Abstract The interaction of the basic antibiotics streptothricin, streptomycin, dihydrostreptomycin, paromomycin, kanamycin, and neomycin with DNA was investigated using spectrophotometric melting and titration and sedimentation measurements. On increasing the antibiotic concentration, precipitation of insoluble DNA-antibiotic complexes was observed. Soluble DNA-antibiotic complexes are formed at low ionic strength as follows from the stabilization of the DNA secondary structure against thermal denaturation by all antibiotics studied. In addition spectrophotometric acid titration profiles shift to lower pH values, and sedimentation coefficients increase with increasing antibiotic concentration. The results suggest that at low ionic strength basic antibiotics of the aminoglycoside type are bound loosely to the negatively charged phosphate groups of the DNA by electrostatic interaction. This bonding can be reversed by increasing the ionic strength to moderate and higher salt concentrations.


The genetical structure of northeastern Brazil


title: Stochastic Economic Dispatch of Power Systems with Renewable Energy Using Sparse Grid Based Stochastic Collocation Method

abstract: The uncertainty of renewable energy has raised significant challenges for power system, which results that conventional dispatch deterministic methods are not well applicable. Therefore, this paper proposes the stochastic collocation method (SCM) for economic dispatch problem based on sparse grid. The SCM utilizes the orthogonal property of generalized polynomial chaos (gPC) to avoid solving the complex optimization model with random variables, by calculating the deterministic gPC coefficients. Meanwhile, to mitigate the computational burden, sparse grid is utilized to decrease the number of collocation points. However, the collocation points selected by conventional sparse grid do not satisfy the nested property. Hence, a strategy of constructing collocation points based on nested sparse grid is proposed to make the selected collocation points satisfy the nested property. The accuracy, effectiveness, and practicality of the proposed algorithm are verified by the simulations on the modified IEEE 39-bus system and a practical power system.


title: Ratings for emotion film clips

abstract: Film clips are widely utilized to elicit emotion in a variety of research studies. Normative ratings for scenes selected for these purposes support the idea that selected clips correspond to the intended target emotion, but studies reporting normative ratings are limited. Using an ethnically diverse sample of college undergraduates, selected clips were rated for intensity, discreteness, valence, and arousal. Variables hypothesized to affect the perception of stimuli (i.e., gender, race–ethnicity, and familiarity) were also examined. Our analyses generally indicated that males reacted strongly to positively valenced film clips, whereas females reacted more strongly to negatively valenced film clips. Caucasian participants tended to react more strongly to the film clips, and we found some variation by race–ethnicity across target emotions. Finally, familiarity with the films tended to produce higher ratings for positively valenced film clips, and lower ratings for negatively valenced film clips. These findings provide normative ratings for a useful set of film clips for the study of emotion, and they underscore factors to be considered in research that utilizes scenes from film for emotion elicitation.


title: Using linguistic cues for the automatic recognition of personality in conversation and text

abstract: It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using self-reports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.


title: Community detection for emerging social networks

abstract: Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.


An Automated Vehicle Counting System Based on Blob Analysis for Traffic Surveillance


title: Gamma band directional interactions between basal forebrain and visual cortex during wake and sleep states

abstract: Abstract The basal forebrain (BF) is an important regulator of cortical excitability and responsivity to sensory stimuli, and plays a major role in wake-sleep regulation. While the impact of BF on cortical EEG or LFP signals has been extensively documented, surprisingly little is known about LFP activity within BF. Based on bilateral recordings from rats in their home cage, we describe endogenous LFP oscillations in the BF during quiet wakefulness, rapid eye movement (REM) and slow wave sleep (SWS) states. Using coherence and Granger causality methods, we characterize directional influences between BF and visual cortex (VC) during each of these states. We observed pronounced BF gamma activity particularly during wakefulness, as well as to a lesser extent during SWS and REM. During wakefulness, this BF gamma activity exerted a directional influence on VC that was associated with cortical excitation. During SWS but not REM, there was also a robust directional gamma band influence of BF on VC. In all three states, directional influence in the gamma band was only present in BF to VC direction and tended to be regulated specifically within each brain hemisphere. Locality of gamma band LFPs to the BF was confirmed by demonstration of phase locking of local spiking activity to the gamma cycle. We report novel aspects of endogenous BF LFP oscillations and their relationship to cortical LFP signals during sleep and wakefulness. We link our findings to known aspects of GABAergic BF networks that likely underlie gamma band LFP activations, and show that the Granger causality analyses can faithfully recapitulate many known attributes of these networks.


title: Congestion Prediction With Big Data for Real-Time Highway Traffic

abstract: By collecting and analyzing a vast quantity and different categories of information, traffic flow and road congestion can be predicted and avoided in intelligent transportation system. However, how to tackle with these big data is vital but challenging. Most of the existing literatures utilized batch method to process a bunch of road data that cannot achieve real-time traffic prediction. In this paper, we use the spouts and bolts in Apache Storm to implement a real-time traffic prediction model by analyzing enormous streaming data, such as road density, traffic events, and rainfall volume. The proposed SVM-based real-time highway traffic congestion prediction (SRHTCP) model collects the road data from the Taiwan Area National Freeway Bureau, the traffic events reported by road users from the Police Broadcasting Service in Taiwan, and the weather data from the Central Weather Bureau in Taiwan. We use fuzzy theory to evaluate the traffic level of road section in real time with considering road speed, road density, road traffic volume, and the rainfall of road sections. In addition, the SRHTCP model predicts the road speed of next time period by exploring streaming traffic and weather data. Results showed that the proposed SRHTCP model improves 25.6% prediction accuracy than the prediction method based on weighted exponential moving average method under the measurement of mean absolute relative error.


title: GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis

abstract: This paper describes in detail the approach carried out by the GTI research group for SemEval 2016 Task 5: Aspect-Based Sentiment Analysis, for the different subtasks proposed, as well as languages and dataset contexts. In particular, we developed a system for category detection based on SVM. Then for the opinion target detection task we developed a system based on CRFs. Both are built for restaurants domain in English and Spanish languages. Finally for aspect-based sentiment analysis we carried out an unsupervised approach based on lexicons and syntactic dependencies, in English language for laptops and restaurants domains.


K-SVD: Design of dictionaries for sparse representation


title: A precision relaxation oscillator with a self-clocked offset-cancellation scheme for implantable biomedical SoCs

abstract: Recently, there has been an increasing demand for SoCs in the biomedical field [1]. In implantable applications, SoCs are designed under very stringent power and area constraints. The analog and mixed-signal circuits as well as digital circuits in those SoCs require a clock source, because clock-based signal-processing techniques, such as sampling and chopper stabilization, are often used. The primary clock source for such SoCs needs to provide good accuracy and long-term stability of the oscillation frequency ƒ OSC to minimize variations and drifts of the system characteristics. A fairly pure clock signal is required to avoid signal distortion when sampling or chopping techniques are applied. Considering such a source is typically a free-running oscillator, and biomedical signals of typical interest reside at low frequencies, the close-in phase noise is important.


title: Testing Protocols for the Internet of Things on the EuWIn Platform

abstract: Several approaches have been considered by research community as possible enablers for the Internet of Things (IoT) implementation. This paper presents the results obtained by testing and comparing three different solutions. In particular, we compare a centralized solution based on software defined network (SDN), called software defined wireless networking (SDWN), with two standard and distributed solutions, that are ZigBee and IPv6 over low-power wireless personal area networks (6LoWPAN). SDWN uses a centralized network layer protocol, where routing policies are defined by an external controller that can be positioned anywhere in the network. The other two solutions are actually the most common protocol stacks for wireless sensor networks, and they both use a distributed routing protocol. The comparison is achieved by experimentations performed on the European Laboratory of Wireless Communications for the Future Internet (EuWIn) platform developed within the network of excellence, NEWCOM#. Results show that SDWN is the best solution in static or quasi-static environments, while the performance degrades in highly dynamic conditions. However, ZigBee has a good reactivity to environmental changes. This paper reports the evaluation of several performance metrics, including packet loss rate, round-trip-time, and overhead generated in the network, under different conditions and considering different kinds of traffic.


title: Empirical identi fi cation of the major facets of Conscientiousness

abstract: article i nfo Conscientiousness is often found to predict academic outcomes, but is defined differently by different models of personality. Highschoolstudents(N=291)completeda large numberof Conscientiousness items fromdifferent models and the Big Five Inventory (BFI). Exploratory and confirmatory factor analysis of the items uncovered eight facets: Industriousness, Perfectionism, Tidiness, Procrastination Refrainment, Control, Cautiousness, Task Planning,andPerseverance.Correlationsbetweenthese facetsandtheBFIrevealedthatallfacetsrelatedstrongly to Conscientiousness. Criterion-related validity was demonstrated by relationships between facets and academic outcomessuchasgrade-point-average,disciplinaryinfractions,andattainmentofacademichonors.Comparedto BFI Conscientiousness, Industriousness and Perfectionism showed significantly stronger prediction of absentee- ism and cognitive test scores, respectively. Results are discussed in terms of the usefulness of facet scores, the interpretation of personality scores for selection, and the development of intervention programs.


title: Cluster-based aggregate forecasting for residential electricity demand using smart meter data

abstract: While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base.


title: Resonant slotted-waveguide antenna array using open-ended SIW resonator

abstract: In this paper, a new resonant slotted-waveguide antenna array based on the open-ended SIW resonator is presented. In the conventional resonant slotted-waveguide arrays an in-phase excitation is achieved by adjusting the distance between slots one half guided wavelength (λ g /2) and alternating themon the opposite sides of the center line. The open-ended SIW resonator is capable of supporting infinite wavelength propagation and hence the distance between slots can be unequal with the same phases. This capability can be used for distance tapering to have low sidelobe level (SLL). A 9-element array with unequal spacing based on this kind of resonator is designed and simulated at 10 GHz. By using genetic algorithm (GA), the distances between slots is determined in order to have a low SLL. The simulation results show that the side lobe level of the designed antenna is 16 dB.


title: Emergent User Roles of a Digital Workplace: A Network Analysis Based on Trace Data

abstract: Communication and collaboration software for knowledge workers are introduced with ::: high expectations, especially in knowledge-intense industries. While advantages of such ::: tools are well documented in theory, many initiatives have yet to achieve the desired ::: outcomes in practice. Research has dealt with roles in the digital workplace and found ::: that one-size-fits-all solutions are not suitable. However, for a lack of real-world data the ::: matter is still not sufficiently understood. To close this gap, we conduct a sequential mixed ::: method study. We perform an exploratory analysis based on trace data within a service ::: organization and reconstruct its social structure. Through a cluster analysis, eight ::: distinct emergent user roles are identified. Additionally, we analyze covariates of cluster ::: membership, such as organizational hierarchy, through statistical testing. Lastly, semistructured ::: interviews help to explain our findings qualitatively. We contribute to research ::: and practice by deepening the understanding of heterogeneous user behavior in a digital ::: workplace.


title: HearHere: smartphone based audio localization using time difference of arrival: demo

abstract: Recent advancements in audio recording on mobile devices have improved audio localization capabilities using phones. Previous research has shown that millimeter level accuracy is capable using an off-the-shelf smartphone. This work demonstrates that such valuable resources in smartphones are readily available for developing consumer applications. We develop a smartphone application called HearHere that utilizes geometric features of sound to categorize a tapping sound within a grid. The geometric features are based on the Time Difference of Arrival (TDoA) of the sound between two microphones. By using this classification, any off the shelf phone with stereo recording capability can be made into a music producing device. The application consists of three major components: system calibration, data collection, and audio localization engine based on TDoA. We demonstrate that we can accurately map the tapping sound six region on various solid surface and turn it into a MIDI controller with six different types of instruments.


title: Attributions of Responsibility and Blame for Procrastination Behavior

abstract: The present study examined the relationship between procrastination, delay, blameworthiness, and moral responsibility. Undergraduate students (N = 240) were provided two scenarios in which the reason for inaction (procrastination, delay), the target (self, other), and the outcome (positive, negative) were manipulated, and students were asked to rate the moral responsibility and blameworthiness of the agent. Results indicated that individuals who procrastinated were seen as more morally responsible and blameworthy than those who otherwise experienced delay. More specifically, after a negative outcome, procrastination was associated with more moral responsibility, whereas delay was associated with less moral responsibility. After a positive outcome, individuals perceived procrastination as deserving of less moral responsibility, and delays as associated with more moral responsibility. Finally, a three-way interaction showed that participants rated procrastination that resulted in failure as deserving of responsibility when engaged in by others as opposed to oneself.