title: An agricultural telemetry system implemented using an Arduino-Android interface

abstract: The research aims to create a wireless telemetry system to be used in the field of agriculture. The Arduino microprocessor was used in conjunction with sensors to measure environmental factors namely the temperature, humidity and soil moisture. Two methods were created in storing the data in the database. The first method is a more direct approach and is limited to scenarios when Wi-Fi connection is available. Data is sent directly to the server via Wi-Fi, The second approach is used when Wi-Fi is not available since data is sent to another android phone via SMS. The second android phone is responsible for sending data to the database. The data collected were compared with actual sensors and past data recorded by PAGASA to test for the accuracy of the system. The identification of each plant could easily be verified since a certain code was attached for each sample. The latitude, longitude and pictures of the samples were recorded in the database via the methods described above.


title: egoSlider: Visual Analysis of Egocentric Network Evolution

abstract: Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.


Information Systems Security Governance Research : A Behavioral Perspective


title: The prevalence, distribution, and mental health correlates of perceived discrimination in the United States.

abstract: associations between perceived discrimination and mental health; and the extent to which differential exposure and differential emotional reactivity to perceived discrimination accountfor the well-known associations between disadvantaged social status and mental health. Although more prevalent among people with disadvantaged social status, results show that perceived discrimination is common in the total population, with 33.5 percent of respondents in the total sample reporting exposure to major lifetime discrimination and 60.9 percent reporting exposure to day-to-day discrimination. The associations of perceived discrimination with mental health are comparable in magnitude to those of other more commonly studied stressors, and these associations do not vary consistently across subsamples defined on the basis of social status. Even though perceived discrimination explains only a small part of the observed associations between disadvantaged social status and mental health, given its high prevalence, wide distribution, and strong associations with mental health, perceived discrimination needs to be treated much more seriously than in the past in future studies of stress and mentdl health.


Multi-Core Program Optimization : Parallel Quick Sort in Intel Cilk Plus


title: Location-aware publish/subscribe

abstract: Location-based services have become widely available on mobile devices. Existing methods employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers.In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an rtree based index structure by integrating textual descriptions into rtree nodes. We devise efficient filtering algorithms and develop effective pruning techniques to improve filtering efficiency. Experimental results show that our method achieves high performance. For example, our method can filter 500 tweets in a second for 10 million registered subscriptions on a commodity computer.


title: Hybrid analog and digital beamforming for OFDM-based large-scale MIMO systems

abstract: Hybrid analog and digital beamforming is a promising technique for large-scale MIMO systems since it can achieve a performance close to the performance of the conventional fully-digital beamforming schemes, but with much lower hardware implementation complexity and power consumption. One of the major challenges in hybrid beamforming is to design the hybrid beamformers for broadband systems with frequency-selective channels. This is because in broadband systems, it is desirable to design the same analog beamformers for the entire band while adapting digital beamformers in each frequency tone. In this paper, we consider the hybrid beamforming design for large-scale MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation. Specifically, we propose a unified heuristic design for two different hybrid beamforming structures, the fully-connected and partially-connected structures, to maximize the overall spectral efficiency of a broadband system. Numerical results show that the proposed algorithm outperforms the existing hybrid beamforming designs and further the proposed algorithm for the fully-connected structure can achieve spectral efficiency close to that of the optimal fully-digital solution with much less number of RF chains.


title: Accessing Structured Health Information through English Queries and Automatic Deduction

abstract: While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research in using natural English text queries and automated deduction to obtain answers based on multiple structured data sources in a specific subject domain. Each English query is transformed using natural language technology into an unambiguous logical form; this is submitted to a theorem prover that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in external databases known to the system. An answer is obtained from the proof, along with an English language explanation of how the answer was obtained. Answers need not be present explicitly in any of the databases, but rather may be deduced or computed from the information they provide. Although English is highly ambiguous, the natural language technology is informed by subject domain knowledge, so that readings of the query that are syntactically plausible but semantically impossible are discarded. When a question is still ambiguous, the system can interrogate the patient to determine what meaning was intended. Additional queries can clarify earlier ones or ask questions referring to previously computed answers. We describe a prototype system, Quadri, which answers questions about HIV treatment using the Stanford HIV Drug Resistance Database and other resources. Natural language processing is provided by PARC’s Bridge, and the deductive mechanism is SRI’s SNARK theorem prover. We discuss some of the problems that must be faced to make this approach work, and some of our solutions.


title: AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion

abstract: This paper describes the systems submitted by Avaya Labs (AVAYA) to SemEval-2013 Task 2 - Sentiment Analysis in Twitter. For the constrained conditions of both the message polarity classification and contextual polarity disambiguation subtasks, our approach centers on training high-dimensional, linear classifiers with a combination of lexical and syntactic features. The constrained message polarity model is then used to tag nearly half a million unlabeled tweets. These automatically labeled data are used for two purposes: 1) to discover prior polarities of words and 2) to provide additional training examples for self-training. Our systems performed competitively, placing in the top five for all subtasks and data conditions. More importantly, these results show that expanding the polarity lexicon and augmenting the training data with unlabeled tweets can yield improvements in precision and recall in classifying the polarity of non-neutral messages and contexts.


title: Social Network Analysis: Foundations and Frontiers on Advantage

abstract: We provide an overview of social network analysis focusing on network advantage as a lens that touches on much of the area. For reasons of good data and abundant research, we draw heavily on studies of people in organizations. Advantage is traced to network structure as a proxy for the distribution of variably sticky information in a population. The network around a person indicates the person's access and control in the distribution. Advantage is a function of information breadth, timing, and arbitrage. Advantage is manifest in higher odds of proposing good ideas, more positive evaluations and recognition, higher compensation, and faster promotions. We discuss frontiers of advantage contingent on personality, cognition, embeddedness, and dynamics.


title: A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN

abstract: Vehicle-IT convergence technology is a rapidly rising paradigm of modern vehicles, in which an electronic control unit (ECU) is used to control the vehicle electrical systems, and the controller area network (CAN), an in-vehicle network, is commonly used to construct an efficient network of ECUs. Unfortunately, security issues have not been treated properly in CAN, although CAN control messages could be life-critical. With the appearance of the connected car environment, in-vehicle networks (e.g., CAN) are now connected to external networks (e.g., 3G/4G mobile networks), enabling an adversary to perform a long-range wireless attack using CAN vulnerabilities. In this paper we show that a long-range wireless attack is physically possible using a real vehicle and malicious smartphone application in a connected car environment. We also propose a security protocol for CAN as a countermeasure designed in accordance with current CAN specifications. We evaluate the feasibility of the proposed security protocol using CANoe software and a DSP-F28335 microcontroller. Our results show that the proposed security protocol is more efficient than existing security protocols with respect to authentication delay and communication load.


title: Intelligence Testing for Autonomous Vehicles: A New Approach

abstract: In this paper, we study how to test the intelligence of an autonomous vehicle. Comprehensive testing is crucial to both vehicle manufactories and customers. Existing testing approaches can be categorized into two kinds: scenario-based testing and functionality-based testing. We first discuss the shortcomings of these two kinds of approaches, and then propose a new testing framework to combine the benefits of them. Based on the new semantic diagram definition for the intelligence of autonomous vehicles, we explain how to design a task for autonomous vehicle testing and how to evaluate test results. Experiments show that this new approach provides a quantitative way to test the intelligence of an autonomous vehicle.


title: The Referential Reader: A Recurrent Entity Network for Anaphora Resolution

abstract: We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operations causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates that the model achieves state-of-the-art performance with purely left-to-right processing of the text.


title: Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

abstract: This work considers the trade-off between accuracy and testtime computational cost of deep neural networks (DNNs) via anytime predictions from auxiliary predictions. Specifically, we optimize auxiliary losses jointly in an adaptive weighted sum, where the weights are inversely proportional to average of each loss. Intuitively, this balances the losses to have the same scale. We demonstrate theoretical considerations that motivate this approach from multiple viewpoints, including connecting it to optimizing the geometric mean of the expectation of each loss, an objective that ignores the scale of losses. Experimentally, the adaptive weights induce more competitive anytime predictions on multiple recognition data-sets and models than non-adaptive approaches including weighing all losses equally. In particular, anytime neural networks (ANNs) can achieve the same accuracy faster using adaptive weights on a small network than using static constant weights on a large one. For problems with high performance saturation, we also show a sequence of exponentially deepening ANNs can achieve near-optimal anytime results at any budget, at the cost of a const fraction of extra computation.


title: Optimal Interactive Coding for Insertions, Deletions, and Substitutions

abstract: Interactive coding, pioneered by Schulman (FOCS 92, STOC 93), is concerned with making communication protocols resilient to adversarial noise. The canonical model allows the adversary to alter a small constant fraction of symbols, chosen at the adversarys discretion, as they pass through the communication channel. Braverman, Gelles, Mao, and Ostrovsky (2015) proposed a far-reaching generalization of this model, whereby the adversary can additionally manipulate the channel by removing and inserting symbols. They showed how to faithfully simulate any protocol in this model with corruption rate up to 1/18, using a constant-size alphabet and a constant-factor overhead in communication. We give an optimal simulation of any protocol in this generalized model of substitutions, insertions, and deletions, tolerating a corruption rate up to 1/4 while keeping the alphabet to a constant size and the communication overhead to a constant factor. Our corruption tolerance matches an impossibility result for corruption rate 1/4 which holds even for substitutions alone (Braverman and Rao, STOC 11).


title: A Reductions Approach to Fair Classification

abstract: We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.


title: The Branches of the First Bank of the United States

abstract: The most interesting and distinctive feature of the first Bank of the United States was its nation-wide operation through a system of branches. It is difficult to present an even reasonably definitive analysis of that system. The records available, although more adequate than formerly, are still sadly incomplete. Enough is known, however, to reveal many complexities, political, entrepreneurial and administrative. This paper is designed to offer only a rather sketchy treatment of the following phases or aspects of the subject: prototypes and predecessors of the Bank with special reference to branch-banking; the establishment of the eight branches of the Bank; the actual operation of the branches; and the relative measure of their liquidity when the Bank was subjected to the acid test of winding up its affairs.


title: A systematic design procedure for a compact DM EMI filter for a 3-phase boost PFC rectifier

abstract: This paper presents a systematic and comprehensive approach for differential mode (DM) EMI filter design in a three-phase boost-type power factor correction rectifier. Since the DM EMI filter forms a significant portion of the overall filter as well as converter volume, reduction of DM filter component size is kept as a major objective in the filter design process in order to improve the power density. In addition to the objectives of volume optimization and conducted emission attenuation requirement to comply with the EMI standard, the filter design process also ensures a near-unity power factor operation with the optimal set of EMI filter parameters. To accomplish this, the paper analyzes the effect of EMI filter component selection on the overall lower order (1–100 kHz) frequency response, which is important for evaluating the dynamic response and stability. The results from the proposed design approach are validated through simulation, the DM Bode plot, and frequency response of the input current with and without EMI filter. As a proof-of-concept verification, the proposed EMI filter is implemented in a 4 kW three-phase boost-PFC prototype, which demonstrates minimal phase displacement (<5°) of the input current.


title: Self-traits and motivations as antecedents of digital media flow and addiction: The Internet, mobile phones, and video games

abstract: In the context of a new paradigm of the media environment, this study aimed to explore levels of media consumption, self-traits and individual motivations as antecedents of media flow and addiction. Focusing specifically on the use of three prominent digital media forms-the Internet, video games, and mobile phones-this study identified four psychological factors-self-esteem, self-efficacy, self-control and dispositional media use motives-and examined the influence of each on media users' experiences of flow and levels of addiction for each of the mediums considered. The results indicated that self-control most significantly affected both users' flow and addiction in relation to their use of the Internet, video games, and mobile phones. In addition, two dimensions of dispositional media use motives, namely pastime and self-presence, exhibited significant associations with flow and addiction for users' consumption of two or all three media types. As expected, the amount of time spent using media was significantly related to the flow and addiction associated with all digital media usage.


title: Risk of Revictimization of Intimate Partner Violence: The Role of Attachment, Anger and Violent Behavior of the Victim

abstract: Victims of intimate partner violence (IPV) are known to be at high risk for revictimization. Yet, to date, the mechanisms explaining the link between victimization and revictimization of IPV have not been extensively studied. In the present prospective study involving 74 female help-seeking victims of IPV, we investigated victim-related psychological mechanisms that may underlie this link. With this study, we aim to contribute to the development of theory addressing these psychological mechanisms and their role in explaining risk for IPV revictimization. Hypotheses regarding possibly relevant psychological mechanisms were derived from two conflicting approaches to IPV: the gender perspective, and the mutual IPV perspective. Results lend further support to the mutual IPV perspective, since our final prediction model indicates that victim-perpetrated IPV is an important risk factor for physical and psychological IPV revictimization. An avoidant attachment style shows to be a strong predictor as well, in particular for victims with high and average anger levels. Findings provide clear indications for risk assessment and treatment of IPV victims, and moreover offer opportunities to empower these victims in order to prevent future violence.


title: Facial Fat Compartments: A Guide to Filler Placement

abstract: Advances in anatomic understanding are frequently the basis upon which surgical techniques are advanced and refined. Recent anatomic studies of the superficial tissues of the face have led to an increased understanding of the compartmentalized nature of the subcutaneous fat. This report provides a review of the locations and characteristics of the facial fat compartments and provides examples of how this knowledge can be used clinically, specifically with regard to soft tissue fillers.


title: Gentle Adaboost algorithm for weld defect classification

abstract: In this paper, we present a new strategy for automatic classification of weld defects in radiographs based on Gentle Adaboost algorithm. Radiographic images were segmented and moment-based features were extracted and given as input to Gentle Adaboost classifier. The performance of our classification system is evaluated using hundreds of radiographic images. The classifier is trained to classify each defect pattern into one of four classes: Crack, Lack of penetration, Porosity, and Solid inclusion. The experimental results show that the Gentle Adaboost classifier is an efficient automatic weld defect classification algorithm and can achieve high accuracy and is faster than support vector machine (SVM) algorithm, for the tested data.


title: Impedance Measurement System for Determination of Capacitive Electrode Coupling

abstract: Capacitive electrodes have been studied as an alternative to gel electrodes, as they allow measurement of biopotentials without conductive contact with the patient. However, because the skin interface is not as precisely defined as with gel electrodes, this could lead to signal deformation and misdiagnoses. Thus, measurement of a capacitive coupling of the electrodes may allow to draw conclusions about the applicability of such systems. In addition, combining capacitive biosignal sensing with an impedance measurement unit may enable bioimpedance measurements, from which additional information on the hydration status can be extracted. A prototype system is introduced which measures impedance over capacitive electrodes in parallel with biopotential measurements. Also presented are the first results on characterization of the skin electrode coupling achieved with the system.


title: Interest-based personalized search

abstract: Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects' answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier than LIST and CAT.


title: EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants

abstract: A re-emergence of research on EEG-neurofeedback followed controlled evidence of clinical benefits and validation of cognitive/affective gains in healthy participants including correlations in support of feedback learning mediating outcome. Controlled studies with healthy and elderly participants, which have increased exponentially, are reviewed including protocols from the clinic: sensory-motor rhythm, beta1 and alpha/theta ratios, down-training theta maxima, and from neuroscience: upper-alpha, theta, gamma, alpha desynchronisation. Outcome gains include sustained attention, orienting and executive attention, the P300b, memory, spatial rotation, RT, complex psychomotor skills, implicit procedural memory, recognition memory, perceptual binding, intelligence, mood and well-being. Twenty-three of the controlled studies report neurofeedback learning indices along with beneficial outcomes, of which eight report correlations in support of a meditation link, results which will be supplemented by further creativity and the performing arts evidence in Part II. Validity evidence from optimal performance studies represents an advance for the neurofeedback field demonstrating that cross fertilisation between clinical and optimal performance domains will be fruitful. Theoretical and methodological issues are outlined further in Part III.


Cross-Cultural Management


title: Lanes Detection Based on Unsupervised and Adaptive Classifier

abstract: This paper describes an algorithm to detect the road lanes based on an unsupervised and adaptive classifier. We have selected this classifier because in the road we do not know the parameters of lanes, although we know that lanes are there, only they need to be classified. First of all, we tested and measured the brightness of the lanes of the road in many videos. Generally, the lines on the road are white. We used the HSV image and we improved the region of study. Then, we used a Hough transform which yields a set of possible lines. These lines have to be classified. The classifier starts with initial parameters because we suppose that the vehicle is on road and in the center of the lane. There are two classes, the first one is the left road line and the second one is the right road line. Each line has two parameters that are: middle point of line and the line slope. These parameters will be changing in order to adjust to the real lanes. A tensor holds the two lines, so these lines will not separate more than the tensor allows. A Kalman filter estimates the new class's parameters and improves the tracking of the lanes. Finally, we use a mask in order to highlight the lane and show to the user a better image.


title: You Can Jam But You Cannot Hide: Defending Against Jamming Attacks for Geo-Location Database Driven Spectrum Sharing

abstract: The emerging paradigm for dynamic spectrum sharing is based on allowing secondary users (SUs) to exploit white space frequency that is not occupied by primary users. White space database provides an opportunity for SUs to obtain spectrum availability information by submitting a location-based query. However, this new paradigm can also be exploited by the attackers to significantly enhance their jamming capability due to the available channel information from spectrum queries, which is expected to increasingly block SUs. The challenge is that the unique characteristics (e.g., lack of the wide range frequencies or continuous broadband) make existing anti-jamming techniques (e.g., direct-sequence spread spectrum and frequency hopping spread spectrum) difficult to be applied. In this paper, we present a novel Jammer Inference-based Jamming Defense (jDefender) framework. The main idea of jDefender is inferring the likelihood of a user being a jammer based on the observed jamming events and then utilizing the inferred attack likelihood to enhance the effectiveness of a series of the proposed anti-jamming strategies. Specifically, we first propose the Channel Allocation-based Jammer Inference scheme to infer the likelihood of an SU being a jammer based on the channels occupied by SUs even under the collusion attack performed by multiple jammers. The strength of the anti-jamming strategies (e.g., puzzle difficulties, available spectrum resources) will be correlated with the possibility of an SU being a jammer to achieve the tradeoff between system performance and jamming tolerance. We then implement the proposed scheme on Universal Software Radio Peripheral and PC. Extensive evaluations are performed to validate the effectiveness of the attacks and countermeasures.


title: A Robust Musical Audio Search Method Based on Diagonal Dynamic Programming Matching of Self-Similarity Matrices

abstract: We propose a new musical audio search method based on audio signal matching that can cope with key and tempo variations. The method employs the self-similarity matrix of an audio signal to represent a key-invariant structure of musical audio. And, we use dynamic programming (DP) matching of self-similarity matrices to deal with time variations. However, conventional DP-based sequence matching methods cannot be directly applied for self-similarity matrices because they cannot treat gaps independently of other time frames. We resolve this problem by introducing “matched element indices,” which reflect the history of matching, to a DP-based sequence matching method. We performed experiments using musical audio signals. The results indicate that the proposed method improves the detection accuracy in comparison to that that obtained by two conventional methods, namely, DP matching with chromabased vector rotations and a simple matching of self-similarity feature vectors.


title: A secure tunable-precision architecture for image processing applications

abstract: Numerous applications involving image and audio presentations, such as video games, can be made available to consumers with varying levels of precision or resolution based on the level of authorization. This paper presents a novel tunable-precision architecture for digital systems. In this architecture, computational precision can be tuned across a large number of modes. A user can pay for and receive a key that unlocks one of the modes of precision which in turn enables a certain level of audio/image presentation and user experience. We present the secure tunable-precision architecture and an image processing application to demonstrate its usage.


Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition.


title: Image database TID 2013 : Peculiarities , results and perspectives

abstract: This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large database.This database contains larger number of distorted images and distortion types.MOS values for all images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described.


title: Recurrent Neural Radio Anomaly Detection

abstract: We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies. This approach holds promise in significantly increasing the ability of naive anomaly detection to detect small anomalies in highly complex complexity multi-user radio bands. We demonstrate the efficacy of this approach on a number of common real over the air radio communications bands of interest and quantify detection performance in terms of probability of detection an false alarm rates across a range of interference to band power ratios and compare to baseline methods.


title: Propagating Uncertainty through the tanh Function with Application to Reservoir Computing

abstract: Many neural networks use the tanh activation function, however when given a probability distribution as input, the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed. One important example is the initialization of the echo state network in reservoir computing, where random initialization of the reservoir requires time to wash out the initial conditions, thereby wasting precious data and computational resources. Motivated by this problem, we propose a novel solution utilizing a moment based approach to propagate uncertainty through an Echo State Network to reduce the washout time. In this work, we contribute two new methods to propagate uncertainty through the tanh activation function and propose the Probabilistic Echo State Network (PESN), a method that is shown to have better average performance than deterministic Echo State Networks given the random initialization of reservoir states. Additionally we test single and multi-step uncertainty propagation of our method on two regression tasks and show that we are able to recover similar means and variances as computed by Monte-Carlo simulations.


title: Deep Reinforcement Learning: An Overview

abstract: In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.


title: Network Capability in Localizing Node Failures via End-to-End Path Measurements

abstract: We investigate the capability of localizing node failures in communication networks from binary states (normal/failed) of end-to-end paths. Given a set of nodes of interest, uniquely localizing failures within this set requires that different observable path states associate with different node failure events. However, this condition is difficult to test on large networks due to the need to enumerate all possible node failures. Our first contribution is a set of sufficient/necessary conditions for identifying a bounded number of failures within an arbitrary node set that can be tested in polynomial time. In addition to network topology and locations of monitors, our conditions also incorporate constraints imposed by the probing mechanism used. We consider three probing mechanisms that differ according to whether measurement paths are: (i) arbitrarily controllable; (ii) controllable but cycle-free; or (iii) uncontrollable (determined by the default routing protocol). Our second contribution is to quantify the capability of failure localization through: 1) the maximum number of failures (anywhere in the network) such that failures within a given node set can be uniquely localized and 2) the largest node set within which failures can be uniquely localized under a given bound on the total number of failures. Both measures in 1) and 2) can be converted into the functions of a per-node property, which can be computed efficiently based on the above sufficient/necessary conditions. We demonstrate how measures 1) and 2) proposed for quantifying failure localization capability can be used to evaluate the impact of various parameters, including topology, number of monitors, and probing mechanisms.


title: A survey on algorithms of hole filling in 3D surface reconstruction

abstract: The surface reconstruction of 3D objects has attracted more and more attention for its widespread application in many areas, such as computer science, cultural heritage restoration, medical facilities, entertainment. However, due to occlusion, reflectance, the scanning angle, raw data preprocessing, it is inevitable to lose some point data, which leads to holes in the reconstruction surface, making it undesirable for various applications. Therefore, methods for filling holes in the process of surface reconstruction are critical to the final results of reconstruction. This paper makes a survey of existing well-known hole-filling algorithms, classifies the algorithms into two main categories, analyzes and compares these algorithms from the viewpoints of theories and experimental results to make a clear introduction of their performance. At the end, the paper points out the possible development direction of hole filling in the future and hopes to be a good guide for other researchers.


title: Design and analysis of magnetically-drive actuator applied for linear compressor

abstract: A novel Magnetically Levitated Linear Actuator (MLLA), mainly consisting of a Halbach magnetized moving-magnet armature, a cylindrical frame, a rod and electromagnetic (EM) poles, is presented and analyzed for linear compressors applications. The Halbach magnetized armature naturally generates a periodically distributed magnetic field which is interacted with that induced by the EM poles. Therefore, an axially reciprocating thrust force is induced that is inherently suitable for high frequency drive for linear compressors. A lateral magnetic repulsive force, due to the eddy current induced at the cylindrical frame, to exert upon the rod is generated as long as the rod is deviated in the radial direction. Once the position of the rod is laterally deviated from the central position, the rod is automatically brought back by this magnetic repulsive force. The magnetic field distributions, axial thrust force and lateral magnetic repulsive force are numerically obtained by Governing Equations Analysis (GEA) under cylindrical coordinate. The Finite Element Method (FEM), by the commercial software ANSOFT Maxwell with Transient Solver, is employed to be compared with and validate the solutions obtained by GEA. It is shown that the GEA is extremely agreed with the results by FEM. At last, the efficacy of the proposed MLLA is examined and verified by intensive computer simulations and experiments.


title: A latency and fault-tolerance optimizer for online parallel query plans

abstract: We address the problem of making online, parallel query plans fault-tolerant: i.e., provide intra-query fault-tolerance without blocking. We develop an approach that not only achieves this goal but does so through the use of different fault-tolerance techniques at different operators within a query plan. Enabling each operator to use a different fault-tolerance strategy leads to a space of fault-tolerance plans amenable to cost-based optimization. We develop FTOpt, a cost-based fault-tolerance optimizer that automatically selects the best strategy for each operator in a query plan in a manner that minimizes the expected processing time with failures for the entire query. We implement our approach in a prototype parallel query-processing engine. Our experiments demonstrate that (1) there is no single best fault-tolerance strategy for all query plans, (2) often hybrid strategies that mix-and-match recovery techniques outperform any uniform strategy, and (3) our optimizer correctly identifies winning fault-tolerance configurations.


title: Fast and accurate ground plane detection for the visually impaired from 3D organized point clouds

abstract: Detecting the ground plane is a prior stage to obstacle avoidance systems for the visually impaired. The ground plane is where the visually impaired can move on. This paper presents an algorithm that help the visually impaired navigate in a fast, safe, reliable and independent way. Using RGB-D scanners, enhanced RANdom SAmple Consensus (RANSAC) algorithm is proposed to eliminate the common RANSAC problems. The proposed algorithm is able to detect the ground plane and obstacles that face the visually impaired. The proposed algorithm consists of three main stages: data preprocessing, ground plane segmentation and object detection. Two sets of experiments have been made using two datasets and real world data. The results show accuracy (99.9%) and speed (21Hz) of our proposed algorithm.


title: Abstract Empirical Evaluation of User Experience in Two Adaptive Mobile Application Prototypes

abstract: Today’s applications such as ubiquitous systems are more and more aware of user’s habits and the context of use. The features of products and the context of use will affect the human’s experiences and preferences about the use of device. Thus, user experience in user-product interaction has been regarded as an important research topic in the mobile application design area. The purpose of this paper is to clarify how user experience can be evaluated in adaptive mobile applications. The user experience evaluations were performed through interviews and observation while test users were using PDA-based adaptive mobile application prototypes. As a result, this paper presents the analysis of the test methods for further user experience evaluations. CR Categories: J.m [Computer Applications]: Miscellaneous; Experimentation; Human Factors


title: Local Histograms for Design of Transfer Functions in Direct Volume Rendering

abstract: Direct volume rendering (DVR) is of increasing diagnostic value in the analysis of data sets captured using the latest medical imaging modalities. The deployment of DVR in everyday clinical work, however, has so far been limited. One contributing factor is that current transfer function (TF) models can encode only a small fraction of the user's domain knowledge. In this paper, we use histograms of local neighborhoods to capture tissue characteristics. This allows domain knowledge on spatial relations in the data set to be integrated into the TF. As a first example, we introduce partial range histograms in an automatic tissue detection scheme and present its effectiveness in a clinical evaluation. We then use local histogram analysis to perform a classification where the tissue-type certainty is treated as a second TF dimension. The result is an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF


title: Where Could We Go?: Recommendations for Groups in Location-Based Social Networks

abstract: Location-Based Social Networks (LBSNs) enable their users to share with their friends the places they go to and whom they go with. Additionally, they provide users with recommendations for Points of Interest (POI) they have not visited before. This functionality is of great importance for users of LBSNs, as it allows them to discover interesting places in populous cities that are not easy to explore. For this reason, previous research has focused on providing recommendations to LBSN users. Nevertheless, while most existing work focuses on recommendations for individual users, techniques to provide recommendations to groups of users are scarce. In this paper, we consider the problem of recommending a list of POIs to a group of users in the areas that the group frequents. Our data consist of activity on Swarm, a social networking app by Foursquare, and our results demonstrate that our proposed Geo-Group-Recommender (GGR), a class of hybrid recommender systems that combine the group geographical preferences using Kernel Density Estimation, category and location features and group check-ins outperform a large number of other recommender systems. Moreover, we find evidence that user preferences differ both in venue category and in location between individual and group activities. We also show that combining individual recommendations using group aggregation strategies is not as good as building a profile for a group. Our experiments show that (GGR) outperforms the baselines in terms of precision and recall at different cutoffs.


title: Enhancing Manufacturing Performance with Erp Systems

abstract: Abstract This article presents five case studies indicating that enterprise resource planning (ERP) systems do enhance the performance of manufacturing organizations. the ERP systems studied are from SAP, Baan, and Oracle. in all five cases, better cross-functional integration was a critical success factor.


title: Single-baseline polarimetric SAR interferometry

abstract: The objective of this paper is to examine the application of single-baseline polarimetric SAR interferometry to the remote sensing and measurement of structure over forested terrain. For this, a polarimetric coherent scattering model for vegetation cover suitable for the estimation of forest parameters from interferometric observables is introduced, discussed and validated. Based on this model, an inversion algorithm which allows the estimation of forest parameters such as tree height, average extinction, and underlying topography from single-baseline fully polarimetric interferometric data is addressed. The performance of the inversion algorithm is demonstrated using fully polarimetric single baseline experimental data acquired by DLR's E-SAR system at L-band.


title: Efficient OFDM Denial in the Absence of Channel Information

abstract: Many modern communications systems use Orthogonal Frequency Division Modulation (OFDM). These systems need to synchronize the receiver and equalize the channel to achieve good performance. The algorithms used to perform timing synchronization and channel estimation/equalization typically are designed without considering whether an adversarial signal could disrupt these subsystems. In typical scenarios, a jammer would not have reliable (or any) knowledge of the channels filtering the target or jamming signals. Thus, in this paper, we consider attack strategies that do not require channel knowledge. We study the efficiency of several jamming strategies targeting each subsystem as measured by the peak and average signal-to-jamming ratio (SJR) required to achieve signal denial for each method and find that jamming the timing can be substantially more efficient than other jamming strategies. We also discuss modifications to the jamming strategies that might be necessary for real-world operation.


title: Clustering of periodic multichannel timeseries data with application to plasma fluctuations

abstract: Abstract A periodic datamining algorithm has been developed and used to extract distinct plasma fluctuations in multichannel oscillatory timeseries data. The technique uses the Expectation Maximisation algorithm to solve for the maximum likelihood estimates and cluster assignments of a mixture of multivariate independent von Mises distributions (EM-VMM). The performance of the algorithm shows significant benefits when compared to a periodic k-means algorithm and clustering using non-periodic techniques on several artificial datasets and real experimental data. Additionally, a new technique for identifying interesting features in multichannel oscillatory timeseries data is described (STFT-clustering). STFT-clustering identifies the coincidence of spectral features over most channels of a multi-channel array using the averaged short time Fourier transform of the signals. These features are filtered using clustering to remove noise. This method is particularly good at identifying weaker features and complements existing methods of feature extraction. Results from applying the STFT-clustering and EM-VMM algorithm to the extraction and clustering of plasma wave modes in the time series data from a helical magnetic probe array on the H-1NF heliac are presented.


title: A three-level, T-type, power electronics building block using Si-SiC hybrid switch for high-speed drives

abstract: This paper presents the design and implementation of a 100 kW three-level T-type (3L-T2), single-phase power electronics building block (PEBB). The PEBB features switching power devices with associated gate drivers, DC-link capacitors and interconnection busbar. A hybrid switch (HyS), which includes a Si IGBT and a SiC MOSFET in parallel, forms the switching power device in the PEBB. An low-inductive multi-layer laminated busbar was designed to have symmetrical current commutation loops. A major contribution is that this is achieved while no three-level T-type power module was used. A loop inductance of 29 nH was achieved which is lower than the existing state-of-the-art three-level designs in literature. The fabricated prototype PEBB achieves a specific power density of 27.7 kW/kg and a volumetric power density of 308.61 W/in3. Single-phase operation of the PEBB has been demonstrated at the switching frequency of 28 kHz.


title: Conflict-Resolution Lifecycles for Governed Decentralized Autonomous Organization Collaboration

abstract: Recent blockchain-technology related innovations enable the governance of collaborating decentralized autonomous organizations (DAO) to engage in agile business-network collaborations that are based on the novel concept of smart contracting. DAOs utilize service-oriented cloud computing in a loosely coupled collaboration lifecycle with the main steps of setup, enactment, possible rollbacks and finally, an orderly termination. This lifecycle supports the selection of services provided and used by DAOs, smart contract negotiations, and behavior monitoring during enactment with the potential for breach management. Based on a sound understanding of the collaboration lifecycle in a Governance- as-a-Service (GaaS)-platform, a new type of conflict management must safeguard business-semantics induced consistency rules. This conflict management involves breach detection with recovery aspects. To fill the detected gap, we employ a formal design-notation that comprises the definition of structural and behavioral properties for exploring conflict-related exception- and compensation management during a decentralized collaboration. With the formal approach, we generate a highly dependable DAO-GaaS conflict model that does not collapse under left-behind clutter such as orphaned processes and exponentially growing database entries that require an unacceptable periodic GaaS reset.


title: The Western Ontario Rotator Cuff Index in Rotator Cuff Disease Patients A Comprehensive Reliability and Responsiveness Validation Study

abstract: Background: The Western Ontario Rotator Cuff Index (WORC) is an increasingly applied condition-specific outcome measure for rotator cuff (RC) conditions. However, in most WORC validation studies, only a limited number of psychometric properties are studied in indistinct patient groups. Purpose: To assess psychometric properties of the WORC according to the Scientific Advisory Committee quality criteria for health questionnaires in 3 patient groups with distinct RC conditions. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: The WORC (range, 0-100; 21 items, 5 domains) was administered twice (T1, T2) in 92 patients (35 RC tears, 35 calcific tendinitis, 22 impingement). Additionally, the Constant score (CS) and the Disabilities of the Arm, Shoulder and Hand score (DASH) were recorded. Calcific tendinitis patients were reassessed 6 weeks after treatment with needling and lavage or a subacromial injection with corticosteroids (T3). We assessed floor and ceiling effects, internal consistency, test-retest reliability, precision, construct validity, minimally detectable change, and responsiveness in the diagnostic subgroups and the total group. Results: Mean age was 55.0 ± 8.7 years, and 49 of 92 (53%) patients were female. Mean baseline WORC was 46.8 ± 20.4, CS was 63.9 ± 15.4, and DASH was 40.9 ± 18.6. Significant differences were found for the CS and DASH between RC tear patients (severe symptoms) and the other patients, but not for the WORC. There were no floor and ceiling effects. Internal consistency was high: the Cronbach alpha coefficient was .95. The intraclass correlation coefficient of .89 and standard error of measurement of 6.9 indicated high reproducibility. Pearson correlations of the WORC with the CS and DASH were .56 and –.65, respectively (both P < .001). At T3, total WORC improved significantly (mean change, 18.8; 95% confidence interval, 11.3-26.2). Correlations of the WORC change scores with CS and DASH changes were .61 and –.84, respectively (both P < .001). Effect size was 0.96, with a standardized response mean of 0.91, indicating good responsiveness. Conclusion: Applied to a variety of RC patients, the WORC had high internal consistency, moderate to good construct validity, high test-retest reliability, and good responsiveness. These findings support the use of the WORC as a condition-specific self-reported outcome measure in RC patients, but its validity in patients with severe symptoms needs further investigation.


title: CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution

abstract: Although the accuracy of super-resolution (SR) methods based on convolutional neural networks (CNN) soars high, the complexity and computation also explode with the increased depth and width of the network. Thus, we propose the convolutional anchored regression network (CARN) for fast and accurate single image super-resolution (SISR). Inspired by locally linear regression methods (A+ and ARN), the new architecture consists of regression blocks that map input features from one feature space to another. Different from A+ and ARN, CARN is no longer relying on or limited by hand-crafted features. Instead, it is an end-to-end design where all the operations are converted to convolutions so that the key concepts, i.e., features, anchors, and regressors, are learned jointly. The experiments show that CARN achieves the best speed and accuracy trade-off among the SR methods. The code is available at https://github.com/ofsoundof/CARN.


title: Passivity based control for a quadrotor UAV transporting a cable-suspended payload with minimum swing

abstract: In the present work a mathematical model and a control methodology for a special class of underactuated mechanical system, composed by an Unmanned Aerial Vehicle (UAV) kind quadrotor transporting a cable-suspended payload, is proposed. The Euler-Lagrange formulation is used to derive the dynamic model of the system, where the integrated dynamics of the quadrotor, cable and payload are considered. An Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) for a quadrotor UAV transporting a cable-suspended payload is developed. The control law designed does not depend on the swing angle of the cable. The control objective is to transport the payload from point to point, with swing suppression along trajectory. Experimental results are shown to evaluate the proposed control law.


title: How to never be wrong

abstract: Human beliefs have remarkable robustness in the face of disconfirmation. This robustness is often explained as the product of heuristics or motivated reasoning. However, robustness can also arise from purely rational principles when the reasoner has recourse to ad hoc auxiliary hypotheses. Auxiliary hypotheses primarily function as the linking assumptions connecting different beliefs to one another and to observational data, but they can also function as a “protective belt” that explains away disconfirmation by absorbing some of the blame. The present article traces the role of auxiliary hypotheses from philosophy of science to Bayesian models of cognition and a host of behavioral phenomena, demonstrating their wide-ranging implications.


title: Stain normalization of histopathology images using generative adversarial networks

abstract: Computational histopathology involves CAD for microscopic analysis of stained histopathological slides to study presence, localization or grading of disease. An important stage in a CAD system, stain color normalization, has been broadly studied. The existing approaches are mainly defined in the context of stain deconvolution and template matching. In this paper, we propose a novel approach to this problem by introducing a parametric, fully unsupervised generative model. Our model is based on end-to-end machine learning in the framework of generative adversarial networks. It can learn a nonlinear transformation of a set of latent variables, which are forced to have a prior Dirichlet distribution and control the color of staining hematoxylin and eosin (H&E) images. By replacing the latent variables of a source image with those extracted from a template image in the trained model, it can generate a new color copy of the source image while preserving the important tissue structures resembling the chromatic information of the template image. Our proposed method can instantly be applied to new unseen images, which is different from previous methods that need to compute some statistical properties on input test data. This is potentially problematic when the test sample sizes are limited. Experiments on H&E images from different laboratories show that the proposed model outperforms most state-of-the-art methods.


title: A Comparative Analysis of H.264 and H.265 with Different Bitrates for on Demand Video Streaming

abstract: Users are changing their traditional communication paradigm based on voice calls or text messages to real-time or on-demand video services consumed on mobile devices. In this sense, the transmission of video content considering an adequate Quality of Experience (QoE) in mobile wireless networking infrastructures is a critical issue in both academic and industrial communities. In this context, both video caching and adaptation schemes improve the QoE of delivered video. However, it is important to evaluate the performance of downloading the video videos with different codec configuration and cached closer to the user to measure the gain from the user perspective. In this paper, we evaluate the performance of downloading the video coded into H264/AVC and H.265 with different bit rate in a scenario with and without network congestion. We collected QoE metrics and energy to analyze the gains for each video configuration. These results are important for understanding the use of cache and transcoding schemes in multimedia networking scenarios.


title: Bayesian Nonparametric Kernel-Learning

abstract: Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approximating kernel is selected, the quality of the finite sample estimator may be greatly affected by the choice of kernel. Furthermore, when directly applying kernel methods, one typically needs to compute a $N \times N$ Gram matrix of pairwise kernel evaluations to work with a dataset of $N$ instances. The computation of this Gram matrix precludes the direct application of kernel methods on large datasets, and makes kernel learning especially difficult. In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels. BaNK places a nonparametric prior on the spectral distribution of random frequencies allowing it to both learn kernels and scale to large datasets. We show that this framework can be used for large scale regression and classification tasks. Furthermore, we show that BaNK outperforms several other scalable approaches for kernel learning on a variety of real world datasets.


title: A Graph-Theoretic Definition of a Sociometric Clique

abstract: The intent of this paper is to provide a definition of a socioraetric clique in the language of graph theory. This problem is viewed from two perspectives: maintaining fidelity to the intuitive notion of a clique; and providing adequate computational mechanics for large bodies of data. Luce's (1950) concept of an n‐clique is used, but further qualifications are added. Two statistics or measures with associated probability distributions are defined for testing the adequacy of a subgraph which qualifies according to the definition.


title: Sound source localization for real-world humanoid robots

abstract: In this paper we study the suitability and performance of conventional time delay of arrival estimation for humanoid robots. Moving away from simulated environments, we look at the influence of real-world robot's shape on the sound source localization. We present a TDOA/GCC based sound source localization method that successfully addresses this influence by utilizing a pre-measured set of TDOAs. The measuring methodology and important aspects of the implementation are thoroughly presented. Finally, an evaluation is made with the humanoid robot Nao. The experimental results are presented and discussed.


title: Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation

abstract: One of the most important problems faced by software developers and users is the prediction of the size of a programming system and its development effort. As an alternative to "size," one might deal with a measure of the "function" that the software is to perform. Albrecht [1] has developed a methodology to estimate the amount of the "function" the software is to perform, in terms of the data it is to use (absorb) and to generate (produce). The "function" is quantified as "function points," essentially, a weighted sum of the numbers of "inputs," "outputs,"master files," and "inquiries" provided to, or generated by, the software. This paper demonstrates the equivalence between Albrecht's external input/output data flow representative of a program (the "function points" metric) and Halstead's [2] "software science" or "software linguistics" model of a program as well as the "soft content" variation of Halstead's model suggested by Gaffney [7].


title: Smile detection in unconstrained scenarios using self-similarity of gradients features

abstract: Smile detection in unconstrained scenarios is a hot research topic with many real-world applications. This paper presents a new approach to practical smile detection and the primary contributions are three-fold. (1) In the image registration procedure, an eyes-mouth alignment strategy is found to be more efficient than popular eyes alignment. (2) In the feature extraction procedure, a novel feature descriptor, Self-Similarity of Gradients (GSS), is proposed and achieved good performance in comparison with baseline approaches. (3) Feature combination and multi-classifier combination strategies are adopted in experiments and excellent results are obtained. Experimental results show that the combined features (HOG+GSS) using AdaBoost+SVM achieve improved performance over state-of-the-art in the GENKI4K benchmark.


A Protocol for Privacy Preserving Neural Network Learning on Horizontally Partitioned Data


title: Ontology-Based Semantic Context Modeling for Object Recognition of Intelligent Mobile Robots

abstract: Object recognitions are challenging tasks, especially partially or fully occluded object recognition in changing and unpredictable robot environments. We propose a novel approach to construct semantic contexts using ontology inference for mobile robots to recognize objects in real-world situations. By semantic contexts we mean characteristic information abstracted from robot sensors. In addition, ontology has been used for better recognizing objects using knowledge represented in the ontology where OWL (Web Ontology Language) has been used for representing object ontologies and contexts. We employ a four-layered robot-centered ontology schema to represent perception, model, context, and activity for intelligent robots. And, axiomatic rules have been used for generating semantic contexts using OWL ontologies. Experiments are successfully performed for recognizing partially occluded objects based on our ontology-based semantic context model without contradictions in real applications.


title: Harnessing Encrypted Data in Cloud for Secure and Efficient Mobile Image Sharing

abstract: Nowadays, large volumes of multimedia data are outsourced to the cloud to better serve mobile applications. Along with this trend, highly correlated datasets can occur commonly, where the rich information buried in correlated data is useful for many cloud data generation/dissemination services. In light of this, we propose to enable a secure and efficient cloud-assisted image sharing architecture for mobile devices, by leveraging outsourced encrypted image datasets with privacy assurance. Different from traditional image sharing, we aim to provide a mobile-friendly design that saves the transmission cost for mobile clients, by directly utilizing outsourced correlated images to reproduce the image of interest inside the cloud for immediate dissemination. First, we propose a secure and efficient index design that allows the mobile client to securely find from encrypted image datasets the candidate selection pertaining to the image of interest for sharing. We then design two specialized encryption mechanisms that support secure image reproduction from encrypted candidate selection. We formally analyze the security strength of the design. Our experiments explicitly show that both the bandwidth and energy consumptions at the mobile client can be saved, while achieving all service requirements and security guarantees.


title: Convolutional neural networks for segmentation and object detection of human semen

abstract: We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due to clutter. Our results indicate that training on full images is superior to training on patches when class-skew is properly handled. Full image training including up-sampling during training proves to be beneficial in deep CNNs for pixel wise accuracy and detection performance. Predicted sperm cells are found by using connected components on the CNN predictions. We investigate optimization of a threshold parameter on the size of detected components. Our best network achieves 93.87% precision and 91.89% recall on our test dataset after thresholding outperforming a classical image analysis approach.


title: One-pot synthesis of carbon-coated Ni5P4nanoparticles and CoP nanorods for high-rate and high-stability lithium-ion batteries

abstract: Carbon-coated transition-metal phosphide (TMPs@C) nanocomposites, including Ni5P4@C nanoparticles and CoP@C nanorods, have been fabricated via a simply developed synthetic route from the reaction of organometallic sources with triphenylphosphine (PPh3) in a sealed quartz tube. These nanocomposites as anode materials for lithium-ion batteries (LIBs) exhibit excellent rate capability and highly stable cycling performances. Typically, the Ni5P4@C nanoparticles present 612 mA h g−1 after 100 cycles at 0.2C, 462 mA h g−1 after 200 cycles at 1.0C and 424 mA h g−1 at 5.0C, and the CoP@C nanorods demonstrate 654 mA h g−1 after 100 cycles at 0.2C, 530 mA h g−1 after 200 cycles at 1.0C, and 384 mA h g−1 at 5.0C, respectively, which would be of great potential in energy storage and conversion.


title: Bridges over troubled waters: education and cognitive neuroscience

abstract: Recently there has been growing interest in and debate about the relation between cognitive neuroscience and education. Our goal is to advance the debate beyond both recitation of potentially education-related cognitive neuroscience findings and the claim that a bridge between fields is chimerical. In an attempt to begin a dialogue about mechanisms among students, educators, researchers and practitioner-scientists, we propose that multiple bridges can be built to make connections between education and cognitive neuroscience, including teacher training, researcher training and collaboration. These bridges – concrete mechanisms that can advance the study of mind, brain and education – will benefit both educators and cognitive neuroscientists, who will gain new perspectives for posing and answering crucial questions about the learning brain.


title: Robust Camera Calibration and Player Tracking in Broadcast Basketball Video

abstract: With the growth of fandom population, a considerable amount of broadcast sports videos have been recorded, and a lot of research has focused on automatically detecting semantic events in the recorded video to develop an efficient video browsing tool for a general viewer. However, a professional sportsman or coach wonders about high level semantics in a different perspective, such as the offensive or defensive strategy performed by the players. Analyzing tactics is much more challenging in a broadcast basketball video than in other kinds of sports videos due to its complicated scenes and varied camera movements. In this paper, by developing a quadrangle candidate generation algorithm and refining the model fitting score, we ameliorate the court-based camera calibration technique to be applicable to broadcast basketball videos. Player trajectories are extracted from the video by a CamShift-based tracking method and mapped to the real-world court coordinates according to the calibrated results. The player position/trajectory information in the court coordinates can be further analyzed for professional-oriented applications such as detecting wide open event, retrieving target video clips based on trajectories, and inferring implicit/explicit tactics. Experimental results show the robustness of the proposed calibration and tracking algorithms, and three practicable applications are introduced to address the applicability of our system.


Seeing the World Behind the Image: Spatial Layout for 3D Scene Understanding


title: Mass-media coverage , its influence on public awareness of climate-change issues , and implications for Japan ’ s national campaign to reduce greenhouse gas emissions

abstract: We analyse Japanese newspaper coverage of global warming from January 1998 to July 2007 and how public opinion during parts of that period were influenced by newspaper coverage. We show that a dramatic increase in newspaper coverage of global warming from January 2007 correlated with an increase in public concern for the issue. Before January 2007, we find that coverage of global warming had an immediate but short-term influence on public concern. With such transitory high levels of media coverage we suggest that for more effective communication of climate change, strategies aimed at maintaining mass-media coverage of global warming are required.


title: Malan syndrome (Sotos syndrome 2) in two patients with 19p13.2 deletion encompassing NFIX gene and novel NFIX sequence variant.

abstract: BACKGROUND AND AIM ::: Sotos syndrome 2 (MIM #614753), known also as Malan syndrome, is caused by heterozygous mutations/deletions of the NFIX gene located on chromosome 19p13.2. It manifests in developmental delay, intellectual impairment, macrocephaly, central nervous system anomalies, postnatal overgrowth, and craniofacial dysmorphism. Unusual behavior with/without autistic traits, ophthalmologic, gastrointestinal, musculo-skeletal, and hand/foot abnormalities are also frequent. Due to the limited number of such cases, no definitive conclusions about genotype-phenotype correlations have been possible. In the following paper, we discuss physical features consistent with Sotos syndrome 2 based on literature review and two new cases [a patient with de novo 19p13.2 deletion encompassing a part of the NFIX gene and a patient with de novo (not described so far) heterozygous missense mutation c.367C>T (p.Arg123Trp) in the NFIX gene]. ::: ::: ::: RESULTS ::: Apart from overgrowth and psychomotor developmental delay, the most consistent physical features of our two patients are dysmorphism including high forehead, downslanting palpebral fissures, pointed chin, and abnormalities of the pinna. Both show abnormal behavior and present with long, tapered fingers and toenail defect. No severe congenital malformations were noted. ::: ::: ::: CONCLUSIONS ::: We hope these data will serve as a material for further studies and provide an opportunity to make more reliable genotype-phenotype correlations.


title: Object-Level Video Advertising: An Optimization Framework

abstract: In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.


title: Bag of contour fragments for robust shape classification

abstract: Shape representation is a fundamental problem in computer vision. Current approaches to shape representation mainly focus on designing low-level shape descriptors which are robust to rotation, scaling and deformation of shapes. In this paper, we focus on mid-level modeling of shape representation. We develop a new shape representation called Bag of Contour Fragments (BCF) inspired by classical Bag of Words (BoW) model. In BCF, a shape is decomposed into contour fragments each of which is then individually described using a shape descriptor, e.g., the Shape Context descriptor, and encoded into a shape code. Finally, a compact shape representation is built by pooling shape codes in the shape. Shape classification with BCF only requires an efficient linear SVM classifier. In our experiments, we fully study the characteristics of BCF, show that BCF achieves the state-of-the-art performance on several well-known shape benchmarks, and can be applied to real image classification problem. HighlightsA new shape representation is proposed by encoding contour fragments in shape.The proposed shape representation is compact yet informative.The proposed shape representation is robust to shape deformation and conclusion.We obtain the state-of-the-art shape classification performance on several bench-mark datasets.


title: Lip-Reading Driven Deep Learning Approach for Speech Enhancement

abstract: This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The approach leverages the complementary strengths of both deep learning and analytical acoustic modeling (filtering-based approach) as compared to benchmark approaches that rely only on deep learning. The proposed audio-visual (AV) speech enhancement framework operates at two levels. In the first level, a novel deep learning based lip-reading regression model is employed. In the second level, lip-reading approximated clean-audio features are exploited, using an enhanced, visually-derived Wiener filter (EVWF), for estimating the clean audio power spectrum. Specifically, a stacked long-short-term memory (LSTM) based lip-reading regression model is designed for estimating the clean audio features using only temporal visual features (i.e., lip reading), by considering a range of prior visual frames. For clean speech spectrum estimation, a new filterbank-domain EVWF is formulated, which exploits the estimated speech features. The EVWF is compared with conventional spectral subtraction and log-minimum mean-square error methods using both ideal AV mapping and LSTM driven AV mapping approaches. The potential of the proposed AV speech enhancement framework is evaluated under four different dynamic real-world scenarios [cafe, street junction, public transport, and pedestrian area] at different SNR levels (ranging from low to high SNRs) using benchmark grid and ChiME3 corpora. For objective testing, perceptual evaluation of speech quality is used to evaluate the quality of restored speech. For subjective testing, the standard mean-opinion-score method is used with inferential statistics. Comparative simulation results demonstrate significant lip-reading and speech enhancement improvements in terms of both speech quality and speech intelligibility. Ongoing work is aimed at enhancing the accuracy and generalization capability of the deep learning driven lip-reading model, using contextual integration of AV cues, leading to context-aware, autonomous AV speech enhancement.


title: Fabrication and Characteristics of Reduced Graphene Oxide Produced with Different Green Reductants

abstract: There has been an upsurge of green reductants for the preparation of graphene materials taking consideration of human health and the environment in recent years. In this paper, reduced graphene oxides (RGOs) were prepared by chemical reduction of graphene oxide (GO) with three green reductants, L-ascorbic acid (L-AA), D-glucose (D-GLC) and tea polyphenol (TP), and comparatively characterized by X-ray photoelectron spectroscopy (XPS), Fourier transform infrared (FTIR) spectra, Raman spectra and electrical conductivity analysis. Results showed that all these three reductants were effective to remove oxygen-containing functional groups in GO and restore the electrical conductivity of the obtained RGO. The RGO sample with L-ascorbic acid as a reductant and reduced with the existence of ammonia had the highest electrical conductivity (9.8 S·cm-1) among all the obtained RGO samples. The mechanisms regarding to the reduction of GO and the dispersion of RGO in water were also proposed. It is the good dispersibility of reduced graphene oxide in water that will facilitate its further use in composite materials and conductive ink.


title: An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers

abstract: In this paper, we attempt to extend Multi Attributive Border Approximation area Comparison (MABAC) approach for multi-attribute decision making (MADM) problems based on type-2 fuzzy sets (IT2FSs). As a special case of IT2FSs interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) are adopted here to deal with uncertainties present in many practical evaluation and selection problems. A systematic description of MABAC based on IT2TrFNs is presented in the current study. The validity and feasibility of the proposed method are illustrated by a practical example of selecting the most suitable candidate for a software company which is heading to hire a system analysis engineer based on few attributes. Finally, a comparison with two other existing MADM methods is described.


title: The physics of hearing: fluid mechanics and the active process of the inner ear

abstract: Most sounds of interest consist of complex, time-dependent admixtures of tones of diverse frequencies and variable amplitudes. To detect and process these signals, the ear employs a highly nonlinear, adaptive, real-time spectral analyzer: the cochlea. Sound excites vibration of the eardrum and the three miniscule bones of the middle ear, the last of which acts as a piston to initiate oscillatory pressure changes within the liquid-filled chambers of the cochlea. The basilar membrane, an elastic band spiraling along the cochlea between two of these chambers, responds to these pressures by conducting a largely independent traveling wave for each frequency component of the input. Because the basilar membrane is graded in mass and stiffness along its length, however, each traveling wave grows in magnitude and decreases in wavelength until it peaks at a specific, frequency-dependent position: low frequencies propagate to the cochlear apex, whereas high frequencies culminate at the base. The oscillations of the basilar membrane deflect hair bundles, the mechanically sensitive organelles of the ear's sensory receptors, the hair cells. As mechanically sensitive ion channels open and close, each hair cell responds with an electrical signal that is chemically transmitted to an afferent nerve fiber and thence into the brain. In addition to transducing mechanical inputs, hair cells amplify them [...]


title: Unified analysis of switched-capacitor resonant converters

abstract: A family of switched-capacitor resonant circuits using only two transistors is presented. The circuit operates under zero-current switching and, therefore, the switching loss is zero. It also offers a wide choice of voltage conversions including fractional as well as multiple and inverted voltage conversion ratios.


title: LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification

abstract: The key challenge of intelligent fault diagnosis is to develop features that can distinguish different categories. Because of the unique properties of mechanical data, predetermined features based on prior knowledge are usually used as inputs for fault classification. However, proper selection of features often requires expertise knowledge and becomes more difficult and time consuming when volume of data increases. In this paper, a novel deep learning network (LiftingNet) is proposed to learn features adaptively from raw mechanical data without prior knowledge. Inspired by convolutional neural network and second generation wavelet transform, the LiftingNet is constructed to classify mechanical data even though inputs contain considerable noise and randomness. The LiftingNet consists of split layer, predict layer, update layer, pooling layer, and full-connection layer. Different kernel sizes are allowed in convolutional layers to improve learning ability. As a multilayer neural network, deep features are learned from shallow ones to represent complex structures in raw data. Feasibility and effectiveness of the LiftingNet is validated by two motor bearing datasets. Results show that the proposed method could achieve layerwise feature learning and successfully classify mechanical data even with different rotating speed and under the influence of random noise.


title: Packing Schemes for Gang Scheduling

abstract: Jobs that do not require all processors in the system can be packed together for gang scheduling. We examine accounting traces from several parallel computers to show that indeed many jobs have small sizes and can be packed together. We then formulate a number of such packing algorithms, and evaluate their effectiveness using simulations based on our workload study. The results are that two algorithms are the best: either perform the mapping based on a buddy system of processors, or use migration to re-map the jobs more tightly whenever a job arrives or terminates. Other approaches, such as mapping to the least loaded PEs, proved to be counterproductive. The buddy system approach depends on the capability to gang-schedule jobs in multiple slots, if there is space. The migration algorithm is more robust, but is expected to suffer greatly due to the overhead of the migration itself. In either case fragmentation is not an issue, and utilization may top 90% with sufficiently high loads.


title: Crossmodal Attentive Skill Learner

abstract: This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.


title: Mental Workload: Assessment, Prediction and Consequences

abstract: I describe below the manner in which workload measurement can be used to validate models that predict workload. These models in turn can be employed to predict the decisions that are made, which select a course of action that is of lower effort or workload, but may also be of lower expected value (or higher expected cost). We then elaborate on four different contexts in which these decisions are made, with non-trivial consequences to performance and learning: switching attention, accessing information, studying material, and behaving safety. Each of these four is illustrated by a series of examples.


title: Alert correlation survey: framework and techniques

abstract: Managing raw alerts generated by various sensors are becoming of more significance to intrusion detection systems as more sensors with different capabilities are distributed spatially in the network. Alert Correlation addresses this issue by reducing, fusing and correlating raw alerts to provide a condensed, yet more meaningful view of the network from the intrusion standpoint. Techniques from a divers range of disciplines have been used by researchers for different aspects of correlation. This paper provides a survey of the state of the art in alert correlation techniques. Our main contribution is a two-fold classification of literature based on correlation framework and applied techniques. The previous works in each category have been described alongside with their strengths and weaknesses from our viewpoint.


title: Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos

abstract: Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.


title: Wireless sensor networks for habitat monitoring

abstract: We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring in general, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island off the coast of Maine streaming useful live data onto the web. The application-driven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and health monitoring.


title: Embedded real-time stereo estimation via Semi-Global Matching on the GPU

abstract: Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640480, 128 disparity levels, and using 4 path directions for the SGM method.


title: Light-Weight Multicast DNS and DNS-SD (lmDNS-SD): IPv6-Based Resource and Service Discovery for the Web of Things

abstract: Internet of Things (IoT) is presenting an enormous growing, in numbers, it is estimated that over 50 billion of devices will be connected to Internet by 2020. Therefore, it presents a high scalability requirement to manage every resource connected to the network. Therefore, It is required a high capability for autonomous registration and discovery of resources and services. In addition, it should be dynamically adapted with the inclusion of new devices in the network and changes of the existing ones. Nowadays, the most extended discovery architecture for the Internet is the Domain Name Systems (DNS), which is offering through the extensions multicast DNS (mDNS) and DNS Service Directory (DNS-SD) the query and discovery of services by type and properties. It has been already carried out some initial works on mDNS and DNS-SD for the discovery of things. Thereby, it can satisfy the discovery of resources from the IoT point of view, and discovery of services, i.e. Web Services such as CoAP from the Web of Things point of view. But, it has not been yet analyzed the impact of DNS for Smart Objects, since it cannot be directly applied, because these protocols are designed for host-based requirements, where they are not taking into account the design issues and constraints from the Smart Objects. For that reason, this paper analyzes the requirements and design issues to apply these discovery techniques in Smart Objects, carries out an overview of the satisfaction of them in the initial solutions for IoT, in order to finally offer an evaluation of different ways to apply mDNS and DNS-SD for Smart Objects, concluding with a set of recommendations and lessons learned to build a lightweight implementation of mDNS and DNS-SD for resource discovery and directory.


Creating a Translation Matrix of the Bible's Names Across 591 Languages.


title: Machine Learning for Performance Prediction in Mobile Cellular Networks

abstract: In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.


title: Distributed control of multi-robot teams: Cooperative baton-passing task

abstract: This research addresses the problem of achieving fault tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. The author describes a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. Since such cooperative teams often work in dynamic and unpredictable environments, the software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, they describe the implementation of this architecture on a team of physical mobile robots performing a cooperative baton passing task. These experiments illustrate the ability of ALLIANCE to achieve adaptive, fault-tolerant cooperative control amidst dynamic changes during the task.


title: Avoiding the "danger zones" when injecting dermal fillers and volume enhancers.

abstract: Aesthetic providers need to be aware of the serious potential complications that can result from occlusion of specific facial arterial and venous structures. This article focuses on the anatomical "danger zones" to avoid during dermal filler and volume enhancer injection procedures. Clinical pearls are provided on how to avoid these "danger zones" and what to do if a rare complication (e.g., skin necrosis or vision loss) does occur.


title: TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process

abstract: Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.