title: Marginalized particle filters for mixed linear/nonlinear state-space models

abstract: The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear state-space model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported.


title: A 65nm CMOS self-terminated open-drain IDAC line driver suitable for fast Ethernet applications

abstract: A self-terminated line driver suitable for fast Ethernet operates in class AB mode and combines digital signal processing with low-power analog circuits. It dissipates 108mW, 48% less than an existing state-of-the-art design. It occupies 0.22mm2 in a 65nm standard CMOS technology and operates from a 2.5V supply.


title: Quality Diversity: A New Frontier for Evolutionary Computation

abstract: While evolutionary computation and evolutionary robotics take inspiration from nature, they have long focused mainly on problems of performance optimization. Yet evolution in nature can be interpreted as more nuanced than a process of simple optimization. In particular, natural evolution is a divergent search that optimizes locally within each niche as it simultaneously diversifies. This tendency to discover both quality and diversity at the same time differs from many of the conventional algorithms of machine learning, and also thereby suggests a different foundation for inferring the approach of greatest potential for evolutionary algorithms. In fact, several recent evolutionary algorithms called quality diversity (QD) algorithms(e.g. novelty search with local competition and MAP-Elites) have drawn inspiration from this more nuanced view, aiming to fill a space of possibilities with the best possible example of each type of achievable behavior. The result is a new class of algorithms that return an archive of diverse, high-quality behaviors in a single run. The aim in this paper is to study the application of QD algorithms in challenging environments (in particular complex mazes) to establish their best practices for ambitious domains in the future. In addition to providing insight into cases when QD succeeds and fails, a new approach is investigated that hybridizes multiple views of behaviors (called behavior characterizations) in the same run, which succeeds in overcoming some of the challenges associated with searching for QD with respect to a behavior characterization that is not necessarily sufficient for generating both quality and diversity at the same time.


title: Larval digestion of different manure types by the black soldier fly (Diptera: Stratiomyidae) impacts associated volatile emissions

abstract: Abstract Volatile emissions from decomposing animal waste are known environmental pollutants. The black soldier fly, Hermetia illucens (L.), is being evaluated for industrialization as a means to recycle wastes and produce protein for use as food and feed. We examined the ability of black soldier fly larvae to reduce odorous compounds associated with animal wastes. Black soldier fly larvae were reared under laboratory conditions on poultry, swine, and dairy manure at feed rates of 18.0 and 27.0 g every other day until 40% reached the prepupal stage. Volatile emissions were collected and analyzed from freshly thawed as well as the digested waste when 90% of the black soldier fly larvae reached the prepupal stage. Volatiles were also collected simultaneously from manure not inoculated with black soldier fly larvae (non-digested) and held under similar conditions. Manure samples were analyzed for relative amounts of nine select odorous volatile organic compounds: phenol, 4-methylphenol, indole, 3-methylindole, propanoic acid, 2-methylpropanoic acid, butanoic acid, 3-methylbutanoic acid and pentanoic acid. Black soldier fly larvae reduced emissions of all volatile organic compounds by 87% or greater. Complete reductions were observed for 2-methly propanoic acid in digested poultry manure, phenol, 4-methylphenol, indole and all five fatty acids in digested swine manure, and 4-methylphenol, indole, 3-methylindole and all five acids in digested dairy manure. This study is the first to identify volatile emissions from manure digested by black soldier fly larvae and compare to those found in non-digested manure. These data demonstrate additional benefits of using black soldier fly larvae as a cost-effective and environmentally friendly means of livestock manure management in comparison to current methods. Capsule Black soldier fly larvae are capable of altering the overall profile of volatile organic compounds and reducing levels of targeted odorous compounds in livestock manure.


title: Decentralized Consensus for Edge-Centric Internet of Things: A Review, Taxonomy, and Research Issues

abstract: With the exponential rise in the number of devices, the Internet of Things (IoT) is geared toward edge-centric computing to offer high bandwidth, low latency, and improved connectivity. In contrast, legacy cloud-centric platforms offer deteriorated bandwidth and connectivity that affect the quality of service. Edge-centric Internet of Things-based technologies, such as fog and mist computing, offer distributed and decentralized solutions to resolve the drawbacks of cloud-centric models. However, to foster distributed edge-centric models, a decentralized consensus system is necessary to incentivize all participants to share their edge resources. This paper is motivated by the shortage of comprehensive reviews on decentralized consensus systems for edge-centric Internet of Things that elucidates myriad of consensus facets, such as data structure, scalable consensus ledgers, and transaction models. Decentralized consensus systems adopt either blockchain or blockchainless directed acyclic graph technologies, which serve as immutable public ledgers for transactions. This paper scrutinizes the pros and cons of state-of-the-art decentralized consensus systems. With an extensive literature review and categorization based on existing decentralized consensus systems, we propose a thematic taxonomy. The pivotal features and characteristics associated with existing decentralized consensus systems are analyzed via a comprehensive qualitative investigation. The commonalities and variances among these systems are analyzed using key criteria derived from the presented literature. Finally, several open research issues on decentralized consensus for edge-centric IoT are presented, which should be highlighted regarding centralization risk and deficiencies in blockchain/blockchainless solutions.


title: Gray spot detection in surveillance video using convolutional neural network

abstract: Video surveillance systems have been widely used in society and plays an important role in the maintenance of public security and social justice. Since the camera has been in the natural environment for a long time it is vulnerable to all kinds of interference resulting in the recorded video information is no longer of monitoring value. In this paperwe propose a detection method based on convolution neural network aiming at the problem of dust speckle interference in video surveillance. We train a fully convolutional network for segmentation and a convolutional neural network for classification simultaneously. Experimental results show that using the result after the classification as the input of the segmentation can reduce the false positives rate of the segmentation result. The experiment shows that our method has achieved good results. In the background of big datait has advantages over traditional algorithms.


title: Reducing cyberbullying: A theory of reasoned action-based video prevention program for college students

abstract: Few studies have evaluated the effectiveness of cyberbullying prevention/intervention programs. The goals of the present study were to develop a Theory of Reasoned Action (TRA)-based video program to increase cyberbullying knowledge (1) and empathy toward cyberbullying victims (2), reduce favorable attitudes toward cyberbullying (3), decrease positive injunctive (4) and descriptive norms about cyberbullying (5), and reduce cyberbullying intentions (6) and cyberbullying behavior (7). One hundred sixty-seven college students were randomly assigned to an online video cyberbullying prevention program or an assessment-only control group. Immediately following the program, attitudes and injunctive norms for all four types of cyberbullying behavior (i.e., unwanted contact, malice, deception, and public humiliation), descriptive norms for malice and public humiliation, empathy toward victims of malice and deception, and cyberbullying knowledge significantly improved in the experimental group. At one-month follow-up, malice and public humiliation behavior, favorable attitudes toward unwanted contact, deception, and public humiliation, and injunctive norms for public humiliation were significantly lower in the experimental than the control group. Cyberbullying knowledge was significantly higher in the experimental than the control group. These findings demonstrate a brief cyberbullying video is capable of improving, at one-month follow-up, cyberbullying knowledge, cyberbullying perpetration behavior, and TRA constructs known to predict cyberbullying perpetration. Considering the low cost and ease with which a video-based prevention/intervention program can be delivered, this type of approach should be considered to reduce cyberbullying. Aggr. Behav. 42:136–146, 2016. © 2015 Wiley Periodicals, Inc.


title: Virtual touch screen “VIRTOS” implementing virtual touch buttons and virtual sliders using a projector and camera

abstract: We propose a large interactive display with virtual touch buttons and sliders on a pale-colored flat wall. Our easy-to-install system consists of a front projector and a single commodity camera. A button touch is detected based on the area of the shadow cast by the user's hand; this shadow becomes very small when the button is touched. The shadow area is segmented by a brief change of the button to a different color when a large foreground (i.e., the hand and its shadow) covers the button region. Therefore, no time consuming operations, such as morphing or shape analysis, are required. Background subtraction is used to extract the foreground region. The reference image for the background is continuously adjusted to match the ambient light. Our virtual slider is based on this touch-button mechanism. When tested, our scheme proved robust to differences in illumination. The response time for touch detection was about 150 ms. Our virtual slider has a quick response and proved suitable as a controller for a Breakout-style game.


title: A Framework for Heuristic Reasoning About Uncertainty

abstract: This paper describes a theory of reasoning about uncertainly, based on a representation of state'i of certainly called endorsements (see Cohen and Grinberg, 1983, for a more detailed discussion of the theory.) The theory of endorsements is an alternative to numerical methods for reasoning about uncertainty, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda, Hart, and Nilsson, 1976) and the Shafer-Dempster theory (Shafer, 1976). The fundamental concern with numerical representations of certainty is that they hide the reasoning that produces them and thus limit one's reasoning about uncertainty. While numbers are easy to propagate over inferences, what the numbers mean is unclear. The theory of endorsements represents the factors that affect certainty and supports multiple strategies for dealing with uncertainty.


title: Leak Detection in Water-Filled Small-Diameter Polyethylene Pipes by Means of Acoustic Emission Measurements

abstract: The implementation of effective strategies to manage leaks represents an essential goal for all utilities involved with drinking water supply in order to reduce water losses affecting urban distribution networks. This study concerns the early detection of leaks occurring in small-diameter customers’ connections to water supply networks. An experimental campaign was carried out in a test bed to investigate the sensitivity of Acoustic Emission (AE) monitoring to water leaks. Damages were artificially induced on a polyethylene pipe (length 28 m, outer diameter 32 mm) at different distances from an AE transducer. Measurements were performed in both unburied and buried pipe conditions. The analysis permitted the identification of a clear correlation between three monitored parameters (namely total Hits, Cumulative Counts and Cumulative Amplitude) and the characteristics of the examined leaks.


title: I like it... i like it not: Evaluating user ratings noise in recommender systems

abstract: Recent growing interest in predicting and influencing consumer behavior has generated a parallel increase in research efforts on Recommender Systems. Many of the state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order to later predict unknown ratings. An underlying assumption in this approach is that the user ratings can be treated as ground truth of the user's taste. However, users are inconsistent in giving their feedback, thus introducing an unknown amount of noise that challenges the validity of this assumption. ::: ::: In this paper, we tackle the problem of analyzing and characterizing the noise in user feedback through ratings of movies. We present a user study aimed at quantifying the noise in user ratings that is due to inconsistencies. We measure RMSE values that range from 0.557 to 0.8156. We also analyze how factors such as item sorting and time of rating affect this noise.


title: Probabilistic movement modeling for intention inference in human–robot interaction

abstract: Intention inference can be an essential step toward efficient human-robot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows the intention to be inferred from observed movements using Bayes' theorem. The IDDM simultaneously finds a latent state representation of noisy and high-dimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e. target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.


title: Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach

abstract: We investigate the performance of two machine learning algorithms in the context of antispam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. At the same time, we compare the performance of the Naive Bayesian filter to an alternative memorybased learning approach, after introducing suitable cost-sensitive evaluation measures. Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader.


title: Physically based models with rigid and deformable components

abstract: A class of physically based models suitable for animating flexible objects in simulated physical environments was proposed earlier by the authors (1987). The original formulation works as well in practice for models whose shapes are moderately to highly deformable, but it tends to become numerically ill conditioned as the rigidity of the models is increased. An alternative formulation of deformable models is presented in which deformations are decomposed into a reference component, which may represent an arbitrary shape, and a displacement component, allowing deformation away from this reference shape. The application of the deformable models to a physically based computer animation project is illustrated. >


title: Energy-Efficient Gait Planning and Control for Biped Robots Utilizing Vertical Body Motion and Allowable ZMP Region

abstract: An energy-efficient gait planning (EEGP) and control system is established for biped robots with three-mass inverted pendulum mode (3MIPM), which utilizes both vertical body motion (VBM) and allowable zero-moment-point (ZMP) region (AZR). Given a distance to be traveled, we newly designed an online gait synthesis algorithm to construct a complete walking cycle, i.e., a starting step, multiple cyclic steps, and a stopping step, in which: 1) ZMP was fully manipulated within AZR; and 2) vertical body movement was allowed to relieve knee bending. Moreover, gait parameter optimization is effectively performed to determine the optimal set of gait parameters, i.e., average body height and amplitude of VBM, number of steps, and average walking speed, which minimizes energy consumption of actuation motors for leg joints under practical constraints, i.e., geometrical constraints, friction force limit, and yawing moment limit. Various simulations were conducted to identify the effectiveness of the proposed method and verify energy-saving performance for various ZMP regions. Our control system was implemented and tested on the humanoid robot DARwIn-OP.


title: A Survey of Dynamic Program Analysis Techniques and Tools

abstract: Dynamic program analysis is a very popular technique for analysis of computer programs. It analyses the properties of a program while it is executing. Dynamic analysis has been found to be more precise than static analysis in handling run-time features like dynamic binding, polymorphism, threads etc. Therefore much emphasis is now being given on dynamic analysis of programs ( instead of static analysis) involving the above mentioned features. Various techniques have been devised over the past several years for the dynamic analysis of programs. This paper provides an overview of the existing techniques and tools for the dynamic analysis of programs. Further, the paper compares these techniques for their merits and demerits and emphasizes the importance of each technique.


title: A Simplified PWM Strategy for a Neutral-Point-Clamped (NPC) Three-Level Converter With Unbalanced DC Links

abstract: A simplified pulse width modulation (PWM) strategy for a neutral-point-clamped three-level converter with unbalanced dc links is proposed in this paper to achieve high-quality line-to-line output voltages and to maximize the linear modulation range. The simplified strategy takes the direct output voltage modulation by calculating the special solutions of the voltage–second balance equations without detecting the position of the reference vector in the asymmetrical and complicated space voltage vector diagrams to reduce the calculation time. A novel solution based on the state transition is proposed to extend the maximum linear modulation index to 1.15. Furthermore, the asymmetric control of the split dc link by the proposed PWM is implemented by adjusting the special solutions. The difference between the conventional space vector PWM and the proposed strategy is conducted to illustrate the advantages of the simplified strategy. The effectiveness of the proposed modulation strategy is verified by simulation and experiment results.


title: Fusion++: Volumetric Object-Level SLAM

abstract: We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to initialise compact per-object Truncated Signed Distance Function (TSDF) reconstructions with object size-dependent resolutions and a novel 3D foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation. Objects are incrementally refined via depth fusion, and are used for tracking, relocalisation and loop closure detection. Loop closures cause adjustments in the relative pose estimates of object instances, but no intra-object warping. Each object also carries semantic information which is refined over time and an existence probability to account for spurious instance predictions. We demonstrate our approach on a hand-held RGB-D sequence from a cluttered office scene with a large number and variety of object instances, highlighting how the system closes loops and makes good use of existing objects on repeated loops. We quantitatively evaluate the trajectory error of our system against a baseline approach on the RGB-D SLAM benchmark, and qualitatively compare reconstruction quality of discovered objects on the YCB video dataset. Performance evaluation shows our approach is highly memory efficient and runs online at 4-8Hz (excluding relocalisation) despite not being optimised at the software level.


title: A novel randomized heuristic for the team orienteering problem

abstract: During the last decades, researchers have shown a great interest in studying the team orienteering problem due to its applications in several fields such as tourist trip planning, technician routing and athlete recruiting. Therefore, solving optimally the team orienteering problem would play a major role in logistics management. In this paper, an unprecedented heuristic is applied to the team orienteering problem. The heuristic is used to generate the initial population for population-based metaheuristics. The heuristic proved its efficiency. Indeed, experiments conducted on the team orienteering problem show that the initial population generated by our heuristic contains the best-known solutions for 67 benchmarks and good solutions for a great number of benchmarks. A good initial population will reduce the computational time and the number of iterations necessary to reaches the best-known solutions.


title: Optimizing well-being: The empirical encounter of two traditions

abstract: Subjective well-being (SWB) is evaluation of life in terms of satisfaction and balance between positive and negative affect; psychological well-being (PWB) entails perception of engagement with existential challenges of life. The authors hypothesized that these research streams are conceptually related but empirically distinct and that combinations of them relate differentially to sociodemographics and personality. Data are from a national sample of 3,032 Americans aged 25–74. Factor analyses confirmed the related-but-distinct status of SWB and PWB. The probability of optimal well-being (high SWB and PWB) increased as age, education, extraversion, and conscientiousness increased and as neuroticism decreased. Compared with adults with higher SWB than PWB, adults with higher PWB than SWB were younger, had more education, and showed more openness to experience. Research on well-being has flourished in recent decades (Diener, Suh, Lucas, & Smith, 1999; Kahneman, Diener, & Schwarz, 1999), with increasing recognition of the different streams of inquiry guiding this broad domain. Ryan and Deci’s (2001) integrative review organized the field of well-being into two broad traditions: one dealing with happiness (hedonic well-being), and one dealing with human potential (eudaimonic well-being; Ryan & Deci, 2001; see also Waterman, 1993). In the present study, we draw and extend these distinctions, which we refer to as traditions of research on subjective well-being (SWB) and psychological well-being (PWB). We use these terms to underscore the fact that studies of SWB have repeatedly included not only affective indicators of happiness (hedonic well-being) but also cognitive assessments of life satisfaction. In addition, some aspects of PWB (e.g., personal growth, purpose in life) but not others (e.g., positive relations with others, self-acceptance) reflect the self-fulfillment meanings of eudaimonic well-being. As described below, SWB and PWB are also the overarching phrases most frequently used in studies that constitute these traditions, both of which are fundamentally concerned with subjective accounts of well-being. Our specific empirical aims are to examine whether indicators of SWB and PWB constitute taxonomically distinct reflections of well-being in a national sample of U.S. adults. Although both approaches assess well-being, they address different features of what it means to be well: SWB involves more global evaluations of affect and life quality, whereas PWB examines perceived thriving vis-a `-vis the existential challenges of life (e.g., pursuing meaningful goals, growing and developing as a person, establishing quality ties to others). We further test the hypothesis that these distinct varieties of well-being are contoured by the broad categories of sociodemographic and personality factors. Specifically, we investigate the role of location in the life course (i.e., age) and position in the socioeconomic hierarchy (e.g., educational status) as well as personality traits in accounting for different profiles of well-being. To put the inquiry in historical context, we provide a brief summary of each tradition.


title: Dual-Band Microstrip Balun With Flexible Frequency Ratio and High Selectivity

abstract: A new planar microstrip balun with two operation bands is presented and investigated in this letter. Originated from the traditional Marchand balun, the proposed dual-band balun is designed on a symmetrical four-port network with one short-circuited port. Derived equations and design charts show that the frequency ratio ( $m = f_{2}/f_{1}$ ) of these two bands could be flexibly controlled in a wide range of 2.0–4.6. Moreover, inherent transmission zeros nearby each band have been introduced into this dual-band balun, aiming to improve the frequency selectivity. For verification, a prototype dual-band balun operating at 2.4 and 5.2 GHz ( $m = 2.17$ ) is implemented and measured. Both simulation and measured results are recorded with good agreement.


title: Design of A Universal User Model for Dynamic Crowd Preference Sensing and Decision-Making Behavior Analysis

abstract: Sharing economy becomes an emerging issue in urban life. It is not a new phenomenon but an assembling of existing techniques to meet specific demands of users. It also points out a better way to implicitly collect users’ contexts and to understand users than the conventional one that requires much user involvement (e.g., tedious inputs). A universal model, for this purpose, that supports dynamic analysis and mining of user-generated content (or contexts) is designed in this paper. Two major factors, sensing and analysis of crowd preference and their decision-making behavior, are especially targeted. This model formulates the given scenario that comprehensively illustrates the possible actors and correlated actions among them with a set of rules to enhance the machine learning results. This model outlines a detail process on pre-/post-process of the data, and indicates the core techniques for user modeling. The raw data collected from on-service website, i.e., Airbnb, are utilized for the preliminary examination of our proposal. We especially look at internal factors (e.g., nationality, gender, and age) and external factors (e.g., device, social media, and time) that reflect implicitly the difference on crowd’s preference and behavior. Results after statistics-based machine learning reveal that the relation among users’ internal and external factors share high similarity with their behavior patterns, and can be applied, considering particular features, for service provision to a specific type of crowds.


title: Analyzing optimization of fluxgate sensor

abstract: Fluxgate sensors are one of the most versatile magnetic field sensors, which are extensively used for various defense and industrial applications, fields ranging from 10-8 T to 108 T cover a wide range of critical applications, applying from space navigation to medical applications. For advanced applications, fluxgate sensor should offer high magnetic field sensitivity as well as directionality. There has been a large demand for the integrated fluxgate sensors containing sensor and the supporting circuit on the same silicon chip. Since integrated circuits have driving current and weight limitation, the objective of this research is studying how to simulation and modeling ring-shaped core sensor smaller in size by geometrical change in order to reduce driving current while the high sensitivity of it doesn't change.


title: A Pilot Study on the Relationships Among Organizational Learning, Change, and Sustainability in a Responsible Romanian Higher Education Institution

abstract: The complexity of the 21st century is highly determined by an incessant change at a global, international, regional, national and local scale. Faced with unpredictable changes at various levels all types of organizations are operating in a turbulent and fiercely competitive environment. Today’s successful organizations are learning organizations that embrace the sustainability paradigm. The aims of our paper are to render the theoretical approaches related to the concepts of organizational learning, change, sustainability, and to the relationships among them, and to analyze the results of a pilot study regarding professors’ opinions on these relationships within a responsible Romanian higher education institution. To such ends, a set of two hypotheses was tested during our research. The data gathered were processed through the SPSS software. The results of our research, limited by its purpose and the size of the sample, show that professors appreciate that team learning and empowerment are the main drivers of change and sustainability in a learning organization.


On Modern Deep Learning and Variational Inference


title: Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms

abstract: Abstract With more and more workflow systems adopting cloud as their e xecution environment, it becomes increasingly challenging on how to effic iently manage various workflows, v irtual machines (VMs) and workflow e xecution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) a rchitecture with independent services. A core part of the architecture is how to efficiently respond continuous workflo w requests from users and schedule their e xecutions in the c loud. Based on diffe rent targets, we p ropose four heuristic workflo w scheduling a lgorith ms for the WFaaS architecture, and analy ze the differences and best usages of the algorith ms in terms of performance, cost and the price/performance ratio via experimental studies.


title: Hybrid DC Circuit Breaker and Fault Current Limiter With Optional Interruption Capability

abstract: Since most of renewable energy resources are dc type, dc microgrids find more attention in deregulated power systems for improvement of reliability and efficiency of the system. In dc microgrids, there is no zero-crossing in current and voltage; so the circuit breaking and fault current limiting are serious concerns. Conventional thyristor-based dc fault current limiters are attractive solutions for tackling this problem. However, they impose permanent interruption on the system in case of temporary fault. In this paper, a novel hybrid circuit breaker (HCB) is proposed to improve power quality of the system by adopting different limiting behaviors for permanent and temporary faults. The proposed HCB transfers load power through a mechanical circuit breaker (MCB) under normal condition. Upon fault occurrence in both sides of the HCB, the fault current is transferred from the MCB to a current limiting path incorporating a zero voltage switching (ZVS) transition mechanism. The HCB limits the fault current for a predefined duration and then interrupts the faulty feeder. In case of temporary fault, the current is returned to the MCB in ZVS. The HCB operation principle is discussed in different operation modes. Capabilities of the proposed HCB are tested and verified by different experiments carried out on a prototype.


title: Achieving autonomous power management using reinforcement learning

abstract: System level power management must consider the uncertainty and variability that come from the environment, the application and the hardware. A robust power management technique must be able to learn the optimal decision from past events and improve itself as the environment changes. This article presents a novel on-line power management technique based on model-free constrained reinforcement learning (Q-learning). The proposed learning algorithm requires no prior information of the workload and dynamically adapts to the environment to achieve autonomous power management. We focus on the power management of the peripheral device and the microprocessor, two of the basic components of a computer. Due to their different operating behaviors and performance considerations, these two types of devices require different designs of Q-learning agent. The article discusses system modeling and cost function construction for both types of Q-learning agent. Enhancement techniques are also proposed to speed up the convergence and better maintain the required performance (or power) constraint in a dynamic system with large variations. Compared with the existing machine learning based power management techniques, the Q-learning based power management is more flexible in adapting to different workload and hardware and provides a wider range of power-performance tradeoff.


title: High-Accuracy All-Digital Resolver-to-Digital Conversion

abstract: In this paper, a high-accuracy all-digital resolverto-digital (R/D) converter is presented. The two basic components of a conventional tracking R/D converter, the phase detector and the loop filter, are software implemented by frequency-shifting techniques and a decoupled double synchronous reference frame-based phase-locked loop (DSRF-PLL). This PLL allows the simultaneous extraction of the angular position and speed of the rotatory resolver, even in the presence of gain and phase errors in the resolver. In order to increase accuracy and to minimize the time lag of the whole system, oversampling methods and downsampling finite-impulse response digital filters are introduced. Finally, DSP implementation issues, like the use of techniques to synchronize the resolver output signals with the excitation one, are discussed. Using these combined techniques and a standard DSP with a 12-bit analog-to-digital converter, resolutions of up to 14 bits can be achieved with a computation cost of about 13% of the total (100 MIPs). The paper presents the main techniques, simulation, and experimental results.


title: Matters of Ethics, Trust, and Potential Liability for Autonomous Systems

abstract: Abstract – The objective of this panel was to discuss issues related to the development and use of autonomous systems, with specific focus on the overriding themes of ethical considerations and potential liability for Human Factors and Ergonomics (HF/E) professionals who are involved in their development. Chris Brill provided opening remarks to frame the discussion and introduce the panelists. James Bliss discussed legal implications related to our collective penchant for developing conservative, false-alarm prone automation. Peter Hancock advocated for human-centered constraints on autonomous systems, as they may, one day, pose an existential threat to humanity. Dietrich Manzey discussed ethical considerations for autonomous systems, including how design can encourage ethical user behavior. Joachim Meyer argued that HF/E professionals have an obligation to help designers understand the ethical implications of poor design, particularly in the context of autonomous systems. Lastly, Alison Vredenburgh provi...


title: Combinatorial Designs by SAT Solvers.

abstract: A technique allowing a terminal device located in a remote place to protect data existing on an information processing system and then control an electric source of the information processing system is provided. An information processing system according to the present invention includes a service processor for discriminating an order issued by a terminal device located in a remote place, an electric source control circuit for controlling connection or disconnection of an electric source in response to an electric source connection or disconnection order issued by the service processor, and an electric source unit for conducting connection or disconnection of an electric source in response to an electric source connection or disconnection order issued by the electric source control circuit. The service processor discriminates an electric source disconnection order issued by a terminal device located in a remote place, terminates application software under execution effected by the information processing system, and thereafter orders the electric source control circuit to disconnect the electric source. In response to an order issued by the electric source control circuit, the electric source unit disconnects the electric source. Furthermore, in response to such a state that electric power is supplied from the auxiliary electric source at the time of an electric power failure, shutdown processing is conducted and then the electric source is disconnected. The terminal device located in a remote place is notified that the shutdown processing will be conducted.


title: A Local 3-D Motion Descriptor for Multi-View Human Action Recognition from 4-D Spatio-Temporal Interest Points

abstract: In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach.


title: A Review of Emerging Technologies for the Management of Diabetes Mellitus

abstract: Objective : High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. Methods : A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. Results : Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. Conclusion : Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. Significance : The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.


title: Time-series mining in a psychological domain

abstract: Analysis of time-series became an inevitable tool in applied settings, such as stock market analysis, process and quality control, observation of natural phenomena, medical treatments, and in the behavioral science, such as psychological research. In this paper, we utilize a new kind of a tool set for time-series analysis (FAP, developed at Department of Mathematics and Informatics, University of Novi Sad) on behavioral data gained from a specific experimental lab system, a so called Socially Augmented Microworld with three human participants (developed by informatics and psychologists for Human Factors Research at Humboldt University Berlin). On the basis of these data (logfiles) we extracted three types of time-series and generated distance matrices using three kinds of time-series similarity measures. Finally, the clustering of generated distance matrices produced dendrograms which serve as the basis for a deeper analysis of human behavior. The outcome of this analysis is two-fold: (a) it allows to select the most suitable similarity measure for this domain of experimental research and (b) these results can serve as a basis for the development of artificial agents, which may in turn replace the human participants in the experiment.


UQAM-NTL: Named entity recognition in Twitter messages.


title: Factors Affecting Career Choice: Comparison Between Students from Computer and Other Disciplines

abstract: The number of student enrolments in computer-related courses remains a serious concern worldwide with far reaching consequences. This paper reports on an extensive survey about career choice and associated motivational factors amongst new students, only some of whom intend to major in computer-related courses, at two South African universities. The data were analyzed using some components of Social Cognitive Career Theory, namely external influences, self-efficacy beliefs and outcome expectations. The research suggests the need for new strategies for marketing computer-related courses and the avenues through which they are marketed. This can to some extent be achieved by studying strategies used by other (non-computer) university courses, and their professional bodies. However, there are also distinct differences, related to self-efficacy and career outcomes, between the computer majors and the ‘other’ group and these need to be explored further in order to find strategies that work well for this group. It is not entirely clear what the underlying reasons are for these differences but it is noteworthy that the perceived importance of “Interest in the career field” when choosing a career remains very high for both groups of students.


title: Separating Disambiguation from Composition in Distributional Semantics

abstract: Most compositional-distributional models of meaning are based on ambiguous vector representations, where all the senses of a word are fused into the same vector. This paper provides evidence that the addition of a vector disambiguation step prior to the actual composition would be beneficial to the whole process, producing better composite representations. Furthermore, we relate this issue with the current evaluation practice, showing that disambiguation-based tasks cannot reliably assess the quality of composition. Using a word sense disambiguation scheme based on the generic procedure of Schutze (1998), we first provide a proof of concept for the necessity of separating disambiguation from composition. Then we demonstrate the benefits of an “unambiguous” system on a composition-only task.


title: Generalized hesitant fuzzy sets and their application in decision support system

abstract: Hesitant fuzzy sets are very useful to deal with group decision making problems when experts have a hesitation among several possible memberships for an element to a set. During the evaluating process in practice, however, these possible memberships may be not only crisp values in [0,1], but also interval values. In this study, we extend hesitant fuzzy sets by intuitionistic fuzzy sets and refer to them as generalized hesitant fuzzy sets. Zadeh's fuzzy sets, intuitionistic fuzzy sets and hesitant fuzzy sets are special cases of the new fuzzy sets. We redefine some basic operations of generalized hesitant fuzzy sets, which are consistent with those of hesitant fuzzy sets. Some arithmetic operations and relationships among them are discussed as well. We further introduce the comparison law to distinguish two generalized hesitant fuzzy sets according to score function and consistency function. Besides, the proposed extension principle enables decision makers to employ aggregation operators of intuitionistic fuzzy sets to aggregate a set of generalized hesitant fuzzy sets for decision making. The rationality of applying the proposed techniques is clarified by a practical example. At last, the proposed techniques are devoted to a decision support system.


title: Wide field-of-view and mid-range distance imaging LIDAR by digital micro-mirror device

abstract: A Digital Micro-mirror Device enables a fast, wide field-of-view, mid-range distance mapping while minimizing requirements for mechanical scanning components. The optical architecture offers a compact, low-cost solution to an imaging LIDAR with a single detector.


title: A CRISPR–Cpf1 system for efficient genome editing and transcriptional repression in plants

abstract: CRISPR–Cpf1 has emerged as an effective genome editing tool in animals. Now, a study shows that this system can edit plant genomes at nearly 100% efficiency at independent sites. Moreover, it can be repurposed for modulating plant transcriptome.


title: Response rates in studies of couples coping with cancer: a systematic review.

abstract: OBJECTIVE ::: Recruiting couples for psychological studies can be challenging. This brief report is the first to examine the average couples' response rate and to systematically review the quality of reporting of couples' response rate in studies of couples coping with cancer. ::: ::: ::: METHOD ::: A systematic review (1980-2011) was conducted, including 83 studies meeting the inclusion criteria of being published in peer-reviewed journals, describing quantitative findings using a cross-sectional or longitudinal design. ::: ::: ::: RESULTS ::: Overall reporting was unsatisfactory in more than half of the included studies. As a consequence, the couples' response rate (CRR; all analyzed couples divided by the number of eligible partnered patients/couples approached) could be calculated for only 33 samples. This CRR varied considerably across studies from 25% to 90% (CRRM = 58%, SD = 17%). The rates reported in the articles (M = 65%) were often higher than the average CRR (CRRM = 57%) of these samples. ::: ::: ::: CONCLUSIONS ::: This systematic review revealed incomplete reporting of response rate. Therefore, it cannot be firmly concluded that the average CRR reported is representative for all studies on couples coping with cancer. Finally, the figures presented, which are often more favorable than the CRR, may create the impression that the sample is more representative of the target population than it actually is. This has consequences for implementing the findings of such studies into practice. The results are critically discussed, and recommendations for improvement are provided.


title: The endoscopic treatment of sciatic nerve entrapment/deep gluteal syndrome.

abstract: Purpose The purpose of this study was to investigate the historical, clinical, and radiographic presentation of deep gluteal syndrome (DGS) patients, describe the endoscopic anatomy associated with DGS, and assess the effectiveness of endoscopic surgical decompression for DGS. Methods Sciatic nerve entrapment was diagnosed in 35 patients (28 women and 7 men). Portals for inspection of the posterior peritrochanteric space (subgluteal space) of the hip were used as well as an auxiliary posterolateral portal. Patients were treated with sciatic nerve decompression by resection of fibrovascular scar bands, piriformis tendon release, obturator internus, or quadratus femoris or by hamstring tendon scarring. Postoperative outcomes were evaluated with the modified Harris Hip Score (MHHS), verbal analog scale (VAS) pain score, and a questionnaire related specifically to sciatic hip pain. Results The mean patient age was 47 years (range, 20 to 66 years). The mean duration of symptoms was 3.7 years (range, 1 to 23 years). The mean preoperative VAS score was 6.9 ± 2.0, and the mean preoperative MHHS was 54.4 ± 13.1 (range, 25.3 to 79.2). Of the patients, 21 reported preoperative use of narcotics for pain; 2 continued to take narcotics postoperatively (unrelated to initial complaint). The mean time of follow-up was 12 months (range, 6 to 24 months). The mean postoperative MHHS increased to 78.0 and VAS score decreased to 2.4. Eighty-three percent of patients had no postoperative sciatic sit pain (inability to sit for >30 minutes). Conclusions Endoscopic decompression of the sciatic nerve appears useful in improving function and diminishing hip pain in sciatic nerve entrapment/DGS. Level of Evidence Level IV, therapeutic case series.


title: Complete Suppression of Ethanol-Induced Formation of Megamitochondria By 4-Hydroxy-2,2,6,6-Tetramethyl-Piperidine-1-Oxyl (4-OH-Tempo)

abstract: An attempt has been made to suppress the ethanol-induced formation of megamitochondria (MG) in the rat liver by 4-hydroxy-2,2,6,6-tetramethyl-piperidine-1-oxyl (4-OH-TEMPO), a free radical scavenger, and by allopurinol (AP), a xanthine oxidase inhibitor. Changes observed in the liver of animals given ethanol (EtOH) for 1 month were remarkable decreases both in the body weight gains during the course of the experiment and in the liver weight at the time of sacrifice compared to those of the control; remarkable increases in the level of thiobarbituric acid reactive substances and lipid soluble fluorophores both in microsomes and mitochondria; decreases in the content of cytochrome a+a3 and b and lowered phosphorylating ability of mitochondria; and formation of MG in the liver. A combined treatment of animals with EtOH plus 4-OH-TEMPO completely suppressed the formation of MG in the liver induced by EtOH and distinctly improved the changes caused by EtOH, as specified above, while AP partly suppressed the MG formation. Results described herein provide additional insight into chronic hepatotoxicity of EtOH besides that previously reported. A novelty of the present work is that we were able for the first time to demonstrate reversibility of EtOH-mediated ultrastructural changes of the liver by a simple administration of aminoxyl-type free radical scavenger, 4-OH-TEMPO. Our results suggest that free radicals may be involved in the mechanism of the formation of MG induced by EtOH.


title: A Comparison between Recursive Neural Networks and Graph Neural Networks

abstract: Recursive neural networks (RNNs) and graph neural networks (GNNs) are two connectionist models that can directly process graphs. RNNs and GNNs exploit a similar processing framework, but they can be applied to different input domains. RNNs require the input graphs to be directed and acyclic, whereas GNNs can process any kind of graphs. The aim of this paper consists in understanding whether such a difference affects the behaviour of the models on a real application. An experimental comparison on an image classification problem is presented, showing that GNNs outperforms RNNs. Moreover the main differences between the models are also discussed w.r.t. their input domains, their approximation capabilities and their learning algorithms.


title: A model-based strategy for mapping human grasps to robotic hands using synergies

abstract: The aim of this paper is to derive the synergies subspace of an anthropomorphic robotic hand using the human hand as a master. A set of grasping postures performed by five subjects in grasping commonly used objects has been mapped to a robotic hand assuming its own kinematics as a simplified model of the human hand. Using an RGB camera and depth sensor for 3D motion capture, the human hand palm pose and fingertip positions have been measured for the reference set of grasping. From the measured fingertip positions a closed-loop inverse kinematics algorithm has been applied to reproduce the joint space configuration of the robotic hand relying on its kinematics, scaled using the human and robotic fingers length ratio. Once the set of grasping has been mapped on the robotic hand, the synergies subspace has been computed applying principal component analysis on the joint configurations. The obtained subspace is tested with experiments on the DEXMART Hand by performing reach to grasp actions on selected objects using the first three predominant synergies. The analysis of these synergies and a comparison with the results on the human hand available in the literature are performed by means of graphical and numerical tools.


title: Social Influence in Mobile Phone Adoption: Evidence from the Bottom of Pyramid in Emerging Asia

abstract: This paper attempts to quantitatively measure the various influences on mobile phone adoption at the bottom of the pyramid (BOP) in Bangladesh, Pakistan, India, Sri Lanka, Philippines and Thailand. Based on an existing theoretical framework adoption is modeled by fitting a logit model to a large six country dataset. The study finds evidence for the importance of social influence in mobile adoption in two modes: one that exerts pressure on individuals to adopt; and another that helps generate benefits via social networks that are tied in with economic and business networks. The paper elaborates on the resulting social policy implications for using mobile telephone services to fight poverty at the BOP in these and similar countries.


Somewhat practical fully homomorphic encryption


Smart to Smarter : Smart Home Systems History , Future and Challenges


title: A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception

abstract: Computational models for image quality assessment (IQA) have been developed by exploring effective features that are consistent with the characteristics of a human visual system (HVS) for visual quality perception. In this paper, we first reveal that many existing features used in computational IQA methods can hardly characterize visual quality perception for local image characteristics and various distortion types. To solve this problem, we propose a new IQA method, called the structural contrast-quality index (SC-QI), by adopting a structural contrast index (SCI), which can well characterize local and global visual quality perceptions for various image characteristics with structural-distortion types. In addition to SCI, we devise some other perceptually important features for our SC-QI that can effectively reflect the characteristics of HVS for contrast sensitivity and chrominance component variation. Furthermore, we develop a modified SC-QI, called structural contrast distortion metric (SC-DM), which inherits desirable mathematical properties of valid distance metricability and quasi-convexity. So, it can effectively be used as a distance metric for image quality optimization problems. Extensive experimental results show that both SC-QI and SC-DM can very well characterize the HVS’s properties of visual quality perception for local image characteristics and various distortion types, which is a distinctive merit of our methods compared with other IQA methods. As a result, both SC-QI and SC-DM have better performances with a strong consilience of global and local visual quality perception as well as with much lower computation complexity, compared with the state-of-the-art IQA methods. The MATLAB source codes of the proposed SC-QI and SC-DM are publicly available online at https://sites.google.com/site/sunghobaecv/iqa .


title: Curation of Physical Objects in Botany: Architecture and Development of a Linked Open Data-Based Application

abstract: Cultural heritage institutions store and manage large volumes of historical information, that have great material and humanitarian value. Their means of data organization, often obsolete, hinders the dissemination and reuse of information to be made in an effective way. In this sense, an application of Linked Open Data (LOD) technology is the possibility to extend the knowledge of a collection, with the use of open data already consolidated on the web to describe artworks or any type of physical object. This paper introduces a generic architecture based on an Extract, Transform, Load (ETL) methodology that connects LOD data from cultural heritage available on the web, generates descriptive content for physical objects and provides the mapping of research institutes engaged in studying them, highlighting their collaboration network. For instance, an implementation of the architecture was dedicated to the domain of Botany and can assist herbarium managers in creating exhibitions about medicinal plants. In this paper, we present a software architecture that provides an automatic method for creating dynamic pages from data stored in interconnected databases, and an application, which supports visitation systems with visualization and interaction mechanisms to encourage visitor learning.


title: Cyber Security Backdrop: A SCADA testbed

abstract: Supervisory Control and Data Acquisition (SCADA) systems are traditionally proprietary and well protected. Due to increasing use of commercial/open source technology and communication protocols, there are growing concerns about the associated security threats. SCADA networks are usually employed in critical infrastructure, therefore, not much technical data of the actual systems is accessible to the research community. Most of the researchers simulate the SCADA functioning through development of testbed. Such projects are usually expensive and requiring financial sponsorship. In this paper we present a simple, inexpensive and flexible approach to develop a SCADA testbed utilizing TrueTime, a MATLAB/ Simulink based tool. The paper describes TrueTime simulation blocks, our control system, simulation of Denial of Service (DoS) attack and its effects. The main aim was to assess the effectiveness and suitability of TrueTime for the intended use in the development of a larger scale SCADA testbed. The results shown reflect that TrueTime can be effectively used for the purpose of SCADA network simulations and collection of necessary data for security analysis.


title: Unsupervised Salience Learning for Person Re-identification

abstract: Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.


title: GLA: Global–Local Attention for Image Description

abstract: In recent years, the task of automatically generating image description has attracted a lot of attention in the field of artificial intelligence. Benefitting from the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), many approaches based on the CNN-RNN framework have been proposed to solve this task and achieved remarkable process. However, two problems remain to be tackled in which the most existing methods use only the image-level representation. One problem is object missing, in which some important objects may be missing when generating the image description and the other is misprediction, when one object may be recognized in a wrong category. In this paper, to address these two problems, we propose a new method called global–local attention (GLA) for generating image description. The proposed GLA model utilizes an attention mechanism to integrate object-level features with image-level feature. Through this manner, our model can selectively pay attention to objects and context information concurrently. Therefore, our proposed GLA method can generate more relevant image description sentences and achieve the state-of-the-art performance on the well-known Microsoft COCO caption dataset with several popular evaluation metrics—CIDEr, METEOR, ROUGE-L and BLEU-1, 2, 3, 4.


Comparison of the effi cacy and safety of new oral anticoagulants with warfarin in patients with atrial fi brillation : a meta-analysis of randomised trials


title: The Psychosocial Distress Questionnaire-Breast Cancer (PDQ-BC) is a useful instrument to screen psychosocial problems

abstract: PURPOSE ::: Recently, the Psychosocial Distress Questionnaire-Breast Cancer (PDQ-BC), a screening instrument specific for patients with early-stage breast cancer, was developed. The aim of this study was to further examine the psychometric properties of the PDQ-BC, in particular the subscales social support, sexual problems and financial problems. ::: ::: ::: METHODS ::: Before patients received treatment (N = 123), they completed the PDQ-BC, the World Health Organization Quality of Life (WHOQOL-100) and the Center for Epidemiologic Studies Depression Scale (CES-D). ::: ::: ::: RESULTS ::: Floor effects were present in 44% of the subscales, whereas ceiling effects were only found in the social support subscale (11%). The PDQ-BC subscales social support, sexual problems and financial problems were highly correlated with the corresponding WHOQOL-100 facets social support, sexual activity and financial resources. Furthermore, the subscale depressive symptoms (PDQ-BC) was highly significantly correlated with the CES-D. Low correlations were found between the PDQ-BC subscales and questionnaires that were expected to be unrelated. Exceptions are the subscales trait anxiety and state anxiety, which had a high correlation with the CES-D. The Cronbach's alpha coefficients of the subscales trait anxiety, state anxiety, depressive symptoms, body image and physical problems ranged from 0.70 to 0.87. Social problems had a low consistency (0.39). Corrected item-total correlations confirmed the PDQ-BC structure. ::: ::: ::: CONCLUSIONS ::: The PDQ-BC has expected floor effects, few ceiling effects and sufficient internal consistency. Furthermore, the construct validity on the PDQ-BC subscales social support, sexual problems and financial problems was good. Thus, the PDQ-BC can be used to screen psychosocial problems in patients with early-stage breast cancer as part of routine care.


title: A rank-order distance based clustering algorithm for face tagging

abstract: We present a novel clustering algorithm for tagging a face dataset (e. g., a personal photo album). The core of the algorithm is a new dissimilarity, called Rank-Order distance, which measures the dissimilarity between two faces using their neighboring information in the dataset. The Rank-Order distance is motivated by an observation that faces of the same person usually share their top neighbors. Specifically, for each face, we generate a ranking order list by sorting all other faces in the dataset by absolute distance (e. g., L1 or L2 distance between extracted face recognition features). Then, the Rank-Order distance of two faces is calculated using their ranking orders. Using the new distance, a Rank-Order distance based clustering algorithm is designed to iteratively group all faces into a small number of clusters for effective tagging. The proposed algorithm outperforms competitive clustering algorithms in term of both precision/recall and efficiency.


title: A model matching for the stabilization of the two Wheels Inverted Pendulum

abstract: In this article, a model matching controller for the stabilization of the Two Wheels Inverted Pendulum is present. The stabilization strategy consists in forcing the closed-loop system to behave as another nonlinear target system with some stability properties. To this end, we solve two matching conditions that allow us to shape a suitable target system and we obtain the stabilizing controller. The resulting closed-loop system is locally asymptotically exponentially stable, with a very large domain of attraction.


title: A compact FPGA-based single-phase cascaded H-bridge multilevel inverter for teaching and research activities

abstract: In this paper is presented the implementation of a compact FPGA-based single-phase cascaded H-bridge multilevel inverter suitable for teaching and research activities. The softwares Matlab/Simulink and Quartus II were used to describe and simulate the PWM algorithm in hardware description language (VHDL), before experimental implementation. A Terasic DE0-Nano board with an Intel Cyclone IV EP4CE22F17C6N FPGA was used to generate the 2.4 kHz PWM switching control signals, which are fed to isolated gate drivers with the HCPL-3180 optocoupler, before being applied to the eight IRF640N MOSFETs in a developed low power prototype inverter board. To validate the proposed inverter, two amplitude modulation indexes were evaluated (0.4 and 0.99) using the phase opposition carriers disposition (POD) technique. Simulation and experimental results to synthesize a three- and a five-level PWM voltage waveform across a resistive and a resistive-inductive-capacitive load show that both are in close agreement, validating the proposed low-cost inverter.


title: On-line Fruit Grading According to their External Quality using Machine Vision

abstract: Abstract This paper presents apple grading into four classes according to European standards. Two varieties were tested: Golden Delicious and Jonagold. The image database included more than a 1000 images of fruits (528 Golden Delicious, 642 Jonagold) belonging to the three acceptable categories—Extra, I and II—and the reject (each class represents, respectively, about 60, 10 and 20% of the sample size).The image grading was achieved in six steps: image acquisition; ground colour classification; defect segmentation; calyx and stem recognition; defects characterisation and finally the fruit classification into quality classes.The proposed method for apple external quality grading showed correct classification rates of 78 and 72%, for Golden Delicious and Jonagold apples, respectively. Taking into account that the healthy fruit were far better graded and considering that this class was under represented in the sample compared with the fruit population, the results of the proposed method (an error rate which drops to 5 and 10%, respectively) are compatible with the requirements of European standards.


title: A Survey of Vision-Based Traffic Monitoring of Road Intersections

abstract: Visual surveillance of dynamic objects, particularly vehicles on the road, has been, over the past decade, an active research topic in computer vision and intelligent transportation systems communities. In the context of traffic monitoring, important advances have been achieved in environment modeling, vehicle detection, tracking, and behavior analysis. This paper is a survey that addresses particularly the issues related to vehicle monitoring with cameras at road intersections. In fact, the latter has variable architectures and represents a critical area in traffic. Accidents at intersections are extremely dangerous, and most of them are caused by drivers' errors. Several projects have been carried out to enhance the safety of drivers in the special context of intersections. In this paper, we provide an overview of vehicle perception systems at road intersections and representative related data sets. The reader is then given an introductory overview of general vision-based vehicle monitoring approaches. Subsequently and above all, we present a review of studies related to vehicle detection and tracking in intersection-like scenarios. Regarding intersection monitoring, we distinguish and compare roadside (pole-mounted, stationary) and in-vehicle (mobile platforms) systems. Then, we focus on camera-based roadside monitoring systems, with special attention to omnidirectional setups. Finally, we present possible research directions that are likely to improve the performance of vehicle detection and tracking at intersections.


title: Antidiuretic Effects of Inactin and Pentobarbital in Chickens

abstract: Summary 1. The present work provides the evidence for the antidiuretic effect of barbiturates in the chicken. 2. Inactin and pentobarbital produce the same level of antidiuretic effect in the chickens if the plane of anesthesia maintained by both drugs is the same.


title: A review of artificial intelligence

abstract: This paper reviews the field of artificial intelligence focusing on embodied artificial intelligence. It also considers models of artificial consciousness, agent-based artificial intelligence and the philosophical commentary on artificial intelligence. It concludes that there is almost no consensus nor formalism in the field and that the achievements of the field are meager.


title: Integrating Models of Diffusion of Innovations: A Conceptual Framework

abstract: ▪ Abstract This chapter provides a conceptual framework for integrating the array of variables defined in diffusion research to explicate their influence on an actor's decision to adopt an innovation. The framework groups the variables into three major components. The first component includes characteristics of the innovation itself, within which two sets of variables are defined concerning public versus private consequences and benefits versus costs of adoption. A second component involves the characteristics of innovators (actors) that influence the probability of adoption of an innovation. Within this component six sets of variables concern societal entity of innovators (either people, organizations, states, etc.), familiarity with the innovation, status characteristics, socioeconomic characteristics, position in social networks, and personal qualities. The third component involves characteristics of the environmental context that modulate diffusion via structural characteristics of the modern world. T...


title: Deep Learning Based Surveillance System for Open Critical Areas

abstract: How to automatically monitor wide critical open areas is a challenge to be addressed. Recent computer vision algorithms can be exploited to avoid the deployment of a large amount of expensive sensors. In this work, we propose our object tracking system which, combined with our recently developed anomaly detection system. can provide intelligence and protection for critical areas. In this work. we report two case studies: an international pier and a city parking lot. We acquire sequences to evaluate the effectiveness of the approach in challenging conditions. We report quantitative results for object counting, detection, parking analysis, and anomaly detection. Moreover, we report state-of-the-art results for statistical anomaly detection on a public dataset.


title: Nonsocial Functions of Hypothalamic Oxytocin

abstract: Oxytocin (OXT) is a hypothalamic neuropeptide composed of nine amino acids. The functions of OXT cover a variety of social and nonsocial activity/behaviors. Therapeutic effects of OXT on aberrant social behaviors are attracting more attention, such as social memory, attachment, sexual behavior, maternal behavior, aggression, pair bonding, and trust. The nonsocial behaviors/functions of brain OXT have also received renewed attention, which covers brain development, reproduction, sex, endocrine, immune regulation, learning and memory, pain perception, energy balance, and almost all the functions of peripheral organ systems. Coordinating with brain OXT, locally produced OXT also involves the central and peripheral actions of OXT. Disorders in OXT secretion and functions can cause a series of aberrant social behaviors, such as depression, autism, and schizophrenia as well as disturbance of nonsocial behaviors/functions, such as anorexia, obesity, lactation failure, osteoporosis, diabetes, and carcinogenesis. As more and more OXT functions are identified, it is essential to provide a general view of OXT functions in order to explore the therapeutic potentials of OXT. In this review, we will focus on roles of hypothalamic OXT on central and peripheral nonsocial functions.


title: Exosomes secreted by cortical neurons upon glutamatergic synapse activation specifically interact with neurons

abstract: Exosomes are nano-sized vesicles of endocytic origin released into the extracellular space upon fusion of multivesicular bodies with the plasma membrane. Exosomes represent a novel mechanism of cell–cell communication allowing direct transfer of proteins, lipids and RNAs. In the nervous system, both glial and neuronal cells secrete exosomes in a way regulated by glutamate. It has been hypothesized that exosomes can be used for interneuronal communication implying that neuronal exosomes should bind to other neurons with some kind of specificity. Here, dissociated hippocampal cells were used to compare the specificity of binding of exosomes secreted by neuroblastoma cells to that of exosomes secreted by cortical neurons. We found that exosomes from neuroblastoma cells bind indiscriminately to neurons and glial cells and could be endocytosed preferentially by glial cells. In contrast, exosomes secreted from stimulated cortical neurons bound to and were endocytosed only by neurons. Thus, our results demonstrate for the first time that exosomes released upon synaptic activation do not bind to glial cells but selectively to other neurons suggesting that they can underlie a novel aspect of interneuronal communication. Keywords: extracellular vesicles; exosomes; neurons; multivesicular bodies; CD63 tetraspanin; tetanus toxin; central nervous system; intercellular communication (Published: 13 November 2014) Citation: Journal of Extracellular Vesicles 2014, 3 : 24722 - http://dx.doi.org/10.3402/jev.v3.24722


A sensitive approach to grasping


title: Development of High Density Fan Out Wafer Level Package (HD FOWLP) with Multi-layer Fine Pitch RDL for Mobile Applications

abstract: Recently, Fan-out Wafer Level Packaging (FOWLP) has been emerged as a promising technology to meet the ever increasing demands of the consumer electronic products. However, conventional FOWLP technology is limited to small size packages with single chip and Low to Mid-range Input/ Output (I/O) count due to die shift, warpage and RDL scaling issues. In this paper, we are presenting new RDL-First FOWLP approach which enables RDL scaling, overcomes the die shift, die protrusion and warpage challenges of conventional FOWLP, and extend the FOWLP technology for multi-chip and high I/O count package applications. RDL-First FOWLP process integration flow was demonstrated and fabricated test vehicles of large multi-chip package of 20 x 20 mm2 with 3 layers fine pitch RDL of LW/LS of 2µm/2µm and ~2400 package I/Os. Two Through Mold Interconnections (TMI) fabrication approaches (tall Cu pillar and vertical Cu wire) were evaluated on this platform for Package-on-Package (PoP) application. Backside RDL process on over molded Chip-to-Wafer (C2W) with carrier wafer was demonstrated for PoP applications. Laser de-bonding and sacrificial release layer material cleaning processes were established, and successfully used in the integration flow to fabricate the test vehicles. Assembly processes were optimized and successfully demonstrated large multi-chip RDL-first FOWLP package and PoP assembly on test boards. The large multi-chip FOWLP packages samples were passed JEDEC component level test Moisture Sensitivity Test Level 1 & Level 3 (MST L1 & MST L3) and 30 drops of board level drop test, and results will be presented.


A Cross-modal Review of Indicators for Depression Detection Systems


title: Design and characterization of a high-power ultrasound driver with ultralow-output impedance

abstract: We describe a pocket-sized ultrasound driver with an ultralow-output impedance amplifier circuit (less than 0.05 ohms) that can transfer more than 99% of the voltage from a power supply to the ultrasound transducer with minimal reflections. The device produces high-power acoustical energy waves while operating at lower voltages than conventional ultrasound driving systems because energy losses owing to mismatched impedance are minimized. The peak performance of the driver is measured experimentally with a PZT-4, 1.54 MHz, piezoelectric ceramic, and modeled using an adjusted Mason model over a range of transducer resonant frequencies. The ultrasound driver can deliver a 100 V(pp) (peak to peak) square-wave signal across 0-8 MHz ultrasound transducers in 5 ms bursts through continuous wave operation, producing acoustic powers exceeding 130 W. Effects of frequency, output impedance of the driver, and input impedance of the transducer on the maximum acoustic output power of piezoelectric transducers are examined. The small size, high power, and efficiency of the ultrasound driver make this technology useful for research, medical, and industrial ultrasonic applications.


Neural Style Transfer Replication Project


title: The Definition of Novelty in Recommendation System

abstract: With the development of information technology and application of the Internet, People gradually entered the time of information overload from information scarcity. User satisfaction with recommender systems is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. Novelty is one of the important metrics of customer satisfaction. There is an increasing realization in the Recommender Systems (RS) field that novelty is fundamental qualities of recommendation effectiveness and added-value. This paper combed research results about definition and algorithm of novel recommendation, and starting from the meaning of "novel", defined novelty of item in recommendation system. Experiment proved using the definition of novelty to recommend can effectively recognize the item that the user is familiar with and ensure certain accuracy.


title: Strong vanet security on a budget

abstract: This article proposes a security authentication process that is well-suited for Vehicular Ad-hoc Networks (VANET). As compared to current Public Key Infrastructure (PKI) proposals for VANET authentication, the scheme is significantly more efficient with regard to bandwidth and computation. The scheme uses time as the creator of asymmetric knowledge. A sender creates a long chain of keys. Each key is used for only a short period of time to sign messages. When a key expires, it is publicly revealed, and then never again used. (The sender subsequently uses the next key in its chain to sign future messages.) Upon receiving a revealed key, recipients authenticate previously received messages. The root of a sender's keychain is given in a certificate signed by an authority. This article describes several possible certificate exchange methods. It also addresses privacy issues in VANET, specifically the tension between anonymity and the ability to revoke certificates.


title: Face recognition using PCA-based method

abstract: The objective of this paper is to develop the image processing and recognize the faces using PCA-based face recognition technique. MATLAB based programs are implemented to identify the faces using Indian databases and the Face recognition data, University of Essex, UK. For matching unknown images with known images, different techniques like sum of absolute difference (SAD), sum of squared difference (SSD), normalized cross correlation (NCC) etc. can be used which has been shown in this paper. Finally, it has been shown that the performance of PCA-based face recognition system is quite satisfactory instead of having some shortcomings of the system. (Abstract)


title: Streaming on twitch: fostering participatory communities of play within live mixed media

abstract: Previously, video streaming sites were at the fringes of online social media. In the past two years, live streams of video games, on sites such as Twitch.tv, have become very popular. Live streams serve as meeting grounds for player communities. The Twitch streaming medium combines broadcast video with open IRC chat channels. In conjunction with gameplay, viewer participation and community building gain emphasis. Twitch streams range in size and nature, from intimate communities with fifty viewers, to massive broadcasts with tens of thousands. In this paper, we present an ethnographic investigation of the live streaming of video games on Twitch. We find that Twitch streams act as virtual third places, in which informal communities emerge, socialize, and participate. Over time, stream communities form around shared identities drawn from streams? contents and participants? shared experiences. We describe processes through which stream communities form, the motivations of members, and emergent issues in the medium. Finally, we draw from our findings to derive implications for design of live mixed-media environments to support participatory online communities.


title: Photometric stereo for outdoor webcams

abstract: We present a photometric stereo technique that operates on time-lapse sequences captured by static outdoor webcams over the course of several months. Outdoor webcams produce a large set of uncontrolled images subject to varying lighting and weather conditions. We first automatically select a suitable subset of the captured frames for further processing, reducing the dataset size by several orders of magnitude. A camera calibration step is applied to recover the camera response function, the absolute camera orientation, and to compute the light directions for each image. Finally, we describe a new photometric stereo technique for non-Lambertian scenes and unknown light source intensities to recover normal maps and spatially varying materials of the scene.


title: Detecting deception in reputation management

abstract: We previously developed a social mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper focuses on the problem of deception in testimony propagation and aggregation. We introduce some models of deception and study how to efficiently detect deceptive agents following those models. Our approach involves a novel application of the well-known weighted majority technique to belief function and their aggregation. We describe simulation experiments to study the number of apparently accurate witnesses found in different settings, the number of witnesses on prediction accuracy, and the evolution of trust networks.


title: Cross-Database Face Antispoofing with Robust Feature Representation

abstract: With the wide applications of user authentication based on face recognition, face spoof attacks against face recognition systems are drawing increasing attentions. While emerging approaches of face antispoofing have been reported in recent years, most of them limit to the non-realistic intra-database testing scenarios instead of the cross-database testing scenarios. We propose a robust representation integrating deep texture features and face movement cue like eye-blink as countermeasures for presentation attacks like photos and replays. We learn deep texture features from both aligned facial images and whole frames, and use a frame difference based approach for eye-blink detection. A face video clip is classified as live if it is categorized as live using both cues. Cross-database testing on public-domain face databases shows that the proposed approach significantly outperforms the state-of-the-art.


title: A White-Box DES Implementation for DRM Applications

abstract: For digital rights management (drm) software implementations incorporating cryptography, white-box cryptography (cryptographic implementation designed to withstand the white-box attack context) is more appropriate than traditional black-box cryptography. In the white-box context, the attacker has total visibility into software implementation and execution. Our objective is to prevent extraction of secret keys from the program. We present methods to make such key extraction difficult, with focus on symmetric block ciphers implemented by substitution boxes and linear transformations. A des implementation (useful also for triple-des) is presented as a concrete example.


title: Predicting Deeper into the Future of Semantic Segmentation

abstract: The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future. We develop an autoregressive convolutional neural network that learns to iteratively generate multiple frames. Our results on the Cityscapes dataset show that directly predicting future segmentations is substantially better than predicting and then segmenting future RGB frames. Prediction results up to half a second in the future are visually convincing and are much more accurate than those of a baseline based on warping semantic segmentations using optical flow.


title: Specifying reusable security requirements

abstract: Unlike typical functional requirements, security requirements can potentially be highly reusable, especially if specified as instances of reusable templates. In this column, I will discuss the concepts underlying security engineering including its quality subfactors. I will then address the issue of security requirements and how they differ from the architectural mechanisms that will fulfill them. Then, I will discuss the value of reusable parameterized templates for specifying security requirements and provide an example of such a template and its associated usage. Finally, I will outline an asset-based riskdriven analysis approach for determining the appropriate actual parameters to use when reusing such parameterized templates to specify security requirements.


title: Star Shape Prior for Graph-Cut Image Segmentation

abstract: In recent years, segmentation with graph cuts is increasingly used for a variety of applications, such as photo/video editing, medical image processing, etc. One of the most common applications of graph cut segmentation is extracting an object of interest from its background. If there is any knowledge about the object shape (i.e. a shape prior), incorporating this knowledge helps to achieve a more robust segmentation. In this paper, we show how to implement a star shape prior into graph cut segmentation. This is a generic shape prior, i.e. it is not specific to any particular object, but rather applies to a wide class of objects, in particular to convex objects. Our major assumption is that the center of the star shape is known, for example, it can be provided by the user. The star shape prior has an additional important benefit - it allows an inclusion of a term in the objective function which encourages a longer object boundary. This helps to alleviate the bias of a graph cut towards shorter segmentation boundaries. In fact, we show that in many cases, with this new term we can achieve an accurate object segmentation with only a single pixel, the center of the object, provided by the user, which is rarely possible with standard graph cut interactive segmentation.


title: Rural tourism: A sustainable alternative

abstract: The aim of this article is to show and share the results of the research we made in order to improve the situation of sustainable interior rural tourism in Spain. We chose our region: Aragon and we studied the case. We had to consider the financial crisis in the hope that our results would contribute to minimize it. We knew beforehand that rural tourism had been less affected than other sectors but still it was important to reach our goal. It had to be proved that rural interior tourism could be sustainable in Spain by means of quantitative secondary data. It is well-known that tourism is the first source and activity in Spain, but due to the increase in the international sector of new exotic destinations abroad, rural tourism could be threatened and sustainable rural interior tourism could be an alternative for present and future times. We got qualitative data through personal interviews to experts and a SWOT analysis, id est, the measure and description of strengths, weaknesses, opportunities and threats, as an instrument to measure the state of the question within a theoretical framework of sustainable tourism. The results of this research have proved it. Rural tourism is existing and viable in Aragon, with important benefits for present and future opportunities.


title: Fast-food consumption among US adults and children: Dietary and nutrient intake profile

abstract: Abstract Objective To examine the dietary profile associated with fast-food use. To compare the dietary intake of individuals on the day that they ate fast food with the day that fast food was not eaten. Design Cross-sectional study design. The dietary intake of individuals who reported eating fast food on one or both survey days was compared with those who did not report eating fast food. Among the individuals who reported eating fast food, dietary intake on the day when fast food was eaten was compared with the day when fast food was not eaten. Weighted comparison of mean intakes and pairwise t-test were used in the statistical analysis. Subjects/setting Data from 17,370 adults and children who participated in the 1994-1996 and 1998 Continuing Survey of Food Intakes by Individuals. Dietary intake data were collected by 2 non-consecutive 24-hour dietary recalls. Results Fast-food use was reported by 37% of the adults and 42% of the children. Adults and children who reported eating fast food had higher intake of energy, fat, saturated fat, sodium, carbonated soft drink, and lower intake of vitamins A and C, milk, fruits and vegetables than those who did not reported eating fast food ( P Conclusions Consumers should be aware that consumption of high-fat fast food may contribute to higher energy and fat intake, and lower intake of healthful nutrients.


title: ECG Security Identification System on the Zynq SoC Platform

abstract: Electrocardiogram (ECG) is one of the most safety-relevant biometrics due to its live indication and the unique specific waveform. This paper presents an approach for a security identification system and its System-on-Chip (SoC) implementation. The design of the proposed solution has been validated using both MATLAB and Vivado high-level synthesis tools. The achieved implementation results show that the proposed ECG identification method has not only met the real-time processing requirements, but it also consumes only 33% of the available on-chip logic resources of a Xilinx Zynq 7010 SoC with a high identification accuracy.


title: Computing LTS Regression for Large Data Sets

abstract: Data mining aims to extract previously unknown patterns or substructures from large databases. In statistics, this is what methods of robust estimation and outlier detection were constructed for, see e.g. Rousseeuw and Leroy (1987). Here we will focus on least trimmed squares (LTS) regression, which is based on the subset of h cases (out of n) whose least squares fit possesses the smallest sum of squared residuals. The coverage h may be set between n/2 and n. The computation time of existing LTS algorithms grows too much with the size of the data set, precluding their use for data mining. In this paper we develop a new algorithm called FAST-LTS. The basic ideas are an inequality involving order statistics and sums of squared residuals, and techniques which we call `selective iteration' and `nested extensions'. We also use an intercept adjustment technique to improve the precision. For small data sets FAST-LTS typically finds the exact LTS, whereas for larger data sets it gives more accurate results than existing algorithms for LTS and is faster by orders of magnitude. This allows us to apply FAST-LTS to large databases.


title: DeepQ: Advancing Healthcare through Artificial Intelligence and Virtual Reality

abstract: Quality, cost, and accessibility together form an iron triangle that has prevented healthcare from achieving accelerated advancement in the last few decades. Improving any one of the three iron triangle vertices may lead to degradation of the other two. For example, a policy that increases healthcare accessibility would lower quality and/or increase cost. Thanks to recent breakthroughs in artificial intelligence (AI) and virtual reality (VR), this iron triangle may finally be shattered. In this talk, I will share our experience of developing DeepQ, an AI platform, for supporting AI-aided diagnosis and VR-aided surgery. I will present two healthcare initiatives that we have been undertaking since 2013: XPRIZE Tricorder and VR surgery, and explain how AI and VR play pivotal roles in improving diagnosis accuracy and treatment effectiveness. More specifically, I will depict how we have dealt with not only big data machine learning, but also small data learning, which is typical in the medical domain. The talk concludes with roadmaps and a list of open research issues in multimodal signal processing, fusion, and mining to achieve precision medicine and precision surgery.


title: Defending from Stealthy Botnets Using Moving Target Defenses

abstract: In today’s IT landscape, organizations are increasingly exposed to an array of novel and sophisticated threats—including advanced persistent threats (APTs) and distributed denial-of-service (DDoS) attacks—which can bypass traditional defenses and persist in target systems indefinitely. Threat actors often rely on networks of compromised and remotely controlled hosts, known as botnets, to execute a number of different cyberattacks and engage in criminal or unauthorized activities. Protecting sensitive and mission-critical data from competitors, state actors, and organized crime has become increasingly critical for the well-being of many organizations. A promising approach to botnet detection and mitigation relies on moving target defense (MTD), a novel and game-changing approach to cyber defense. MTD creates asymmetric uncertainty, providing the defender with a tactical advantage over the attacker. MTD techniques are designed to continuously change or shift a system’s attack surface, thus increasing cost and complexity for the threat actors. We show how the botnet detection and mitigation problem can be decomposed in three related and relatively simpler challenges, and how these challenges can be effectively tackled adopting an MTD approach, ultimately limiting the ability of a botnet to persist within a target system.