title: Inter-frame Video Forgery Detection Based on Block-Wise Brightness Variance Descriptor

abstract: Video forensics becomes more and more important than ever before. In this paper a new methodology based on Block-wise Brightness Variance Descriptor (BBVD) is proposed. It is capable of fast detecting video inter-frame forgery. Our proposed algorithm has been tested on a database consisting of 240 original and forged videos. The experiments have demonstrated that the precision rate is about 94.09 % in detecting the insertion forgery and the precision rate is 79.45 % in the forgery localization. Moreover, the time utilized for forgery detecting is shorter than the time used for video replay. On average the time of forgery detection is only about 73.4 % in video replay.


title: Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems

abstract: The widescale use of machine learning algorithms to drive decision-making has highlighted the critical importance of ensuring the interpretability of such models in order to engender trust in their output. The state-of-the-art recommendation systems use black-box latent factor models that provide no explanation of why a recommendation has been made, as they abstract their decision processes to a high-dimensional latent space which is beyond the direct comprehension of humans. We propose a novel approach for extracting explanations from latent factor recommendation systems by training association rules on the output of a matrix factorisation black-box model. By taking advantage of the interpretable structure of association rules, we demonstrate that predictive accuracy of the recommendation model can be maintained whilst yielding explanations with high fidelity to the black-box model on a unique industry dataset. Our approach mitigates the accuracy-interpretability trade-off whilst avoiding the need to sacrifice flexibility or use external data sources. We also contribute to the ill-defined problem of evaluating interpretability.


title: Software-Defined Software: A Perspective of Machine Learning-Based Software Production

abstract: As the Moore's Law is ending, and increasingly high demand of software development continues in the human society, we are facing two serious challenges in the computing field. First, the general-purpose computing ecosystem that has been developed for more than 50 years will have to be changed by including many diverse devices for various specialties in high performance. Second, human-based software development is not sustainable to respond the requests from all the fields in the society. We envision that we will enter a time of developing high quality software by machines, and we name this as Software-defined Software (SDS). In this paper, we will elaborate our vision, the goals and its roadmap.


title: Comparing growth in surface and cave morphs of the species Astyanax mexicanus: insights from scales

abstract: Background ::: Life in the darkness of caves is accompanied, throughout phyla, by striking phenotypic changes including the loss or severe reduction in eyes and pigmentation. On the other hand, cave animals have undergone constructive changes, thought to be adaptive, to survive in this extreme environment. The present study addresses the question of the evolution of growth in caves, taking advantage of the comparison between the river-dwelling and the cave-dwelling morphs of the Mexican tetra, Astyanax mexicanus. ::: ::: ::: Results ::: A sclerochronology approach was undertaken to document the growth of the species in these two very distinct habitats. Scales from 158 wild Astyanax mexicanus specimens were analyzed from three caves (Pachón, Tinaja and Subterráneo) and two rivers (Rio Gallinas and Arroyo Lagarto) in San Luis Potosi and Tamaulipas, Mexico. A 10-13% reduction in scales size was observed in the cave morphs compared to the surface morphs. Age could be reliably inferred from annual growth increments on the scales from the two morphs of the species. Further comparisons with growth curves in laboratory conditions, obtained using the von Bertalanffy growth model, were also performed. In the wild and in the laboratory, cavefish originating from the Pachón cave reached smaller sizes than surface fish from three different locations: Rio Gallinas and Arroyo Lagarto (wild sampling) and Texas (laboratory population), respectively. Wild Pachón cavefish also seemed to grow to smaller sizes than the two other wild cavefish populations studied, Tinaja and Subterráneo. Finally, growth in the laboratory was faster than in the wild, particularly in the two first years of life. ::: ::: ::: Conclusions ::: These data suggest that cavefish originating from the Pachón cave are subjected to an intrinsic limitation of their final size, which is at least in part independent from energy/food availability. This growth limitation may be an advantageous way of limiting energy expenditure and food needs in the cave environment. Moreover, growth regulation evolved differently in independently evolved cave populations. These results are discussed with regard to the sources of energy or general ecological conditions present in caves, and to the differences in behavior or feeding skills known in cavefish.


title: High Speed Networks Need Proactive Congestion Control

abstract: As datacenter speeds scale to 100 Gb/s and beyond, traditional congestion control algorithms like TCP and RCP converge slowly to steady sending rates, which leads to poorer and less predictable user performance. These reactive algorithms use congestion signals to perform gradient descent to approach ideal sending rates, causing poor convergence times. In this paper, we propose a proactive congestion control algorithm called PERC, which explicitly computes rates independently of congestion signals in a decentralized fashion. Inspired by message-passing algorithms with traction in other fields (e.g., modern Low Density Parity Check decoding algorithms), PERC improves convergence times by a factor of 7 compared to reactive explicit rate control protocols such as RCP. This fast convergence reduces tail flow completion time (FCT) significantly in high speed networks; for example, simulations of a realistic workloads in a 100 Gb/s network show that PERC achieves up to 4x lower 99th percentile FCT compared to RCP.


title: Deep Learning for Recommender Systems

abstract: Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex machine learning tasks such as computer vision, natural language processing and speech recognition. After its relatively slow uptake by the recommender systems community, deep learning for recommender systems became widely popular in 2016. We believe that a tutorial on the topic of deep learning will do its share to further popularize the topic. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the tutorial is to encourage the application of Deep Learning techniques in Recommender Systems, to further promote research in deep learning methods for Recommender Systems.


title: Malaria in Children

abstract: Malaria remains widespread throughout the planet and increasing global travel continues to lead to imported cases of malaria in travelers, including children. This article provides an overview of pediatric malaria, including its epidemiology, clinical features, diagnosis, treatment, and prevention in travelers.


title: Moral conviction: another contributor to attitude strength or something more?

abstract: Attitudes held with strong moral conviction (moral mandates) were predicted to have different interpersonal consequences than strong but nonmoral attitudes. After controlling for indices of attitude strength, the authors explored the unique effect of moral conviction on the degree that people preferred greater social (Studies 1 and 2) and physical (Study 3) distance from attitudinally dissimilar others and the effects of moral conviction on group interaction and decision making in attitudinally homogeneous versus heterogeneous groups (Study 4). Results supported the moral mandate hypothesis: Stronger moral conviction led to (a) greater preferred social and physical distance from attitudinally dissimilar others, (b) intolerance of attitudinally dissimilar others in both intimate (e.g., friend) and distant relationships (e.g., owner of a store one frequents), (c) lower levels of good will and cooperativeness in attitudinally heterogeneous groups, and (d) a greater inability to generate procedural solutions to resolve disagreements.


title: Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data

abstract: In this paper, we develop a methodology for forecasting key macroeconomic indicators, based on business survey data. We estimate a large set of models, using an autoregressive specification, with regressors selected from business and household survey data. Our methodology is based on the Bayesian averaging of classical estimates method. Additionally, we examine the impact of deterministic and stochastic seasonality of the business survey time series on the outcome of the forecasting process. We propose an intuitive procedure for incorporating both types of seasonality into the forecasting process. After estimating the specified models, we check the accuracy of the forecasts. Copyright Springer Science+Business Media New York 2014


title: Constant Current Fast Charging of Electric Vehicles via a DC Grid Using a Dual-Inverter Drive

abstract: Existing integrated chargers are configured to charge from single- or three-phase ac networks. With the rapid emergence of dc grids, there is growing interest in the development of high-efficiency low-cost integrated chargers interfaced with dc power outlets. This paper introduces a new integrated charger offering electric vehicle fast charging from emerging dc distribution networks. In absence of a dc grid, the charger can alternatively be fed from a simple uncontrolled rectifier. The proposed charger leverages the dual-inverter topology previously developed for high-speed drive applications. By connecting the charger inlet to the differential ends of the traction inverters, charging is enabled for a wide battery voltage range previously unattainable using an integrated charger based on the single traction drive. An 11-kW experimental setup demonstrates rapid charging using constant current control and energy balancing of dual storage media. To minimize the harmonic impact of the charger on the dc distribution network, a combination of complementary and interleaved switching methods is demonstrated.


title: SigMal: a static signal processing based malware triage

abstract: In this work, we propose SigMal, a fast and precise malware detection framework based on signal processing techniques. SigMal is designed to operate with systems that process large amounts of binary samples. It has been observed that many samples received by such systems are variants of previously-seen malware, and they retain some similarity at the binary level. Previous systems used this notion of malware similarity to detect new variants of previously-seen malware. SigMal improves the state-of-the-art by leveraging techniques borrowed from signal processing to extract noise-resistant similarity signatures from the samples. SigMal uses an efficient nearest-neighbor search technique, which is scalable to millions of samples. We evaluate SigMal on 1.2 million recent samples, both packed and unpacked, observed over a duration of three months. In addition, we also used a constant dataset of known benign executables. Our results show that SigMal can classify 50% of the recent incoming samples with above 99% precision. We also show that SigMal could have detected, on average, 70 malware samples per day before any antivirus vendor detected them.


title: Scrum of scrums solution for large size teams using scrum methodology

abstract: Scrum is a structured framework to support complex product development. However, Scrum methodology faces a challenge of managing large teams. To address this challenge, in this paper we propose a solution called Scrum of Scrums. In Scrum of Scrums, we divide the Scrum team into teams of the right size, and then organize them hierarchically into a Scrum of Scrums. The main goals of the proposed solution are to optimize communication between teams in Scrum of Scrums; to make the system work after integration of all parts; to reduce the dependencies between the parts of system; and to prevent the duplication of parts in the system.


title: Speech enhancement using β-divergence based NMF with update bases

abstract: In this paper, combination of statistical model based approach and Non-negative matrix factorization (NMF) based approach with on-line update of speech and noise bases for speech enhancement is proposed. Template based approaches are more robust and performs better to non-stationary noises compared to the statistical model based approaches. However, the template based approach is dependent on a priori information. Combining the approaches avoids the drawbacks of both. To improve the performance further, speech and noise bases are adapted simultaneously in NMF approach with the help of the estimated speech presence probability (SPP). The proposed approach yields better results than statistical based approach, NMF based approach and also combination of both approaches without on-line update in non-stationary noise environments.


title: Automatically characterizing places with opportunistic crowdsensing using smartphones

abstract: Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present CrowdSense@Place (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place "hints" mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places into a variety of categories with an overall accuracy of 69%, outperforming currently available alternative solutions.


title: Rapid and Reliable Adaptation of Video Game AI

abstract: Current approaches to adaptive game AI typically require numerous trials to learn effective behavior (i.e., game adaptation is not rapid). In addition, game developers are concerned that applying adaptive game AI may result in uncontrollable and unpredictable behavior (i.e., game adaptation is not reliable). These characteristics hamper the incorporation of adaptive game AI in commercially available video games. In this paper, we discuss an alternative to these current approaches. Our alternative approach to adaptive game AI has as its goal adapting rapidly and reliably to game circumstances. Our approach can be classified in the area of case-based adaptive game AI. In the approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource-intensive learning) to evoke effective behavior in a controlled manner in online play. We performed experiments that test case-based adaptive game AI on three different maps in a commercial real-time strategy (RTS) game. From our results, we may conclude that case-based adaptive game AI provides a strong basis for effectively adapting game AI in video games.


title: Introduction: Moral Emotions

abstract: The claim that the psychological study of morality underwent a revolution at the beginning of the twenty-first century and that this revolution was an affective one is now commonplace: emotions, rather than reflection and reasoning from explicit moral principles, were suddenly supposed to play the prominent role in moral thought and behaviour (Haidt 2001, 2007). Though supporters of this revolution might crudely underestimate the importance other fields—such as philosophy—had already allocated to emotions in moral thinking and behaviour, there is no denying that this change of attitudes within psychology has had such an impact that it has affected other disciplines such as philosophy, neuroscience and sociology and has led researchers throughout these fields to pay more attention to the role emotions play in everyday morality. One consequence of this renewed interest in the interplay between emotions and morality was a sudden increase in the use of the expression ‘‘moral emotions’’ (see for example: De Sousa 2001; Haidt 2003; Tangney et al. 2007; Mulligan 2008). Though this expression is now widespread—and this surely reflects the general realization that emotions matter for morality—, it is still debated what counts as a ‘‘moral emotion’’ and what emotions should be considered ‘‘moral’’. Haidt (2003) tentatively defined moral emotions as ‘‘those emotions that are linked to the interests or welfare either of society as a whole or at least of persons other than the judge or agent.’’(p. 853) However, such a definition leaves unexplained the crucial problem of the exact ‘‘link’’ between emotions and morality and arbitrarily restricts the field of morality (excluding, for example, possible moral duties towards oneself). So, what kinds of ‘‘link’’ are there between emotions and morality, and which ones are relevant for a satisfactory characterisation of moral emotions? It is now widely accepted that emotions present their object (their ‘‘intentional object’’) in a certain evaluative way (e.g. De Sousa 1987 and Deonna and Teroni 2012 for a thorough overview of what this claim may mean). Thus, fear presents a certain object as dangerous. The value that an emotion presents its object as instantiating is this emotion’s ‘‘formal object’’. Thus, some emotions might be said to be moral in virtue of their ‘‘formal object’’, that is: because they present their object as instantiating a certain moral value (e.g. Mulligan 2008). For example, some consider that guilt presents the person who feels it as responsible for a transgression or wrongdoing, and others consider indignation as a particular brand of anger that presents a certain act as being unjust. In this first sense, an & Florian Cova florian.cova@gmail.com


title: Building Detection from Satellite Imagery Using a Composite Loss Function

abstract: In this paper, we present a LinkNet-based architecture with SE-ResNeXt-50 encoder and a novel training strategy that strongly relies on image preprocessing and incorporating distorted network outputs. The architecture combines a pre-trained convolutional encoder and a symmetric expanding path that enables precise localization. We show that such a network can be trained on plain RGB images with a composite loss function and achieves competitive results on the DeepGlobe challenge on building extraction from satellite images


title: Endmember extraction algorithms from hyperspectral images

abstract: During the last years, several high-resolution sensors have been developed for hyperspectral remote sensing applications. ::: Some of these sensors are already available on space-borne devices. Space-borne sensors are currently ::: acquiring a continual stream of hyperspectral data, and new efficient unsupervised algorithms are required to ::: analyze the great amount of data produced by these instruments. The identification of image endmembers is a ::: crucial task in hyperspectral data exploitation. Once the individual endmembers have been identified, several ::: methods can be used to map their spatial distribution, associations and abundances. This paper reviews the Pixel ::: Purity Index (PPI), N-FINDR and Automatic Morphological Endmember Extraction (AMEE) algorithms developed ::: to accomplish the task of finding appropriate image endmembers by applying them to real hyperspectral ::: data. In order to compare the performance of these methods a metric based on the Root Mean Square Error ::: (RMSE) between the estimated and reference abundance maps is used.


title: Automatic Arabic Text Classification

abstract: Automated document classification is an important text mining task especially with the rapid growth of the number of online documents present in Arabic language. Text classification aims to automatically assign the text to a predefined category based on linguistic features. Such a process has different useful applications including, but not restricted to, e-mail spam detection, web page content filtering, and automatic message routing. This paper presents the results of experiments on document classification achieved on seven different Arabic corpora using statistical methodology. The performance of two popular classification algorithms in classifying the aforementioned corpora has been evaluated.


title: CycleGAN, a Master of Steganography

abstract: CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to "hide" information about a source image into the images it generates in a nearly imperceptible, high-frequency signal. This trick ensures that the generator can recover the original sample and thus satisfy the cyclic consistency requirement, while the generated image remains realistic. We connect this phenomenon with adversarial attacks by viewing CycleGAN's training procedure as training a generator of adversarial examples and demonstrate that the cyclic consistency loss causes CycleGAN to be especially vulnerable to adversarial attacks.


title: Permanent-Magnet Temperature Distribution Estimation in Permanent-Magnet Synchronous Machines Using Back Electromotive Force Harmonics

abstract: Permanent magnet (PM) temperature measurement/estimation in permanent-magnet synchronous machines (PMSMs) is convenient for both torque control and monitoring purposes. Most of the existing methods provide an averaged/punctual temperature. However, PM temperature distribution is not uniform, which can have unexpected adverse effects such as reduced accuracy estimating the torque produced by the machine or local demagnetization e.g., due to hot spots. PM temperature distribution can be measured or estimated. Inner and outer PM surfaces are not visible once the machine is assembled. Therefore, measurement of the PM temperature distribution cannot be realized by visual means. An array of temperature sensors attached to the PM can be used in this case. While this is technically feasible, it is unaffordable in commercial products due to cost and robustness issues. Alternatively, the PM temperature distribution can be estimated. This paper analyzes the use of back electromotive force (BEMF) harmonics to estimate the differential temperature in the PM of a PMSM, i.e., the temperature difference between the hottest and coldest parts of the magnets. Combined with average PM temperature estimation methods, this information can be used to estimate the PM temperature spatial distribution. The proposed method has been developed on an empirical basis.


title: Modeling, Identification, and Control of Tendon-Based Actuation Systems

abstract: In this paper, we deal with several aspects related to the control of tendon-based actuation systems for robotic devices. In particular, the problems that are considered in this paper are related to the modeling, identification, and control of tendons sliding on curved pathways, subject to friction and viscoelastic effects. Tendons made in polymeric materials are considered, and therefore, hysteresis in the transmission system characteristic must be taken into account as an additional nonlinear effect because of the plasticity and creep phenomena typical of these materials. With the aim of reproducing these behaviors, a viscoelastic model is used to model the tendon compliance. Particular attention has been given to the friction effects arising from the interaction between the tendon pathway and the tendon itself. This phenomenon has been characterized by means of a LuGre-like dynamic friction model to consider the effects that cannot be reproduced by employing a static friction model. A specific setup able to measure the tendon's tension in different points along its path has been designed in order to verify the tension distribution and identify the proper parameters. Finally, a simple control strategy for the compensation of these nonlinear effects and the control of the force that is applied by the tendon to the load is proposed and experimentally verified.


title: Learning deep representation from big and heterogeneous data for traffic accident inference

abstract: With the rapid development of urbanization and public transportation system, the number of traffic accidents have significantly increased globally over the past decades and become a big problem for human society. Facing these possible and unexpected traffic accidents, understanding what causes traffic accident and early alarms for some possible ones will play a critical role on planning effective traffic management. However, due to the lack of supported sensing data, research is very limited on the field of updating traffic accident risk in real-time. Therefore, in this paper, we collect big and heterogeneous data (7 months traffic accident data and 1.6 million users' GPS records) to understand how human mobility will affect traffic accident risk. By mining these data, we develop a deep model of Stack denoise Autoencoder to learn hierarchical feature representation of human mobility. And these features are used for efficient prediction of traffic accident risk level. Once the model has been trained, our model can simulate corresponding traffic accident risk map with given real-time input of human mobility. The experimental results demonstrate the efficiency of our model and suggest that traffic accident risk can be significantly more predictable through human mobility.


title: NIPS 2016 Tutorial: Generative Adversarial Networks

abstract: This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises.


title: Communities, modules and large-scale structure in networks

abstract: Networks have proved to be useful representations of complex systems. Within these networks, there are typically a number of subsystems defined by only a subset of nodes and edges. Detecting these structures often provides important information about the organization and functioning of the overall network. Here, progress towards quantifying medium- and large-scale structures within complex networks is reviewed.


title: Real-Time Anomalous Behavior Detection and Localization in Crowded Scenes

abstract: In this paper, we propose an accurate and real-time anomaly detection and localization in crowded scenes, and two descriptors for representing anomalous behavior in video are proposed. We consider a video as being a set of cubic patches. Based on the low likelihood of an anomaly occurrence, and the redundancy of structures in normal patches in videos, two (global and local) views are considered for modeling the video. Our algorithm has two components, for (1) representing the patches using local and global descriptors, and for (2) modeling the training patches using a new representation. We have two Gaussian models for all training patches respect to global and local descriptors. The local and global features are based on structure similarity between adjacent patches and the features that are learned in an unsupervised way. We propose a fusion strategy to combine the two descriptors as the output of our system. Experimental results show that our algorithm performs like a state-of-the-art method on several standard datasets, but even is more time-efficient.


title: Efficient Time-Domain Image Formation with Precise Topography Accommodation for General Bistatic SAR Configurations

abstract: Due to the lack of an appropriate symmetry in the acquisition geometry, general bistatic synthetic aperture radar (SAR) cannot benefit from the two main properties of low-to-moderate resolution monostatic SAR: azimuth-invariance and topography-insensitivity. The precise accommodation of azimuth-variance and topography is a real challenge for efficent image formation algorithms working in the Fourier domain, but can be quite naturally handled by time-domain approaches. We present an efficient and practical implementation of a generalised bistatic SAR image formation algorithm with an accurate accommodation of these two effects. The algorithm has a common structure with the monostatic fast-factorised backprojection (FFBP), and is therefore based on subaperture processing. The images computed over the different subapertures are displayed in an advantageous elliptical coordinate system capable of incorporating the topographic information of the imaged scene in an analogous manner as topography-dependent monostatic SAR algorithms do. Analytical expressions for the Nyquist requirements using this coordinate system are derived. The overall discussion includes practical implementation hints and a realistic computational burden estimation. The algorithm is tested with both simulated and actual bistatic SAR data. The actual data correspond to the spaceborne-airborne experiment between TerraSAR-X and F-SAR performed in 2007 and to the DLR-ONERA airborne experiment carried out in 2003. The presented approach proves its suitability for the precise SAR focussing of the data acquired in general bistatic configurations.


Oral Complications of Nonsurgical Cancer Therapies.


title: Lessons learned from building model-driven development tools

abstract: Tools to support modelling in system and software engineering are widespread, and have reached a degree of maturity where their use and availability are accepted. Tools to support model-driven development (MDD)--where models are manipulated and managed throughout the system/software engineering lifecycle--have, over the last 10 years, seen much research and development attention. Over the last 10 years, we have had significant experience in the design, development and deployment of MDD tools in practical settings. In this paper, we distill some of the important lessons we have learned in developing and deploying two MDD tools: Epsilon and VIATRA. In doing so, we aim to identify some of the key principles of developing successful MDD tools, as well as some hints of the pitfalls and risks.


title: User-Service Rating Prediction by Exploring Social Users' Rating Behaviors

abstract: With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking. In this paper, we propose a user-service rating prediction approach by exploring social users' rating behaviors. In order to predict user-service ratings, we focus on users' rating behaviors. In our opinion, the rating behavior in recommender system could be embodied in these aspects: 1) when user rated the item, 2) what the rating is, 3) what the item is, 4) what the user interest that we could dig from his/her rating records is, and 5) how the user's rating behavior diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users' daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users' rating behaviors. In the proposed user-service rating prediction approach, we fuse four factors—user personal interest (related to user and the item's topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users' rating behavior habits), and interpersonal rating behavior diffusion (related to users' behavior diffusions)—into a unified matrix-factorized framework. We conduct a series of experiments in the Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach.


title: Signal detection effects on deep neural networks utilizing raw IQ for modulation classification

abstract: Recently, automatic modulation classification techniques using convolutional neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making classification decisions directly on the raw IQ samples allows for reduced system complexity and removes the need for expertly crafted transformations and feature extractions. In practice, RF environments are typically very dense, and a receiver must first detect and isolate each signal of interest before classification can be performed. The errors introduced by this detection and isolation process will affect the accuracy of convolutional neural networks making automatic modulation classification decisions using only raw IQ samples. To quantify this impact, a representative convolutional neural network designed to distinguish between 8 modulation classes (2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM, and 64QAM), over a generalized parameter set, is analyzed. The classification accuracy of this neural network is investigated as a function of errors in carrier frequency estimation and errors in sample rate estimation. The importance of defining upper limits on frequency and sample rate estimation errors in a detector is highlighted, and the negative effects of over-estimating or under-estimating these limits is explored.


title: Comparison of text mining tools

abstract: Most of the data is in the form of text these days. While databases store only structured data, most of the data is unstructured like text documents, web pages, emails etc. Text mining is what is required if useful information needs to be extracted from tons of text. But where to begin, what are the popular tools, which techniques are used, what are the features. Beginning is always the toughest, so this paper tries to explore the tools available for text mining to help new researchers and practitioners in the field of text mining.


title: The Complete Business Process Handbook: Body of Knowledge from Process Modeling to BPM, Volume I

abstract: This is the most comprehensive body of knowledge on business processes. Written as a practical guide for Executives, Practitioners, Managers and Students by the authorities that have shaped the way we think and work with process today. This book is one of tree volumes, representing the most comprehensive body of knowledge publised on business process. Second volume uniquely bridging theory with how BPM is applied today with the most extensive information on extended BPM. The third volume explores award winning real-life examples of leading business process practices and how it can be replaced to your advantage. Covering what Practitioners, Managers, Executives and Students need to know about Key Features Learn what Business Process is and how to get started Comprehensive historical process evolution In-depth look at the Process Anatomy, Semantics and Ontology Find out how to link Strategy to Operation with value driven BPM Uncover how to establish a way of Thinking, Working, Modelling and Implementation Explore comprehensive Frameworks, Methods and Approaches How to build BPM competencies and establish a Center of Excellence Discover how to apply Social BPM, Sustainable and Evidence based BPM Learn how Value & Performance Measurement and Management Learn how to roll-out and deploy process Explore how to enable Process Owners, Roles and Knowledge Workers Discover how to Process and Application Modelling Uncover Process Lifecycle, Maturity, Alignment and Continuous Improvement Practical continuous improvement with the way of Governance Future BPM trends that will affect business Explore the BPM Body of Knowledge


title: Adaptive video highlights for wired and wireless platforms

abstract: Many new applications such as pervasive healthcare, pervasive infotainment and pervasive training, require relevant video segments (i.e. video highlights) to be delivered to a variety of devices operating under both wired and wireless platforms. This brings in a requirement of (a) way to describe the content as well as the environment such as device and network in which it is to be delivered, and (b) a method to adapt the content for the environment and the application. Maximizing user viewing experiences and minimizing resources required is the main aim of the adaptation process. This involves identifying relevant segments within a video and scaling down the video in different dimensions so that it can be delivered to the end users any time any where on any device. Towards this we have developed a prototype system for delivery of adaptive video (DAVE). In this paper, we explain how DAVE delivers video highlights in both wired and wireless platforms. Our focus in this paper is on content and environment description and content adaptation aspects of DAVE.


title: A Transient-enhan With

abstract: This paper presents a modified folded cascode error amplifier of low dropout (LDO) regulator and a slew-rate enhancement circuit to minimize compensation capacitance and improve transient response. The proposed error amplifier eliminates the tradeoffs between small and large slew-rate that is imposed by the tail-current in conventional error amplifier design. The design is implemented in a standard UMC 0.18 im standard CMOS process. Simulation results show that, the LDO regulator consumes a quiescent current of 49.64μA only with a total power consumption of .079mW. It regulates the output voltage at 1.4v from 1.6-1.8v supply. The overshoot/undershoot in the output voltage under the extreme load transients are 220.7mV/280.26mV for load current range of 0 to 100mA. The line regulation is 1.244mV/V at 1.8V, load regulation is 40.6mV/A. This circuit finds its beneficial behavior for chip-level power management units requiring high-area efficiency as compensation capacitors are avoided


title: Bayesian Optimization of Text Representations

abstract: When applying machine learning to problems in NLP, there are many choices to make about how to represent input texts. These choices can have a big effect on performance, but they are often uninteresting to researchers or practitioners who simply need a module that performs well. We propose an approach to optimizing over this space of choices, formulating the problem as global optimization. We apply a sequential model-based optimization technique and show that our method makes standard linear models competitive with more sophisticated, expensive state-of-the-art methods based on latent variable models or neural networks on various topic classification and sentiment analysis problems. Our approach is a first step towards black-box NLP systems that work with raw text and do not require manual tuning.


title: Swimming Stroke Kinematic Analysis with BSN

abstract: The recent maturity of body sensor networks has enabled a wide range of applications in sports, well-being and healthcare. In this paper, we hypothesise that a single unobtrusive head-worn inertial sensor can be used to infer certain biomotion details of specific swimming techniques. The sensor, weighing only seven grams is mounted on the swimmer's goggles, limiting the disturbance to a minimum. Features extracted from the recorded acceleration such as the pitch and roll angles allow to recognise the type of stroke, as well as basic biomotion indices. The system proposed represents a non-intrusive, practical deployment of wearable sensors for swimming performance monitoring.


title: Recent Advances in Techniques for Hyperspectral Image Processing

abstract: Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific appli- cations, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of- the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms.


title: Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs

abstract: Unmanned Aerial Vehicle (UAV) imagery allows for a new way of obtaining geographic information. In this work, a Geographical Information System (GIS) open source application was developed in QGIS software that estimates several parameters and metrics on tree crown through image analysis techniques (image segmentation and image classification) and fractal analysis. The metrics that have been estimated were: area, perimeter, number of trees, distance between trees, and a missing tree check. This methodology was tested on three different plantations: olive, eucalyptus, and vineyard. The application developed is free, open source and takes advantage of QGIS integration with external software. Several tools available from Orfeo Toolbox and Geographic Resources Analysis Support System (GRASS) GIS were employed to generate a classified raster image which allows calculating the metrics referred before. The application was developed in the Python 2.7 language. Also, some functions, modules, and classes from the QGIS Application Programming Interface (API) and PyQt4 API were used. This new plugin is a valuable tool, which allowed for automatizing several parameters and metrics on tree crown using GIS analysis tools, while considering data acquired by UAV.


title: Character transfer: Example-based individuality retargeting for facial animations

abstract: A key disadvantage of blendshape animation is the labor-intensive task of sculpting blendshapes with individual expressions for each character. In this paper, we propose a novel system ”Character Transfer”, that automatically sculpts blendshapes with individual expressions by extracting them from training examples; this extraction creates a mapping that drives the sculpting process. Comparing our approach with the naive method of transferring facial expressions from other characters, Character Transfer effectively sculpted blendshapes without the need to create such unnecessary blendshapes for other characters. Character Transfer is applicable even the training examples are limited to only a few number by using region segmentations of the face and the blending of the mappings.


title: The relationship between energy, phase charge, impedance, and perceived sensation in electrotactile stimulation

abstract: Electrotactile stimulation is a common method of sensory substitution and haptic feedback. One problem with this method has been the large variability in perceived sensation that derives from changes in the impedance of the electrode-skin interface. One way to reduce this variability is to modulate stimulation parameters (current amplitude and pulse duration) in response to impedance changes, which are reflected in the time domain by changes in measured peak resistance, Rp. To work well, this approach requires knowing precisely the relationship between stimulation parameters, peak resistance, and perceived sensation. In this paper, experimental results show that at a constant level of perceived sensation there are linear relationships between Rp and both peak pulse energy, Ep, and phase charge, Q, from which stimulation parameters are easily computed. These linear relationships held across different subjects, sessions, magnitudes of sensation, stimulation locations, and electrode sizes. The average R2 values for these linear relationships were 0.957 for Ep vs. Rp and 0.960 for Q vs. Rp, indicating a nearly perfect fit.


title: Flexible Depth of Field Photography

abstract: The range of scene depths that appear focused in an image is known as the depth of field (DOF). Conventional cameras are limited by a fundamental trade-off between depth of field and signal-to-noise ratio (SNR). For a dark scene, the aperture of the lens must be opened up to maintain SNR, which causes the DOF to reduce. Also, today's cameras have DOFs that correspond to a single slab that is perpendicular to the optical axis. In this paper, we present an imaging system that enables one to control the DOF in new and powerful ways. Our approach is to vary the position and/or orientation of the image detector during the integration time of a single photograph. Even when the detector motion is very small (tens of microns), a large range of scene depths (several meters) is captured, both in and out of focus. Our prototype camera uses a micro-actuator to translate the detector along the optical axis during image integration. Using this device, we demonstrate four applications of flexible DOF. First, we describe extended DOF where a large depth range is captured with a very wide aperture (low noise) but with nearly depth-independent defocus blur. Deconvolving a captured image with a single blur kernel gives an image with extended DOF and high SNR. Next, we show the capture of images with discontinuous DOFs. For instance, near and far objects can be imaged with sharpness, while objects in between are severely blurred. Third, we show that our camera can capture images with tilted DOFs (Scheimpflug imaging) without tilting the image detector. Finally, we demonstrate how our camera can be used to realize nonplanar DOFs. We believe flexible DOF imaging can open a new creative dimension in photography and lead to new capabilities in scientific imaging, vision, and graphics.


abstract: A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. A general formulation for editing graphs is used to derive a graph edit distance that is proven to be a metric, provided the cost function for individual edit operations is a metric. Then, a binary linear program is developed for computing this graph edit distance, and polynomial time methods for determining upper and lower bounds on the solution of the binary program are derived by applying solution methods for standard linear programming and the assignment problem. A recognition problem of comparing a sample input graph to a database of known prototype graphs in the context of a chemical information system is presented as an application of the new method. The costs associated with various edit operations are chosen by using a minimum normalized variance criterion applied to pairwise distances between nearest neighbors in the database of prototypes. The new metric is shown to perform quite well in comparison to existing metrics when applied to a database of chemical graphs


title: Pancultural self-enhancement reloaded: a meta-analytic reply to Heine (2005).

abstract: C. Sedikides, L. Gaertner, and Y. Toguchi (2003; see record 2002-08440-007) reported findings favoring the universality of self-enhancement. S. J. Heine (2005; see record 2005-13803-005) challenged the authors' research on evidential and logical grounds. In response, the authors carried out 2 meta-analytic investigations. The results backed the C. Sedikides et al. (2003) theory and findings. Both Westerners and Easterners self-enhanced tactically. Westerners self-enhanced on attributes relevant to the cultural ideal of individualism, whereas Easterners self-enhanced on attributes relevant to the cultural ideal of collectivism (in both cases, because of the personal importance of the ideal). Self-enhancement motivation is universal, although its manifestations are strategically sensitive to cultural context. The authors respond to other aspects of Heine's critique by discussing why researchers should empirically validate the comparison dimension (individualistic vs. collectivistic) and defending why the better-than-average effect is a valid measure of self-enhancement.


title: Mycoplasma in synovial fluid from the patients with rheumatoid arthritis.

abstract: Mycoplasma colonies were found on culture of synovial fluid in 14 of 18 cases of rheumatoid arthritis. It was possible to demonstrate the colonies in primary culture medium only by the method of hot water fixation and Giemsa staining. The organisms grown on solid medium were also observed electron-microscopically.


title: A collaborative filtering similarity measure based on singularities

abstract: Recommender systems play an important role in reducing the negative impact of information overload on those websites where users have the possibility of voting for their preferences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to calculate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed.


title: Deep Identity-aware Transfer of Facial Attributes

abstract: This paper presents a Deep convolutional network model for Identity-Aware Transfer (DIAT) of facial attributes. Given the source input image and the reference attribute, DIAT aims to generate a facial image that owns the reference attribute as well as keeps the same or similar identity to the input image. In general, our model consists of a mask network and an attribute transform network which work in synergy to generate a photo-realistic facial image with the reference attribute. Considering that the reference attribute may be only related to some parts of the image, the mask network is introduced to avoid the incorrect editing on attribute irrelevant region. Then the estimated mask is adopted to combine the input and transformed image for producing the transfer result. For joint training of transform network and mask network, we incorporate the adversarial attribute loss, identity-aware adaptive perceptual loss, and VGG-FACE based identity loss. Furthermore, a denoising network is presented to serve for perceptual regularization to suppress the artifacts in transfer result, while an attribute ratio regularization is introduced to constrain the size of attribute relevant region. Our DIAT can provide a unified solution for several representative facial attribute transfer tasks, e.g., expression transfer, accessory removal, age progression, and gender transfer, and can be extended for other face enhancement tasks such as face hallucination. The experimental results validate the effectiveness of the proposed method. Even for the identity-related attribute (e.g., gender), our DIAT can obtain visually impressive results by changing the attribute while retaining most identity-aware features.


title: Delineation of 2q32q35 Deletion Phenotypes: Two Apparent “Proximal” and “Distal” Syndromes

abstract: We report on three patients with interstitial deletions of the long arm of chromosome 2 involving bands 2q32.1–q35. They presented with wide-ranging phenotypic variation including facial dysmorphisms, cleft palate, learning difficulties, behavioural issues and severe heart defects. Microarray analysis confirmed an 8.6 Mb deletion in patients 1 and 2 and a 24.7 Mb deletion in patient 3. We discuss the genes involved in the deleted regions including MYO1B, GLS, FRZB, SATB2, and CPS1 and compare the phenotype with those reported in the literature. Taken together, these data suggest that there is a spectrum of disease severity such that patients with deletions encompassing the region of 2q32.1q32.2, which includes the FRZB gene, show an apparently milder phenotype compared to those that lie further distal in 2q32.3q35 that encompasses the SATB2 gene.


Emergent Behaviors in Mixed-Autonomy Traffic.


title: The W5 Framework for Computation Offloading in the Internet of Things

abstract: The Internet of Things (IoT) aims at providing things in the world with Internet connectivity. The goal is to facilitate communication among such IoT devices in addition to accessing and/or controlling them as needed. IoT devices are typically smart devices with constrained resources. Accordingly, computation offloading from such clients to more powerful servers is anticipated. Mobile cloud computing was first proposed for computation offloading from mobile devices, especially smartphones, to the cloud. Nevertheless, this scenario proved to be unsuitable for latency-sensitive applications such as multimedia applications due to the relatively long distance between the mobile clients and the cloud data centers. Accordingly, cloudlets and mobile clouds, which are ideally located at the edge of the Internet within one hop from mobile clients have been proposed as alternative solutions. Recently, other forms of edge computing such as micro and nano data centers, mini and micro clouds, fog computing, and mobile edge computing have also been proposed for both mobile and IoT devices. In this paper, we formulate offloading in the IoT as a decision problem concerned with what , where , who , when/why , and how to offload. Accordingly, we present the W5 framework, which takes those factors into consideration, and so can be used as a reference model to guide researchers in this area. As an example, we developed a client-based agent for three computation offloading scenarios among constrained smartphones in the IoT. Through detailed discussion and an extensive set of experiments, we attempt to answer the above wh-questions.


Superpower Your Browser with LibX and Zotero


title: A Topological Descriptor of Acoustic Images for Navigation and Mapping

abstract: The use of robots in underwater exploration is increasing in the last years. The automation of the monitoring, inspection and underwater maintenance tasks require a good mapping and localization system. One of the key issues of these systems is how to summarize the sensory information in order to recognize an area that has already been visited. This paper proposes a description method of acoustic images acquired by a forward looking sonar (FLS) using a graph of Gaussian probability density function. This structure represents both shape and topological relation. Furthermore, we also presented a method to match the descriptors in a efficient way. We evaluated the method in a real dataset acquired by a underwater vehicle performing autonomous navigation and mapping tasks in a marine environment.


title: Towards a Framework to Detect Multi-stage Advanced Persistent Threats Attacks

abstract: Detecting and defending against Multi-Stage Advanced Persistent Threats (APT) Attacks is a challenge for mechanisms that are static in its nature and are based on blacklisting and malware signature techniques. Blacklists and malware signatures are designed to detect known attacks. But multi-stage attacks are dynamic, conducted in parallel and use several attack paths and can be conducted in multi-year campaigns, in order to reach the desired effect. In this paper the design principles of a framework are presented that model Multi-Stage Attacks in a way that both describes the attack methods as well as the anticipated effects of attacks. The foundation to model behaviors is by the combination of the Intrusion Kill-Chain attack model and defense patterns (i.e. a hypothesis based approach of known patterns). The implementation of the framework is made by using Apache Hadoop with a logic layer that supports the evaluation of a hypothesis.


title: A video-based traffic violation detection system

abstract: Traffic violation detection systems are effective tools to help traffic administration to monitor the traffic condition. It can detect traffic violations, such as running red lights, speeding, and vehicle retrogress in real time. In this paper, we propose an improved background-updating algorithm by using wavelet transform on dynamic background, and then track moving vehicles by feature-based tracking method. A complete traffic violation detection system is realized in C++ with OpenCV. Keywords—traffic violation detection, vehicle tracking, red running.


Learning Algorithms from Data


title: Stealthy Denial of Service Strategy in Cloud Computing

abstract: The success of the cloud computing paradigm is due to its on-demand, self-service, and pay-by-use nature. According to this paradigm, the effects of Denial of Service (DoS) attacks involve not only the quality of the delivered service, but also the service maintenance costs in terms of resource consumption. Specifically, the longer the detection delay is, the higher the costs to be incurred. Therefore, a particular attention has to be paid for stealthy DoS attacks. They aim at minimizing their visibility, and at the same time, they can be as harmful as the brute-force attacks. They are sophisticated attacks tailored to leverage the worst-case performance of the target system through specific periodic, pulsing, and low-rate traffic patterns. In this paper, we propose a strategy to orchestrate stealthy attack patterns, which exhibit a slowly-increasing-intensity trend designed to inflict the maximum financial cost to the cloud customer, while respecting the job size and the service arrival rate imposed by the detection mechanisms. We describe both how to apply the proposed strategy, and its effects on the target system deployed in the cloud.


DecaWave : Exploring State of the Art Commercial Localization


title: Customer Satisfaction At Low Cost Airlines: A Case Study Of Jetstar Pacific Airlines (JPA)

abstract: The appearance of LCA model in recent decades has rapidly penetrated into airline markets. Passengers have more options for their travel. Many people believe that low fares mean low quality. However, some of them argue that LCA can deliver good quality as FCA does. LCA providers always find ways to prove to their customers that it offers low prices but not low quality. Many studies have been explored on the subjects of the service quality, cost, and customer satisfaction in the LCA around the world but still rarely any related researches is in Vietnamese. This paper examines the influence of factors on customer satisfaction and identifies the satisfaction levels with service that customers have acquired from the low-cost airline industry in Vietnam, with the case study of JPA that is the first LCA in Vietnam. The examination is based on the SERVPERF model includes Reliability, Responsiveness, Assurance, Empathy, Tangibility dimensions, and the Price dimension which is added afterwards. The hypotheses of six dimensions have been assumed to have positive impacts on customer satisfaction. Those hypotheses are tested to determine the correlation between initial dimensions and identify the real representative factors affecting customer satisfaction with services that JPA has supplied. In addition, the paper determines the scores of corresponding to the representative factors that are evaluated by its customers. Through those findings, the management has an overview and insight of performance that JPA executes to adjust the behaviors and to give the most appropriate direction. Furthermore, the identified factors are also evaluated to determine effected coefficients that have impact on customer satisfaction. Based on the effected coefficients the recommendations for improving those factors are given. The survey and analysis result proves that the Behavior – Performance factor has the strongest influence on customer satisfaction; however JPA’s passengers are not very pleased about JPA’s performance on this factor, therefore JPA should focus on it at the highest priority for improvement. In the context of the current economic difficulties, Price - Convenience is also considered as the second factor which has relatively impressive impact on passengers’ satisfaction; JPA is regarded pretty good at this aspect in customer assessment, so that JPA needs to promote and continue to improve in order to keep a competitive advantage. Besides, the analysis also indicates that the Tangibility – Commitment factor has little affect on customer satisfaction, however followed by the two above factors, the JPA should improve this element.


Numerical methods for nondestructive identification of defects


title: 2 Portfolio Optimization with Mental Accounts

abstract: We integrate appealing features of Markowitz’s mean-variance portfolio theory (MVT) and Shefrin and Statman’s behavioral portfolio theory (BPT) into a new mental accounting (MA) framework. Features of the MA framework include an MA structure of portfolios, a definition of risk as the probability of failing to reach the threshold level in each mental account, and attitudes toward risk that vary by account. We demonstrate a mathematical equivalence between MVT, MA, and risk management using value at risk (VaR). The aggregate allocation across MA subportfolios is mean-variance efficient with short selling. Short-selling constraints on mental accounts impose very minor reductions in certainty equivalents, only if binding for the aggregate portfolio, offsetting utility losses from errors in specifying risk-aversion coefficients in MVT applications. These generalizations of MVT and BPT via a unified MA framework result in a fruitful connection between investor consumption goals and portfolio production.


title: Quantum clustering algorithms

abstract: By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k-medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.


title: Wind-Aware Emergency Landing Assistant Based on Dubins Curves

abstract: A total engine failure poses a major threat to passengers as well as the aircraft and requires a fast decision by the pilot. We develop an assistant system to support the pilot in this decision process. An aircraft is able to glide a certain distance without thrust power by converting the potential energy of the altitude into distance. The objective of our work is to calculate an approach route which allows the aircraft to reach a suitable landing field at an appropriate altitude. This is a non-trivial problem because of many free parameters like wind direction and wind velocity. Our solution computes an approach route with two tangents and two co-rotating circular segments. For this purpose, valid approach routes can be calculated for many general cases. The method has a constant complexity and can dispense with iterative approaches. The route is calculated entirely in the wind frame which avoids complex calculations with trochoids. The central idea is to take the wind into account by moving the target towards the wind direction.


title: We the Media: Grassroots Journalism by the People, for the People

abstract: "We the Media, has become something of a bible for those who believe the online medium will change journalism for the better." - "Financial Times". Big Media has lost its monopoly on the news, thanks to the Internet. Now that it's possible to publish in real time to a worldwide audience, a new breed of grassroots journalists are taking the news into their own hands. Armed with laptops, cell phones, and digital cameras, these readers-turned-reporters are transforming the news from a lecture into a conversation. In "We the Media", nationally acclaimed newspaper columnist and blogger Dan Gillmor tells the story of this emerging phenomenon and sheds light on this deep shift in how we make - and consume - the news. Gillmor shows how anyone can produce the news, using personal blogs, Internet chat groups, email, and a host of other tools. He sends a wake-up call to newsmakers - politicians, business executives, celebrities - and the marketers and PR flacks who promote them. He explains how to successfully play by the rules of this new era and shift from "control" to "engagement." And, he makes a strong case to his fell journalists that, in the face of a plethora of Internet-fueled news vehicles, they must change or become irrelevant. Journalism in the 21st century will be fundamentally different from the Big Media oligarchy that prevails today. "We the Media" casts light on the future of journalism, and invites us all to be part of it. Dan Gillmor is founder of Grassroots Media Inc., a project aimed at enabling grassroots journalism and expanding its reach. The company's first launch is Bayosphere.com, a site "of, by, and for the San Francisco Bay Area." From 1994-2004, Gillmor was a columnist at the "San Jose Mercury News", Silicon Valley's daily newspaper, and wrote a weblog for SiliconValley.com. He joined the "Mercury News" after six years with the Detroit Free Press. Before that, he was with the "Kansas City Times" and several newspapers in Vermont. He has won or shared in several regional and national journalism awards. Before becoming a journalist, he played music professionally for seven years.


title: Autoencoding beyond pixels using a learned similarity metric

abstract: We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.


title: On information coverage for location category based point-of-interest recommendation

abstract: Point-of-interest (POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users' preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories (like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city. In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city. The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms.


title: Causal Learning and Inference as a Rational Process: The New Synthesis

abstract: Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with Bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating Bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.


title: Joint Emotion-Topic Modeling for Social Affective Text Mining

abstract: This paper is concerned with the problem of social affective text mining, which aims to discover the connections between social emotions and affective terms based on user-generated emotion labels. We propose a joint emotion-topic model by augmenting latent Dirichlet allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model.


Predictors of resiliency in women affected by Hurricane Katrina


title: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

abstract: We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences.


title: Active pages: a computation model for intelligent memory

abstract: Microprocessors and memory systems suffer from a growing gap in performance. We introduce Active Pages, a computation model which addresses this gap by shifting data-intensive computations to the memory system. An Active Page consists of a page of data and a set of associated functions which can operate upon that data. We describe an implementation of Active Pages on RADram (Reconfigurable Architecture DRAM), a memory system based upon the integration of DRAM and reconfigurable logic. Results from the SimpleScalar simulator [BA97] demonstrate up to 1000X speedups on several applications using the RADram system versus conventional memory systems. We also explore the sensitivity of our results to implementations in other memory technologies.


title: The Neural Bases of Social Cognition and Story Comprehension

abstract: A great deal of research exists on the neural basis of theory-of-mind (ToM) or mentalizing. Qualitative reviews on this topic have identified a mentalizing network composed of the medial prefrontal cortex, posterior cingulate/precuneus, and bilateral temporal parietal junction. These conclusions, however, are not based on a quantitative and systematic approach. The current review presents a quantitative meta-analysis of neuroimaging studies pertaining to ToM, using the activation-likelihood estimation (ALE) approach. Separate ALE meta-analyses are presented for story-based and nonstory-based studies of ToM. The conjunction of these two meta-analyses reveals a core mentalizing network that includes areas not typically noted by previous reviews. A third ALE meta-analysis was conducted with respect to story comprehension in order to examine the relation between ToM and stories. Story processing overlapped with many regions of the core mentalizing network, and these shared regions bear some resemblance to a n...


title: Standalone Wearable Driver Drowsiness Detection System in a Smartwatch

abstract: Drowsiness while driving is one of the main causes of fatal accidents, especially on monotonous routes such as highways. The goal of this paper is to design a completely standalone, distraction-free, and wearable system for driver drowsiness detection by incorporating the system in a smartwatch. The main objective is to detect the driver’s drowsiness level based on the driver behavior derived from the motion data collected from the built-in motion sensors in the smartwatch, such as the accelerometer and the gyroscope. For this purpose, the magnitudes of hand movements are extracted from the motion data and are used to calculate the time, spectral, and phase domain features. The features are selected based on the feature correlation method. Eight features serve as an input to a support vector machine (SVM) classifier. After the SVM training and testing, the highest obtained accuracy was 98.15% (Karolinska sleepiness scale). This user-predefined system can be used by both left-handed and right-handed users, because different SVM models are used for different hands. This is an effective, safe, and distraction-free system for the detection of driver drowsiness.


Alternative work schedules


title: Digital Entanglements: Craft, Computation and Collaboration in Fine Art Furniture Production

abstract: This paper joins a growing body of CSCW and HCI work exploring questions of creativity and collaboration at the intersection of digital and material practices of craft. Drawing on studio visits and interviews with fine art furniture maker Wendell Castle and his team, we investigate one studio's experience with integrating digital fabrication tools into their studio practice, and its implications for the collective organization of work and creativity. We explore how the introduction of new computational and industrial machine objects (here, Computer Numerical Controllers) remediates traditional relations of craft and the forms of human-object value, care, and creativity built around them. We also chart new forms of creative practice and material flow that emerge from this encounter, and show how remediations of craft in the Castle studio may pose questions and opportunities for wider CSCW concerns around craft, creativity, and design.


title: Simpson's Paradox: An Anatomy

abstract: Simpson's Paradox: An Anatomy Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles, CA 90024 judea@cs.ucla.edu Judea Pearl This report discusses the reversal e ect known as Simpson's paradox from a causal- theoretic viewpoint. It analyzes the reasons why the e ect has been (and still is) considered paradoxical and why its resolution has been so late in coming. The re- port is extracted from a forthcoming book Causality Pearl, 2000], and assumes some familiarity with causal diagrams and the do ( ) (or set ( )) notation (e.g., Pearl, 1995]). Abstract 0.1 A Tale of a Non-Paradox Simpson's paradox Simpson, 1951 Blyth, 1972], rst encountered by Pearson in 1899 Aldrich, 1995], refers to the phenomenon whereby an event C increases the probability of E in a given population p and, at the same time, decreases the probability of E in ev- ery subpopulation of p . In other words, if F and : F are two complementary properties describing two subpopulations, we might well encounter the inequalities P ( E j C ) > P ( E j: C ) P ( E j C F ) < P ( E j: C F ) P ( E j C : F ) < P ( E j: C : F ) : Although such order reversal might not surprise students of probability, it is paradoxical when given causal interpretation. For example, if we associate C (connoting cause ) with taking a certain drug, E (connoting e ect ) with recovery, and F with being a female then| under the causal interpretation of (2){(3)|the drug seems to be harmful to both males and females yet bene cial to the population as a whole (equation (1)). Intuition deems such a result impossible, and correctly so. The tables in Fig. 1 represent Simpson's reversal numerically. We see that, overall, the recovery rate for patients receiving the drug ( C ) at 50% exceeds that of the control ( : C ) at 40% and so the drug treatment is apparently to be preferred. However, when we inspect the separate tables for males and females, the recovery rate for the untreated patients is 10% higher than that for the treated ones, for males and females both.


Cold-Dependent Expression and Alternative Splicing of Arabidopsis Long Non-coding RNAs


title: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera

abstract: KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.


title: A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012

abstract: This paper describes the solution of National Taiwan University for track 2 of KDD Cup 2012. Track 2 of KDD Cup 2012 aims to predict the click-through rate of ads on Tencent proprietary search engine. We exploit classication, regression, ranking, and factorization models to utilize a variety of dierent signatures captured from the dataset. We then blend our individual models to boost the performance through two stages, one on an internal validation set and one on the external test set. Our solution achieves 0.8069 AUC on the public test set and 0.8089 AUC on the private test set.


title: Evidential map-building approaches for multi-UAV cooperative search

abstract: This paper addresses the map building problem for cooperative search by a team of uninhabited air vehicles (UAVs) operating in an unknown and uncertain environment . We present and compare two evidential map-building approaches based on Bayesian theory and Dempster-Shafer theory respectively. We illustrate how to utilize the generated maps into the UAVs path planning procedure so that they could cooperatively localize targets in the environment. The simulation results illustrate the effectiveness of the proposed strategies.


title: Groundwater chemistry at Yucca Mountain, Nevada, and vicinity

abstract: The chemistry of groundwater at Yucca Mountain and vicinity has been reviewed and compared with the chemistry of water from the Nevada Test Site and surrounding areas such as the Amargosa Desert and Oasis Valley. Sodium is the primary cation and carbonate is the primary anion in water from the saturated zone of the tuffaceous aquifer at Yucca Mountain. Other major cations present are calcium, potassium, and magnesium; other major anions are sulfate and chloride, with lesser quantities of fluoride and nitrate. Aqueous silica is also present. The primary purpose of this review was to survey water-composition data and look for relations among the compositional variables that could provide insight into the processes that control the composition and would ultimately affect radionuclide transport. The following conclusions were inferred from the review. Major cation concentrations are controlled by rock dissolution and mineral precipitation reactions as well as by cation exchange with existing minerals. Aqueous carbonate initially comes from atmospheric and soil-zone carbon dioxide, but there is evidence at Yucca Mountain that carbon dioxide in the gas phase of the unsaturated zone supplies additional carbonate to saturated-zone water in the tuffaceous aquifer as mineral dissolution and precipitation reactions raise the pH ofmore » the water. This combination is effectively mineral dissolution and precipitation in a system that is open with respect to carbon dioxide. A carbon model for this process is discussed.« less


title: Three-Dimensional Visualization of Human Vocal Organ on the Web

abstract: In the field of image technology, three-dimensional (3D) image display can give the intuitive information more efficiently. In medical science, 3D visualization is used for the purpose of education and diagnosis and is becoming increasingly more important. In this article, several structures of the larynx are visualized by 3D image and displayed on the Web to provide users an easy approach to the image. VRML is used on Web3D display because it is popular and easy to manipulate.


title: Vegetable oils in fermentation: beneficial effects of low-level supplementation

abstract: To evaluate their potential to enhance fermentation performance, vegetable oils were investigated in a model tetracycline fermentation. With sucrose as the carbon source, the fermentation efficiency of Streptomyces aureofaciens (ATCC 10762) was enhanced by the inclusion in the medium of low levels of vegetable oil. Soybean and sunflower oils significantly improved the rate of sucrose consumption and tetracycline production suggesting that oil is an excellent adjuvant for improving fermentation productivity. For optimum benefit, the dosage level was critical. Little difference was observed between crude and refined oils. These data contribute to the assessment of industrially available fermentation feedstocks, and to the development of new feedstock products for specific fermentation applications.


MALDI-TOF MS for Microbial Identification: Years of Experimental Development to an Established Protocol


Information Hiding using Image Embedding in QR Codes for Color Images : A Review


title: Cloud-Based Software Platform for Big Data Analytics in Smart Grids

abstract: This article focuses on a scalable software platform for the Smart Grid cyber-physical system using cloud technologies. Dynamic Demand Response (D2R) is a challenge-application to perform intelligent demand-side management and relieve peak load in Smart Power Grids. The platform offers an adaptive information integration pipeline for ingesting dynamic data; a secure repository for researchers to share knowledge; scalable machine-learning models trained over massive datasets for agile demand forecasting; and a portal for visualizing consumption patterns, and validated at the University of Southern California's campus microgrid. The article examines the role of clouds and their tradeoffs for use in the Smart Grid Cyber-Physical Sagileystem.


title: Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble

abstract: The ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANNs) for TSF. In this work, we propose a novel EANN approach, where a weighted n-fold validation fitness scheme is used to build an ensemble of neural networks, under four different combination methods: mean, median, softmax and rank-based. Several experiments were held, using six real-world time series with different characteristics and from distinct domains. Overall, the proposed approach achieved competitive results when compared with a non-weighted n-fold EANN ensemble, the simpler 0-fold EANN and also the popular Holt-Winters statistical method.


title: Dual-feed circularly polarized microstrip antenna for S-Band transmitter of Synthetic Aperture Radar (SAR) system

abstract: In this research, a single patch microstrip antenna has been made, using dual-feed line technique to produce a circular polarization. A rectangular slot has been used in order to reduce antenna dimension and increase the bandwidth. To increase the gain, front-end parasitic elements has been used. The antenna has a circular polarization with unidirectional radiation pattern. The center frequency is 2.35 GHz. The obtained value of VSWR is less than 1.5 with the bandwidth of 165.9 MHz, and the gain is 6.36 dBic.


title: Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine RidgeStructure Dictionary

abstract: Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate (“lights-out” capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving “lights-out” latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these latent patches. The ridge quality of a patch, which is used for latent segmentation, is defined as the structural similarity between the patch and its reconstruction. Orientation and frequency fields, which are used for latent enhancement, are then extracted from the reconstructed patch. To balance robustness and accuracy, a coarse to fine strategy is proposed. Experimental results on two latent fingerprint databases (i.e., NIST SD27 and WVU DB) show that the proposed algorithm outperforms the state-of-the-art segmentation and enhancement algorithms and boosts the performance of a state-of-the-art commercial latent matcher.


title: Real-time partitioned convolution for Ambiophonics surround sound

abstract: Ambiophonics, as one of the most realistic music reproduction methods, requires multi-channel convolution with very long impulse responses for creation of believable reverberation. It is shown how the heavy processing task required for a real-time Ambiophonics system, or similar convolution-intensive system, can be handled by a low-cost personal computer, by means of partitioned convolution. The only theoretical advantage of partitioned convolution is that it provides low input/output delay. However, since the intensive part of it is very easily made optimal for the target platform, which normally is not the case for the standard overlap-and-save algorithm, it often provides the fastest convolution as well.


title: A unifying view on dataset shift in classification

abstract: The field of dataset shift has received a growing amount of interest in the last few years. The fact that most real-world applications have to cope with some form of shift makes its study highly relevant. The literature on the topic is mostly scattered, and different authors use different names to refer to the same concepts, or use the same name for different concepts. With this work, we attempt to present a unifying framework through the review and comparison of some of the most important works in the literature.


title: Robust Real-Time Periodic Motion Detection , Analysis , and Applications

abstract: We describe new techniques to detect and analyze periodic motion as seen from both a static and a moving camera. By tracking objects of interest, we compute an object's self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic and we apply time-frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robustly using the 2D lattice structures inherent in similarity matrices. A real-time system has been implemented to track and classify objects using periodicity. Examples of object classification (people, running dogs, vehicles), person counting, and nonstationary periodicity are provided.


title: Exploring the ambient assisted living domain: a systematic review

abstract: Ambient assisted living (AAL) is focused on providing assistance to people primarily in their natural environment. Over the past decade, the AAL domain has evolved at a fast pace in various directions. The stakeholders of AAL are not only limited to patients, but also include their relatives, social services, health workers, and care agencies. In fact, AAL aims at increasing the life quality of patients, their relatives and the health care providers with a holistic approach. This paper aims at providing a comprehensive overview of the AAL domain, presenting a systematic analysis of over 10 years of relevant literature focusing on the stakeholders’ needs, bridging the gap of existing reviews which focused on technologies. The findings of this review clearly show that until now the AAL domain neglects the view of the entire AAL ecosystem. Furthermore, the proposed solutions seem to be tailored more on the basis of the available existing technologies, rather than supporting the various stakeholders’ needs. Another major lack that this review is pointing out is a missing adequate evaluation of the various solutions. Finally, it seems that, as the domain of AAL is pretty new, it is still in its incubation phase. Thus, this review calls for moving the AAL domain to a more mature phase with respect to the research approaches.


title: Letter-To-Sound Conversion For Urdu Text-To-Speech System

abstract: Urdu is spoken by more than 100 million people across a score countries and is the national language of Pakistan (http://www.ethnologue.com). There is a great need for developing a text-to-speech system for Urdu because this population has low literacy rate and therefore speech interface would greatly assist in providing them access to information. One of the significant parts of a text-to-speech system is a natural language processor which takes textual input and converts it into an annotated phonetic string. To enable this, it is necessary to develop models which map textual input onto phonetic content. These models may be very complex for various languages having unpredictable behaviour (e.g. English), but Urdu shows a relatively regular behaviour and thus Urdu pronunciation may be modelled from Urdu text by defining fairly regular rules. These rules have been identified and explained in this paper.


MCMCTS PCG 4 SMB : Monte Carlo Tree Search to Guide Platformer Level Generation


title: Autonomous Agents and Ethical Decision-Making

abstract: Machine ethics, also known as artificial morality, is a newly emerging field concerned with ensuring appropriate behavior of machines toward humans and other machines. In this article, we discuss the importance of machine ethics and present a computational model of ethical decision-making for autonomous agents. The proposed model implements a mechanism for integrating the results of diverse assessments into a unique cue, and takes into account the agent’s preferences, good and bad past experiences, ethical rules, and current emotional state as the main factors involved in choosing the most appropriate option. The design of the model is based on theories and models developed in fields such as neuroscience, psychology, artificial intelligence, and cognitive informatics. In particular, the model attempts to emulate neural mechanisms of the human brain involved in ethical decision-making.


title: Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study

abstract: This paper presents the experimental pilot study to investigate the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) in response to photoplethysmographic (PPG), electrocardiographic (ECG), electroencephalographic (EEG) activity. The assessment of wavelet transform (WT) as a feature extraction method was used in representing the electrophysiological signals. Considering that classification is often more accurate when the pattern is simplified through representation by important features, the feature extraction and selection play an important role in classifying systems such as neural networks. The PPG, ECG, EEG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and the statistical features were calculated to depict their distribution. Our pilot study investigation for any possible electrophysiological activity alterations due to ELF PEMF exposure, was evaluated by the efficiency of DWT as a feature extraction method in representing the signals. As a result, this feature extraction has been justified as a feasible method.


title: A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

abstract: Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. Furthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. Finally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naive Bayes (NB), Random Forest (RF), and C4.5. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.59% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 3.34% and 6.00% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.