title: M2M Communications for Smart City: An Event-Based Architecture

abstract: Machine to machine (M2M) communications will lead to dramatic changes in the applications and services offered to citizens, allowing smart city to become a reality. In this article, we first introduce the new tendency in M2M development and analyze the construction frame of application systems in smart city. Then, an event-based architecture depending on SOFIA project is built to allow the management and cooperation among M2M components by mean of event manager. Based on this architecture, we conduct a case study in a vehicular context. M2M network architecture is introduced for vehicular networks. Finally, an event processing flow of vehicle maintenance services is designed to manage the mission-critical wireless messages.


title: Procedural Game Adaptation: Framing Experience Management as Changing an MDP

abstract: In this paper, we present the Procedural Game Adaptation (PGA) framework, a designer-controlled way to change a game's dynamics during end-user play. We formalize a video game as a Markov Decision Process, and frame the problem as maximizing the reward of a given player by modifying the game's transition function. By learning a model of each player to estimate her rewards, PGA managers can change the game's dynamics in a player-informed way. Following a formal definition of the components of the framework, we illustrate its versatility by using it to represent two existing adaptive systems: PaSSAGE, and Left 4 Dead's AI Director. instead of 75%? We investigate such questions in the con- text of Artificial Intelligence (AI), with the goal of taking advantage of different players' preferences to improve each player's experience. By learning from the additional data that becomes available when players start interacting with the game, an online adaptation agent can serve as an extra- informed proxy for the game's designers, carrying out their decisions in light of what it learns about each player (e.g., the AI Director used in Left 4 Dead). We focus on such agents for the remainder of this work, under the assumption that designers can make better decisions when they know more about their players on an individual basis. In this paper, we present the Procedural Game Adaptation (PGA) framework: a designer-controlled way to adapt the dynamics of a given video game during end-user play. When implemented, this framework produces a deterministic, on- line adaptation agent (called an experience manager (Riedl et al. 2011)) that automatically performs two tasks: 1) it gathers information about a game's current player, 2) it uses that information to estimate which of several different changes to the game's dynamics will maximize some player- specific value (e.g., fun, sense of influence, etc.).


title: Face Recognition with Local Binary Patterns

abstract: This paper is about providing efficient face recognition i.e. feature extraction and face matching system using local binary patterns (LBP) method. It is a texture based algorithm for face recognition which describes the texture and shape of digital images. The preprocessed or facial image is first divided into small blocks from which LBP histograms are formed and then concatenated into a single feature vector. This feature vector plays a vital role in efficient representation of the face and is used to measure similarities by calculating the distance between Images. This paper presents the principles of the method and implementation to perform face recognition. Experiments have been carried out on Yale data set; high recognition rates are obtained, especially compared to other face recognition methods. Also few extensions are investigated and implemented successfully to further improve the performance of the method.


title: PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction

abstract: Traffic problems have seriously affected people's life quality and urban development, and forecasting the short-term traffic congestion is of great importance to both individuals and governments. However, understanding and modeling the traffic conditions can be extremely difficult, and our observations from real traffic data reveal that (1) similar traffic congestion patterns exist in the neighboring time slots and on consecutive workdays; (2) the levels of traffic congestion have clear multiscale properties. To capture these characteristics, we propose a novel method named PCNN based on deep Convolutional Neural Network, modeling Periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. It first temporally folds the time series and constructs a two-dimensional matrix as the network input, such that both the real-time traffic conditions and past traffic patterns are well considered; then with a series of convolutions over the input matrix, it is able to model the local temporal dependency and multiscale traffic patterns. In particular, the global trend of congestion can be addressed at the macroscale; whereas more details and variations of the congestion can be captured at the microscale. Experimental results on a real-world urban traffic dataset confirm that folding time series data into a two-dimensional matrix is effective and PCNN outperforms the baselines significantly for the task of short-term congestion prediction.


title: Software Engineering: Challenges and their Solution in Mobile App Development

abstract: Mobile app development is increasing rapidly due to the popularity of smartphones. With billions of apps downloads, the Apple App Store and Google Play Store succeeded to overcome mobile devices. Throughout last 10 years, the amount of smartphones and mobile applications has been perpetually growing. Android and iOS are two mobile platforms that cowl most smartphones within the world in 2017. However, this success challenges app developers to publish high-quality apps to stay attracting and satisfying end-users. Developing a mobile app involves ?rst to select the platforms the app can run, so to develop speci?c solutions (i.e., native apps). During application development a developer come across multiple challenges. In this paper, we have tried to find out challenges faced by developer during their development life cycle with their possible solution.


title: Effect of Self-myofascial Release on Reduction of Physical Stress: A Pilot Study

abstract: [Purpose] This study aims to examined the effect of the self-myofascial release induced with a foam roller on the reduction of stress by measuring the serum concentration of cortisol. [Subjects and Methods] The subjects of this study were healthy females in their 20s. They were divided into the experimental and control groups. Both groups, each consisting of 12 subjects, were directed to walk for 30 minutes on a treadmill. The control group rested for 30 minutes of rest by lying down, whereas the experimental group was performed a 30 minutes of self-myofascial release program. [Results] Statistically significant levels of cortisol concentration reduction were observed in both the experimental group, which used the foam roller, and the control group. There was no statistically significant difference between the two groups. [Conclusion] The Self-myofascial release induced with a foam roller did not affect the reduction of stress.


title: Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

abstract: Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.


title: Hispanic-American rhinoplasty.

abstract: Hispanic Americans have quickly become a major ethnic group in the United States who are interested in nasal cosmetic surgery. They are derived from a variety of ethnicities with a corresponding diversity in nasal anatomy. Rhinoplasty in Hispanic Americans has evolved from imitating neoclassical canons to preservation of ethnic characteristics. Common problems addressed in this population are narrowing and augmenting the height of the nasal dorsum; increasing projection, rotation, and definition of the nasal tip; and reducing alar flare and base width. Anatomic goals for surgery should satisfy both individual desires and ethnic features that are to be created or to be preserved. Here we describe the techniques used by the senior author (R.W.H.K.).


title: Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

abstract: After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.


title: Enduring Influence of Stereotypical Computer Science Role Models on Women’s Academic Aspirations

abstract: The current work examines whether a brief exposure to a computer science role model who fits stereotypes of computer scientists has a lasting influence on women’s interest in the field. One-hundred undergraduate women who were not computer science majors met a female or male peer role model who embodied computer science stereotypes in appearance and stated interests or the same role model who did not embody these stereotypes. Participants and role models engaged in an interaction that lasted approximately 2 minutes. Interest in majoring in computer science was assessed following the interaction and 2 weeks later outside the laboratory. Results revealed that exposure to the stereotypical role model had both an immediate and an enduring negative effect on women’s interest in computer science. Differences in interest at both times were mediated by women’s reduced sense of belonging in computer science upon interacting with the stereotypical role model. Gender of the role model had no effect. Whether a potent...


title: ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files

abstract: Email communication carrying malicious attachments or links is often used as an attack vector for initial penetration of the targeted organization. Existing defense solutions prevent executables from entering organizational networks via emails, therefore recent attacks tend to use non-executable files such as PDF. Machine learning algorithms have recently been applied for detecting malicious PDF files. These techniques, however, lack an essential element --they cannot be updated daily. In this study we present ALPD, a framework that is based on active learning methods that are specially designed to efficiently assist anti-virus vendors to focus their analytical efforts. This is done by identifying and acquiring new PDF files that are most likely malicious, as well as informative benign PDF documents. These files are used for retraining and enhancing the knowledge stores. Evaluation results show that in the final day of the experiment, Combination, one of our AL methods, outperformed all the others, enriching the anti-virus's signature repository with almost seven times more new PDF malware while also improving the detection model's performance on a daily basis.


title: Missing Value Estimation for Mixed-Attribute Data Sets

abstract: Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great successes on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). This paper studies a new setting of missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of different types), referred to as imputing mixed-attribute data sets. Although many real applications are in this setting, there is no estimator designed for imputing mixed-attribute data sets. This paper first proposes two consistent estimators for discrete and continuous missing target values, respectively. And then, a mixture-kernel-based iterative estimator is advocated to impute mixed-attribute data sets. The proposed method is evaluated with extensive experiments compared with some typical algorithms, and the result demonstrates that the proposed approach is better than these existing imputation methods in terms of classification accuracy and root mean square error (RMSE) at different missing ratios.


title: It's just a matter of perspective(s): Crowd-Powered Consensus Organization of Corpora

abstract: We study the problem of organizing a collection of objects - images, videos - into clusters, using crowdsourcing. This problem is notoriously hard for computers to do automatically, and even with crowd workers, is challenging to orchestrate: (a) workers may cluster based on different latent hierarchies or perspectives; (b) workers may cluster at different granularities even when clustering using the same perspective; and (c) workers may only see a small portion of the objects when deciding how to cluster them (and therefore have limited understanding of the "big picture"). We develop cost-efficient, accurate algorithms for identifying the consensus organization (i.e., the organizing perspective most workers prefer to employ), and incorporate these algorithms into a cost-effective workflow for organizing a collection of objects, termed ORCHESTRA. We compare our algorithms with other algorithms for clustering, on a variety of real-world datasets, and demonstrate that ORCHESTRA organizes items better and at significantly lower costs.


title: Assessment of positive functioning in clinical psychology: Theoretical and practical issues

abstract: Abstract Positive psychology has led to an increasing emphasis on the promotion of positive functioning in clinical psychology research and practice, raising issues of how to assess the positive in clinical setting. Three key considerations are presented. First, existing clinical measures may already be assessing positive functioning, if positive and negative functioning exist on a single continuum (such as on bipolar dimensions from happiness to depression, and from anxiety to relaxation). Second, specific measures of positive functioning (e.g., eudemonic well-being) could be used in conjunction with existing clinical scales. Third, completely different measures would be needed depending on whether well-being is defined as emotional or medical functioning, or as humanistically orientated growth (e.g., authenticity). It is important that clinical psychologists introduce positive functioning into their research and practice in order to widen their armoury of therapeutic interventions, but in doing so researchers and practitioners need also to be aware that they are shifting the agenda of clinical psychology. As such, progress in clinical psychology moving toward the adoption of positive functioning requires reflection on epistemological foundations.


title: A ransomware research framework: poster

abstract: This research develops a series of research frameworks for addressing the problem of ransomware from the perspective of multiple disciplines, begins to develop theories about ransomware, and identifies needs for future research. Approaches include criminal justice, hardware and software technologies, file structures, critical infrastructure impact, an abstract theoretic approach, and money tracking. To date, poster presentations are available and formal articles from each researcher are forthcoming..


title: Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology

abstract: In this paper, we derive theoretical bounds for the long-term influence of a node in an Independent Cascade Model (ICM). We relate these bounds to the spectral radius of a particular matrix and show that the behavior is sub-critical when this spectral radius is lower than $1$. More specifically, we point out that, in general networks, the sub-critical regime behaves in $O(\sqrt{n})$ where $n$ is the size of the network, and that this upper bound is met for star-shaped networks. We apply our results to epidemiology and percolation on arbitrary networks, and derive a bound for the critical value beyond which a giant connected component arises. Finally, we show empirically the tightness of our bounds for a large family of networks.


title: A Proposal of a Password Manager Satisfying Security and Usability by Using the Secret Sharing and a Personal Server

abstract: Password managers protect users' passwords by using a master password or a security token. The security of them using the master password is weakened if users use weak master passwords. The usability of them using the security token is low since users always need the token to log in. In this paper, we propose a new framework of password managers which has a high security and a high usability by employing the secret sharing and a personal servers for a user.


title: Interaction-aware driver maneuver inference in highways using realistic driver models

abstract: In this work, we address the problem of lane change maneuver prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. Our prediction approach is two-fold. First, a driver model learned from demonstrations via Inverse Reinforcement Learning is used to equip a host vehicle with the anticipatory behavior reasoning capability of common drivers. Second, inference on an interaction-aware augmented Switching State-Space Model allows the approach to account for the dynamic evidence observed. The use of a driver model that correctly balances the driving and risk-aversive preferences of a driver allows the computation of a planning-based maneuver prediction. Integrating this anticipatory prediction into the maneuver inference engine brings a degree of scene understanding into the estimate and leads to faster lane change detections compared to those obtained by relying on dynamics alone. The performance of the presented framework is evaluated using highway data collected with an instrumented vehicle. The combination of model-based maneuver prediction and filtering-based state and maneuver tracking is shown to outperform an Interacting Multiple Model filter in the detection of highway lane change maneuvers regarding accuracy, detection latency — by an average of 0.4 seconds- and false-positive rates.


title: CarePre: An Intelligent Clinical Decision Assistance System

abstract: Clinical decision support systems (CDSS) are widely used to assist with medical decision making. However, CDSS typically require manually curated rules and other data which are difficult to maintain and keep up-to-date. Recent systems leverage advanced deep learning techniques and electronic health records (EHR) to provide more timely and precise results. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, while the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his/her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support the diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of CarePre system by reporting results from a quantities evaluation of the prediction algorithm and a case study and three interviews with senior physicians.


title: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs

abstract: New work on microRNAs (miRNAs) sheds light on the nature of tissue-specific gene expression in humans. miRNAs act in plants and animals to regulate gene expression. Microarray analysis of messenger RNAs expressed in human cells containing miR-124, an miRNA expressed in the brain, or miR-1, found in muscle, shows that the miRNAs skew a cell's expression profile to resemble that of the miRNA's ‘home’ cells, regardless of source. This involves the downregulation of many target mRNA transcripts, as well as reduction of the amount of protein produced. MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals1,2. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3′ untranslated regions of these messages had a significant propensity to pair to the 5′ region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs3. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.


title: Multi-features taxi destination prediction with frequency domain processing

abstract: The traditional taxi prediction methods model the taxi trajectory as a sequence of spatial points. It cannot represent two-dimensional spatial relationships between trajectory points. Therefore, many methods transform the taxi GPS trajectory into a two-dimensional image, and express the spatial correlations by trajectory image. However, the trajectory image may have noise and sparsity according to trajectory data characteristics. So, we import image frequency domain processing to taxi destination prediction to reduce noise and sparsity, then propose multi-features taxi destination prediction with frequency domain processing (MTDP-FD) method. Firstly, we transform the spatial domain trajectory image into frequency-domain representation by fast Fourier transform and reduce the noise of the trajectory images. Convolutional Neural Network (CNN) is adapted to extract the deep features from the processed trajectory image as CNN has a significant learning ability to images. Recurrent Neural Network (RNN) is adapted to predict the taxi destination as multiple hidden layers of RNN can store dependencies between input data to achieve better prediction. The deep features of the trajectory images are combined with trajectory metadata, trajectory data to act as the input to RNN. The experiments based on the taxi trajectory dataset of Porto show that the average distance error of MTDP-FD is reduced by 0.14km compared with the existing methods, and the GTOHL is the best combination of data and features to improve the prediction accuracy.


title: Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

abstract: Many classical algorithms are found until several years later to outlive the confines in which they were conceived, and continue to be relevant in unforeseen settings. In this paper, we show that SVRG is one such method: being originally designed for strongly convex objectives, it is also very robust in non-strongly convex or sum-of-non-convex settings. ::: More precisely, we provide new analysis to improve the state-of-the-art running times in both settings by either applying SVRG or its novel variant. Since non-strongly convex objectives include important examples such as Lasso or logistic regression, and sum-of-non-convex objectives include famous examples such as stochastic PCA and is even believed to be related to training deep neural nets, our results also imply better performances in these applications.


title: Faster algorithms for the shortest path problem

abstract: Efficient implementations of Dijkstra's shortest path algorithm are investigated. A new data structure, called the radix heap , is proposed for use in this algorithm. On a network with n vertices, m edges, and nonnegative integer arc costs bounded by C , a one-level form of radix heap gives a time bound for Dijkstra's algorithm of O ( m + n log C ). A two-level form of radix heap gives a bound of O ( m + n log C /log log C ). A combination of a radix heap and a previously known data structure called a Fibonacci heap gives a bound of O ( m + n a


title: Class-E LCCL for capacitive power transfer system

abstract: This paper presented the design of a capacitive power transfer (CPT) system by implementing a Class-E inverter due to its high efficiency, which in theory can reach close to 100% performance. However, the Class-E inverter is highly sensitive to its circuit parameters under the scenario of having small capacitance at the coupling plate. As a solution, an additional capacitor can be integrated into the Class-E inverter for increased coupling capacitance for a better performance. Both simulation and experimental investigations were carried out to verify the high efficiency CPT system based on Class-E inverter with additional capacitor. The outcome of the investigation exhibited 96% of overall DC-DC power transfer efficiency with 0.97 W output at 1MHz operating frequency.


title: Better than nothing privacy with bloom filters: to what extent?

abstract: Bloom filters are probabilistic data structures which permit to conveniently represent set membership. Their performance/memory efficiency makes them appealing in a huge variety of scenarios. Their probabilistic operation, along with the implicit data representation, yields some ambiguity on the actual data stored, which, in scenarios where cryptographic protection is unviable or unpractical, may be somewhat considered as a better than nothing privacy asset. Oddly enough, even if frequently mentioned, to the best of our knowledge the (soft) privacy properties of Bloom filters have never been explicitly quantified. This work aims to fill this gap. Starting from the adaptation of probabilistic anonymity metrics to the Bloom filter setting, we derive exact and (tightly) approximate formulae which permit to readily relate privacy properties with filter (and universe set) parameters. Using such relations, we quantitatively investigate the emerging privacy/utility trade-offs. We finally preliminary assess the advantages that a tailored insertion of a few extra (covert) bits achieves over the commonly employed strategy of increasing ambiguity via addition of random bits.


title: Adaptive Deep Learning Through Visual Domain Localization

abstract: A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision.


title: 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

abstract: While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applications, especially in the medical domain. Unlabeled data, on the other hand, is much more abundant. Semi-supervised learning techniques, such as co-training, could provide a powerful tool to leverage unlabeled data. In this paper, we propose a novel framework, uncertainty-aware multi-view co-training (UMCT), to address semi-supervised learning on 3D data, such as volumetric data from medical imaging. In our work, co-training is achieved by exploiting multi-viewpoint consistency of 3D data. We generate different views by rotating or permuting the 3D data and utilize asymmetrical 3D kernels to encourage diversified features in different sub-networks. In addition, we propose an uncertainty-weighted label fusion mechanism to estimate the reliability of each view's prediction with Bayesian deep learning. As one view requires the supervision from other views in co-training, our self-adaptive approach computes a confidence score for the prediction of each unlabeled sample in order to assign a reliable pseudo label. Thus, our approach can take advantage of unlabeled data during training. We show the effectiveness of our proposed semi-supervised method on several public datasets from medical image segmentation tasks (NIH pancreas&LiTS liver tumor dataset). Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.


title: Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

abstract: In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.


title: Natural language processing: a prolog perspective

abstract: Natural language processing (NLP) is a vibrant field of interdisciplinary Computer Science research. Ultimately, NLP seeks to build intelligence into software so that software will be able to process a natural language as skillfully and artfully as humans. Prolog, a general purpose logic programming language, has been used extensively to develop NLP applications or components thereof. This report is concerned with introducing the interested reader to the broad field of NLP with respect to NLP applications that are built in Prolog or from Prolog components.


title: Iso-X: A Flexible Architecture for Hardware-Managed Isolated Execution

abstract: We consider the problem of how to provide an execution environment where the application's secrets are safe even in the presence of malicious system software layers. We propose Iso-X --- a flexible, fine-grained hardware-supported framework that provides isolation for security-critical pieces of an application such that they can execute securely even in the presence of untrusted system software. Isolation in Iso-X is achieved by creating and dynamically managing compartments to host critical fragments of code and associated data. Iso-X provides fine-grained isolation at the memory-page level, flexible allocation of memory, and a low-complexity, hardware-only trusted computing base. Iso-X requires minimal additional hardware, a small number of new ISA instructions to manage compartments, and minimal changes to the operating system which need not be in the trusted computing base. The run-time performance overhead of Iso-X is negligible and even the overhead of creating and destroying compartments is modest. Iso-X offers higher memory flexibility than the recently proposed SGX design from Intel, allowing both fluid partitioning of the vailable memory space and dynamic growth of compartments. An FPGA implementation of Iso-X runtime mechanisms shows a negligible impact on the processor cycle time.


title: Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set

abstract: We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present O(n^3) global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the "second-best-in-class" result on the English Penn Treebank.


Neural Coreference Resolution


title: Suitability analysis of in-wheel motor direct drives for electric and hybrid electric vehicles

abstract: In most conventional EV applications, a central high speed electric motor is mechanically coupled to the wheels by a single speed reduction gearbox and a mechanical differential. An innovative alternative utilizes low speed, high torque, gearless, electric motors, mounted completely inside the rim of the wheels, to provide instantaneous torque and eliminate driveline transmission losses. These in-wheel motors have many advantages, including no mechanical linkages and independent and precise torque control of each wheel. Furthermore, advanced control functions like Antilock Braking System (ABS), Anti Slip Regulation (ASR), Electronic Stability Program (ESP), and steering assistance can be easily integrated. In this paper, various motors and control strategies for such in-wheel motor drives for 2-wheel and 4-wheel drive vehicles have been presented.


Cloud-Native, Event-Based Programming for Mobile Applications


title: Comparison of Euler Estimate using Extended Kalman Filter, Madgwick and Mahony on Quadcopter Flight Data

abstract: A magnetic and inertial measurement unit (MIMU) provides raw, real-time acceleration, angular velocity, and a measure of earth's magnetic field. By itself, this data is subject to significant noise, bias, and drift (without constant re-calibration). A data fusion algorithm can be applied to significantly reduce these errors. In the past, many approaches have been adopted for filtering gyroscope data with inertial measurements, and the most commonly used techniques are Extended Kalman filtering and complementary filters. Thus, this paper compares three methods: two complementary filters known as Madgwick and Mahony, and the Extended Kalman Filter (EKF). Simulation experiments are conducted using quadcopter data and results show that Mahony provides better orientation estimation than both Madgwick and EKF when using optimum parameters.


title: Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition

abstract: Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity annotations. In this work, we show that unlabeled face data can be as effective as the labeled ones. Here, we consider a setting closely mimicking the real-world scenario, where the unlabeled data are collected from unconstrained environments and their identities are exclusive from the labeled ones. Our main insight is that although the class information is not available, we can still faithfully approximate these semantic relationships by constructing a relational graph in a bottom-up manner. We propose Consensus-Driven Propagation (CDP) to tackle this challenging problem with two modules, the “committee” and the “mediator”, which select positive face pairs robustly by carefully aggregating multi-view information. Extensive experiments validate the effectiveness of both modules to discard outliers and mine hard positives. With CDP, we achieve a compelling accuracy of 78.18% on MegaFace identification challenge by using only 9% of the labels, comparing to 61.78% when no unlabeled data are used and 78.52% when all labels are employed.


title: Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks

abstract: We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resolution images. However, existing approaches have limitations in achieving both high image quality and structural consistency at the same time. Our method tackles the limitations by progressively increasing the resolution of both generated images and structural conditions during training. In this paper, we empirically demonstrate the effectiveness of this method by showing the comparison with existing approaches and video generation results of diverse anime characters at 1024x1024 based on target pose sequences. We also create a novel dataset containing full-body 1024x1024 high-resolution images and exact 2D pose keypoints using Unity 3D Avatar models.


title: Local directional relation pattern for unconstrained and robust face retrieval

abstract: Face recognition is still a very demanding area of research. This problem becomes more challenging in unconstrained environment and in the presence of several variations like pose, illumination, expression, etc. Local descriptors are widely used for this task. The most of the existing local descriptors consider only few immediate local neighbors and not able to utilize the wider local information to make the descriptor more discriminative. The wider local information based descriptors mainly suffer due to the increased dimensionality. In this paper, this problem is solved by encoding the relationship among directional neighbors in an efficient manner. The relationship between the center pixel and the encoded directional neighbors is utilized further to form the proposed local directional relation pattern (LDRP). The descriptor is inherently uniform illumination invariant. The multi-scale mechanism is also adapted to further boost the discriminative ability of the descriptor. The proposed descriptor is evaluated under the image retrieval framework over face databases. Very challenging databases like PaSC, LFW, PubFig, ESSEX, FERET, AT&T, and FaceScrub are used to test the discriminative ability and robustness of LDRP descriptor. Results are also compared with the recent state-of-the-art face descriptors such as LBP, LTP, LDP, LDN, LVP, DCP, LDGP and LGHP. Very promising performance is observed using the proposed descriptor over very appealing face databases as compared to the existing face descriptors. The proposed LDRP descriptor also outperforms the pre-trained ImageNet CNN models over large-scale FaceScrub face dataset. Moreover, it also outperforms the deep learning based DLib face descriptor in many scenarios.


title: Online Deduplication for Databases

abstract: dbDedup is a similarity-based deduplication scheme for on-line database management systems (DBMSs). Beyond block-level compression of individual database pages or operation log (oplog) messages, as used in today's DBMSs, dbDedup uses byte-level delta encoding of individual records within the database to achieve greater savings. dbDedup's single-pass encoding method can be integrated into the storage and logging components of a DBMS to provide two benefits: (1) reduced size of data stored on disk beyond what traditional compression schemes provide, and (2) reduced amount of data transmitted over the network for replication services. To evaluate our work, we implemented dbDedup in a distributed NoSQL DBMS and analyzed its properties using four real datasets. Our results show that dbDedup achieves up to 37x reduction in the storage size and replication traffic of the database on its own and up to 61x reduction when paired with the DBMS's block-level compression. dbDedup provides both benefits with negligible effect on DBMS throughput or client latency (average and tail).


title: Investigation of cloudless solar radiation with PV module employing Matlab-Simulink

abstract: This paper focuses an novel approach on the prediction of Voltage-Current(V-I) characteristics of a Photovoltaic panel under varying weather conditions and also the modelling of hourly cloudless solar radiation to provide the insolation on a PV module of any orientation, located at any site. The empirical model developed in this study uses standard specifications together with actual solar radiation and cell temperature. This proposed work develops a Matlab-Simulink block to generate solar radiation at any location and for any time of the year. A new model for Current-Voltage (I-V) characteristics and Maximum Power operation of a Photovoltaic (PV) module is also presented which aims to model the effect on I-V and P-V curves of varying climatic conditions. Moreover, this model has been implemented using the Matlab-Simulink and is used to investigate the effect of meteorological conditions on the performance of a PV module generator. Thus the combined model of cloudless solar radiation and the photovoltaic module provide a tool that may be loaded in the library for analysis purpose. It is found that the predicted solar radiation strongly agrees with the experimental data.


title: Fall Detection Using Deep Learning in Range-Doppler Radars

abstract: In this paper, we propose an approach that uses deep learning to detect a human fall. The proposed approach automatically captures the intricate properties of the radar returns. In order to minimize false alarms, we fuse information from both the time-frequency and range domains. Experimental data is used to demonstrate the superiority of the deep learning based approach in comparison with the principal component analysis method and those methods incorporating predefined physically interpreted features.


title: Super-resolution imaging using a camera array

abstract: The angular resolution of many commercial imaging systems is limited, not by diffraction or optical aberrations, but by pixilation effects. Multiaperture imaging has previously demonstrated the potential for super-resolution (SR) imaging using a lenslet array and single detector array. We describe the practical demonstration of SR imaging using an array of 25 independent commercial-off-the-shelf cameras. This technique demonstrates the potential for increasing the angular resolution toward the diffraction limit, but without the limit on angular resolution imposed by the use of a single detector array.


title: A Fast Parallel Clustering Algorithm for Large Spatial Databases

abstract: The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the ‘shared-nothing’ architecture with multiple computers interconnected through a network. A fundamental component of a shared-nothing system is its distributed data structure. We introduce the dRa-tree, a distributed spatial index structure in which the data is spread among multiple computers and the indexes of the data are replicated on every computer. We implemented our method using a number of workstations connected via Ethernet (10 Mbit). A performance evaluation shows that PDBSCAN offers nearly linear speedup and has excellent scaleup and sizeup behavior.


title: Online optimization for scheduling preemptable tasks on IaaS cloud systems

abstract: In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.


title: Vector quantization using the firefly algorithm for image compression

abstract: The vector quantization (VQ) was a powerful technique in the applications of digital image compression. The traditionally widely used method such as the Linde-Buzo-Gray (LBG) algorithm always generated local optimal codebook. Recently, particle swarm optimization (PSO) was adapted to obtain the near-global optimal codebook of vector quantization. An alterative method, called the quantum particle swarm optimization (QPSO) had been developed to improve the results of original PSO algorithm. The honey bee mating optimization (HBMO) was also used to develop the algorithm for vector quantization. In this paper, we proposed a new method based on the firefly algorithm to construct the codebook of vector quantization. The proposed method uses LBG method as the initial of FF algorithm to develop the VQ algorithm. This method is called FF-LBG algorithm. The FF-LBG algorithm is compared with the other four methods that are LBG, particle swarm optimization, quantum particle swarm optimization and honey bee mating optimization algorithms. Experimental results show that the proposed FF-LBG algorithm is faster than the other four methods. Furthermore, the reconstructed images get higher quality than those generated form the LBG, PSO and QPSO, but it is no significant superiority to the HBMO algorithm.


title: LodLive, exploring the web of data

abstract: LodLive project, http://en.lodlive.it/, provides a demonstration of the use of Linked Data standard (RDF, SPARQL) to browse RDF resources. The application aims to spread linked data principles with a simple and friendly interface and reusable techniques. In this report we present an overview of the potential of LodLive, mentioning tools and methodologies that were used to create it.


title: A novel sensor cell architecture and sensing circuit scheme for capacitive fingerprint sensors

abstract: Novel capacitive fingerprint sensor techniques are described. We propose a novel sensor cell architecture to obtain high sensitivity, wide output dynamic range, and contrast adjustment. For the architecture, three circuit techniques were developed. A sensing circuit with a differential charge-transfer amplifier enhances sensitivity while it suppresses the influence of the parasitic capacitance of the sensor plate. A wide output dynamic range, which is needed for high-resolution analog-to-digital (A/D) conversion, is achieved by transforming the sensed voltage to a time-variant signal. Finally, the sensing circuit includes an automatic contrast enhancement scheme that uses a variable-threshold Schmitt trigger circuit to distinguish the ridges and valleys of a fingerprint well. The characteristics of a test chip using the 0.5-/spl mu/m CMOS process show a high sensitivity to less than 80 fF as the detected signal, while the variation of the output signal is suppressed to less than 3% at /spl plusmn/20% variation of the parasitic capacitance. The dynamic range of the time-variant signal is 70 /spl mu/s, which is wide enough for A/D conversion. The automatic contrast enhancement scheme widens the time-variant signal 100 /spl mu/s more. A single-chip fingerprint sensor/identifier LSI using the proposed sensing circuit scheme confirms the scheme's effectiveness.


title: Wireless Sensor Network and Internet of Things in Precision Agriculture

abstract: Internet of Things is one of the most popular subjects nowadays where sensors and smart devices facilitate the provision of information and communication. In IoT, one of the main concepts is wireless sensor networks in which data is collected from all the sensors in a network characterized by low power consumption and a wide range of communication. In this study, an architecture to monitor soil moisture, temperature and humidity on small farms is provided. The main motivation for this study is to decrease water consumption whilst increasing productivity on small agricultural farms and precisions on them. This motivation is further propelled by the fact that agriculture is the backbone of some towns and most villages in most of the countries. Furthermore, some countries depend on farming as the main source of income. Putting the above-mentioned factors into consideration, the farm is divided into regions; the proposed system monitors soil moisture, humidity and temperature in the respective regions using wireless sensor networks, internet of things and sends a report to the end user. The report contains, as part of the information, a 10-day weather forecast. We believe that with the above information, the end user (farmer) can more efficiently schedule farm cultivation, harvesting, irrigation, and fertilization.


title: mTOR Pathway and mTOR Inhibitors in Head and Neck Cancer

abstract: Head and neck cancer is the sixth most common type of Cancer worldwide. Since conventional treatment regimens are nonselective and are associated with systemic toxicities, intense investigations focus on molecular targeted therapy with high selectivity and low adverse effects. mTOR signaling pathway has been found to be activated in head and neck cancer, making it attractive for targeted therapy. In addition, expression levels of mTOR and downstream targets eIF4E, 4EBP1, S6K1, and S6 are potential diagnostic and prognostic biomarkers for head and neck cancer. mTOR inhibitors, such as rapamycin and its derivatives temsirolimus and everolimus, exhibit inhibitory effects on head and neck cancer in both in vitro cell line model and in vivo xenograft model. A large number of clinical trials have been initiated to evaluate the therapeutic effects of mTOR inhibitors on patients with head and neck cancer. mTOR inhibitor has potential as a single therapeutic agent or in combination with radiation, chemotherapeutic agents, or other targeted therapeutic agents to obtain synergistic repression on head and neck cancer.


title: Phase-Shift-Controlled Isolated Buck-Boost Converter With Active-Clamped Three-Level Rectifier (AC-TLR) Featuring Soft-Switching Within Wide Operation Range

abstract: An active-clamped three-level rectifier (AC-TLR) is derived from the diode-clamped three-level inverter, by replacing the active switches and the diodes in the three-level inverter with the diodes and active switches, respectively. Novel isolated buck-boost converters, featuring single-stage conversion and soft-switching within wide operation range, are developed based on the proposed AC-TLR. By utilizing the AC-TLR, the voltage stress on the power devices and passive components, including the rectifying diodes, the active-clamping switches, the flying capacitor, and output filter capacitors, is reduced to the half of the output voltage. Low-voltage rating switching devices with better switching and conduction performances and a transformer with reduced turns ratio and parasitic parameters are used to enhance the efficiency. The full-bridge isolated buck-boost converter with the proposed AC-TLR is analyzed in detail as an example. An optimized phase-shift control strategy is employed to realize isolated buck and boost conversion. Soft switching of all of the switching devices in both the primary- and secondary-side circuits is achieved within the whole operation range by using the proposed AC-TLR and the phase-shift control strategy. Experimental results on a prototype with 380-V output verify the effectiveness of the proposed AC-TLR and its derived isolated buck-boost converters.


title: The MetaCrawler Architecture for Resource Aggregation on the Web

abstract: The paper discusses the MetaCrawler Softbot parallel Web search service that has been available at the University of Washington since June 1995. It provides users with a single interface for querying popular general-purpose Web search services, such as Lycos and AltaVista, and has some sophisticated features that allow it to obtain results of much higher quality than simply regurgitating the output from each search service.


title: Automatic temporal layout mechanisms

abstract: A traditional static document has a spatial layout that indicates where objects in the document appear. Because multimedia documents incorporate time, they also require a temporal layout, or schedule, that indicates when events in the document occur. This paper argues that multimedia document systems should provide mechanisms for automatically producing temporal layouts for documents. The major advantage of this approach is that it makes it easier for authors to create and modify multimedia documents. ::: ::: This paper constructs a framework for understanding automatic temporal formatters and explores the basic issues surrounding them. It also describes the Firefly multimedia document system, which has been developed to test the potential of automatic temporal formatting.


title: iOLAP: Managing Uncertainty for Efficient Incremental OLAP

abstract: The size of data and the complexity of analytics continue to grow along with the need for timely and cost-effective analysis. However, the growth of computation power cannot keep up with the growth of data. This calls for a paradigm shift from traditional batch OLAP processing model to an incremental OLAP processing model. In this paper, we propose iOLAP, an incremental OLAP query engine that provides a smooth trade-off between query accuracy and latency, and fulfills a full spectrum of user requirements from approximate but timely query execution to a more traditional accurate query execution. iOLAP enables interactive incremental query processing using a novel mini-batch execution model---given an OLAP query, iOLAP first randomly partitions the input dataset into smaller sets (mini-batches) and then incrementally processes through these mini-batches by executing a delta update query on each mini-batch, where each subsequent delta update query computes an update based on the output of the previous one. The key idea behind iOLAP is a novel delta update algorithm that models delta processing as an uncertainty propagation problem, and minimizes the recomputation during each subsequent delta update by minimizing the uncertainties in the partial (including intermediate) query results. We implement iOLAP on top of Apache Spark and have successfully demonstrated it at scale on over 100 machines. Extensive experiments on a multitude of queries and datasets demonstrate that iOLAP can deliver approximate query answers for complex OLAP queries orders of magnitude faster than traditional OLAP engines, while continuously delivering updates every few seconds.


A Twitter Corpus for Hindi-English Code Mixed POS Tagging


A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension


Detecting Offensive Tweets in Hindi-English Code-Switched Language


title: Learning Background-Aware Correlation Filters for Visual Tracking

abstract: Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which can result in suboptimal performance. Recent tracking algorithms have suggested to resolve this drawback by either learning CFs from more discriminative deep features (e.g. DeepSRDCF [9] and CCOT [11]) or learning complex deep trackers (e.g. MDNet [28] and FCNT [33]). While such methods have been shown to work well, they suffer from high complexity: extracting deep features or applying deep tracking frameworks is very computationally expensive. This limits the real-time performance of such methods, even on high-end GPUs. This work proposes a Background-Aware CF based on hand-crafted features (HOG [6]) that can efficiently model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient- and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers.


Design and optimization of a magnetic wheel for a grit-blasting robot for use on ship hulls


title: Looking good: factors affecting the likelihood of having cosmetic surgery

abstract: The present study examined various factors associated with the likelihood of having cosmetic surgery in a community sample of Austrian participants. One-hundred and sixty-eight women and 151 men completed a questionnaire measuring how likely they were to consider common cosmetic procedures. The results showed that women were more likely than men to consider most cosmetic procedures. Path analysis revealed that personal experience of having had cosmetic surgery was a significant predictor of future likelihood, while media exposure (viewing advertisements or television programs, or reading articles about cosmetic surgery) mediated the influence of vicarious experience and sex. These results are discussed in relation to previous work examining the factors associated with the likelihood of having cosmetic surgery.


title: Satellite Image Resolution Enhancement Using Complex Wavelet Transform

abstract: In this letter, a satellite image resolution enhancement technique based on interpolation of the high-frequency subband images obtained by dual-tree complex wavelet transform (DT-CWT) is proposed. DT-CWT is used to decompose an input low-resolution satellite image into different subbands. Then, the high-frequency subband images and the input image are interpolated, followed by combining all these images to generate a new high-resolution image by using inverse DT-CWT. The resolution enhancement is achieved by using directional selectivity provided by the CWT, where the high-frequency subbands in six different directions contribute to the sharpness of the high-frequency details such as edges. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional bicubic interpolation, wavelet zero padding, and Irani and Peleg based image resolution enhancement techniques.


title: Use of relative code churn measures to predict system defect density

abstract: Software systems evolve over time due to changes in requirements, optimization of code, fixes for security and reliability bugs etc. Code churn, which measures the changes made to a component over a period of time, quantifies the extent of this change. We present a technique for early prediction of system defect density using a set of relative code churn measures that relate the amount of churn to other variables such as component size and the temporal extent of churn. Using statistical regression models, we show that while absolute measures of code chum are poor predictors of defect density, our set of relative measures of code churn is highly predictive of defect density. A case study performed on Windows Server 2003 indicates the validity of the relative code churn measures as early indicators of system defect density. Furthermore, our code churn metric suite is able to discriminate between fault and not fault-prone binaries with an accuracy of 89.0 percent.


title: Lower versus higher dose of enteral caloric intake in adult critically ill patients: a systematic review and meta-analysis

abstract: BACKGROUND ::: There is conflicting evidence about the relationship between the dose of enteral caloric intake and survival in critically ill patients. The objective of this systematic review and meta-analysis is to compare the effect of lower versus higher dose of enteral caloric intake in adult critically ill patients on outcome. ::: ::: ::: METHODS ::: We reviewed MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from inception through November 2015. We included randomized and quasi-randomized studies in which there was a significant difference in the caloric intake in adult critically ill patients, including trials in which caloric restriction was the primary intervention (caloric restriction trials) and those with other interventions (non-caloric restriction trials). Two reviewers independently extracted data on study characteristics, caloric intake, and outcomes with hospital mortality being the primary outcome. ::: ::: ::: RESULTS ::: Twenty-one trials mostly with moderate bias risk were included (2365 patients in the lower caloric intake group and 2352 patients in the higher caloric group). Lower compared with higher caloric intake was not associated with difference in hospital mortality (risk ratio (RR) 0.953; 95 % confidence interval (CI) 0.838-1.083), ICU mortality (RR 0.885; 95 % CI 0.751-1.042), total nosocomial infections (RR 0.982; 95 % CI 0.878-1.077), mechanical ventilation duration, or length of ICU or hospital stay. Blood stream infections (11 trials; RR 0.718; 95 % CI 0.519-0.994) and incident renal replacement therapy (five trials; RR 0.711; 95 % CI 0.545-0.928) were lower with lower caloric intake. The associations between lower compared with higher caloric intake and primary and secondary outcomes, including pneumonia, were not different between caloric restriction and non-caloric restriction trials, except for the hospital stay which was longer with lower caloric intake in the caloric restriction trials. ::: ::: ::: CONCLUSIONS ::: We found no association between the dose of caloric intake in adult critically ill patients and hospital mortality. Lower caloric intake was associated with lower risk of blood stream infections and incident renal replacement therapy (five trials only). The heterogeneity in the design, feeding route and timing and caloric dose among the included trials could limit our interpretation. Further studies are needed to clarify our findings.


title: Time domain synchronous OFDM based on simultaneous multi-channel reconstruction

abstract: Time domain synchronous OFDM (TDS-OFDM) can achieve a higher spectrum efficiency than standard cyclic prefix OFDM (CP-OFDM). Currently, it can support constellations up to 64QAM, but cannot support higher-order constellations like 256QAM due to the residual mutual interferences between the pseudorandom noise (PN) guard interval and the OFDM data block. To solve this problem, we break the traditional approach of iterative interference cancellation and propose the idea of using multiple inter-block-interference (IBI)-free regions of very small size to realize simultaneous multi-channel reconstruction under the framework of structured compressive sensing, whereby the sparsity nature of wireless channels as well as the characteristic that path delays vary much slower than path gains are jointly exploited. In this way, the mutually conditional time-domain channel estimation and frequency-domain data demodulation in TDS-OFDM can be decoupled without the use of IBI removal. We then propose the adaptive simultaneous orthogonal matching pursuit (A-SOMP) algorithm with low complexity to realize accurate multi-channel reconstruction, whose performance is close to the Cramer-Rao lower bound (CRLB). Simulation results confirm that the proposed scheme can support 256QAM without changing the current signal structure, so the spectrum efficiency can be increased by about 30%.


title: FFRob: An Efficient Heuristic for Task and Motion Planning

abstract: Manipulation problems involving many objects present substantial challenges for motion planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. In this paper, we show how to extend the heuristic ideas from one of the most successful symbolic planners in recent years, the FastForward (FF) planner, to motion planning, and to compute it efficiently. We use a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects.


title: Real-Time Erosion Using Shallow Water Simulation

abstract: We present a new real-time hydraulic erosion simulation for Computer Graphics. In our system water runs over the surface and disintegrates the underlying layer of soil. The grit is simulated as a fluid with higher viscosity and moves on the ground of the water pool. When water evaporates, or the dissolved soil exceeds a critical level, the dissolved matter is deposited back on the ground for accumulation to occur. The grit motion as well as the water simulation are calculated by the shallow water simulation that is a 2D simplification of Navier-Stokes equations. This simulation has proven to be useful in many Computer Graphics applications because of the speed of calculation and the visual plausibility of the results. Our experiments show that the shallow water-based erosion is suitable for real-time simulation of a wide variety of phenomena including river and lake formation due to rain and evaporation, erosion of surfaces affected by a sudden splash of high level of water, mountain erosion, etc. The speed of simulation makes the algorithm suitable for real-time surface modeling and editing.


TR 13-007 Nebula : Data Intensive Computing over Widely Distributed Voluntary Resources


title: A novel strategy for three-phase/switch/level (Vienna) rectifier under severe unbalanced grids

abstract: Unbalanced grids introduce performance deterioration for Vienna rectifier topology by producing twice fundamental frequency ripples in dc-link voltage and input active/reactive power. A common current reference generation for the purpose of eliminating the input power ripple, such as dual-frame hybrid vector control, can maintain constant input power and eliminate ripples in dc-link voltage under light voltage unbalanced grids. Under severe unbalanced grids, this type of a control method will fail to work. This paper first analyzes the theoretical operation area of a constant power control method under unbalanced grids, and then a novel control method is proposed. The proposed control method can work under severe unbalanced grids by injecting a small amount of input power ripple and balance the performance of working area and output dc voltage ripples. Finally, the experiment results using the constant power control method are given and validate the performance of the proposed control method.


title: Use of selected waste materials in concrete mixes

abstract: A modern lifestyle, alongside the advancement of technology has led to an increase in the amount and type of waste being generated, leading to a waste disposal crisis. This study tackles the problem of the waste that is generated from construction fields, such as demolished concrete, glass, and plastic. In order to dispose of or at least reduce the accumulation of certain kinds of waste, it has been suggested to reuse some of these waste materials to substitute a percentage of the primary materials used in the ordinary portland cement concrete (OPC). The waste materials considered to be recycled in this study consist of glass, plastics, and demolished concrete. Such recycling not only helps conserve natural resources, but also helps solve a growing waste disposal crisis. Ground plastics and glass were used to replace up to 20% of fine aggregates in concrete mixes, while crushed concrete was used to replace up to 20% of coarse aggregates. To evaluate these replacements on the properties of the OPC mixes, a number of laboratory tests were carried out. These tests included workability, unit weight, compressive strength, flexural strength, and indirect tensile strength (splitting). The main findings of this investigation revealed that the three types of waste materials could be reused successfully as partial substitutes for sand or coarse aggregates in concrete mixtures.


title: Dynapack: space-time compression of the 3D animations of triangle meshes with fixed connectivity

abstract: Dynapack exploits space-time coherence to compress the consecutive frames of the 3D animations of triangle meshes of constant connectivity. Instead of compressing each frame independently (space-only compression) or compressing the trajectory of each vertex independently (time-only compression), we predict the position of each vertex v of frame f from three of its neighbors in frame f and from the positions of v and of these neighbors in the previous frame (space-time compression). We introduce here two extrapolating spacetime predictors: the ELP extension of the Lorenzo predictor, developed originally for compressing regularly sampled 4D data sets, and the Replica predictor. ELP may be computed using only additions and subtractions of points and is a perfect predictor for portions of the animation undergoing pure translations. The Replica predictor is slightly more expensive to compute, but is a perfect predictor for arbitrary combinations of translations, rotations, and uniform scaling. For the typical 3D animations that we have compressed, the corrections between the actual and predicted value of the vertex coordinates may be compressed using entropy coding down to an average ranging between 1.37 and 2.91 bits, when the quantization used ranges between 7 and 13 bits. In comparison, space-only compression yields a range of 1.90 to 7.19 bits per coordinate and time-only compressions yields a range of 1.77 to 6.91 bits per coordinate. The implementation of the Dynapack compression and decompression is trivial and extremely fast. It perform a sweep through the animation, only accessing two consecutive frames at a time. Therefore, it is particularly well suited for realtime and out-of-core compression, and for streaming decompression.


title: Characterization and conductivity studies of nanocomposite films with polymer networks containing polyaniline-alginate/titanium dioxide

abstract: Polyaniline nanocomposite films were chemically synthesized in the presence of alginate template by varying the concentration of TiO2 in the composites. Characterization of the composite samples by FTIR, UV-Vis spectra (UV), and X-ray diffraction (XRD) indicates the formation of polyaniline-alginate/titanium dioxide (PAT) composites. The morphology analysis by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) also supports the formation of the composites. Temperature-dependent DC conductivity of the polyaniline-alginate (PA) and PAT composites was studied in the range of 300 ≤ T ≤ 500 K. UV-Vis and FTIR spectral studies reveal that the alginate is a good template for the chemical interaction between polyaniline and TiO2, which suggests that the micelles formed by the anilinium-alginate cations containing TiO2 are responsible for the transport properties of the PAT composites. POLYM. COMPOS., 31:1754–1761, 2010. © 2010 Society of Plastics Engineers.


title: High-Frequency Modeling of TSVs for 3-D Chip Integration and Silicon Interposers Considering Skin-Effect, Dielectric Quasi-TEM and Slow-Wave Modes

abstract: Through-silicon vias (TSVs) in low, medium and high resistivity silicon for 3-D chip integration and interposers are modeled and thoroughly characterized from 100 MHz to 130 GHz, considering the slow-wave, dielectric quasi-TEM and skin-effect modes. The frequency ranges of these modes and their transitions are predicted using resistivity-frequency domain charts. The impact of the modes on signal integrity is quantified, and three coaxial TSV configurations are proposed to minimize this impact. Finally, conventional expressions for calculating the per-unit-length circuit parameters of transmission lines are extended and used to analytically capture the frequency dependent behavior of TSVs, considering the impact of the mixed dielectric (silicon dioxide-silicon-silicon dioxide) around the TSVs. Excellent correlation is obtained between the analytical calculations using the extended expressions and electromagnetic field simulations up to 130 GHz. These extended expressions can be implemented directly in electronic design automation tools to facilitate performance evaluation of TSVs, prior to system design.


title: Understanding fraudulent activities in online ad exchanges

abstract: Online advertisements (ads) provide a powerful mechanism for advertisers to effectively target Web users. Ads can be customized based on a user's browsing behavior, geographic location, and personal interests. There is currently a multi-billion dollar market for online advertising, which generates the primary revenue for some of the most popular websites on the Internet. In order to meet the immense market demand, and to manage the complex relationships between advertisers and publishers (i.e., the websites hosting the ads), marketplaces known as "ad exchanges" are employed. These exchanges allow publishers (sellers of ad space) and advertisers(buyers of this ad space) to dynamically broker traffic through ad networks to efficiently maximize profits for all parties. Unfortunately, the complexities of these systems invite a considerable amount of abuse from cybercriminals, who profit at the expense of the advertisers. In this paper, we present a detailed view of how one of the largest ad exchanges operates and the associated security issues from the vantage point of a member ad network. More specifically, we analyzed a dataset containing transactions for ingress and egress ad traffic from this ad network. In addition, we examined information collected from a command-and-control server used to operate a botnet that is leveraged to perpetrate ad fraud against the same ad exchange.


title: Evidence for a basic level in a taxonomy of everyday action sounds

abstract: We searched for evidence that the auditory organization of categories of sounds produced by actions includes a privileged or “basic” level of description. The sound events consisted of single objects (or substances) undergoing simple actions. Performance on sound events was measured in two ways: sounds were directly verified as belonging to a category, or sounds were used to create lexical priming. The category verification experiment measured the accuracy and reaction time to brief excerpts of these sounds. The lexical priming experiment measured reaction time benefits and costs caused by the presentation of these sounds prior to a lexical decision. The level of description of a sound varied in how specifically it described the physical properties of the action producing the sound. Both identification and priming effects were superior when a label described the specific interaction causing the sound (e.g. trickling) in comparison to the following: (1) more general descriptions (e.g. pour, liquid: trickling is a specific manner of pouring liquid), (2) more detailed descriptions using adverbs to provide detail regarding the manner of the action (e.g. trickling evenly). These results are consistent with neuroimaging studies showing that auditory representations of sounds produced by actions familiar to the listener activate motor representations of the gestures involved in sound production.


title: Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions

abstract: There are natural synergies between shared autonomous vehicle (AV) fleets and electric vehicle (EV) technology, since fleets of AVs resolve the practical limitations of today’s non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous electric vehicles (SAEVs) in a regional, discrete-time, agent-based model. The simulation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas.


title: Learning Fast Classifiers for Image Spam.

abstract: Recently, spammers have proliferated “image spam”, emails which contain the text of the spam message in a human readable image instead of the message body, making detection by conventional content filters difficult. New techniques are needed to filter these messages. Our goal is to automatically classify an image directly as being spam or ham. We present features that focus on simple properties of the image, making classification as fast as possible. Our evaluation shows that they accurately classify spam images in excess of 90% and up to 99% on real world data. Furthermore, we introduce a new feature selection algorithm that selects features for classification based on their speed as well as predictive power. This technique produces an accurate system that runs in a tiny fraction of the time. Finally, we introduce Just in Time (JIT) feature extraction, which creates features at classification time as needed by the classifier. We demonstrate JIT extraction using a JIT decision tree that further increases system speed. This paper makes image spam classification practical by providing both high accuracy features and a method to learn fast classifiers.


title: Review of flywheel based energy storage systems

abstract: In flywheel based energy storage systems, a flywheel stores mechanical energy that interchanges in form of electrical energy by means of an electrical machine with a bidirectional power converter. Flywheel based energy storage systems are suitable whenever numerous charge and discharge cycles (hundred of thousands) are needed with medium to high power (kW to MW) during short periods (seconds). The materials for the flywheel, the type of electrical machine, the type of bearings and the confinement atmosphere determine the energy efficiency (>85%) of the flywheel based energy storage systems. Monitoring of state of charge is simple and reliable as only spinning speed is needed. Flywheel based energy storage systems are commercially available with more than a dozen of manufacturers. Amongst the applications of flywheel based energy storage systems are: uninterruptible power supplies, hybrid power systems, power grids feeding trains, hybrid vehicles and space satellites.


Identifying Word Correspondences In Parallel Texts


title: Sum and Difference Histograms for Texture Classification

abstract: The sum and difference of two random variables with same variances are decorrelated and define the principal axes of their associated joint probability function. Therefore, sum and difference histograms are introduced as an alternative to the usual co-occurrence matrices used for texture analysis. Two maximum likelihood texture classifiers are presented depending on the type of object used for texture characterization (sum and difference histograms or some associated global measures). Experimental results indicate that sum and difference histograms used conjointly are nearly as powerful as cooccurrence matrices for texture discrimination. The advantage of the proposed texture analysis method over the conventional spatial gray level dependence method is the decrease in computation time and memory storage.


title: Mosaic: Processing a Trillion-Edge Graph on a Single Machine

abstract: Processing a one trillion-edge graph has recently been demonstrated by distributed graph engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single heterogeneous machine with fast storage media (e.g., NVMe SSD) and massively parallel coprocessors (e.g., Xeon Phi) to reach similar dimensions. By fully exploiting the heterogeneous devices, we design a new graph processing engine, named Mosaic, for a single machine. We propose a new locality-optimizing, space-efficient graph representation---Hilbert-ordered tiles, and a hybrid execution model that enables vertex-centric operations in fast host processors and edge-centric operations in massively parallel coprocessors. Our evaluation shows that for smaller graphs, Mosaic consistently outperforms other state-of-the-art out-of-core engines by 3.2-58.6x and shows comparable performance to distributed graph engines. Furthermore, Mosaic can complete one iteration of the Pagerank algorithm on a trillion-edge graph in 21 minutes, outperforming a distributed disk-based engine by 9.2×.


title: Is the manchester mobility score a valid and reliable measure of physical function within the intensive care unit

abstract: Early and structured rehabilitation programmes have been shown to decrease both critical care and hospital length of stay (LOS) [1] as well as improve functional ability at the point of critical care discharge [2]. At present there is no general or universally accepted method for measuring mobility within the critical care unit or to track rehabilitation progress [3]. The Manchester Mobility Score was developed in 2005 as one such tool to describe the levels of mobility seen within critical care. Since development, the MMS has been used and adapted in several large critical care units within the UK, but has not previously been investigated in terms of validity and reliability


title: Verbalization: narration of autonomous robot experience

abstract: Autonomous mobile robots navigate in our spaces by planning and executing routes to destinations. When a mobile robot appears at a location, there is no clear way to understand what navigational path the robot planned and experienced just by looking at it. In this work, we address the generation of narrations of autonomous mobile robot navigation experiences. We contribute the concept of verbalization as a parallel to the well-studied concept of visualization. Through verbalizations, robots can describe through language what they experience, in particular in their paths. For every executed path, we consider many possible verbalizations that could be generated. We introduce the verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans. We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. We believe our verbalization space and algorithm are applicable to different narrative aspects for many mobile robots, including autonomous cars.


title: Low-Pass and Bandpass Filters With Ultra-Broad Stopband Bandwidth Based on Directional Couplers

abstract: The design of planar sharp-rejection low-pass and bandpass filters (LPFs and BPFs) featuring a very broad stopband is addressed in this paper. For the LPF case, two new classes of bandstop signal-interference transversal filtering section (TFS) consisting of power directional couplers in reflection mode are proposed. In the BPF application, additional short-circuit stubs are inserted at the TFS input/output accesses. Based on these filtering cells, the devised LPF and BPF approaches are then derived from the cascade of multiple TFSs with frequency-contiguous stopbands. Thus, an overall attenuated band showing an ultra-large spectral width and deep rejection levels through the generation of several transmission zeros (TZs) is synthesized. Furthermore, such unprecedented filtering performances are obtained for much lower circuit complexity than available solutions. The operating principle of the conceived LPF and BPF topologies and guidelines for their realization are detailed. This includes formulas for their analytically designable TZs. For practical validation, three LPF prototypes and one wideband BPF circuit are also fabricated in microstrip technology and tested.


title: A Fully Integrated HF-Band Passive RFID Tag IC Using 0.18- $\mu\hbox{m}$ CMOS Technology for Low-Cost Security Applications

abstract: We present a fully integrated small-size HF-band passive RF identification (RFID) tag chip with authentication and security functions. The design of the RF transceiver and digital control of the tag IC is based on the International Organization for Standardization-14443 type-B protocol. The design of the key analog part of the tag IC is presented, which includes a robust demodulator for 10% amplitude shift keying envelope detection, a high-quality random number generator, and a voltage regulator that can handle a range of output load currents. To implement the secure data transaction with a reader, a 128-b advanced encryption standard (AES) with a new cyclic key generation is used for the data encryption and decryption. An on-chip 4-kb electrically erasable programmable ROM (EEPROM) is used to support the AES operation, tag identification, and tag self-destruction. The read and write accesses of the EEPROM are performed using a 128-b wide buffer with self-timed bursts. The tag chip is fabricated in a one-poly six-metal low-power 0.18-μm CMOS process with a CoSi2 Schottky diode and EEPROM process. Using the scaled-down CMOS technology, the size of the tag chip is only 1.1 × 1 mm2, providing a cost-effective solution for everyday RFID applications.


title: Big data analytics: The stakes for students, scientists&managers - a management perspective

abstract: For a developing nation, deploying big data (BD) technology and introducing data science in higher education is a challenge. A pessimistic scenario is: Mis-use of data in many possible ways, waste of trained manpower, poor BD certifications from institutes, under-utilization of resources, disgruntled management staff, unhealthy competition in the market, poor integration with existing technical infrastructures. Also, the questions in the minds of students, scientists, engineers, teachers and managers deserve wider attention. Besides the stated perceptions and analyses perhaps ignoring socio-political and scientific temperaments in developing nations, the following questions arise: How did the BD phenomenon naturally occur, post technological developments in Computer and Communications Technology and how did different experts react to it? Are academicians elsewhere agreeing on the fact that BD is a new science? Granted that big data science is a new science what are its foundations as compared to conventional topics in Physics, Chemistry or Biology? Or, is it similar to astronomy or nuclear science? What are the technological and engineering implications and how these can be advantageously used to augment business intelligence, for example? Will the industry adopt the changes due to tactical advantages? How can BD success stories be carried over elsewhere? How will BD affect the Computer Science and other curricula? How will BD benefit different segments of our society on a large scale? To answer these, an appreciation of the BD as a science and as a technology is necessary. This paper presents a quick BD overview, relying on the contemporary literature; it addresses: characterizations of BD and the BD people, the background required for the students and teachers to join the BD bandwagon, the management challenges in embracing BD.


title: Bringing Deep Learning at the Edge of Information-Centric Internet of Things

abstract: Various Internet solutions take their power processing and analysis from cloud computing services. Internet of Things (IoT) applications started discovering the benefits of computing, processing, and analysis on the device itself aiming to reduce latency for time-critical applications. However, on-device processing is not suitable for resource-constraints IoT devices. Edge computing (EC) came as an alternative solution that tends to move services and computation more closer to consumers, at the edge. In this letter, we study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network (RNN), and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept. Therefore, a CNN model can be used in the IoT area to exploit reliably data from a complex environment. Moreover, RL and RNN have been recently integrated into IoT, which can be used to take the multi-modality of data in real-time applications into account.


Challenges of adopting agile methods in a public organization


title: QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts

abstract: Large inter-datacenter transfers are crucial for cloud service efficiency and are increasingly used by organizations that have dedicated wide area networks between datacenters. A recent work uses multicast forwarding trees to reduce the bandwidth needs and improve completion times of point-to-multipoint transfers. Using a single forwarding tree per transfer, however, leads to poor performance because the slowest receiver dictates the completion time for all receivers. Using multiple forwarding trees per transfer alleviates this concern--the average receiver could finish early; however, if done naively, bandwidth usage would also increase and it is apriori unclear how best to partition receivers, how to construct the multiple trees and how to determine the rate and schedule of flows on these trees. This paper presents QuickCast, a first solution to these problems. Using simulations on real-world network topologies, we see that QuickCast can speed up the average receiver's completion time by as much as $10\times$ while only using $1.04\times$ more bandwidth; further, the completion time for all receivers also improves by as much as $1.6\times$ faster at high loads.


title: Adaptive Quad-Element Multi-Wideband Antenna Array for User-Effective LTE MIMO Mobile Terminals

abstract: An LTE MIMO antenna array, which is adaptive to the effects of the user's body, is presented for mobile terminals. The bands of 750-960 MHz and 1700-2700 MHz are covered with a good efficiency in free space. Three kinds of user effects are studied, namely, “SAM head and PDA hand,” “PDA hand,” and “dual hands.” The array is formed by selecting the best two elements out of four, with the two ports and grounding points of the other two elements open. The user effects on MIMO channel capacity are reduced through the adaptive selection of two optimal antenna elements. The total efficiency, envelope correlation coefficient, and multiplexing efficiency are presented for the three kinds of user effects. In the lower band, the decreased correlation due to the optimal selection improves the multiplexing efficiency. The underlying physical mechanisms and some general rules are discussed. The specific absorption rate (SAR) in the “SAM head and PDA hand” case is studied. Experiments for the three kinds of user effects are also carried out.


title: Predictive Models for Imbalanced Data: A School Dropout Perspective

abstract: Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the students who have the tendency to dropout. This problem often encounters a phenomenon that masks out the obtained results. This study delves into this phenomenon and provides a reliable educational data mining technique that accurately predicts the dropout rates. In particular, the three data classifying techniques, namely, decision tree, neural networks and Balanced Bagging, are used. The performances of these classifies are tested with and without the use of a downsample, SMOTE and ADASYN data balancing. It is found that among other parameters geometric mean and UAR provides reliable results while predicting the dropout rates using Balanced Bagging classifying techniques.


title: Stance detection in online discussions

abstract: This paper describes our system created to detect stance in online discussions. The goal is to identify whether the author of a comment is in favor of the given target or against. Our approach is based on a maximum entropy classifier, which uses surface-level, sentiment and domain-specific features. The system was originally developed to detect stance in English tweets. We adapted it to process Czech news commentaries.