title: Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

abstract: When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.


title: Stuxnet worm impact on industrial cyber-physical system security

abstract: Industrial systems consider only partially security, mostly relying on the basis of “isolated” networks, and controlled access environments. Monitoring and control systems such as SCADA/DCS are responsible for managing critical infrastructures operate in these environments, where a false sense of security assumptions is usually made. The Stuxnet worm attack demonstrated widely in mid 2010 that many of the security assumptions made about the operating environment, technological capabilities and potential threat risk analysis are far away from the reality and challenges modern industrial systems face. We investigate in this work the highly sophisticated aspects of Stuxnet, the impact that it may have on existing security considerations and pose some thoughts on the next generation SCADA/DCS systems from a security perspective.


title: A Shallow Network with Combined Pooling for Fast Traffic Sign Recognition

abstract: Traffic sign recognition plays an important role in intelligent transportation systems. Motivated by the recent success of deep learning in the application of traffic sign recognition, we present a shallow network architecture based on convolutional neural networks (CNNs). The network consists of only three convolutional layers for feature extraction, and it learns in a backward optimization way. We propose the method of combining different pooling operations to improve sign recognition performance. In view of real-time performance, we use the activation function ReLU to improve computational efficiency. In addition, a linear layer with softmax-loss is taken as the classifier. We use the German traffic sign recognition benchmark (GTSRB) to evaluate the network on CPU, without expensive GPU acceleration hardware, under real-world recognition conditions. The experiment results indicate that the proposed method is effective and fast, and it achieves the highest recognition rate compared with other state-of-the-art algorithms.


title: An Entropy-based Pruning Method for CNN Compression

abstract: This paper aims to simultaneously accelerate and compress off-the-shelf CNN models via filter pruning strategy. The importance of each filter is evaluated by the proposed entropy-based method first. Then several unimportant filters are discarded to get a smaller CNN model. Finally, fine-tuning is adopted to recover its generalization ability which is damaged during filter pruning. Our method can reduce the size of intermediate activations, which would dominate most memory footprint during model training stage but is less concerned in previous compression methods. Experiments on the ILSVRC-12 benchmark demonstrate the effectiveness of our method. Compared with previous filter importance evaluation criteria, our entropy-based method obtains better performance. We achieve 3.3x speed-up and 16.64x compression on VGG-16, 1.54x acceleration and 1.47x compression on ResNet-50, both with about 1% top-5 accuracy decrease.


title: Hierarchical Convolutional Features for Visual Tracking

abstract: Visual object tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we exploit features extracted from deep convolutional neural networks trained on object recognition datasets to improve tracking accuracy and robustness. The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations. However, their spatial resolution is too coarse to precisely localize targets. In contrast, earlier convolutional layers provide more precise localization but are less invariant to appearance changes. We interpret the hierarchies of convolutional layers as a nonlinear counterpart of an image pyramid representation and exploit these multiple levels of abstraction for visual tracking. Specifically, we adaptively learn correlation filters on each convolutional layer to encode the target appearance. We hierarchically infer the maximum response of each layer to locate targets. Extensive experimental results on a largescale benchmark dataset show that the proposed algorithm performs favorably against state-of-the-art methods.


title: AraNLP: a Java-based Library for the Processing of Arabic Text.

abstract: We present a free, Java-based library named “AraNLP” that covers various Arabic text preprocessing tools. Although a good number of tools for processing Arabic text already exist, integration and compatibility problems continually occur. AraNLP is an attempt to gather most of the vital Arabic text preprocessing tools into one library that can be accessed easily by integrating or accurately adapting ::: existing tools and by developing new ones when required. The library includes a sentence detector, tokenizer, light stemmer, root stemmer, part-of-speech tagger (POS-tagger), word segmenter, normalizer, and a punctuation and diacritic remover.


title: Holey Graphene as a Weed Barrier for Molecules

abstract: We demonstrate the use of “holey” graphene as a mask against molecular adsorption. Prepared porous graphene is transferred onto a Au{111} substrate, annealed, and then exposed to dilute solutions of 1-adamantanethiol. In the pores of the graphene lattice, we find islands of organized, self-assembled molecules. The bare Au in the pores can be regenerated by postdeposition annealing, and new molecules can be self-assembled in the exposed Au region. Graphene can serve as a robust, patternable mask against the deposition of self-assembled monolayers.


Zero-shot Learning of Classifiers from Natural Language Quantification


title: Sparse Deep Transfer Learning for Convolutional Neural Network

abstract: Extensive studies have demonstrated that the representations of convolutional neural networks (CNN), which are learned from a large-scale data set in the source domain, can be effectively transferred to a new target domain. However, compared to the source domain, the target domain often has limited data in practice. In this case, overfitting may significantly depress transferability, due to the model redundancy of the intensive CNN structures. To deal with this difficulty, we propose a novel sparse deep transfer learning approach for CNN. There are three main contributions in this work. First, we introduce a Sparse-SourceNet to reduce the redundancy in the source domain. Second, we introduce a Hybrid-TransferNet to improve the generalization ability and the prediction accuracy of transfer learning, by taking advantage of both model sparsity and implicit knowledge. Third, we introduce a Sparse-TargetNet, where we prune our Hybrid-TransferNet to obtain a highly-compact, source-knowledge-integrated CNN in the target domain. To examine the effectiveness of our methods, we perform our sparse deep transfer learning approach on a number of benchmark transfer learning tasks. The results show that, compared to the standard fine-tuning approach, our proposed approach achieves a significant pruning rate on CNN while improves the accuracy of transfer learning.


title: Experiments with Convolutional Neural Network Models for Answer Selection

abstract: In recent years, neural networks have been applied to many text processing problems. One example is learning a similarity function between pairs of text, which has applications to paraphrase extraction, plagiarism detection, question answering, and ad hoc retrieval. Within the information retrieval community, the convolutional neural network model proposed by Severyn and Moschitti in a SIGIR 2015 paper has gained prominence. This paper focuses on the problem of answer selection for question answering: we attempt to replicate the results of Severyn and Moschitti using their open-source code as well as to reproduce their results via a de novo (i.e., from scratch) implementation using a completely different deep learning toolkit. Our de novo implementation is instructive in ascertaining whether reported results generalize across toolkits, each of which have their idiosyncrasies. We were able to successfully replicate and reproduce the reported results of Severyn and Moschitti, albeit with minor differences in effectiveness, but affirming the overall design of their model. Additional ablation experiments break down the components of the model to show their contributions to overall effectiveness. Interestingly, we find that removing one component actually increases effectiveness and that a simplified model with only four word overlap features performs surprisingly well, even better than convolution feature maps alone.


Years of Piezoelectric Transformers . Trends In The Technology


title: A new upsampling method for mobile LiDAR data

abstract: We present a novel method to upsample mobile LiDAR data using panoramic images collected in urban environments. Our method differs from existing methods in the following aspects: First, we consider point visibility with respect to a given viewpoint, and use only visible points for interpolation; second, we present a multi-resolution depth map based visibility computation method; third, we present ray casting methods for upsampling mobile LiDAR data incorporating constraints from color information of spherical images. The experiments show the effectiveness of the proposed approach.


Temporal Reasoning in Videos using Convolutional Gated Recurrent Units


title: VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users

abstract: This paper presents a mathematical model that has been created from the subjective MOS, instead of modifying or improving the existing objective measurement methods (e.g., E-model) for VoIP quality measurement. The proposed model of VoIP quality measurement method is based on native Thai users who communicate to each other using Thai language, which is a tonal language, unlike English and most western languages. The data have been gathered using conversation-opinion tests with 400 and 354 native Thai subjects for two popular codecs, G.711 and G.729, respectively, referring to effects from two major network factors, packet loss and packet delay. This model is called the Thai subjective VoIP quality evaluation model (ThaiVQE). It has been evaluated using two test sets of subjective MOS, from 50 native Thai subjects for G.711 and 64 native Thai subjects for G.729, then the results have been compared with the E-model results. Based on native Thai users, the evaluation result surprisingly shows that ThaiVQE can contribute better accuracy and reliability than the standard E-model with error reduction of over 13 % for G.711 and 28 % for G.729. Therefore, this is an example study for other countries that have their own languages and cultures to create their subjective MOS model.


title: Computing Partitions with Applications to the Knapsack Problem

abstract: Given <italic>r</italic> numbers <italic>s</italic><subscrpt>1</subscrpt>, ···, <italic>s<subscrpt>r</subscrpt></italic>, algorithms are investigated for finding all possible combinations of these numbers which sum to <italic>M</italic>. This problem is a particular instance of the 0-1 unidimensional knapsack problem. All of the usual algorithms for this problem are investigated in terms of both asymptotic computing times and storage requirements, as well as average computing times. We develop a technique which improves all of the dynamic programming methods by a square root factor. Empirical studies indicate this new algorithm to be generally superior to all previously known algorithms. We then show how this improvement can be incorporated into the more general 0-1 knapsack problem obtaining a square root improvement in the asymptotic behavior. A new branch and search algorithm that is significantly faster than the Greenberg and Hegerich algorithm is also presented. The results of extensive empirical studies comparing these knapsack algorithms are given


title: Efficient video text recognition using multiple frame integration

abstract: Text superimposed on the video frames provides supplemental but important information for video indexing and retrieval. Many efforts have been made for videotext detection and recognition (video OCR). The main difficulties of video OCR are the low resolution and the background complexity. We present efficient schemes to deal with the second difficulty by sufficiently utilizing multiple frames that contain the same text to get every clear word from these frames. Firstly, we use multiple frame verification to reduce text detection false alarms. We then choose those frames where the text is most likely clear, thus it is more possible to be correctly recognized. We then detect and joint every clear text block from those frames to form a clearer "man-made" frame. Later we apply a block-based adaptive thresholding procedure on these "man-made" frames. Finally, the binarized frames are sent to an OCR engine for recognition. Experiments show that the word recognition rate has been increased over 28% by these methods.


title: Smart Grid: The Electric Energy System of the Future

abstract: Three dominant factors are impacting future electric systems of the world: governmental policies at both federal and state levels, customer efficiency needs, and new intelligent computer software and hardware technologies. In addition, environmental concerns are driving the entire energy system to efficiency, conservation, and renewable sources of electricity. Customers are becoming more proactive and are being empowered to engage in energy consumption decisions affecting their day-to-day lives. At the same time, energy needs are continually expanding. For example, consumer participation will soon include extensive use of electric vehicles (both cars and trucks), remote control of in-home appliances to promote energy conservation, ownership of distributed generation from ever more renewable energy sources, and management of electricity storage to locally match supply to demand. The availability of new technologies such as distributed sensors, two-way secure communication, advanced software for data management, and intelligent and autonomous controllers has opened up new opportunities for changing the energy system. For instance, while networking technologies and systems have been greatly enhanced, the Smart Grid faces challenges in terms of reliability and security in both wired and wireless communication environments. In particular, smart home appliances represent a major part of the Smart Grid vision, which aims at increasing energy efficiency. To achieve this goal, home appliances need to communicate with entities and players in other Smart Grid domains via home area networks. Therefore, the electric system of the future will address all these needs and concerns by using new advanced technologies to create a smarter, more efficient and sustainable grid. Although many different definitions have been proposed for the Smart Grid, in most cases, the users have chosen narrowly focused definitions related to their specific applications and local needs. The main objective of this special issue is to report on some, if not all, of the technical challenges posed by this conversion. While acknowledging its limited coverage, this special issue offers a range of valuable contributions. For the benefit of readers before beginning your excursion, we first provide a description of the current conventional electric energy system. We then identify the key areas that must change in order to provide the intelligence and control necessary to convert to the safe, secure, and efficient Smart Grid of the future.


title: A Hybrid Model for Day-Ahead Price Forecasting

abstract: This paper presents a hybrid time-series and adaptive wavelet neural network (AWNN) model for the day-ahead electricity market clearing price forecast. Instead of using price series, one-period continuously compounded return series is used to achieve more attractive statistical properties. The autoregressive moving average with exogenous variables (ARMAX) model is used to catch the linear relationship between price return series and explanatory variable load series, the generalized autoregressive conditional heteroscedastic (GARCH) model is used to unveil the heteroscedastic character of residuals, and AWNN is used to present the nonlinear, nonstationary impact of load series on electricity prices. The Monte Carlo method is adopted to generate more evenly distributed random numbers used for time series and AWNN models to accelerate the convergence. Several criteria such as average mean absolute percentage error (AMAPE) and the variance of forecast errors are used to assess the model and measure the forecasting accuracy. Illustrative price forecasting examples of the PJM market are presented to show the efficiency of the proposed method.


title: Assessing self-critical perfectionism in clinical depression.

abstract: Several facets of perfectionism have been strongly associated with depression and anxiety. Dunkley and Blankstein (2000) combined these maladaptive traits with self-criticism to create a general construct labeled self-critical perfectionism. In this study, we employed confirmatory factor analysis to evaluate a model for assessing self-critical perfectionism in a clinically depressed sample using scales from 3 instruments. Participants were 356 depressed adult outpatients who completed 2 multidimensional measures of perfectionism and a measure of self-criticism. A confirmatory factor model that separated a self-critical perfectionism construct from a more adaptive, achievement-striving component of perfectionism was supported. A composite scale assessing self-critical perfectionism demonstrated much larger correlations with distress measures compared to a composite scale assessing achievement striving and also showed evidence of discriminant validity. In this study, we provided further support for the valid assessment of self-critical perfectionism and extended evidence for its assessment to a clinically depressed sample.


Predictive Analytics of E-Commerce Search Behavior for Conversion


title: Understanding and mitigating refresh overheads in high-density DDR4 DRAM systems

abstract: Recent DRAM specifications exhibit increasing refresh latencies. A refresh command blocks a full rank, decreasing available parallelism in the memory subsystem significantly, thus decreasing performance. Fine Granularity Refresh (FGR) is a feature recently announced as part of JEDEC's DDR4 DRAM specification that attempts to tackle this problem by creating a range of refresh options that provide a trade-off between refresh latency and frequency. In this paper, we first conduct an analysis of DDR4 DRAM's FGR feature, and show that there is no one-size-fits-all option across a variety of applications. We then present Adaptive Refresh (AR), a simple yet effective mechanism that dynamically chooses the best FGR mode for each application and phase within the application. When looking at the refresh problem more closely, we identify in high-density DRAM systems a phenomenon that we call command queue seizure, whereby the memory controller's command queue seizes up temporarily because it is full with commands to a rank that is being refreshed. To attack this problem, we propose two complementary mechanisms called Delayed Command Expansion (DCE) and Preemptive Command Drain (PCD). Our results show that AR does exploit DDR4's FGR effectively. However, once our proposed DCE and PCD mechanisms are added, DDR4's FGR becomes redundant in most cases, except in a few highly memory-sensitive applications, where the use of AR does provide some additional benefit. In all, our simulations show that the proposed mechanisms yield 8% (14%) mean speedup with respect to traditional refresh, at normal (extended) DRAM operating temperatures, for a set of diverse parallel applications.


title: Mining evolutionary multi-branch trees from text streams

abstract: Understanding topic hierarchies in text streams and their evolution patterns over time is very important in many applications. In this paper, we propose an evolutionary multi-branch tree clustering method for streaming text data. We build evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which considers both the likelihood of the current tree and conditional prior given the previous tree. We also introduce a constraint model to compute the conditional prior of a tree in the multi-branch setting. Experiments on real world news data demonstrate that our algorithm can better incorporate historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm.


title: Response of brain tissue to chronically implanted neural electrodes

abstract: Chronically implanted recording electrode arrays linked to prosthetics have the potential to make positive impacts on patients suffering from full or partial paralysis. Such arrays are implanted into the patient's cortical tissue and record extracellular potentials from nearby neurons, allowing the information encoded by the neuronal discharges to control external devices. While such systems perform well during acute recordings, they often fail to function reliably in clinically relevant chronic settings. Available evidence suggests that a major failure mode of electrode arrays is the brain tissue reaction against these implants, making the biocompatibility of implanted electrodes a primary concern in device design. This review presents the biological components and time course of the acute and chronic tissue reaction in brain tissue, analyses the brain tissue response of current electrode systems, and comments on the various material science and bioactive strategies undertaken by electrode designers to enhance electrode performance.


title: (Short Paper) Burning Zerocoins for Fun and for Profit - A Cryptographic Denial-of-Spending Attack on the Zerocoin Protocol

abstract: Zerocoin (Miers et. al, IEEE S&P'13), designed as an extension to Bitcoin and similar cryptocurrencies, was the first anonymous cryptocurrency proposal which supports large anonymity sets. We identify a cryptographic denial-of-spending attack on the original Zerocoin protocol and a second Zerocoin protocol (Groth and Kohlweiss, EUROCRYPT'15), which enables a network attacker to destroy money of honest users. The attack leads to real-world vulnerabilities in multiple cryptocurrencies, which rely on implementations of the original Zerocoin protocol. The existence of the attack does not contradict the formal security analyses of the two Zerocoin protocols but exposes the lack of an important missing property in the security model of Zerocoin. While the security definitions model that the attacker should not be able to create money out of thin air or steal money from honest users, they do not model that the attacker cannot destroy money of honest users. Fortunately, there are simple fixes for the security model and for both protocols.


Library as safe haven: Disaster planning, response and recovery: A how-to-do-it manual for librarians


title: Artificial vision by multi-layered neural networks: Neocognitron and its advances

abstract: The neocognitron is a neural network model proposed by Fukushima (1980). Its architecture was suggested by neurophysiological findings on the visual systems of mammals. It is a hierarchical multi-layered network. It acquires the ability to robustly recognize visual patterns through learning. Although the neocognitron has a long history, modifications of the network to improve its performance are still going on. For example, a recent neocognitron uses a new learning rule, named add-if-silent, which makes the learning process much simpler and more stable. Nevertheless, a high recognition rate can be kept with a smaller scale of the network. Referring to the history of the neocognitron, this paper discusses recent advances in the neocognitron. We also show that various new functions can be realized by, for example, introducing top-down connections to the neocognitron: mechanism of selective attention, recognition and completion of partly occluded patterns, restoring occluded contours, and so on.


title: Vertex reconstruction of neutrino interactions using deep learning

abstract: Deep learning offers new tools to improve our understanding of many important scientific problems. Neutrinos are the most abundant particles in existence and are hypothesized to explain the matter-antimatter asymmetry that dominates our universe. Definitive tests of this conjecture require a detailed understanding of neutrino interactions with a variety of nuclei. Many measurements of interest depend on vertex reconstruction — finding the origin of a neutrino interaction using data from the detector, which can be represented as images. Traditionally, this has been accomplished by utilizing methods that identify the tracks coming from the interaction. However, these methods are not ideal for interactions where an abundance of tracks and cascades occlude the vertex region. Manual algorithm engineering to handle these challenges is complicated and error prone. Deep learning extracts rich, semantic features directly from raw data, making it a promising solution to this problem. In this work, deep learning models are presented that classify the vertex location in regions meaningful to the domain scientists improving their ability to explore more complex interactions.


title: Crypto trust point (cTp) for secure data sharing among intelligent vehicles

abstract: Tremendous amount of research is going on in the field of Intelligent vehicles (IVs)in industries and academics. Although, IV supports a better convenience for the society, but it also suffers from some concerns. Security is the major concern in Intelligent vehicle technology, due to its high exposure to data and information communication. The environment of the IV communication has many security vulnerabilities, which cannot be solved by Traditional Security approaches due to their fixed capabilities. Among security, trust, data accuracy and reliability of communication data in the communication channel are the other issues in IV communication. Blockchain is a peer-to-peer, distributed and decentralized technology which is used by the digital currency Bit-coin, to build trust and reliability and it has capability and is feasible to use Blockchain in IV Communication. In this paper, we propose, Blockchain based crypto Trust point (cTp) mechanism for IV communication. Using cTp in the IVs communication environment can provide IV data security and reliability. cTp mechanism accounts for the legitimate and illegitimate vehicles behavior, and rewarding thereby building trust among the vehicles. We also propose a reward based system using cTp (exchange of some cTp among IVs, during successful communication). We use blockchain technology in the Intelligent Transportation System (ITS) for the data management of the cTp. Using ITS, cTp details of every vehicle can be accessed ubiquitously by IVs. We evaluation, our proposal using the intersection use case scenario for intelligent vehicles communication.


title: A Scalable Wireless Body Area Network for Bio-Telemetry

abstract: In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This is a scalable platform that requires minimum human interaction during set-up and monitoring. Its main components include a biosensor, a smart gateway to automatically set up the body area network, a mechanism for delivering data to an Internet monitoring server, and automatic data collection, profiling and feature extraction from bio-potentials. Such a system could increase the quality of life and significantly lower healthcare costs for everyone in general, and for the elderly and those with disabilities in particular.


Chapter 4 Constraint Programming


title: Motivational, Structural and Tenure Factors that Impact Online Community Photo Sharing

abstract: In recent years, we have witnessed a significant growth of “social computing” services, or online communities where users contribute content in various forms, including images, text or video. Content contribution from members is critical to the viability of these online communities. It is therefore important to understand what drives users to share content with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with users’ photo sharing in an online community, drawing on motivation theories as well as on analysis of basic structural properties. Our results indicate that photo sharing declines in respect to the users’ tenure in the community. We also show that users with higher commitment to the community and greater “structural embeddedness” tend to share more content. We demonstrate that the motivation of self-development is negatively related to photo sharing, and that tenure in the community moderates the effect of self-development on photo sharing. Directions for future research, as well as implications for theory and practice are discussed.


title: BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks

abstract: We present a simple and effective framework for simultaneous semantic segmentation and instance segmentation with Fully Convolutional Networks (FCNs). The method, called BiSeg, predicts instance segmentation as a posterior in Bayesian inference, where semantic segmentation is used as a prior. We extend the idea of position-sensitive score maps used in recent methods to a fusion of multiple score maps at different scales and partition modes, and adopt it as a robust likelihood for instance segmentation inference. As both Bayesian inference and map fusion are performed per pixel, BiSeg is a fully convolutional end-to-end solution that inherits all the advantages of FCNs. We demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.


title: Parameters design evaluation in 3-level flying capacitor boost converter

abstract: Flying capacitor boost converter (FCBC) is one of the possible power converter for high power applications such as solar systems and electric vehicles system and so on. The aim of this paper is to establish relationship between the capacitance of the flying capacitor and output voltage ripple. 3-level FCBC prototype has been constructed for parameters design confirmation. Experimental result shows that the input current ripple is within design value. By identifying the input current ripple, the inductance of the boost inductor is estimated and it needs approximately only 25% compared to the 2-level conventional boost converter. Besides, 3-level FCBC only requires inductor core volume of 35% compared to the 2-level conventional boost converter. Moreover, it is confirmed that the relationship between the capacitance of the flying capacitor and output voltage ripple is independent. This finding is concretely proven by the simulation and experimental results. Therefore, the boost inductor and capacitance of the flying capacitor can be designed independently without considering output voltage ripple.


title: Geometric path tracking algorithm for autonomous driving in pedestrian environment

abstract: This paper proposes an alternative formulation to the pure pursuit path tracking algorithm for autonomous driving. The current approach has tendencies to cut corners, and therefore results in poor path tracking accuracy. The proposed method considers not only the relative position of the pursued point, but also the orientation of the path at that point. A steering control law is designed in accordance with the kinematic equations of motion of the vehicle. The effectiveness of the algorithm is then tested by implementing it on an autonomous golf cart, driving in a pedestrian environment. The experimental result shows that the new algorithm reduces the root mean square (RMS) cross track error for the same given pre-programmed path by up to 46 percent, while having virtually no extra computational cost, and still maintaining the chatter free property of the original pure pursuit controller.


title: The Transformation of Open Source Software

abstract: A frequent characterization of open source software is the somewhat outdated, mythical one of a collective of supremely talented software hackers freely volunteering their services to produce uniformly high-quality software. I contend that the open source software phenomenon has metamorphosed into a more mainstream and commercially viable form, which I label as OSS 2.0. I illustrate this transformation using a framework of process and product factors, and discuss how the bazaar metaphor, which up to now has been associated with the open source development process, has actually shifted to become a metaphor better suited to the OSS 2.0 product delivery and support process. Overall the OSS 2.0 phenomenon is significantly different from its free software antecedent. Its emergence accentuates the fundamental alteration of the basic ground rules in the software landscape, signifying the end of the proprietary-driven model that has prevailed for the past 20 years or so. Thus, a clear understanding of the characteristics of the emergent OSS 2.0 phenomenon is required to address key challenges for research and practice.


title: The Iterated Sigma Point Kalman Filter with Applications to Long Range Stereo

abstract: This paper investigates the use of statistical linearization to improve iterative non-linear least squares estimators. In particular, we look at improving long range stereo by filtering feature tracks from sequences of stereo pairs. A novel filter called the Iterated Sigma Point Kalman Filter (ISPKF) is developed from first principles; this filter is shown to achieve superior performance in terms of efficiency and accuracy when compared to the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Gauss-Newton filter. We also compare the ISPKF to the optimal Batch filter and to a Gauss-Newton Smoothing filter. For the long range stereo problem the ISPKF comes closest to matching the performance of the full batch MLE estimator. Further, the ISPKF is demonstrated on real data in the context of modeling environment structure from long range stereo data.


Optical Measurements of Surface Plasmons in Gold


title: Consequences of the Pauli exclusion principle for the Bose-Einstein condensation of atoms and excitons

abstract: The bosonic atoms used in present day experiments on Bose-Einstein condensation are made up of fermionic electrons and nucleons. In this Letter we demonstrate how the Pauli exclusion principle for these constituents puts an upper limit on the Bose-Einstein-condensed fraction. Detailed numerical results are presented for hydrogen atoms in a cubic volume and for excitons in semiconductors and semiconductor bilayer systems. The resulting condensate depletion scales differently from what one expects for bosons with a repulsive hard-core interaction. At high densities, Pauli exclusion results in significantly more condensate depletion. These results also shed a new light on the low condensed fraction in liquid helium II.


title: Class Noise vs. Attribute Noise: A Quantitative Study

abstract: Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification accuracy, time in building a classifier and the size of the classifier. Accordingly, most existing learning algorithms have integrated various approaches to enhance their learning abilities from noisy environments, but the existence of noise can still introduce serious negative impacts. A more reasonable solution might be to employ some preprocessing mechanisms to handle noisy instances before a learner is formed. Unfortunately, rare research has been conducted to systematically explore the impact of noise, especially from the noise handling point of view. This has made various noise processing techniques less significant, specifically when dealing with noise that is introduced in attributes. In this paper, we present a systematic evaluation on the effect of noise in machine learning. Instead of taking any unified theory of noise to evaluate the noise impacts, we differentiate noise into two categories: class noise and attribute noise, and analyze their impacts on the system performance separately. Because class noise has been widely addressed in existing research efforts, we concentrate on attribute noise. We investigate the relationship between attribute noise and classification accuracy, the impact of noise at different attributes, and possible solutions in handling attribute noise. Our conclusions can be used to guide interested readers to enhance data quality by designing various noise handling mechanisms.


title: The functions and unique features of long intergenic non-coding RNA

abstract: Long intergenic non-coding RNA (lincRNA) genes have diverse features that distinguish them from mRNA-encoding genes and exercise functions such as remodelling chromatin and genome architecture, RNA stabilization and transcription regulation, including enhancer-associated activity. Some genes currently annotated as encoding lincRNAs include small open reading frames (smORFs) and encode functional peptides and thus may be more properly classified as coding RNAs. lincRNAs may broadly serve to fine-tune the expression of neighbouring genes with remarkable tissue specificity through a diversity of mechanisms, highlighting our rapidly evolving understanding of the non-coding genome.


title: Video enhancement using per-pixel virtual exposures

abstract: We enhance underexposed, low dynamic range videos by adaptively and independently varying the exposure at each photoreceptor in a post-process. This virtual exposure is a dynamic function of both the spatial neighborhood and temporal history at each pixel. Temporal integration enables us to expand the image's dynamic range while simultaneously reducing noise. Our non-linear exposure variation and denoising filters smoothly transition from temporal to spatial for moving scene elements. Our virtual exposure framework also supports temporally coherent per frame tone mapping. Our system outputs restored video sequences with significantly reduced noise, increased exposure time of dark pixels, intact motion, and improved details.


title: Semantic 3D Media and Content: A declarative approach to procedural modeling of virtual worlds

abstract: With the ever increasing costs of manual content creation for virtual worlds, the potential of creating it automatically becomes too attractive to ignore. However, for most designers, traditional procedural content generation methods are complex and unintuitive to use, hard to control, and generated results are not easily integrated into a complete and consistent virtual world. We introduce a novel declarative modeling approach that enables designers to concentrate on stating what they want to create instead of on describing how they should model it. It aims at reducing the complexity of virtual world modeling by combining the strengths of semantics-based modeling with manual and procedural approaches. This article describes two of its main contributions to procedural modeling of virtual worlds: interactive procedural sketching and virtual world consistency maintenance. We discuss how these techniques, integrated in our modeling framework SketchaWorld, build up to enable designers to create a complete 3D virtual world in minutes. Procedural sketching provides a fast and more intuitive way to model virtual worlds, by letting designers interactively sketch their virtual world using high-level terrain features, which are then procedurally expanded using a variety of integrated procedural methods. Consistency maintenance guarantees that the semantics of all terrain features is preserved throughout the modeling process. In particular, it automatically solves conflicts possibly emerging from interactions between terrain features. We believe that these contributions together represent a significant step towards providing more user control and flexibility in procedural modeling of virtual worlds. It can therefore be expected that by further reducing its complexity, virtual world modeling will become accessible to an increasingly broad group of users.


title: Vertex-Centric Graph Processing: The Good, the Bad, and the Ugly

abstract: We study distributed graph algorithms that adopt an iterative vertex-centric framework for graph processing, popularized by the Google's Pregel system. Since then, there are several attempts to implement many graph algorithms in a vertex-centric framework, as well as efforts to design optimization techniques for improving the efficiency. However, to the best of our knowledge, there has not been any systematic study to compare these vertex-centric implementations with their sequential counterparts. Our paper addresses this gap in two ways. (1) We analyze the computational complexity of such implementations with the notion of time-processor product, and benchmark several vertex-centric graph algorithms whether they perform more work with respect to their best-known sequential solutions. (2) Employing the concept of balanced practical Pregel algorithms, we study if these implementations suffer from imbalanced workload and large number of iterations. Our findings illustrate that with the exception of Euler tour tree algorithm, all other algorithms either perform more work than their best-known sequential approach, or suffer from imbalanced workload/ large number of iterations, or even both. We also emphasize on graph algorithms that are fundamentally difficult to be expressed in vertex-centric frameworks, and conclude by discussing the road ahead for distributed graph processing.


title: Learning Fine-Grained Image Similarity with Deep Ranking

abstract: Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is also proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models.


title: OB-PWS: Obfuscation-Based Private Web Search

abstract: Obfuscation-based private web search (OB-PWS) solutions allow users to search for information in the Internet while concealing their interests. The basic privacy mechanism in OB-PWS is the automatic generation of dummy queries that are sent to the search engine along with users' real requests. These dummy queries prevent the accurate inference of search profiles and provide query deniability. In this paper we propose an abstract model and an associated analysis framework to systematically evaluate the privacy protection offered by OB-PWS systems. We analyze six existing OB-PWS solutions using our framework and uncover vulnerabilities in their designs. Based on these results, we elicit a set of features that must be taken into account when analyzing the security of OB-PWS designs to avoid falling into the same pitfalls as previous proposals.


title: Intraorganizational communication with intranets

abstract: Intranets have been used to unify geographically dispersed work forces. They have been used to empower employees around the world (especially telecommuters and salespeople on the road) through a communication tool supporting collaboration, interaction, and real-time sharing of corporate information across functional boundaries and organizational levels. This form of distributed information infrastructure may even enable some corporate managers to redefine their computing strategies and organizational control to better address competitive business environments. Most studies of intranet performance have been conceptual or anecdotal; none has sought to collect empirical data from intranet implementers and synthesize their experience in order to guide other organizations to plan, control, implement, and evaluate intranets. That’s why I participated in “Intranet Impact and Effectiveness,” a study of the 500 largest organizations in Hong Kong, targeting five goals: investigate the reasons for intranet adoption; explore the extent intranets support intraorganizational communications; identify the problems and benefits associated with intranet implementation; evaluate the effect of organization size on intranet use; and analyze the effect of intranets on overall organizational performance, team collaboration, and business process reengineering (see the sidebar “How the Study Was Done”). Intranet adoption and diffusion. In today’s dynamic business environment, decisions about adopting technological innovations are influenced by the expectation that they will streamline a business process and improve overall organizational performance. In particular, organizations facing keen market competition have compelling incentives to evaluate innovative technologies to help them maintain or enhance their competitiveness. Intranet The technical performance and business contribution of intranet applications has proved to many organizations worldwide that intranet technology is a costeffective and efficient approach to supporting corporate distributed computing strategies [3]. Increasingly, all kinds of organizations are taking advantage of intranets to disseminate corporate documents, forms, news, policies, phone directories, product specifications, and pricing information. In addition to using the technology to integrate individual, group, departmental, and corporate communications, business managers are exploring intranets to enhance their organizations’ business strategies.


Comparative Study of Different Clustering Algorithms


Irreversibility and Heat Generation in the Computing Process


title: DISTRIBUTED SOFTWARE DEVELOPMENT : TOWARD AN UNDERSTANDING OF THE RELATIONSHIP BETWEEN PROJECT TEAM , USERS AND CUSTOMERS

abstract: The objective of this paper is to propose a typology for distributed software development comprising the relation between the three main stakeholders: project team (developers, analysts, managers, testers, system administrator, etc), customers and users. We propose a set of criteria to define geographically distributed environments. As a result, a model to define the distribution level for an organization in a Distributed Software Development (DSD) environment is presented. The model is applied to two exploratory case studies and its results discussed. These cases studies involve companies with headquarters in the United States and a development unit in Brazil. Advantages of this model as well as some aspects of the increasing distribution of software development particularly in a few Brazilian organizations are discussed.


title: A probabilistic justification for using tf×idf term weighting in information retrieval

abstract: This paper presents a new probabilistic model of information retrieval. The most important modeling assumption made is that documents and queries are defined by an ordered sequence of single terms. This assumption is not made in well known existing models of information retrieval, but is essential in the field of statistical natural language processing. Advances already made in statistical natural language processing will be used in this paper to formulate a probabilistic justification for using tf.idf term weighting. The paper shows that the new probabilistic interpretation of tf.idf term weighting might lead to better understanding of statistical ranking mechanisms, for example by explaining how they relate to coordination level ranking. A pilot experiment on the TREC collection shows that the linguistically motivated weighting algorithm outperforms the popular BM25 weighting algorithm.


title: Bidirectional recurrent neural networks as generative models

abstract: Bidirectional recurrent neural networks (RNN) are trained to predict both in the positive and negative time directions simultaneously. They have not been used commonly in unsupervised tasks, because a probabilistic interpretation of the model has been difficult. Recently, two different frameworks, GSN and NADE, provide a connection between reconstruction and probabilistic modeling, which makes the interpretation possible. As far as we know, neither GSN or NADE have been studied in the context of time series before. As an example of an un-supervised task, we study the problem of filling in gaps in high-dimensional time series with complex dynamics. Although unidirectional RNNs have recently been trained successfully to model such time series, inference in the negative time direction is non-trivial. We propose two probabilistic interpretations of bidirectional RNNs that can be used to reconstruct missing gaps efficiently. Our experiments on text data show that both proposed methods are much more accurate than unidirectional reconstructions, although a bit less accurate than a computationally complex bidirectional Bayesian inference on the unidirectional RNN. We also provide results on music data for which the Bayesian inference is computationally infeasible, demonstrating the scalability of the proposed methods.


title: The "Lesson Sheets" and their Application in the Instructional Procedure

abstract: This study concerns the application of tools and methods of Open and Distance Learning (ODL) in the traditional class. More concretely, the design of an instructional aid called «lesson sheets» is discussed, according to specific guidelines, which evolve from the corresponding educational theories. Accordingly, the application of the proposed tool in four classes is described, as perceived by the participating subjects. The results of the study show a good integration of the proposed tool, however its construction must be performed according to the provided guidelines. Finally, some further research aspects are discussed, which are necessary in order to investigate the effective application of this tool in ODL environments as well.


title: Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size

abstract: A key problem in research on adversarial examples is that vulnerability to adversarial examples is usually measured by running attack algorithms. Because the attack algorithms are not optimal, the attack algorithms are prone to overestimating the size of perturbation needed to fool the target model. In other words, the attack-based methodology provides an upper-bound on the size of a perturbation that will fool the model, but security guarantees require a lower bound. CLEVER is a proposed scoring method to estimate a lower bound. Unfortunately, an estimate of a bound is not a bound. In this report, we show that gradient masking, a common problem that causes attack methodologies to provide only a very loose upper bound, causes CLEVER to overestimate the size of perturbation needed to fool the model. In other words, CLEVER does not resolve the key problem with the attack-based methodology, because it fails to provide a lower bound.


title: Fast and Effective Orchestration of Compiler Optimizations for Automatic Performance Tuning

abstract: Although compile-time optimizations generally improve program performance, degradations caused by individual techniques are to be expected. One promising research direction to overcome this problem is the development of dynamic, feedback-directed optimization orchestration algorithms, which automatically search for the combination of optimization techniques that achieves the best program performance. The challenge is to develop an orchestration algorithm that finds, in an exponential search space, a solution that is close to the best, in acceptable time. In this paper, we build such a fast and effective algorithm, called combined elimination (CE). The key advance of CE over existing techniques is that it takes the least tuning time (57% of the closest alternative), while achieving the same program performance. We conduct the experiments on both a Pentium IV machine and a SPARC II machine, by measuring performance of SPEC CPU2000 benchmarks under a large set of 38 GCC compiler options. Furthermore, through orchestrating a small set of optimizations causing the most degradation, we show that the performance achieved by CE is close to the upper bound obtained by an exhaustive search algorithm. The gap is less than 0.2% on average.


title: Hemolysis indexes for biochemical tests and immunoassays on Roche analyzers: determination of allowable interference limits according to different calculation methods.

abstract: OBJECTIVES ::: To determine the hemolysis interference on biochemical tests and immunoassays performed on Roche Diagnostics analyzers, according to different maximum allowable limits. ::: ::: ::: DESIGN AND METHODS ::: Heparinized plasma and serum pools, free of interferences, were overloaded by increasing amounts of a hemoglobin-titrated hemolysate. This interference was evaluated for 45 analytes using Modular(®) and Cobas(®) analyzers. For each parameter, the hemolysis index (HI) corresponding to the traditional ± 10% change of concentrations from baseline (± 10%Δ) was determined, as well as those corresponding to the analytical change limit (ACL), and to the reference change value (RCV). Then, the relative frequencies distribution (% RFD) of hemolyzed tests performed in a hospital laboratory over a 25-day period were established for each HI as allowable limit. ::: ::: ::: RESULTS ::: Considering the ± 10%Δ, the analyte concentrations enhanced by hemolysis were: Lactate dehydrogenase (LDH), aspartate aminotransferase (AST), folate, potassium, creatine kinase, phosphorus, iron, alanine aminotransferase, lipase, magnesium and triglycerides, decreasingly. The analyte concentrations decreased by hemolysis were: Haptoglobin, high-sensitive troponin T and alkaline phosphatase. Over the 25-day period, the % RFD of tests impacted more than 10%Δ by hemolysis were < 7% for LDH; < 5% for AST, folates and iron; and < 1% for the other analytes. Considering the ACL, HI were lower, giving % RFD substantially increased for many analytes, whereas only four analytes remain sensitive to hemolysis when considering RCV. ::: ::: ::: CONCLUSION ::: This study proposes new HI based on different allowable limits, and can therefore serve as a starting point for future harmonization of hemolysis interference evaluation needed in routine laboratory practice.


title: Workflow ControlFlow Patterns: A Revised View

abstract: The Workflow Patterns Initiative was established with the aim of delineating the fundamental requirements that arise during business process modelling on a recurring basis and describe them in an imperative way. The first deliverable of this research project was a set of twenty patterns describing the control-flow perspective of workflow systems. Since their release, these patterns have been widely used by practitioners, vendors and academics alike in the selection, design and development of workflow systems [vdAtHKB03]. This paper presents the first systematic review of the original twenty control-flow patterns and provides a formal description of each of them in the form of a Coloured Petri-Net (CPN) model. It also identifies twenty three new patterns relevant to the control-flow perspective. Detailed context conditions and evaluation criteria are presented for each pattern and their implementation is assessed in fourteen commercial offerings including workflow and case handling systems, business process modelling formalisms and business process execution languages.


title: Real-time object detection using a sparse 4-layer LIDAR

abstract: The robust detection of obstacles, on a given road path by vehicles equipped with range measurement devices represents a requirement for many research fields including autonomous driving and advanced driving assistance systems. One particular sensor system used for measurement tasks, due to its known accuracy, is the LIDAR (Light Detection and Ranging). The commercial price and computational intensiveness of such systems generally increase with the number of scanning layers. For this reason, in this paper, a novel six step based obstacle detection approach using a 4-layer LIDAR is presented. In the proposed pipeline we tackle the problem of data correction and temporal point cloud fusion and we present an original method for detecting obstacles using a combination between a polar histogram and an elevation grid. The results have been validated by using objects provided from other range measurement sensors.


title: Detecting epistasis in human complex traits

abstract: Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.


title: An Intrusion Detection Model Based on Deep Belief Networks

abstract: This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain. The deep hierarchical model is a deep neural network classifier of a combination of multilayer unsupervised learning networks, which is called as Restricted Boltzmann Machine, and a supervised learning network, which is called as Back-propagation network. The experimental results on KDD CUP 1999 dataset demonstrate that the performance of Deep Belief Networks model is better than that of SVM and ANN.


title: Fighting for my space: coping mechanisms for sns boundary regulation

abstract: Sharing information online via social network sites (SNSs) is at an all-time high, yet research shows that users often exhibit a marked dissatisfaction in using such sites. A compelling explanation for this dichotomy is that users are struggling against their SNS environment in an effort to achieve their preferred levels of privacy for regulating social interactions. Our research investigates users' SNS boundary regulation behavior. This paper presents results from a qualitative interview-based study to identify "coping mechanisms" that users devise outside explicit boundary-regulation interface features in order to manage interpersonal boundaries. Our categorization of such mechanisms provides insight into interaction design issues and opportunities for new SNS features.


title: Accurate indoor localization with zero start-up cost

abstract: Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Past work on SAR requires measuring mechanically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse's core contribution is the ability to perform SAR on handheld devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localization with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in complex indoor settings.


title: A Classifier-Based Parser With Linear Run-Time Complexity

abstract: We present a classifier-based parser that produces constituent trees in linear time. The parser uses a basic bottom-up shift-reduce algorithm, but employs a classifier to determine parser actions instead of a grammar. This can be seen as an extension of the deterministic dependency parser of Nivre and Scholz (2004) to full constituent parsing. We show that, with an appropriate feature set used in classification, a very simple one-path greedy parser can perform at the same level of accuracy as more complex parsers. We evaluate our parser on section 23 of the WSJ section of the Penn Treebank, and obtain precision and recall of 87.54% and 87.61%, respectively.


title: Impact-noise suppression with phase-based detection

abstract: This paper proposes impact noise suppression with a new phase-based detection. Different from any other conventional algorithms which rely on magnitude-based or model-based detection, a new impact-noise detection utilizing phase linearity of the input noisy signal is developed based on an analysis of an impulse. Phase slopes of the input noisy signal is compared with an ideal phase slope obtained from the peak magnitude of the time-domain noisy signal. Phase unwrapping problem is alleviated by the use of a rotation vector of frequency domain components. Evaluation with PESQ for push-button clicks shows an improvement of 0.9 over a conventional noise suppressor for voice communication. Comparison of enhanced signal spectrogram with that of clean speech demonstrates superior enhanced signal quality. The proposed phase-based detection may be combined with an amplitude-based detection for more accurate results.


title: Empathy versus personal distress - recent evidence from social neuroscience

abstract: Philosophers and psychologists have long debated the nature of empathy (e.g., Ickes, 2003; Thompson, 2001), and whether the capacity to share and understand other people’s emotions sets humans apart from other species (e.g., de Waal, 2005). Here, we consider empathy as a construct accounting for a sense of similarity in feelings experienced by the self and the other, without confusion between the two individuals (Decety & Jackson, 2004; Decety & Lamm, 2006). The experience of empathy can lead to sympathy (concern for another based on the apprehension or comprehension of the other’s emotional state or condition), or even personal distress (i.e., an aversive, self-focused emotional reaction to the apprehension or comprehension of another’s emotional state or condition) when there is confusion between self and other. Knowledge of empathic behavior is essential for an understanding of human social and moral development (Eisenberg et al., 1994). Furthermore, various psychopathologies are marked by empathy deficits, and a wide array of psychotherapeutic approaches stress the importance of clinical empathy as a fundamental component of treatment (Farrow & Woodruff, 2007).


title: Compact Watershed and Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation Algorithms

abstract: A major insight from our previous work on extensive comparison of super pixel segmentation algorithms is the existence of several trade-offs for such algorithms. The most intuitive is the trade-off between segmentation quality and runtime. However, there exist many more between these two and a multitude of other performance measures. In this work, we present two new super pixel segmentation algorithms, based on existing algorithms, that provide better balanced trade-offs. Better balanced means, that we increase one performance measure by a large amount at the cost of slightly decreasing another. The proposed new algorithms are expected to be more appropriate for many real time computer vision tasks. The first proposed algorithm, Preemptive SLIC, is a faster version of SLIC, running at frame-rate (30 Hz for image size 481x321) on a standard desktop CPU. The speed-up comes at the cost of slightly worse segmentation quality. The second proposed algorithm is Compact Watershed. It is based on Seeded Watershed segmentation, but creates uniformly shaped super pixels similar to SLIC in about 10 ms per image. We extensively evaluate the influence of the proposed algorithmic changes on the trade-offs between various performance measures.


title: Granger Causality for Time-Series Anomaly Detection

abstract: Recent developments in industrial systems provide us with a large amount of time series data from sensors, logs, system settings and physical measurements, etc. These data are extremely valuable for providing insights about the complex systems and could be used to detect anomalies at early stages. However, the special characteristics of these time series data, such as high dimensions and complex dependencies between variables, as well as its massive volume, pose great challenges to existing anomaly detection algorithms. In this paper, we propose Granger graphical models as an effective and scalable approach for anomaly detection whose results can be readily interpreted. Specifically, Granger graphical models are a family of graphical models that exploit the temporal dependencies between variables by applying L1-regularized learning to Granger causality. Our goal is to efficiently compute a robust "correlation anomaly" score for each variable via Granger graphical models that can provide insights on the possible reasons of anomalies. We evaluate the effectiveness of our proposed algorithms on both synthetic and application datasets. The results show the proposed algorithm achieves significantly better performance than other baseline algorithms and is scalable for large-scale applications.


title: EMOVO Corpus: an Italian Emotional Speech Database

abstract: This article describes the first emotional corpus, named EMOVO, applicable to Italian language,. It is a database built from the voices of up to 6 actors who played 14 sentences simulating 6 emotional states (disgust, fear, anger, joy, surprise, sadness) plus the neutral state. These emotions are the well-known Big Six found in most of the literature related to emotional speech. The recordings were made with professional equipment in the Fondazione Ugo Bordoni laboratories. The paper also describes a subjective validation test of the corpus, based on emotion-discrimination of two sentences carried out by two different groups of 24 listeners. The test was successful because it yielded an overall recognition accuracy of 80%. It is observed that emotions less easy to recognize are joy and disgust, whereas the most easy to detect are anger, sadness and the neutral state.


title: Argumentation Mining in Persuasive Essays and Scientific Articles from the Discourse Structure Perspective

abstract: In this paper, we analyze and discuss approaches to argumentation mining from the discourse structure perspective. We chose persuasive essays and scientific articles as our example domains. By analyzing several example arguments and providing an overview of previous work on argumentation mining, we derive important tasks that are currently not addressed by existing argumentation mining systems, most importantly, the identification of argumentation structures. We discuss the relation of this task to automated discourse analysis and describe preliminary results of two annotation studies focusing on the annotation of argumentation structure. Based on our findings, we derive three challenges for encouraging future research on argumentation mining.


title: Wikify!: linking documents to encyclopedic knowledge

abstract: This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks. The paper also shows how the two methods can be combined into a system able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages. Evaluations of the system show that the automatic annotations are reliable and hardly distinguishable from manual annotations.


Design and Implementation of a Real-time Traffic Light Control System Based on FPGA


title: Creation of DICOM—Aware Applications Using ImageJ

abstract: The demand for image-processing software for radiology applications has been increasing, fueled by advancements in both image-acquisition and image-analysis techniques. The utility of existing image-processing software is often limited by cost, lack of flexibility, and/or specific hardware requirements. In particular, many existing packages cannot directly utilize images formatted using the specifications in part 10 of the DICOM standard (“DICOM images”). We show how image analyses can be performed directly on DICOM images by using ImageJ, a free, Java-based image-processing package (http://rsb.info.nih.gov/ij/). We demonstrate how plug-ins written in our laboratory can be used along with the ImageJ macro script language to create flexible, low-cost, multiplatform image-processing applications that can be directed by information contained in the DICOM image header.


title: Implementing a skin BSSRDF: (or several...)

abstract: In the seminal paper from 2001: A Practical Model for Subsurface Light Transport ([Jensen '01]), Henrik Wann Jensen et al employed the concept of a BSSRDF (a bidirectional surface scattering distribution function) as a means to overcome the limitations of BRDFs.


title: From e-government to social government: : Twitter use by Korea's central government

abstract: Purpose – This study aims to explore Twitter use by Korea's central government by classifying the government's Twitter-based networking strategies into government-to-citizen (G2C) and government-to-government (G2G) strategies. Design/methodology/approach – The study investigates the nature of social media interactions and networking strategies in the Korean government by extracting tweets, follower/following relationships, and hyperlinks for 32 ministries. Network patterns and networking strategies are reviewed through descriptive statistical analysis and social network analysis to map the government's Twitter activity. Findings – The results indicate that the government's direct networking strategy targeting citizens does not necessarily motivate their participation in the government's social media activities but that it plays an instrumental role in reinforcing G2G relationships. Originality/value – This study investigates the social media use patterns (e.g. network properties and co-link analyses) and ...


title: Kinematics modeling and experimental verification of baxter robot

abstract: The Baxter® humanoid robot made by Rethink Robotics™ offers users an affordable platform with guaranteed safety for both academic and industrial applications. The platform provides the users a good opportunity to carry out research on dual-arm robot manipulation and vision based control. For simulation of the Baxter® robot, a proper kinematic and dynamic model should be built. The Baxter robot uses URDF file to describe the robot kinematics, i.e., the relationship between adjacent joint and link. Consider the large difference between the structure of URDF and the conventional Denavit-Hartenburg(DH) notations which are widely used in robotics literature, we perform rigorous theoretic analysis of kinematics of Baxter® robot including all limbs. A kinematics model has been built following DH convention, and has been implemented in the MATLAB Robotics Toolbox. Extensive comparison between simulation and experimental results has verified the validity of the kinematic model.


title: The effects of different durations of static stretching within a comprehensive warm-up on voluntary and evoked contractile properties

abstract: Evidence for performance decrements following prolonged static stretching (SS) has led to a paradigm shift in stretching routines within a warm-up. Rather than SS, dynamic stretching (DS) and dynamic activity (DA) have replaced SS within warm-up routines. The objective of the present study was to compare the effect of differing lower limb SS durations (30 [SS30s], 60 [SS60s] or 120 s [SS120s] of SS per muscle group or no-stretch control) within a comprehensive warm-up protocol consisting of aerobic activity, DS and DA. Sixteen male participants completed the four stretching conditions in a randomized order, after a 5-min low-intensity (cycle) warm-up and before a DS/DA component on separate days. Tests included passive hip and knee ranges of motion (ROM), maximum voluntary knee extensor/flexor force, force produced at 100 ms (F100), vertical jump height and evoked knee extensor contractile properties. For hip flexion (hamstrings) ROM, SS120s provided the largest increase (5.6–11.7%) followed by SS60s (4.3–11.4%), control (4.4–10.6%) and SS30s (3.6–11.1%). For knee flexion (quadriceps) ROM, SS30s provided the largest increase (9.3–18.2%) followed by SS120s (6.5–16.3%), SS60s (7.2–15.2%) and control (6.3–15.2%). There were decreases in quadriceps F100 following SS in SS120s (29.6%) only. There were increases in vertical jump performance in the control (6.2%), SS60s (4.6%) and SS30s (3.3%). While 120 s SS per muscle increased ROM, even within a comprehensive warm-up routine, it also elicited notable performance decrements. However, moderate durations of SS were observed to improve ROM whilst either having negligible or beneficial (but not detrimental) effects on specific aspects of athletic performance.


An implicit function theorem for nondifferentiable mappings


title: A simple and effective control of single phase solar inverter

abstract: In this paper, a single phase effective closed loop control for solar inverter is proposed. As solar irradiance level changes with atmospheric conditions, output of the inverter varies. To maintain the output voltage of the inverter constant a close loop is implemented using PWM technique. A PWM signal is generated by comparing the output of PI controller with two identical triangular signals having a phase difference of 180°. The output voltage is constant within ±0.5% of reference value. The circuit is validated using PSIM software. The simulated AC output waveform has THD within 2%.


title: Calibrated Confidence Scoring for Biometric Identification

abstract: Existing biometric identification systems, such as those used in trusted traveler programs, attempt to identify an individual’s identity from an enrollment database of n people. The output is either the name of an enrolled person, or a rejection message indicating that no match was found. Traditionally, no measure of confidence is given to the output; an individual is either granted or denied access. In this paper, we propose an extension to existing biometric systems by applying a calibration function to the n matching scores. We introduce a computationally-light calculation that can be applied either as a post-processing filter or embedded directly into an algorithm to yield perfectly calibrated probability-based scores. In addition to attaching a meaningful confidence measure to the output, the proposed methodology is also shown to improve the overall performance of a biometric system. The theoretical proof of the calibration formula is followed by its application to iris biometrics, on a data set consisting of nearly 60,000 iris images. By comparing the detection error trade-o (DET) curves, we show that our score calibration post-processing filter reduces the area under the DET curve from 2.41 to 0.17, and reduces the equal error rate (EER) from 5.40% to 2.84%.


title: ExecRecorder: VM-based full-system replay for attack analysis and system recovery

abstract: Log-based recovery and replay systems are important for system reliability, debugging and postmortem analysis/recovery of malware attacks. These systems must incur low space and performance overhead, provide full-system replay capabilities, and be resilient against attacks. Previous approaches fail to meet these requirements: they replay only a single process, or require changes in the host and guest OS, or do not have a fully-implemented replay component. This paper studies full-system replay for uniprocessors by logging and replaying architectural events. To limit the amount of logged information, we identify architectural nondeterministic events, and encode them compactly. Here we present ExecRecorder, a full-system, VM-based, log and replay framework for post-attack analysis and recovery. ExecRecorder can replay the execution of an entire system by checkpointing the system state and logging architectural nondeterministic events, and imposes low performance overhead (less than 4% on average). In our evaluation its log files grow at about 5.4 GB/hour (arithmetic mean). Thus it is practical to log on the order of hours or days between checkpoints. It can also be integrated naturally with an IDS and a post-attack analysis tool for intrusion analysis and recovery.


title: Overlapping sound event recognition using local spectrogram features and the generalised hough transform

abstract: Abstract In this paper, we address the challenging task of simultaneous recognition of overlapping sound events from single channel audio. Conventional frame-based methods are not well suited to the problem, as each time frame contains a mixture of information from multiple sources. Missing feature masks are able to improve the recognition in such cases, but are limited by the accuracy of the mask, which is a non-trivial problem. In this paper, we propose an approach based on Local Spectrogram Features (LSFs) which represent local spectral information that is extracted from the two-dimensional region surrounding “keypoints” detected in the spectrogram. The keypoints are designed to locate the sparse, discriminative peaks in the spectrogram, such that we can model sound events through a set of representative LSF clusters and their occurrences in the spectrogram. To recognise overlapping sound events, we use a Generalised Hough Transform (GHT) voting system, which sums the information over many independent keypoints to produce onset hypotheses, that can detect any arbitrary combination of sound events in the spectrogram. Each hypothesis is then scored against the class distribution models to recognise the existence of the sound in the spectrogram. Experiments on a set of five overlapping sound events, in the presence of non-stationary background noise, demonstrate the potential of our approach.


title: Hierarchical parallel coordinates for exploration of large datasets

abstract: Our ability to accumulate large, complex (multivariate) data sets has far exceeded our ability to effectively process them in searching for patterns, anomalies and other interesting features. Conventional multivariate visualization techniques generally do not scale well with respect to the size of the data set. The focus of this paper is on the interactive visualization of large multivariate data sets based on a number of novel extensions to the parallel coordinates display technique. We develop a multi-resolution view of the data via hierarchical clustering, and use a variation of parallel coordinates to convey aggregation information for the resulting clusters. Users can then navigate the resulting structure until the desired focus region and level of detail is reached, using our suite of navigational and filtering tools. We describe the design and implementation of our hierarchical parallel coordinates system which is based on extending the XmdvTool system. Lastly, we show examples of the tools and techniques applied to large (hundreds of thousands of records) multivariate data sets.


Controlled Generation of Models with Defined Properties.


title: Bias in Wikipedia

abstract: While studies have shown that Wikipedia articles exhibit quality that is comparable to conventional encyclopedias, research still proves that Wikipedia, overall, is prone to many different types of Neutral Point of View (NPOV) violations that are explicitly or implicitly caused by bias from its editors. Related work focuses on political, cultural and gender bias. We are developing an approach for detecting both explicit and implicit bias in Wikipedia articles and observing its evolution over time. Our approach is based on different factors of bias, with the most important ones being language style, editors, and citations. In this paper we present the approach, methodology and a first analysis.


title: Real-time Motion-based Gesture Recognition Using the GPU

abstract: In this paper we describe a real-time system for gesture recognition. Given an input video, we derive a set of motion features based on smoothed optical flow estimates. A user-centric representation of these features is obtained using face detection, and an efficient classifier is learned to discriminate between gestures. We develop a real-time system using GPU programming for implementing the classifier. We provide experimental results demonstrating the speed and efficacy of our system.


title: Implementation of the SHA-2 Hash Family Standard Using FPGAs

abstract: The continued growth of both wired and wireless communications has triggered the revolution for the generation of new cryptographic algorithms. SHA-2 hash family is a new standard in the widely used hash functions category. An architecture and the VLSI implementation of this standard are proposed in this work. The proposed architecture supports a multi-mode operation in the sense that it performs all the three hash functions (256, 384 and 512) of the SHA-2 standard. The proposed system is compared with the implementation of each hash function in a separate FPGA device. Comparing with previous designs, the introduced system can work in higher operation frequency and needs less silicon area resources. The achieved performance in the term of throughput of the proposed system/architecture is much higher (in a range from 277 to 417%) than the other hardware implementations. The introduced architecture also performs much better than the implementations of the existing standard SHA-1, and also offers a higher security level strength. The proposed system could be used for the implementation of integrity units, and in many other sensitive cryptographic applications, such as, digital signatures, message authentication codes and random number generators.


title: Total nitrogen inflow and outflow from a large river swamp basin to the Gulf of Mexico

abstract: Abstract River corridor wetland restoration and freshwater diversion from the lower Mississippi River are being considered as two major options to reduce nitrogen input to the Gulf of Mexico. However, it is largely uncertain how much nitrogen can actually be retained from the overflowing waters by these wetland systems. This study quantified the nitrogen inflow and outflow for the largest distributary basin of the Mississippi River, the Atchafalaya River Swamp basin. The goal of the study was to seek answers to three critical questions: (a) Does the Atchafalaya River Swamp remove a significant amount of nitrogen from the overflowing water, or is it releasing more nitrogen into the Gulf? (b) How do the nitrogen removal or release rates fluctuate seasonally and annually? (c) What are the relationships between the nitrogen removal capacity and the hydrological conditions in the basin such as river stage and discharge? By utilizing the long-term (1978–2002) river discharge and water quality data, monthly and annual nitrogen fluxes were quantified, and their relationships with the basin hydrological conditions investigated. A total nitrogen—sum of the total Kjeldahl nitrogen (TKN) and nitrate plus nitrite nitrogen (NO3+NO2)—mass input—output balance between the upstream (Simmesport) and downstream (Morgan City and Wax Lake Outlet) locations was established to examine the nitrogen removal potential for this, the largest freshwater swamp basin in North America. The results from this study showed that, over the past 25 years, the Atchafalaya River Swamp basin acted as a source for NO3+NO2 nitrogen, although the average annual output of NO3+NO2 nitrogen (174 584 Mg) was only slightly higher (2.3%) than the average annual input of NO3+NO2 nitrogen (170 721 Mg). The higher NO3+NO2 mass outflow occurred throughout summer and autumn, indicating an active role of biological processes on nitrogen in the overflowing waters of the Atchafalaya. However, this swamp basin has served as a major sink for organic nitrogen: the annual averages of TKN mass input and output were 200 323 and 145 917 Mg year−1, respectively, presenting a 27.2% removal rate by the basin. This large TKN reduction appeared high during springs and low during late summers, corresponding with the fluctuation of the hydrological conditions of the river.


title: Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references

abstract: Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an advanced statistical technique—segmented regression analysis—which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.


title: Viral Marketing - Establishing Customer Relationships by 'Word-of-mouse'

abstract: Newcomers to electronic markets are forced to accumulate customers as rapidly as possible. One strategy to fulfil this aim is so called viral marketing, which seems an appropriate term for describing the pattern in which Internet companies spread by making use of customer referrals. The aim of this article is to describe and explain common features of viral marketing strategies and to outline chances and risks associated with using Internet word-of-mouth in consumer settings.


title: Representation learning for measuring entity relatedness with rich information

abstract: Incorporating multiple types of relational information from heterogeneous networks has been proved effective in data mining. Although Wikipedia is one of the most famous heterogeneous network, previous works of semantic analysis on Wikipedia are mostly limited on single type of relations. In this paper, we aim at incorporating multiple types of relations to measure the semantic relatedness between Wikipedia entities. We propose a framework of coordinate matrix factorization to construct lowdimensional continuous representation for entities, categories and words in the same semantic space. We formulate this task as the completion of a sparse entity-entity association matrix, in which each entry quantifies the strength of relatedness between corresponding entities. We evaluate our model on the task of judging pair-wise word similarity. Experiment result shows that our model outperforms both traditional entity relatedness algorithms and other representation learning models.


HIGH RESOLUTION POINT CLOUD GENERATION FROM KINECT AND HD CAMERAS USING GRAPH CUT


title: Broadcast-based distributed alternating direction method of multipliers

abstract: We consider a multi agent optimization problem where a network of agents collectively solves a global optimization problem with the objective function given by the sum of locally known convex functions. We propose a fully distributed broadcast-based Alternating Direction Method of Multipliers (ADMM), in which each agent broadcasts the outcome of his local processing to all his neighbors. We show that both the objective function values and the feasibility violation converge with rate O(1/T), where T is the number of iterations. This improves upon the O(1/√T) convergence rate of subgradient-based methods. We also characterize the effect of network structure and the choice of communication matrix on the convergence speed. Because of its broadcast nature, the storage requirements of our algorithm are much more modest compared to the distributed algorithms that use pairwise communication between agents.


title: Enjoyment for High-Intensity Interval Exercise Increases during the First Six Weeks of Training: Implications for Promoting Exercise Adherence in Sedentary Adults

abstract: This is the first study to show that enjoyment for high-intensity interval exercise increases with chronic training. Prior acute studies typically report high-intensity interval training (HIT) as being more enjoyable than moderate continuous training (MCT) unless the high-intensity intervals are too strenuous or difficult to complete. It follows that exercise competency may be a critical factor contributing to the enjoyment of HIT, and therefore building competency through chronic training may be one way to increase its enjoyment. To test this, we randomly assigned sedentary young adults to six weeks of HIT or MCT, and tracked changes in their enjoyment for the exercise. Enjoyment for HIT increased with training whereas enjoyment for MCT remained constant and lower. Changes in exercise enjoyment were predicted by increases in workload, suggesting that strength adaptions may be important for promoting exercise enjoyment. The results point to HIT as a promising protocol for promoting exercise enjoyment and adherence in sedentary young adults.


title: Online consumer retention: contingent effects of online shopping habit and online shopping experience

abstract: In this study, we further develop the information systems continuance model in the context of online shopping, using a contingency theory that accounts for the roles of online shopping habit and online shopping experience. Specifically, we argue and empirically demonstrate that although conceptually distinct, online shopping habit and online shopping experience have similar effects on repurchase intention. They both have positive mediated effects through satisfaction and moderate the relationship between satisfaction and online repurchase intention. The results of a survey study involving 122 online customers provide strong support for our research model. We also identify after-sale service, transaction efficiency, security, convenience, and cost savings as important online shopping usefulness drivers. Theoretical and practical implications include establishing a contingency theory to more fully explain online customer retention as well as guidelines for development of customer relationship management initiatives.


title: Eugenol-induced suppression of biofilm-forming genes in Streptococcus mutans: An approach to inhibit biofilms.

abstract: Streptococcus mutans is well documented as a major aetiological agent of dental caries. The ability to form a biofilm on tooth surfaces is the major virulence factor of this bacterium. The objective of this study was to evaluate the effect of eugenol on suppression of biofilm- and quorum sensing (QS)-related genes of S. mutans and to determine its putative mode of action. Eugenol was evaluated for its inhibitory activity against virulence properties such as adherence and biofilm formation. Morphological changes in the architecture of S. mutans and in the biofilm were analysed and observed using confocal laser scanning microscopy and transmission electron microscopy. The effects of eugenol on expression of biofilm- and QS-related genes (gtfB, gtfC, comDE, smu630, vicR, brpA, ftf, relA, gbpB and spaP) were checked by quantitative real-time PCR (qRT-PCR). The present data revealed that eugenol at a sub-minimum inhibitory concentration (sub-MIC) significantly downregulated the expression of tested genes but did not affect bacterial growth. These results suggest that a sub-MIC of eugenol can effectively suppress virulence genes. Thus, the results indicated that eugenol can inhibit caries-associated biofilm and showed its therapeutic potential against oral biofilm.


Pig feeds rich in rapeseed products and organic selenium increased omega-3 fatty acids and selenium in pork meat and backfat


title: A Dual-Beam Eight-Element Antenna Array With Compact CPWG Crossover Structure

abstract: In this letter, a dual-beam eight-element antenna array is proposed with compact coplanar waveguide with ground (CPWG) crossover structure. The feeding network is composed of a microstrip-line feeding network with four 90° hybrids and a CPWG feeding network with compact crossover structures. Compared to the conventional Butler matrix feeding network, the proposed network has fewer 90° hybrids and circuit is less complex. By exciting different port of the feeding network, the eight-element antenna can be excited by phase step of 22.5° or –22.5° and achieve two beams pointing at two different directions. The measured gain is 10.2 dBi, and beam directions of the two peaks are ±7° at 5.8 GHz.


title: Changing teaching techniques and adapting new technologies to improve student learning in an introductory meteorology and climate course

abstract: Responding to the call for reform in science education, changes were made in an introductory meteorology and climate course offered at a large public university. These changes were a part of a larger project aimed at deepening and extending a program of science content courses that model effective teaching strategies for prospective middle school science teachers. Therefore, revisions were made to address misconceptions about meteorological phenomena, foster deeper understanding of key concepts, encourage engagement with the text, and promote inquiry-based learning. Techniques introduced include: use of a flash cards, student reflection questionnaires, writing assignments, and interactive discussions on weather and forecast data using computer technology such as Integrated Data Viewer (IDV). The revision process is described in a case study format. Preliminary results (self-reflection by the instructor, surveys of student opinion, and measurements of student achievement), suggest student learning has been positively influenced. This study is supported by three grants: NSF grant No. 0202923, the Unidata Equipment Award, and the Lucia Harrison Endowment Fund.


Linear distance metric learning for large-scale generic image recognition


title: Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

abstract: We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size - up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics.