title: Jaql: A Scripting Language for Large Scale Semistructured Data Analysis

abstract: This paper describes Jaql, a declarative scripting language for analyzing large semistructured datasets in parallel using Hadoop’s MapReduce framework. Jaql is currently used in IBM’s InfoSphere BigInsights [5] and Cognos Consumer Insight [9] products. Jaql’s design features are: (1) a flexible data model, (2) reusability, (3) varying levels of abstraction, and (4) scalability. Jaql’s data model is inspired by JSON and can be used to represent datasets that vary from flat, relational tables to collections of semistructured documents. A Jaql script can start without any schema and evolve over time from a partial to a rigid schema. Reusability is provided through the use of higher-order functions and by packaging related functions into modules. Most Jaql scripts work at a high level of abstraction for concise specification of logical operations (e.g., join), but Jaql’s notion of physical transparency also provides a lower level of abstraction if necessary. This allows users to pin down the evaluation plan of a script for greater control or even add new operators. The Jaql compiler automatically rewrites Jaql scripts so they can run in parallel on Hadoop. In addition to describing Jaql’s design, we present the results of scale-up experiments on Hadoop running Jaql scripts for intranet data analysis and log processing.


title: A Scaleable FIR Filter Implementation Using 32-bit Floating- Point Complex Arithmetic on a FPGA Based Custom Computing Platform

abstract: This thesis presents a linear phase finite impulse response filter implementation developed on a custom computing platform called WILDFORCE. The work has been motivated by ways to off-load intensive computing tasks to hardware for indoor communications channel modeling. The design entails complex convolution filters with customized lengths that can support channel impulse response profiles generated by SIRCIM. The paper details the partitioning for a fully pipelined convolution algorithm onto field programmable gate arrays through VHDL synthesis. Using WILDFORCE, the filter can achieve calculations at 160 MFLOPs/s.


title: A Multi-Agent Based Simulation Model for Rail–Rail Transshipment: An Engineering Approach for Gantry Crane Scheduling

abstract: Le Havre Port Authority is putting into service a multimodal hub terminal with massified hinterland links (trains and barges) in order to restrict the intensive use of roads, to achieve a more attractive massification share of hinterland transportation and to provide a river connection to its maritime terminals that do not currently have one. This paper focuses on the rail–rail transshipment yard of this new terminal. In the current organizational policy, this yard is divided into two equal operating areas, and, in each one, a crane is placed, and it is equipped with reach stackers to enable container moves across both operating areas. However, this policy causes poor scheduling of crane moves, because it gives rise to many crane interference situations. For the sake of minimizing the occurrence of these undesirable situations, this paper proposes a multi-agent simulation model including an improved strategy for crane scheduling. This strategy is inspired by the ant colony approach and it is governed by a new configuration for the rail yard’s working area that eliminates the use of reach stackers. The proposed simulation model is based on two planner agents, to each of which a time-horizon planning is assigned. The simulation results show that the model developed here is very successful in significantly reducing unproductive times and moves (undesirable situations), and it outperforms other existing simulation models based on the current organizational policy.


title: Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks

abstract: One of the most important goals in genetic epidemiology is the identification of genetic factors/features that predict complex diseases. The ubiquitous nature of gene-gene interactions in the underlying etiology of common diseases creates an important analytical challenge, spurring the introduction of novel, computational approaches. One such method is a grammatical evolution neural network (GENN) approach. GENN has been shown to have high power to detect such interactions in simulation studies, but previous studies have ignored an important feature of most genetic data: linkage disequilibrium (LD). LD describes the non-random association of alleles not necessarily on the same chromosome. This results in strong correlation between variables in a dataset, which can complicate analysis. In the current study, data simulations with a range of LD patterns are used to assess the impact of such correlated variables on the performance of GENN. Our results show that not only do patterns of strong LD not decrease the power of GENN to detect genetic associations, they actually increase its power.


title: Asymmetric Division of Labor in Human Skilled Bimanual Action: The Kinematic Chain as a Model

abstract: This article presents a tentative theoretical framework for the study of asymmetry in the context of human bimanual action. It is emphasized that in humans most skilled manual activities involve two hands playing different roles, a fact that has been often overlooked in the experimental study of human manual lateralization. As an alternative to the current concepts of manual preference and manual superiority, whose relevance is limited to the particular case of unimanual actions, the more general concept of lateral preference is proposed, to denote preference for one of the two possible ways of assigning two roles to two hands. A simple model describing intermanual division of labor in the variety of human skilled manual activities is outlined. The two main assumptions of the model are the following. 1) The two hands represent two motors, that is, devices serving to create motion, whose internal (biomechanical and physiological) complexity is ignored in the suggested approach. 2) In humans, the two manual motors cooperate with one another as if they were assembled in series, thereby forming a kinematic chain: In right-handers allowed to follow their lateral preferences, motion produced by the right hand tends to articulate with motion produced by the left. It is suggested that the kinematic chain model may help in understanding the adaptive advantage of human manual specialization.


title: Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

abstract: Very high resolution (VHR) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.


title: Harmony Search for Multi-objective Optimization

abstract: This paper investigates the efficiency of Harmony Search based algorithms for solving multi-objetive problems. For this task, four variants of the Harmony Search algorithm were adapted in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) framework. Harmony Search is a recent proposed music-inspired metaheuristic while NSGA-II is a very successful evolutionary multi-objective algorithm. The four proposed methods are tested against each other using a set of benchmark instances proposed in CEC 2009. The best proposed algorithm is also compared with NSGA-II. The preliminary results are very promising and stand the proposed approach as a candidate to the State-of-the-art for multi-objective optimization, encouraging further researches in the hybridization of the Harmony Search and Multi-objective Evolutionary Algorithms.


title: An Ultra-Low Power QRS Complex Detection Algorithm Based on Down-Sampling Wavelet Transform

abstract: Low-power design has become a key technology for battery-power biomedical devices in Wireless Body Area Network. In order to meet the requirement of low-power dissipation for electrocardiogram related applications, a down-sampling QRS complex detection algorithm is proposed. Based on Wavelet Transform (WT), this letter characterizes the energy distribution of QRS complex corresponding to the frequency band of WT. Then this letter details for the first time the process of down-sampled filter design, and presents the time and frequency response of the filter. The algorithm is evaluated in fixed point on MIT-BIH and QT database. Compared with other existing results, our work reduces the power dissipation by 23%, 61%, and 72% for 1 $\,\times $ , 2 $\,\times $ , and 3 $\,\times $ down-sampling rate, respectively, while maintaining almost constant detection performance.


title: Error Performance for Adaptive Transmission on Fading Channels

abstract: A communication system is analyzed in which the transmission power and/or rate are adjusted to the observed state (attenuation) of a slowly fading channel. The optimum control of power and rate with channel attenuation is determined. Some suboptimum control rules are also investigated. The average probability of error is evaluated for various types of adaptive transmission, imposing different constraints on power, rate, and bandwidth. It is shown that rate control is superior to power control and that simultaneous power and rate control further improves the performance. The most effective power and rate control presupposes a high peakpower-average-power ratio and a high peak-rate-peak-rate-average-rate ratio; with no peak power and bandwidth limitation, arbitrary small error probability is attainable for a given power and average rate.


title: A border-based approach for hiding sensitive frequent itemsets

abstract: Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach.


title: CCFL backlight solution for low-cost liquid crystal televisions without image artifacts

abstract: Recently, a single-phase CCFL (cold cathode fluorescent lamp) driving method has been adopted to provide a low cost implementation. However, whereas dual-phase driving compensates the coupling effect on the pixel electrodes, single-phase high voltage driving signals are coupled to pixel electrodes through parasitic capacitance between the lamps and panel electrodes. As a result, image artifacts appear on the display screen. This paper describes the analysis results of this phenomenon. Furthermore, the proposed automatic delay compensation scheme eliminates the artifacts caused by the coupling effect, regardless of how much delay is generated by the lamp driving circuit.


title: Gender nonconformity as a target of prejudice, discrimination, and violence against LGB individuals

abstract: ABSTRACT Research into antigay violence has been limited by a lack of attention to issues of gender presentation. Understanding gender nonconformity is important for addressing antigay prejudice and hate crimes. We assessed experiences of gender-nonconformity-related prejudice among 396 Black, Latino, and White lesbian, gay, and bisexual individuals recruited from diverse community venues in New York City. We assessed the prevalence and contexts of prejudice-related life events and everyday discrimination using both quantitative and qualitative approaches. Gender nonconformity had precipitated major prejudice events for 9% of the respondents and discrimination instances for 19%. Women were more likely than men to report gender-nonconformity-related discrimination but there were no differences by other demographic characteristics. In analysis of events narratives, we show that gender nonconformity prejudice is often intertwined with antigay prejudice. Our results demonstrate that both constructs should be ...


title: Identifying time lags in the restoration of grassland butterfly communities: A multi-site assessment

abstract: Although grasslands are crucial habitats for European butterflies, large-scale declines in quality and area have devastated many species. Grassland restoration can contribute to the recovery of butterfly populations, although there is a paucity of information on the long-term effects of management. Using eight UK data sets (9–21 years), we investigate changes in restoration success for (1) arable reversion sites, were grassland was established on bare ground using seed mixtures, and (2) grassland enhancement sites, where degraded grasslands are restored by scrub removal followed by the re-instigation of cutting/grazing. We also assessed the importance of individual butterfly traits and ecological characteristics in determining colonisation times. Consistent increases in restoration success over time were seen for arable reversion sites, with the most rapid rates of increase in restoration success seen over the first 10 years. For grasslands enhancement there were no consistent increases in restoration success over time. Butterfly colonisation times were fastest for species with widespread host plants or where host plants established well during restoration. Low mobility butterfly species took longer to colonise. We show that arable reversion is an effective tool for the management of butterfly communities. We suggest that as restoration takes time to achieve, its use as a mitigation tool against future environmental change (i.e. by decreasing isolation in fragmented landscapes) needs to take into account such time lags.


title: The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

abstract: This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution.


title: Source-Connected p-GaN Gate HEMTs for Increased Threshold Voltage

abstract: A pathway to increase the threshold voltage $(V_{\rm TH})$ of p-GaN gate high-electron-mobility transistors (HEMTs) is presented. The hole depletion width in the p-GaN layer at the gate interface is one of the key controlling factors of $V_{\rm TH}$ in p-GaN gate HEMTs. In order to increase the depletion width, we devise a new device structure of p-GaN gate HEMT having a source-connected p-GaN bridge. We demonstrate that a bridged p-GaN gate HEMT structure increases the $V_{\rm TH}$ from 0.93 to 2.44 V.


title: Embark: securely outsourcing middleboxes to the cloud

abstract: It is increasingly common for enterprises and other organizations to outsource network processing to the cloud. For example, enterprises may outsource firewalling, caching, and deep packet inspection, just as they outsource compute and storage. However, this poses a threat to enterprise confidentiality because the cloud provider gains access to the organization's traffic. ::: ::: We design and build Embark, the first system that enables a cloud provider to support middlebox outsourcing while maintaining the client's confidentiality. Embark encrypts the traffic that reaches the cloud and enables the cloud to process the encrypted traffic without decrypting it. Embark supports a wide-range of middleboxes such as firewalls, NATs, web proxies, load balancers, and data exfiltration systems. Our evaluation shows that Embark supports these applications with competitive performance.


title: Appearance Manifold of Facial Expression

abstract: This paper investigates the appearance manifold of facial expression: embedding image sequences of facial expression from the high dimensional appearance feature space to a low dimensional manifold. We explore Locality Preserving Projections (LPP) to learn expression manifolds from two kinds of feature space: raw image data and Local Binary Patterns (LBP). For manifolds of different subjects, we propose a novel alignment algorithm to define a global coordinate space, and align them on one generalized manifold. Extensive experiments on 96 subjects from the Cohn-Kanade database illustrate the effectiveness of the alignment algorithm. The proposed generalized appearance manifold provides a unified framework for automatic facial expression analysis.


A Survey of Event Correlation Techniques and Related Topics


title: Text recognition of low-resolution document images

abstract: Cheap and versatile cameras make it possible to easily and quickly capture a wide variety of documents. However, low resolution cameras present a challenge to OCR because it is virtually impossible to do character segmentation independently from recognition. In this paper we solve these problems simultaneously by applying methods borrowed from cursive handwriting recognition. To achieve maximum robustness, we use a machine learning approach based on a convolutional neural network. When our system is combined with a language model using dynamic programming, the overall performance is in the vicinity of 80-95% word accuracy on pages captured with a 1024/spl times/768 webcam and 10-point text.


title: Detect-SLAM: Making Object Detection and SLAM Mutually Beneficial

abstract: Although significant progress has been made in SLAM and object detection in recent years, there are still a series of challenges for both tasks, e.g., SLAM in dynamic environments and detecting objects in complex environments. To address these challenges, we present a novel robotic vision system, which integrates SLAM with a deep neural networkbased object detector to make the two functions mutually beneficial. The proposed system facilitates a robot to accomplish tasks reliably and efficiently in an unknown and dynamic environment. Experimental results show that compare to the state-of-the-art robotic vision systems, the proposed system has three advantages: i) it greatly improves the accuracy and robustness of SLAM in dynamic environments by removing unreliable features from moving objects leveraging the object detector, ii) it builds an instance-level semantic map of the environment in an online fashion using the synergy of the two functions for further semantic applications; and iii) it improves the object detector so that it can detect/recognize objects effectively under more challenging conditions such as unusual viewpoints, poor lighting condition, and motion blur, by leveraging the object map.


title: Design of a 5-W power receiver for 6.78 MHz resonant wireless power transfer system with power supply switching circuit

abstract: A 6.78 MHz resonant wireless power transfer (WPT) system with a 5 W fully integrated power receiver is presented. A conventional low-dropout (LDO) linear regulator supplies power for operating the circuit in the power receiver. However, as the required operating current increases, the power consumption of the LDO regulator increases, which degrades the power efficiency. In order to increase the power efficiency of the receiver, this work proposes a power supply switching circuit (PSSC). When operation starts, the PSSC changes the power source from the low-efficiency LDO regulator to the high-efficiency step-down DC-DC converter. The LDO regulator operates only for initialization. This chip has been fabricated using 0.18 μm high-voltage bipolar- CMOS-DMOS (double-diffused metal-oxide-semiconductor) (BCD) technology with a die area of 2.5 mm x 2.5 mm. A maximum power transfer efficiency of 81% is measured.


Generalized Bloom Filters


title: Information Theoretically Aided Reinforcement Learning for Embodied Agents

abstract: Reinforcement learning for embodied agents is a challenging problem. The accumulated reward to be optimized is often a very rugged function, and gradient methods are impaired by many local optimizers. We demonstrate, in an experimental setting, that incorporating an intrinsic reward can smoothen the optimization landscape while preserving the global optimizers of interest. We show that policy gradient optimization for locomotion in a complex morphology is significantly improved when supplementing the extrinsic reward by an intrinsic reward defined in terms of the mutual information of time consecutive sensor readings.


title: Reducing combinatorics in GUI testing of android applications

abstract: The rising popularity of Android and the GUI-driven nature of its apps have motivated the need for applicable automated GUI testing techniques. Although exhaustive testing of all possible combinations is the ideal upper bound in combinatorial testing, it is often infeasible, due to the combinatorial explosion of test cases. This paper presents TrimDroid, a framework for GUI testing of Android apps that uses a novel strategy to generate tests in a combinatorial, yet scalable, fashion. It is backed with automated program analysis and formally rigorous test generation engines. TrimDroid relies on program analysis to extract formal specifications. These specifications express the app’s behavior (i.e., control flow between the various app screens) as well as the GUI elements and their dependencies. The dependencies among the GUI elements comprising the app are used to reduce the number of combinations with the help of a solver. Our experiments have corroborated TrimDroid’s ability to achieve a comparable coverage as that possible under exhaustive GUI testing using significantly fewer test cases.


title: Color naming models for color selection, image editing and palette design

abstract: Our ability to reliably name colors provides a link between visual perception and symbolic cognition. In this paper, we investigate how a statistical model of color naming can enable user interfaces to meaningfully mimic this link and support novel interactions. We present a method for constructing a probabilistic model of color naming from a large, unconstrained set of human color name judgments. We describe how the model can be used to map between colors and names and define metrics for color saliency (how reliably a color is named) and color name distance (the similarity between colors based on naming patterns). We then present a series of applications that demonstrate how color naming models can enhance graphical interfaces: a color dictionary & thesaurus, name-based pixel selection methods for image editing, and evaluation aids for color palette design.


title: Interval-level measurement with visual analogue scales in Internet-based research: VAS Generator

abstract: The present article describes VAS Generator (www.vasgenerator.net), a free Web service for creating a wide range of visual analogue scales that can be used as measurement devices in Web surveys and Web experimentation, as well as for local computerized assessment. A step-by-step example for creating and implementing a visual analogue scale with visual feedback is given. VAS Generator and the scales it generates work independently of platforms and use the underlying languages HTML and JavaScript. Results from a validation study with 355 participants are reported and show that the scales generated with VAS Generator approximate an interval-scale level. In light of previous research on visual analogue versus categorical (e.g., radio button) scales in Internet-based research, we conclude that categorical scales only reach ordinal-scale level, and thus visual analogue scales are to be preferred whenever possible.


How Can Psychology Help Arti cial Intelligence


title: Article Information Theory in Scientific Visualization

abstract: In recent years, there is an emerging direction that leverages information theory to solve many challenging problems in scientific data analysis and visualization. In this article, we review the key concepts in information theory, discuss how the principles of information theory can be useful for visualization, and provide specific examples to draw connections between data communication and data visualization in terms of how information can be measured quantitatively. As the amount of digital data available to us increases at an astounding speed, the goal of this article is to introduce the interested readers to this new direction of data analysis research, and to inspire them to identify new applications and seek solutions using information theory.


Au th or ' s pe rs on al co py A fast and memory-efficient N-gram language model lookup method for large vocabulary continuous speech recognition


title: Modern intrusion detection, data mining, and degrees of attack guilt

abstract: This chapter examines the state of modern intrusion detection, with a particular emphasis on the emerging approach of data mining. The discussion parallels two important aspects of intrusion detection: general detection strategy (misuse detection versus anomaly detection) and data source (individual hosts versus network traffic). Misuse detection attempts to match known patterns of intrusion, while anomaly detection searches for deviations from normal behavior. Between the two approaches, only anomaly detection has the ability to detect unknown attacks. A particularly promising approach to anomaly detection combines association raining with other forms of machine learning such as classification. Moreover, the data source that an intrusion detection system employs significantly impacts the types of attacks it can detect. There is a tradeoff in the level of detailed information available versus data volume. We introduce a novel way of characterizing intrusion detection activities: degree of attack guilt. It is useful for qualifying the degree of confidence associated with detection events, providing a framework in which we analyze detection quality versus cost.


title: Procedural generation of stylized 2D maps

abstract: Outdoor worlds are often the setting for games and game worlds are often accompanied by a stylized version of the world drawn by an artist as a 2D map. Procedurally generating the terrain allows games to have a higher replay value. A limitation of procedural terrain generation is an artistic map of the terrain cannot be created by an artist beforehand. We propose an algorithm for generating a stylized 2D map from a simple procedurally generated 2D basis map. Our algorithm could be used in a game to generate stylized maps at execution time or in an offline application to serve as a guideline for an artist.


title: Malignant melanoma associated with a blue naevus: a case report

abstract: IntroductionCombined malignant naevi are characterised pathologically by the association of a melanoma with one or more different types of benign melanocytic naevi in a single lesion.Case PresentationWe show here a case of malignant combined naevus made up of a blue naevus and a melanoma, presenting as a slowly progressing and asymptomatic pigmented lesion on the trunk of a 35-year-old man. Dermoscopic examination was not conclusive for a malignant lesion, showing only an atypical brown pigment network. The definitive diagnosis was reached only at the hystopathological examination.ConclusionThis finding suggests that combined naevi should be always excised and histologically examined to achieve a correct diagnosis and avoid risk of misclassification.


title: Personalised rating prediction for new users using latent factor models

abstract: In recent years, personalised recommendations have gained importance in helping users deal with the abundance of information available online. Personalised recommendations are often based on rating predictions, and thus accurate rating prediction is essential for the generation of useful recommendations. Recently, rating prediction algorithms that are based on matrix factorisation have become increasingly popular, due to their high accuracy and scalability. However, these algorithms still deliver inaccurate rating predictions for new users, who submitted only a few ratings. In this paper, we address the new user problem by introducing several extensions to the basic matrix factorisation algorithm, which take user attributes into account when generating rating predictions. We consider both demographic attributes, explicitly supplied by users, and attributes inferred from user-generated texts. Our results show that employing our text-based user attributes yields personalised rating predictions that are more accurate than our baselines, while not requiring users to explicitly supply any information about themselves and their preferences.


title: Dealing with Uncertainties? Combining System Dynamics with Multiple Criteria Decision Analysis or with Exploratory Modelling

abstract: System Dynamics is often used to explore issues that are characterised by uncertainties. This paper discusses first of all dierent types of uncertainties that system dynamicists need to deal with and the tools they already use to deal with these uncertainties. From this discussion it is concluded that stand-alone System Dynamics is often not sucient to deal with uncertainties. Then, two venues for improving the capacity of System Dynamics to deal with uncertainties are discussed, in both cases, by matching System Dynamics with other method(ologie)s: first with Multi-Attribute Multiple Criteria Decision Analysis, and finally with Exploratory Modelling.


A Hierarchy of Authentication Specifications


title: Targeted genome modification technologies and their applications in crop improvements

abstract: Recent advances in genome engineering indicate that innovative crops developed by targeted genome modification (TGM) using site-specific nucleases (SSNs) have the potential to avoid the regulatory issues raised by genetically modified organisms. These powerful SSNs tools, comprising zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regulatory interspaced short palindromic repeats/CRISPR-associated systems, enable precise genome engineering by introducing DNA double-strand breaks that subsequently trigger DNA repair pathways involving either non-homologous end-joining or homologous recombination. Here, we review developments in genome-editing tools, summarize their applications in crop organisms, and discuss future prospects. We also highlight the ability of these tools to create non-transgenic TGM plants for next-generation crop breeding.


Use of Long Pulse Nd : YAG 1064 nm Laser for Treatment of Rosacea Telangiectatica


title: Influence of Jitter on Limit Cycles in Bang-Bang Clock and Data Recovery Circuits

abstract: In bang-bang (BB) clock and data recovery circuits (CDR) limit cycles can occur, but these limit cycles are undesired for a good operation of the BB-CDR. Surprisingly, however, a little bit of noise in the system is beneficial, because it will quench the limit cycles. Until now, authors have always assumed that there is enough noise in a BB-CDR such that no limit cycle occurs. In this work, a pseudo-linear analysis based on describing functions is used to investigate this. In particular, the relationship between the input noise and the amplitude of eventual limit cycles is investigated. An important result of the theory is that it allows to quantify the influence of the different loop parameters on the minimal amount of input jitter needed to destroy the limit cycle. Additionally, for the case that there is not enough noise, the worst case amplitude of the limit cycle (which is unavoidable in this case) is quantified as well. The presented analysis exhibits excellent matching with time domain simulations and leads to very simple analytical expressions.


title: Computing and Information RECENT ADVANCES IN PERSONAL RECOMMENDER SYSTEMS

abstract: In the past years we have witnessed an explosive growth of the data and information on the World Wide Web, which makes it difficult for normal users to find the information that they are interested in. On the other hand, the majority of the data and resources are very unpopular, which can be considered as "hidden information", and are very difficult tofind. By building a bridge between the users and the objects and constructing their similarities, the Personal Recommender System (PRS) can recommend the objects that the users are potentially interested in. PRS plays an important role in not only social and economic life but also scientific analysis. The interdisciplinary PRS attracts attention from the communities of information science, computational mathematics, statistical physics, management science, and consumer behaviors, etc. In fact, PRS is one of the most efficient tools to solve the i nformation over- load problem. According to the recommendation algorithms, we introduce four typical systems, including the collaborating filtering system, the content-based system, the structure-based system, and the hybrid system. In addition, some improved algorithms are proposed to overcome the limitations of traditional systems. This review article may shed some light on the study of PRS from different backgrounds.


title: Apprenticeship Learning About Multiple Intentions

abstract: In this paper, we apply tools from inverse reinforcement learning (IRL) to the problem of learning from (unlabeled) demonstration trajectories of behavior generated by varying "intentions" or objectives. We derive an EM approach that clusters observed trajectories by inferring the objectives for each cluster using any of several possible IRL methods, and then uses the constructed clusters to quickly identify the intent of a trajectory. We show that a natural approach to IRL—a gradient ascent method that modifies reward parameters to maximize the likelihood of the observed trajectories—is successful at quickly identifying unknown reward functions. We demonstrate these ideas in the context of apprenticeship learning by acquiring the preferences of a human driver in a simple highway car simulator.


title: Adaptive Boosting for Synthetic Aperture Radar Automatic Target Recognition

abstract: We propose a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the MSTAR public release database. First, MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, we decompose our multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature.


title: Stretchable and Flexible E-Fiber Wire Antennas Embedded in Polymer

abstract: A new process is presented to fabricate stretchable and flexible wire antennas using conductive fibers (E-fibers). Stretchability and flexibility are important for wire antennas that operate in environments subjecting them to shape deformation (e.g., automotive, wearable, implantable applications, etc.). The concept is to embroider the wire antenna using E-fibers and embed it into a stretchy polymer. This also preserves the integrity of the textile threads and protects them from corrosion. A key challenge is to remove the non-stretchable fabric on which the textile antenna was embroidered while still preserving its original shape. To validate our technology, we fabricate and test a stretchable and flexible wire antenna operating at 915 MHz. The E-fiber prototype is found to achieve excellent performance, comparable to that of its non-stretchable and non-flexible copper-wire counterpart. A future goal is to embed such antennas into hostile environments, such as a truck tire, for collecting automotive data.


title: Initiating and utilizing shared leadership in teams: The role of leader humility, team proactive personality, and team performance capability.

abstract: The present study was designed to produce novel theoretical insight regarding how leader humility and team member characteristics foster the conditions that promote shared leadership and when shared leadership relates to team effectiveness. Drawing on social information processing theory and adaptive leadership theory, we propose that leader humility facilitates shared leadership by promoting leadership-claiming and leadership-granting interactions among team members. We also apply dominance complementary theory to propose that team proactive personality strengthens the impact of leader humility on shared leadership. Finally, we predict that shared leadership will be most strongly related to team performance when team members have high levels of task-related competence. Using a sample composed of 62 Taiwanese professional work teams, we find support for our proposed hypothesized model. The theoretical and practical implications of these results for team leadership, humility, team composition, and shared leadership are discussed. (PsycINFO Database Record


Erratum to: FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method


Supplementary Information Link Communities Reveal Multi-Scale Complexity in Networks by


title: Why Deep Neural Networks for Function Approximation?

abstract: Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation. First, we consider univariate functions on a bounded interval and require a neural network to achieve an approximation error of $\varepsilon$ uniformly over the interval. We show that shallow networks (i.e., networks whose depth does not depend on $\varepsilon$) require $\Omega(\text{poly}(1/\varepsilon))$ neurons while deep networks (i.e., networks whose depth grows with $1/\varepsilon$) require $\mathcal{O}(\text{polylog}(1/\varepsilon))$ neurons. We then extend these results to certain classes of important multivariate functions. Our results are derived for neural networks which use a combination of rectifier linear units (ReLUs) and binary step units, two of the most popular type of activation functions. Our analysis builds on a simple observation: the multiplication of two bits can be represented by a ReLU.


title: D2D communication underlay uplink cellular network with fractional frequency reuse

abstract: Embedding device-to-device (D2D) communication into a cellular network can benefit the entire system by efficiently reusing cellular licensed frequency resources[2][3]. However, the interference between D2D user equipments (DUEs) and cellular user equipments (CUEs) must be properly handled to realize the expected gain. Fractional frequency reuse (FFR) is a promising technique to mitigate the interference between DUEs and CUEs and hence improve the performance as well as reliability of the entire system [4]. In this paper, the coverage performance of both CUEs and DUEs are analyzed in an FFR based D2D underlay uplink cellular network. We model the spacial distribution of DUEs as a homogeneous Poisson Point Process (PPP), which can facilitate tractable mathematical coverage analysis. Theoretical analysis is validated by numerical results.


title: Getting up to speed with transcription elongation by RNA polymerase II

abstract: Recent advances in sequencing techniques that measure nascent transcripts and that reveal the positioning of RNA polymerase II (Pol II) have shown that the pausing of Pol II in promoter-proximal regions and its release to initiate a phase of productive elongation are key steps in transcription regulation. Moreover, after the release of Pol II from the promoter-proximal region, elongation rates are highly dynamic throughout the transcription of a gene, and vary on a gene-by-gene basis. Interestingly, Pol II elongation rates affect co-transcriptional processes such as splicing, termination and genome stability. Increasing numbers of factors and regulatory mechanisms have been associated with the steps of transcription elongation by Pol II, revealing that elongation is a highly complex process. Elongation is thus now recognized as a key phase in the regulation of transcription by Pol II.


title: Robust Heart Rate Measurement from Video Using Select Random Patches

abstract: The ability to remotely measure heart rate from videos without requiring any special setup is beneficial to many applications. In recent years, a number of papers on heart rate (HR) measurement from videos have been proposed. However, these methods typically require the human subject to be stationary and for the illumination to be controlled. For methods that do take into account motion and illumination changes, strong assumptions are still made about the environment (e.g. background can be used for illumination rectification). In this paper, we propose an HR measurement method that is robust to motion, illumination changes, and does not require use of an environment's background. We present conditions under which cardiac activity extraction from local regions of the face can be treated as a linear Blind Source Separation problem and propose a simple but robust algorithm for selecting good local regions. The independent HR estimates from multiple local regions are then combined in a majority voting scheme that robustly recovers the HR. We validate our algorithm on a large database of challenging videos.


title: Neural Networks in Building QSAR Models

abstract: This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resulting models, the issues of generalization and memorization, the problems of overfitting and overtraining, the learning dynamics, regularization, and the use of neural network ensembles. The next part of the chapter focuses attention on the use of descriptors. It reviews different descriptor selection and preprocessing techniques; considers the use of the substituent, substructural, and superstructural descriptors in building common QSAR models; the use of molecular field descriptors in three-dimensional QSAR studies; along with the prospects of “direct” graph-based QSAR analysis. The chapter starts with a short historical survey of the main milestones in this area.


title: On Probabilistic Analysis of Disagreement in Synchronous Consensus Protocols

abstract: This paper presents a probabilistic analysis of disagreement for a family of simple synchronous consensus algorithms aimed at solving the 1-of-n selection problem in presence of unrestricted communication failures. In this problem, a set of n nodes are to select one common value among n proposed values. There are two possible outcomes of each node's selection process: decide to select a value or abort. We have disagreement if some nodes select the same value while other nodes decide to abort. Previous research has shown that it is impossible to guarantee agreement among the nodes subjected to an unbounded number of message losses. Our aim is to find decision algorithms for which the probability of disagreement is as low as possible. In this paper, we investigate two different decision criteria, one optimistic and one pessimistic. We assume two communication failure models, symmetric and asymmetric. For symmetric communication failures, we present the closed-form expressions for the probability of disagreement. For asymmetric failures, we analyse the algorithm using a probabilistic model checking tool. Our results show that the choice of decision criterion significantly influences the probability of disagreement for the 1-of-n selection algorithm. The optimistic decision criterion shows a lower probability of disagreement compare to the pessimistic one when the probability of message loss is less than 30% to 70%. On the other hand, the optimistic decision criterion has in general a higher maximum probability of disagreement compared to the pessimistic criterion.


mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation


title: T.: Scalable Splitting of Massive Data Streams

abstract: Scalable execution of continuous queries over massive data streams often requires splitting input streams into parallel sub-streams over which query operators are executed in parallel. Automatic stream splitting is in general very difficult, as the optimal parallelization may depend on application semantics. To enable application specific stream splitting, we introduce splitstream functions where the user specifies non-procedural stream partitioning and replication. For high-volume streams, the stream splitting itself becomes a performance bottleneck. A cost model is introduced that estimates the performance of splitstream functions with respect to throughput and CPU usage. We implement parallel splitstream functions, and relate experimental results to cost model estimates. Based on the results, a splitstream function called autosplit is proposed, which scales well for high degrees of parallelism, and is robust for varying proportions of stream partitioning and replication. We show how user defined parallelization using autosplit provides substantially improved scalability (L = 64) over previously published results for the Linear Road Benchmark.


title: Deity and Destiny Patterns of Fatalistic Thinking in Christian and Hindu Cultures

abstract: The current studies investigate whether different forms of fatalistic thinking follow from the Christian and Hindu cosmologies. The authors found that fatalistic interpretations of one’s own life events center on deity influence for Christians, especially for those high in religiosity; however, Hindu interpretations of one’s own life emphasized destiny as much as deity (Study 1). Also, the focus on fate over chance when explaining others’ misfortunes depends on the presence of known misdeeds for Christians, but not for Hindus (Study 2). Finally, Christians prefer petitionary prayer over divination as a strategy for managing uncontrollable future risks (Studies 3a and 3c), and preference for these strategies can be primed in bicultural Hindu Americans by a Hindi-accented telephone interviewer (Study 3b).


title: Low-cost and Device-free Activity Recognition System with Energy Harvesting PIR and Door Sensors

abstract: Progress of IoT and ubiquitous computing technologies has strong anticipation to realize smart services in households such as efficient energy-saving appliance control and elderly monitoring. In order to put those applications into practice, high-accuracy and low-cost in-home living activity recognition is essential. Many researches have tackled living activity recognition so far, but the following problems remain: (i)privacy exposure due to utilization of cameras and microphones; (ii) high deployment and maintenance costs due to many sensors used; (iii) burden to force the user to carry the device and (iv) wire installation to supply power and communication between sensor node and server; (v) few recognizable activities; (vi) low recognition accuracy. In this paper, we propose an in-home living activity recognition method to solve all the problems. To solve the problems (i)--(iv), our method utilizes only energy harvesting PIR and door sensors with a home server for data collection and processing. The energy harvesting sensor has a solar cell to drive the sensor and wireless communication modules. To solve the problems (v) and (vi), we have tackled the following challenges: (a) determining appropriate features for training samples; and (b) determining the best machine learning algorithm to achieve high recognition accuracy; (c) complementing the dead zone of PIR sensor semipermanently. We have conducted experiments with the sensor by five subjects living in a home for 2-3 days each. As a result, the proposed method has achieved F-measure: 62.8% on average.


Co-creation of a digital tool for the empowerment of parents of children with physical disabilities


BlurBurst: Removing Blur Due to Camera Shake using Multiple Images ATSUSHI ITO Sony Corporation and


title: The Physical Visualization of Information: Designing Data Sculptures in an Educational Context

abstract: This paper is a qualitative case study analysis of the issues involved in designing and implementing data sculptures, the physical “embodiment” of data in a tangible presence, shape or form, within the context of an undergraduate design studio. It demonstrates how approaches and theories from the field of data visualization can form a rich contextual resource and motivational tool for teaching principles of creative design. A relatively short, two-week assignment challenged students to investigate and convey a meaningful data-driven insight through a tangible representation. In this paper, we analyze the resulting collection of physical artifacts developed by the students to reveal notable design approaches of embodying abstract information. We present a novel classification of data sculptures based on a semiotic taxonomy (symbolic, iconic, or indexical) and consider the qualities of representational fidelity and narrative formulation, for instance when the unfolding of the data-driven narrative is seen as a process, rather than an outcome. In addition, we investigate how the introduction of digital fabrication techniques influenced the design strategies chosen by the students versus handmade approaches.


title: Digital forensics of microscopic images for printed source identification

abstract: When trying to identify a printed forged document, examining digital evidence can prove to be a challenge. In this study, microscopic images are used for printed source identification due to their high magnification properties resulting in detailed texture and structure information. Prior research implemented a scanner as a digitizing technique to resolve very fine printed identification, but this technique provided limited information on the resolution and magnification of the sample. In contrast, the performance of microscopy techniques can retrieve the shape and surface texture of a printed document with differing micro structures among printer sources. To explore the relationship between source printers and images obtained by the microscope, the proposed approach utilizes image processing techniques and data exploration methods to calculate many important statistical features, including: Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, the Wiener filter, the Gabor filter, Haralick, and SFTA features. Among the different set of features, the LBP approach achieves the highest identification rate and is significantly superior to other methods. As a result, the proposed technique using microscopic images achieves a high classification accuracy rate, which shows promising applications for real world digital forensics research.


title: S^3FD: Single Shot Scale-Invariant Face Detector

abstract: This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.


title: The AMPA receptor antagonist perampanel robustly rescues amyotrophic lateral sclerosis (ALS) pathology in sporadic ALS model mice

abstract: Both TDP-43 pathology and failure of RNA editing of AMPA receptor subunit GluA2, are etiology-linked molecular abnormalities that concomitantly occur in the motor neurons of the majority of patients with amyotrophic lateral sclerosis (ALS). AR2 mice, in which an RNA editing enzyme adenosine deaminase acting on RNA 2 (ADAR2) is conditionally knocked out in the motor neurons, exhibit a progressive ALS phenotype with TDP-43 pathology in the motor neurons through a Ca(2+)-permeable AMPA receptor-mediated mechanism. Therefore, amelioration of the increased Ca(2+) influx by AMPA receptor antagonists may be a potential ALS therapy. Here, we showed that orally administered perampanel, a selective, non-competitive AMPA receptor antagonist significantly prevented the progression of the ALS phenotype and normalized the TDP-43 pathology-associated death of motor neurons in the AR2 mice. Given that perampanel is an approved anti-epileptic drug, perampanel is a potential candidate ALS drug worthy of a clinical trial.


title: Architectural issues in software reuse: it's not just the functionality, it's the packaging

abstract: Effective reuse depends not only on finding and reusing components, but also on the ways those components are combined. The informal folklore of software engineering provides a number of diverse styles for organizing software systems. These styles, or architectures, show how to compose systems from components; different styles expect different kinds of component packaging and different kinds of interactions between the components. Unfortunately, these styles and packaging distinctions are often implicit; as a consequence, components with appropriate functionality may fail to work together. This talk surveys common architectural styles, including important packaging and interaction distinctions, and proposes an approach to the problem of reconciling architectural mismatches.


title: Multi-Residual Networks.

abstract: In this article, we take one step toward understanding the learning behavior of deep residual networks, and supporting the hypothesis that deep residual networks are exponential ensembles by construction. We examine the effective range of ensembles by introducing multi-residual networks that significantly improve classification accuracy of residual networks. The multi-residual networks increase the number of residual functions in the residual blocks. This is shown to improve the accuracy of the residual network when the network is deeper than a threshold. Based on a series of empirical studies on CIFAR-10 and CIFAR-100 datasets, the proposed multi-residual network yield $6\%$ and $10\%$ improvement with respect to the residual networks with identity mappings. Comparing with other state-of-the-art models, the proposed multi-residual network obtains a test error rate of $3.92\%$ on CIFAR-10 that outperforms all existing models.


Actuality, Potentiality, and Relativity’s Block Universe


title: TCP Westwood: congestion control with faster recovery

abstract: In this paper we propose a new version of the TCP protocol, called TCP Westwood, which enhances the performance of TCP window congestion control by using as feedback the end-to-end measurement of the bandwidth available along a TCP connection. The available bandwidth is estimated at the TCP source by measuring and low-pass filtering the returning rate of acknowledgments. The estimated bandwidth is then used to properly set the congestion window and the slow start threshold after a congestion episode, that is after a timeout or 3 duplicate acknowledgments. The rationale of this strategy is simple: TCP Westwood sets a slow start threshold and a congestion window which are consistent with the network capacity measured at the time congestion is experienced. In particular, TCP Westwood introduces a mechanism of faster recovery to avoid overly conservative reduction of the congestion window after a congestion episode by taking into account the end-to-end estimation of available bandwidth. The advantage of the proposed mechanism is that the TCP sender recovers faster after losses especially over connections with large round trip times, or running over wireless links where sporadic losses are due to unreliable links rather than congestion. The proposed modifications follow the end-to-end design principle of TCP. They require only slight modifications at the sender side and are backward-compatible. Simulation results show a considerable throughput increment in comparison with TCP Reno and TCP SACK over wired networks and even more over wireless networks.


title: HEAL ME - An Architecture for Health Software Ecosystem Evaluation

abstract: Software Ecosystems are complex environments. Several actors are grouped on a software platform, facilitating investments in research, development, and reuse of components. However, such investments may be lost if the ecosystem is not solid. Therefore, it is important to assess the health of an ecosystem. This work presents HEAL-ME architecture, which main goal is to evaluate the health of software ecosystems. A pilot case study using a scientific software ecosystem is also presented. Enhancements will be provided for a complete SECO health assessment.


title: Facial reconstruction - anatomical art or artistic anatomy?

abstract: Facial reconstruction is employed in the context of forensic investigation and for creating three-dimensional portraits of people from the past, from ancient Egyptian mummies and bog bodies to digital animations of J. S. Bach. This paper considers a facial reconstruction method (commonly known as the Manchester method) associated with the depiction and identification of the deceased from skeletal remains. Issues of artistic licence and scientific rigour, in relation to soft tissue reconstruction, anatomical variation and skeletal assessment, are discussed. The need for artistic interpretation is greatest where only skeletal material is available, particularly for the morphology of the ears and mouth, and with the skin for an ageing adult. The greatest accuracy is possible when information is available from preserved soft tissue, from a portrait, or from a pathological condition or healed injury.


title: Learning bounds for domain adaptation

abstract: Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classifier from a source domain with a large amount of training data to different target domain with very little training data. In this work we give uniform convergence bounds for algorithms that minimize a convex combination of source and target empirical risk. The bounds explicitly model the inherent trade-off between training on a large but inaccurate source data set and a small but accurate target training set. Our theory also gives results when we have multiple source domains, each of which may have a different number of instances, and we exhibit cases in which minimizing a non-uniform combination of source risks can achieve much lower target error than standard empirical risk minimization.


title: Cyber–Physical Security of a Smart Grid Infrastructure

abstract: It is often appealing to assume that existing solutions can be directly applied to emerging engineering domains. Unfortunately, careful investigation of the unique challenges presented by new domains exposes its idiosyncrasies, thus often requiring new approaches and solutions. In this paper, we argue that the “smart” grid, replacing its incredibly successful and reliable predecessor, poses a series of new security challenges, among others, that require novel approaches to the field of cyber security. We will call this new field cyber-physical security. The tight coupling between information and communication technologies and physical systems introduces new security concerns, requiring a rethinking of the commonly used objectives and methods. Existing security approaches are either inapplicable, not viable, insufficiently scalable, incompatible, or simply inadequate to address the challenges posed by highly complex environments such as the smart grid. A concerted effort by the entire industry, the research community, and the policy makers is required to achieve the vision of a secure smart grid infrastructure.


title: AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System

abstract: Wi-Fi positioning is currently the mainstream indoor positioning method, and the construction of fingerprint database is crucial to Wi-Fi based localization system. However, the accuracy requirement needs to sample enough data at many reference points, which consumes significant manpower and time. In this paper, we convert the CSI data collected at reference points into amplitude feature maps and then extend the fingerprint database using the proposed Amplitude Feature Deep Convolutional Generative Adversarial Network (AF-DCGAN) model. Using this model, the convergence process in the training phase can be accelerated, and the diversity of the CSI amplitude feature maps can be increased significantly. Based on the extended fingerprint database, the accuracy of indoor localization system can be improved with reduced human effort.


title: Predicting Vulnerable Components via Text Mining or Software Metrics? An Effort-Aware Perspective

abstract: In order to identify vulnerable software components, developers can take software metrics as predictors or use text mining techniques to build vulnerability prediction models. A recent study reported that text mining based models have higher recall than software metrics based models. However, this conclusion was drawn without considering the sizes of individual components which affects the code inspection effort to determine whether a component is vulnerable. In this paper, we investigate the predictive power of these two kinds of prediction models in the context of effort-aware vulnerability prediction. To this end, we use the same data sets, containing 223 vulnerabilities found in three web applications, to build vulnerability prediction models. The experimental results show that: (1) in the context of effort-aware ranking scenario, text mining based models only slightly outperform software metrics based models, (2) in the context of effort-aware classification scenario, text mining based models perform similarly to software metrics based models in most cases, and (3) most of the effect sizes (i.e. the magnitude of the differences) between these two kinds of models are trivial. These results suggest that, from the viewpoint of practical application, software metrics based models are comparable to text mining based models. Therefore, for developers, software metrics based models are practical choices for vulnerability prediction, as the cost to build and apply these models is much lower.


title: The Lion's Gate and the Persian Wall in Byblos opening the doors of digital representation to the cultural heritage of a resilient city in Lebanon

abstract: Byblos, modern Jbeil, is a northern Lebanese city considered as one of the oldest continuously inhabited cities since Neolithic times, designated as a UNESCO World Heritage site in 1984. Thus, the archaeological area, strictly surrounded by city boundaries, is the result of several civilizations that came over the centuries producing many layers of architectural buildings now in precarious state of conservation. The recent nomination of Byblos as one of the “100 Resilient Cities”, a project pioneered by the Rockefeller Foundation, fostered some new intents aimed at the preservation of the urban space and the archaeological site. In order to support a possible valorization strategy, the Lion's Gate at the Persian Wall has been chosen as a case study to experiment how digital photogrammetric modeling of meaningful remains can improve dissemination of cultural legacy to remote visitors and transfer knowledge to town planners. This paper presents the preliminary research, which is supposed to be integrated by some more detailed surveys in the near future, in order to populate a shared web repository in which scholars, tourists and planners will find useful information on the archaeological park.


title: Complications Associated With Injectable Soft-Tissue Fillers

abstract: IMPORTANCE ::: Even when administered by experienced hands, injectable soft-tissue fillers can cause various unintended reactions, ranging from minor and self-limited responses to severe complications requiring prompt treatment and close follow-up. ::: ::: ::: OBJECTIVES ::: To review the complications associated with injectable soft-tissue filler treatments administered in the Williams Rejuva Center during a 5-year period and to discuss their management. ::: ::: ::: DESIGN AND SETTING ::: Retrospective medical record review in a private practice setting. ::: ::: ::: PARTICIPANTS ::: Patients receiving injectable soft-tissue fillers and having a treatment-related complication. ::: ::: ::: INTERVENTIONS ::: Injectable soft-tissue filler treatments. ::: ::: ::: MAIN OUTCOME MEASURES ::: A retrospective medical record review was conducted of patients undergoing treatment with injectable soft-tissue fillers between January 1, 2007, and December 31, 2011, and identified as having a treatment-related complication. ::: ::: ::: RESULTS ::: A total of 2089 injectable soft-tissue filler treatments were performed during the study period, including 1047 with hyaluronic acid, 811 with poly-L-lactic acid, and 231 with calcium hydroxylapatite. Fourteen complications were identified. The most common complication was nodule or granuloma formation. Treatment with calcium hydroxylapatite had the highest complication rate. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Complications are rare following treatment with injectable soft-tissue fillers. Nevertheless, it is important to be aware of the spectrum of potential adverse sequelae and to be comfortable with their proper management. ::: ::: ::: LEVEL OF EVIDENCE ::: 4.


title: Cross-lingual polarity detection with machine translation

abstract: Recent advancements in machine translation foster an interest of its use in sentiment analysis. In this paper, we investigate prospects and limitations of machine translation in sentiment analysis for cross-lingual polarity detection task. We focus on improving classification accuracy in a cross-lingual setting where we have available labeled training instances about particular domain in different languages. We experiment with movie review and product review datasets consisting of polar texts in English and Turkish. The results of the study show that expanding training size with new instances taken from another corpus does not necessarily increase classification accuracy. And this happens primarily not due to (not always accurate) machine translation, but because of the inherent differences in corpora between two subsets written in different languages. Similarly, in case of co-training classification with machine translation we observe from the results that accuracy improvement can be explained by semi-supervised learning with unlabeled data coming from the same domain, but not due to cross-language co-training itself. Our results also show that amount of artificial noise added by machine translation services does not hinder classifiers much in polarity detection task. However, it is important to distinguish the effect of machine translation from the effect of merging different cross-lingual data sources and that like in case of transfer learning we may need to search for ways to account for cross-lingual data distribution differences.


title: Action Tube Extraction Based 3D-CNN for RGB-D Action Recognition

abstract: In this paper we propose a novel action tube extractor for RGB-D action recognition in trimmed videos. The action tube extractor takes as input a video and outputs an action tube. The method consists of two parts: spatial tube extraction and temporal sampling. The first part is built upon MobileNet-SSD and its role is to define the spatial region where the action takes place. The second part is based on the structural similarity index (SSIM) and is designed to remove frames without obvious motion from the primary action tube. The final extracted action tube has two benefits: 1) a higher ratio of ROI (subjects of action) to background; 2) most frames contain obvious motion change. We propose to use a two-stream (RGB and Depth) I3D architecture as our 3D-CNN model. Our approach outperforms the state-of-the-art methods on the OA and NTU RGB-D datasets.


title: Contactless Multiple Wavelength Photoplethysmographic Imaging: A First Step Toward “SpO2 Camera” Technology

abstract: We describe a route toward contactless imaging of arterial oxygen saturation (SpO2) distribution within tissue, based upon detection of a two-dimensional matrix of spatially resolved optical plethysmographic signals at different wavelengths. As a first step toward SpO2-imaging we built a monochrome CMOS-camera with apochromatic lens and 3lambda-LED-ringlight (lambda1 = 660 nm, lambda2 = 810 nm, lambda3 = 940 nm; 100 LEDs lambda(-1)). We acquired movies at three wavelengths while simultaneously recording ECG and respiration for seven volunteers. We repeated this experiment for one volunteer at increased frame rate, additionally recording the pulse wave of a pulse oximeter. Movies were processed by dividing each image frame into discrete Regions of Interest (ROIs), averaging 10 x 10 raw pixels each. For each ROI, pulsatile variation over time was assigned to a matrix of ROI-pixel time traces with individual Fourier spectra. Photoplethysmograms correlated well with respiration reference traces at three wavelengths. Increased frame rates revealed weaker pulsations (main frequency components 0.95 and 1.9 Hz) superimposed upon respiration-correlated photoplethysmograms, which were heartbeat-related at three wavelengths. We acquired spatially resolved heartbeat-related photoplethysmograms at multiple wavelengths using a remote camera. This feasibility study shows potential for non-contact 2-D imaging reflection-mode pulse oximetry. Clinical devices, however, require further development.


title: Learning a Sparse Representation for Object Detection

abstract: We present an approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects. A vocabulary of information-rich object parts is automatically constructed from a set of sample images of the object class of interest. Images are then represented using parts from this vocabulary, along with spatial relations observed among them. Based on this representation, a feature-efficient learning algorithm is used to learn to detect instances of the object class. The framework developed can be applied to any object with distinguishable parts in a relatively fixed spatial configuration. We report experiments on images of side views of cars. Our experiments show that the method achieves high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation.In addition, we discuss and offer solutions to several methodological issues that are significant for the research community to be able to evaluate object detection approaches.


title: Acquisition of Characteristic Tree Patterns with VLDC's by Genetic Programming and Edit Distance

abstract: Knowledge discovery from structured data is an important task in machine learning and data mining. We propose a learning method for acquiring characteristic tree patterns with VLDC's from positive and negative tree structured data by using Genetic Programming and tree edit distance. We report experimental results on applying our method to glycan data.


title: Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients

abstract: A decrease in the abundance and biodiversity of intestinal bacteria within the dominant phylum Firmicutes has been observed repeatedly in Crohn disease (CD) patients. In this study, we determined the composition of the mucosa-associated microbiota of CD patients at the time of surgical resection and 6 months later using FISH analysis. We found that a reduction of a major member of Firmicutes, Faecalibacterium prausnitzii, is associated with a higher risk of postoperative recurrence of ileal CD. A lower proportion of F. prausnitzii on resected ileal Crohn mucosa also was associated with endoscopic recurrence at 6 months. To evaluate the immunomodulatory properties of F. prausnitzii we analyzed the anti-inflammatory effects of F. prausnitzii in both in vitro (cellular models) and in vivo [2,4,6-trinitrobenzenesulphonic acid (TNBS)-induced] colitis in mice. In Caco-2 cells transfected with a reporter gene for NF-κB activity, F. prausnitzii had no effect on IL-1β-induced NF-κB activity, whereas the supernatant abolished it. In vitro peripheral blood mononuclear cell stimulation by F. prausnitzii led to significantly lower IL-12 and IFN-γ production levels and higher secretion of IL-10. Oral administration of either live F. prausnitzii or its supernatant markedly reduced the severity of TNBS colitis and tended to correct the dysbiosis associated with TNBS colitis, as demonstrated by real-time quantitative PCR (qPCR) analysis. F. prausnitzii exhibits anti-inflammatory effects on cellular and TNBS colitis models, partly due to secreted metabolites able to block NF-κB activation and IL-8 production. These results suggest that counterbalancing dysbiosis using F. prausnitzii as a probiotic is a promising strategy in CD treatment.


title: Clinical and research methods for evaluating cortical excitability.

abstract: Summary The evaluation of motor cortical output after transcranial magnetic stimulation (TMS) is a means of investigating how the motor cortex reacts to external stimuli (i.e., a method to assess the excitability of the motor cortex). The recording of the descending volleys at the surface of the spinal cord provides a direct measure of the motor cortical output. However, this approach is highly invasive and can be used only during particular conditions. On the other hand, electromyographic recordings of the motor phenomena induced by TMS provide a completely painless, noninvasive, indirect measure of the cortical output, with these phenomena obviously reflecting the excitability of the spinal motoneurons as well as that of the muscle itself. The authors review how the electromyographic activity induced by TMS can provide valuable information about motor cortical excitability for use in clinical practice and research.


title: Skip-gram word embeddings in hyperbolic space

abstract: Embeddings of tree-like graphs in hyperbolic space were recently shown to surpass their Euclidean counterparts in performance by a large margin. Inspired by these results, we present an algorithm for learning word embeddings in hyperbolic space from free text. An objective function based on the hyperbolic distance is derived and included in the skip-gram architecture from word2vec. The hyperbolic word embeddings are then evaluated on word similarity and analogy benchmarks. The results demonstrate the potential of hyperbolic word embeddings, particularly in low dimensions, though without clear superiority over their Euclidean counterparts. We further discuss problems in the formulation of the analogy task resulting from the curvature of hyperbolic space.


title: Memory Replay GANs: learning to generate images from new categories without forgetting

abstract: Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (i.e. forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories.


title: Deformable Spatial Pyramid Matching for Fast Dense Correspondences

abstract: We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel level, we propose a pyramid graph model that simultaneously regularizes match consistency at multiple spatial extents-ranging from an entire image, to coarse grid cells, to every single pixel. This novel regularization substantially improves pixel-level matching in the face of challenging image variations, while the "deformable" aspect of our model overcomes the strict rigidity of traditional spatial pyramids. Results on Label Me and Caltech show our approach outperforms state-of-the-art methods (SIFT Flow [15] and Patch-Match [2]), both in terms of accuracy and run time.


title: Analyzing the Literature on Knowledge Management Frameworks: Towards a Normative Knowledge Management Classification Schema

abstract: Knowledge Management (KM) is a young and interdisciplinary subdomain of the IS discipline and it covers a considerable number of different topics. Due to its interdisciplinary character, a common understanding of KM is still missing. Several studies have already focused their efforts on harmonizing the discipline’s topics by developing KM frameworks. The purpose of this paper is to explore and integrate these frameworks in order to contribute to a common KM understanding. The procedure is twofold: 74 studies are identified through a structured literature review and compared using a concept matrix. The studies discuss already existing KM frameworks from research and practice or present the results of reviewing the KM domain through the development of frameworks. Based on these results, a normative KM classification schema is introduced. This schema comprises seven main KM categories and offers a summary of the common grounds in the domain of KM frameworks. The study provides guidance where to focus future research efforts and helps to identify potentially relevant topics, which, despite their relevance to the KM field, have not been considered up until now. Moreover, businesses can use the schema to get an overview of existing frameworks, which can be adapted in their organization.


title: The Internet of Things: A Review of Enabled Technologies and Future Challenges

abstract: The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm.


title: On the emotion of users in app reviews

abstract: App store analysis has become an important discipline in recent software engineering research. It empirically studies apps using information mined from their distribution platforms. Information provided by users, such as app reviews, are of high interest to developers. Commercial providers such as App Annie analyzing this information became an important source for companies developing and marketing mobile apps. In this paper, we perform an exploratory study, which analyzes over seven million reviews from the Apple AppStore regarding their emotional sentiment. Since recent research in this field used sentiments to detail and refine their results, we aim to gain deeper insights into the nature of sentiments in user reviews. In this study we try to evaluate whether or not the emotional sentiment can be an informative feature for software engineers, as well as pitfalls of its usage. We present our initial results and discuss how they can be interpreted from the software engineering perspective.


title: Planning Walking Patterns for a Biped Robot

abstract: Biped robots have better mobility than conventional wheeled robots, but they tend to tip over easily. To be able to walk stably in various environments, such as on rough terrain, up and down slopes, or in regions containing obstacles, it is necessary for the robot to adapt to the ground conditions with a foot motion, and maintain its stability with a torso motion. When the ground conditions and stability constraint are satisfied, it is desirable to select a walking pattern that requires small torque and velocity of the joint actuators. We first formulate the constraints of the foot motion parameters. By varying the values of the constraint parameters, we can produce different types of foot motion to adapt to ground conditions. We then propose a method for formulating the problem of the smooth hip motion with the largest stability margin using only two parameters, and derive the hip trajectory by iterative computation. Finally, the correlation between the actuator specifications and the walking patterns is described through simulation studies, and the effectiveness of the proposed methods is confirmed by simulation examples and experimental results.


title: Mobility performance evaluation of lunar rover and optimization of rocker-bogie suspension parameters

abstract: In order to provide a steady platform for scientific instruments, decrease the energy consumption, it is important to analyze and improve the mobility performance of lunar rovers. The mobility performance indexes are built based on four kinds of work conditions by summarizing the lunar surface terrain characteristics. The performance of overturning stability, load equalization of the wheels and trafficability are analyzed. The optimization mathematical model of the suspension parameters is established. Based on the sequence quadratic programming (SQP) algorithm, the optimal parameters of the suspension are obtained by taking the maximum energy consumption of motor and stationarity of mass center as the optimization objectives and overturning stability and trafficability parameters as constraints. The results indicate that the energy consumption of the motors, displacement and pitch angle of the mass center reduces significantly.


title: Structured Time Series Analysis for Human Action Segmentation and Recognition

abstract: We address the problem of structure learning of human motion in order to recognize actions from a continuous monocular motion sequence of an arbitrary person from an arbitrary viewpoint. Human motion sequences are represented by multivariate time series in the joint-trajectories space. Under this structured time series framework, we first propose Kernelized Temporal Cut (KTC), an extension of previous works on change-point detection by incorporating Hilbert space embedding of distributions, to handle the nonparametric and high dimensionality issues of human motions. Experimental results demonstrate the effectiveness of our approach, which yields realtime segmentation, and produces high action segmentation accuracy. Second, a spatio-temporal manifold framework is proposed to model the latent structure of time series data. Then an efficient spatio-temporal alignment algorithm Dynamic Manifold Warping (DMW) is proposed for multivariate time series to calculate motion similarity between action sequences (segments). Furthermore, by combining the temporal segmentation algorithm and the alignment algorithm, online human action recognition can be performed by associating a few labeled examples from motion capture data. The results on human motion capture data and 3D depth sensor data demonstrate the effectiveness of the proposed approach in automatically segmenting and recognizing motion sequences, and its ability to handle noisy and partially occluded data, in the transfer learning module.


title: Knowledge transfer with low-quality data: A feature extraction issue

abstract: Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research the goal is to transfer knowledge between sources of data, particularly when ground truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary sources of data becomes a necessity. Towards seamless knowledge transfer among tasks, effective representation of the data is a critical but yet not fully explored research area for the data engineer and data miner. Here we present a technique based on the idea of sparse coding, which essentially attempts to find an embedding for the data by assigning feature values based on subspace cluster membership. We modify the idea of sparse coding by focusing the identification of shared clusters between data when source and target data may have different distributions. In our paper, we point out cases where a direct application of sparse coding will lead to a failure of knowledge transfer. We then present the details of our extension to sparse coding, by incorporating distribution distance estimates for the embedded data, and show that the proposed algorithm can overcome the shortcomings of the sparse coding algorithm on synthetic data and achieve improved predictive performance on a real world chemical toxicity transfer learning task.


title: Computerized heart sounds analysis

abstract: This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency (TF) PCG signal characteristics and consequently aid diagnosis. Similarly, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties. The studied techniques (FT, STFT, WD, CWT, DWT and PWT) of analysis can thus be regarded as complementary in the TF analysis of the PCG signal; each will relate to a part distinct from the analysis in question.


title: Bone erosion in rheumatoid arthritis: mechanisms, diagnosis and treatment

abstract: Bone erosion is a central feature of rheumatoid arthritis and is associated with disease severity and poor functional outcome. Erosion of periarticular cortical bone, the typical feature observed on plain radiographs in patients with rheumatoid arthritis, results from excessive local bone resorption and inadequate bone formation. The main triggers of articular bone erosion are synovitis, including the production of proinflammatory cytokines and receptor activator of nuclear factor κB ligand (RANKL), as well as antibodies directed against citrullinated proteins. Indeed, both cytokines and autoantibodies stimulate the differentiation of bone-resorbing osteoclasts, thereby stimulating local bone resorption. Although current antirheumatic therapy inhibits both bone erosion and inflammation, repair of existing bone lesions, albeit physiologically feasible, occurs rarely. Lack of repair is due, at least in part, to active suppression of bone formation by proinflammatory cytokines. This Review summarizes the substantial progress that has been made in understanding the pathophysiology of bone erosions and discusses the improvements in the diagnosis, monitoring and treatment of such lesions.


title: User Experience in Service Design: A Case Study from Algeria

abstract: The design of crisis management services is crucial for emerging countries such as Algeria. It must take into account the experiences of diverse stakeholders. The authors investigate user experience (UX) practices from a service design perspective and describe a case study from Algeria exploring UX-driven service design for crisis management.


title: A weighted spectrum metric for comparison of internet topologies

abstract: Comparison of graph structures is a frequently encountered problem across a number of problem domains. Comparing graphs requires a metric to discriminate which features of the graphs are considered important. The spectrum of a graph is often claimed to contain all the information within a graph, but the raw spectrum contains too much information to be directly used as a useful metric. In this paper we introduce a metric, the weighted spectral distribution, that improves on the raw spectrum by discounting those eigenvalues believed to be unimportant and emphasizing the contribution of those believed to be important. We use this metric to optimize the selection of parameter values for generating Internet topologies. Our metric leads to parameter choices that appear sensible given prior knowledge of the problem domain: the resulting choices are close to the default values of the topology generators and, in the case of some generators, fall within the expected region. This metric provides a means for meaningfully optimizing parameter selection when generating topologies intended to share structure with, but not match exactly, measured graphs.


title: Learning from Demonstrations: Is It Worth Estimating a Reward Function?

abstract: This paper provides a comparative study between Inverse Reinforcement Learning (IRL) and Apprenticeship Learning (AL). IRL and AL are two frameworks, using Markov Decision Processes (MDP), which are used for the imitation learning problem where an agent tries to learn from demonstrations of an expert. In the AL framework, the agent tries to learn the expert policy whereas in the IRL framework, the agent tries to learn a reward which can explain the behavior of the expert. This reward is then optimized to imitate the expert. One can wonder if it is worth estimating such a reward, or if estimating a policy is sufficient. This quite natural question has not really been addressed in the literature right now. We provide partial answers, both from a theoretical and empirical point of view.


title: Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

abstract: Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).


title: Psl model checking and run-time verification via testers

abstract: The paper introduces the construct of temporal testers as a compositional basis for the construction of automata corresponding to temporal formulas in the PSL logic. Temporal testers can be viewed as (non-deterministic) transducers that, at any point, output a boolean value which is 1 iff the corresponding temporal formula holds starting at the current position. ::: ::: The main advantage of testers, compared to acceptors (such as Buchi automata) is that they are compositional. Namely, a tester for a compound formula can be constructed out of the testers for its sub-formulas. In this paper, we extend the application of the testers method from LTL to the logic PSL. ::: ::: Besides providing the construction of testers for PSL, we indicate how the symbolic representation of the testers can be directly utilized for efficient model checking and run-time monitoring