title: Virus Detection using Artificial Neural Networks

abstract: A virus is defined as a program that spreads or replicates by copying itself, and generally has malicious effects. The antivirus systems used today mainly detect malware on the basis of known virus patterns, making detection of a new virus very difficult. This deficiency can be overcome by training an artificial neural network with the inputs from Portable Executable (PE) Structure of executable files, as they learn from the training data and will be able to identify unknown virus patterns. PE Structure contains various fields by which one can identify virus infected executable files from the legitimate ones without executing them, and Fisher Score can be used to select the most relevant features (fields) to speed up the analysis. A new technique of identifying virus infected files by using Fisher Score and applying them as input to the neural network is proposed. General Terms Virus, Program, Patterns, Executable files


title: A Shortly and Densely Connected Convolutional Neural Network for Vehicle Re-identification

abstract: In this paper, we propose a shortly and densely connected convolutional neural network (SDC-CNN) for vehicle re-identification. The proposed SDC-CNN mainly consists of short and dense units (SDUs), necessary pooling and normalization layers. The main contribution lies at the design of short and dense connection mechanism, which would effectively improve the feature learning ability. Specifically, in the proposed short and dense connection mechanism, each SDU contains a short list of densely connected convolutional layers and each convolutional layer is of the same appropriate channels. Consequently, the number of connections and the input channel of each convolutional layer are limited in each SDU, and the architecture of SDC-CNN is simple. Extensive experiments on both VeRi and VehicleID datasets show that the proposed SDC-CNN is obviously superior to multiple state-of-the-art vehicle re-identification methods.


title: An End-to-End Neural Network for Multi-line License Plate Recognition

abstract: Currently, license plate recognition plays an important role in numerous applications and a number of technologies have been proposed. However, most of them can only work with single-line license plates. In the practical application scenarios, there are also existing many multi-line license plates. The traditional approaches need to segment the original input images for double-line license plates. This is a very difficult problem in the complex scenes. In order to solve this problem, we propose an end-to-end neural network for both single-line and double-line license plate recognition. It is segmentation-free for the original input license plate images. We view each of these whole images as a unit on feature maps after deep convolution neural network directly. A large number of experiments show that our method is effective. It is better than the state-of-the-art algorithms in SYSU-ITS license plate library data.


title: Visual Data Mining: An Introduction and Overview

abstract: In our everyday life we interact with various information media, which present us with facts and opinions, supported with some evidence, based, usually, on condensed information extracted from data. It is common to communicate such condensed information in a visual form --- a static or animated, preferably interactive, visualisation. For example, when we watch familiar weather programs on the TV, landscapes with cloud, rain and sun icons and numbers next to them quickly allow us to build a picture about the predicted weather pattern in a region. Playing sequences of such visualisations will easily communicate the dynamics of the weather pattern, based on the large amount of data collected by many thousands of climate sensors and monitors scattered across the globe and on weather satellites. These pictures are fine when one watches the weather on Friday to plan what to do on Sunday --- after all if the patterns are wrong there are always alternative ways of enjoying a holiday. Professional decision making would be a rather different scenario. It will require weather forecasts at a high level of granularity and precision, and in real-time. Such requirements translate into requirements for high volume data collection, processing, mining, modelling and communicating the models quickly to the decision makers. Further, the requirements translate into high-performance computing with integrated efficient interactive visualisation. From practical point of view, if a weather pattern can not be depicted fast enough, then it has no value. Recognising the power of the human visual perception system and pattern recognition skills adds another twist to the requirements --- data manipulations need to be completed at least an order of magnitude faster than real-time in order to combine them with a variety of highly interactive visualisations, allowing easy remapping of data attributes to the features of the visual metaphor, used to present the data. In this few steps in the weather domain, we have specified some requirements towards a visual data mining system.


title: Corpus-Based Induction Of Syntactic Structure: Models Of Dependency And Constituency

abstract: We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product model outperforms both components on their respective evaluation metrics, giving the best published figures for unsupervised dependency parsing and unsupervised constituency parsing. We also demonstrate that the combined model works and is robust cross-linguistically, being able to exploit either attachment or distributional regularities that are salient in the data.


title: A mechanically driven form of Kirigami as a route to 3D mesostructures in micro/nanomembranes

abstract: Assembly of 3D micro/nanostructures in advanced functional materials has important implications across broad areas of technology. Existing approaches are compatible, however, only with narrow classes of materials and/or 3D geometries. This paper introduces ideas for a form of Kirigami that allows precise, mechanically driven assembly of 3D mesostructures of diverse materials from 2D micro/nanomembranes with strategically designed geometries and patterns of cuts. Theoretical and experimental studies demonstrate applicability of the methods across length scales from macro to nano, in materials ranging from monocrystalline silicon to plastic, with levels of topographical complexity that significantly exceed those that can be achieved using other approaches. A broad set of examples includes 3D silicon mesostructures and hybrid nanomembrane–nanoribbon systems, including heterogeneous combinations with polymers and metals, with critical dimensions that range from 100 nm to 30 mm. A 3D mechanically tunable optical transmission window provides an application example of this Kirigami process, enabled by theoretically guided design.


title: Prenatal weight gain patterns and spontaneous preterm birth among nonobese black and white women.

abstract: OBJECTIVE ::: To examine the relationship between prenatal weight gain and spontaneous preterm delivery, using the Institute of Medicine (IOM) guidelines. ::: ::: ::: METHODS ::: Nonobese low-income black (677 subjects) and white (338) women were grouped by ethnicity and prepregnancy body mass index (BMI) as low (less than 19.8) or normal (19.8-26.0). The relationship of total gain (first trimester) and weekly rate of gain (second and third trimester) to spontaneous preterm delivery was determined while controlling for sociodemographic and reproductive variables as well as for time between last weight observation and delivery. ::: ::: ::: RESULTS ::: For all women combined, the mean (+/- standard deviation) weight gain during the first trimester was 2.48 +/- 3.36 kg, and the mean rate of gain during the second and third trimesters was 0.49 +/- 0.21 and 0.45 +/- 0.28 kg/week, respectively. Low first- or second-trimester weight gain was not associated with increased adjusted odds ratios (OR) for spontaneous preterm delivery. Third-trimester rates of gain below the lower limit of the IOM-recommended range (less than 0.38 kg/week with low BMI, less than 0.37 kg/week with normal BMI) were associated with increased preterm delivery among all women (OR 2.46, 95% confidence interval [CI] 1.53-3.92), all black women (OR 1.98, 95% CI 1.16-3.41), and all white women (OR 4.05, 95% CI 1.41-11.66). ::: ::: ::: CONCLUSION ::: These observations suggest that a low third-trimester rate of weight gain, defined using IOM guidelines, is associated with an increased risk of spontaneous preterm delivery among nonobese black and white women.


title: HiCS: High Contrast Subspaces for Density-Based Outlier Ranking

abstract: Outlier mining is a major task in data analysis. Outliers are objects that highly deviate from regular objects in their local neighborhood. Density-based outlier ranking methods score each object based on its degree of deviation. In many applications, these ranking methods degenerate to random listings due to low contrast between outliers and regular objects. Outliers do not show up in the scattered full space, they are hidden in multiple high contrast subspace projections of the data. Measuring the contrast of such subspaces for outlier rankings is an open research challenge. In this work, we propose a novel subspace search method that selects high contrast subspaces for density-based outlier ranking. It is designed as pre-processing step to outlier ranking algorithms. It searches for high contrast subspaces with a significant amount of conditional dependence among the subspace dimensions. With our approach, we propose a first measure for the contrast of subspaces. Thus, we enhance the quality of traditional outlier rankings by computing outlier scores in high contrast projections only. The evaluation on real and synthetic data shows that our approach outperforms traditional dimensionality reduction techniques, naive random projections as well as state-of-the-art subspace search techniques and provides enhanced quality for outlier ranking.


title: Toward Automated Online Photo Privacy

abstract: Online photo sharing is an increasingly popular activity for Internet users. More and more users are now constantly sharing their images in various social media, from social networking sites to online communities, blogs, and content sharing sites. In this article, we present an extensive study exploring privacy and sharing needs of users’ uploaded images. We develop learning models to estimate adequate privacy settings for newly uploaded images, based on carefully selected image-specific features. Our study investigates both visual and textual features of images for privacy classification. We consider both basic image-specific features, commonly used for image processing, as well as more sophisticated and abstract visual features. Additionally, we include a visual representation of the sentiment evoked by images. To our knowledge, sentiment has never been used in the context of image classification for privacy purposes. We identify the smallest set of features, that by themselves or combined together with others, can perform well in properly predicting the degree of sensitivity of users’ images. We consider both the case of binary privacy settings (i.e., public, private), as well as the case of more complex privacy options, characterized by multiple sharing options. Our results show that with few carefully selected features, one may achieve high accuracy, especially when high-quality tags are available.


title: MemSpy: analyzing memory system bottlenecks in programs

abstract: To cope with the increasing difference between processor and main memory speeds, modern computer systems use deep memory hierarchies. In the presence of such hierarchies, the performance attained by an application is largely determined by its memory reference behavior—if most references hit in the cache, the performance is significantly higher than if most references have to go to main memory. Frequently, it is possible for the programmer to restructure the data or code to achieve better memory reference behavior. Unfortunately, most existing performance debugging tools do not assist the programmer in this component of the overall performance tuning task. This paper describes MemSpy, a prototype tool that helps programmers identify and fix memory bottlenecks in both sequential and parallel programs. A key aspect of MemSpy is that it introduces the notion of data oriented, in addition to code oriented, performance tuning. Thus, for both source level code objects and data objects, MemSpy provides information such as cache miss rates, causes of cache misses, and in multiprocessors, information on cache invalidations and local versus remote memory misses. MemSpy also introduces a concise matrix presentation to allow programmers to view both code and data oriented statistics at the same time. This paper presents design and implementation issues for MemSpy, and gives a detailed case study using MemSpy to tune a parallel sparse matrix application. It shows how MemSpy helps pinpoint memory system bottlenecks, such as poor spatial locality and interference among data structures, and suggests paths for improvement.


title: A Comparison of Parallel Graph Processing Benchmarks.

abstract: The rapidly growing number of large network analysis problems has led to the emergence of many parallel and distributed graph processing systems---one survey in 2014 identified over 80. Since then, the landscape has evolved; some packages have become inactive while more are being developed. Determining the best approach for a given problem is infeasible for most developers. To enable easy, rigorous, and repeatable comparison of the capabilities of such systems, we present an approach and associated software for analyzing the performance and scalability of parallel, open-source graph libraries. We demonstrate our approach on five graph frameworks: GraphMat, the Graph500, the Graph Algorithm Platform, GraphBIG, and PowerGraph using synthetic and real-world datasets. We examine previously overlooked aspects of parallel graph processing performance such as phases of execution and energy usage for three algorithms: breadth first search, single source shortest paths, and PageRank and compare our results to Graphalytics.


title: Psychometric Properties of the Intrinsic Motivation Inventory in a Competitive Sport Setting: A Confirmatory Factor Analysis

abstract: Abstract The present study was designed to assess selected psychometric properties of the Intrinsic Motivation Inventory (IMI) (Ryan, 1982), a multidimensional measure of subjects' experience with regard to experimental tasks. Subjects (N = 116) competed in a basketball free-throw shooting game, following which they completed the IMI. The LISREL VI computer program was employed to conduct a confirmatory factor analysis to assess the tenability of a five factor hierarchical model representing four first-order factors or dimensions and a second-order general factor representing intrinsic motivation. Indices of model acceptability tentatively suggest that the sport data adequately fit the hypothesized five factor hierarchical model. Alternative models were tested but did not result in significant improvements in the goodness-of-fit indices, suggesting the proposed model to be the most accurate of the models tested. Coefficient alphas for the four dimensions and the overall scale indicated adequate reliabilit...


title: A compliant hand based on a novel pneumatic actuator

abstract: The RBO Hand is a novel, highly compliant robotic hand. It exhibits robust grasping performance, is easy to build and prototype, and very cheap to produce. One of the primary design motivations is the extensive leverage of compliance to achieve robust shape matching between the hand and the grasped object. This effect results in robust grasping performance under sensing, model, and actuation uncertainty. We show the feasibility of our approach to constructing robotic hands in extensive grasping experiments on objects with varying properties, included water bottles, eye glasses, and sheets of fabric. The RBO hand is based on a novel pneumatic actuator, called PneuFlex, which exhibits desirable properties for robotic fingers.


title: Interactive Protection System Simulation Using ATP MODELS and C++

abstract: This paper presents a new approach for interactive protection system simulation. In this approach, the power system network is modeled by the ATP program while the "compiled foreign model" mechanism of MODELS language is employed to model the digital protective relay in C++ language. This allows "object-oriented" relay modeling as well as building a "seamless" interface between the power system network model and the relay model. An example is used to demonstrate the interactive protection system simulation developed using the new approach


title: Why People Watch Reality TV

abstract: We assessed the appeal of reality TV by asking 239 adults to rate themselves on each of 16 basic motives using the Reiss Profile standardized instrument and to rate how much they watched and enjoyed various reality television shows. The results suggested that the people who watched reality television had above-average trait motivation to feel self-important and, to a lesser extent, vindicated, friendly, free of morality, secure, and romantic, as compared with large normative samples. The results, which were dose-dependent, showed a new method for studying media. This method is based on evidence that people have the potential to experience 16 different joys. People prefer television shows that stimulate the feelings they intrinsically value the most, which depends on individuality.


title: Efficient OLAP operations for RDF analytics

abstract: RDF is the leading data model for the Semantic Web, and dedicated query languages such as SPARQL 1.1, featuring in particular aggregation, allow extracting information from RDF graphs. A framework for analytical processing of RDF data was introduced in [1], where analytical schemas and analytical queries (cubes) are fully re-designed for heterogeneous, semantic-rich RDF graphs. In this novel analytical setting, we consider the following optimization problem: how to reuse the materialized result of a given RDF analytical query (cube) in order to compute the answer to another cube. We provide view-based rewriting algorithms for these cube transformations, and demonstrate experimentally their practical interest.


title: Electric load forecasting methods: Tools for decision making

abstract: For decision makers in the electricity sector, the decision process is complex with several different levels that have to be taken into consideration. These comprise for instance the planning of facilities and an optimal day-to-day operation of the power plant. These decisions address widely different time-horizons and aspects of the system. For accomplishing these tasks load forecasts are very important. Therefore, finding an appropriate approach and model is at core of the decision process. Due to the deregulation of energy markets, load forecasting has gained even more importance. In this article, we give an overview over the various models and methods used to predict future load demands.


title: Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

abstract: Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.


title: ETLator - a scripting ETL framework

abstract: ETL (Extract Transform Load) process is the industry standard term for data extraction, transformation and loading into the Data Warehouse (DW). ETL process is the most resource demanding process in DW implementation and typically has to be evolved and maintained for the duration of the DW. To facilitate the development and maintenance of ETL processes many ETL tools have been developed featuring Graphical User Interfaces and various built-in functionalities (parallelism, logging, rich transformation libraries, documentation generation, etc.). The downside of such GUI ETL tools is that development is carried out heavily using mouse operations and less by writing programming code, which feels unnatural for some developers, especially with many similar, repetitive tasks. In this paper we present an alternative approach - an ETL framework “ETLator” based on Python scripting language where ETL tasks are defined by writing Python code. ETLator implements various typical ETL transformations and allows the user to simply and efficiently define complex ETL tasks with multiple sources and parallel tasks whilst leveraging full flexibility of Python. ETLator also provides logging and can document ETL tasks by generating data flow images. On a test case we show that ETLator simplifies ETL development and rivals the GUI approach.


title: Effects of Situated Mobile Learning Approach on Learning Motivation and Performance of EFL Students

abstract: Introduction Globalization has generated a trend toward learning English as a second language. Numerous students from non-English-speaking countries attend colleges, language schools, and institutes to learn English (Ballou, 2009; Liu & Chu, 2010). Several studies have shown that vocabulary plays a role as a necessary foundation in learning English (Sun, Huang, and Liu, 2011). Wilkins (1972) asserted that communication is possible without grammar but not without vocabulary. Oxford (1990) indicated that the major obstacle that English learners confront is the requirement that a large vocabulary must be memorized, and that English as a foreign language (EFL) students often learn vocabulary by rote (Kim & Gilman, 2008). In traditional teaching methods, teachers typically give students vocabulary textbooks and use the blackboard to demonstrate word spelling and invite students to practice writing words. A lack of situational assistance necessitates that EFL students memorize vocabulary by rote (Kim & Gilman, 2008). Thus, some researchers have proposed an alternative method--situated learning--to assist EFL students in learning vocabulary (Winn, 1993; Hay 1993). Situated learning is when learners acquire knowledge and skills by performing activities in real, interactive situations; this enables them to rationally and meaningfully interpret the knowledge and skills acquired during such a process (Brown, Collins, and Duguid, 1989; McLellan, 1996). The new knowledge acquired by learners through situated learning is not isolated or fragmented but complete and comprehensive knowledge that is truly mastered following reorganization (Harley, 1993). Because of the limitations of traditional English teaching venues, situated learning cannot be adequately implemented. Therefore, numerous researchers have adopted situational teaching aids and multimedia technology to implement situated learning in teaching venues and establish a situational English classroom (Chen & Chung, 2008). Situational English classrooms are immersive and simulate real-life situations; they enable EFL students to learn English vocabulary that can be used in those situations. Numerous studies have shown that situated learning effectively improves the performance of EFL students in learning vocabulary (Winn, 1993; Hay, 1993; McLellan, 1996). However, some researchers have indicated that learning vocabulary in real situations rather than simulated situations enables students to develop a stronger sense of immediacy and, consequently, enhances learning motivation and outcomes. A mobile learning tool is an optimal aid for this type of learning (Sandberg, Maris, & de Geus, 2011; Liu & Chu, 2010). To facilitate enhanced vocabulary learning performances among EFL students, novel learning aids must be integrated with effective vocabulary learning strategies (Herrington & Oliver, 2000). Brown and Payne (1994) proposed a 5step vocabulary learning strategy (FSVL) that comprised the steps of encountering, getting, comprehending, consolidating, and using. This study adopted the FSVL strategy combined with a mobile learning tool featuring guiding, recording, and prompting functions to support situational English vocabulary learning environments. In a situational English vocabulary learning environment, the tool can be employed to assist EFL students in completing the learning steps of the FSVL strategy. This study investigated the effects that the mobile learning tool and traditional learning tools exerted on the learning performance of EFL students given an identical vocabulary learning strategy and situational English vocabulary learning environment. In addition, regarding motivation theory, the empirical study provided evidence on how extrinsic motivation (EM) and intrinsic motivation (IM) are used in foreign language field (Noels, Pelletier, Clement, and Vallerand, 2000). Extrinsic motivation is related to achieving some intended goals, such as earning a reward or avoiding punishment. …


title: Camera Calibration Using Symmetric Objects

abstract: This paper proposes a novel method for camera calibration using images of a mirror symmetric object. Assuming unit aspect ratio and zero skew, we show that interimage homographies can be expressed as a function of only the principal point. By minimizing symmetric transfer errors, we thus obtain an accurate solution for the camera parameters. We also extend our approach to a calibration technique using images of a 1-D object with a fixed pivoting point. Unlike existing methods that rely on orthogonality or pole-polar relationship, our approach utilizes new inter-image constraints and does not require knowledge of the 3-D coordinates of feature points. To demonstrate the effectiveness of the approach, we present results for both synthetic and real images.


title: ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction

abstract: We have constructed ADVISOR, a two-agent machine learning architecture for intelligent tutoring systems (ITS). The purpose of this architecture is to centralize the reasoning of an ITS into a single component to allow customization of teaching goals and to simplify improving the ITS. The first agent is responsible for learning a model of how students perform using the tutor in a variety of contexts. The second agent is provided this model of student behavior and a goal specifying the desired educational objective. Reinforcement learning is used by this agent to derive a teaching policy that meets the specified educational goal. Component evaluation studies show each agent performs adequately in isolation. We have also conducted an evaluation with actual students of the complete architecture. Results show ADVISOR was successful in learning a teaching policy that met the educational objective provided. Although this set of machine learning agents has been integrated with a specific intelligent tutor, the general technique could be applied to a broad class of ITS.


title: Remote Moisture Sensing utilizing Ordinary RFID Tags

abstract: The paper presents a concept where pairs of ordinary RFID tags are exploited for use as remotely read moisture sensors. The pair of tags is incorporated into one label where one of the tags is embedded in a moisture absorbent material and the other is left open. In a humid environment the moisture concentration is higher in the absorbent material than the surrounding environment which causes degradation to the embedded tag's antenna in terms of dielectric losses and change of input impedance. The level of relative humidity or the amount of water in the absorbent material is determined for a passive RFID system by comparing the difference in RFID reader output power required to power up respectively the open and embedded tag. It is similarly shown how the backscattered signal strength of a semi-active RFID system is proportional to the relative humidity and amount of water in the absorbent material. Typical applications include moisture detection in buildings, especially from leaking water pipe connections hidden beyond walls. Presented solution has a cost comparable to ordinary RFID tags, and the passive system also has infinite life time since no internal power supply is needed. The concept is characterized for two commercial RFID systems, one passive operating at 868 MHz and one semi-active operating at 2.45 GHz.


title: Robust and false positive free watermarking in IWT domain using SVD and ABC

abstract: Watermarking is used to protect the copyrighted materials from being misused and help us to know the lawful ownership. The security of any watermarking scheme is always a prime concern for the developer. In this work, the robustness and security issue of IWT (integer wavelet transform) and SVD (singular value decomposition) based watermarking is explored. Generally, SVD based watermarking techniques suffer with an issue of false positive problem. This leads to even authenticating the wrong owner. We are proposing a novel solution to this false positive problem; that arises in SVD based approach. Firstly, IWT is employed on the host image and then SVD is performed on this transformed host. The properties of IWT and SVD help in achieving high value of robustness. Singular values are used for the watermark embedding. In order to further improve the quality of watermarking, the optimization of scaling factor (mixing ratio) is performed with the help of artificial bee colony (ABC) algorithm. A comparison with other schemes is performed to show the superiority of proposed scheme.


title: A new object proposal generation method for object detection in RGB-D data

abstract: This paper proposes a modified selective search method that generates object proposals on RGB-D data in indoor scenes. The proposed method first applies color flattening to generate monotonous color variations in RGB image data. Then, from the color-flattened image and depth map data, cost function-based segment grouping and depth segmentation are applied to produce desirable segmentation results. Segment grouping using cost function on image data computes dissimilarities in color, texture, and size between two adjacent regions with pre-learned weights. Depth segmentation uses the height difference of grid cells in the binned depth grid map. The final set of object proposal regions extracted from the RGB image and depth map data is organized by considering the overlapping between two data modalities. Finally, the extracted set of object proposal regions is fed into AlexNet or VGG-16, both of which are widely used for object classification, to evaluate our method on object detection and classification tasks. The proposed segment-based method can precisely detect meaningful object regions using a smaller number of proposals than other methods. Further, its detection and classification performance are better than those of previous methods.


Distributed File Systems : A Survey


title: An affordable and flexible architecture for deep space exploration

abstract: This paper describes a cislunar proving ground architecture as part of an affordable, evolvable program that maximizes return on investment in the International Space Station (ISS), the Space Launch System (SLS), and Orion spacecraft. The cislunar proving ground architecture is capable of performing/supporting key missions and objectives, including deep space habitation stays for increasing durations, an asteroid redirect mission, lunar excursions, and other Mars mission enabling demonstrations. Key approaches to affordability include maximizing the use of SLS/Orion co-manifested payload launch mass and assuming incorporation of International Partner (IP) contributions and commercial logistics support. Additional focus on affordability was placed in the areas of standardizing key interfaces, incorporating commonality where feasible, and making use of ISS as a technology demonstration facility The primary conclusion from the study is that an incremental architecture can support meaningful cislunar proving ground missions/objectives as well as provide a framework for international and commercial cooperation. The underlying long duration mission capabilities and partnerships developed with the ISS in concert with the transportation system enabled by the SLS and Orion make this architecture feasible.


title: SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks

abstract: Abstract SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field of structural health monitoring. The authors present benchmark results for crack detection using SDNET2018 and a crack detection algorithm based on the AlexNet DCNN architecture. SDNET2018 is freely available at https://doi.org/10.15142/T3TD19 .


title: Stochastic Gradient Descent as Approximate Bayesian Inference

abstract: Stochastic Gradient Descent with a constant learning rate (constant SGD) simulates a Markov chain with a stationary distribution. With this perspective, we derive several new results. (1) We show that constant SGD can be used as an approximate Bayesian posterior inference algorithm. Specifically, we show how to adjust the tuning parameters of constant SGD to best match the stationary distribution to a posterior, minimizing the Kullback-Leibler divergence between these two distributions. (2) We demonstrate that constant SGD gives rise to a new variational EM algorithm that optimizes hyperparameters in complex probabilistic models. (3) We also propose SGD with momentum for sampling and show how to adjust the damping coefficient accordingly. (4) We analyze MCMC algorithms. For Langevin Dynamics and Stochastic Gradient Fisher Scoring, we quantify the approximation errors due to finite learning rates. Finally (5), we use the stochastic process perspective to give a short proof of why Polyak averaging is optimal. Based on this idea, we propose a scalable approximate MCMC algorithm, the Averaged Stochastic Gradient Sampler.


title: Auto-tuning technology for fuzzy algorithms based DC brushless servo systems

abstract: A feasible auto-tuning method for DC brushless servo systems that introduces fuzzy algorithms is described. This is basically considered as a technique to adjust two gains of a proportional-integral (PI) compensator under the principle of providing an additional reference generator, observer, and fuzzy inferencer with a general-purpose speed control loop including PI regulator. It is possible to adjust the gains so as to be an optimum level through repeating 10 responses or more step responses in relation to variations of load inertia and mechanical vibration in addition to control characteristic fluctuations. It is proved that a DC brushless servo system can be automatically tuned to provide an optimum response if it is in a completely unstable state on the basis of the experimental results. >


title: A multisided generalization of Bézier surfaces

abstract: In this paper we introduce a class of surface patch representations, called S-patches, that unify and generalize triangular and tensor product Bezier surfaces by allowing patches to be defined over any convex polygonal domain; hence, S-patches may have any number of boundary curves. Other properties of S-patches are geometrically meaningful control points, separate control over positions and derivatives along boundary curves, and a geometric construction algorithm based on de Casteljau's algorithm. Of special interest are the regular S-patches, that is, S-patches defined on regular domain polygons. Also presented is an algorithm for smoothly joining together these surfaces with C k continuity.


title: Exploiting the Natural Exploration In Contextual Bandits

abstract: The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation trade-off. In particular, greedy policies that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy policies are desirable in many practical settings where exploration may be prohibitively costly or unethical (e.g. clinical trials). We prove that, for a general class of context distributions, the greedy policy benefits from a natural exploration obtained from the varying contexts and becomes asymptotically rate-optimal for the two-armed contextual bandit. Through simulations, we also demonstrate that these results generalize to more than two arms if the dimension of contexts is large enough. Motivated by these results, we introduce Greedy-First, a new algorithm that uses only observed contexts and rewards to determine whether to follow a greedy policy or to explore. We prove that this algorithm is asymptotically optimal without any additional assumptions on the distribution of contexts or the number of arms. Extensive simulations demonstrate that Greedy-First successfully reduces experimentation and outperforms existing (exploration-based) contextual bandit algorithms such as Thompson sampling, UCB, or $\epsilon$-greedy.


Investigating the frequency of multi-drug resistant strains of escherichia coli isolated from urinary tract infection in children


title: LSTM Neural Networks for Language Modeling

abstract: Neural networks have become increasingly popular for the task of language modeling. Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks can take into account all of the predecessor words. On the other hand, it is well known that recurrent networks are difficult to train and therefore are unlikely to show the full potential of recurrent models. These problems are addressed by a the Long Short-Term Memory neural network architecture. In this work, we analyze this type of network on an English and a large French language modeling task. Experiments show improvements of about 8 % relative in perplexity over standard recurrent neural network LMs. In addition, we gain considerable improvements in WER on top of a state-of-the-art speech recognition system.


title: Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes

abstract: Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. In this paper, an optical flow based moving object detection framework is proposed to address this problem. We utilize homography matrixes to online construct a background model in the form of optical flow. When judging out moving foregrounds from scenes, a dual-mode judge mechanism is designed to heighten the system's adaptation to challenging situations. In experiment part, two evaluation metrics are redefined for more properly reflecting the performance of methods. We quantitatively and qualitatively validate the effectiveness and feasibility of our method with videos in various scene conditions. The experimental results show that our method adapts itself to different situations and outperforms the state-of-the-art methods, indicating the advantages of optical flow based methods.


Being There: The Subjective Experience of Presence


Automatically Constructing Compositional and Recursive Learners


title: A X-Band Reconfigurable Phased Array Antenna System Using 0.13-µm SiGe BiCMOS IC with 5-Bit IF Phase Shifters

abstract: A X-band reconfigurable active phased array antenna system is presented. The phased array system consists of interconnected tile modules of which number can be flexibly changed depending on system requirements. The PCB integrated tile module assembles 4×4 patch antennas and flip-chipped phased array 0.13-μm SiGe BiCMOS ICs with 5-bit IF phase shifters. The concept of scalable phased array is verified by narrowing beamwidth in beamforming pattern and improving SNR in data transmission of 64-QAM OFDM signal as increasing the number of antenna elements.


title: A Review of the State of the Practice in Requirements Modeling

abstract: A field study of ten organizations was conducted to determine their current practices on definition, interpretation, analysis, and use of the requirements for their software systems and products. The field study consisted of a series of in-depth, structured interviews with practitioners of various kinds. The findings of this study are summarized, and the implications for improving practice either by organizational and methodological interventions or by introducing new technology are explained. >


title: Tool-path planning for direction-parallel area milling

abstract: Abstract Presented in the paper is a tool-path planning algorithm for direction-parallel area milling consisting of three modules: (1) finding the optimal inclination; (2) calculating and storing tool-path elements; and (3) tool-path linking. For the optimal inclination, we suggest an algorithm that selects an inclination by reflecting the shape of the machining area as well as the tool-path interval. We make use of the concept of a monotone chain and the plane-sweep paradigm to calculate the tool-path elements. The concept of a monotone chain brings clarity and tight-time complexity to the proposed algorithm. The tool-path linking problem is modeled as a TPE-Net (tool-path element net) traversing problem. For the two direction-parallel milling topologies, one-way and zigzag, tool-path linking algorithms are proposed. Empirical tests show that the proposed algorithm fulfils its requirements.


title: Cooperative Control of Multiple Nonholonomic Mobile Agents

abstract: This paper considers two cooperative control problems for nonholonomic mobile agents. In the first problem, we discuss the design of cooperative control laws such that a group of nonholonomic mobile agents cooperatively converges to some stationary point under various communication scenarios. Dynamic control laws for each agent are proposed with the aid of sigma-processes and results from graph theory. In the second problem, we discuss the design of cooperative control laws such that a group of mobile agents converges to and tracks a target point which moves along a desired trajectory under various communication scenarios. By introducing suitable variable transformations, cooperative control laws are proposed. Since communication delay is inevitable in cooperative control, in each of the above cooperative control problems, we analyze the effect of delayed communication on the proposed controllers. As applications of the proposed results, formation control of wheeled mobile robots is discussed. It is shown that our results can be successfully used to solve formation control problem. To show effectiveness of the proposed approach, simulation results are included.


title: Factors affecting engineers' acceptance of asynchronous e-learning systems in high-tech companies

abstract: With the rapid change in all types of working environment, there is a need to implement electronic learning (e-learning) systems to train people in new technologies, products, and services. However, the large investment in e-learning has made user acceptance an increasingly critical issue for technology implementation and management. Although user acceptance received fairly extensive attention in prior research, efforts were needed to examine or validate previous results, especially in different technologies, user populations, and/or organizational contexts. We therefore proposed a new construct, perceived credibility, to examine the applicability of the technology acceptance model (TAM) in explaining engineers' decisions to accept e-learning, and address a pragmatic technology management issue. Based on a sample of 140 engineers taken from six international companies, the results strongly support the extended TAM in predicting engineers' intention to use e-learning.


title: Multi-label convolutional neural network based pedestrian attributeclassification

abstract: Recently, pedestrian attributes like gender, age, clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database. Multi-label convolutional neural network for pedestrian attribute classification.The proposed MLCNN can simultaneously predict multiple pedestrian attributes.Experiments on the PETA database have shown the superiority of the proposed MLCNN.


title: Pneumatic robotic systems for upper limb rehabilitation

abstract: The aim of rehabilitation robotic area is to research on the application of robotic devices to therapeutic procedures. The goal is to achieve the best possible motor, cognitive and functional recovery for people with impairments following various diseases. Pneumatic actuators are attractive for robotic rehabilitation applications because they are lightweight, powerful, and compliant, but their control has historically been difficult, limiting their use. This article first reviews the current state-of-art in rehabilitation robotic devices with pneumatic actuation systems reporting main features and control issues of each therapeutic device. Then, a new pneumatic rehabilitation robot for proprioceptive neuromuscular facilitation therapies and for relearning daily living skills: like taking a glass, drinking, and placing object on shelves is described as a case study and compared with the current pneumatic rehabilitation devices.


title: Analytical Calculation of Back EMF Waveform for Linear PM Motors in Slotted and Slotless Structures

abstract: This paper presents the analytical calculation of back electromotive force (EMF) waveform for linear permanent magnet motors in either slotted or slotless structures. The predictions are based on the analysis of the 2-D magnetic field distributions and the 3-D end effects. In the slotted structure, the relative permeance function obtained by the Schwarz-Christoffel conformal mapping is used to take into account the effects of slotting and to calculate the air-gap flux density. Subsequently, to evaluate the leakage components resulting from the slot opening, slot opening factor is introduced to reflect the elements which do not follow the intended path to produce the back EMF. In the slotless structure, the effective values of the magnetic field quantities, together with the extended theory of winding factors, are employed to evaluate the back EMF waveform. For the analysis of the axial end effects, the 3-D leakage factor is incorporated into the analytical solutions by adding the end leakage permeance to the entire magnetic circuit models. The validity of the analytical results for each topology is confirmed by 2-D finite element and experimental results.


title: Detecting Pulse from Head Motions in Video

abstract: We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat. Our method tracks features on the head and performs principal component analysis (PCA) to decompose their trajectories into a set of component motions. It then chooses the component that best corresponds to heartbeats based on its temporal frequency spectrum. Finally, we analyze the motion projected to this component and identify peaks of the trajectories, which correspond to heartbeats. When evaluated on 18 subjects, our approach reported heart rates nearly identical to an electrocardiogram device. Additionally we were able to capture clinically relevant information about heart rate variability.


title: An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis

abstract: In Multiple Sclerosis (MS), detection of T2-hyperintense white matter (WM) lesions on magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and predicting prognosis in early disease. Automated lesion detection is not only desirable with regard to time and cost effectiveness but also constitutes a prerequisite to minimize user bias. Here, we developed and evaluated an algorithm for automated lesion detection requiring a three-dimensional (3D) gradient echo (GRE) T1-weighted and a FLAIR image at 3 Tesla (T). Our tool determines the three tissue classes of gray matter (GM) and WM as well as cerebrospinal fluid (CSF) from the T1-weighted image, and, then, the FLAIR intensity distribution of each tissue class in order to detect outliers, which are interpreted as lesion beliefs. Next, a conservative lesion belief is expanded toward a liberal lesion belief. To this end, neighboring voxels are analyzed and assigned to lesions under certain conditions. This is done iteratively until no further voxels are assigned to lesions. Herein, the likelihood of belonging to WM or GM is weighed against the likelihood of belonging to lesions. We evaluated our algorithm in 53 MS patients with different lesion volumes, in 10 patients with posterior fossa lesions, and 18 control subjects that were all scanned at the same 3T scanner (Achieva, Philips, Netherlands). We found good agreement with lesions determined by manual tracing (R2 values of over 0.93 independent of FLAIR slice thickness up to 6mm). These results require validation with data from other protocols based on a conventional FLAIR sequence and a 3D GRE T1-weighted sequence. Yet, we believe that our tool allows fast and reliable segmentation of FLAIR-hyperintense lesions, which might simplify the quantification of lesions in basic research and even clinical trials.


title: Integrated photovoltaic maximum power point tracking converter

abstract: A low-power low-cost highly efficient maximum power point tracker (MPPT) to be integrated into a photovoltaic (PV) panel is proposed. This can result in a 25% energy enhancement compared to a standard photovoltaic panel, while performing functions like battery voltage regulation and matching of the PV array with the load. Instead of using an externally connected MPPT, it is proposed to use an integrated MPPT converter as part of the PV panel. It is proposed that this integrated MPPT uses a simple controller in order to be cost effective. Furthermore, the power converter has to be very efficient, in order to transfer more energy to the load than a directly-coupled system. This is achieved by using a simple soft-switched topology. A much higher conversion efficiency at lower cost will then result, making the MPPT an affordable solution for small PV energy systems.


Characterization of Cloud Obscured Free Space Optical Channels


title: Salient region detection and segmentation

abstract: Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. In this paper we present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image. We demonstrate the use of the algorithm in the segmentation of semantically meaningful whole objects from digital images.


title: Vision-aided inertial navigation for resource-constrained systems

abstract: In this paper we present a resource-adaptive framework for real-time vision-aided inertial navigation. Specifically, we focus on the problem of visual-inertial odometry (VIO), in which the objective is to track the motion of a mobile platform in an unknown environment. Our primary interest is navigation using miniature devices with limited computational resources, similar for example to a mobile phone. Our proposed estimation framework consists of two main components: (i) a hybrid EKF estimator that integrates two algorithms with complementary computational characteristics, namely a sliding-window EKF and EKF-based SLAM, and (ii) an adaptive image-processing module that adjusts the number of detected image features based on the availability of resources. By combining the hybrid EKF estimator, which optimally utilizes the feature measurements, with the adaptive image-processing algorithm, the proposed estimation architecture fully utilizes the system's computational resources. We present experimental results showing that the proposed estimation framework is capable of real-time processing of image and inertial data on the processor of a mobile phone.


title: Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

abstract: Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to"bridge the gap"between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.


title: Monosynaptic Circuit Tracing with Glycoprotein-Deleted Rabies Viruses.

abstract: ### Introduction ::: ::: Since the introduction of methods using glycoprotein (G)-deleted rabies virus (RV dG ) to identify the direct monosynaptic inputs to genetically targeted neurons eight years ago ([Wickersham et al., 2007b][1]), this approach has been widely used for sophisticated circuit-tracing


title: Realizing an electronically tunable reflectarray using varactor diode-tuned elements

abstract: Electronically tunable reflectarrays hold significant promise as cost effective architectures for RF beamforming applications. The successful operation of these arrays depends on the phase agility of the cells used to realize the array. This letter describes the design of an electronically tunable reflectarray based on a novel cell architecture that provides a large degree of phase agility. Experimental results demonstrating the beamforming capabilities of the array at 5.8 GHz are presented.


Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering


title: Bridge the terminology gap between recruiters and candidates: A multilingual skills base built from social media and linked data

abstract: A major part of the job offers and candidates profiles are now available online. Leveraging this public data, Multiposting, a subsidiary of SAP, aims at providing in real-time an exhaustive job market analysis through the SmartSearch project. One big issue in this project, and more generally in the e-recruitment and the human resources management, is to extract the skills from the raw texts in order to associate a job or a candidate to its corresponding skills. This paper proposes to generate a multilingual base of skills in a novel bottom-up approach that finds its roots from the terminology used by candidates in professional social networks. The knowledge base is built by leveraging the Linked Open Data project DBpedia, as well as the tags of a Q&A website, StackOverflow. The large-scale experiments on real-world job offers show that the coverage and precision of the skills extraction are higher using this base than existing bases. The system has been implemented in industrial context and is used daily to extract the skills from thousands of documents, leading to advanced statistics as illustrated at the end this paper.


title: Serratus anterior paralysis in the young athlete.

abstract: Ten cases of isolated, complete paralysis of the serratus anterior muscle were diagnosed in young athletes during a three-year period. One patient had recurrent partial paralysis of the serratus anterior muscle, the first such case reported. From studies on cadavera and clinical observations, we concluded that paralysis of the serratus anterior muscle results from a traction injury to the long thoracic nerve of Bell. Since full recovery usually occurs in an average of nine months, surgical methods of treatment should be reserved for patients in whom function fails to return after a two-year period. Non-strenuous use of the involved extremity with avoidance of the precipitating activity, followed by exercises designed to maintain the range of motion of the shoulder and to increase the strength of associated muscles, is advocated for treatment of acute or repetitive injuries to the long thoracic nerve of Bell.


title: FreeRider: Non-Local Adaptive Network-on-Chip Routing with Packet-Carried Propagation of Congestion Information

abstract: Non-local adaptive routing techniques, which utilize statuses of both local and distant links to make routing decisions, have recently been shown to be effective solutions for promoting the performance of Network-on-Chip (NoC). The essence of non-local adaptive routing was an additional network dedicated to propagate congestion information of distant links on the NoC. While the dedicated Congestion Propagation Network (CPN) helps routers to make promising routing decisions, it incurs additional wiring and power costs and becomes an unnecessary decoration when the load of NoC is light. Moreover, the CPN has to be extended if one would utilize more sophisticated congestion information to enhance the performance of NoC, bringing in even larger wiring and power costs. This paper proposes an innovative non-local adaptive routing technique called FreeRider, which does not use a dedicated CPN but instead leverages free bits in head flits of existing packets to carry and propagate rich congestion information without introducing additional wires or flits. In order to balance the network load, FreeRider adopts a novel three-stage strategy of output link selection, which adequately utilizes the propagated information to make routing decisions. Experimental results on both synthetic traffic patterns and application traces show that FreeRider achieves better throughput, shorter latency, and smaller power consumption than a state-of-the-art adaptive routing technique with dedicated CPN.


title: Stein Variational Autoencoder

abstract: A new method for learning variational autoencoders is developed, based on an application of Stein's operator. The framework represents the encoder as a deep nonlinear function through which samples from a simple distribution are fed. One need not make parametric assumptions about the form of the encoder distribution, and performance is further enhanced by integrating the proposed encoder with importance sampling. Example results are demonstrated across multiple unsupervised and semi-supervised problems, including semi-supervised analysis of the ImageNet data, demonstrating the scalability of the model to large datasets.


title: Attention-Deficit/Hyperactivity Disorder, Stimulant Therapy, and the Patient with Congenital Heart Disease: Evidence and Reason

abstract: Attention-deficit/hyperactivity disorder (ADHD) is the most common neurobehavioral disorder of childhood, and congenital heart disease (CHD) is the most common form of birth defect. Children with CHD are at increased risk for neurodevelopmental disorders such as ADHD. Stimulant medications, specifically methylphenidates and amphetamines, are frequently prescribed and effective in reducing the symptoms of ADHD. Despite their efficacy and long history of use, the safety of these medications has recently come into question due to isolated reports describing sudden unexplained death of children undergoing treatment. This review summarizes the current literature on the cardiovascular risks associated with the use of pharmacologic therapy for ADHD, with an emphasis on patients who had CHD.


title: Agile Model-Driven Modernization to the Service Cloud

abstract: Migration of legacy systems to more advanced technologies and platforms is a current issue for many software organizations. Model-Driven Modernization combined with Software as a Service delivery model is a very promising approach, which possesses a lot of advantages, including reduced costs, automation of migration activities and reuse of system functionality. However, a drawback of such an innovative modernization approach is that it lacks mature software process models to guide its adoption. Thus, a methodology for seamless execution of different migration and deployment activities is quite needed. On the other hand, agile development methods have been successfully adopted in various projects, which partly or thoroughly use the engineering and delivery models exploited in the modernization process. This paper presents how a particular methodology for Model-Driven Modernization with deployment to the Cloud is enriched with agile techniques to address different challenging issues. The extended agile methodology could be used by organizations which have already applied agile software development as well as by organizations that plan to introduce it in their work. KeywordsCloud computing; Agile Methodology; Modeldriven Modernization; Software as a service


title: 0.35mm pitch wafer level package board level reliability: Studying effect of ball de-population with varying ball size

abstract: Board level reliability studies have been performed on wafer level packages (WLP) of various die sizes with 0.35mm ball pitch, SAC405 solder alloy, and two solder ball sizes. The 0.35mm pitch test vehicles included 5mm × 5mm and 6mm × 6mm package sizes. All test vehicles had areas of depopulation in the ball array, but used the same design rules regardless of ball size. The parts were assembled at 3 different suppliers. All of the WLPs studied passed the minimum drop shock requirements of 30 drops. For board level temperature cycle, a minimum of 500 cycles was targeted as a passing result. The 220um ball size on 5mm × 5mm 188 pin WLP package failed at both the corner most solder balls and solder balls near the corner at all suppliers. Only one of three suppliers' corner balls passed using the 230um ball. For interior balls, the 220um ball size on 5mm × 5mm 188 pin WLP package failed temperature cycle at two of three suppliers, while two of three suppliers passed with 230um ball. For the larger 6mm × 6mm test vehicle, the corner most solder balls were also very sensitive to temperature cycle fatigue failures. The 220um ball size failed at the corner at all three suppliers. For interior balls, the 220um ball size failed temperature cycle at one of three suppliers, while two of three suppliers passed 500 cycles only marginally. This study shows that depopulated WLP ball arrays of 5mm × 5mm and 6mm × 6mm are sensitive to temperature cycle fatigue failures at 0.35mm pitch with SAC405 alloy.


title: Maximum a Posteriori Video Super-Resolution Using a New Multichannel Image Prior

abstract: Super-resolution (SR) is the term used to define the process of estimating a high-resolution (HR) image or a set of HR images from a set of low-resolution (LR) observations. In this paper we propose a class of SR algorithms based on the maximum a posteriori (MAP) framework. These algorithms utilize a new multichannel image prior model, along with the state-of-the-art single channel image prior and observation models. A hierarchical (two-level) Gaussian nonstationary version of the multichannel prior is also defined and utilized within the same framework. Numerical experiments comparing the proposed algorithms among themselves and with other algorithms in the literature, demonstrate the advantages of the adopted multichannel approach.


title: Variational Bi-LSTMs

abstract: Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs) on the other hand model sequences along both forward and backward directions and are generally known to perform better at such tasks because they capture a richer representation of the data. In the training of Bi-LSTMs, the forward and backward paths are learned independently. We propose a variant of the Bi-LSTM architecture, which we call Variational Bi-LSTM, that creates a channel between the two paths (during training, but which may be omitted during inference); thus optimizing the two paths jointly. We arrive at this joint objective for our model by minimizing a variational lower bound of the joint likelihood of the data sequence. Our model acts as a regularizer and encourages the two networks to inform each other in making their respective predictions using distinct information. We perform ablation studies to better understand the different components of our model and evaluate the method on various benchmarks, showing state-of-the-art performance.


title: The Double-Edged Sword of Leader Charisma: Understanding the Curvilinear Relationship Between Charismatic Personality and Leader Effectiveness

abstract: This study advanced knowledge on charisma by (a) introducing a new personality-based model to conceptualize and assess charisma and by (b) investigating curvilinear relationships between charismatic personality and leader effectiveness. Moreover, we delved deeper into this curvilinear association by (c) examining moderation by the leader’s level of adjustment and by (d) testing a process model through which the effects of charismatic personality on effectiveness are explained with a consideration of specific leader behaviors. Study 1 validated HDS charisma (Hogan Development Survey) as a useful trait-based measure of charisma. In Study 2 a sample of leaders (N = 306) were assessed in the context of a 360-degree development center. In line with the too-much-of-a-good-thing effect, an inverted U-shaped relationship between charismatic personality and observer-rated leader effectiveness was found, indicating that moderate levels are better than low or high levels of charisma. Study 3 (N = 287) replicated this curvilinear relationship and further illustrated the moderating role of leader adjustment, in such a way that the inflection point after which the effects of charisma turn negative occurs at higher levels of charisma when adjustment is high. Nonlinear mediation modeling further confirmed that strategic and operational leader behaviors fully mediate the curvilinear relationship. Leaders low on charisma are less effective because they lack strategic behavior; highly charismatic leaders are less effective because they lack operational behavior. In sum, this work provides insight into the dispositional nature of charisma and uncovers the processes through which and conditions under which leader charisma translates into (in)effectiveness. (PsycINFO Database Record (c) 2018 APA, all rights reserved)


title: 3D printed soft skin for safe human-robot interaction

abstract: The purpose of this research is the development of a soft skin module with a built-in airtight cavity in which air pressure can be sensed. A pressure feedback controller is implemented on a robotic system using this module for contact sensing and gentle grasping. The soft skin module is designed to meet size and safety criteria appropriate for a toy-sized interactive robot. All module prototypes are produced using a muti-material 3D printer. Experimental results from collision tests show that this module significantly reduces the impact forces due to collision. Also, using the measured pressure information from the module, the robotic system to which these modules are attached is capable of very gentle physical interaction with soft objects.


title: Critical failure factors in ERP implementation

abstract: This study firstly examines the current literature concerning ERP implementation problems during implementation phases and causes of ERP implementation failure. A multiple case study research methodology was adopted to understand "why" and "how" these ERP systems could not be implemented successfully. Different stakeholders (including top management, project manager, project team members and ERP consultants) from these case studies were interviewed, and EPP implementation documents were reviewed for triangulation. An ERP life cycle framework was applied to study the ERP implementation process and the associated problems in each phase of ERP implementation. Fourteen critical failure factors were identified and analyzed, and three common critical failure factors (poor consultant effectiveness, project management effectiveness and poo555ir quality of business process re-engineering) were examined and discussed Future research on ERP implementation and critical failure factors is discussed It is hoped that this research will help to bridge the current literature gap and provide practical advice for both academics and practitioners.


title: Decoy Password Vaults: At Least as Hard as Steganography?

abstract: Cracking-resistant password vaults have been recently proposed with the goal of thwarting offline attacks. This requires the generation of synthetic password vaults that are statistically indistinguishable from real ones. In this work, we establish a conceptual link between this problem and steganography, where the stego objects must be undetectable among cover objects. We compare the two frameworks and highlight parallels and differences. Moreover, we transfer results obtained in the steganography literature into the context of decoy generation. Our results include the infeasibility of perfectly secure decoy vaults and the conjecture that secure decoy vaults are at least as hard to construct as secure steganography.


title: Question Answering with Character-Level LSTM Encoders and Model-Based Data Augmentation

abstract: This paper presents a character-level encoder-decoder modeling method for question answering (QA) from large-scale knowledge bases (KB). This method improves the existing approach [9] from three aspects. First, long short-term memory (LSTM) structures are adopted to replace the convolutional neural networks (CNN) for encoding the candidate entities and predicates. Second, a new strategy of generating negative samples for model training is adopted. Third, a data augmentation strategy is applied to increase the size of the training set by generating factoid questions using another trained encoder-decoder model. Experimental results on the SimpleQuestions dataset and the Freebase5M KB demonstrates the effectiveness of the proposed method, which improves the state-of-the-art accuracy from 70.3% to 78.8% when augmenting the training set with 70,000 generated triple-question pairs.


Comparing Clusterings- An Overview


title: Geometric Mean for Subspace Selection

abstract: Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.


title: Mining product reputations on the Web

abstract: Knowing the reputations of your own and/or competitors' products is important for marketing and customer relationship management. It is, however, very costly to collect and analyze survey data manually. This paper presents a new framework for mining product reputations on the Internet. It automatically collects people's opinions about target products from Web pages, and it uses text mining techniques to obtain the reputations of those products.On the basis of human-test samples, we generate in advance syntactic and linguistic rules to determine whether any given statement is an opinion or not, as well as whether such any opinion is positive or negative in nature. We first collect statements regarding target products using a general search engine, and then, using the rules, extract opinions from among them and attach three labels to each opinion, labels indicating the positive/negative determination, the product name itself, and an numerical value expressing the degree of system confidence that the statement is, in fact, an opinion. The labeled opinions are then input into an opinion database.The mining of reputations, i.e., the finding of statistically meaningful information included in the database, is then conducted. We specify target categories using label values (such as positive opinions of product A) and perform four types of text mining: extraction of 1) characteristic words, 2) co-occurrence words, 3) typical sentences, for individual target categories, and 4) correspondence analysis among multiple target categories.Actual marketing data is used to demonstrate the validity and effectiveness of the framework, which offers a drastic reduction in the overall cost of reputation analysis over that of conventional survey approaches and supports the discovery of knowledge from the pool of opinions on the web.


title: Revisiting the origin and diversification of vascular plants through a comprehensive Bayesian analysis of the fossil record

abstract: Plants have a long evolutionary history, during which mass extinction events dramatically affected Earth's ecosystems and its biodiversity. The fossil record can shed light on the diversification dynamics of plant life and reveal how changes in the origination-extinction balance have contributed to shaping the current flora. We use a novel Bayesian approach to estimate origination and extinction rates in plants throughout their history. We focus on the effect of the 'Big Five' mass extinctions and on estimating the timing of origin of vascular plants, seed plants and angiosperms. Our analyses show that plant diversification is characterized by several shifts in origination and extinction rates, often matching the most important geological boundaries. The estimated origin of major plant clades predates the oldest macrofossils when considering the uncertainties associated with the fossil record and the preservation process. Our findings show that the commonly recognized mass extinctions have affected each plant group differently and that phases of high extinction often coincided with major floral turnovers. For instance, after the Cretaceous-Paleogene boundary we infer negligible shifts in diversification of nonflowering seed plants, but find significantly decreased extinction in spore-bearing plants and increased origination rates in angiosperms, contributing to their current ecological and evolutionary dominance.


title: A2-D7 GoPubMed: ontology-based literature search

abstract: With the ever increasing size of scientific literature, finding relevant documents and answering questions has become even more of a challenge. Recently, ontologies — hierarchical, controlled vocabularies — have been introduced to annotate genomic data. They can also improve the question answering and the selection of relevant documents in the literature search. Search engines such as GoPubMed.org use ontological background knowledge to give an overview over large query results and to answer questions. Here we give an overview over GoPubMed. We show how it can answer questions using the GeneOntology and the Medical subject Headings as background knowledge. We also demonstrate that GoPubMed is general by applying it to the problem of associating genes, tissues, and developmental stages, as described in the Edinburgh Mouse Atlas. GoPubMed builds on background knowledge in the form of ontologies, which are given for the previous two applications. We describe a method to automatically generate the vocabulary for ontologies and compare our method to 3 other approaches in the context of a lipid metabolism ontology. The deliverable comprises three sections. GoPubMed is described in Section 1, MousePubMed in Section 2 and ontology generation in Section 3.


title: Finding Optimal Bayesian Networks

abstract: In this paper, we derive optimality results for greedy Bayesian-network search algorithms that perform single-edge modifications at each step and use asymptotically consistent scoring criteria. Our results extend those of Meek (1997) and Chickering (2002), who demonstrate that in the limit of large datasets, if the generative distribution is perfect with respect to a DAG defined over the observable variables, such search algorithms will identify this optimal (i.e. generative) DAG model. We relax their assumption about the generative distribution, and assume only that this distribution satisfies the composition property over the observable variables, which is a more realistic assumption for real domains. Under this assumption, we guarantee that the search algorithms identify an inclusion-optimal model; that is, a model that (1) contains the generative distribution and (2) has no sub-model that contains this distribution. In addition, we show that the composition property is guaranteed to hold whenever the dependence relationships in the generative distribution can be characterized by paths between singleton elements in some generative graphical model (e.g. a DAG, a chain graph, or a Markov network) even when the generative model includes unobserved variables, and even when the observed data is subject to selection bias.


title: Gender reassignment surgery - a 13 year review of surgical outcomes

abstract: PURPOSE: The aim of this study is to thoroughly report on surgical outcomes from 332 patients who underwent male to female gender reassignment surgery (GRS). MATERIAL AND METHODS: Records from 332 patients who underwent GRS from 1995 to 2008 were reviewed. All patients were submitted to penile inversion vaginoplasty with glans-derived sensate clitoroplasty. Mean age was 36.7 years (range 19-68 years). Surgical complications were stratified in 6 main groups: genital region, urinary tract, gastrointestinal events, wound healing disorders and unspecific events. RESULTS: Progressive obstructive voiding disorder due to meatal stenosis was the main complication observed in 40% of the patients, feasibly corrected during the second setting. Stricture recurrence was found in 15%. Stricture of vaginal introitus was observed in 15% of the cases followed by 12% and 8% of vaginal stenosis and lost of vaginal depth, respectively. Rectal injury was seen in 3% and minor wound healing disorders in 33% of the subjects. CONCLUSION: Regarding male to female GRS, a review of the current literature demonstrated scarce description of complications and their treatment options. These findings motivated a review of our surgical outcomes. Results showed a great number of adverse events, although functionality preserved. Comparision of our outcomes with recent publications additionally showed that treatment options provide satisfying results. Moreover, outcomes reaffirm penile inversion vaginoplasty in combination with glans-derived sensate clitoroplasty as a safe technique. Nevertheless, discussing and improving surgical techniques in order to reduce complications and their influence on patient's quality of life is still strongly necessary and theme of our future reports.


title: Decision tree based Support Vector Machine for Intrusion Detection

abstract: Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems in Intrusion Detection Systems (IDS). This method can decrease the training and testing time of the IDS, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying two decision tree approaches: Hierarchical multiclass SVM and Tree structured multiclass SVM, to construct multiclass intrusion detection system.


title: Physical Access Control Based on QR Code

abstract: Nowadays, the authentication methods of physical access control such as key, smart card, and OTP (One Time Password) card require extra hardware, and it is not easy to be aware of if they are lost because of the low using rate. Nowadays, mobile phone is one of the most common used and carry-on device. It would be detected in a shorter time when a mobile phone is lost because it was frequently used in daily time. One of the advantage of OTP is that it is only used once and it is securer compared to the fixed password. QR code is a kind of two-dimension matrix code, which has high encryption and decryption speed; it can not only store large information but also has the error-correction ability. In this paper, we try to develop a safe and efficient authentication way by using mobile phone and implement it for access control system. We take the advantage of one-time usage by using OTP, and store the secret information into QR code. Therefore, the proposed system enhances the security of physical access control systems.


title: A novel log-periodic dipole antenna with distributed inductive load

abstract: This article studied a novel Log-Periodic Dipole Antenna (LPDA) with distributed inductive load for size reduction. By adding a short circuit stub at top of the each element, the dimensions of the LPDA are reduced by nearly 50% compared to the conventional one. The impedance bandwidth of the presented antenna is nearly 122% (54∼223MHz) (S11<10dB), and this antenna is very suited for BROADCAST and TV applications.


title: Event-Based Visual Inertial Odometry

abstract: Event-based cameras provide a new visual sensing model by detecting changes in image intensity asynchronously across all pixels on the camera. By providing these events at extremely high rates (up to 1MHz), they allow for sensing in both high speed and high dynamic range situations where traditional cameras may fail. In this paper, we present the first algorithm to fuse a purely event-based tracking algorithm with an inertial measurement unit, to provide accurate metric tracking of a cameras full 6dof pose. Our algorithm is asynchronous, and provides measurement updates at a rate proportional to the camera velocity. The algorithm selects features in the image plane, and tracks spatiotemporal windows around these features within the event stream. An Extended Kalman Filter with a structureless measurement model then fuses the feature tracks with the output of the IMU. The camera poses from the filter are then used to initialize the next step of the tracker and reject failed tracks. We show that our method successfully tracks camera motion on the Event-Camera Dataset in a number of challenging situations.


title: Symbol acquisition for probabilistic high-level planning

abstract: We introduce a framework that enables an agent to autonomously learn its own symbolic representation of a low-level, continuous environment. Propositional symbols are formalized as names for probability distributions, providing a natural means of dealing with uncertain representations and probabilistic plans. We determine the symbols that are sufficient for computing the probability with which a plan will succeed, and demonstrate the acquisition of a symbolic representation in a computer game domain.


Linear-time computability of combinatorial problems on series-parallel graphs


title: A Customer Churn Prediction Model in Telecom Industry Using Boosting

abstract: With the rapid growth of digital systems and associated information technologies, there is an emerging trend in the global economy to build digital customer relationship management (CRM) systems. This trend is more obvious in the telecommunications industry, where companies become increasingly digitalized. Customer churn prediction is a main feature of in modern telecomcommunication CRM systems. This research conducts a real-world study on customer churn prediction and proposes the use of boosting to enhance a customer churn prediction model. Unlike most research that uses boosting as a method to boost the accuracy of a given basis learner, this paper tries to separate customers into two clusters based on the weight assigned by the boosting algorithm. As a result, a higher risk customer cluster has been identified. Logistic regression is used in this research as a basis learner, and a churn prediction model is built on each cluster, respectively. The result is compared with a single logistic regression model. Experimental evaluation reveals that boosting also provides a good separation of churn data; thus, boosting is suggested for churn prediction analysis.


title: Behind Phishing: An Examination of Phisher Modi Operandi

abstract: Phishing costs Internet users billions of dollars a year. Using various data sets collected in real-time, this paper analyzes various aspects of phisher modi operandi. We examine the anatomy of phishing URLs and domains, registration of phishing domains and time to activation, and the machines used to host the phishing sites. Our findings can be used as heuristics in filtering phishing-related emails and in identifying suspicious domain registrations.


title: Active Learning with Committees for Text Categorization

abstract: In many real-world domains, supervised learning requires a large number of training examples. In this paper, we describe an active learning method that uses a committee of learners to reduce the number of training examples required for learning. Our approach is similar to the Query by Committee framework, where disagreement among the committee members on the predicted label for the input part of the example is used to signal the need for knowing the actual value of the label. Our experiments are conducted in the text categorization domain, which is characterized by a large number of features, many of which are irrelevant. We report here on experiments using a committee of Winnowbased learners and demonstrate that this approach can reduce the number of labeled training examples required over that used by a single Winnow learner by l-2 orders of magnitude. 1. Hntroduction


title: Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction

abstract: Artificial Intelligence models (AI) which computerize human reasoning has found a challenging test bed for various paradigms in many areas including financial time series prediction. Extensive researches have resulted in numerous financial applications using AI models. Since stock investment is a major investment activity, Lack of accurate information and comprehensive knowledge would result in some certain loss of investment. Hence, stock market prediction has always been a subject of interest for most investors and professional analysts. Stock market prediction is a challenging problem because uncertainties are always involved in the market movements. This paper proposes a hybrid intelligent model for stock exchange index prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and Levenberg-Marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as data transformation and input variables selection for improving the accuracy of the model. The capability of the proposed method is tested by applying it for predicting some stock exchange indices used in the literature. The results show that the proposed approach is able to cope with the fluctuations of stock market values and also yields good prediction accuracy. So it can be used to model complex relationships between inputs and outputs or to find data patterns while performing financial prediction.


title: Document Layout Analysis: A Maximum Homogeneous Region Approach

abstract: This paper presents a method for document layout analysis. This method applies the analyzing of whitespace in maximum homogeneous regions. This method focuses on the balance between processing time and performance. It consists of two main stages: classification and segmentation. Firstly, by using the analysis of whitespace analysis on Maximum multi-layer horizontal homogeneous regions, the text and non-text elements are classified. Then, text regions are extracted by using mathematical morphology. Besides, non-text elements are classified into separators, tables, images via a machine learning approach. The proposed method's effectiveness is proved by the tests on UW-III (A1) datasets.


Enhancing Utility and Privacy using t-closeness For Multiple Sensitive Attributes


title: Automatic cell tracking applied to analysis of cell migration in wound healing assay

abstract: The wound healing assay in vitro is widely used for research and discovery in biology and medicine. This assay allows for observing the healing process in vitro in which the cells on the edges of the artificial wound migrate toward the wound area. The influence of different culture conditions can be measured by observing the change in the size of the wound area. For further investigation, more detailed measurements of the cell behaviors are required. In this paper, we present an application of automatic cell tracking in phase-contrast microscopy images to wound healing assay. The cell behaviors under three different culture conditions have been analyzed. Our cell tracking system can track individual cells during the healing process and provide detailed spatio-temporal measurements of cell behaviors. The application demonstrates the effectiveness of automatic cell tracking for quantitative and detailed analysis of the cell behaviors in wound healing assay in vitro.


title: Geometric Considerations for the Design of Rigid Origami Structures

abstract: Transformable polyhedral surfaces with rigid facets, i.e., rigid origami, are useful for designing kinetic and deployable structures. In order to apply rigid origami to various architectural and other engineering design purposes, it is essential to consider the geometry of origami in kinetic motion and provide sufficiently generalized methods to produce controlled variations of shapes that suit the given design conditions. In this paper, we introduce the author’s recent studies and their extensions on the geometry of rigid origami for designing transformable and deployable structures.


title: Unsupervised learning to detect loops using deep neural networks for visual SLAM system

abstract: This paper is concerned of the loop closure detection problem for visual simultaneous localization and mapping systems. We propose a novel approach based on the stacked denoising auto-encoder (SDA), a multi-layer neural network that autonomously learns an compressed representation from the raw input data in an unsupervised way. Different with the traditional bag-of-words based methods, the deep network has the ability to learn the complex inner structures in image data, while no longer needs to manually design the visual features. Our approach employs the characteristics of the SDA to solve the loop detection problem. The workflow of training the network, utilizing the features and computing the similarity score is presented. The performance of SDA is evaluated by a comparison study with Fab-map 2.0 using data from open datasets and physical robots. The results show that SDA is feasible for detecting loops at a satisfactory precision and can therefore provide an alternative way for visual SLAM systems.


title: Guillain–Barré syndrome and Zika virus outbreaks

abstract: PURPOSE OF REVIEW ::: Arboviruses have been associated with central and peripheral nervous system injuries, in special the flaviviruses. Guillain-Barré syndrome (GBS), transverse myelitis, meningoencephalitis, ophthalmological manifestations, and other neurological complications have been recently associated to Zika virus (ZIKV) infection. In this review, we aim to analyze the epidemiological aspects, possible pathophysiology, and what we have learned about the clinical and laboratory findings, as well as treatment of patients with ZIKV-associated neurological complications. ::: ::: ::: RECENT FINDINGS ::: In the last decades, case series have suggested a possible link between flaviviruses and development of GBS. Recently, large outbreaks of ZIKV infection in Asia and the Americas have led to an increased incidence of GBS in these territories. Rapidly, several case reports and case series have reported an increase of all clinical forms and electrophysiological patterns of GBS, also including cases with associated central nervous system involvement. Finally, cases suggestive of acute transient polyneuritis, as well as acute and progressive postinfectious neuropathies associated to ZIKV infection have been reported, questioning the usually implicated mechanisms of neuronal injury. ::: ::: ::: SUMMARY ::: The recent ZIKV outbreaks have triggered the occurrence of a myriad of neurological manifestations likely associated to this arbovirosis, in special GBS and its variants.


title: A place-based model for understanding community resilience to natural disasters

abstract: There is considerable research interest on the meaning and measurement of resilience from a variety of research perspectives including those from the hazards/disasters and global change communities. The identification of standards and metrics for measuring disaster resilience is one of the challenges faced by local, state, and federal agencies, especially in the United States. This paper provides a new framework, the disaster resilience of place (DROP) model, designed to improve comparative assessments of disaster resilience at the local or community level. A candidate set of variables for implementing the model are also presented as a first step towards its implementation.


title: Achieving k-Barrier Coverage in Hybrid Directional Sensor Networks

abstract: Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection) where directional sensors (e.g., cameras) are becoming more popular than omni-directional scalar sensors (e.g., microphones). However, barrier coverage cannot be guaranteed after initial random deployment of sensors, especially for directional sensors with limited sensing angles. In this paper, we study how to efficiently use mobile sensors to achieve \(k\) -barrier coverage. In particular, two problems are studied under two scenarios. First, when only the stationary sensors have been deployed, what is the minimum number of mobile sensors required to form \(k\) -barrier coverage? Second, when both the stationary and mobile sensors have been pre-deployed, what is the maximum number of barriers that could be formed? To solve these problems, we introduce a novel concept of weighted barrier graph (WBG) and prove that determining the minimum number of mobile sensors required to form \(k\) -barrier coverage is related with finding \(k\) vertex-disjoint paths with the minimum total length on the WBG. With this observation, we propose an optimal solution and a greedy solution for each of the two problems. Both analytical and experimental studies demonstrate the effectiveness of the proposed algorithms.