title: Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights

abstract: Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headlights detection to reduce false alarms caused by reflections. Second, to take full advantage of the complementary nature of grouping and tracking, we alternately optimize grouping and tracking. For grouping, motion features produced by tracking are used by headlights pairing. We use a maximal independent set framework for effective pairing, which is more robust than traditional pairing-by-rules methods. For tracking, context information provided by pairing is employed by multiple object tracking. The experiments on challenging datasets and quantitative evaluation show promising performance of our method.


title: Integrated Design and Realization of a Hubless Rim-driven Thruster

abstract: This paper presents the integrated design and realization of a hubless rim-driven thruster for remotely operated vehicles (ROV). The design process considers the integration of hydrodynamics, propeller design, motor design and driver design. The integration technique is particularly emphasized, where the output of the motor should match the characteristic of the propeller. Also, for such a hubless rim-driven construction, the motor is embraced within the duct, and the motor size will consequently determine the duct thickness. Therefore, the drag force due to the duct should be analyzed and calculated as a design consideration for the propeller and motor. The specifications of the thruster are determined based on the thrust requirement of the target ROV. Several types of software are used for analyses of hydrodynamics, motor magnetic field, etc, and the design information is exchanged around. The rim-driven thrust is successfully designed, and a prototype is manufactured and tested for functionality.


title: Internal and external Field of View: computer games and cybersickness

abstract: In an experiment with a computer game environment, we studied the effect of Field-of-View (FOV) on cybersickness. In particular, we examined the effect of differences between the internal FOV (IFOV, the FOV which the graphics generator is using to render its images) and the external FOV (EFOV, the FOV of the presented images as seen from the physical viewpoint of the observer). We hypothesized that incongruent IFOVs and EFOVs would lead to a larger incidence of cybersickness. However, this was not found, and the opposite seems to be true instead. The unexpected result may be explained by the relative large differences between IFOV and EFOV we used.


title: A software-hardware cooperative implementation of bird's-eye view system for camera-on-vehicle

abstract: Rear camera or other wide-angle camera mounted on vehicle has serious perspective effect which makes driver unable to feel distance correctly and is not helpful for succeeding image analysis. To remove perspective effect, it's necessary to change diagonal view to bird's-eye view. In this paper, a software-hardware cooperative bird's-eye view system is proposed, which can estimates the transformation matrix and changes the view automatically in real-time. The matrix estimation module is implemented by software, because it only needs to calculate once. The transformation part is implemented by hardware, because it must be done repetitively and it requires high computation power. To optimize hardware performance and save cost, three optimization approaches like one-step transformation, memory pre-estimation and look-up table (LUT) have been employed.


Summaries of 107 Computer Vision-Based Human Motion Capture Papers


A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence


title: Integrated Grasp Planning and Visual Object Localization For a Humanoid Robot with Five-Fingered Hands

abstract: In this paper we present a framework for grasp planning with a humanoid robot arm and a five-fingered hand. The aim is to provide the humanoid robot with the ability of grasping objects that appear in a kitchen environment. Our approach is based on the use of an object model database that contains the description of all the objects that can appear in the robot workspace. This database is completed with two modules that make use of this object representation: An exhaustive offline grasp analysis system and a real-time stereo vision system. The offline grasp analysis system determines the best grasp for the objects by employing a simulation system, together with CAD models of the objects and the five-fingered hand. The results of this analysis are added to the object database using a description suited to the requirements of the grasp execution modules. A stereo camera system is used for a real-time object localization using a combination of appearance-based and model-based methods. The different components are integrated in a controller architecture to achieve manipulation task goals for the humanoid robot.


Evaluating different query reformulation techniques for the Geographical Information Retrieval task considering geospatial entities as textual terms


title: Moderating Effects of Distrust and Social Influence on Aesthetic Experience of Augmented Reality: Motivation--Opportunity--Ability Model Perspective

abstract: Augmented Reality (AR) applications have been actively developed by numerous tourism institutions or organizations around the world in order to provide tourists with unique and memorable experience. Tourists can be physically and virtually immersed in tourism sites with aesthetic experience provided by AR application. However, AR applications are cutting-edge technology and consequently tourists have to undergo some annoying process in order to use it. Thus, not enough motivation, opportunity and ability of tourists can be barriers to enhancing aesthetic experience. So we adopted Motivation-Opportunity-Ability (MOA) model in order to assessing the predictors of aesthetic experience. Further, assuming that the relationship between MOA and aesthetic experience can be moderated by negative perception about technology and extrinsic influence, we adopted distrust of technology and social influence as moderating variables. The results show that only (intrinsic) motivation (enjoyment) and ability (self-efficacy) have effects on aesthetic experience. Distrust of technology showed that it moderates the relationship between ability (self-efficacy) and aesthetic experience. Based on these key findings, we present theoretical and practical implications with suggestions for future research.


title: Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

abstract: The status quo approach to training object detectors requires expensive bounding box annotations. Our framework takes a markedly different direction: we transfer tracked object boxes from weakly-labeled videos to weakly-labeled images to automatically generate pseudo ground-truth boxes, which replace manually annotated bounding boxes. We first mine discriminative regions in the weakly-labeled image collection that frequently/rarely appear in the positive/negative images. We then match those regions to videos and retrieve the corresponding tracked object boxes. Finally, we design a hough transform algorithm to vote for the best box to serve as the pseudo GT for each image, and use them to train an object detector. Together, these lead to state-of-the-art weakly-supervised detection results on the PASCAL 2007 and 2010 datasets.


Zero Knowledge Proofs: A Survey


abstract: The problem of fitting a union of subspaces to a collection of data points drawn from multiple subspaces is considered in this paper. In the traditional low rank representation model, the dictionary used to represent the data points is chosen as the data points themselves and thus the dictionary is corrupted with noise. This problem is solved in the low rank subspace clustering model which decomposes the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and gross errors. Also, the clustering results of the low rank representation model can be enhanced by using a graph of data similarity. This model is called Laplacian regularized low rank representation model with a graph regularization term added to the objective function. Inspired from the above two ideas, in this paper a Laplacian regularized low rank subspace clustering model is proposed. This model uses a clean dictionary to represent the data points and a graph regularization term is also incorporated in the objective function. Experimental results show that, compared with the traditional low rank representation model, low rank subspace clustering model and several other state-of-the-art subspace clustering model, the model proposed in this paper can get better subspace clustering results with lower clustering error.


title: ohStore: Ontology hierarchy solution to improve RDF data management

abstract: RDF data is growing exponentially and expanding rapidly from different sources everyday. Storing large-scale RDF data has been a matter of concern since the beginning of the Semantic Web. In the recent past, multiple approaches for storing RDF data have been suggested, ranging from simple storages to advanced methods like clustering predicates, which belong to a single class, or vertical partitioning on the properties. Unfortunately, it is still a challenge to store and retrieve huge quantities of RDF triples, due, in part, to the unpredictable nature of data encoded in RDF. Current RDF stores in RDB scale poorly, which may exacerbate poor performance behavior for querying and retrieving RDF triples. In light of this, we propose a framework, called ohStore, which improves state-of-the-art scaling methods by developing a storage model based on the ontology hierarchy and by clustering classes with the help of their semantic relatedness. Our experiment shows that ohStore achieves encouraging and competitive performance by reducing main bottlenecks of property-table, one of the state-of-the-art RDF storage method into RDBMS, on both row and column store database systems.


title: Palm vein recognition using adaptive Gabor filter

abstract: Vein pattern recognition is one of the newest biometric techniques researched today. One of the reliable and robust personal identification authentication approaches using palm vein patterns is presented in this paper. In our work, we consider the palm vein as a piece of texture and apply texture-based feature extraction techniques to palm vein authentication. A Gabor filter provides the optimized resolution in both the spatial and frequency domains, thus it is a basis for extracting local features in the palm vein recognition. However, Gabor filter has many potential parameter combinations to use, and it is a common practice now to use multiple Gabor filters or to determine desired single combination by experience. The overall aim of this work is to discuss the optimization algorithm that determines the best parameter values of a single Gabor filter for palm vein recognition. In order to obtain effective pattern of palm vascular, we proposed an innovative and robust adaptive Gabor filter method to encode the palm vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalized Hamming distance. A total of 4140 palm vein images were collected form 207 persons to verify the validity of the proposed palm vein recognition approach. High accuracy has been obtained by the proposed method and the speed of this method is rapid enough for real-time palm vein recognition. Experimental results demonstrate that our proposed approach is feasible and effective in palm vein recognition.


title: Prediction of 4-year college student performance using cognitive and noncognitive predictors and the impact on demographic status of admitted students.

abstract: This study was conducted to determine the validity of noncognitive and cognitive predictors of the performance of college students at the end of their 4th year in college. Results indicate that the primary predictors of cumulative college grade point average (GPA) were Scholastic Assessment Test/American College Testing Assessment (SAT/ACT) scores and high school GPA (HSGPA) though biographical data and situational judgment measures added incrementally to this prediction. SAT/ACT scores and HSGPA were collected and used in various ways by participating institutions in the admissions process while situational judgment measures and biodata were collected for research purposes only during the first few weeks of the participating students’ freshman year. Alternative outcomes such as a self-report of performance on a range of student performance dimensions and a measure of organizational citizenship behavior, as well as class absenteeism, were best predicted by noncognitive measures. The racial composition of a student body selected with only cognitive measures or both cognitive and noncognitive measures under various levels of selectivity as well as the performance of students admitted under these scenarios is also reported. The authors concluded that both the biodata and situational judgment measures could be useful supplements to cognitive indexes of student potential in college admissions.


title: Multi Document Summarization.

abstract: This thesis describes the development and deployment of a search based, automatic, multi-document summarizing system, and shows that technologies regularly used in search engines are applicable to the task of automatic summarization. The summarizer works by first indexing a set of documents as well as extracting sentences from them. The summarizer can then summarize document collections, which are dynamically constructed from the documents in the index. This is done as an addition to the Solr search server, which provides an interface to the Lucene index; the summarizer uses Lucene to find sentences which it can use to create summaries. The summarizer uses methods similar to search engines, and enjoys the same benefits in speed that modern search engines provide when summarizing documents.


title: Open-World Knowledge Graph Completion

abstract: Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the present work we relax this assumption and propose a new open-world KGC task. As a first attempt to solve this task we introduce an open-world KGC model called ConMask. This model learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KG. To mitigate the presence of noisy text descriptions, ConMask uses a relationship-dependent content masking to extract relevant snippets and then trains a fully convolutional neural network to fuse the extracted snippets with entities in the KG. Experiments on large data sets, both old and new, show that ConMask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task.


title: Application of compressed sensing for rapid MR imaging

abstract: The sparsity which is implicit in MR images is exploited to significantly undersample k-space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain-for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressed-sensing, images with a sparse representation can be recovered from randomly undersampled k-space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the l(1) norm of a transformed image, subject to data fidelity constraints. Examples demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin-echo brain imaging and 3D contrast enhanced angiography.


title: Intercellular Spread of Protein Aggregates in Neurodegenerative Disease

abstract: Most neurodegenerative diseases are characterized by the accumulation of protein aggregates, some of which are toxic to cells. Mounting evidence demonstrates that in several diseases, protein aggre...


Interaction and Transition Model for Speech Emotion Recognition in Dialogue


title: General-purpose image forensics using patch likelihood under image statistical models

abstract: This paper proposes a new, conceptually simple and effective forensic method to address both the generality and the fine-grained tampering localization problems of image forensics. Corresponding to each kind of image operation, a rich GMM (Gaussian Mixture Model) is learned as the image statistical model for small image patches. Thereafter, the binary classification problem, whether a given image block has been previously processed, can be solved by comparing the average patch log-likelihood values calculated on overlapping image patches under different GMMs of original and processed images. With comparisons to a powerful steganalytic feature, experimental results demonstrate the efficiency of the proposed method, for multiple image operations, on whole images and small blocks.


title: False News On Social Media: A Data-Driven Survey

abstract: In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news.


title: Adaptive Neural Trees

abstract: Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neural trees (ANTs) that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers). We demonstrate that, whilst achieving competitive performance on classification and regression datasets, ANTs benefit from (i) lightweight inference via conditional computation, (ii) hierarchical separation of features useful to the task e.g. learning meaningful class associations, such as separating natural vs. man-made objects, and (iii) a mechanism to adapt the architecture to the size and complexity of the training dataset.


title: Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis

abstract: This paper proposes a forward attention method for the sequenceto- sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences. Only the alignment paths that satisfy the monotonic condition are taken into consideration at each decoder timestep. The modified attention probabilities at each timestep are computed recursively using a forward algorithm. A transition agent for forward attention is further proposed, which helps the attention mechanism to make decisions whether to move forward or stay at each decoder timestep. Experimental results show that the proposed forward attention method achieves faster convergence speed and higher stability than the baseline attention method. Besides, the method of forward attention with transition agent can also help improve the naturalness of synthetic speech and control the speed of synthetic speech effectively.


title: Cooking activities recognition in egocentric videos using hand shape feature with openpose

abstract: Recently people can easily obtain wearable cameras and it is easy to record egocentric videos by using them. Therefore, daily activity recognition from egocentric videos is one of the hot topics in computer vision. In this research, we propose a new cooking activity recognition method for egocentric videos. Our proposed method has following characteristic points; 1) hand regions are detected with bounding box by using SSD, 2) hand keypoints(articular points) are estimated by using OpenPose, and hand features are extracted from the keypoint positions and 3) fully-connected multi layer neural network is utilized to recognize cooking activities with the extracted features. From the experimental results with our benchmark including eight cooking activities, we have confirmed that our proposed method allows us to recognize cooking activities with 58.9% on average.


title: Achievable information rate for outdoor free space optical communication with intensity modulation and direct detection

abstract: This work investigates the achievable information rate with the state-of-the-art turbo coding and intensity modulation/direct detection for outdoor long-distance free-space optic (FSO) communications. The channel under weak atmospheric turbulence is modeled as a log-normal intensity fading channel where on-off keying makes it look asymmetric. While no effort is made to spectrally match the code to the asymmetry of the channel, the decoding strategy is optimally adjusted to match to the channel response. In addition to fixed rate turbo coding, a family of variable rate turbo codes are constructed and discussed. Shannon capacity is also briefly visited to denote the theoretic limit. It is shown that under low turbulence a single long turbo code is sufficient to get within 1 dB from the capacity, but when the turbulence gets strong, adaptive coding is necessary to close the gap. We expect these results to be useful for current and immediate future systems.


title: Channel coding for NOMA schemes with a JD or SIC receiver

abstract: In this paper, we investigate the impact of channel coding on two kinds of non-orthogonal multiple access (NOMA) schemes, either with a successive interference cancellation (SIC) receiver or with a joint decoding (JD) receiver. Firstly, the information-theoretic individual achievable signal-to-noise ratio (SNR) regions of SIC and JD are analyzed. Then single-user-oriented (SU) code and multi-users-oriented (MU) code are considered for SIC and JD, and the practical individual achievable SNR regions of SIC and JD with these two kinds of channel codes are obtained, which provides an intuitive way to evaluate system performance under different channel conditions and transmission rates. Simulation results show that, the practical boundaries of individual achievable SNR region of SIC generally lie inside the boundaries of JD, as predicted by the theoretical analysis. Moreover, SU code, which is capacity-approaching over point-to-point channel, performs better than MU code for SIC, while the choice of channel codes for JD depends on transmission rates and channel conditions.


title: Sensory Integration: Cross-Modal Communication Between the Olfactory and Visual Systems in Zebrafish

abstract: Cross-modal sensory communication is an innate biological process that refers to the combination and/or interpretation of different types of sensory input in the brain. Often, this process conjugates with neural modulation, by which the neural signals that convey sensory information are adjusted, such as intensity, frequency, complexity, and/or novelty. Although the anatomic pathways involved in cross-modal sensory integration have been previously described, the course of development and the physiological roles of multisensory signaling integration in brain functions remain to be elucidated. In this article, I review some of the recent findings in sensory integration from research using the zebrafish models. In zebrafish, cross-modal sensory integration occurs between the olfactory and visual systems. It is mediated by the olfacto-retinal centrifugal (ORC) pathway, which originates from the terminalis nerve (TN) in the olfactory bulb and terminates in the neural retina. In the retina, the TNs synapse with the inner nuclear layer dopaminergic interplexiform cells (DA-IPCs). Through the ORC pathway, stimulation of the olfactory neurons alters the cellular activity of TNs and DA-IPCs, which in turn modulates retinal neural function and increases behavioral visual sensitivity.


title: Two Challenges in Cognitive Musicology

abstract: Two themes in music cognition research are highlighted—inspired by the contributions in this volume: (a) statistical learning and (b) evolutionary theorizing. Our ability to test alternatives to statistical learning is threatened by the rapidly diminishing opportunities for cross-cultural studies unconfounded by bimusicalism. Our ability to infer possible evolutionary origins for music is confounded by the “hedonic plenitude” of modern music-making—where multiple pleasure channels are activated simultaneously. Cognitively inspired music research will benefit by studying a wider range of musical cultures. Evolutionary theorizing will benefit by further work involving comparative animal behavior.


title: Facial Assessment and Injection Guide for Botulinum Toxin and Injectable Hyaluronic Acid Fillers: Focus on the Upper Face

abstract: Techniques for the administration of injectable fillers and neuromodulators for facial aesthetic rejuvenation and enhancement continue to evolve. As the number of physicians with limited experience in providing aesthetic treatments expands, the need for guidance and training from more experienced injectors has become apparent. The use of a slow, careful, and methodical injection technique is imperative in all treatment settings and for all facial areas. Constant attention to local anatomy, particularly arteries, veins, and nerve bundles, is critical for minimizing complications. This first article of a three-part series addresses techniques and recommendations for aesthetic treatment of the upper face. Traditionally, the upper face has been considered a basic area for treatment with neuromodulators but an advanced area for treatment with fillers. Injectable fillers may be used for temple volumization, eyebrow shaping, and forehead contouring. Neuromodulators are well suited for diminishing the appearance of dynamic facial lines such as forehead, glabellar, and crow's feet lines, and eyebrow lifting and eye-aperture widening. These techniques may be used independently or together, sequentially or concurrently, to address rejuvenation of individual or multiple facial regions. Overall, this series provides a practical framework of techniques for physicians who desire to perform safe and effective aesthetic treatments using a multimodal approach.


title: Cross-layer modeler: a tool for flexible multilevel modeling with consistency checking

abstract: Model-driven engineering has become a popular methodology in software engineering. Most available modeling tools support the creation of models based on a fixed metamodel. Typically, tool users cannot change the metamodel to reflect domain changes or newly emerged requirements. As a consequence, an updated version of the tool with an evolved metamodel must be developed and models as well as constraints that ensure model consistency have to be co-evolved, often manually, to conform to the new metamodel. Both, tool evolution and the necessary co-evolutions, are time consuming and error prone tasks. Furthermore, common tools often restrict the number of metalevels that can be modeled and force modelers to use workarounds to express certain facts. To overcome these issues we present the Cross-Layer Modeler (XLM), a modeling tool that supports multilevel modeling and allows co-evolution of metamodels and models. The XLM automatically performs co-evolution of constraints and gives instant feedback about model consistency. We illustrate the novel modeling approach of our tool and discuss its main capabilities.


title: Distant Supervision for Emotion Classification with Discrete Binary Values

abstract: In this paper, we present an experiment to identify emotions in tweets. Unlike previous studies, which typically use the six basic emotion classes defined by Ekman, we classify emotions according to a set of eight basic bipolar emotions defined by Plutchik (Plutchik's "wheel of emotions"). This allows us to treat the inherently multi-class problem of emotion classification as a binary problem for four opposing emotion pairs. Our approach applies distant supervision, which has been shown to be an effective way to overcome the need for a large set of manually labeled data to produce accurate classifiers. We build on previous work by treating not only emoticons and hashtags but also emoji, which are increasingly used in social media, as an alternative for explicit, manual labels. Since these labels may be noisy, we first perform an experiment to investigate the correspondence among particular labels of different types assumed to be indicative of the same emotion. We then test and compare the accuracy of independent binary classifiers for each of Plutchik's four binary emotion pairs trained with different combinations of label types. Our best performing classifiers produce results between 75-91%, depending on the emotion pair; these classifiers can be combined to emulate a single multi-label classifier for Plutchik's eight emotions that achieves accuracies superior to those reported in previous multi-way classification studies.


title: Serious games for language learning: How much game, how much AI

abstract: Modern computer games show potential not just for engaging and entertaining users, but also in promoting learning. Game designers employ a range of techniques to promote long-term user engagement and motivation. These techniques are increasingly being employed in so-called serious games, games that have non-entertainment purposes such as education or training. Although such games share the goal of AIED of promoting deep learner engagement with subject matter, the techniques employed are very different. Can AIED technologies complement and enhance serious game design techniques, or does good serious game design render AIED techniques superfluous? This paper explores these questions in the context of the Tactical Language Training System (TLTS), a program that supports rapid acquisition of foreign language and cultural skills. The TLTS combines game design principles and game development tools with learner modelling, pedagogical agents, and pedagogical dramas. Learners carry out missions in a simulated game world, interacting with non-player characters. A virtual aide assists the learners if they run into difficulties, and gives performance feedback in the context of preparatory exercises. Artificial intelligence plays a key role in controlling the behaviour of the non-player characters in the game; intelligent tutoring provides supplementary scaffolding.


title: An improved random forest classifier for image classification

abstract: This paper proposes an improved random forest algorithm for image classification. This algorithm is particularly designed for analyzing very high dimensional data with multiple classes whose well-known representative data is image data. A novel feature weighting method and tree selection method are developed and synergistically served for making random forest framework well suited to classify image data with a large number of object categories. With the new feature weighting method for subspace sampling and tree selection method, we can effectively reduce subspace size and improve classification performance without increasing error bound. Experimental results on image datasets with diverse characteristics have demonstrated that the proposed method could generate a random forest model with higher performance than the random forests generated by Breiman's method.


title: The effects of modern mathematics computer games on mathematics achievement and class motivation

abstract: This study examined the effects of a computer game on students' mathematics achievement and motivation, and the role of prior mathematics knowledge, computer skill, and English language skill on their achievement and motivation as they played the game. A total of 193 students and 10 teachers participated in this study. The teachers were randomly assigned to experimental and control groups. A mixed method of quantitative and interviews were used with Multivariate Analysis of Co-Variance to analyze the data. The results indicated significant improvement of the achievement of the experimental versus control group. No significant improvement was found in the motivation of the groups. Students who played the games in their classrooms and school labs reported greater motivation compared to the ones who played the games only in the school labs. Prior knowledge, computer and English language skill did not play significant roles in achievement and motivation of the experimental group.


title: A 13.5-mW 10-Gb/s 4-PAM Serial Link Transmitter in 0.13- $\mu\hbox{m}$ CMOS Technology

abstract: A 13.5-mW 10-Gb/s four-level pulse-amplitude modulation (4-PAM) serial link transmitter is presented. To improve the power efficiency, a voltage-mode 4-PAM driver is proposed. It consists of voltage-scaled pull-up and pull-down networks, instead of conventional current switching networks. Not employing a tail current source, the proposed 4-PAM driver achieves the higher output voltage swing and lower power dissipation than conventional 4-PAM drivers. As a result, the proposed 4-PAM transmitter implemented in a 0.13-μm CMOS process achieved 10-Gb/s data rate with only 13.5-mW power dissipation.


Integrating Out Multinomial Parameters in Latent Dirichlet Allocation and Naive Bayes for Collapsed Gibbs Sampling


title: Temporal regularized matrix factorization for high-dimensional time series prediction

abstract: Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand needs to be forecast might be as large as 50,000. In addition, the data is generally noisy and full of missing values. Thus, modern applications require methods that are highly scalable, and can deal with noisy data in terms of corruptions or missing values. However, classical time series methods usually fall short of handling these issues. In this paper, we present a temporal regularized matrix factorization (TRMF) framework which supports data-driven temporal learning and forecasting. We develop novel regularization schemes and use scalable matrix factorization methods that are eminently suited for high-dimensional time series data that has many missing values. Our proposed TRMF is highly general, and subsumes many existing approaches for time series analysis. We make interesting connections to graph regularization methods in the context of learning the dependencies in an autoregressive framework. Experimental results show the superiority of TRMF in terms of scalability and prediction quality. In particular, TRMF is two orders of magnitude faster than other methods on a problem of dimension 50,000, and generates better forecasts on real-world datasets such as Wal-mart E-commerce datasets.


title: On the Benefit of Incorporating External Features in a Neural Architecture for Answer Sentence Selection

abstract: Incorporating conventional, unsupervised features into a neural architecture has the potential to improve modeling effectiveness, but this aspect is often overlooked in the research of deep learning models for information retrieval. We investigate this incorporation in the context of answer sentence selection, and show that combining a set of query matching, readability, and query focus features into a simple convolutional neural network can lead to markedly increased effectiveness. Our results on two standard question-answering datasets show the effectiveness of the combined model.


title: Hand gesture recognition for Human-Robot Interaction for service robot

abstract: With advances in technology, robots play an important role in our lives. Nowadays, we have more chance to see robots service in our society such as intelligent robot for rescue and for service. Therefore, Human-Robot interaction becomes an essential issue for research. In this paper we introduce a combining method for hand sign recognition. Hand sign recognition is an essential way for Human-Robot Interaction (HRI). Sign language is the most intuitive and direct way to communication for impaired or disabled people. Through the hand or body gestures, the disabled can easily let caregiver or robot know what message they want to convey. In this paper, we propose a combining hands gesture recognition algorithm which combines two distinct recognizers. These two recognizers collectively determine the hand's sign via a process called CAR equation. These two recognizers are aimed to complement the ability of discrimination. To achieve this goal, one recognizer recognizes hand gesture by hand skeleton recognizer (HSR), and the other recognizer is based on support vector machines (SVM). In addition, the corresponding classifiers of SVM are trained using different features like local binary pattern (LBP) and raw data. Furthermore, the trained images are using Bosphorus Hand Database and in addition to taking by us. A set of rules including recognizer switching and combinatorial approach recognizer CAR equation is devised to synthesize the distinctive methods. We have successfully demonstrated gesture recognition experimentally with successful proof of concept.


title: The Role of Social Norms in the Structural Model Explaining Alcohol Use Among Students

abstract: Few studies have explored the complex relationships among several factors determining alcohol use or explained how norms operate in the relationships between alcohol use and other psychological constructs. This study aims to explore the complex relationships in the proposed model explaining students' alcohol use with special emphasis on descriptive and injunctive norms. Cross-sectional data were collected from 719 university students (59.0% females; Mage = 21.25; SD = 1.99), who were asked to fill in a set of AUDIT items, cognitive and affective attitudes, descriptive and injunctive norms, self-regulation, self- -determination, drinking motives, and alcohol expectancies. Structural equation modelling was used for data analysis. It was found that (1) self-regulation was neither directly nor indirectly associated with alcohol use; (2) self-determination was indirectly associated with alcohol use through injunctive norms, attitudes and drinking motives; (3) descriptive norms were associated with alcohol use directly; (4) injunctive norms were associated with alcohol use directly as well as indirectly via alcohol expectancies, attitudes and drinking motives; (5) alcohol expectancies were associated with alcohol use directly. In addition to identifying individual associations among variables, this study also explains which factors contribute to students' alcohol use and norms and how these factors interact, which can be useful for the development of prevention programmes.


Malicious Pdf Document Detection Based on Feature Extraction and Entropy


title: Ubiquitous Personal Learning Environment (UPLE)

abstract: Web 2.0 technologies opened up new perspectives in learning and teaching activities. Collaboration, communication and sharing between learners contribute to the self-regulated learning, a bottom-up approach. The market for smartphones and tablets are growing rapidly. They are being used more often in everyday life. This allows us to support self-regulated learning in a way that learning resources and applications are accessible any time and at any place. This publication focuses on the Personal Learning Environment (PLE) that was launched at Graz University of Technology in 2010. After a first prototype a complete redesign was carried out to fulfill a change towards learner-centered framework. Statistical data show a high increase of attractiveness of the whole system in general. As the next step a mobile version is integrated. A converter for browser-based learning apps within PLE to native smartphone apps leads to the Ubiquitous PLE, which is discussed in this paper in detail.


title: A novel corner-fed patch to reduce cross-polarization for a microstrip antenna array

abstract: This paper presents a novel corner-fed microstrip square patch. On the patch there is a slot along the diagonal. With this slot, the current distribution on the patch is changed. The currents related to co-polarization become more dominant and those related to cross-polarization become more symmetrical. These two factors result in a reduction in the cross-polarization level. An eight element shunt connected series-fed linear array has been designed, manufactured and tested. The measured results show that the cross-polarization level is reduced by 10.5 dB, while the other performances of the array are nearly the same as those of a standard one.


title: A SPACE-EFFICIENT FLASH TRANSLATION LAYER FOR COMPACTFLASH SYSTEMS

abstract: Flash memory is becoming increasingly important as nonvolatile storage for mobile consumer electronics due to its low power consumption and shock resistance. However, it imposes technical challenges in that a write should be preceded by an erase operation, and that this erase operation can be performed only in a unit much larger than the write unit. To address these technical hurdles, an intermediate software layer called a flash translation layer (FTL) is generally employed to redirect logical addresses from the host system to physical addresses in flash memory. Previous approaches have performed this address translation at the granularity of either a write unit (page) or an erase unit (block). We propose a novel FTL design that combines the two different granularities in address translation. This is motivated by the idea that coarse grain address translation lowers the resources required to maintain translation information, which is crucial in mobile consumer products for cost and power consumption reasons, while fine grain address translation is efficient in handling small size writes. Performance evaluation based on trace-driven simulation shows that the proposed scheme significantly outperforms previously proposed approaches.


title: Accuracy of linear and angular measurements on panoramic radiographs taken at various positions in vitro.

abstract: The accuracy of measurement of tooth length and angulation on dental panoramic tomograms (DPTs) is thought to be highly dependent on head positioning technique. A model representing the dentition and the functional occlusal plane was designed using an acrylic framework and stainless steel wires. The aim was to investigate whether varying the position of the model affects the linear and angular measurements on DPTs. Four different positions were investigated: initial position representing natural head posture (NHP) (T1); lateral right cant of the occlusal plane (T2); lateral left cant of the occlusal plane (T3); and tilting the occlusal plane up anteriorly (T4). On each DPT, four sets of measurements were recorded: (1) Vertical linear measurements of the stainless steel pins and ratio calculations of the 'crown' and 'root' segments (represented by the wire above and below the occlusal plane, respectively); (2) angular measurements of the pins relative to the occlusal plane; (3) angular measurements of the pins relative to a constructed reference line; and (4) angular measurements of pins relative to each other in the same segment. The results showed a significant error (P < 0.05) in all measurements when the occlusal plane was tilted up anteriorly by 8 degrees. A lateral cant of the occlusal plane by less than 10 degrees without an upward anterior rotation showed no significant effect on the measurements. This would suggest that there is some tolerance of variation in head position.


title: Synthesis and characterization of tetrafunctional lactic acid oligomers: A potential in situ forming degradable orthopaedic biomaterial

abstract: Tetrafunctional lactic acid oligomers with low molecular weight ethylene glycol cores were synthesized and characterized to assess their applicability to orthopaedics. Utilizing a visible light photoinitiating system, these oligomers polymerize within minutes to form highly crosslinked networks and, thus, have potential for in situ formation. Varying the oligomer structure readily alters the physical properties of the resultant polymer networks. For instance, mechanical properties were highly dependent on the number of lactic acid and ethylene glycol units in the oligomer backbone. Additionally, polymer mass loss ranged from ∼30 to 60% within 8 weeks of degradation time depending on the oligomer chemistry. Mechanical properties decreased with degradation of these polymers, indicating a bulk degradation mechanism. Finally, scaffolds with a controlled architecture were fabricated from these oligomers that show potential for tissue-engineering applications. © 2001 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 39: 683–692, 2001


title: The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation

abstract: Most popular evolutionary algorithms for multiobjective optimisation maintain a population of solutions from which individuals are selected for reproduction. In this paper, we introduce a simpler evolution scheme for multiobjective problems, called the Pareto archived evolution strategy (PAES). We argue that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm is identified as being a (1+1) evolution strategy, using local search from a population of one but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. PAES is intended as a good baseline approach, against which more involved methods may be compared, and may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. The performance of the new algorithm is compared with that of a MOEA based on the niched Pareto GA on a real world application from the telecommunications field. In addition, we include results from experiments carried out on a suite of four test functions, to demonstrate the algorithm's general capability.


title: Grounding Action Descriptions in Videos

abstract: Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used. In this paper, we consider the problem of grounding sentences describing actions in visual information extracted from videos . We present a general purpose corpus that aligns high quality videos with multiple natural language descriptions of the actions portrayed in the videos, together with an annotation of how similar the action descriptions are to each other. Experimental results demonstrate that a text-based model of similarity between actions improves substantially when combined with visual information from videos depicting the described actions.


title: Deriving a pattern language syntax for context-patterns

abstract: In a previous publication we introduced a catalog of context-patterns. Each context pattern describes common structures and stakeholders for a specific domain. The common elements of the context were obtained from observations about the domain in terms of standards, domain specific-publications, and implementations. Whenever the domain of a system-to-be is already described by a context-pattern, one can use this context-pattern to elicit domain knowledge by instantiating the corresponding context-pattern. Moreover, we analyzed the common concepts in our context-patterns and created a meta-model to describe the relations between these concepts. This meta-model was the initial step towards a pattern language for context-patterns. In this work, we show the consequent next step for the definition of a pattern language syntax for context-patterns. Thus, we describe how to derive the connections between the existing context-pattern in a structured way and present the results.


title: Seamless patches for GPU-based terrain rendering

abstract: In this paper we present a novel approach for interactive rendering of large terrain datasets. Our approach is based on subdividing a terrain into rectangular patches at different resolutions. Each patch is represented by four triangular tiles that are selected form different resolutions, and four strips which are used to stitch the four tiles in a seamless manner. Such a scheme maintains resolution changes within patches through the stitching strips, and not across patches. At runtime, these patches are used to construct a level-of-detail representation of the input terrain based on view-parameters. A selected level of detail only includes the layout of the patches and their boundary edges resolutions. The layout includes the location and dimension of each patch. Within the graphics hardware, the GPU generates the meshes of the patches by using scaled instances of cached tiles and assigns elevation for each vertex from cached textures. Since adjacent rectangular patches agree on the resolution of the common edges, the resulted mesh does not include cracks or degenerate triangles. Our algorithm manages to achieve quality images at high frame rates while providing seamless transition between different levels of detail.


title: Patient predictors of response to cognitive behaviour therapy and schema therapy for depression

abstract: Objective:Few studies have examined differential predictors of response to psychotherapy for depression. Greater understanding about the factors associated with therapeutic response may better enable therapists to optimise response by targeting therapy for the individual. The aim of the current exploratory study was to examine patient characteristics associated with response to cognitive behaviour therapy and schema therapy for depression.Methods:Participants were 100 outpatients in a clinical trial randomised to either cognitive behaviour therapy or schema therapy. Potential predictors of response examined included demographic, clinical, functioning, cognitive, personality and neuropsychological variables.Results:Individuals with chronic depression and increased levels of pre-treatment negative automatic thoughts had a poorer response to both cognitive behaviour therapy and schema therapy. A treatment type interaction was found for verbal learning and memory. Lower levels of verbal learning and memory im...


title: Ambient affiliation: A linguistic perspective on Twitter

abstract: This article explores how language is used to build community with the microblogging service, Twitter (www.twitter.com). Systemic Functional Linguistic (SFL), a theory of language use in its social context, is employed to analyse the structure and meaning of ‘tweets’ (posts to Twitter) in a corpus of 45,000 tweets collected in the 24 hours after the announcement of Barak Obama’s victory in the 2008 US presidential elections. This analysis examines the evaluative language used to affiliate in tweets. The article shows how a typographic convention, the hashtag, has extended its meaning potential to operate as a linguistic marker referencing the target of evaluation in a tweet (e.g. #Obama). This both renders the language searchable and is used to upscale the call to affiliate with values expressed in the tweet. We are currently witnessing a cultural shift in electronic discourse from online conversation to such ‘searchable talk’.


title: Semantic Linking of Learning Object Repositories to DBpedia

abstract: Large-sized repositories of learning objects (LOs) are difficult to create and also to maintain. In this paper we propose a way to reduce this drawback by improving the classification mechanisms of the LO repositories. Specifically, we present a solution to automate the LO classification of the Universia repository, a collection of more than 15 million of LOs represented according to the IEEE LOM standard. Although a small part of these LOs is correctly classified, most are unclassified and therefore searching and accessing information is difficult. Our solution makes use of the categories provided by DBpedia, a linked data repository, to automatically improve its classification through a graph-based filtering algorithm, which selects the most suitable categories for describing a LO. Once selected, these categories will classify the LO, linking its classification metadata with a set of DBpedia categories.


title: Online recruiting: The effects of organizational familiarity, website usability, and website attractiveness on viewers' impressions of organizations

abstract: Previous research on Internet recruitment has made the implicit assumption that recruitment websites influence viewers' opinions of recruiting organizations. This study tested this assumption using a pretest/posttest design. Findings revealed that participants' organizational favorability, image as employer, and organizational attractiveness perceptions were affected by their viewing of organizational recruitment websites. Greater increases in favorable organizational evaluations from the pretest measures to the posttest measures occurred with organizations maintaining websites that were easy to navigate and/or that were appealing. Contrary to predictions made by signaling theory, recruitment websites had similar effects on the organizational impressions of all individuals, regardless of their familiarity with the organizations maintaining the recruitment websites that they viewed.


title: A cognitive neuroscience perspective on psychopathy : Evidence for paralimbic system dysfunction

abstract: Psychopathy is a complex personality disorder that includes interpersonal and affective traits such as glibness, lack of empathy, guilt or remorse, shallow affect, and irresponsibility, and behavioral characteristics such as impulsivity, poor behavioral control, and promiscuity. Much is known about the assessment of psychopathy; however, relatively little is understood about the relevant brain disturbances. The present review integrates data from studies of behavioral and cognitive changes associated with focal brain lesions or insults and results from psychophysiology, cognitive psychology and cognitive and affective neuroscience in health and psychopathy. The review illustrates that the brain regions implicated in psychopathy include the orbital frontal cortex, insula, anterior and posterior cingulate, amygdala, parahippocampal gyrus, and anterior superior temporal gyrus. The relevant functional neuroanatomy of psychopathy thus includes limbic and paralimbic structures that may be collectively termed ‘the paralimbic system’. The paralimbic system dysfunction model of psychopathy is discussed as it relates to the extant literature on psychopathy.


title: Compact Orthomode Transducer Polarizer Based on a Tilted-Waveguide T-Junction

abstract: An alternative device to the well-known septum polarizer is proposed, which also provides the double function of an orthomode and a polarizer. It is based on a waveguide T-junction between a common dual-polarization waveguide and a tilted rectangular single-mode waveguide, and offers an intrinsically shorter geometry in the axial dimension, an advisable property when searching for compact antena assemblies. The analysis of this new device is presented, along with the derivation and introduction of the $S$ -parameters referred to the circularly polarized modes in the common waveguide. The working principle of the proposed T-junction is also explained in detail. A breadboard is designed, and its simulation and measurement results are presented. As a conclusion, the proposed device seems worthy of comparison in its electrical performances to the septum polarizer, showing some advantages in terms of geometrical dimensions and manufacturability.


title: A high dynamic range, high precision framework for measuring machinery part using structured light

abstract: In this research, we present a framework for industrial measurement using 3D scanning technology. Until recently, metal parts with glossy surfaces still pose a big challenge for low dynamic range CMOS sensor in 3D scanner. Several approaches have partially solved this problem either by trading off scanning time or by increasing system's complexity. Unfortunately, these methods defeat the “low cost” and “fast” advantages of structured light scanning. In our framework, we employ several strategies such as: utilize smart encoding schemes for better signal-to-noise ratio, dynamically adjust the projector to minimize specular light, utilize multiple cameras and customized algorithms to shorten processing time. As a result, our setup greatly improves measurement quality while only incur a small time and monetary cost.


title: A neurophilosophical slant on consciousness research

abstract: Abstract Explaining the nature and mechanisms of conscious experience in neurobiological terms seems to be an attainable, if yet unattained, goal. Research at many levels is important, including research at the cellular level that explores the role of recurrent pathways between thalamic nuclei and the cortex, and research that explores consciousness from the perspective of action. Conceptually, a clearer understanding of the logic of expressions such as “causes” and “correlates”, and about what to expect from a theory of consciousness are required. The logic of some terms, such as “qualia” and “reductionism”, continues to generate misunderstandings about the scientific possibilities and limits. Experimentally, a deeper understanding of the role of the thalamus in coordinating activity across cortical levels, and a readiness to reconsider the orthodox approach to thalamocortical organization are also required.


title: A Hardware-Assisted Energy-Efficient Processing Model for Activity Recognition Using Wearables

abstract: Wearables are being widely utilized in health and wellness applications, primarily due to the recent advances in sensor and wireless communication, which enhance the promise of wearable systems in providing continuous and real-time monitoring and interventions. Wearables are generally composed of hardware/software components for collection, processing, and communication of physiological data. Practical implementation of wearable monitoring in real-life applications is currently limited due to notable obstacles. The wearability and form factor are dominated by the amount of energy needed for sensing, processing, and communication. In this article, we propose an ultra-low-power granular decision-making architecture, also called screening classifier, which can be viewed as a tiered wake-up circuitry, consuming three orders of magnitude-less power than the state-of-the-art low-power microcontrollers. This processing model operates based on computationally simple template matching modules, based on coarse- to fine-grained analysis of the signals with on-demand and gradually increasing the processing power consumption. Initial template matching rejects signals that are clearly not of interest from the signal processing chain, keeping the rest of processing blocks idle. If the signal is likely of interest, the sensitivity and the power of the template matching modules are gradually increased, and ultimately, the main processing unit is activated. We pose optimization techniques to efficiently split a full template into smaller bins, called mini-templates, and activate only a subset of bins during each classification decision. Our experimental results on real data show that this signal screening model reduces power consumption of the processing architecture by a factor of 70p while the sensitivity of detection remains at least 80p.


title: Transcriptional Control of a Plant Stem Cell Niche

abstract: Summary Despite the independent evolution of multicellularity in plants and animals, the basic organization of their stem cell niches is remarkably similar. Here, we report the genome-wide regulatory potential of WUSCHEL, the key transcription factor for stem cell maintenance in the shoot apical meristem of the reference plant Arabidopsis thaliana . WUSCHEL acts by directly binding to at least two distinct DNA motifs in more than 100 target promoters and preferentially affects the expression of genes with roles in hormone signaling, metabolism, and development. Striking examples are the direct transcriptional repression of CLAVATA1 , which is part of a negative feedback regulation of WUSCHEL , and the immediate regulation of transcriptional repressors of the TOPLESS family, which are involved in auxin signaling. Our results shed light on the complex transcriptional programs required for the maintenance of a dynamic and essential stem cell niche.


title: Toward Scalable Indexing and Search on Distributed and Unstructured Data

abstract: The ubiquity of Big Data has greatly influenced the direction and the development of storage technologies. To meet the needs of storing and analyzing Big Data, researchers and administrators have turned to parallel and distributed storage and compute architectures. While the problems of securely and consistently storing and accessing data in large parallel and distributed file systems have been largely addressed in both the research and production systems, efficiently searching across large unstructured data and metadata has largely been overlooked. According to the International Data Corporation, more than 90% of data found in the digital universe is unstructured, emphasizing the importance of developing efficient solutions for querying distributed data. This paper proposes a novel indexing solution, called FusionDex, that provides an efficient model for querying across distributed file systems. FusionDex leverages state-of-the-art, open-source indexing modules as its building blocks to deliver an integrated system for enabling efficient user-specified queries over distributed and unstructured data. FusionDex has been evaluated on a cluster of 64 nodes, and results show that it outperforms existing tools (in some cases by several orders of magnitude), such as Hadoop Grep and Cloudera Search.


title: Automatic Gait Optimization with Gaussian Process Regression

abstract: Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.


title: DIRECT algorithm: A new definition of potentially optimal hyperrectangles

abstract: Abstract We propose a new version of potentially optimal intervals for the DIRECT algorithm. A two-point based sampling method is presented. The method starts from a distinguished point (the peak point) by forming an initial triangle. The idea is to sample the midpoint of a specific interval: the basis of the resulting triangle. This specific interval is obtained by translating the initial interval towards the lowest function value: min{f(ci), f(ci+1)} and then overcoming the disadvantage if the global minimum lies at the boundaries. Two-dimensional version of our subdivision and sampling method is also discussed.


title: SVM ranking with backward search for feature selection in type II diabetes databases

abstract: Clinical databases have accumulated large quantities of information about patients and their clinical history. Data mining is the search for relationships and patterns within this data that could provide useful knowledge for effective decision-making. Classification analysis is one of the widely adopted data mining techniques for healthcare applications to support medical diagnosis, improving quality of patient care, etc. Usually medical databases are high dimensional in nature. If a training dataset contains irrelevant features (i.e., attributes), classification analysis may produce less accurate results. Data pre-processing is required to prepare the data for data mining and machine learning to increase the predictive accuracy. Feature selection is a preprocessing technique commonly used on high-dimensional data and its purposes include reducing dimensionality, removing irrelevant and redundant features, reducing the amount of data needed for learning, improving algorithms' predictive accuracy, and increasing the constructed models' comprehensibility. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. The importance of feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of features. Feature selection may provide us with the means to reduce the number of clinical measures made while still maintaining or even enhancing accuracy and reducing false negative rates. In medical diagnosis, reduction in false negative rate can, literally, be the difference between life and death. In this paper we propose a feature selection approach for finding an optimum feature subset that enhances the classification accuracy of Naive .Bayes classifier. Experiments were conducted on the Pima Indian Diabetes Dataset to assess the effectiveness of our approach. The results confirm that SVM Ranking with Backward Search approach leads to promising improvement on feature selection and enhances classification accuracy.


Railway track settlements - a literature review


DISTRIBUTION STATEMENT A Approved for Public Release Distribution Unlimited Thread Migration and Communication Minimization in DSM Sjstems


title: Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

abstract: Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.


title: Light-Field Image Super-Resolution Using Convolutional Neural Network

abstract: Commercial light field cameras provide spatial and angular information, but their limited resolution becomes an important problem in practical use. In this letter, we present a novel method for light field image super-resolution (SR) to simultaneously up-sample both the spatial and angular resolutions of a light field image via a deep convolutional neural network. We first augment the spatial resolution of each subaperture image by a spatial SR network, then novel views between super-resolved subaperture images are generated by three different angular SR networks according to the novel view locations. We improve both the efficiency of training and the quality of angular SR results by using weight sharing . In addition, we provide a new light field image dataset for training and validating the network. We train our whole network end-to-end, and show state-of-the-art performances on quantitative and qualitative evaluations.


title: Cooperative quadrotors carrying a suspended load

abstract: This work proposes the representation of the high level dynamic modelling of a formation of two quadrotor UAVs cooperatively carrying a suspended load. The two quadrotors are attached to the load by massless and stretchable cables, which can slack and taut during the task accomplishment. The cable is modeled as a high stiffness spring with a damping coefficient, according to the Hooke's law, and its behavior is taken into account in the disturbance estimation. A fictitious repulsion force is also introduced to minimize the risk of a collision between the UAVs during lateral displacements. Finally, to dealt with disturbance rejection caused by the load compensation and the cable tension variations, a nonlinear controller is proposed to guide the UAVs cooperatively during positioning and trajectory-tracking tasks, considering that the movement of the UAVs is restricted to the XZ-plane. Finally, simulated results are presented, in order to validate the proposal.


title: Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms

abstract: The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.


title: A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets

abstract: We assume that the instantaneous riskless rate reverts towards a central tendency which in turn, is changing stochastically over time. As a result, current short-term rates are not" sufficient to predict future short-term rates movements, as would be the case if the central" tendency was constant. However, since longer-maturity bond prices incorporate information" about the central tendency, longer-maturity bond yields can be used to predict future short-term" rate movements. We develop a two-factor model of the term-structure which implies that a" linear combination of any two rates can be used as a proxy for the central tendency. Based on" this central-tendency proxy, we estimate a model of the one-month rate which performs better" than models which assume the central tendency to be constant.


title: Optimized artificial muscles for an inherently safe robotic arm

abstract: Safe interaction between rehabilitation robots and patients requires the development of dedicated technology. Inherently compliant actuators, such as artificial muscles (McKibben actuators) can help achieve this goal. This paper presents McKibben actuators with an initial length that is invariant with pressure. They also feature improved range of stiffness and increase range of extension. An application of the developed actuators in an anthropomorphic robotic arm presented. A stiffness control scheme was implemented that allows it to safely shake hands with humans.


title: Intrusion Detection in the Cloud

abstract: Intrusion Detection Systems (IDS) have been used widely to detect malicious behaviors in network communication and hosts. IDS management is an important capability for distributed IDS solutions, which makes it possible to integrate and handle different types of sensors or collect and synthesize alerts generated from multiple hosts located in the distributed environment. Facing new application scenarios in Cloud Computing, the IDS approaches yield several problems since the operator of the IDS should be the user, not the administrator of the Cloud infrastructure. Extensibility, efficient management, and compatibility to virtualization-based context need to be introduced into many existing IDS implementations.Additionally, the Cloud providers need to enable possibilities to deploy and configure IDS for the user. Within this paper, we summarize several requirements for deploying IDS in the Cloud and propose an extensible IDS architecture for being easily used in a distributed cloud infrastructure.


title: Design principles of massive, robust prediction systems

abstract: Most data mining research is concerned with building high-quality classification models in isolation. In massive production systems, however, the ability to monitor and maintain performance over time while growing in size and scope is equally important. Many external factors may degrade classification performance including changes in data distribution, noise or bias in the source data, and the evolution of the system itself. A well-functioning system must gracefully handle all of these. This paper lays out a set of design principles for large-scale autonomous data mining systems and then demonstrates our application of these principles within the m6d automated ad targeting system. We demonstrate a comprehensive set of quality control processes that allow us monitor and maintain thousands of distinct classification models automatically, and to add new models, take on new data, and correct poorly-performing models without manual intervention or system disruption.


title: A Hybrid Genetic Algorithm For The Vehicle Routing Problem With Time Windows

abstract: This paper suggests a new hybrid genetic algorithm for the 2D Euclidean vehicle routing problem with time windows. The natural crossover, proposed for the 2D Euclidean traveling salesman problem, was adopted with some modification in the suggested genetic algorithm. The most notable feature of the natural crossover is that it uses the 2D image of a solution itself for chromosomal cutting. We also investigate the usefulness of parents' decision variables such as arrival times during recombination. The suggested genetic algorithm found optimal solutions for 26 out of 31 instances with known optimal solutions.


title: Product as a Service: Enabling Physical Products as Service End-Points

abstract: Manufacturers of consumer goods are trying to establish a direct link to consumers and to strengthen their competitive position by offering additional services. Even for simple services like a free warranty extension, manufacturers often rely on old-fashioned mailin coupons and have to encourage consumers to reach out to them over inefficient channels. We suspect that the transaction costs for initiating such relationships are prohibitively high and limit the potential. Therefore, we conducted a survey among 1012 consumers to determine the current and desired level of product-related service consumptions. Our findings suggest an increase of 22% in intention to use if search cost were reduced. In a second step, we quantify the potential of reducing search cost by developing and evaluating a mobile IS for services. The results show a potential of effort reduction by factor 11 and identify opportunities for enabling an increase in productrelated service consumption with information systems.


title: A COGNITIVE SEMANTIC APPROACH TO PERSIAN SPATIAL PREPOSITIONS, A PEDAGOGICAL PERSPECTIVE, CASE STUDY: PERSIAN PREPOSITION PREPOSITION /DÆR/

abstract: We will argue that the common practice in the literature of giving a long list of senses for spatial prepositions, implying that they are unrelated to one another is not adequate. The more adequate way is presenting a network of related senses based on a prototype. Also, speaking specifically about Persian preposition /daer/, we will argue that the meanings of spatial prepositions arise as a consequence of our daily bodily experiences in the world and our conceptualization thereof. It is also shown that the cognitive semantic approach taken in this paper sheds more light on the semantic structure of spatial prepositions in general and /daer/ in particular, a light which can be very useful in teaching prepositions.


title: Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms

abstract: Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. In Burger et al. (2012), we show that multi-layer perceptrons can achieve outstanding image denoising performance for various types of noise (additive white Gaussian noise, mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes). In this work we discuss in detail which trade-os have to be considered during the training procedure. We will show how to achieve good results and which pitfalls to avoid. By analysing the activation patterns of the hidden units we are able to make observations regarding the functioning principle of multi-layer perceptrons trained for image denoising.


title: On Tikhonov Regularization, Bias and Variance in Nonlinear System Identification

abstract: Abstract Regularization is a general method for solving ill-posed and ill-conditioned problems. Traditionally, ill-conditioning in system identification problems is approached using regularization methods such as ridge regression and principal component regression. In this work it is argued that the Tikhonov regularization method is a powerful alternative for regularization of nonlinear system identification problems by introducing smoothness of the model as a prior. Its properties is discussed in terms of an analysis of bias and variance, and illustrated by a semirealistic simulation example.


title: An overview of audio information retrieval

abstract: The problem of audio information retrieval is familiar to anyone who has returned from vacation to find ananswering machine full of messages. While there is not yetan "AltaVista" for the audio data type, many workers arefinding ways to automatically locate, index, and browse audio using recent advances in speech recognition and machinelistening. This paper reviews the state of the art in audioinformation retrieval, and presents recent advances in automatic speech recognition, word spotting, speaker and music identification, and audio similarity with a view towardsmaking audio less "opaque". A special section addresses intelligent interfaces for navigating and browsing audio andmultimedia documents, using automatically derived information to go beyond the tape recorder metaphor.


title: Efficient batchwise dropout training using submatrices

abstract: Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off some of the units---a different pattern of dropout is applied to every sample in the minibatch. We explore a very simple alternative to the dropout mask. Instead of masking dropped out units by setting them to zero, we perform matrix multiplication using a submatrix of the weight matrix---unneeded hidden units are never calculated. Performing dropout batchwise, so that one pattern of dropout is used for each sample in a minibatch, we can substantially reduce training times. Batchwise dropout can be used with fully-connected and convolutional neural networks.


title: Emotion recognition through facial expression analysis based on a neurofuzzy network

abstract: Extracting and validating emotional cues through analysis of users' facial expressions is of high importance for improving the level of interaction in man machine communication systems. Extraction of appropriate facial features and consequent recognition of the user's emotional state that can be robust to facial expression variations among different users is the topic of this paper. Facial animation parameters (FAPs) defined according to the ISO MPEG-4 standard are extracted by a robust facial analysis system, accompanied by appropriate confidence measures of the estimation accuracy. A novel neurofuzzy system is then created, based on rules that have been defined through analysis of FAP variations both at the discrete emotional space, as well as in the 2D continuous activation-evaluation one. The neurofuzzy system allows for further learning and adaptation to specific users' facial expression characteristics, measured though FAP estimation in real life application of the system, using analysis by clustering of the obtained FAP values. Experimental studies with emotionally expressive datasets, generated in the EC IST ERMIS project indicate the good performance and potential of the developed technologies.


title: Fully Convolutional Region Proposal Networks for Multispectral Person Detection

abstract: Multispectral images that combine visual-optical (VIS) and infrared (IR) image information are a promising source of data for automatic person detection. Especially in automotive or surveillance applications, challenging conditions such as insufficient illumination or large distances between camera and object occur regularly and can affect image quality. This leads to weak image contrast or low object resolution. In order to detect persons under such conditions, we apply deep learning for effectively fusing the VIS and IR information in multispectral images. We present a novel multispectral Region Proposal Network (RPN) that is built up on the pre-trained very deep convolutional network VGG-16. The proposals of this network are further evaluated using a Boosted Decision Trees classifier in order to reduce potential false positive detections. With a log-average miss rate of 29:83% on the reasonable test set of the KAIST Multispectral Pedestrian Detection Benchmark, we improve the current state-of-the-art by about 18%.


title: Feature selection using linear classifier weights: interaction with classification models

abstract: This paper explores feature scoring and selection based on weights from linear classification models. It investigates how these methods combine with various learning models. Our comparative analysis includes three learning algorithms: Naive Bayes, Perceptron, and Support Vector Machines (SVM) in combination with three feature weighting methods: Odds Ratio, Information Gain, and weights from linear models, the linear SVM and Perceptron. Experiments show that feature selection using weights from linear SVMs yields better classification performance than other feature weighting methods when combined with the three explored learning algorithms. The results support the conjecture that it is the sophistication of the feature weighting method rather than its apparent compatibility with the learning algorithm that improves classification performance.


title: Adversarial Deep Learning for Robust Detection of Binary Encoded Malware

abstract: Malware is constantly adapting in order to avoid detection. Model-based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to adversarial examples of images have been developed using saddle-point optimization formulations. We are inspired by them to develop similar methods for the discrete, e.g. binary, domain which characterizes the features of malware. A specific extra challenge of malware is that the adversarial examples must be generated in a way that preserves their malicious functionality. We introduce methods capable of generating functionally preserved adversarial malware examples in the binary domain. Using the saddle-point formulation, we incorporate the adversarial examples into the training of models that are robust to them. We evaluate the effectiveness of the methods and others in the literature on a set of Portable Execution (PE) files. Comparison prompts our introduction of an online measure computed during training to assess general expectation of robustness.


title: Object detection by contour segment networks

abstract: We propose a method for object detection in cluttered real images, given a single hand-drawn example as model. The image edges are partitioned into contour segments and organized in an image representation which encodes their interconnections: the Contour Segment Network. The object detection problem is formulated as finding paths through the network resembling the model outlines, and a computationally efficient detection technique is presented. An extensive experimental evaluation on detecting five diverse object classes over hundreds of images demonstrates that our method works in very cluttered images, allows for scale changes and considerable intra-class shape variation, is robust to interrupted contours, and is computationally efficient.


title: Generative networks as inverse problems with Scattering transforms

abstract: Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be optimized with a discriminator or an encoder. The embedding is Lipschitz continuous to deformations so that generators transform linear interpolations between input white noise vectors into deformations between output images. This embedding is computed with a wavelet Scattering transform. Numerical experiments demonstrate that the resulting Scattering generators have similar properties as GANs or VAEs, without learning a discriminative network or an encoder.


Generating Captions for Medical Images with a Deep Learning Multi-hypothesis Approach: ImageCLEF 2017 Caption Task


title: A copper ReRAM cell for Storage Class Memory applications

abstract: Hybrid memory systems that incorporate Storage Class Memory (SCM) as non-volatile cache or DRAM data backup are expected to bolster system efficiency and cost because SCM promises higher density than DRAM cache and higher speed than the storage I/F. This paper demonstrates a Cu-based resistive random access memory (ReRAM) cell that meets the SCM performance specifications for a 16Gb ReRAM with 200MB/s write and 1GB/s read [1].


title: A hybrid-based filtering approach for user authentication

abstract: Big data based user authentication is a new approach that leverages the power of Big Data analytics to develop a fertile field for the next generation user authentication. This new approach relies on “something you do”-based verification methods, where the users' dynamic behaviors are analyzed in order to generate real-time uniquely identifiable information about them. Once the unique user's identification is generated “authentication on demand” can be achieved through user challenging questions. In this paper, a new model is proposed to generate these users' identifiable information, where the main concepts of creating users' profiles in Recommendation Systems (RS) is used. RS are using the users' profiles to determine the user's preferences so that they can suggest a list of recommendations to other users with similar preferences. However, in the proposed model these profiles are employed to determine the user's personality traits that have a substantial influence on his/her identity verification. Based on these users' profiles the challenging questions will be issued only once to protect the users' responses from being compromised, and will be generated for the most recent user actions to help the legitimate user to easily remember and successfully complete the challenge.


title: Analysis of assets for virtual reality applications in neuropsychology

abstract: Virtual reality (VR) technology offers new opportunities for the development of innovative neuropsychological assessment and rehabilitation tools. VR-based testing and training scenarios that would be difficult, if not impossible, to deliver using conventional neuropsychological methods are now being developed that take advantage of the assets available with VR technology. If empirical studies continue to demonstrate effectiveness, virtual environment applications could provide new options for targeting cognitive and functional impairments due to traumatic brain injury, neurological disorders, and learning disabilities. This article focuses on specifying the assets that are available with VR for neuropsychological applications along with discussion of current VRbased research that serves to illustrate each asset. VR allows for the precise presentation and control of dynamic multi-sensory 3D stimulus environments, as well as providing advanced methods for recording behavioural responses. This serves as the basis for a diverse set of VR assets for neuropsychological approaches that are detailed in this article. We take the position that when combining these assets within the context of functionally relevant, ecologically valid virtual environments, fundamental advancements can emerge in how human cognition and functional behaviour is assessed and rehabilitated.


title: The Intel 80/spl times/86 processor architecture: pitfalls for secure systems

abstract: An in-depth analysis of the 80/spl times/86 processor families identifies architectural properties that may have unexpected, and undesirable, results in secure computer systems. In addition, reported implementation errors in some processor versions render them undesirable for secure systems because of potential security and reliability problems. We discuss the imbalance in scrutiny for hardware protection mechanisms relative to software, and why this imbalance is increasingly difficult to justify as hardware complexity increases. We illustrate this difficulty with examples of architectural subtleties and reported implementation errors. >


title: Reconfigurable Antennas: Design and Applications

abstract: The advancement in wireless communications requires the integration of multiple radios into a single platform to maximize connectivity. In this paper, the design process of reconfigurable antennas is discussed. Reconfigurable antennas are proposed to cover different wireless services that operate over a wide frequency range. They show significant promise in addressing new system requirements. They exhibit the ability to modify their geometries and behavior to adapt to changes in surrounding conditions. Reconfigurable antennas can deliver the same throughput as a multiantenna system. They use dynamically variable and adaptable single-antenna geometry without increasing the real estate required to accommodate multiple antennas. The optimization of reconfigurable antenna design and operation by removing unnecessary redundant switches to alleviate biasing issues and improve the system's performance is discussed. Controlling the antenna reconfiguration by software, using Field Programmable Gate Arrays (FPGAs) or microcontrollers is introduced herein. The use of Neural Networks and its integration with graph models on programmable platforms and its effect on the operation of reconfigurable antennas is presented. Finally, the applications of reconfigurable antennas for cognitive radio, Multiple Input Multiple Output (MIMO) channels, and space applications are highlighted.


title: Genetic diversity of the Pvk12 gene in Plasmodium vivax from the China-Myanmar border area

abstract: BackgroundPlasmodium falciparum resistance to artemisinin emerged in the Greater Mekong Sub-region has been associated with mutations in the propeller domain of the kelch gene Pfk13.MethodsHere the polymorphisms in Pvk12 gene, the orthologue of Pfk13 in Plasmodium vivax, were determined by PCR and sequencing in 262 clinical isolates collected in recent years (2012–2015) from the China-Myanmar border area.ResultsSequencing of full-length Pvk12 genes from these isolates identified three synonymous mutations (N172N, S360S, S697S) and one non-synonymous mutation M124I, all of which were at very low prevalence (2.0–3.1%). Moreover, these mutations were non-overlapping between the two study sites on both sides of the border. Molecular evolutionary analysis detected signature of purifying selection on Pvk12.ConclusionsThere is no direct evidence that Pvk12 is involved in artemisinin resistance in P. vivax, but it remains a potential candidate requiring further investigation. Continuous monitoring of potential drug resistance in this parasite is needed in order to facilitate the regional malaria elimination campaign.


title: Examining the critical success factors in the adoption of enterprise resource planning

abstract: This paper presents a literature review of the critical success factors (CSFs) in the implementation of enterprise resource planning (ERP) across 10 different countries/regions. The review covers journals, conference proceedings, doctoral dissertation, and textbooks from these 10 different countries/regions. Through a review of the literature, 18 CSFs were identified, with more than 80 sub-factors, for the successful implementation of ERP. The findings of our study reveal that 'appropriate business and IT legacy systems', 'business plan/vision/goals/justification', 'business process reengineering', 'change management culture and programme', 'communication', 'ERP teamwork and composition', 'monitoring and evaluation of performance', 'project champion', 'project management', 'software/system development, testing and troubleshooting', 'top management support', 'data management', 'ERP strategy and implementation methodology', 'ERP vendor', 'organizational characteristics', 'fit between ERP and business/process', 'national culture' and 'country-related functional requirement' were the commonly extracted factors across these 10 countries/regions. In these 18 CSFs, 'top management support' and 'training and education' were the most frequently cited as the critical factors to the successful implementation of ERP systems.