title: Training of goal-directed attention regulation enhances control over neural processing for individuals with brain injury

abstract: Deficits in attention and executive control are some of the most common, debilitating and persistent consequences of brain injuries. Understanding neural mechanisms that support clinically significant improvements, when they do occur, may help advance treatment development. Intervening via rehabilitation provides an opportunity to probe such mechanisms. Our objective was to identify neural mechanisms that underlie improvements in attention and executive control with rehabilitation training. We tested the hypothesis that intensive training enhances modulatory control of neural processing of perceptual information in patients with acquired brain injuries. Patients ( n = 12) participated either in standardized training designed to target goal-directed attention regulation, or a comparison condition (brief education). Training resulted in significant improvements on behavioural measures of attention and executive control. Functional magnetic resonance imaging methods adapted for testing the effects of intervention for patients with varied injury pathology were used to index modulatory control of neural processing. Pattern classification was utilized to decode individual functional magnetic resonance imaging data acquired during a visual selective attention task. Results showed that modulation of neural processing in extrastriate cortex was significantly enhanced by attention regulation training. Neural changes in prefrontal cortex, a candidate mediator for attention regulation, appeared to depend on individual baseline state. These behavioural and neural effects did not occur with the comparison condition. These results suggest that enhanced modulatory control over visual processing and a rebalancing of prefrontal functioning may underlie improvements in attention and executive control.


title: Evaluating the Predictive Validity of the Compas Risk and Needs Assessment System

abstract: This study examines the statistical validation of a recently developed, fourth-generation (4G) risk—need assessment system (Correctional Offender Management Profiling for Alternative Sanctions; COMPAS) that incorporates a range of theoretically relevant criminogenic factors and key factors emerging from meta-analytic studies of recidivism. COMPAS's automated scoring provides decision support for correctional agencies for placement decisions, offender management, and treatment planning. The article describes the basic features of COMPAS and then examines the predictive validity of the COMPAS risk scales by fitting Cox proportional hazards models to recidivism outcomes in a sample of presentence investigation and probation intake cases (N = 2,328). Results indicate that the predictive validities for the COMPAS recidivism risk model, as assessed by the area under the receiver operating characteristic curve (AUC), equal or exceed similar 4G instruments. The AUCs ranged from .66 to .80 for diverse offender sub...


The Stellar Consensus Protocol : A Federated Model for Internet-level Consensus


title: Rule-Based Named Entity Recognition For Greek Financial Texts

abstract: The identification and classification of proper names (named entity recognition) is considered an important task in the area of Information Retrieval and Extraction. A typical named entity recognition (NER) system mainly consists of a lexicon and a grammar. When moving to a new domain, these lexical resources should be customised, either manually or exploiting machine learning techniques. In this paper, we present a NER system based on hand crafted lexical resources. The system is part of a Greek information extraction system and was tested on a Greek corpus of financial news with satisfactory results.


title: Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

abstract: This study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector representations. We evaluate three machine learning models based on the speech, actions and predicatives of the main characters, and show that especially the lexical-semantic features significantly outperform the baselines. The most predictive features correspond to reported findings in personality psychology.


title: An efficiently computable metric for comparing polygonal shapes

abstract: A method for comparing polygons that is a metric, invariant under translation, rotation, and change of scale, reasonably easy to compute, and intuitive is presented. The method is based on the L/sub 2/ distance between the turning functions of the two polygons. It works for both convex and nonconvex polygons and runs in time O(mn log mn), where m is the number of vertices in one polygon and n is the number of vertices in the other. Some examples showing that the method produces answers that are intuitively reasonable are presented. >


title: Obfuscation resilient binary code reuse through trace-oriented programming

abstract: With the wide existence of binary code, it is desirable to reuse it in many security applications, such as malware analysis and software patching. While prior approaches have shown that binary code can be extracted and reused, they are often based on static analysis and face challenges when coping with obfuscated binaries. This paper introduces trace-oriented programming (TOP), a general framework for generating new software from existing binary code by elevating the low-level binary code to C code with templates and inlined assembly. Different from existing work, TOP gains benefits from dynamic analysis such as resilience against obfuscation and avoidance of points-to analysis. Thus, TOP can be used for malware analysis, especially for malware function analysis and identification. We have implemented a proof-of-concept of TOP and our evaluation results with a range of benign and malicious software indicate that TOP is able to reconstruct source code from binary execution traces in malware analysis and identification, and binary function transplanting.


title: Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images

abstract: BackgroundA reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods.ResultsA new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice.ConclusionsOur method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.


title: Skeleton based action recognition with convolutional neural network

abstract: Temporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network (CNN) performs well in representation learning. In this paper, we propose an end-to-end hierarchical architecture for skeleton based action recognition with CNN. Firstly, we represent a skeleton sequence as a matrix by concatenating the joint coordinates in each instant and arranging those vector representations in a chronological order. Then the matrix is quantified into an image and normalized to handle the variable-length problem. The final image is fed into a CNN model for feature extraction and recognition. For the specific structure of such images, the simple max-pooling plays an important role on spatial feature selection as well as temporal frequency adjustment, which can obtain more discriminative joint information for different actions and meanwhile address the variable-frequency problem. Experimental results demonstrate that our method achieves the state-of-art performance with high computational efficiency, especially surpassing the existing result by more than 15 percentage on the challenging ChaLearn gesture recognition dataset.


title: Generic Framework to Predict Repeat Behavior of Customers Using Their Transaction History

abstract: There exists a class of problems in e-commerce and retail businesses where the shopping behavior of customers is analyzed in order to predict their repeat behavior for products or retail stores. This analysis plays a crucial role in advertisement budgeting, product placement and relevant customer targeting. Researchers have addressed this problem by using standard predictive models, which use ad hoc features. We propose a metamodel that abstracts the different dimensions of data present in transactional datasets. These dimensions can be customer, product, offer, target, marketplace and transactions. Our framework also has abstract functions for comprehensive feature set generation, and includes different machine learning algorithms to learn prediction model. Our framework works end-to-end from feature engineering to reporting repeat probabilities of customers for products (or marketplace, brand, website or storechain). Moreover, the predicted repeat behavior of customers for different products along with their transactional history is used by our offer optimization model i-Prescribe to suggest products to be offered to customers with the goal of maximizing the return on investment of given marketing budget. We prove that our abstract features work on two different data-challenge datasets, by sharing experimental results.


title: Histological validation of carotid plaque characterization by in-vivo T2 mapping in patients with recent cerebrovascular events: preliminary results

abstract: Background Multicontrast CMR is the conventional method for invivo characterization of carotid plaques. However, its non-quantitative nature and the need for extensive postacquisition interpretation limit its clinical application. We have recently proposed using in-vivo quantitative T2 mapping for atherosclerotic plaque characterization [Biasiolli et al. JCMR 2013, 15:69]. T2 maps have the advantage of providing an absolute physical measure of plaque components that can be standardized among different MR systems and widely adopted in multi-centre studies. This pilot study investigates the agreement between T2 mapping and histology using AHA plaque type classification.


title: SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

abstract: Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet ). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen’s kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.


title: 160-GBd (320-Gb/s) PAM4 Transmission Using 97-GHz Bandwidth Analog Multiplexer

abstract: We demonstrate the transmission of a single-polarization four-level pulsed amplitude modulation (PAM4) optical signal over 10-km single-mode fiber at the gross bit rate of 320 Gb/s and the net data rate of 266.7 Gb/s. A digital-preprocessed analog-multiplexed digital-to-analog converter with a 97-GHz bandwidth analog multiplexer module enables us to generate the Nyquist-shaped PAM4 signal at the high baud rate of 160 Gbd.


title: Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

abstract: Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.


title: Movie Recommendation Using Metadata Based Word2Vec Algorithm

abstract: Nowadays, recommending preferable item among huge number of item is essential on online market. Many content platforms, such as YouTube and Amazon, use recommendation techniques to recommend items. Therefore, various techniques have been studied to recommend desirable item for each users. In this paper, we propose a method for effectively recommending preferable movies for each users by using community user's movie rating information and movie metadata information with deep learning technology. The proposed method shows 0.165 performance improvement based on Rcall©100 as compared with the basline method.


title: NestCloud: Towards practical nested virtualization

abstract: This paper describes a nested virtualization solution, which allows virtual machine monitor (VMM) with virtual machine to run within another virtual machine with low overhead. Previous nested virtualization solutions on x86 platform are mainly based on emulation, which result in poor performance and poor usability. We propose and implement NestCloud, a practical high performance nested virtualization architecture, which fully employs the hardware virtualization extensions. Furthermore, three optimizations are provided to reduce the overhead of nested guests: (1) Guest Page Fault Bypassing, which permits nested guests to handle page faults without VM Exit; (2) Virtual EPT (Extended Page Table), which eliminates unnecessary page faults introduced by shadow page table in nested VMM; (3) PV VMCS, which provides more effective VMCS accessing for nested VMM. Experimental results show that the performance of NestCloud guest is close to single level guest in both CPU-intensive and memory-intensive benchmarks. The CPU overhead is 5.22% and the memory overhead is 5.69%, which makes the nested guest of NestCloud comparable with a conventional one.


title: DroidScribe: Classifying Android Malware Based on Runtime Behavior

abstract: The Android ecosystem has witnessed a surge in malware, which not only puts mobile devices at risk but also increases the burden on malware analysts assessing and categorizing threats. In this paper, we show how to use machine learning to automatically classify Android malware samples into families with high accuracy, while observing only their runtime behavior. We focus exclusively on dynamic analysis of runtime behavior to provide a clean point of comparison that is dual to static approaches. Specific challenges in the use of dynamic analysis on Android are the limited information gained from tracking low-level events and the imperfect coverage when testing apps, e.g., due to inactive command and control servers. We observe that on Android, pure system calls do not carry enough semantic content for classification and instead rely on lightweight virtual machine introspection to also reconstruct Android-level inter-process communication. To address the sparsity of data resulting from low coverage, we introduce a novel classification method that fuses Support Vector Machines with Conformal Prediction to generate high-accuracy prediction sets where the information is insufficient to pinpoint a single family.


title: Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns

abstract: Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Bi- nary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.


title: Kernels and Regularization on Graphs

abstract: We introduce a family of kernels on graphs based on the notion of regularization operators. This generalizes in a natural way the notion of regularization and Greens functions, as commonly used for real valued functions, to graphs. It turns out that diffusion kernels can be found as a special case of our reasoning. We show that the class of positive, monotonically decreasing functions on the unit interval leads to kernels and corresponding regularization operators.


title: A Model of Proximity Control for Information-Presenting Robots

abstract: In this paper, we report a model that allows a robot to appropriately control its position as it presents information to a user. This capability is indispensable, since in the future, many robots will function in daily situations such as shopkeepers presenting products to customers or museum guides presenting information to visitors. Psychology research suggests that people adjust their positions to establish a joint view toward a target object. Similarly, when a robot presents an object, it should stand at an appropriate position that considers the positions of both the listener and the object to optimize the listener's field of view and establish a joint view. We observed human-human interaction situations, where people presented objects, and developed a model for an information-presenting robot to appropriately adjust its position. Our model consists of four constraints to establish O-space: 1) proximity to listener; 2) proximity to object; 3) listener's field of view; and 4) presenter's field of view. We also experimentally evaluate the effectiveness of our model.


title: Sentiment Analysis in MOOC Discussion Forums: What does it tell us?

abstract: Sentiment analysis is one of the great accomplishments of the last decade in the field of Language Technologies. In this paper, we explore mining collective sentiment from forum posts in a Massive Open Online Course (MOOC) in order to monitor students’ trending opinions towards the course and major course tools, such as lecture and peer-assessment. We observe a correlation between sentiment ratio measured based on daily forum posts and number of students who drop out each day. On a user-level, we evaluate the impact of sentiment on attrition over time. A qualitative analysis clarifies the subtle differences in how these language behaviors are used in practice across three MOOCs. Implications for research and practice are discussed.


title: How Cooperative Learning Works for Special Education and Remedial Students

abstract: This study reports new analyses from an earlier study by Antil, Jenkins, Wayne, & Vadasy (1998) in which 21 general education classroom teachers were interviewed about their use of cooperative learning. We report teachers' perceptions of how cooperative learning benefits special education and remedial students, the percentage of these students who consistently participated in classroom cooperative learning activities, its efficacy for these students, and the kind of modifications teachers made for students with special needs. Teachers were generally positive about cooperative learning's efficacy for students with learning problems, while acknowledging that it worked better for some students than others. Major benefits were improved self-esteem, a safe learning environment, and better classroom success rates and products. The primary modification for special and remedial education students was selecting suitable partners for them.


title: Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting

abstract: Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furthermore, this study proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for determining the parameters of MSVR (abbreviated as FA-MSVR). Three globally traded broad market indices are used to compare the performance of the proposed FA-MSVR method with selected counterparts. The quantitative and comprehensive assessments are performed on the basis of statistical criteria, economic criteria, and computational cost. In terms of statistical criteria, we compare the out-of-sample forecasting using goodness-of-forecast measures and testing approaches. In terms of economic criteria, we assess the relative forecast performance with a simple trading strategy. The results obtained in this study indicate that the proposed FA-MSVR method is a promising alternative for forecasting interval-valued financial time series.


title: A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3

abstract: To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v3 (You Only Look Once, v3) is constructed. The structural parameters of the YOLO v3 algorithm are modified to make it more suitable for lane detection. To improve the training efficiency, a method for automatic generation of the lane label images in a simple scenario, which provides label data for the training of the first-stage network, is proposed. Then, an adaptive edge detection algorithm based on the Canny operator is used to relocate the lane detected by the first-stage model. Furthermore, the unrecognized lanes are shielded to avoid interference in subsequent model training. Then, the images processed by the above method are used as label data for the training of the second-stage model. The experiment was carried out on the KITTI and Caltech datasets, and the results showed that the accuracy and speed of the second-stage model reached a high level.


title: Differential involvement of left prefrontal cortexin inductive and deductive reasoning

abstract: While inductive and deductive reasoning are considered distinct logical and psychological processes, little is known about their respective neural basis. To address this issue we scanned 16 subjects with fMRI, using an event-related design, while they engaged in inductive and deductive reasoning tasks. Both types of reasoning were characterized by activation of left lateral prefrontal and bilateral dorsal frontal, parietal, and occipital cortices. Neural responses unique to each type of reasoning determined from the Reasoning Type (deduction and induction) by Task (reasoning and baseline) interaction indicated greater involvement of left inferior frontal gyrus (BA 44) in deduction than induction, while left dorsolateral (BA 8/9) prefrontal gyrus showed greater activity during induction than deduction. This pattern suggests a dissociation within prefrontal cortex for deductive and inductive reasoning.


Introduction: Digital games and learning


title: Text-based geolocation prediction of social media users with neural networks

abstract: Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many other tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural networks to the problem of geolocation prediction and experiment with multiple techniques to improve neural networks for geolocation inference based solely on text. Experimental results on three Twitter datasets suggest that choosing appropriate network architecture, activation function, and performing Batch Normalization, can all increase performance on this task.


title: Integrating constraints and metric learning in semi-supervised clustering

abstract: Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.


title: A low-offset dynamic comparator with area-efficient and low-power offset cancellation

abstract: A low-offset two-stage dynamic comparator has been proposed for parallel multi-channel processing. Low offset is achieved from two aspects: 1st-stage offset cancellation and 2nd-stage offset suppression. A fully dynamic offset cancellation scheme based on current auto-zeroing is adopted to effectively cancel out the 1st-stage offset. It features small area overhead and low energy consumption. For the 2nd-stage offset suppression, a high gain is designed for the 1st-stage dynamic amplifier by optimizing the overdrive voltage of input transistors. To maintain low offset performance across a wide range of input commonmode voltages, the overdrive voltage of the input pair is required to stay low. Therefore, a tail current source is employed for the 1st stage to ensure constant common-mode discharging current. As a result, the overdrive voltage can be stably kept low under various operation conditions. The proposed comparator has been designed in a standard CMOS 0.18 μm process. It operates under a supply voltage of 1.2 V at 10 MHz. Simulation results have verified the low-offset property of the comparator. The input-referred offset (1 σ) is reduced from 19.25 mV to 1.296 mV after cancellation and it remains constant with the input commonmode voltage changing from 0 V to 0.8 V. The offset is further reduced to 771 μV when the 2nd-stage input pair are enlarged by 4 times. At the same time, the energy consumption is increased from 147 fJ/Conv to 168 fJ/Conv.


title: Generative Local Metric Learning for Nearest Neighbor Classification

abstract: We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning to dimensionality reduction from a novel perspective, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models such as a Gaussian.


title: Neural Architectures for Open-Type Relation Argument Extraction

abstract: In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q authored notable work with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e.g. X: the title of a book or a work of art) from the corpus. A distantly supervised dataset based on WikiData relations is obtained and released to address the task. ::: We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger gives the best results.


title: A Wearable Virtual Usher for Vision-Based Cognitive Indoor Navigation

abstract: Inspired by progresses in cognitive science, artificial intelligence, computer vision, and mobile computing technologies, we propose and implement a wearable virtual usher for cognitive indoor navigation based on egocentric visual perception. A novel computational framework of cognitive wayfinding in an indoor environment is proposed, which contains a context model, a route model, and a process model. A hierarchical structure is proposed to represent the cognitive context knowledge of indoor scenes. Given a start position and a destination, a Bayesian network model is proposed to represent the navigation route derived from the context model. A novel dynamic Bayesian network (DBN) model is proposed to accommodate the dynamic process of navigation based on real-time first-person-view visual input, which involves multiple asynchronous temporal dependencies. To adapt to large variations in travel time through trip segments, we propose an online adaptation algorithm for the DBN model, leading to a self-adaptive DBN. A prototype system is built and tested for technical performance and user experience. The quantitative evaluation shows that our method achieves over 13% improvement in accuracy as compared to baseline approaches based on hidden Markov model. In the user study, our system guides the participants to their destinations, emulating a human usher in multiple aspects.


Chief clinical information officers: clinical leadership for a digital age.


title: Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

abstract: In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.


title: Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network

abstract: We propose a new deep learning based approach for camera relocalization. Our approach localizes a given query image by using a convolutional neural network (CNN) for first retrieving similar database images and then predicting the relative pose between the query and the database images, whose poses are known. The camera location for the query image is obtained via triangulation from two relative translation estimates using a RANSAC based approach. Each relative pose estimate provides a hypothesis for the camera orientation and they are fused in a second RANSAC scheme. The neural network is trained for relative pose estimation in an end-to-end manner using training image pairs. In contrast to previous work, our approach does not require scene-specific training of the network, which improves scalability, and it can also be applied to scenes which are not available during the training of the network. As another main contribution, we release a challenging indoor localisation dataset covering 5 different scenes registered to a common coordinate frame. We evaluate our approach using both our own dataset and the standard 7 Scenes benchmark. The results show that the proposed approach generalizes well to previously unseen scenes and compares favourably to other recent CNN-based methods.


title: Design of a novel dual band patch antenna loaded with CELC resonator for WLAN/WiMAX applications

abstract: A Complementary Electric Field Coupled (CELC) resonator, loaded in the ground plane of a conventional patch antenna to achieve dual band operation is presented here. Two antenna designs have been presented. In the first design, one CELC is etched out from the ground plane of patch antenna which leads to generation of a lower order mode in addition to the conventional patch mode. In order to achieve better impedance bandwidth for the lower order resonant mode two CELC resonators with closely spaced resonant frequencies are etched out from the ground plane of patch antenna in the second design. The proposed antennas can be used in WLAN (Wireless Local Area Network) and WiMAX (Worldwide Interoperability for Microwave Access) applications.


title: 3D Human Action Recognition Using a Single Depth Feature and Locality-Constrained Affine Subspace Coding

abstract: This paper addresses the problem of recognizing human actions from depth videos. We propose a depth-based local descriptor and affine subspace coding representation with locality-constrained affine subspace coding (LASC) for 3D action recognition. First, each depth video sequence is divided into a set of subsequences (i.e., multi-scale sub-actions) based on the normalized motion energy vector. Next, depth motion map-based gradient local auto-correlation features are employed to capture the shape information and motion cues of each sub-action. In order to obtain discriminative and compact representation, we extract the local high-order information of the depth video using LASC. Through experiments, we show that the use of LASC exhibits better performance compared with existing methods such as locality-constrained linear coding. We compared LASC with the state-of-the-art methods based on similar principle, using features extracted from a single modality, on four datasets, and with those using multiple features or nonlinear recognition machines. The results on four datasets clearly show the effectiveness of the proposed method.


title: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest

abstract: In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watersheds in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term power watersheds. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watersheds to optimize more general models of use in application beyond image segmentation.


title: Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features

abstract: Indexing and classification tools for Content Based Visual Information Retrieval (CBVIR) have been penetrating the universe of medical image analysis. They have been recently investigated for Alzheimer's disease (AD) diagnosis. This is a normal "knowledge diffusion" process, when methodologies developed for multimedia mining penetrate a new application area. The latter brings its own specificities requiring an adjustment of methodologies on the basis of domain knowledge. In this paper, we develop an automatic classification framework for AD recognition in structural Magnetic Resonance Images (MRI). The main contribution of this work consists in considering visual features from the most involved region in AD (hippocampal area) and in using a late fusion to increase precision results. Our approach has been first evaluated on the baseline MR images of 218 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and then tested on a 3T weighted contrast MRI obtained from a subsample of a large French epidemiological study: "Bordeaux dataset". The experimental results show that our classification of patients with AD versus NC (Normal Control) subjects achieves the accuracies of 87 % and 85 % for ADNI subset and "Bordeaux dataset" respectively. For the most challenging group of subjects with the Mild Cognitive Impairment (MCI), we reach accuracies of 78.22 % and 72.23 % for MCI versus NC and MCI versus AD respectively on ADNI. The late fusion scheme improves classification results by 9 % in average for these three categories. Results demonstrate very promising classification performance and simplicity compared to the state-of-the-art volumetric AD diagnosis methods.


title: Smart Wearable Hand Device for Sign Language Interpretation System With Sensors Fusion

abstract: Gesturing is an instinctive way of communicating to present a specific meaning or intent. Therefore, research into sign language interpretation using gestures has been explored progressively during recent decades to serve as an auxiliary tool for deaf and mute people to blend into society without barriers. In this paper, a smart sign language interpretation system using a wearable hand device is proposed to meet this purpose. This wearable system utilizes five flex-sensors, two pressure sensors, and a three-axis inertial motion sensor to distinguish the characters in the American sign language alphabet. The entire system mainly consists of three modules: 1) a wearable device with a sensor module; 2) a processing module; and 3) a display unit mobile application module. Sensor data are collected and analyzed using a built-in embedded support vector machine classifier. Subsequently, the recognized alphabet is further transmitted to a mobile device through Bluetooth low energy wireless communication. An Android-based mobile application was developed with a text-to-speech function that converts the received textinto audible voice output. Experiment results indicate that a true sign language recognition accuracy rate of 65.7% can be achieved on average in the first version without pressure sensors. A second version of the proposed wearable system with the fusion of pressure sensors on the middle finger increased the recognition accuracy rate dramatically to 98.2%. The proposed wearable system outperforms the existing method, for instance, although background lights, and other factors are crucial to a vision-based processing method, they are not for the proposed system.


title: A Novel Transformerless Interleaved High Step-Down Conversion Ratio DC–DC Converter With Low Switch Voltage Stress

abstract: In this paper, a novel transformerless interleaved high step-down conversion ratio dc-dc converter with low switch voltage stress is proposed. In the proposed converter, two input capacitors are series-charged by the input voltage and parallel-discharged by a new two-phase interleaved buck converter for providing a much higher step-down conversion ratio without adopting an extreme short duty cycle. Based on the capacitive voltage division, the main objectives of the new voltage-divider circuit in the converter are for both storing energy in the blocking capacitors for increasing the step-down conversion ratio and reducing voltage stresses of active switches. As a result, the proposed converter topology possesses the low switch voltage stress characteristic. This will allow one to choose lower voltage rating MOSFETs to reduce both switching and conduction losses, and the overall efficiency is consequently improved. Moreover, due to the charge balance of the blocking capacitor, the converter features automatic uniform current sharing characteristic of the interleaved phases without adding extra circuitry or complex control methods. The operation principles and relevant analysis of the proposed converter are presented in this paper. Finally, a 400-V input voltage, 25-V output voltage, and 400-W output power prototype circuit is implemented in the laboratory to verify the performance.


title: Online Search and Buying Behaviour in Consumer Markets

abstract: Online search behaviour is analysed using a novel methodology based on an international panel of two million users. Consumer search is measured by the size and distribution of online consideration sets and the use of price comparison engines in a range of US and UK consumer markets. It is shown that most online researchers who are considering competing suppliers only view two or three competitor websites, which results in an average online consideration set of between 2.1 and 2.8, regardless of the use of price comparison websites. Consumer perceived risk is negatively correlated with the size of online consideration sets and online price competition intensity. Using international data from fifteen countries it is shown that online research and online purchasing are negatively correlated with shop density. The implications for managers are outlined, in particular the importance of branding and advertising to improve the likelihood of inclusion in online consideration sets.


title: Aerodynamic Loads on Tall Buildings: Interactive Database

abstract: Under the action of wind, tall buildings oscillate simultaneously in the alongwind, acrosswind, and torsional directions. While the alongwind loads have been successfully treated using quasi-steady and strip theories in terms of gust loading factors, the acrosswind and torsional loads cannot be treated in this manner, since these loads cannot be related in a straightforward manner to the fluctuations in the approach flow. Accordingly, most current codes and standards provide little guidance for the acrosswind and torsional response. To fill this gap, a preliminary, interactive database of aerodynamic loads is presented, which can be accessed by any user with Microsoft Explorer at the URL address http://www.nd.edu/;nathaz/. The database is comprised of high-frequency base balance measurements on a host of isolated tall building models. Combined with the analysis procedure provided, the nondimensional aerodynamic loads can be used to compute the wind-induced response of tall buildings. The influence of key parameters, such as the side ratio, aspect ratio, and turbulence characteristics for rectangular sections, is also discussed. The database and analysis procedure are viable candidates for possible inclusion as a design guide in the next generation of codes and standards.


title: The diamond dipole: a Gaussian impulse antenna

abstract: When a typical narrowband antenna is excited by an ultra-wideband (UWB) impulse, the antenna rings at its resonant frequency, stretching out the waveform in time. A resonant antenna, like the bow tie, discone, or bicone have been available for years. A bow tie has a relatively high input impedance, and requires a matching balun to make it useable with a 50 /spl Omega/ system. A discone or a bicone antenna is more suitable for use with UWB impulses, but these are 3-D structures that are difficult to manufacture. The "diamond dipole" design arose from a long search for an antenna that combines an ultra-wideband, non-time dispersive response with a matched input impedance: all qualities essential for a successful ultra-wide band impulse radio system. A diamond dipole is an inverted bow tie with isosceles triangular elements whose height and base are scaled to be 1/4 /spl lambda/ at the center frequency of interest.


A Control Scheme for an AC-DC Single-Stage Buck-Boost PFC Converter with Improved Output Ripple Reduction


title: Front-end receiver electronics for high-frequency monolithic CMUT-on-CMOS imaging arrays

abstract: This paper describes the design of CMOS receiver electronics for monolithic integration with capacitive micromachined ultrasonic transducer (CMUT) arrays for highfrequency intravascular ultrasound imaging. A custom 8-inch (20-cm) wafer is fabricated in a 0.35-μm two-poly, four-metal CMOS process and then CMUT arrays are built on top of the application specific integrated circuits (ASICs) on the wafer. We discuss advantages of the single-chip CMUT-on-CMOS approach in terms of receive sensitivity and SNR. Low-noise and high-gain design of a transimpedance amplifier (TIA) optimized for a forward-looking volumetric-imaging CMUT array element is discussed as a challenging design example. Amplifier gain, bandwidth, dynamic range, and power consumption trade-offs are discussed in detail. With minimized parasitics provided by the CMUT-on-CMOS approach, the optimized TIA design achieves a 90 fA/√Hz input-referred current noise, which is less than the thermal-mechanical noise of the CMUT element. We show successful system operation with a pulseecho measurement. Transducer-noise-dominated detection in immersion is also demonstrated through output noise spectrum measurement of the integrated system at different CMUT bias voltages. A noise figure of 1.8 dB is obtained in the designed CMUT bandwidth of 10 to 20 MHz.


Human Activity Recognition Using Deep Recurrent Neural Networks and Complexity-based Motion Features


title: Integrating the Front End

abstract: Fifth-generation (5G) mobile communications will need to accommodate huge traffic demands in the near future. Massive multipleinput/multiple-output (MIMO) technology utilizing hundreds of antenna elements has drawn attention as a key antenna configuration for envisioned 5G applications. Realizing the massive MIMO concept of active phased-array antennas (APAAs) for 5G will require small-size, low-power-consumption, and highly accurate phase control over the wide-band frequency range, which poses significant challenges for the RF front end. This article describes prototyped highly integrated RF front ends for high super-high-frequency (SHF) wide-band massive MIMO in 5G.


title: Hiding Transaction Amounts and Balances in Bitcoin

abstract: Bitcoin is gaining increasing adoption and popularity nowadays. In spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place in the system are publicly announced. ::: ::: The literature contains a number of proposals that aim at evaluating and enhancing user privacy in Bitcoin. To the best of our knowledge, ZeroCoin ZC is the first proposal which prevents the public tracing of coin expenditure in Bitcoin by leveraging zero-knowledge proofs of knowledge and one-way accumulators. While ZeroCoin hardens the traceability of coins, it does not hide the amount per transaction, nor does it prevent the leakage of the balances of Bitcoin addresses. In this paper, we propose, EZC, an extension of ZeroCoin which i enables the construction of multi-valued ZCs whose values are only known to the sender and recipient of the transaction and ii supports the expenditure of ZCs among users in the Bitcoin system, without the need to convert them back to Bitcoins. By doing so, EZC hides transaction values and address balances in Bitcoin, for those users who opt-out from exchanging their coins to BTCs. We performed a preliminary assessment of the performance of EZC; our findings suggest that EZC improves the communication overhead incurred in ZeroCoin.


title: Minimum Time vs Maximum Exit Velocity Path Optimization During Cornering

abstract: Numerical optimization has been used as an extension of vehicle dynamics simulation in order to repro- duce trajectories and driving techniques used by expert race drivers and investigate the effects of several vehicle param- eters in the stability limit operation of the vehicle. In this work we investigate how different race-driving techniques may be reproduced by considering different optimization cost functions. We introduce a bicycle model with suspension dynamics and study the role of the longitudinal load transfer in limit vehicle operation, i.e., when the tires operate at the adhesion limit. Finally we demonstrate that for certain vehicle configurations the optimal trajectory may include large slip angles (drifting), which matches the techniques used by rally-race drivers. I. INTRODUCTION


title: Document Forgery Detection with SVM Classifier and Image Quality Measures

abstract: This paper presents a detection scheme for a fraudulent document made by printers. The fraud document is indistinguishable by the naked eye from a genuine document because of the technological advances in printing methods. Even though we cannot find any visual evidence of forgery, the fraud document includes inherent device features. We propose a method to uncover these features. 17 image quality measures are applied to discriminate between genuine and fake documents. The results of each measure are used as training and testing parameters of SVM classifier to determine fake documents. Preliminary experimental results are presented based on the fraud gift voucher made by several color printers.


title: Comparison of SQL injection detection and prevention tools based on attack type and deployment requirements

abstract: SQL injection is a type of attack which the attacker adds Structured Query Language code to a web form input box to gain access or make changes to data. SQL injection vulnerability allows an attacker to flow commands directly to a web application's underlying database and destroy functionality or confidentiality. Researchers have proposed different tools to detect and prevent this vulnerability. In this paper we present all SQL injection attack types and also different tools which can detect or prevent these attacks. Finally we compared these tools against attack types and deployment requirements. Keyword: SQL Injection Attacks, detection, prevention, tool, comparison.


title: An architectural framework for roadmapping: Towards visual strategy

abstract: Abstract Since the first application of technology roadmapping in the late 1970s to support integrated product-technology planning, roadmapping concepts and techniques have been widely adopted at product, technology, company, sector and policy levels. The roadmapping approach is flexible and scalable, and can be customized to suit many different strategic and innovation contexts. However, this demands careful planning and design, including consideration of roadmap structure, process and participation. This paper explores the issues of how to design and architect roadmaps and roadmapping processes, which is crucial if the approach is to provide a framework for supporting effective dialogue and communication within and between organizations. The structure of the roadmap, and the process for developing and maintaining the roadmap, should be designed to serve the purpose for which the activity is intended to satisfy, providing a ‘common language and structure' for both development and deployment of strategy.


title: Learning to Extract Motion from Videos in Convolutional Neural Networks

abstract: This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN). The proposed model constitutes a potential building block for deeper architectures to allow using motion without resorting to an external algorithm, e.g. for recognition in videos. We derive our network architecture from signal processing principles to provide desired invariances to image contrast, phase and texture. We constrain weights within the network to enforce strict rotation invariance and substantially reduce the number of parameters to learn. We demonstrate end-to-end training on only 8 sequences of the Middlebury dataset, orders of magnitude less than competing CNN-based motion estimation methods, and obtain comparable performance to classical methods on the Middlebury benchmark. Importantly, our method outputs a distributed representation of motion that allows representing multiple, transparent motions, and dynamic textures. Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation.


title: Association between substandard classroom ventilation rates and students' academic achievement.

abstract: UNLABELLED ::: This study focuses on the relationship between classroom ventilation rates and academic achievement. One hundred elementary schools of two school districts in the southwest United States were included in the study. Ventilation rates were estimated from fifth-grade classrooms (one per school) using CO(2) concentrations measured during occupied school days. In addition, standardized test scores and background data related to students in the classrooms studied were obtained from the districts. Of 100 classrooms, 87 had ventilation rates below recommended guidelines based on ASHRAE Standard 62 as of 2004. There is a linear association between classroom ventilation rates and students' academic achievement within the range of 0.9-7.1 l/s per person. For every unit (1 l/s per person) increase in the ventilation rate within that range, the proportion of students passing standardized test (i.e., scoring satisfactory or above) is expected to increase by 2.9% (95%CI 0.9-4.8%) for math and 2.7% (0.5-4.9%) for reading. The linear relationship observed may level off or change direction with higher ventilation rates, but given the limited number of observations, we were unable to test this hypothesis. A larger sample size is needed for estimating the effect of classroom ventilation rates higher than 7.1 l/s per person on academic achievement. ::: ::: ::: PRACTICAL IMPLICATIONS ::: The results of this study suggest that increasing the ventilation rates toward recommended guideline ventilation rates in classrooms should translate into improved academic achievement of students. More studies are needed to fully understand the relationships between ventilation rate, other indoor environmental quality parameters, and their effects on students' health and achievement. Achieving the recommended guidelines and pursuing better understanding of the underlying relationships would ultimately support both sustainable and productive school environments for students and personnel.


title: Embedding Text in Hyperbolic Spaces

abstract: Natural language text exhibits hierarchical structure in a variety of respects. Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data. Recent work by Nickel & Kiela (2017) proposed using hyperbolic instead of Euclidean embedding spaces to represent hierarchical data and demonstrated encouraging results when embedding graphs. In this work, we extend their method with a re-parameterization technique that allows us to learn hyperbolic embeddings of arbitrarily parameterized objects. We apply this framework to learn word and sentence embeddings in hyperbolic space in an unsupervised manner from text corpora. The resulting embeddings seem to encode certain intuitive notions of hierarchy, such as word-context frequency and phrase constituency. However, the implicit continuous hierarchy in the learned hyperbolic space makes interrogating the model's learned hierarchies more difficult than for models that learn explicit edges between items. The learned hyperbolic embeddings show improvements over Euclidean embeddings in some -- but not all -- downstream tasks, suggesting that hierarchical organization is more useful for some tasks than others.


title: Prosopagnosia: A reclassification

abstract: We report a quantitative investigation of three patients who presented with a profound prosopagnosia. They were tested on a series of tests of face perception, face recognition, and paired associate learning using faces. A patient with marked perceptual difficulties but no difficulty whatever in recognizing faces was tested as a control. The control patient's performance was as bad or worse on the tests of perception of faces, and it is concluded that impaired performance on tests of face perception does not contribute to difficulties in recognizing familiar faces. The major difference between the three prosopagnosic patients was the evidence of covert recognition in two of these cases but not in the third. We use this evidence to argue that the perceptual/mnestic distinction is an inappropriate classification and would suggest instead that the two types may be described in terms either of a disconnection of the face recognition units or of damage to the units themselves.


Why Machine Learning Works : A Search ( for Simplicity )


title: Lattice Cryptography for the Internet

abstract: In recent years, lattice-based cryptography has been recognized for its many attractive properties, such as strong provable security guarantees and apparent resistance to quantum attacks, flexibility for realizing powerful tools like fully homomorphic encryption, and high asymptotic efficiency. Indeed, several works have demonstrated that for basic tasks like encryption and authentication, lattice-based primitives can have performance competitive with (or even surpassing) those based on classical mechanisms like RSA or Diffie-Hellman. However, there still has been relatively little work on developing lattice cryptography for deployment in real-world cryptosystems and protocols.


title: Keloid and Hypertrophic Scars Are the Result of Chronic Inflammation in the Reticular Dermis

abstract: Keloids and hypertrophic scars are caused by cutaneous injury and irritation, including trauma, insect bite, burn, surgery, vaccination, skin piercing, acne, folliculitis, chicken pox, and herpes zoster infection. Notably, superficial injuries that do not reach the reticular dermis never cause keloidal and hypertrophic scarring. This suggests that these pathological scars are due to injury to this skin layer and the subsequent aberrant wound healing therein. The latter is characterized by continuous and histologically localized inflammation. As a result, the reticular layer of keloids and hypertrophic scars contains inflammatory cells, increased numbers of fibroblasts, newly formed blood vessels, and collagen deposits. Moreover, proinflammatory factors, such as interleukin (IL)-1α, IL-1β, IL-6, and tumor necrosis factor-α are upregulated in keloid tissues, which suggests that, in patients with keloids, proinflammatory genes in the skin are sensitive to trauma. This may promote chronic inflammation, which in turn may cause the invasive growth of keloids. In addition, the upregulation of proinflammatory factors in pathological scars suggests that, rather than being skin tumors, keloids and hypertrophic scars are inflammatory disorders of skin, specifically inflammatory disorders of the reticular dermis. Various external and internal post-wounding stimuli may promote reticular inflammation. The nature of these stimuli most likely shapes the characteristics, quantity, and course of keloids and hypertrophic scars. Specifically, it is likely that the intensity, frequency, and duration of these stimuli determine how quickly the scars appear, the direction and speed of growth, and the intensity of symptoms. These proinflammatory stimuli include a variety of local, systemic, and genetic factors. These observations together suggest that the clinical differences between keloids and hypertrophic scars merely reflect differences in the intensity, frequency, and duration of the inflammation of the reticular dermis. At present, physicians cannot (or at least find it very difficult to) control systemic and genetic risk factors of keloids and hypertrophic scars. However, they can use a number of treatment modalities that all, interestingly, act by reducing inflammation. They include corticosteroid injection/tape/ointment, radiotherapy, cryotherapy, compression therapy, stabilization therapy, 5-fluorouracil (5-FU) therapy, and surgical methods that reduce skin tension.


title: U-shaped slot-array antenna for RFID shelf in the UHF

abstract: This paper presents a linearly polarized slot-array antenna that consists of slots series-fed by a microstrip line for RFID shelf applications in the UHF band in which the end of the line is shorted to the ground plane by the via-hole. The radiating element of the antenna is the U-shaped slot fed by the travelling and standing current wave formed on the microstrip line left shorted-end. The gain of the designed 3×6 slot-array antenna is about 4dBi and the fractional return-loss bandwidth of less than −10dB about 5.5% at 920MHz.


title: Reduction of Computational Efforts in Finite Element-Based Permanent Magnet Traction Motor Optimization

abstract: This paper presents a method for reducing computational time in constrained single objective optimization problems related to permanent magnet motors modeled using computationally intensive finite element method. The method is based on differential evolution algorithm. The principal approach is to interrupt the evaluation of inequality constraints after encountering the constraint which is violated and continue their evaluation only if better solutions are obtained in comparison with the candidate vector from the previous generation both in terms of inequality constraints and the cost function. This approach avoids unnecessary time consuming finite element calculations required for solving the inequality constraints and saves the overall optimization time without affecting the convergence rate. The main features of this approach and how it complements the existing method for handling inequality constraints in Differential Evolution algorithm is demonstrated on design optimization of an interior permanent magnet motor for the low-floor tram TMK 2200. The motor geometry is optimized for maximum torque density.


title: Orchid diversity: an evolutionary consequence of deception?

abstract: The Orchidaceae are one of the most species-rich plant families and their floral diversity and pollination biology have long intrigued evolutionary biologists. About one-third of the estimated 18 500 species are thought to be pollinated by deceit. To date, the focus has been on how such pollination evolved, how the different types of deception work, and how it is maintained, but little progress has been made in understanding its evolutionary consequences. To address this issue, we discuss here how deception affects orchid mating systems, the evolution of reproductive isolation, speciation processes and neutral genetic divergence among species. We argue that pollination by deceit is one of the keys to orchid floral and species diversity. A better understanding of its evolutionary consequences could help evolutionary biologists to unravel the reasons for the evolutionary success of orchids.


title: Dependency Parsing by Belief Propagation

abstract: We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and empirically efficient. Even with second-order features or latent variables, which would make exact parsing considerably slower or NP-hard, BP needs only O(n3) time with a small constant factor. Furthermore, such features significantly improve parse accuracy over exact first-order methods. Incorporating additional features would increase the runtime additively rather than multiplicatively.


title: Piperine‐pro‐nanolipospheres as a novel oral delivery system of cannabinoids: Pharmacokinetic evaluation in healthy volunteers in comparison to buccal spray administration

abstract: Nowadays, therapeutic indications for cannabinoids, specifically Δ9-tetrahydrocannabinol (THC) and Cannabidiol (CBD) are widening. However, the oral consumption of the molecules is very limited due to their highly lipophilic nature that leads to poor solubility at the aqueous environment. Additionally, THC and CBD are prone to extensive first pass mechanisms. These absorption obstacles render the molecules with low and variable oral bioavailability. To overcome these limitations we designed and developed the advanced pro-nanolipospheres (PNL) formulation. The PNL delivery system is comprised of a medium chain triglyceride, surfactants, a co-solvent and the unique addition of a natural absorption enhancer: piperine. Piperine was selected due to its distinctive inhibitory properties affecting both Phase I and Phase II metabolism. This constellation self emulsifies into nano particles that entrap the cannabinoids and the piperine in their core and thus improve their solubility while piperine and the other PNL excipients inhibit their intestinal metabolism. Another clear advantage of the formulation is that its composition of materials is approved for human consumption. The safe nature of the excipients enabled their direct evaluation in humans. In order to evaluate the pharmacokinetic profile of the THC-CBD-piperine-PNL formulation, a two-way crossover, single administration clinical study was conducted. The trial comprised of 9 healthy volunteers under fasted conditions. Each subject received a THC-CBD (10.8mg, 10mg respectively) piperine (20mg)-PNL filled capsule and an equivalent dose of the oromucosal spray Sativex® with a washout period in between treatments. Single oral administration of the piperine-PNL formulation resulted in a 3-fold increase in Cmax and a 1.5-fold increase in AUC for THC when compared to Sativex®. For CBD, a 4-fold increase in Cmax and a 2.2-fold increase in AUC was observed. These findings demonstrate the potential this formulation has in serving as a standardized oral cannabinoid formulation. Moreover, the concept of improving oral bioavailability described here, can pave the way for other potential lipophilic active compounds requiring enhancement of their oral bioavailability.


title: Sentential Structure And Discourse Parsing

abstract: In this paper, we describe how the LIDAS System (Linguistic Discourse Analysis System), a discourse parser built as an implementation of the Unified Linguistic Discourse Model (U-LDM) uses information from sentential syntax and semantics along with lexical semantic information to build the Open Right Discourse Parse Tree (DPT) that serves as a representation of the structure of the discourse (Polanyi et al., 2004; Thione 2004a, b). More specifically, we discuss how discourse segmentation, sentence-level discourse parsing, and text-level discourse parsing depend on the relationship between sentential syntax and discourse. Specific discourse rules that use syntactic information are used to identify possible attachment points and attachment relations for each Basic Discourse Unit to the DPT.


title: Learning scrum by doing real-life projects

abstract: Being Scrum the agile software development framework most commonly used in the software industry, its applicability is attracting great attention to the academia. That is why this topic is quite often included in computer science and related university programs. In this article, we present a course design of a Software Engineering course where an educational framework and an open-source agile project management tool were used to develop real-life projects by undergraduate students. During the course, continuous guidance was given by the teaching staff to facilitate the students' learning of Scrum. Results indicate that students find it easy to use the open-source tool and helpful to apply Scrum to a real-life project. However, the unavailability of the client and conflicts among the team members have negative impact on the realization of projects. The guidance given to students along the course helped identify five common issues faced by students through the learning process.


title: Computational cost of nonrigid registration algorithms based on fluid dynamics [MRI time series application]

abstract: Though fluid dynamics offer a good approach to nonrigid registration and give accurate results, even with large-scale deformations, its application is still very time consuming. We introduce and discuss different approaches to solve the core problem of nonrigid registration, the partial differential equation of fluid dynamics. We focus on the solvers, their computational costs and the accuracy of registration. Numerical experiments show that relaxation is currently the best approach, especially when reducing the cost/iteration by focusing the updates on deformation spots.


title: A UML-Based Approach to System Testing

abstract: System testing is concerned with testing an entire system based on its specifications. In the context of object-oriented, UML development, this means that system test requirements are derived from UML analysis artifacts such as use cases, their corresponding sequence and collaboration diagrams, class diagrams, and possibly Object Constraint Language (OCL) expressions across all these artifacts. Our goal here is to support the derivation of functional system test requirements, which will be transformed into test cases, test oracles, and test drivers once we have detailed design information. In this paper, we describe a methodology in a practical way and illustrate it with an example. In this context, we address testability and automation issues, as the ultimate goal is to fully support system testing activities with high-capability tools.


title: Covariance Tracking using Model Update Based on Lie Algebra

abstract: We propose a simple and elegant algorithm to track nonrigid objects using a covariance based object description and a Lie algebra based update mechanism. We represent an object window as the covariance matrix of features, therefore we manage to capture the spatial and statistical properties as well as their correlation within the same representation. The covariance matrix enables efficient fusion of different types of features and modalities, and its dimensionality is small. We incorporated a model update algorithm using the Lie group structure of the positive definite matrices. The update mechanism effectively adapts to the undergoing object deformations and appearance changes. The covariance tracking method does not make any assumption on the measurement noise and the motion of the tracked objects, and provides the global optimal solution. We show that it is capable of accurately detecting the nonrigid, moving objects in non-stationary camera sequences while achieving a promising detection rate of 97.4 percent.


title: Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs

abstract: State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifolds, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.


title: Causal Theories: A Categorical Perspective on Bayesian Networks

abstract: In this dissertation we develop a new formal graphical framework for causal reasoning. Starting with a review of monoidal categories and their associated graphical languages, we then revisit probability theory from a categorical perspective and introduce Bayesian networks, an existing structure for describing causal relationships. Motivated by these, we propose a new algebraic structure, which we term a causal theory. These take the form of a symmetric monoidal category, with the objects representing variables and morphisms ways of deducing information about one variable from another. A major advantage of reasoning with these structures is that the resulting graphical representations of morphisms match well with intuitions for flows of information between these variables. These categories can then be modelled in other categories, providing concrete interpretations for the variables and morphisms. In particular, we shall see that models in the category of measurable spaces and stochastic maps provide a slight generalisation of Bayesian networks, and naturally form a category themselves. We conclude with a discussion of this category, classifying the morphisms and discussing some basic universal constructions. ::: ERRATA: (i) Pages 41-42: Objects of a causal theory are words, not collections, in $V$, and we include swaps as generating morphisms, subject to the identities defining a symmetric monoidal category. (ii) Page 46: A causal model is a strong symmetric monoidal functor.


title: Attacker behavior analysis in multi-stage attack detection system

abstract: Today’s internet world is facing attacks from different types of attackers who launch is multistage attack. Besides discovering, visualizing, and predicting multi-stage attacks, a method to understand and profile behaviors of attackers is important to protect network security. We use the Hidden Markov Model (HMM) to analyze and predict the attacker behavior based on what was learned from observed alerts and intrusions. We use data mining to process alerts to generate input for the HMM to calculate the required probability distribution. Our system is able to stream real-time Snort alerts and predict intrusions based on our learned rules. Our system is able to automatically discover patterns in multistage attack, classify attackers based on their behavior pattern. By doing this, our system can effectively predict behavior and attackers and assess danger level of different groups of attackers.


title: Convolutional networks and applications in vision

abstract: Intelligent tasks, such as visual perception, auditory perception, and language understanding require the construction of good internal representations of the world (or "features")? which must be invariant to irrelevant variations of the input while, preserving relevant information. A major question for Machine Learning is how to learn such good features automatically. Convolutional Networks (ConvNets) are a biologically-inspired trainable architecture that can learn invariant features. Each stage in a ConvNets is composed of a filter bank, some nonlinearities, and feature pooling layers. With multiple stages, a ConvNet can learn multi-level hierarchies of features. While ConvNets have been successfully deployed in many commercial applications from OCR to video surveillance, they require large amounts of labeled training samples. We describe new unsupervised learning algorithms, and new non-linear stages that allow ConvNets to be trained with very few labeled samples. Applications to visual object recognition and vision navigation for off-road mobile robots are described.


title: Dyscalculia: Characteristics, Causes, and Treatments

abstract: Developmental Dyscalculia (DD) is a learning disorder affecting the ability to acquire school-level arithmetic skills, affecting approximately 3-6% of individuals. Progress in understanding the root causes of DD and how best to treat it have been impeded by lack of widespread research and variation in characterizations of the disorder across studies. However, recent years have witnessed significant growth in the field, and a growing body of behavioral and neuroimaging evidence now points to an underlying deficit in the representation and processing of numerical magnitude information as a potential core deficit in DD. An additional product of the recent progress in understanding DD is the resurgence of a distinction between ‘primary’ and ‘secondary’ developmental dyscalculia. The first appears related to impaired development of brain mechanisms for processing numerical magnitude information, while the latter refers to mathematical deficits stemming from external factors such as poor teaching, low socio-economic status, and behavioral attention problems or domain-general cognitive deficits. Increased awareness of this distinction going forward, in combination with longitudinal empirical research, offers great potential for deepening our understanding of the disorder and developing effective educational interventions.


title: Speech emotion classification using acoustic features

abstract: Emotion recognition from speech is a challenging research area with wide applications. In this paper we explore one of the key aspects of building an emotion recognition system: generating suitable feature representation. We extract features from four angles: (1) low-level acoustic features such as intensity, F0, jitter, shimmer and spectral contours etc. and statistical functions over these features, (2) a set of features derived from segmental cepstral-based features scored against emotion-dependent Gaussian mixture models, (3) a set of features derived from a set of low-level acoustic codewords and (4) GMM supervectors constructed by stacking the means or covariance or weights of the adapted mixture components on each utterance. We apply these features for emotion recognition independently and jointly and compare their performance within this task. We build a support vector machine (SVM) classifier based on these features on the IEMOCAP database. The four-class emotion recognition accuracy of 71.9% of our system outperforms the previously reported best results on this dataset.


title: Real-time image super resolution using an FPGA

abstract: Image super resolution is the process of combining a set of overlapping low-resolution images to produce a single high-resolution image. In this paper, a novel real-time super-resolution system is presented which is based on a weighted mean super-resolution algorithm combined with the existing fast and robust multi-frame super-resolution algorithm. The resource requirements of the proposed architecture scale linearly with the targeted image quality, making it ideally suited for a variety of real-time applications such as HDTV. Simulation results demonstrate a speed-up of three orders of magnitude over optimized software implementations with negligible loss to image quality.


title: Bayesian intermittent demand forecasting for large inventories

abstract: We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items.


title: Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos

abstract: In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. This paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. The proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. Then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.


title: Using Pregel-like Large Scale Graph Processing Frameworks for Social Network Analysis

abstract: Pregel is a system for large scale graph processing developed at Google. It provides a scalable framework for running graph analytics on clusters of commodity machines. In this paper, we present several important undirected graph algorithms for social network analysis which fit within this framework. We discuss various graph componentisation methods, diameter estimation, degrees of separations, along with triangle, k-core and k-truss finding and computing clustering coefficients. Finally we present some experimental results using our own implementation of the Pregel framework, and examine key features of the general framework and algorithmic design.


title: Self-Attention with Relative Position Representations

abstract: Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. (2017) achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs. In this work we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively. Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graph-labeled inputs.


title: Causal Discovery Using Proxy Variables

abstract: Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two random entities $X$ and $Y$, given $n$ samples from $P(X,Y)$. In this paper, we develop a framework to estimate the cause-effect relation between two static entities $x$ and $y$: for instance, an art masterpiece $x$ and its fraudulent copy $y$. To this end, we introduce the notion of proxy variables, which allow the construction of a pair of random entities $(A,B)$ from the pair of static entities $(x,y)$. Then, estimating the cause-effect relation between $A$ and $B$ using an observational causal discovery algorithm leads to an estimation of the cause-effect relation between $x$ and $y$. For example, our framework detects the causal relation between unprocessed photographs and their modifications, and orders in time a set of shuffled frames from a video. As our main case study, we introduce a human-elicited dataset of 10,000 pairs of casually-linked pairs of words from natural language. Our methods discover 75% of these causal relations. Finally, we discuss the role of proxy variables in machine learning, as a general tool to incorporate static knowledge into prediction tasks.


title: Learning Stochastic Regular Grammars by Means of a State Merging Method

abstract: We propose a new algorithm which allows for the identification of any stochastic deterministic regular language as well as the determination of the probabilities of the strings in the language. The algorithm builds the prefix tree acceptor from the sample set and merges systematically equivalent states. Experimentally, it proves very fast and the time needed grows only linearly with the size of the sample set.


title: Optimization of Complex Dataflows with User-Defined Functions

abstract: In many fields, recent years have brought a sharp rise in the size of the data to be analyzed and the complexity of the analysis to be performed. Such analyses are often described as dataflows specified in declarative dataflow languages. A key technique to achieve scalability for such analyses is the optimization of the declarative programs; however, many real-life dataflows are dominated by user-defined functions (UDFs) to perform, for instance, text analysis, graph traversal, classification, or clustering. This calls for specific optimization techniques as the semantics of such UDFs are unknown to the optimizer. In this article, we survey techniques for optimizing dataflows with UDFs. We consider methods developed over decades of research in relational database systems as well as more recent approaches spurred by the popularity of Map/Reduce-style data processing frameworks. We present techniques for syntactical dataflow modification, approaches for inferring semantics and rewrite options for UDFs, and methods for dataflow transformations both on the logical and the physical levels. Furthermore, we give a comprehensive overview on declarative dataflow languages for Big Data processing systems from the perspective of their build-in optimization techniques. Finally, we highlight open research challenges with the intention to foster more research into optimizing dataflows that contain UDFs.


title: Deception Through Telling the Truth?! Experimental Evidence From Individuals and Teams

abstract: Informational asymmetries abound in economic decision making and often provide an incentive for deception through telling a lie or misrepresenting information. In this paper I use a cheap-talk sender-receiver experiment to show that telling the truth should be classified as deception too if the sender chooses the true message with the expectation that the receiver will not follow the sender?s (true) message. The experimental data reveal a large degree of ?sophisticated? deception through telling the truth. The robustness of my broader definition of deception is confirmed in an experimental treatment where teams make decisions.


ISUALIZING A F RAMEWORK FOR T ANGIBILITY IN M ULTIMEDIA L EARNING FOR P RESCHOOLERS


title: A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features

abstract: Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems. For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.


title: 3D Object Classification via Spherical Projections

abstract: In this paper, we introduce a new method for classifying 3D objects. Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical projection. We introduce two complementary projections. The first captures depth variations of a 3D object, and the second captures contour-information viewed from different angles. Spherical projections combine key advantages of two main-stream 3D classification methods: image-based and 3D-based. Specifically, spherical projections are locally planar, allowing us to use massive image datasets (e.g, ImageNet) for pre-training. Also spherical projections are similar to voxel-based methods, as they encode complete information of a 3D object in a single neural network capturing dependencies across different views. Our novel network design can fully utilize these advantages. Experimental results on ModelNet40 and ShapeNetCore show that our method is superior to prior methods.


title: An effective test case selection for software testing improvement

abstract: One problem of testing software is selecting the suitable test cases from the test suit regarding the size of the programs. If the size of selected test cases is big, then it can affect the whole performance of software development life cycle. Accordingly, it increases testing time and produce many bugs. Therefore, this paper proposes the improvement of software testing for selecting the appropriate and small number of test cases by considering the amounts of the functions modified, lines of code changed, and numbers of bugs produced after modifying programs. The reason of proposing the software testing improvement model is to prepare effective algorithm, while numbers of bugs are lower than the traditional methods. According to the experimental results, the size of the selected test cases by using the proposed model is less than Retest All, Random, and a Safe Test about 98.70%, 87.86%, and 84.67% respectively. Moreover, the ability of STI is higher than the comparative studies about 1–20 times regarding the number of bugs found after modifying a program.


title: Reliable information retrieval evaluation with incomplete and biased judgements

abstract: Information retrieval evaluation based on the pooling method is inherently biased against systems that did not contribute to the pool of judged documents. This may distort the results obtained about the relative quality of the systems evaluated and thus lead to incorrect conclusions about the performance of a particular ranking technique. We examine the magnitude of this effect and explore how it can be countered by automatically building an unbiased set of judgements from the original, biased judgements obtained through pooling. We compare the performance of this method with other approaches to the problem of incomplete judgements, such as bpref, and show that the proposed method leads to higher evaluation accuracy, especially if the set of manual judgements is rich in documents, but highly biased against some systems.


title: A new and easy approach to create BCI models

abstract: In this article, new lumped-parameter models of current clamps are proposed and discussed. The models were extracted from measured S parameters of a clamp mounted onto a calibration fixture. The generated bulk current injection (BCI) and current probe simulation models were used in the electromagnetic (EMC) testing of a realistic cable harness according to the ISO 11452-4 standard. The accuracy and efficiency of the proposed simulation model were estimated by comparing the simulated results with measured data.


ABSTRACT On the Role of Color in the Perception of Motion


title: The Your Disease Risk Index for Colorectal Cancer Is an Inaccurate Risk Stratification Tool for Advanced Colorectal Neoplasia at Screening Colonoscopy

abstract: Tailoring the use of screening colonoscopy based on the risk of advanced colorectal neoplasia (ACN) could optimize the cost-effectiveness of colorectal cancer (CRC) screening. Our goal was to assess the accuracy of the Your Disease Risk (YDR) CRC risk index for stratifying average risk patients into low- versus intermediate/high-risk categories for ACN. The YDR risk assessment tool was administered to 3,317 asymptomatic average risk patients 50 to 79 years of age just before their screening colonoscopy. Associations between YDR-derived relative risk (RR) scores and ACN prevalence were examined using logistic regression and χ(2) analyses. ACN was defined as a tubular adenoma ≥1 cm, tubulovillous or villous adenoma of any size, and the presence of high-grade dysplasia or cancer. The overall prevalence of ACN was 5.6%. Although YDR-derived RR scores were linearly associated with ACN after adjusting for age and gender (P = 0.033), the index was unable to discriminate "below average" from "above/average" risk patients [OR, 1.01; 95% confidence interval (CI), 0.75-1.37]. Considerable overlap in rates of ACN was also observed between the different YDR risk categories in our age- and gender-stratified analyses. The YDR index lacks accuracy for stratifying average risk patients into low- versus intermediate/high-risk categories for ACN.


title: Bayesian autocalibration for surveillance

abstract: In the context of visual surveillance of human activity knowledge about a camera's internal and external parameters is useful, as it allows for the establishment of a connection between image and world measurements. Unfortunately, calibration information is rarely available and difficult to obtain after a surveillance system has been installed. In this paper, a method for camera autocalibration based on information gathered by tracking people is developed. It brings two main contributions: first, we show how a foot-to-head plane homology can be used to obtain the calibration parameters and then we show an approach how to efficiently estimate initial parameter estimates from measurements; second, we present a Bayesian solution to the calibration problem that can elegantly handle measurement uncertainties, outliers, as well as prior information. It is shown how the full posterior distribution of calibration parameters given the measurements can be estimated, which allows making statements about the accuracy of both the calibration parameters and the measurements involving them