title: Efficacy of 2LPAPI<sup>®</sup>, a Micro-Immunotherapy Drug, in Patients with High-Risk Papillomavirus Genital Infection

abstract: Human papillomaviruses ::: (HPVs) are well known for being linked to the development of cervical cancers, ::: most of them being caused by the high-risk (HR) oncogenic genotypes, mainly 16 ::: and 18. The efficacy of 2LPAPI® (Labo’Life), a micro-immunotherapy ::: homeopathic drug, has been evaluated in HR-HPV infected women (n = 18), in a ::: private gynecology practice, by comparing them to an untreated control group (n ::: = 18). Patients were 20 to 45 years old and had cytology with Atypical Squamous ::: Cells of Undetermined Significance (ASCUS) or Low grade Superficial Intra ::: Lesions/ Cervical Intraepithelial Neoplasia Grade I (LSIL/CINI). Patients ::: freely chose to be treated with the drug or not. Those deciding not to take the ::: drug remained untreated and were followed as a control group. The drug was ::: taken at the regimen of one capsule per day during 6 months. HR-HPV and ::: cytology were evaluated at 6 and 12 months. After 12 months, HR-HPV was cleared ::: in 78% of the patients taking the drug versus 44% in those not taking it (p = ::: 0.086). In patients over 25 years, HR-HPV clearance in the treated group was ::: significantly higher (81.3%) than in the control group (20%) (p = 0.004). The difference ::: in the regression of the lesion grades almost reached statistical significance ::: (p = 0.053). This follow-up confirms that the micro-immunotherapy drug 2LPAPI® is a safe and effective therapeutic ::: approach to treat HR-HPV cervical lesions in women over 25 years.


Computer Vision Applied to Flower, Fruit and Vegetable Processing


title: Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays

abstract: PURPOSE ::: To improve detection of pulmonary and pleural abnormalities caused by pneumonia or tuberculosis (TB) in digital chest X-rays (CXRs). ::: ::: ::: METHODS ::: A method was developed and tested by combining shape and texture features to classify CXRs into two categories: TB and non-TB cases. Based on observation that radiologist interpretation is typically comparative: between left and right lung fields, the algorithm uses shape features to describe the overall geometrical characteristics of the lung fields and texture features to represent image characteristics inside them. ::: ::: ::: RESULTS ::: Our algorithm was evaluated on two different datasets containing tuberculosis and pneumonia cases. ::: ::: ::: CONCLUSIONS ::: Using our proposed algorithm, we were able to increase the overall performance, measured as area under the (ROC) curve (AUC) by 2.4 % over our previous work.


title: A survey on IEEE 802 . 11 ah : An enabling networking technology for smart cities

abstract: We study carefully the IEEE 802.11ah draft standard published in July 2014.We overview use cases of .11ah, especially related to smart cities scenarios.We describe in details novel mechanisms and explain why they are needed in .11ah. Smart technologies play a key role in sustainable economic growth. They transform houses, offices, factories, and even cities into autonomic, self-controlled systems acting often without human intervention and thus sparing people routine connected with information collecting and processing. The paper gives an overview of a novel Wi-Fi technology, currently under development, which aims to organize communication between various devices used in such applications as smart grids, smart meters, smart houses, smart healthcare systems, smart industry, etc.


title: Facilitating Pervasive Community Policing on the Road with Mobile Roadwatch

abstract: We consider community policing on the road with pervasive recording technologies such as dashcams and smartphones where citizens are actively volunteering to capture and report various threats to traffic safety to the police via mobile apps. This kind of novel community policing has recently gained significant popularity in Korea and India. In this work, we identify people's general attitude and concerns toward community policing on the road through an online survey. We then address the major concerns by building a mobile app that supports easy event capture/access, context tagging, and privacy preservation. Our two-week user study (n = 23) showed Roadwatch effectively supported community policing activities on the road. Further, we found that the critical factors for reporting are personal involvement and seriousness of risks, and participants were mainly motivated by their contribution to traffic safety. Finally, we discuss several practical design implications to facilitate community policing on the road.


title: Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition

abstract: Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.


A New Local Search Algorithm Providing High Quality Solutions to Vehicle Routing Problems


title: Semi-supervised learning for anomalous trajectory detection

abstract: A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a human operator. We consider the behaviour of pedestrians in terms of motion trajectories, and parametrise these trajectories using the control points of approximating cubic spline curves. This paper demonstrates an incremental semi-supervised one-class learning procedure in which unlabelled trajectories are combined with occasional examples of normal behaviour labelled by a human operator. This procedure is found to be effective on two different datasets, indicating that a human operator could potentially train the system to detect anomalous behaviour by providing only occasional interventions (a small percentage of the total number of observations).


title: A Full Continuous Model of Polymorphism

abstract: We introduce a model of the second-order lambda calculus. Such a model is a Scott domain whose elements are themselves Scott domains, and in it polymorphic maps are interpreted by generic continous maps.


title: Bead: explorations in information visualization

abstract: We describe work on the visualization of bibliographic data and, to aid in this task, the application of numerical techniques for multidimensional scaling. Many areas of scientific research involve complex multivariate data. One example of this is Information Retrieval. Document comparisons may be done using a large number of variables. Such conditions do not favour the more well-known methods of visualization and graphical analysis, as it is rarely feasible to map each variable onto one aspect of even a three-dimensional, coloured and textured space. Bead is a prototype system for the graphically-based exploration of information. In this system, articles in a bibliography are represented by particles in 3-space. By using physically-based modelling techniques to take advantage of fast methods for the approximation of potential fields, we represent the relationships between articles by their relative spatial positions. Inter-particle forces tend to make similar articles move closer to one another and dissimilar ones move apart. The result is a 3D scene which can be used to visualize patterns in the high-D information space.


title: Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings

abstract: Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language. This one-to-one mapping between words in different languages is too strict. Also the context is not considered at all. In this paper, we propose a cross-lingual SCL based on distributed representation of words; it can learn meaningful one-to-many mappings for pivot words using large amounts of monolingual data and a small dictionary. We conduct experiments on NLP\&CC 2013 cross-lingual sentiment analysis dataset, employing English as source language, and Chinese as target language. Our method does not rely on the parallel corpora and the experimental results show that our approach is more competitive than the state-of-the-art methods in cross-lingual sentiment classification.


title: SimEd: Simulating Education as a Multi Agent System

abstract: This paper describes our efforts in creating SimEd, a simulation of the education system. The longterm aim of this work is to be able to model the types of interactions and interplays that occur between students, teachers and administrators, resulting in a toolkit and a methodology that will allow policy makers on various levels to experiment with their decisions, examining effects over time and across levels. Here, we outline our reasoning for selecting the multi agent system paradigm and the particular techniques chosen. We present details of two components within the SimEd toolkit and show results of experimental simulations executed with each. In constructing these tools, we have begun to identify special attributes of the education environment that set it aside from more typical agent-based application areas, such as e-commerce. We highlight these aspects and discuss current and future work extending agent techniques to accommodate these types of environments.


title: Verification Requirements for Secure and Reliable Cloud Computing

abstract: Cloud computing has been widely adopted in a large variety of applications. This brings besides others also many new challenges in the fields of security and reliability. Like any other security and correctness sensitive computer system, it is beneficial to fully verify cloud solutions to ensure their correctness. Verification and validation of distributed systems has been widely studied, however, it has not been analyzed whether verification approaches in distributed systems can be directly applied to cloud computing or not. In this study, we present a comparison of verification requirements for distributed computing and cloud computing to establish the need of a specialized verification model/architecture for cloud computing. We differentiate the verification in distributed and cloud systems based on their business models, architecture models, programming models and security models. We argue that service level agreements (SLAs), which span across the cloud, become more critical in the verification process. Based on our study, we present an abstract verification model focusing specifically on cloud computing, paving the way to an implementation of a viable verification model which can practically verify complex cloud systems.


title: Text Steganography Based on Feature Coding Method

abstract: Stegnography is a sheltered communication in which information such as text, image, audio and video is kept under shelter of another message. The message providing the shelter can also be text, image, video etc. No one except the dispatcher and projected addressee can have any idea if any sort of hidden writing exists. In this paper, we present and evaluate an approach for text steganography which is hiding text underneath text. The approach is based on feature encoding and random character generation method. Our focus is primarily on the shape of the capital alphabets of the English language and hence the algorithm has been termed as capital alphabet shape encoding method.


title: Geometric Decision Tree

abstract: In this paper, we present a new algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess the goodness of hyperplanes at each node while learning a decision tree in top-down fashion. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy for assessing the hyperplanes in such a way that the geometric structure in the data is taken into account. At each node of the decision tree, we find the clustering hyperplanes for both the classes and use their angle bisectors as the split rule at that node. We show through empirical studies that this idea leads to small decision trees and better performance. We also present some analysis to show that the angle bisectors of clustering hyperplanes that we use as the split rules at each node are solutions of an interesting optimization problem and hence argue that this is a principled method of learning a decision tree.


title: Design and control of a micro ball-balancing robot (MBBR) with orthogonal midlatitude omniwheel placement

abstract: Ball-balancing robots (BBRs) are endowed with rich dynamics. When properly designed and stabilized via feedback to eliminate jitter, and intuitively coordinated with a well-designed smartphone interface, BBRs exhibit a uniquely fluid and organic motion. Unlike mobile inverted pendulums (MIPs, akin to unmanned Segways), BBRs stabilize both fore/aft and left/right motions with feedback, and bank when turning. Previous research on BBRs focused on vehicles from 50cm to 2m in height; the present work is the first to build significantly smaller BBRs, with heights under 25cm. We consider the unique issues arising when miniaturizing a BBR to such a scale, which are characterized by faster time scales and reduced weight (and, thus, reduced normal force and stiction between the omniwheels and the ball). Two key patent-pending aspects of our design are (a) moving the omniwheels to contact the ball down to around 20 to 30 deg N latitude, which increases the normal force between the omniwheels and the ball, and (b) orienting the omniwheels into mutually-orthogonal planes, which improves efficiency. Design iterations were facilitated by rapid prototyping and leveraged low-cost manufacturing principles and inexpensive components. Classical successive loop closure control strategies are implemented, which prove to be remarkably effective when the BBR isn't spinning quickly, and thus the left/right and fore/aft stabilization problems decompose into two decoupled MIP problems.


title: Joint Training of Generic CNN-CRF Models with Stochastic Optimization

abstract: We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.


How Parents Influence Deviant Behavior among Adolescents : An Analysis of their Family Life , their Community , and their Peers


title: A framework for the design and integration of collaborative classroom games

abstract: The progress registered in the use of video games as educational tools has not yet been successfully transferred to the classroom. In an attempt to close this gap, a framework was developed that assists in the design and classroom integration of educational games. The framework addresses both the educational dimension and the ludic dimension. The educational dimension employs Bloom's revised taxonomy to define learning objectives and applies the classroom multiplayer presential game (CMPG) pedagogical model while the ludic dimension determines the gaming elements subject to constraints imposed by the educational dimension. With a view to validating the framework, a game for teaching electrostatics was designed and experimentally implemented in a classroom context. An evaluation based on pre/post testing found that the game increased the average number of correct answers by students participating in the experiment from 6.11 to 10.00, a result found to be statistically significant. Thus validated, the framework offers a promising basis for further exploration through the development of other games and fine-tuning of its components.


context2vec: Learning Generic Context Embedding with Bidirectional LSTM


title: Validation of the Five-Factor Model of Personality Across Instruments and Observers

abstract: Two data sources—self-reports and peer ratings—and two instruments—adjective factors and questionnaire scales—were used to assess the five-factor model of personality. As in a previous study of self-reports {McCrae & Costa, 1985b), adjective factors of neuroticism, extraversion, openness to experience, agreeableness-antagonism, and conscientiousness-undirectedness were identified in an analysis of 738 peer ratings of 275 adult subjects. Intraclass correlations among raters, ranging from .30 to .65, and correlations between mean peer ratings and self-reports, from .25 to .62, showed substantial cross-observer agreement on all five adjective factors. Similar results were seen in analyses of scales from the NEO Personality Inventory. Items from the adjective factors were used as guides in a discussion of the nature of the five factors. These data reinforce recent appeals for the adoption of the five-factor model in personality research and assessment.


title: GPU-based Fast Ray Casting for a Large Number of

abstract: Metaballs are implicit surfaces widely used to model curved objects, represented by the isosurface of a density field defined by a set of points. Recently, the results of particle-based simulations have been often visualized using a large number of metaballs, however, such visualizations have high rendering costs. In this paper we propose a fast technique for rendering metaballs on the GPU. Instead of using polygonization, the isosurface is directly evaluated in a per-pixel manner. For such evaluation, all metaballs contributing to the isosurface need to be extracted along each viewing ray, on the limited memory of GPUs. We handle this by keeping a list of metaballs contributing to the isosurface and efficiently update it. Our method neither requires expensive precomputation nor acceleration data structures often used in existing ray tracing techniques. With several optimizations, we can display a large number of moving metaballs quickly.


title: Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices

abstract: In a recent conference paper, we have reported a rhythm transcription method based on a merged-output hidden Markov model HMM that explicitly describes the multiple-voice structure of polyphonic music. This model solves a major problem of conventional methods that could not properly describe the nature of multiple voices as in polyrhythmic scores or in the phenomenon of loose synchrony between voices. In this paper, we present a complete description of the proposed model and develop an inference technique, which is valid for any merged-output HMMs, for which output probabilities depend on past events. We also examine the influence of the architecture and parameters of the method in terms of accuracies of rhythm transcription and voice separation and perform comparative evaluations with six other algorithms. Using MIDI recordings of classical piano pieces, we found that the proposed model outperformed other methods by more than 12 points in the accuracy for polyrhythmic performances and performed almost as good as the best one for non-polyrhythmic performances. This reveals the state-of-the-art methods of rhythm transcription for the first time in the literature. Publicly available source codes are also provided for future comparisons.


title: An overview on hyperspectral unmixing: Geometrical, statistical, and sparse regression based approaches

abstract: Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquired at different spectral bands from a given pixel) acquired by the hyperspectral scanners are actually mixtures of the spectral signatures of the materials present in the scene.


title: Image Captioning with Semantic Attention

abstract: Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing. Existing approaches are either top-down, which start from a gist of an image and convert it into words, or bottom-up, which come up with words describing various aspects of an image and then combine them. In this paper, we propose a new algorithm that combines both approaches through a model of semantic attention. Our algorithm learns to selectively attend to semantic concept proposals and fuse them into hidden states and outputs of recurrent neural networks. The selection and fusion form a feedback connecting the top-down and bottom-up computation. We evaluate our algorithm on two public benchmarks: Microsoft COCO and Flickr30K. Experimental results show that our algorithm significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.


title: A survey: cluster ensemble techniques

abstract: The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of unsupervised classification solutions. So far, many contributions have been done to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, firstly, we introduce clustering ensembles, representation of multiple partitions, its challenges and present taxonomy of combination algorithms. Secondly, we describe consensus functions in clustering ensembles including Hypergraph partitioning, Voting approach, Mutual information, Co-association based functions and Finite mixture model, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques.


title: Multiple asynchronous stimulus- and task-dependent hierarchies (STDH) within the visual brain's parallel processing systems

abstract: Results from a variety of sources, some many years old, lead ineluctably to a re-appraisal of the twin strategies of hierarchical and parallel processing used by the brain to construct an image of the visual world. Contrary to common supposition, there are at least three ‘feed-forward’ anatomical hierarchies that reach the primary visual cortex (V1) and the specialized visual areas outside it, in parallel. These anatomical hierarchies do not conform to the temporal order with which visual signals reach the specialized visual areas through V1. Furthermore, neither the anatomical hierarchies nor the temporal order of activation through V1 predict the perceptual hierarchies. The latter shows that we see (and become aware of) different visual attributes at different times, with colour leading form (orientation) and directional visual motion, even though signals from fast-moving, high-contrast stimuli are among the earliest to reach the visual cortex (of area V5). Parallel processing, on the other hand, is much more ubiquitous than commonly supposed but is subject to a barely noticed but fundamental aspect of brain operations, namely that different parallel systems operate asynchronously with respect to each other and reach perceptual endpoints at different times. This re-assessment leads to the conclusion that the visual brain is constituted of multiple, parallel and asynchronously operating task- and stimulus-dependent hierarchies (STDH); which of these parallel anatomical hierarchies have temporal and perceptual precedence at any given moment is stimulus and task related, and dependent on the visual brain's ability to undertake multiple operations asynchronously.


title: Fine-Grained Vehicle Model Recognition Using A Coarse-to-Fine Convolutional Neural Network Architecture

abstract: Fine-grained vehicle model recognition is a challenging problem in intelligent transportation systems due to the subtle intra-category appearance variation. In this paper, we demonstrate that this problem can be addressed by locating discriminative parts, where the most significant appearance variation appears, based on the large-scale training set. We also propose a corresponding coarse-to-fine method to achieve this, in which these discriminative regions are detected automatically based on feature maps extracted by convolutional neural network. A mapping from feature maps to the input image is established to locate the regions, and these regions are repeatedly refined until there are no more qualified ones. The global and local features are then extracted from the whole vehicle images and the detected regions, respectively. Based upon the holistic cues and the subordinate-level variation within these global and local features, an one-versus-all support vector machine classifier is applied for classification. The experimental results show that our framework outperforms most of the state-of-the-art approaches, achieving 98.29% accuracy over 281 vehicle makes and models.


title: Parallelizing WFST speech decoders

abstract: The performance-intensive part of a large-vocabulary continuous speech-recognition system is the Viterbi computation that determines the sequence of words that are most likely to generate the acoustic-state scores extracted from an input utterance. This paper presents an efficient parallel algorithm for Viterbi. The key idea is to partition the per-frame computation among threads to minimize inter-thread communication despite traversing a large irregular acoustic and language model graphs. Together with a per-thread beam search, load balancing language-model lookups, and memory optimizations, we achieve a 6.67x speedup over an highly-optimized production-quality WFST-based speech decoder. On a 200,000 word vocabulary and a 59 million ngram model, our decoder runs at 0.27x real time while achieving a word-error rate of 14.81% on 6214 labeled utterances from Voice Search data.


title: Speaker Diarization with LSTM

abstract: For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of deep learning in various domains, neural network based audio embeddings, also known as d-vectors, have consistently demonstrated superior speaker verification performance. In this paper, we build on the success of d-vector based speaker verification systems to develop a new d-vector based approach to speaker diarization. Specifically, we combine LSTM-based d-vector audio embeddings with recent work in non-parametric clustering to obtain a state-of-the-art speaker diarization system. Our system is evaluated on three standard public datasets, suggesting that d-vector based diarization systems offer significant advantages over traditional i-vector based systems. We achieved a 12.0% diarization error rate on NIST SRE 2000 CALLHOME, while our model is trained with out-of-domain data from voice search logs.


title: Abiotic and Biotic Stress Responses in Group Phureja DM1-3 516 R44 as Measured through Whole Transcriptome Sequencing

abstract: This study conducted an in-depth analysis of the potato (Solanum tuberosum L.) transcriptome in response to abiotic (salinity, drought, and heat) and biotic (Phytophthora infestans, DL-bamino-n-butyric acid, and acibenzolar-s-methyl) stresses and plant hormone treatment (abscisic acid, 6-benzylaminopurine, gibberellic acid, and indole-3-acetic acid) using ribonucleic acid sequencing (RNA-seq) of the doubled monoploid S. tuberosum Group Phureja DM1-3 516 R44 clone. Extensive changes in gene expression were observed with 37% of the expressed genes being differentially expressed in at least one comparison of stress to control tissue. Stress-inducible genes known to function in stress tolerance or to be involved in the regulation of gene expression were among the highest differentially expressed. Members of the MYB, APETALA2 (AP2)/ethylene-responsive element binding factor (ERF), and NAM, ATAF1/2, and CUC2 (NAC) transcription factor families were the most represented regulatory genes. A weighted gene co-expression network analysis yielded 37 co-expression modules containing 9198 genes. Fifty percent of the genes within these co-expression modules were specific to a stress condition indicating conditionspecific responses. Cross-species comparison between potato and Arabidopsis thaliana (L.) Heynh. uncovered differentially expressed orthologs and defined evolutionary conserved genes. Collectively, the transcriptional profiling of RNA-seq data presented here provide a valuable reference for potato stress responses to environmental factors that is supported by statistically significant differences in expression changes, highly interconnected genes in co-expression networks, and evidence of evolutionary conservation.


title: Measuring foot pronation using RFID sensor networks

abstract: Running efficiency is an important factor to consider in order to avoid injury. In particular, foot pronation, the angle of the foot as it hits the ground, is a common cause for many types of injuries among runners. Though pronation is common, diagnosing pronation is difficult and imprecise. Currently there is no method of diagonsis which can quantify the severity of pronation. In this paper we propose using WISP (Wireless Identification and Sensing Platform) sensors to help identify and quantify foot pronation.


title: darkNoC: Designing energy-efficient network-on-chip with multi-Vt cells for dark silicon

abstract: In this paper, we propose a novel NoC architecture, called darkNoC, where multiple layers of architecturally identical, but physically different routers are integrated, leveraging the extra transistors available due to dark silicon. Each layer is separately optimized for a particular voltage-frequency range by the adroit use of multi-Vt circuit optimization. At a given time, only one of the network layers is illuminated while all the other network layers are dark. We provide architectural support for seamless integration of multiple network layers, and a fast inter-layer switching mechanism without dropping in-network packets. Our experiments on a 4 × 4 mesh with multi-programmed real application workloads show that darkNoC improves energy-delay product by up to 56% compared to a traditional single layer NoC with state-of-the-art DVFS. This illustrates darkNoC can be used as an energy-efficient communication fabric in future dark silicon chips.


title: DIFFERENCES IN MINERAL NUTRIENT CONCENTRATION OF DORMANT CHERRY SPURS AS AFFECTED BY ROOTSTOCK, SCION, AND ORCHARD SITE

abstract: Dormant sweet (Prunus avium) and sour (P. cerasus) cherry spur nutrient levels can influence a number of initial growth activities in spring, including fruit set and leaf expansion. Rootstock genotypes may vary in uptake and translocation of mineral nutrients to sites of utilization within the canopy. Consequently, at the conclusion of the most recent 10-year North American regional (NC-140) cherry rootstock trial, a collaborative study was initiated to analyze and compare nutrient concentrations from dormant flowering spurs at three of the trial sites: Traverse City, Michigan (MI); Geneva, New York (NY); and Hotchkiss, Colorado (CO). Replicated spur tissue samples of 'Hedelfinger' (MI, NY) and 'Bing' (CO) sweet cherry on up to 18 rootstocks (11 common to all three sites) and of 'Montmorency' sour cherry on up to 12 rootstocks (10 common to both tart cherry sites) were collected in winter, prepared at Michigan State University, and analyzed for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn), boron (B), copper (Cu), sulfur (S), manganese (Mn), aluminum (Al), and sodium (Na). The rootstock comparisons included Mazzard seedling, Mahaleb seedling, Gisela (Gi) 3, Gi.5, Gi.6, Gi.7, Giessen 195/20, Tabel Edabriz, P50, Weiroot (W) 10, W.13, W.72, W.158, Mazzard x Mahaleb (MxM) 2, MxM.60, and the Hungarian Mahaleb inbred seedling lines CT500, CT2753, and Erdi V. The soil types at the test sites included a sandy loam to loamy sand in Michigan, a fine sandy loam in New York, and a stony loam in Colorado. In general, few consistent trends regarding rootstock influences on dormant spur nutrient levels were found across sites or scions.


title: Novel Diversity/MIMO PIFA Antenna With Broadband Circular Polarization for Multimode Satellite Navigation

abstract: A broadband circular polarized two-element planar inverted-F antenna (PIFA) with pattern diversity and high isolation for multimode satellite navigation is reported. Circular polarization characteristic of the proposed antenna is achieved by introducing a cross branch at its corner of the ground. The modified PIFA structure gives the antenna a broadband impedance bandwidth characteristic. A prototype is fabricated and measured. The results show that this circular polarized antenna with compact dimension has broadband from 1.1 to 1.7 GHz and good isolation of 14 dB between the two elements over the whole band, which makes the antenna suitable for handheld terminal application of multimode satellite navigation.


title: Recommending Outfits from Personal Closet

abstract: We consider grading a fashion outfit for recommendation, where we assume that users have a closet of items and we aim at producing a score for an arbitrary combination of items in the closet. The challenge in outfit grading is that the input to the system is a bag of item pictures that are unordered and vary in size. We build a deep neural network-based system that can take variable-length items and predict a score. We collect a large number of outfits from a popular fashion sharing website, Polyvore, and evaluate the performance of our grading system. We compare our model with a random-choice baseline, both on the traditional classification evaluation and on people's judgment using a crowdsourcing platform. With over 84% in classification accuracy and 91% matching ratio to human annotators, our model can reliably grade the quality of an outfit. We also build an outfit recommender on top of our grader to demonstrate the practical application of our model for a personal closet assistant.


title: Investigation of strengthening effect on the machining rigidity in longitudinal torsional ultrasonic milling of thin-plate structures

abstract: Weak-stiffness structures are difficult to be machined because of their low stiffness, large deformation and poor stability through conventional machining. Ultrasonic-assisted machining has the sup...


What You Corrupt Is Not What You Crash: Challenges in Fuzzing Embedded Devices


title: 3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder

abstract: Researchers have proposed various methods to extract 3D keypoints from the surface of 3D mesh models over the last decades, but most of them are based on geometric methods, which lack enough flexibility to meet the requirements for various applications. In this paper, we propose a new method on the basis of deep learning by formulating the 3D keypoint detection as a regression problem using deep neural network (DNN) with sparse autoencoder (SAE) as our regression model. Both local information and global information of a 3D mesh model in multi-scale space are fully utilized to detect whether a vertex is a keypoint or not. SAE can effectively extract the internal structure of these two kinds of information and formulate high-level features for them, which is beneficial to the regression model. Three SAEs are used to formulate the hidden layers of the DNN and then a logistic regression layer is trained to process the high-level features produced in the third SAE. Numerical experiments show that the proposed DNN based 3D keypoint detection algorithm outperforms current five state-of-the-art methods for various 3D mesh models.


title: New evolving ensemble classifier for handling concept drifting data streams

abstract: Data streams mining have become a novel research topic of growing interest in knowledge discovery. The data streams which are generated from applications, such as network analysis, real time surveillance systems, sensor networks and financial generate huge data streams. These data streams consist of millions or billions of updates and must be processed to extract the useful information. Because of the high speed and huge size of data set in data streams, the traditional classification technologies are no longer applicable. In recent years a great deal of research has been done on this problem, most intends to efficiently solve the data streams mining problem with concept drift. This paper presents a novel approach for data stream classification which handles concept drift. This approach uses weighted majority approach with adaptive sliding window strategies. The experimental result shows that this novel approach works better than other methods.


title: Functional role of growth/differentiation factor 5 in chondrogenesis of limb mesenchymal cells

abstract: Growth/Differentiation Factor 5 (GDF5) plays an important role in limb mesenchymal cell condensation and chondrogenesis. Here we demonstrate, using high density cultures of chick embryonic limb mesenchyme, that GDF5 misexpression increased condensation of chondroprogenitor cells and enhanced chondrogenic differentiation. These effects were observed in the absence of altered cellular viability or biosynthetic activity, suggesting that GDF5 action might be directed at the level of cellular adhesion or cell-cell communication. GDF5- enhanced condensation occurred independent of cell density or N-cadherin mediated adhesion and signaling, but was inhibited upon interference of gap junction mediated communication. p38 MAP kinase signaling was required for the GDF5 effect on chondrocyte differentiation, but not for mesenchymal condensation. These findings suggest gap junction involvement in the action of GDF5 in developmental chondrogenesis.


title: Map merging of Multi-Robot SLAM using Reinforcement Learning

abstract: Using ‘Simultaneous Localization and Mapping’ (SLAM), mobile robots can become truly autonomous in the exploration of their environment. However, once these environments becomes too large, Multi-Robot SLAM becomes a requirement. This paper will outline how a mobile robot should decide when best to merge its maps with another robot's upon rendezvous, as opposed to doing so immediately. This decision will be based on the current status of the mapping particle filters and the current status of the environment. Using Reinforcement Learning, a model can be established and then trained upon to determine a policy capable of deciding when best to merge. This will allow the robot to incur less error during a merge compared to simply merging immediately. This policy is trained and validated using simulated mobile robot datasets.


title: Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy

abstract: We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a discriminator attempts to tell these apart from data samples. In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples. Second, the MMD can be used to evaluate the performance of a generative model, by testing the model’s samples against a reference data set. In the latter role, the optimized MMD is particularly helpful, as it gives an interpretable indication of how the model and data distributions differ, even in cases where individual model samples are not easily distinguished either by eye or by classifier.


title: Effects of taper and space settings of telescopic Ce-TZP/A crowns on retentive force and settling

abstract: The aim of this study was to investigate the effect of the taper and space setting of using Ce-TZP/A on retentive force and secondary crown settling. The taper were 2°, 4°, and 6°, and the space settings were 0 and 10 μm. The applied loads were 50 and 100 N. The taper had a significant effect on retentive force and settling at both loads (p<0.05). The space settings did not have a significant effect on retentive force or settling at either load (p<0.05). The taper of the telescopic crowns and the load affected the retentive force and the settling.


A Distribution-based Model to Learn Bilingual Word Embeddings


title: CARLA: An Open Urban Driving Simulator

abstract: We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end-to-end model trained via reinforcement learning. The approaches are evaluated in controlled scenarios of increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform's utility for autonomous driving research. The supplementary video can be viewed at this https URL


title: Best Practices for Compact Modeling in Verilog-A

abstract: Verilog-A is the de facto standard language that the semiconductor industry uses to define compact models. Unfortunately, it is easy to write models poorly in Verilog-A, and this can lead to unphysical model behavior, poor convergence, and difficulty in understanding and maintaining model codes. This paper details best practices for writing compact models in Verilog-A, to try to help raise the quality of compact modeling throughout the industry.


title: A survey of microarchitectural timing attacks and countermeasures on contemporary hardware

abstract: Microarchitectural timing channels expose hidden hardware states though timing. We survey recent attacks that exploit microarchitectural features in shared hardware, especially as they are relevant for cloud computing. We classify types of attacks according to a taxonomy of the shared resources leveraged for such attacks. Moreover, we take a detailed look at attacks used against shared caches. We survey existing countermeasures. We finally discuss trends in attacks, challenges to combating them, and future directions, especially with respect to hardware support.


title: Planned complex suicide. An unusual suicide by hanging and gunshot

abstract: We report a case of planned complex suicide (PCS) by a young man who had previously tried to commit suicide twice. He was found dead hanging by his neck, with a shot in his head. The investigation of the scene, the method employed, and previous attempts at suicide altogether pointed toward a suicidal etiology. The main difference between PCS and those cases defined in the medicolegal literature as combined suicides lies in the complex mechanism used by the victim as a protection against a failure in one of the mechanisms. Language: en


title: Decision support systems or business intelligence : what can help in decision making?

abstract: The world of tools supporting decision-making is very broad and full of confusing buzzwords. In this paper, we will focus on two basic types of software solutions that can be used to support decision making; DSS-decision support systems and BI-business intelligence.


title: An SIW fed dual-band and dual-polarized lens antenna at K/Ka-band

abstract: A dual-band and dual-polarized antenna element for K/Ka-band satellite communication systems is presented. An orthomode transducer (OMT) excites circular polarization in a square substrate integrated waveguide (SIW), that feeds an extended hemispherical dielectric lens. The feed structure is composed of a multilayer printed circuit board (PCB) and tapered superstrates. The design procedure of the components is explained and the antenna performance is presented. The measured scattering and far-field parameters validate the concept.


Making Open Data Transparent: Data Discovery on Open Data


title: Leadership , Followership , and Evolution Some Lessons From the Past

abstract: This article analyzes the topic of leadership from an evolutionary perspective and proposes three conclusions that are not part of mainstream theory. First, leading and following are strategies that evolved for solving social coordination problems in ancestral environments, including in particular the problems of group movement, intragroup peacekeeping, and intergroup competition. Second, the relationship between leaders and followers is inherently ambivalent because of the potential for exploitation of followers by leaders. Third, modern organizational structures are sometimes inconsistent with aspects of our evolved leadership psychology, which might explain the alienation and frustration of many citizens and employees. The authors draw several implications of this evolutionary analysis for leadership theory, research, and practice.


title: A Code for RanGDP Binding in Ankyrin Repeats Defines a Nuclear Import Pathway

abstract: Summary Regulation of nuclear import is fundamental to eukaryotic biology. The majority of nuclear import pathways are mediated by importin-cargo interactions. Yet not all nuclear proteins interact with importins, necessitating the identification of a general importin-independent nuclear import pathway. Here, we identify a code that determines importin-independent nuclear import of ankyrin repeats (ARs), a structural motif found in over 250 human proteins with diverse functions. AR-containing proteins (ARPs) with a hydrophobic residue at the 13th position of two consecutive ARs bind RanGDP efficiently, and consequently enter the nucleus. This code, experimentally tested in 17 ARPs, predicts the nuclear-cytoplasmic localization of over 150 annotated human ARPs with high accuracy and is acquired by the most common familial melanoma-associated CDKN2A mutation, leading to nuclear accumulation of mutant p16ink4a. The RaDAR (RanGDP/AR) pathway represents a general importin-independent nuclear import pathway and is frequently used by AR-containing transcriptional regulators, especially those regulating NF-κB/p53.


title: Identifying fixations and saccades in eye-tracking protocols

abstract: The process of fixation identification—separating and labeling fixations and saccades in eye-tracking protocols—is an essential part of eye-movement data analysis and can have a dramatic impact on higher-level analyses. However, algorithms for performing fixation identification are often described informally and rarely compared in a meaningful way. In this paper we propose a taxonomy of fixation identification algorithms that classifies algorithms in terms of how they utilize spatial and temporal information in eye-tracking protocols. Using this taxonomy, we describe five algorithms that are representative of different classes in the taxonomy and are based on commonly employed techniques. We then evaluate and compare these algorithms with respect to a number of qualitative characteristics. The results of these comparisons offer interesting implications for the use of the various algorithms in future work.


title: A flexible R package for nonnegative matrix factorization

abstract: BackgroundNonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community.ResultsOur objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results.ConclusionsThe NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN.


title: Solving the Fluid Pressure Poisson Equation Using Multigrid—Evaluation and Improvements

abstract: In many numerical simulations of fluids governed by the incompressible Navier-Stokes equations, the pressure Poisson equation needs to be solved to enforce mass conservation. Multigrid solvers show excellent convergence in simple scenarios, yet they can converge slowly in domains where physically separated regions are combined at coarser scales. Moreover, existing multigrid solvers are tailored to specific discretizations of the pressure Poisson equation, and they cannot easily be adapted to other discretizations. In this paper we analyze the convergence properties of existing multigrid solvers for the pressure Poisson equation in different simulation domains, and we show how to further improve the multigrid convergence rate by using a graph-based extension to determine the coarse grid hierarchy. The proposed multigrid solver is generic in that it can be applied to different kinds of discretizations of the pressure Poisson equation, by using solely the specification of the simulation domain and pre-assembled computational stencils. We analyze the proposed solver in combination with finite difference and finite volume discretizations of the pressure Poisson equation. Our evaluations show that, despite the common assumption, multigrid schemes can exploit their potential even in the most complicated simulation scenarios, yet this behavior is obtained at the price of higher memory consumption.


title: A new map of Hollywood and the world

abstract: In this paper, I offer a reinterpretation of the economic geography of the so-called new Hollywood. The argument proceeds in six main stages. First, I briefly examine the debate on industrial organization in Hollywood that has gone on in the literature since the mid-1980s, and I conclude that the debate has become unnecessarily polarized. Second, I attempt to show how an approach that invokes both flexible specialization and systems-house forms of production is necessary to any reasonably complete analysis of the organization of production in the new Hollywood. Third, and on this basis, I argue that the Hollywood production system is deeply bifurcated into two segments comprising (a) the majors and their cohorts of allied firms on the one hand, and (b) the mass of independent production companies on the other. Fourth, I reaffirm the continuing tremendous agglomerative attraction of Hollywood as a locale for motion-picture production, but I also describe in analytical and empirical terms how selected kinds of activities seek out satellite production locations in other parts of the world. Fifth, I show how the majors continue to extend their global reach by means of their ever more aggressive marketing and distribution divisions, and I discuss how that this state of affairs depends on and amplifies the competitive advantages of Hollywood. Sixth and finally, I reflect upon some of the challenges that Hollywood must face up to as new cultural-products agglomerations arise all over the globe, offering potential challenges to its hegemony. Key words: motion-picture industry; cultural economy; Hollywood; agglomeration; regional development; globalization


title: Towards a unified visual notation for OWL ontologies: insights from a comparative user study

abstract: Browsing ontologies and making sense of their concepts and relationships are important activities in knowledge engineering. They are supported by a large number of tools that often provide visual representations of the ontologies for better illustration. However, different notations are used to represent the ontologies which can be confusing when switching tools. Furthermore, many representations provide only basic overviews of the ontologies that are not sufficient to get a deeper understanding of the concepts and relationships. A unified visual notation for OWL ontologies would be most helpful to overcome these limitations. Having this goal in mind, we compare two different notations for OWL ontologies in this paper: the UML profile of the Ontology Definition Metamodel (ODM) and the Visual Notation for OWL Ontologies (VOWL). We report on a comparative user study of these notations and discuss benefits and limitations raised by the study participants. Based on these findings, we draw some general conclusions regarding the development of a unified visual notation for OWL ontologies.


title: Childhood Motor Skill Proficiency as a Predictor of Adolescent Physical Activity

abstract: Abstract Purpose Cross-sectional evidence has demonstrated the importance of motor skill proficiency to physical activity participation, but it is unknown whether skill proficiency predicts subsequent physical activity. Methods In 2000, children's proficiency in object control (kick, catch, throw) and locomotor (hop, side gallop, vertical jump) skills were assessed in a school intervention. In 2006/07, the physical activity of former participants was assessed using the Australian Physical Activity Recall Questionnaire. Linear regressions examined relationships between the reported time adolescents spent participating in moderate-to-vigorous or organized physical activity and their childhood skill proficiency, controlling for gender and school grade. A logistic regression examined the probability of participating in vigorous activity. Results Of 481 original participants located, 297 (62%) consented and 276 (57%) were surveyed. All were in secondary school with females comprising 52% (144). Adolescent time in moderate-to-vigorous and organized activity was positively associated with childhood object control proficiency. Respective models accounted for 12.7% ( p = .001), and 18.2% of the variation ( p = .003). Object control proficient children became adolescents with a 10% to 20% higher chance of vigorous activity participation. Conclusions Object control proficient children were more likely to become active adolescents. Motor skill development should be a key strategy in childhood interventions aiming to promote long-term physical activity.


title: Castalia Network Animator (CNA): A Visualization Tool for Castalia Wireless Sensor Network Simulator

abstract: Castalia is among the specialized simulators that researchers in the area of low power personal networks utilize for various purposes. However, the lack of visualization tool for any simulator causes the decrease of thorough studies carried out. Aiming at further facilitating Castalia simulator, the current study introduces a network visualizer, CNA, which is designed and implemented for animating simulation scenarios produced by Castalia. The proposed animator is designed so that it performs all the operations necessary for animating a simulated network. It includes several features from supporting various trace files to software capabilities which are outlined in details. The case studies prove the precision and proper functioning of the proposed animator.


title: A Watt-Level Stacked-FET Linear Power Amplifier in Silicon-on-Insulator CMOS

abstract: A single-stage stacked field-effect transistor (FET) linear power amplifier (PA) is demonstrated using 0.28-?m 2.5-V standard I/O FETs in a 0.13-?m silicon-on-insulator (SOI) CMOS technology. To overcome the low breakdown voltage limit of MOSFETs, a stacked-FET structure is employed, where four transistors are connected in series so that their output voltage swings are added in phase. With a 6.5-V supply, the measured PA achieves a small-signal gain of 14.6 dB, a saturated output power of 32.4 dBm, and a maximum power-added efficiency (PAE) of 47% at 1.9 GHz. Using a reverse-link IS-95 code division multiple access modulated signal, the PA shows an average output power of up to 28.7 dBm with a PAE of 41.2% while meeting the adjacent channel power ratio requirement. Using an uplink wideband code division multiple access modulated signal, the PA shows an average output power of up to 29.4 dBm with a PAE of 41.4% while meeting the adjacent channel leakage ratio requirement. The stacked-FET PA is designed to withstand up to 9 V of supply voltage before reaching its breakdown limit. This is the first reported stacked-FET linear PA in submicrometer SOI CMOS technology that delivers watt-level output power in the gigahertz frequency range with efficiency and linearity performance comparable to those of GaAs-based PAs.


title: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

abstract: One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing on the correspondence between the graph Laplacian, the Laplace Beltrami operator on the manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for representing the high-dimensional data. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality-preserving properties and a natural connection to clustering. Some potential applications and illustrative examples are discussed.


Switched Linear Systems: Control and Design


title: Hybrid LQ-optimization using dynamic programming

abstract: In this paper we study the linear quadratic optimal control problem for linear hybrid systems in which transitions between different discrete locations occur autonomously when the continuous state intersects given switching surfaces. In particular, we make an explicit connection between the newly developed, Pontryagin-type Hybrid Maximum Principle and the Bellman Dynamic Programming approach. As a consequence, we extend the classic Riccati-formalism, derive the associated Riccati-type equations, and prove the discontinuity of the full “hybrid” Riccati matrix. Finally, we discuss some computational aspects of the obtained theoretical results and propose a numerical algorithm in the framework of an optimal feedback control law.


title: Does an iridotomy provide protection against narrowing of the anterior chamber angle during Valsalva maneuvre in eyes with primary angle closure

abstract: PURPOSE ::: To evaluate changes at the anterior chamber angle during Valsalva manoeuver, in eyes having primary angle closure (PAC) and a patent laser peripheral iridotomy. ::: ::: ::: METHODS ::: Twenty-three eyes of 23 consecutive patients underwent a recording of applanation tonometry, measurement of the anterior chamber angle recess, angle opening distance, iris thickness, anterior chamber depth, and pupil size on ultrasound biomicroscopy before and during the Valsalva maneuver. The Valsalva manoeuver was standardized to a pressure of 40 mmHg for 15 s, using a manometer. ::: ::: ::: RESULTS ::: The mean baseline intraocular pressure changed from 18.86+3.79 to 26.73+4.73 mmHg during Valsalva, (P<0.0001). The anterior chamber angle recess narrowed from 16.62+6.24 to 6.5+4.02 degrees (P<0.0001). There was a significant increase in the thickness of the ciliary body from 0.92+0.25 to 1.17+0.31 mm (P=0.0006) and in the iris thickness from 0.47+0.17 to 0.57+0.11 mm (P=0.007). A significant increase in pupillary diameter (P=0.008) and a decrease in the angle opening distance (P<0.0001) also occurred during Valsalva, whereas there was no significant change in the anterior chamber depth (P=0.056). The angle recess during Valsalva had a positive correlation with the baseline anterior chamber angle (r=0.41, P=0.05) and a negative correlation with the ciliary body thickness (r=-0.52, P=0.046). ::: ::: ::: CONCLUSIONS ::: The induction of Valsalva maneuver in day-to-day activities can lead to significant anterior segment angle shallowing and can lead to progression from the PAC stage to primary angle closure glaucoma in such predisposed eyes. The presence of a patent laser iridotomy may not prevent irido trabecular apposition during the Valsalva maneuver.


title: Concurrent software architectures for exploratory data analysis

abstract: Decades ago, increased volume of data made manual analysis obsolete and prompted the use of computational tools with interactive user interfaces and rich palette of data visualizations. Yet their classic, desktop-based architectures can no longer cope with the ever-growing size and complexity of data. Next-generation systems for explorative data analysis will be developed on client-server architectures, which already run concurrent software for data analytics but are not tailored to for an engaged, interactive analysis of data and models. In explorative data analysis, the key is the responsiveness of the system and prompt construction of interactive visualizations that can guide the users to uncover interesting data patterns. In this study, we review the current software architectures for distributed data analysis and propose a list of features to be included in the next generation frameworks for exploratory data analysis. The new generation of tools for explorative data analysis will need to address integrated data storage and processing, fast prototyping of data analysis pipelines supported by machine-proposed analysis workflows, pre-emptive analysis of data, interactivity, and user interfaces for intelligent data visualizations. The systems will rely on a mixture of concurrent software architectures to meet the challenge of seamless integration of explorative data interfaces at client site with management of concurrent data mining procedures on the servers. WIREs Data Mining Knowl Discov 2015, 5:165-180. doi: 10.1002/widm.1155


title: Comprehensible software fault and effort prediction

abstract: HighlightsWe argue that comprehensibility is crucial in software effort and fault prediction.We extracted new datasets based on the Android repository.ALPA extracts a tree that mimics the performance of the complex model.The extracted trees are not only comprehensible but also more accurate. Software fault and effort prediction are important tasks to minimize costs of a software project. In software effort prediction the aim is to forecast the effort needed to complete a software project, whereas software fault prediction tries to identify fault-prone modules. In this research both tasks are considered, thereby using different data mining techniques. The predictive models not only need to be accurate but also comprehensible, demanding that the user can understand the motivation behind the model's prediction. Unfortunately, to obtain predictive performance, comprehensibility is often sacrificed and vice versa. To overcome this problem, we extract trees from well performing Random Forests (RFs) and Support Vector Machines for regression (SVRs) making use of a rule extraction algorithm ALPA. This method builds trees (using C4.5 and REPTree) that mimic the black-box model (RF, SVR) as closely as possible. The proposed methodology is applied to publicly available datasets, complemented with new datasets that we have put together based on the Android repository. Surprisingly, the trees extracted from the black-box models by ALPA are not only comprehensible and explain how the black-box model makes (most of) its predictions, but are also more accurate than the trees obtained by working directly on the data.


title: Visual Measurement and Prediction of Ball Trajectory for Table Tennis Robot

abstract: A high-speed stereovision system with two smart cameras is presented to track a table tennis ball, which adopts a distributed parallel processing architecture based on a local area network. A set of novel algorithms with little computation and good robustness running in the smart cameras is also proposed to recognize and track the ball in the images. A computer receives the image coordinates of the ball from the cameras via the local area network and computes its 3-D positions in the working frame. Then, the flying trajectory of the ball is estimated and predicted according to the measured positions and the flying and rebound models. The main motion parameters of the ball such as the landing point and striking point are calculated from its predicted trajectory. Experimental results show that the developed image-processing algorithms are robust enough to distinguish the ball from a complex dynamic background. The predicted landing point and striking point of the ball have satisfactory precision.


title: Bus tracking and monitoring using RFID

abstract: The rapid growth in population in India causes more crowding at public bus stops. People long wait for arriving of buses and suddenly gather near bus when it arrives and travel in overcrowded buses on footboards which leads to accidents. Theft is another risk of overcrowding. All this happens due to lack of proper information of arriving of buses at many of the bus stops. To solve this problem in a cost effective way, we have developed an IoT based bus tracking system to show the current location of the bus and seat availability in the arriving buses. We have used RFID technology for tracking the bus and Thingspeak web server for displaying location of the bus and seat availability in android application in a smart phone. This system enables the commuters to know the exact location of the bus, and occupancy level in the bus, which enable the commuters to take decision, whether to board this bus or next. This reduces the waiting time and overcrowding at bus stops. It also reduces the risk of accidents and thefts.


title: An exact equivalence between the discrete- and continuous-time formulations of the Kalman filter

abstract: It is shown that if the definition of the covariance of a white noise sequence in discrete-time is derived from the accepted mathematical description for the covariance of a white noise process in continuous-time, compatibility between the discrete- and continuous-time versions of the Kalman filter is complete. Consequently the approach to the limit of the discrete-time filter to obtain its continuous-time equivalent no longer depends on Kalman's non-rigorous argument for dividing the covariance of a white noise sequence by the sampling interval Δt. Such an exact equivalence is essential for comparing the accuray of discrete-time computations with results obtained by numerically integrating the continuous-time filter equations. This approach provides a pragmatic technique for the determination of the most suitable sampling interval for discrete-time Kalman filtering.


Integrating Machine Learning and Workflow Management to Support Acquisition and Adaptation of Workflow Models


title: On the Relationship between Sum-Product Networks and Bayesian Networks

abstract: In this paper, we establish some theoretical connections between Sum-Product Networks (SPNs) and Bayesian Networks (BNs). We prove that every SPN can be converted into a BN in linear time and space in terms of the network size. The key insight is to use Algebraic Decision Diagrams (ADDs) to compactly represent the local conditional probability distributions at each node in the resulting BN by exploiting context-specific independence (CSI). The generated BN has a simple directed bipartite graphical structure. We show that by applying the Variable Elimination algorithm (VE) to the generated BN with ADD representations, we can recover the original SPN where the SPN can be viewed as a history record or caching of the VE inference process. To help state the proof clearly, we introduce the notion of {\em normal} SPN and present a theoretical analysis of the consistency and decomposability properties. We conclude the paper with some discussion of the implications of the proof and establish a connection between the depth of an SPN and a lower bound of the tree-width of its corresponding BN.


title: Municipal solid waste source-separated collection in China: A comparative analysis.

abstract: A pilot program focusing on municipal solid waste (MSW) source-separated collection was launched in eight major cities throughout China in 2000. Detailed investigations were carried out and a comprehensive system was constructed to evaluate the effects of the eight-year implementation in those cities. This paper provides an overview of different methods of collection, transportation, and treatment of MSW in the eight cities; as well as making a comparative analysis of MSW source-separated collection in China. Information about the quantity and composition of MSW shows that the characteristics of MSW are similar, which are low calorific value, high moisture content and high proportion of organisms. Differences which exist among the eight cities in municipal solid waste management (MSWM) are presented in this paper. Only Beijing and Shanghai demonstrated a relatively effective result in the implementation of MSW source-separated collection. While the six remaining cities result in poor performance. Considering the current status of MSWM, source-separated collection should be a key priority. Thus, a wider range of cities should participate in this program instead of merely the eight pilot cities. It is evident that an integrated MSWM system is urgently needed. Kitchen waste and recyclables are encouraged to be separated at the source. Stakeholders involved play an important role in MSWM, thus their responsibilities should be clearly identified. Improvement in legislation, coordination mechanisms and public education are problematic issues that need to be addressed.


title: A physics-based coordinate transformation for 3-D image matching

abstract: Many image matching schemes are based on mapping coordinate locations, such as the locations of landmarks, in one image to corresponding locations in a second image. A new approach to this mapping (coordinate transformation), called the elastic body spline (EBS), is described. The spline is based on a physical model of a homogeneous, isotropic three-dimensional (3-D) elastic body. The model can approximate the way that some physical objects deform. The EBS as well as the affine transformation, the thin plate spline [1], [2] and the volume spline [3] are used to match 3-D magnetic resonance images (MRI's) of the breast that are used in the diagnosis and evaluation of breast cancer. These coordinate transformations are evaluated with different types of deformations and different numbers of corresponding (paired) coordinate locations. In all but one of the cases considered, using the EBS yields more similar images than the other methods.


title: Learning Structured Text Representations

abstract: In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluation across different tasks and datasets shows that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.


title: Visplause: Visual Data Quality Assessment of Many Time Series Using Plausibility Checks

abstract: Trends like decentralized energy production lead to an exploding number of time series from sensors and other sources that need to be assessed regarding their data quality (DQ). While the identification of DQ problems for such routinely collected data is typically based on existing automated plausibility checks, an efficient inspection and validation of check results for hundreds or thousands of time series is challenging. The main contribution of this paper is the validated design of Visplause, a system to support an efficient inspection of DQ problems for many time series. The key idea of Visplause is to utilize meta-information concerning the semantics of both the time series and the plausibility checks for structuring and summarizing results of DQ checks in a flexible way. Linked views enable users to inspect anomalies in detail and to generate hypotheses about possible causes. The design of Visplause was guided by goals derived from a comprehensive task analysis with domain experts in the energy sector. We reflect on the design process by discussing design decisions at four stages and we identify lessons learned. We also report feedback from domain experts after using Visplause for a period of one month. This feedback suggests significant efficiency gains for DQ assessment, increased confidence in the DQ, and the applicability of Visplause to summarize indicators also outside the context of DQ.


title: Massively Parallel Logic Simulation with GPUs

abstract: In this article, we developed a massively parallel gate-level logical simulator to address the ever-increasing computing demand for VLSI verification. To the best of the authors’ knowledge, this work is the first one to leverage the power of modern GPUs to successfully unleash the massive parallelism of a conservative discrete event-driven algorithm, CMB algorithm. A novel data-parallel strategy is proposed to manipulate the fine-grain message passing mechanism required by the CMB protocol. To support robust and complete simulation for real VLSI designs, we establish both a memory paging mechanism and an adaptive issuing strategy to efficiently utilize the GPU memory with a limited capacity. A set of GPU architecture-specific optimizations are performed to further enhance the overall simulation performance. On average, our simulator outperforms a CPU baseline event-driven simulator by a factor of 47.4X. This work proves that the CMB algorithm can be efficiently and effectively deployed on modern GPUs without the performance overhead that had hindered its successful applications on previous parallel architectures.


title: On the performance modelling and optimisation of DOCSIS HFC networks

abstract: The DOCSIS protocol defines the MAC and physical layer operations governing two-way transmission of voice, video and multimedia data over HFC cable networks, thus constituting a complex system with many interdependent parameters. This tutorial employs simulation and analytic methodologies for the performance modelling and optimisation of DOCSIS 1.1/2.0 HFC networks with particular focus on the contention resolution algorithm, upstream bandwidth allocation strategies, flow-priority scheduling disciplines, QoS provisioning and TCP applications. In this context two performance evaluation case studies are reviewed in detail - based respectively on two open queueing network models of DOCSIS 1.1/2.0 HFC networks. The first study evaluates, via 'ns' simulation, the effect of carrying TCP/IP traffic on network performance whilst the second optimises analytically the upstream network bandwidth allocation. It is expected that many of the performance affecting operational behaviours exhibited by former releases of DOCSIS-based HFC networks will also exist under the latest DOCSIS 3.0 protocol and future extensions.


title: Nutrition Knowledge in Athletes: A Systematic Review

abstract: Context: Nutrition education aims to enhance knowledge and improve dietary intake in athletes. Understanding athletes’ nutrition knowledge and its influence on dietary intake will inform nutrition-education programs in this population. Purpose: To systematically review the level of nutrition knowledge in athletes, benchmark this against nonathlete comparison groups, and determine the impact of nutrition knowledge on dietary intake. Methods: An extensive literature search from the earliest record to March 2010 using the terms nutrition knowledge or diet knowledge and athlete or sport was conducted. Included studies recruited able or physically disabled, male or female, competitive (recreational or elite) athletes over the age of 13 yr. Quantitative assessment of knowledge and, if available, diet intake was required. Because of variability in the assessment of nutrition knowledge and dietary intake, meta-analysis was not conducted. Results: Twenty-nine studies (17 published before 2000) measuring nutrition ...


title: Split face comparative study of microneedling with PRP versus microneedling with vitamin C in treating atrophic post acne scars

abstract: INTRODUCTION ::: Acne scars are largely preventable complications of acne. 95% of the scars occur over the face thus impacting the quality of life. Correction of scars is the priority for acne patients. ::: ::: ::: MATERIALS AND METHODS ::: Thirty patients with post acne atrophic facial scars attending the OPD during the period from April to October 2013 were offered four sittings of microneedling with PRP on one side and microneedling with vitamin C on other side of the face at an interval of 1 month. ::: ::: ::: RESULTS ::: Twenty-seven out of the total 30 patients completed the treatment schedule. Two patients were lost to follow up and one dropped out of the study due to severe PIH. Mean age of the patients was 27.5 years. Out of 30 patients, 23 achieved reduction in scarring by one or two grades. Excellent response was seen in five (18.5%) patients with platelet-rich plasma (PRP) as compared to two (7%) patients who received treatment with vitamin C according to physician's assessment. As far as up gradation by 1 score is considered, i.e., good response, it was similar in both cases. Vitamin C did not prove to be as efficacious as PRP since 10 (37%) patients had poor response in vitamin C-treated area compared to only 6 (22.2%) patients who underwent PRP therapy, but vitamin C proved to be efficacious in dealing with post inflammatory hyper-pigmentation secondary to acne. Patients were more satisfied with PRP as compared to vitamin C. The results were evaluated and statistical analysis was done using SPSS 16.0.2. ::: ::: ::: CONCLUSIONS ::: Overall results were better with microneedling and PRP. Vitamin C combined with microneedling also showed improvement with respect to firmness and smoothness of skin; as well as post inflammatory hyper-pigmentation. Microneedling combined with PRP proved to be good in treating boxcar and rolling scars but had limited efficacy in dealing with ice pick scars.


title: A taxonomic reevaluation of Phrynops (Testudines: Chelidae) with the description of two new genera and a new species of Batrachemys.

abstract: Relationships among turtle species loosely categorized within the South American genus Phrynops are explored. Three once recognized genera (Batrachemys, Mesoclemmys and Phrynops) that were demoted to subgenera, and then synonymized with Phrynops, are demonstrated to warrant full recognition based on morphometric analysis, skull osteology, and mitochondrial and nuclear gene sequencing. Mesoclemmys is resurrected from the synonymy of Phrynops as a monotypic genus including M. gibba. The genus Rhinemys, previously a synonym of Phrynops, is resurrected for the species R. rufipes. Ranacephala gen. nov. is described to include the species R. hogei. The genus Batrachemys is resurrected from the synonymy of Phrynops and includes B. dahli, B. nasuta, B. raniceps, B. tuberculata, and B. zuliae. The taxon vanderhaegei is placed in Bufocephala gen. nov. The genus Phrynops is redefined to include the taxa P. geoffroanus, P. hilarii, P. tuberosus, and P. williamsi. Cladistic analysis of morphological data supports this taxonomy. A new species of Batrachemys is described from the western Amazon region, and is distinguished by having facial markings in juveniles, a relatively wide head, and a flattened shell. The new species, B. heliostemma sp. nov., is sympatric with and most similar to the recently resurrected form Batrachemys raniceps in the upper Amazonian region of Peru and adjacent Brazil, Ecuador, Venezuela, and Colombia. Lastly, morphometric data from living and museum specimens of all species of Batrachemys are presented.


title: Learning from unscripted deictic gesture and language for human-robot interactions

abstract: As robots become more ubiquitous, it is increasingly important for untrained users to be able to interact with them intuitively. In this work, we investigate how people refer to objects in the world during relatively unstructured communication with robots. We collect a corpus of deictic interactions from users describing objects, which we use to train language and gesture models that allow our robot to determine what objects are being indicated. We introduce a temporal extension to state-of-the-art hierarchical matching pursuit features to support gesture understanding, and demonstrate that combining multiple communication modalities more effectively capture user intent than relying on a single type of input. Finally, we present initial interactions with a robot that uses the learned models to follow commands.


title: Toward Reconstructing Surfaces With Arbitrary Isotropic Reflectance : A Stratified Photometric Stereo Approach

abstract: We consider the problem of reconstructing the shape of a surface with an arbitrary, spatially varying isotropic bidirectional reflectance distribution function (BRDF), and introduce a novel, stratified photometric stereo method. By using a particular configuration of lights, it is possible to use symmetry in the image measurements resulting from BRDF isotropy to estimate at each point a plane containing the surface normal. For differentiable surfaces, this allows us to recover the isocontours of the depth map, but not the actual depth associated with each contour. The isocontour structure provides topological information about the surface (critical points, Reeb graph, etc.). By using additional cues in the image data or imposing additional constraints on the surface (e.g., shadows, specular highlights, Helmholtz reciprocity, uniform BRDF), the unknown height of each isocontour can be estimated and the metric structure is resolved. We validate this technique on real and synthetic data by successfully recovering the isocontours of the depth map from images.


title: High power microwave sources and applications

abstract: There has been considerable interest recently in high power microwave (HPM) sources for nonlethal directed energy weaponry applications. Many of these sources that are being developed are derivatives of sources that are well known to the vacuum electronics community. Others are unique to the HPM community and have no analog in traditional microwave sources. For electronic attack (referred to in the media as the "e-bomb"), HPM sources are being developed that provide peak powers that exceed 1 GW at frequencies on the order of 1 GHz in short pulses, typically on the order of 100 ns. The technology that is used to drive such sources has its roots in pulsed power, and a comprehensive understanding of their behavior requires the use of tools that have been developed in the plasma physics community. The ever-increasing reliance on the use of microprocessors that have increasing density of circuits packaged on a chip makes such systems increasingly vulnerable to HPM. This paper provides an overview of HPM sources, their applications, and will highlight areas of intense, ongoing research activity.


title: A Koch-Shaped Log-Periodic Dipole Array (LPDA) Antenna for Universal Ultra-High-Frequency (UHF) Radio Frequency Identification (RFID) Handheld Reader

abstract: In this communication, we propose a printed log-periodic dipole array (LPDA) antenna covering the universal ultra-high-frequency (UHF) radio frequency identification (RFID) band of operation. Koch-shaped dipoles together with closely-coupled parasitic elements as directors were utilized for compactness in size and improvement in front-to-back ratio (F/B ratio). The experimental results demonstrate that the proposed antenna features good impedance match, moderate gain, better than 80% total radiation efficiency and excellent front-to-back ratio over the universal UHF RFID band from 840 MHz to 960 MHz. The antenna is well suitable for applications in RFID handheld readers.


title: A flexible, wideband RFID tag antenna for metallic surfaces

abstract: A flexible, wideband antenna for radio frequency identification tags mountable on metallic surfaces is presented. A novel balanced feed structure, in which a longitudinal two-step staircase slit is inserted at one of the radiating edges of a patch and a tag chip feed is placed between the two steps, is proposed to perform simple and wideband impedance matching between an antenna and a tag chip. There is no via connection between the patch and ground plane. The measured 3-dB return loss bandwidth of the proposed antenna is about 84 MHz, and its read range is about 8.1 m.


title: An Augmented Reality Environment for Astronomy Learning in Elementary Grades: An Exploratory Study

abstract: This paper describes an ongoing research comparing two 3D astronomical tangible models: an Augmented Reality model versus a physical model. According to IBSE principles, learners should investigate and manipulate in order to become conscious of the origin of astronomical phenomena, construct scientific knowledge and change their misconceptions. In primary French schools, physical models are usually used. However, children do not take advantage of these models and form new synthetic models instead of scientific ones. We aim at providing an adapted pedagogical environment support. An Augmented Reality environment was designed for inquiry-based learning. This tangible AR model shows augmented views of the celestial bodies and supports the pupils' investigations using spatial visual guides and views from a terrestrial observer. The AR model not only exposes the phenomena as in several Virtual Environments, but also allows pupils to virtually move the celestial bodies and test "as for real" their hypotheses. Our results show that the AR environment is particularly suitable for astronomy learning compared to the physical one. Only AR users have developed scientific conceptions of the explored astronomical phenomena and learnings have been significantly improved. Furthermore, we present some arguments in order to support the assumption that the AR model assists the process of scaffolding and motivation dynamic by enhancing task controllability and by promoting collaborative learning.


title: WORT: write optimal radix tree for persistent memory storage systems

abstract: Recent interest in persistent memory (PM) has stirred development of index structures that are efficient in PM. Recent such developments have all focused on variations of the B-tree. In this paper, we show that the radix tree, which is another less popular indexing structure, can be more appropriate as an efficient PM indexing structure. This is because the radix tree structure is determined by the prefix of the inserted keys and also does not require tree rebalancing operations and node granularity updates. However, the radix tree as-is cannot be used in PM. As another contribution, we present three radix tree variants, namely, WORT (Write Optimal Radix Tree), WOART (Write Optimal Adaptive Radix Tree), and ART+CoW. Of these, the first two are optimal for PM in the sense that they only use one 8-byte failure-atomic write per update to guarantee the consistency of the structure and do not require any duplicate copies for logging or CoW. Extensive performance studies show that our proposed radix tree variants perform considerable better than recently proposed B-tree variants for PM such NVTree, wB+Tree, and FPTree for synthetic workloads as well as in implementations within Memcached.


title: NHTSA driver distraction research: past, present, and future

abstract: Driver distraction has been identified as a high-priority topic by the National Highway Traffic Safety Administration, reflecting concerns about the compatibility of certain in-vehicle technologies with the driving task, whether drivers are making potentially dangerous decisions about when to interact with in-vehicle technologies while driving, and that these trends may accelerate as new technologies continue to become available. Since 1991, NHTSA has conducted research to understand the factors that contribute to driver distraction and to develop methods to assess the extent to which in-vehicle technologies may contribute to crashes. This paper summarizes significant findings from past NHTSA research in the area of driver distraction and workload, provides an overview of current ongoing research, and describes upcoming research that will be conducted, including research using the National Advanced Driving Simulator and work to be conducted at NHTSA's Vehicle Research and Test Center. Preliminary results of the ongoing research are also presented. For the covering abstract see ITRD E111577.


title: Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

abstract: The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis (CAD) of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and over-segmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e. no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimisation algorithm for lesion segmentation.


A taxonomy of camera calibration and video projection correction methods


title: Is Joint Training Better for Deep Auto-Encoders?

abstract: Traditionally, when generative models of data are developed via deep architectures, greedy layer-wise pre-training is employed. In a well-trained model, the lower layer of the architecture models the data distribution conditional upon the hidden variables, while the higher layers model the hidden distribution prior. But due to the greedy scheme of the layerwise training technique, the parameters of lower layers are fixed when training higher layers. This makes it extremely challenging for the model to learn the hidden distribution prior, which in turn leads to a suboptimal model for the data distribution. We therefore investigate joint training of deep autoencoders, where the architecture is viewed as one stack of two or more single-layer autoencoders. A single global reconstruction objective is jointly optimized, such that the objective for the single autoencoders at each layer acts as a local, layer-level regularizer. We empirically evaluate the performance of this joint training scheme and observe that it not only learns a better data model, but also learns better higher layer representations, which highlights its potential for unsupervised feature learning. In addition, we find that the usage of regularizations in the joint training scheme is crucial in achieving good performance. In the supervised setting, joint training also shows superior performance when training deeper models. The joint training framework can thus provide a platform for investigating more efficient usage of different types of regularizers, especially in light of the growing volumes of available unlabeled data.


title: 3D human body modeling based on single Kinect

abstract: 3D human body models have been widely used in all fields. For acquiring precise human body models, the research about their recognition and reconstruction has become a major topic. Here we present a new method for modeling 3D human body based on single Kinect from the viewpoint of cost and operability. In this method, human body 3D information is recognized and acquired by only one Kinect while 3D human body models are reconstructed by using the tools of Processing and Point Cloud Library. To achieve the reconstruction objective, the iterative closest point algorithm is adopted for registering the captured upper human body 3D point cloud data with the standard reference human body data. The experiment results demonstrate that this method is feasible.


title: Ballet hero: building a garment for memetic embodiment in dance learning

abstract: This paper describes the analysis and design of a wearable technology garment intended to aid with the instruction of ballet technique to adult beginners. A phenomenological framework is developed and used to assess physiological training tools. Following this, a garment is developed that incorporates visual feedback inspired by animation techniques that more directly convey the essential movements of ballet. The garment design is presented, and a discussion is provided on the challenges of constructing an e-textile garment using contemporary materials and techniques.


title: A Comprehensive Review of Audio Steganalysis Methods

abstract: Recently, merging signal processing techniques with information security services has drawn a lot of attention. Steganography and steganalysis are among those trends. Like their counterparts in cryptology, steganography and steganalysis are in a constant battle. Steganography methods try to hide the presence of covert messages in innocuous-looking data, whereas steganalysis methods try to break steganography algorithms and reveal the existence of hidden messages. The streaming nature of audio signals, their popularity, and their wide spread usage make them very good candidates for steganography. This has led to a very rich literature on both steganography and steganalysis of audio signals. This study intends to conduct a comprehensive review of audio steganalysis methods aggregated over near 15 years. To that end, both compressed and con-compressed methods are reviewed, and then their important details are presented in different tables. Furthermore, some of the most recent audio steganalysis methods (both non-compressed and compressed ones) are implemented and comparative analyses on their performances are conducted. Finally, the study provides some possible directions for future research studies on audio steganalysis.