title: Factors Affecting the Adoption of Formal Accounting Systems by SMEs

abstract: Small to medium-sized enterprises’ (SMEs) poor performance is often ascribed to external factors such as burdensome character of the legal framework, limited scope to penetrate the export market and access to finance. However, internal factors such as marketing, operation and in particular accounting services may be equally responsible to such state of affairs. This study therefore attempts to analyse the importance attached to formal accounting systems among the small to medium-sized Mauritian manufacturing firms operating in six main industry groups. The research findings are based on a comprehensive survey of the financial and working capital management practices of 141 SMEs. Additional rigour to the research findings was possible through the use of 12 mini case studies.


title: SwGridNet: A Deep Convolutional Neural Network based on Grid Topology for Image Classification

abstract: Deep convolutional neural networks (CNNs) achieve remarkable performance on image classification tasks. Recent studies, however, have demonstrated that generalization abilities are more important than the depth of neural networks for improving performance on image classification tasks. Herein, a new neural network called SwGridNet is proposed. A SwGridNet includes many convolutional processing units which connect mutually as a grid network where many processing paths exist between input and output. A SwGridNet has high generalization capability because the multipath architecture has the same effect of ensemble learning. As described in this paper, details of the SwGridNet network architecture are presented. Experimentally obtained results presented in this paper show that SwGridNets respectively achieve test error rates of 2.95% and 15.67% in a CIFAR-10 and CIFAR-100 classification tasks. The results indicate that the SwGridNet performance approximates that of state-of-the-art deep CNNs.


title: Design of coaxial cavity for high power magnetron

abstract: This paper presents electromagnetic design of coaxial cavity for high power X-band coaxial magnetron. The initial dimensions of the cavity have been calculated by solving transcendental equation using graphical method. The eigenmode simulation has been carried out by using CST MW Studio. It is found that TE011 mode occurs at 9.3 GHz. Output coupler of cavity is designed to launch RF power into WR112. Proposed design is analyzed in terms of performance parameters i.e., unloaded, loaded, and external quality factors. Circuit efficiency and coupling coefficient are analyzed by varying critical geometrical parameters. Unloaded Q factor of 13108 is achieved for circuit efficiency of 93%.


title: Wireless Sensor Network for Precise Agriculture Monitoring

abstract: Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments.


Relational Database to NoSQL Conversion by Schema Migration and Mapping


title: Experiences of integration and performance testing of multilingual OCR for printed Indian scripts

abstract: This paper presents integration and testing scheme for managing a large Multilingual OCR Project. The project is an attempt to implement an integrated platform for OCR of different Indian languages. Software engineering, workflow management and testing processes have been discussed in this paper. The OCR has now been experimentally deployed for some specific applications and currently is being enhanced for handling the space and time constraints, achieving higher recognition accuracies and adding new functionalities.


Entity-Relationship Modeling


Ion Pairs Spooky Action


title: Deep Learning Computed Tomography

abstract: In this paper, we demonstrate that image reconstruction can be expressed in terms of neural networks. We show that filtered back-projection can be mapped identically onto a deep neural network architecture. As for the case of iterative reconstruction, the straight forward realization as matrix multiplication is not feasible. Thus, we propose to compute the back-projection layer efficiently as fixed function and its gradient as projection operation. This allows a data-driven approach for joint optimization of correction steps in projection domain and image domain. As a proof of concept, we demonstrate that we are able to learn weightings and additional filter layers that consistently reduce the reconstruction error of a limited angle reconstruction by a factor of two while keeping the same computational complexity as filtered back-projection. We believe that this kind of learning approach can be extended to any common CT artifact compensation heuristic and will outperform hand-crafted artifact correction methods in the future.


title: Mediated experts for deep convolutional networks

abstract: We present a new supervised architecture termed Mediated Mixture-of-Experts (MMoE) that allows us to improve classification accuracy of Deep Convolutional Networks (DCN). Our architecture achieves this with the help of expert networks: A network is trained on a disjoint subset of a given dataset and then run in parallel to other experts during deployment. A mediator is employed if experts contradict each other. This allows our framework to naturally support incremental learning, as adding new classes requires (re-)training of the new expert only. We also propose two measures to control computational complexity: An early-stopping mechanism halts experts that have low confidence in their prediction. The system allows to trade-off accuracy and complexity without further retraining. We also suggest to share low-level convo-lutional layers between experts in an effort to avoid computation of a near-duplicate feature set. We evaluate our system on a popular dataset and report improved accuracy compared to a single model of same configuration.


title: Plastics and Health Risks

abstract: By 2010, the worldwide annual production of plastics will surpass 300 million tons. Plastics are indispensable materials in modern society, and many products manufactured from plastics are a boon to public health (e.g., disposable syringes, intravenous bags). However, plastics also pose health risks. Of principal concern are endocrine-disrupting properties, as triggered for example by bisphenol A and di-(2-ethylhexyl) phthalate (DEHP). Opinions on the safety of plastics vary widely, and despite more than five decades of research, scientific consensus on product safety is still elusive. This literature review summarizes information from more than 120 peer-reviewed publications on health effects of plastics and plasticizers in lab animals and humans. It examines problematic exposures of susceptible populations and also briefly summarizes adverse environmental impacts from plastic pollution. Ongoing efforts to steer human society toward resource conservation and sustainable consumption are discussed, including the concept of the 5 Rs--i.e., reduce, reuse, recycle, rethink, restrain--for minimizing pre- and postnatal exposures to potentially harmful components of plastics.


title: Applying particle swarm optimization to software testing

abstract: Evolutionary structural testing is an approach to automatically generating test cases that achieve high structural code coverage. It typically uses genetic algorithms (GAs) to search for relevant test cases. In recent investigations particle swarm optimization (PSO), an alternative search technique, often outperformed GAs when applied to various problems. This raises the question of how PSO competes with GAs in the context of evolutionary structural testing.In order to contribute to an answer to this question, we performed experiments with 25 small artificial test objects and 13 more complex industrial test objects taken from various development projects. The results show that PSO outperforms GAs for most code elements to be covered in terms of effectiveness and efficiency.


title: The Transporters Associated with Antigen Processing (TAP)

abstract: Class I molecules of the major histocompatibility complex (MHC) present peptides derived from endogenous proteins at the cell surface. During viral infection or malignant transformation a different set of peptides is displayed by MHC class I molecules. These antigen-loaded class I complexes are recognized by cytotoxic T cells via the T cell receptors as nonself, thus leading to the destruction of the abnormal cell.


title: Merkle Tree Traversal in Log Space and Time

abstract: We present a technique for Merkle tree traversal which requires only logarithmic space and time. For a tree with N leaves, our algorithm computes sequential tree leaves and authentication path data in time 2log 2 (N) and space less than 3log 2 (N), where the units of computation are hash function evaluations or leaf value computations, and the units of space are the number of node values stored. This result is an asymptotic improvement over all other previous results (for example, measuring cost = space * time). We also prove that the complexity of our algorithm is optimal: There can exist no Merkle tree traversal algorithm which consumes both less than O(log 2 (N)) space and less than O(log 2 (N)) time. Our algorithm is especially of practical interest when space efficiency is required.


title: Approximation algorithms for bipartite matching with metric and geometric costs

abstract: Let G = G(A∪B,A×B), with |A| = |B| = n, be a weighted bipartite graph, and let d(·,·) be the cost function on the edges. Let w(M) denote the weight of a matching in G, and M* a minimum-cost perfect matching in G. We call a perfect matching M c-approximate, for c ≥ 1, if w(M) ≤ c · w(M*). We present three approximation algorithms for computing minimum-cost perfect matchings in G. First, we consider the case when d(·,·) is a metric. For any Δ > 0, we present an algorithm that, in O(n2+Δ log n log2(1/Δ)) time, computes a O(1/Δα)-approximate matching of G, where α = log3 2 ≈ 0.631. Next, we assume the existence of a dynamic data structure for answering approximate nearest neighbor (ANN) queries under d(··). Given two parameters e, d e) is the query and update time of an (e/2)-ANN data structure. Finally, we present an algorithm that works even if d(·,·) is not a metric but admits an ANN data structure for d(·,·). In particular, we present an algorithm that computes, in O(e---1n3/2τ (n, e) log4(n/e) log Δ) time, a (1 + e)-approximate matching of A and B; here Δ is the ratio of the largest to the smallest-cost edge in G, and τ (n, e) is the query and update time of an (e/c)-ANN data structure for some constant c > 1. We show that our results lead to faster matching algorithms for many geometric settings.


LPAD : Building Secure Enclave Storage using Authenticated Log-Structured Merge Trees


title: MTD(f), A Minimax Algorithm Faster Than NegaScout

abstract: MTD(f) is a new minimax search algorithm, simpler and more efficient than previous algorithms. In tests with a number of tournament game playing programs for chess, checkers and Othello it performed better, on average, than NegaScout/PVS (the AlphaBeta variant used in practically all good chess, checkers, and Othello programs). One of the strongest chess programs of the moment, MIT's parallel chess program Cilkchess uses MTD(f) as its search algorithm, replacing NegaScout, which was used in StarSocrates, the previous version of the program.


title: Corpus-level Fine-grained Entity Typing

abstract: Extracting information about entities remains an important research area. This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class, such as “food” or “artist”. The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that computes scores based on global information of an entity and (ii) a context model that first evaluates the individual occurrences of an entity and then aggregates the scores. Each of the two proposed models has specific properties. For the global model, learning highquality entity representations is crucial because it is the only source used for the predictions. Therefore, we introduce representations using the name and contexts of entities on the three levels of entity, word, and character. We show that each level provides complementary information and a multi-level representation performs best. For the context model, we need to use distant supervision since there are no context-level labels available for entities. Distantly supervised labels are nois and this harms the performance of models. Therefore, we introduce and apply new algorithms for noise mitigation using multi-instance learning. We show the effectiveness of our models on a large entity typing dataset built from Freebase.


title: Measuring The Semantic Similarity Of Texts

abstract: This paper presents a knowledge-based method for measuring the semantic-similarity of texts. While there is a large body of previous work focused on finding the semantic similarity of concepts and words, the application of these word-oriented methods to text similarity has not been yet explored. In this paper, we introduce a method that combines word-to-word similarity metrics into a text-to-text metric, and we show that this method outperforms the traditional text similarity metrics based on lexical matching.


title: Learning to Synthesize and Manipulate Natural Images

abstract: Humans are avid consumers of visual content. Every day, people watch videos, play games, and share photos on social media. However, there is an asymmetry-while everybody is able to consume visual data, only a chosen few are talented enough to express themselves visually. For the rest of us, most attempts at creating realistic visual content end up quickly "falling off" what we could consider to be natural images. In this thesis, we investigate several machine learning approaches for preserving visual realism while creating and manipulating photographs. We use these methods as training wheels for visual content creation. These methods not only help users easily synthesize realistic photos but also enable previously not possible visual effects.


title: Fail-safe and fail-operational systems safeguarded with coded processing

abstract: Safety has the highest priority because it helps contribute to customer confidence and thereby ensures further growth of the new markets, like electromobility. Therefore in series production redundant hardware concepts like dual core microcontrollers running in lock-step-mode are used to reach for example ASIL D safety requirements given from the ISO 26262. Coded processing is capable of reducing redundancy in hardware by adding diverse redundancy in software, e.g. by specific coding of data and instructions. A system with two coded processing channels is considered. Both channels are active. When one channel fails, the service can be continued with the other channel. It is imaginable that the two channels with implemented coded processing are running with time redundancy on a single core or on a multi core system where for example different ASIL levels are partitioned on different cores. In this paper a redundancy concept based on coded processing will be taken into account. The improvement of the Mean Time To Failure by safeguarding the system with coded processing will be computed for fail-safe as well as for fail-operational systems. The use of the coded processing approach in safeguarding failsafe systems is proved.


title: Differentially private data aggregation with optimal utility

abstract: Computing aggregate statistics about user data is of vital importance for a variety of services and systems, but this practice has been shown to seriously undermine the privacy of users. Differential privacy has proved to be an effective tool to sanitize queries over a database, and various cryptographic protocols have been recently proposed to enforce differential privacy in a distributed setting, e.g., statical queries on sensitive data stored on the user's side. The widespread deployment of differential privacy techniques in real-life settings is, however, undermined by several limitations that existing constructions suffer from: they support only a limited class of queries, they pose a trade-off between privacy and utility of the query result, they are affected by the answer pollution problem, or they are inefficient. This paper presents PrivaDA, a novel design architecture for distributed differential privacy that leverages recent advances in secure multiparty computations on fixed and floating point arithmetics to overcome the previously mentioned limitations. In particular, PrivaDA supports a variety of perturbation mechanisms (e.g., the Laplace, discrete Laplace, and exponential mechanisms) and it constitutes the first generic technique to generate noise in a fully distributed manner while maintaining the optimal utility. Furthermore, PrivaDA does not suffer from the answer pollution problem. We demonstrate the efficiency of PrivaDA with a performance evaluation, and its expressiveness and flexibility by illustrating several application scenarios such as privacy-preserving web analytics.


title: ORIGINAL PAPER Promoting Altruism Through Meditation: An 8-Week Randomized Controlled Pilot Study

abstract: The purpose of this study was to investigate the effects of a Buddhist meditation intervention on empathy, perceived stress, mindfulness, self-compassion, and of particular interest, the dispositional tendency to feel empathic concern rather than personal distress when perceiving another as in need, termed altruistic orientation. Participants were randomly assigned to an intervention group (n = 20) or a waiting list control group (n = 22). Results indicated a trend towards increases in altruistic orientation in the intervention group—an increase that significantly correlated with meditation time, decreases in perceived stress, and increases in self-compassion and mindfulness. Additionally, compared to the controls, significant increases in mindfulness and self-compassion and a significant decrease in perceived stress were obtained for the intervention group.


Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks


title: cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey

abstract: The"cvpaper.challenge"is a group composed of members from AIST, Tokyo Denki Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers on computer vision, pattern recognition, and related fields. For this particular review, we focused on reading the ALL 602 conference papers presented at the CVPR2015, the premier annual computer vision event held in June 2015, in order to grasp the trends in the field. Further, we are proposing"DeepSurvey"as a mechanism embodying the entire process from the reading through all the papers, the generation of ideas, and to the writing of paper.


title: Building a RCP (Rate Control Protocol) Test Network

abstract: We recently proposed the Rate Control Protocol (RCP) as way to minimize download times (or flow-completion times). Simulations suggest that if RCP were widely deployed, downloads would frequently finish ten times faster than with TCP. This is because RCP involves explicit feedback from the routers along the path, allowing a sender to pick a fast starting rate, and adapt quickly to network conditions. RCP is particularly appealing because it can be shown to be stable under broad operating conditions, and its performance is independent of the flow-size distribution and the RTT. Although it requires changes to the routers, the changes are small: The routers keep no per-flow state or per-flow queues, and the per-packet processing is minimal. However, the bar is high for a new congestion control mechanism - introducing a new scheme requires enormous change, and the argument needs to be compelling. And so, to enable some scientific and repeatable experiments with RCP, we have built and tested an open and public implementation of RCP; we have made available both the end- host software, and the router hardware. In this paper we describe our end-host implementation of RCP in Linux, and our router implementation in Verilog (on the NetFPGA platform). We hope that others will be able to use these implementations to experiment with RCP and further our understanding of congestion control.


title: StreamOp: An Innovative Middleware for Supporting Data Management and Query Functionalities over Sensor Network Streams Efficiently

abstract: During the last decade, a lot of progress has happened in the field of autonomous embedded devices (also known as motes) and wireless sensor networks. In this context, we envision next-generation systems to have autonomous database management functionalities on each mote, for supporting easy management of data on flash disks, adequate querying capabilities, and ubiquitous computing with no-effort. Following this major vision, in this paper we describe application scenarios, middleware approach and data management algorithms of a novel system, called Stream Operation (StreamOp), which effectively and efficiently realizes the depicted challenges. In particular, StreamOp supports heterogeneity, i.e. It works on top of different platforms, efficiency, i.e. It returns responses quickly, and autonomy, i.e. It saves battery power. We show how StreamOp provides these features, along with some experimental results that clearly validate our main assumptions.


title: Data Streams: Models and Algorithms

abstract: This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.


title: Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment

abstract: Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from large amounts of raw text. However, state-of-the-art systems that employ such representations yield modest gains in PP attachment accuracy. In this paper, we show that word vector representations can yield significant PP attachment performance gains. This is achieved via a non-linear architecture that is discriminatively trained to maximize PP attachment accuracy. The architecture is initialized with word vectors trained from unlabeled data, and relearns those to maximize attachment accuracy. We obtain additional performance gains with alternative representations such as dependency-based word vectors. When tested on both English and Arabic datasets, our method outperforms both a strong SVM classifier and state-of-the-art parsers. For instance, we achieve 82.6% PP attachment accuracy on Arabic, while the Turbo and Charniak self-trained parsers obtain 76.7% and 80.8% respectively.


title: Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study

abstract: Subjects with Alzheimer’s disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial role in detecting such changes from normal conditions. However, despite the existence of clinical measures that are used to classify and diagnose AD, a large amount of subjectivity continues to exist. Our aim was to assess the potential of machine learning in quantifying this process and optimizing or even reducing the amount of neuropsychological tests used to classify AD patients, also at an early stage of impairment. We investigated the role of twelve state-of-the-art neuropsychological tests in the automatic classification of subjects with none, mild, or severe impairment as measured by the clinical dementia rating (CDR). Data were obtained from the ADNI database. In the groups of measures used as features, we included measures of both cognitive domains and subdomains. Our findings show that some tests are more frequently best predictors for the automatic classification, namely, LM, ADAS-Cog, AVLT, and FAQ, with a major role of the ADAS-Cog measures of delayed and immediate memory and the FAQ measure of financial competency.


title: Traffic light recognition in varying illumination using deep learning and saliency map

abstract: The accurate detection and recognition of traffic lights is important for autonomous vehicle navigation and advanced driver aid systems. In this paper, the authors present a traffic light recognition algorithm for varying illumination conditions using computer vision and machine learning. More specifically, a convolutional neural network is used to extract and detect features from visual camera images. To improve the recognition accuracy, an on-board global position system (GPS) sensor is employed to identify the region-of-interest, in the visual image, that contains the traffic light. In addition, a saliency map containing the traffic light location is generated using the normal illumination recognition to assist the recognition under low illumination conditions. The proposed algorithm was evaluated on the authors data sets acquired in a variety of real world environments and compared with the performance of a baseline traffic signal recognition algorithm. The experimental results demonstrate the high recognition accuracy of the proposed algorithm in varied illumination conditions.


title: High-Frequency Electrical Characterization of a New Coaxial Silicone Rubber Socket for High-Bandwidth and High-Density Package Test

abstract: This paper, for the first time, proposes and verifies a new coaxial silicone rubber socket for high-bandwidth and high-density package test using a fabricated sample. In addition, this paper also characterizes and verifies the coaxial silicone rubber socket. Because of the proposed coaxial socket’s novel coaxial structure, the proposed socket successfully achieves the electrical performance improvement. For verification, we compare the proposed socket and the previous noncoaxial socket in time domain. The proposed socket has greater eye height and eye width in the measured eye diagram than those of the noncoaxial socket. Moreover, the slope in the eye diagram is also improved in the case of the proposed socket. Therefore, the measured eye diagram for the coaxial socket shows the improvement in electrical performances. This paper also characterizes the equivalent RLGC model for the coaxial silicone rubber socket. In order to verify the RLGC model, we compare the insertion losses and eye diagrams from measurement, 3-D electromagnetic simulation, and the proposed RLGC model, respectively. Their insertion losses are comparable up to 20 GHz. Furthermore, the obtained eye diagrams are almost identical at the data rate of 9.6 Gb/s. In conclusion, this paper successfully proposes, verifies, and characterizes a new coaxial silicone rubber socket for the first time.


title: Split learning for health: Distributed deep learning without sharing raw patient data

abstract: Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.


title: Return of the Devil in the Details: Delving Deep into Convolutional Nets

abstract: The latest generation of Convolutional Neural Networks (CNN) have achieved impressive results in challenging benchmarks on image recognition and object detection, significantly raising the interest of the community in these methods. Nevertheless, it is still unclear how different CNN methods compare with each other and with previous state-of-the-art shallow representations such as the Bag-of-Visual-Words and the Improved Fisher Vector. This paper conducts a rigorous evaluation of these new techniques, exploring different deep architectures and comparing them on a common ground, identifying and disclosing important implementation details. We identify several useful properties of CNN-based representations, including the fact that the dimensionality of the CNN output layer can be reduced significantly without having an adverse effect on performance. We also identify aspects of deep and shallow methods that can be successfully shared. In particular, we show that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost. Source code and models to reproduce the experiments in the paper is made publicly available.


title: The AmphiHex: A novel amphibious robot with transformable leg-flipper composite propulsion mechanism

abstract: The amphibious robot is so attractive and challenging for its broad application and its complex working environment. It should walk on rough ground, maneuver underwater and pass through transitional terrain such as sand and mud, simultaneously. To tackle with such a complex task, a novel amphibious robot (AmphiHex-I) with transformable leg-flipper composite propulsion is proposed and developed. This paper presents the detailed structure design of the transformable leg-flipper propulsion mechanism and its drive module, which enables the amphibious robot passing through the terrain, water and transitional zone between them. A preliminary theoretical analysis is conducted to study the interaction between the elliptic leg and transitional environment such as granular medium. An orthogonal experiment is designed to study the leg locomotion in the sandy and muddy terrain with different water content. Finally, basic propulsion experiments of AmphiHex-I are launched, which verified the locomotion capability on land and underwater is achieved by the transformable leg-flipper mechanism.


title: Dataset Augmentation for Pose and Lighting Invariant Face Recognition

abstract: The performance of modern face recognition systems is a function of the dataset on which they are trained. Most datasets are largely biased toward "near-frontal" views with benign lighting conditions, negatively effecting recognition performance on images that do not meet these criteria. The proposed approach demonstrates how a baseline training set can be augmented to increase pose and lighting variability using semi-synthetic images with simulated pose and lighting conditions. The semi-synthetic images are generated using a fast and robust 3-d shape estimation and rendering pipeline which includes the full head and background. Various methods of incorporating the semi-synthetic renderings into the training procedure of a state of the art deep neural network-based recognition system without modifying the structure of the network itself are investigated. Quantitative results are presented on the challenging IJB-A identification dataset using a state of the art recognition pipeline as a baseline.


title: Algorithm selection of off-policy reinforcement learning algorithm

abstract: Dialogue systems rely on a careful reinforcement learning design: the learning algorithm and its state space representation. In lack of more rigorous knowledge, the designer resorts to its practical experience to choose the best option. In order to automate and to improve the performance of the aforementioned process, this article formalises the problem of online off-policy reinforcement learning algorithm selection. A meta-algorithm is given for input a portfolio constituted of several off-policy reinforcement learning algorithms. It then determines at the beginning of each new trajectory, which algorithm in the portfolio is in control of the behaviour during the full next trajectory, in order to maximise the return. The article presents a novel meta-algorithm, called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is to freeze the policy updates at each epoch, and to leave a rebooted stochastic bandit in charge of the algorithm selection. Under some assumptions, a thorough theoretical analysis demonstrates its near-optimality considering the structural sampling budget limitations. Then, ESBAS is put to the test in a set of experiments with various portfolios, on a negotiation dialogue game. The results show the practical benefits of the algorithm selection for dialogue systems, in most cases even outperforming the best algorithm in the portfolio, even when the aforementioned assumptions are transgressed.


Mushroom Polysaccharides: Chemistry and Antiobesity, Antidiabetes, Anticancer, and Antibiotic Properties in Cells, Rodents, and Humans


title: Face Detection using Local SMQT Features and Split up Snow Classifier

abstract: The purpose of this paper is threefold: firstly, the local successive mean quantization transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up sparse network of winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the receiver operation characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors. A Matlab version of the face detection algorithm can be downloaded from http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13701& objectType=FILE.


title: (www.interscience.wiley.com) DOI: 10.1002/sim.2672 Paper Celebrating the 25th Anniversary of Statistics in Medicine Bayesian statistics in medicine: A 25 year review ‡

abstract: This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies. Copyright © 2006 John Wiley & Sons, Ltd.


title: Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates

abstract: Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study utilizes recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach utilizes the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationall...


title: A preliminary study of emotional intelligence, empathy and exam performance in first year medical students ☆

abstract: Abstract A group of 156 first year medical students completed measures of emotional intelligence (EI) and physician empathy, and a scale assessing their feelings about a communications skills course component. Females scored significantly higher than males on EI. Exam performance in the autumn term on a course component (Health and Society) covering general issues in medicine was positively and significantly related to EI score but there was no association between EI and exam performance later in the year. High EI students reported more positive feelings about the communication skills exercise. Females scored higher than males on the Health and Society component in autumn, spring and summer exams. Structural equation modelling showed direct effects of gender and EI on autumn term exam performance, but no direct effects other than previous exam performance on spring and summer term performance. EI also partially mediated the effect of gender on autumn term exam performance. These findings provide limited evidence for a link between EI and academic performance for this student group. More extensive work on associations between EI, academic success and adjustment throughout medical training would clearly be of interest.


title: Faun tail: a rare cutaneous sign of spinal dysraphism

abstract: Faun tail is a triangle-shaped hypertrichosis of the lumbosacral region. It is a rare condition and it can be a cutaneous marker of underlying spinal cord anomaly. We report on a 17-year-old female patient with hypertrichosis on the lumbosacral area since birth that was later diagnosed with tethered cord in magnetic resonance imaging.


title: A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology

abstract: Routing Protocol for Low power and Lossy network (RPL) topology attacks can downgrade the network performance significantly by disrupting the optimal protocol structure. To detect such threats, we propose a RPL-specification, obtained by a semi-auto profiling technique that constructs a high-level abstract of operations through network simulation traces, to use as reference for verifying the node behaviors. This specification, including all the legitimate protocol states and transitions with corresponding statistics, will be implemented as a set of rules in the intrusion detection agents, in the form of the cluster heads propagated to monitor the whole network. In order to save resources, we set the cluster members to report related information about itself and other neighbors to the cluster head instead of making the head overhearing all the communication. As a result, information about a cluster member will be reported by different neighbors, which allow the cluster head to do cross-check. We propose to record the sequence in RPL Information Object (DIO) and Information Solicitation (DIS) messages to eliminate the synchronized issue created by the delay in transmitting the report, in which the cluster head only does cross-check on information that come from sources with the same sequence. Simulation results show that the proposed Intrusion Detection System (IDS) has a high accuracy rate in detecting RPL topology attacks, while only creating insignificant overhead (about 6.3%) that enable its scalability in large-scale network.


title: Face recognition performance comparison between fake faces and live faces

abstract: Face recognition is a widely used biometric technology because it is both user friendly and more convenient to use than other biometric approaches. However, naive face recognition systems that do not support any type of liveness detection can be easily spoofed using just a photograph of a valid user. Face liveness detection is a key issue in the field of security systems that use a camera. Unfortunately, it is not easy to detect face liveness using existing methods, assuming that there are print failures and overall image blur. With the development of display devices and image capturing technology, it is possible to reproduce face images similar to real faces. Therefore, the number of attacks using a photograph or video displayed on a screen rather than paper will increase. In this study, we compare test results using live faces and high-definition face videos from light-emitting diode (LED) display devices and analyze the changes in face recognition performance according to the lighting direction. Experimental results show that there is no significant difference between live faces and not live faces under good lighting conditions. We suggest the use of gamma to reduce the performance gap between the two faces under poor lighting conditions. From these results, we can provide key solutions to resolve the issues associated with texture-based approaches.


title: Chronic granulomatous disease

abstract: A 2½-year-old child presented with multiple discrete granulomatous lesions on the face and flexural regions since the age of 2 months along with lymphadenopathy. The patient also had recurrent bouts of pyodermas and respiratory tract infections. Biopsy of the lesion showed necrosis of tissue with suppuration and histiocytes but no evidence of tuberculosis, fungal infections or atypical mycobacteria. Lymph node biopsy also showed necrosis with suppuration but no infective organism. Nitroblue tetrazolium test was negative indicating that the neutrophils failed to oxidize the dye. We are reporting here a rare case of chronic granulomatous disease.


title: Underwater 3D reconstruction using BlueView imaging sonar

abstract: This paper addresses the problem of 3D scene reconstruction from 2D imaging sonars. Reconstruction using imaging sonar being a non-trivial problem, we present an algorithm enabling the generation of a 3D map of the observed scene from a sequence of imaging sonar images. The inherent problem when using large aperture sonars being the unavailability of the elevation angle information, our algorithm solves this by associating multiple point of views and taking advantage of the sonar imaging process. Using this technique, we enable the surface reconstruction of an inspected structure using standard sonars, frequently embedded on many ROVs and AUVs on the market. In addition to performing tests in a simulated environment, tests made with real inspection data showed that accurate 3D mapping was possible using noisy standard sonar and AUV navigation. Results can be obtained in real operation time, paving the way for further 3D online applications.


title: VisFlowConnect: netflow visualizations of link relationships for security situational awareness

abstract: We present a visualization design to enhance the ability of an administrator to detect and investigate anomalous traffic between a local network and external domains. Central to the design is a parallel axes view which displays NetFlow records as links between two machines or domains while employing a variety of visual cues to assist the user. We describe several filtering options that can be employed to hide uninteresting or innocuous traffic such that the user can focus his or her attention on the more unusual network flows. This design is implemented in the form of VisFlowConnect, a prototype application which we used to study the effectiveness of our visualization approach. Using VisFlowConnect, we were able to discover a variety of interesting network traffic patterns. Some of these were harmless, normal behavior, but some were malicious attacks against machines on the network.


title: Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis

abstract: Aspect Based Sentiment Analysis (ABSA) provides further insight into the analysis of social media. Understanding user opinion about different aspects of products, services or policies can be used for improving and innovating in an effective way. Thus, it is becoming an increasingly important task in the Natural Language Processing (NLP) realm. The standard pipeline of aspect-based sentiment analysis consists of three phases: aspect category detection, Opinion Target Extraction (OTE) and sentiment polarity classification. In this article, we propose an alternative pipeline: OTE, aspect classification, aspect context detection and sentiment classification. As it can be observed, the opinionated words are first detected and then are classified into aspects. In addition, the opinionated fragment of every aspect is delimited before performing the sentiment analysis. This paper is focused on the aspect classification and aspect context detection phases and proposes a twofold contribution. First, we propose a hybrid model consisting of a word embeddings model used in conjunction with semantic similarity measures in order to develop an aspect classifier module. Second, we extend the context detection algorithm by Mukherjee et al. to improve its performance. The system has been evaluated using the SemEval2016 datasets. The evaluation shows through several experiments that the use of hybrid techniques that aggregate different sources of information improves the classification performance.


Implementing Lock-Free Queues


title: A Theoretical Model of Augmented Reality Acceptance

abstract: Recent tourism research increasingly explored the opportunities of ::: using Augmented Reality (AR) in order to boost tourism and increase ::: the value for tourists while travelling within a destination. The ::: Technology Acceptance Model (TAM) has been applied to a number of ::: research disciplines, lately also AR however, studies focusing on the ::: tourism context are still scarce. As this field is expected to increase in ::: importance rapidly due to technological advancements and research ::: into functionality, acceptance and usefulness, it is important to identify ::: what the basic requirements are for AR to be accepted by users. ::: Furthermore, the provision of a conceptual model provides researchers ::: with a starting point on which they can base their future research. ::: Therefore, this paper proposes an AR acceptance model including five ::: external variables that might be included in future AR acceptance ::: research.


title: Complex convolution Kernel for deep networks

abstract: Deep Convolutional Neural Network (CNN) is one of the most popular methods for image processing and recognition. There are many research works to improve the performance of CNNs. However, as an important part of CNNs, convolution kernel has rarely been discussed. As one Original Convolution Kernel (OCK) can only detect one type of visual feature with a fixed deformation, the networks using OCKs may learn many duplicate kernels with multiple deformations for one feature. In this paper, we propose a Complex Convolution Kernel (CCK), which can put the duplicate kernels together. Experiments on four popular datasets show the performance of networks is greatly improved by using CCKs, and suggest how to choose convolution kernels while designing networks.


title: Standard Function Blocks for Flexible IED in IEC 61850-Based Substation Automation

abstract: Flexible intelligent electronic devices (IEDs) are highly desirable to support free allocation of function to IED by means of software reconfiguration without any change of hardware. The application of generic hardware platforms and component-based software technology seems to be a good solution. Due to the advent of IEC 61850, generic hardware platforms with a standard communication interface can be used to implement different kinds of functions with high flexibility. The remaining challenge is the unified function model that specifies various software components with appropriate granularity and provides a framework to integrate them efficiently. This paper proposes the function-block (FB)-based function model for flexible IEDs. The standard FBs are established by combining the IEC 61850 model and the IEC 61499 model. The design of a simplified distance protection IED using standard FBs is described and investigated. The testing results of the prototype system in MATLAB/Simulink demonstrate the feasibility and flexibility of FB-based IEDs.


title: Cold, cholinergic and aquagenic urticaria in children: presentation of three cases and review of the literature.

abstract: Urticaria can be induced with a wide variety of environmental stimuli, such as cold, pressure, vibration, sunlight, exercise, temperature changes, heat, and water. Urticaria caused by physical factors has been reported as the cause of 6-17% of the chronic urticaria in children. We present three cases here cholinergic, cold and aquagenic urticaria - in which the diagnosis was proven with exercise, ice-cube and water provocation tests, respectively.


title: A Wideband Dual-Polarized L-Probe Antenna Array With Hollow Structure and Modified Ground Plane for Isolation Enhancement

abstract: A two-element wideband dual-polarized L-probe antenna array with hollow structure is presented in this letter. The hollow structure is applied to offer space for hosting other antennas (e.g., the concept of shared-aperture antenna). Each radiating element is designed with a compact size of 51 mm × 51 mm × 24.5 mm (0.374λ0  × 0.374λ0 × 0.18λ0 at 2.2 GHz) and has a hollow volume of 31 mm × 31 mm × 24.5 mm. With a slotted square ring patch and unique L-probes, the antenna element exhibits a fairly large fractional bandwidth of 45%, ranging from 1.71 to 2.69 GHz. Moreover, a narrow slot and T-shaped metallic structures on the ground plane are introduced to increase the input-port isolation of the proposed antenna array to 26 dB across the entire operating bandwidth. The experimental results indicate that the antenna array has an average realized gain of 10.5 dBi, which correlates well with the simulations.


title: Scannerless 3D imaging sensors

abstract: This contribution describes the research activity on the development of different smart pixel topologies aimed at three-dimensional (3D) vision applications exploiting the multiple-pulse indirect time-of-flight (TOF) and standard direct TOF techniques. The proposed approaches allow for the realization of scannerless laser ranging systems capable of fast collection of 3D data sets, as required in a growing number of applications like, automotive, security, surveillance and robotic guidance. Single channel approach, as well as matrix-organized sensors, will be described, facing the demanding constraints of specific applications, like the high dynamic range capability and the background immunity. Real time range (3D) and intensity (2D) imaging of non-cooperative targets, also in presence of strong background illumination, has been successfully performed in the 2m-9m range with a precision better than 5% and an accuracy of about 1%.


title: Mono- and Biarticular Muscle Activity During Jumping in Different Directions

abstract: This study examined the role of mono- and biarticular muscles in control of countermovement jumps (CMJ) in different directions. It was hypothesized that monoarticular muscles would demonstrate the same activity regardless of jump direction, based on previous studies which suggest their role is to generate energy to maximize center-of-mass (CM) velocity. In contrast, biarticular activity patterns were expected to change to control the direction of the ground reaction force (GRF) and CM velocity vectors. Twelve participants performed maximal CMJs in four directions: vertical, forward, intermediate forward, and backward. Electromyographical data from 4 monoarticular and 3 biarticular lower extremity muscles were analyzed with respect to segmental kinematics and kinetics during the jumps. The biarticular rectus femoris (RF), hamstrings (HA), and gastrocnemius all exhibited changes in activity magnitude and pattern as a function of jump angle. In particular, HA and RF demonstrated reciprocal trends, with HA a...


title: SceneGrok: inferring action maps in 3D environments

abstract: With modern computer graphics, we can generate enormous amounts of 3D scene data. It is now possible to capture high-quality 3D representations of large real-world environments. Large shape and scene databases, such as the Trimble 3D Warehouse, are publicly accessible and constantly growing. Unfortunately, while a great amount of 3D content exists, most of it is detached from the semantics and functionality of the objects it represents. In this paper, we present a method to establish a correlation between the geometry and the functionality of 3D environments. Using RGB-D sensors, we capture dense 3D reconstructions of real-world scenes, and observe and track people as they interact with the environment. With these observations, we train a classifier which can transfer interaction knowledge to unobserved 3D scenes. We predict a likelihood of a given action taking place over all locations in a 3D environment and refer to this representation as an action map over the scene. We demonstrate prediction of action maps in both 3D scans and virtual scenes. We evaluate our predictions against ground truth annotations by people, and present an approach for characterizing 3D scenes by functional similarity using action maps.


title: Functional Signatures and Pseudorandom Functions

abstract: We introduce two new cryptographic primitives: functional digital signatures and functional pseudorandom functions. ::: ::: In a functional signature scheme, in addition to a master signing key that can be used to sign any message, there are signing keys for a functionf, which allow one to sign any message in the range of f. As a special case, this implies the ability to generate keys for predicates P, which allow one to sign any message m for which Pm=1. ::: ::: We show applications of functional signatures to constructing succinct non-interactive arguments and delegation schemes. We give several general constructions for this primitive based on different computational hardness assumptions, and describe the trade-offs between them in terms of the assumptions they require and the size of the signatures. ::: ::: In a functional pseudorandom function, in addition to a master secret key that can be used to evaluate the pseudorandom function F on any point in the domain, there are additional secret keys for a functionf, which allow one to evaluate F on any y for which there exists an x such that fx=y. As a special case, this implies pseudorandom functions with selective access, where one can delegate the ability to evaluate the pseudorandom function on inputs y for which a predicate Py=1 holds. We define and provide a sample construction of a functional pseudorandom function family for prefix-fixing functions. This construction yields, in particular, punctured pseudorandom functions, which have proven an invaluable tool in recent advances in obfuscation Sahai and Waters ePrint 2013.


title: Social Network and Sentiment Analysis on Twitter: Towards a Combined Approach

abstract: Twitter is a platform which may contain opinions, thoughts, facts and other information. Within it, many and various communities are originated by users with common interests, or with similar ways to feel part of the community. This paper presents a possible combined approach between Social Network Analysis and Sentiment Analysis. In particular, we have tried to associate a sentiment to the nodes of the graphs showing the social connections, and this may highlight the potential correlations. The idea behind it is that, on the one hand, the network topology can contextualize and then, in part, unmask some incorrect results of the Sentiment Analysis; on the other hand, the polarity of the feeling on the network can highlight the role of semantic connections in the hierarchy of the communities that are present in the network. In this work, we illustrate the approach to the issue, together with the system architecture and, then, we discuss our first results.


title: Mutual Constraints on Representation and Inference

abstract: We examine the nature of representations from first principles, with the goal of providing an autonomous resource-limited agent with the ability to construct an appropriate language in which to express its beliefs. The principal cognitive constraints on representations for such agents are correctness of conclusions and efficiency of use. For deductive systems, the choice of formulation can have a great impact on efficiency, and we present a generative technique for automatic reformulation that creates abstractions to improve performance. The competence (ability to solve problems correctly) of non-deductive systems depends strongly on choice of representation. We illustrate this dependence using two simple cases from nonmonotonic reasoning and give some ideas for choosing representations that allow successful analogical reasoning. In sum, we propose a new division of labor between representation choice and inference.


title: Architectures for Deep Learning

abstract: Modern mobile devices are equipped with highperformance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even recently, specialized silicons as neural engines are being used for mobile devices. However, most mobile devices are still not capable of performing real-time inference using very deep models. Computations associated with deep models for today's intelligent applications are typically performed solely on the cloud. This cloud-only approach requires significant amounts of raw data to be uploaded to the cloud over the mobile wireless network and imposes considerable computational and communication load on the cloud server. Recent studies have shown that the latency and energy consumption of deep neural networks in mobile applications can be notably reduced by splitting the workload between the mobile device and the cloud. In this approach, referred to as collaborative intelligence, intermediate features computed on the mobile device are offloaded to the cloud instead of the raw input data of the network, reducing the size of the data needed to be sent to the cloud. In this paper, we design a new collaborative intelligence friendly architecture by introducing a unit responsible for reducing the size of the feature data needed to be offloaded to the cloud to a greater extent, where this unit is placed after a selected layer of a deep model. This unit is referred to as the butterfly unit. The butterfly unit consists of the reduction unit and the restoration unit. The outputs of the reduction unit is offloaded to the cloud server on which the computations associated with the restoration unit and the rest of the inference network are performed. Both the reduction and restoration units use a convolutional layer as their main component. The inference outcomes are sent back to the mobile device. The new network architecture, including the introduced butterfly unit after a selected layer of the underlying deep model, is trained end-to-end. Our proposed method, across different wireless networks, achieves on average 53x improvements for end-to-end latency and 68 x improvements for mobile energy consumption compared to the status quo cloud-only approach for ResNet-50, while the accuracy loss is less than 2 %.


Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models


title: Weakly-supervised relation classification for information extraction

abstract: This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed BootProject algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.


title: Key Estimation in Electronic Dance Music

abstract: In this paper we study key estimation in electronic dance music, an umbrella term referring to a variety of electronic music subgenres intended for dancing at nightclubs and raves. We start by defining notions of tonality and key before outlining the basic architecture of a template-based key estimation method. Then, we report on the tonal characteristics of electronic dance music, in order to infer possible modifications of the method described. We create new key profiles combining these observations with corpus analysis, and add two pre-processing stages to the basic algorithm. We conclude by comparing our profiles to existing ones, and testing our modifications on independent datasets of pop and electronic dance music, observing interesting improvements in the performance or our algorithms, and suggesting paths for future research.


The Technology and Economics of In-Wheel Motors


title: An Analysis and Visualization Tool for DBLP Data

abstract: The Digital Bibliography and Library Project (DBLP) is a popular computer science bibliography website hosted at the University of Trier in Germany. It currently contains 2,722,212 computer science publications with additional information about the authors and conferences, journals, or books in which these are published. Although the database covers the majority of papers published in this field of research, it is still hard to browse the vast amount of textual data manually to find insights and correlations in it, in particular time-varying ones. This is also problematic if someone is merely interested in all papers of a specific topic and possible correlated scientific words which may hint at related papers. To close this gap, we propose an interactive tool which consists of two separate components, namely data analysis and data visualization. We show the benefits of our tool and explain how it might be used in a scenario where someone is confronted with the task of writing a state-of-the art report on a specific topic. We illustrate how data analysis, data visualization, and the human user supported by interaction features can work together to find insights which makes typical literature search tasks faster.


title: CIPA: A collaborative intrusion prevention architecture for programmable network and SDN

abstract: Abstract Coordinated intrusion, like DDoS, Worm outbreak and Botnet, is a major threat to network security nowadays and will continue to be a threat in the future. To ensure the Internet security, effective detection and mitigation for such attacks are indispensable. In this paper, we propose a novel collaborative intrusion prevention architecture, i.e. CIPA, aiming at confronting such coordinated intrusion behavior. CIPA is deployed as a virtual network of an artificial neural net over the substrate of networks. Taking advantage of the parallel and simple mathematical manipulation of neurons in a neural net, CIPA can disperse its lightweight computation power to the programmable switches of the substrate. Each programmable switch virtualizes one to several neurons. The whole neural net functions like an integrated IDS/IPS. This allows CIPA to detect distributed attacks on a global view. Meanwhile, it does not require high communication and computation overhead. It is scalable and robust. To validate CIPA, we have realized a prototype on Software-Defined Networks. We also conducted simulations and experiments. The results demonstrate that CIPA is effective.


title: Combining labeled and unlabeled data for text classification with a large number of categories

abstract: We develop a framework to incorporate unlabeled data in the error-correcting output coding (ECOC) setup by decomposing multiclass problems into multiple binary problems and then use co-training to learn the individual binary classification problems. We show that our method is especially useful for classification tasks involving a large number of categories where co-training doesn't perform very well by itself and when combined with ECOC, outperforms several other algorithms that combine labeled and unlabeled data for text classification in terms of accuracy, precision-recall tradeoff, and efficiency.


title: Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction

abstract: Vector-space models, from word embeddings to neural network parsers, have many advantages for NLP. But how to generalise from fixed-length word vectors to a vector space for arbitrary linguistic structures is still unclear. In this paper we propose bag-of-vector embeddings of arbitrary linguistic graphs. A bag-of-vector space is the minimal nonparametric extension of a vector space, allowing the representation to grow with the size of the graph, but not tying the representation to any specific tree or graph structure. We propose efficient training and inference algorithms based on tensor factorisation for embedding arbitrary graphs in a bag-of-vector space. We demonstrate the usefulness of this representation by training bag-of-vector embeddings of dependency graphs and evaluating them on unsupervised semantic induction for the Semantic Textual Similarity and Natural Language Inference tasks.


title: Synaptic plasticity and depression: new insights from stress and rapid-acting antidepressants

abstract: Depression is a common, devastating illness. Current pharmacotherapies help many patients, but high rates of a partial response or no response, and the delayed onset of the effects of antidepressant therapies, leave many patients inadequately treated. However, new insights into the neurobiology of stress and human mood disorders have shed light on mechanisms underlying the vulnerability of individuals to depression and have pointed to novel antidepressants. Environmental events and other risk factors contribute to depression through converging molecular and cellular mechanisms that disrupt neuronal function and morphology, resulting in dysfunction of the circuitry that is essential for mood regulation and cognitive function. Although current antidepressants, such as serotonin-reuptake inhibitors, produce subtle changes that take effect in weeks or months, it has recently been shown that treatment with new agents results in an improvement in mood ratings within hours of dosing patients who are resistant to typical antidepressants. Within a similar time scale, these new agents have also been shown to reverse the synaptic deficits caused by stress.


title: A production transfer procedure based on risk management principles

abstract: Purpose ::: ::: ::: ::: ::: This paper aims to develop a procedure for preparing production transfers based on risk management principles. The procedure should help companies reduce the amount of supply chain disruptions during transfers and achieve their outsourcing/offshoring objectives. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The procedure was developed during a three-year Design Science study. First, a literature review and case studies were conducted to frame the research problem. Second, a preliminary procedure was developed based on preventive risk mitigation actions from the production transfer literature. Third, the procedure was implemented during an electronics-offshoring case and refined during workshops with the sender and receiver’s transfer personnel. Fourth, during a seminar, transfer practitioners verified the procedure by applying it to outsourcing/offshoring cases with which they had experience. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Most of the preventive actions were evaluated as relevant for the transfers the procedure was applied to, regardless of industry and relocation type. Moreover, the electronics-offshoring case showed that the success of a production transfer not only depends on the physical, knowledge and supply chain transfers, as presented in earlier research, but also on the administrative transfer and on the organisation, project and quality management actions. This paper also attempts to enhance the production transfer literature by clarifying transfer risk management. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The procedure can be used during the production transfer phase as a preparation procedure. Moreover, it informs the decision-making process during the relocation-decision and supplier-selection phases. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: To the authors’ knowledge, this is the first production-transfer-preparation procedure based on risk management principles.


title: Course and exercise sequencing using metadata in adaptive hypermedia learning systems

abstract: In the last few years the (semi-) automatic sequencing of course material has become an important research issue, particularly the standardization of metadata for educational resources. Sequencing can help to generate hypermedia documents which, at their best match the learner's needs. To perform (semi-) automatic course sequencing, a knowledge library as well as modular resources can be used. Both must be described by metadata. First, metadata standards (IEEE Learning Objects Metadata, Instructional Mangement Systems Global Learning Consortium, Dublin Core) are analyzed with regard to course sequencing. As an application example, Multibook, an adaptive hypermedia system used to teach multimedia technology, is described. Multibook uses metadata to create course sequences semi-automatically. In this article we explain how a knowledge library can be used to create exercises automatically. We give an example of how courses can be sequenced in general by analyzing the creation of exercises. An evaluation of our system shows the advantages and drawbacks of the automatic sequencing approach. are analyzed with regard to course sequencing. As an application example, Multibook uses metadata to create course sequences semi-automatically. In this article we explain how a knowledge library can be used to create automatically. We give an example of how courses can be sequenced in general by analyzing the creation of exercises. An evaluation of our system shows the advantages and drawbacks of the automatic course sequencing approach.


title: Core Ontologies for Safe Autonomous Driving

abstract: Representing the knowledge of driving environments in a structured machine-readable format is necessary for safe autonomous driving. We use ontologies to represent the knowledge of maps, driving paths, and driving environments to improve safety for smart vehicles. In this paper, we introduce core ontologies that are used for developing Advanced Driver Assistance Systems. The ontologies can be reused and extended for constructing Knowledge Base for smart vehicles as well as for implementing different types of Advanced Driver Assistance Systems.


CURVE FITTING FOR COARSE DATA USING ARTIFICIAL NEURAL NETWORK


title: A Low-Frequency Versatile Wireless Power Transfer Technology for Biomedical Implants

abstract: Implantable biomedical sensors and actuators are highly desired in modern medicine. In many cases, the implant's electrical power source profoundly determines its overall size and performance. The inductively coupled coil pair operating at the radio-frequency (RF) has been the primary method for wirelessly delivering electrical power to implants for the last three decades. Recent designs significantly improve the power delivery efficiency by optimizing the operating frequency, coil size and coil distance. However, RF radiation hazard and tissue absorption are the concerns in the RF wireless power transfer technology (RF-WPTT) [4], [5]. Also, it requires an accurate impedance matching network that is sensitive to operating environments between the receiving coil and the load for efficient power delivery. In this paper, a novel low-frequency wireless power transfer technology (LF-WPTT) using rotating rare-earth permanent magnets is demonstrated. The LF-WPTT is able to deliver 2.967 W power at ~ 180 Hz to an 117.1 Ω resistor over 1 cm distance with 50% overall efficiency. Because of the low operating frequency, RF radiation hazard and tissue absorption are largely avoided, and the power delivery efficiency from the receiving coil to the load is independent of the operating environment. Also, there is little power loss observed in the LF-WPTT when the receiving coil is enclosed by non-magnetic implant-grade stainless steel.


title: Performance analysis of downlink NOMA networks over Rayleigh fading channels

abstract: This paper analyzes the performance of downlink non-orthogonal multiple-access (NOMA) networks over independent but not necessarily identically distributed Rayleigh fading channels for arbitrary number of network users. Specifically, closed-form expressions for the average SNR, average achievable rate, and outage probability are derived, where it has been shown that network users achieve diversity orders equivalent to their ordered channel gains. Numerical evaluations are presented to validate the derived closed-form expressions for the different performance metrics, which are found to be in agreement with the network simulation results.


title: Internet of Things Forensics: The Need, Process Models, and Open Issues

abstract: The Internet of Things (IoT) brings a set of unique and complex challenges to the field of digital forensics. To take advantage of the volume and variety of data captured by and stored in ubiquitous IoT services, forensic investigators need to draw upon evidence-acquisition methods and techniques from all areas of digital forensics and possibly create new IoT-specific investigation processes. Although a number of conceptual process models have been developed to address the unique characteristics of the IoT, many challenges remain unresolved.


title: Smart speed bump detection and estimation with kinect

abstract: Prior identification of speed bumps allows drivers and the car suspension system to respond before passing over them. This may prevent bad driving experience, vehicle damage and in the extreme case, accidents. This paper proposes a cost effective method of speed bumps detection and height estimation by using the Microsoft Kinect Sensor. 3D scene reconstruction is based on both the colour and depth images acquired from Kinect's RGB and IR cameras. In addition, a smart speed bump detector is trained to recognize speed bumps and finally extract its properties such as its height and distance from the Kinect sensor.


Toward an Understanding of Uses and Gratifications Theory and the Sense of Virtual Community on Knowledge Sharing in Online Game Communities


title: Optical amplify-and-forward multihop WDM/FSO for all-optical access networks

abstract: Multihop free space optical (FSO) system using optical amplify-and-forward (OAF) relaying technique combined with wavelength division multiplexing (WDM) is proposed for all-optical access networks. The proposed system can provide a low cost, flexible, and high-bandwidth access network for multiple users. To investigate the system performance, we consider a special case of dual-hop WDM-FSO system taking into account the effects of all noises, interchannel crosstalk, as well as path loss and geometric spreading of optical beam over atmospheric turbulence channels. In addition, pulse position modulation (PPM) is employed for improving the overall performance. Our results show that OAF technique combined with PPM scheme can be a good solution for mitigating the effect of atmospheric turbulence. Moreover, the required amplifier gain corresponding to a specific value of BER, transmission distance, and turbulence strength is quantitatively discussed. Finally, the adverse effect of interchannel crosstalk in the upstream transmission is also evaluated.


Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory


title: Inhibition of mobilization of acetylcholine: the weak link in neuromuscular transmission during partial neuromuscular block with d-tubocurarine.

abstract: The authors have demonstrated earlier, by direct measurement of acetylcholine (ACh), that d-tubocurarine (d-Tc) and other nondepolarizing muscle relaxants decrease the release of ACh from the indirectly stimulated mouse hemidiaphragm preparation. It was the purpose of the present study to determine whether the decrease of the stimulated release of ACh and the increase in the intensity of the partial neuromuscular block observed at higher stimulation rates is caused by the inhibition of mobilization of ACh from reserve depots to release sites or by inhibition of the release process itself. To attain our objective, the authors have investigated the influence of the progressive increase of the rate of stimulation from 0.1 to 1, 2, 3, and 5 Hz on the force of contraction on the in vitro phrenic nerve-hemidiaphragm and in vivo sciatic nerve-tibialis anterior preparation in the absence of drugs and after about 20% neuromuscular block produced by d-Tc or by Mg2+. The latter is known to inhibit the Ca2+ dependent release of ACh. In the absence of drugs increasing the stimulation rate increased the force of contraction, in vivo and in vitro, during both indirect and direct stimulation. In the phrenic nerve-hemidiaphragm preparation the increase was significant at 1, 2, 3, and 5 Hz (P less than 0.001) with both types of stimulation. In the sciatic nerve-tibialis anterior preparation the force of contraction was only higher at 3 and 5 Hz (P less than 0.01). The similar magnitude of the increase of the force of contraction during direct and indirect stimulation indicates that it is caused by facilitation of the contraction of the muscle fibers.(ABSTRACT TRUNCATED AT 250 WORDS)


title: A movie recommendation algorithm based on genre correlations

abstract: Since the late 20th century, the number of Internet users has increased dramatically, as has the number of Web searches performed on a daily basis and the amount of information available. A huge amount of new information is transferred to the Web on a daily basis. However, not all data are reliable and valuable, which implies that it may become more and more difficult to obtain satisfactory results from Web searches. We often iterate searches several times to find what we are looking for. To solve this problem, researchers have suggested the use of recommendation systems. Instead of searching for the same information several times, a recommendation system proposes relevant information. In the Web 2.0 era, recommendation systems often rely on collaborative filtering by users. In general, a collaborative filtering approach based on user information such as gender, location, or preference is effective. However, the traditional approach can fail due to the cold-start problem or the sparsity problem, because initial user information is required for this approach to be effective. Recently, several attempts have been made to tackle these collaborative filtering problems. One such attempt used category correlations of contents. For instance, a movie has genre information provided by movie experts and directors. This category information is more reliable than user ratings. Moreover, newly created content always has category information, allowing avoidance of the cold-start problem. In this study, we consider a movie recommendation system and improve the previous algorithms based on genre correlations to correct its shortcomings. We also test the modified algorithm and analyze the results with respect to two characteristics of genre correlations.


title: Terrain Response Estimation Using an Instrumented Rocker-Bogie Mobility System

abstract: This paper presents a procedure to model the drawbar pull and resistive torque of an unknown terrain as a function of normal load and slip using on-board rover instruments. Kapvik , which is a planetary micro-rover prototype with a rocker-bogie mobility system, is simulated in two dimensions. A suite of sensors is used to take relevant measurements; in addition to typical rover measurements, forces above the wheel hubs and rover forward velocity are sensed. An estimator determines the drawbar pull, resistive torque, normal load, and slip of the rover. The collected data are used to create a polynomial fit model that closely resembles the real terrain response.


title: On-Line Multi-View Video Summarization for Wireless Video Sensor Network

abstract: Battery lifetime is critical for wireless video sensors. To enable battery-powered wireless video sensors, low-power design is required. In this paper, we consider applying multi-view summarization to wireless video sensors to remove redundant contents such that the compression and transmission power can be reduced. A low-complexity online multi-view video summarization scheme is proposed. Experiments show that the proposed summarization method successfully reduces the video content while keeping important events. A power analysis of the system also shows that a significant amount of energy can be saved.


title: Guided inpainting and filtering for Kinect depth maps

abstract: Depth maps captured by Kinect-like cameras are lack of depth data in some areas and suffer from heavy noise. These defects have negative impacts on practical applications. In order to enhance the depth maps, this paper proposes a new inpainting algorithm that extends the original fast marching method (FMM) to reconstruct unknown regions. The extended FMM incorporates an aligned color image as the guidance for inpainting. An edge-preserving guided filter is further applied for noise reduction. To validate our algorithm and compare it with other existing methods, we perform experiments on both the Kinect data and the Middlebury dataset which, respectively, provide qualitative and quantitative results. The results show that our method is efficient and superior to others.


title: Memcomputing Numerical Inversion with Self-Organizing Logic Gates

abstract: We propose to use digital memcomputing machines (DMMs), implemented with self-organizing logic gates (SOLGs), to solve the problem of numerical inversion. Starting from fixed-point scalar inversion, we describe the generalization to solving linear systems and matrix inversion. This method, when realized in hardware, will output the result in only one computational step. As an example, we perform simulations of the scalar case using a 5-bit logic circuit made of SOLGs, and show that the circuit successfully performs the inversion. Our method can be extended efficiently to any level of precision, since we prove that producing $n$ -bit precision in the output requires extending the circuit by at most $n$ bits. This type of numerical inversion can be implemented by DMM units in hardware; it is scalable, and thus of great benefit to any real-time computing application.


Compatible Item Recommendation


title: Effective graph classification based on topological and label attributes

abstract: Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different global topological attributes, as well as global label features. The main idea is that the graphs from the same class should have similar topological and label attributes. Our method is simple and easy to implement, and via a detailed comparison on real benchmark datasets, we show that our topological and label feature-based approach delivers competitive classification accuracy, with significantly better results on those datasets that have large unlabeled graph instances. Our method is also substantially faster than most other graph kernels. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc.


title: The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations

abstract: The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Ital-ian, and Dutch). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations , assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) seg-mentation of the text in sentences and lexical items; (ii) syntactic parsing with Com-binatory Categorial Grammar; (iii) universal semantic tagging; (iv) symboliza-tion; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semi-supervised manner. The employed annotation models are all language-neutral. Our first results are promising.


The Effect of Social Media on Student ’ s Engagement and Collaboration : a case study of University of Venda using Facebook


title: Data flow oriented UI testing: exploiting data flows and UI elements to test Android applications

abstract: Testing user interfaces (UIs) is a challenging task. Ideally, every sequence of UI elements should be tested to guarantee that the application works correctly. This is, however, unfeasible due to the number of UI elements in an application. A better approach is to limit the evaluation to UI elements that affect a specific functionality. In this paper I present a novel technique to identify the relation between UI elements using the statically extracted data flows. I also present a method to refine these relations using dynamic analysis, in order to ensure that relations extracted from unreachable data flows are removed. Using these relations it is possible to more efficiently test a functionality. Finally, I present an approach to evaluate how these UI-aware data flows can be used as an heuristic to measure test coverage.


title: Growth and instability of the liquid rim in the crown splash regime

abstract: We study the formation, growth, and disintegration of jets following impact of a drop on a thin film of the same liquid for We<1000 and Re<2000 using a combination of numerical simulations and linear stability theory (Agbaglah et al. 2013). Our simulations faithfully capture this phenomena and are in good agreement with experimental profiles obtained from high-speed X-ray imaging.We obtain scaling relations from our simulations and use these as inputs to our stability analysis. The resulting prediction for the most unstable wavelength are in excellent agreement with experimental data. Our calculations show that the dominant destabilizing mechanism is a competition between capillarity and inertia but that deceleration of the rim provides an additional boost to growth. We also predict over the entire parameter range of our study the number and timescale for formation of secondary droplets formed during a splash, based on the assumption that the most unstable mode sets the droplet number.


title: A general formal framework for pathfinding problems with multiple agents

abstract: Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain.


title: Build your own visual-inertial odometry aided cost-effective and open-source autonomous drone

abstract: This article describes an approach to building a cost-effective and research-grade visual-inertial (VI) odometry-aided vertical takeoff and landing (VTOL) platform. We utilize an off-the-shelf VI sensor, an onboard computer, and a quadrotor platform, all of which are factory calibrated and mass produced, thereby sharing similar hardware and sensor specifications [e.g., mass, dimensions, intrinsic and extrinsic of camera-inertial measurement unit (IMU) systems, and signal-to-noise ratio]. We then perform system calibration and identification, enabling the use of our VI odometry, multisensor fusion (MSF), and model predictive control (MPC) frameworks with off-the-shelf products. This approach partially circumvents the tedious parameter-tuning procedures required to build a full system. The complete system is extensively evaluated both indoors using a motioncapture system and outdoors using a laser tracker while performing hover and step responses and trajectory-following tasks in the presence of external wind disturbances. We achieve root-mean-square (rms) pose errors of 0.036 m with respect to reference hover trajectories. We also conduct relatively long distance (.180 m) experiments on a farm site, demonstrating a 0.82% drift error of the total flight distance. This article conveys the insights we acquired about the platform and sensor module and offers open-source code with tutorial documentation to the community.


title: Towards A Distributed Arabic OCR Based on the DTW Algorithm: Performance Analysis

abstract: In spite of the diversity of printed Arabic optical character recognition products and proposals, the problem seems to be not yet well solved. The complex morphology and calligraphy of the Arabic writing on one hand and the use of some light approaches on the other hand are behind the poorness of these products. However, some strong proposed approaches didn’t find the opportunity to be commercialised because of generally their corresponding complex computing. The dynamic time warping algorithm is considered as one among these strong approaches. In fact, several studies and experiments have shown and confirmed that the printed Arabic optical character recognition based on dynamic time warping algorithm provides a very interesting recognition rate especially for large and huge vocabularies. One of the attractive sides of the dynamic time warping algorithm is its ability to recognize properly connected or cursive characters (words or sub words) without prior segmentation. Furthermore, this algorithm performs the recognition process from within a reference library of isolated characters and owns a very good immunity against noises. Unfortunately, the big amount of its computing during the recognition process makes its execution time very slow and, hence, restricts its utilization. Many researchers attempted to speedup the execution time of this algorithm. Unfortunately, the corresponding proposed solutions require generally specific high cost architectures. Loosely coupled architectures such as grapes or grid computing can provide enough power without additional cost to distribute the complexity of some greedy applications. Consequently, we report in this paper the performance analysis of an analytical and an experimental study of a distributed Arabic optical character recognition based on the dynamic time warping algorithm within loosely coupled architectures. Obtained results confirm that loosely coupled architectures and more specifically grid computing present a very interesting framework to speedup the Arabic optical character recognition based on the dynamic time warping algorithm.


title: An Improved k-Nearest Neighbor Algorithm for Text Categorization

abstract: k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process, k nearest documents to the test one in the training set are determined firstly. Then, the predication can be made according to the category distribution among these k nearest neighbors. Generally speaking, the class distribution in the training set is uneven. Some classes may have more samples than others. Therefore, the system performance is very sensitive to the choice of the parameter k. And it is very likely that a fixed k value will result in a bias on large categories. To deal with these problems, we propose an improved kNN algorithm, which uses different numbers of nearest neighbors for different categories, rather than a fixed number across all categories. More samples (nearest neighbors) will be used for deciding whether a test document should be classified to a category, which has more samples in the training set. Preliminary experiments on Chinese text categorization show that our method is less sensitive to the parameter k than the traditional one, and it can properly classify documents belonging to smaller classes with a large k. The method is promising for some cases, where estimating the parameter k via cross-validation is not allowed.


title: Covering a Set of Points with a Minimum Number of Lines

abstract: We consider the minimum line covering problem: given a set S of n points in the plane, we want to find the smallest number l of straight lines needed to cover all n points in S. We show that this problem can be solved in O(n log l) time if I ∈ O(log 1-e n), and that this is optimal in the algebraic computation tree model (we show that the Ω(nlogl) lower bound holds for all values of I up to O(√n)). Furthermore, a O(log l)-factor approximation can be found within the same O(nlogl) time bound if l ∈ O(4√n). For the case when l ∈ Ω(logn) we suggest how to improve the time complexity of the exact algorithm by a factor exponential in l.


title: Cache cookies for browser authentication

abstract: Like conventional cookies, cache cookies are data objects that servers store in Web browsers. Cache cookies, however, are unintentional byproducts of protocol design for browser caches. They do not enjoy any explicit interface support or security policies. In this paper, we show that despite limitations, cache cookies can play a useful role in the identification and authentication of users. Many users today block conventional cookies in their browsers as a privacy measure. The cache-cookie tools we propose can help restore lost usability and convenience to such users while maintaining good privacy. As we show, our techniques can also help combat online security threats such as phishing and pharming that ordinary cookies cannot. The ideas we introduce for cache-cookie management can strengthen ordinary cookies as well. The full version of this paper may be referenced at www.ravenwhite.com.


title: Pushover Analysis of Multistoried Building

abstract: A large number of multi-storey reinforced concrete (R/C) framed building structures in urban India are constructed with masonry in fills for architectural, aesthetic or economic reasons. We have investigated the effect of the layout of masonry infill panels over the elevation of masonry in filled R/C frames on the seismic performance and potential seismic damage of the frame under strong ground motions using nonlinear static push-over analysis based on realistic and efficient computational models. From output non-linear analysis, we compare Base shear and Displacement in bare frame, in fill wall frame and ground, it seen that at roof level, displacement in bare frame is more than other two frames and displacement at ground floor in weak story is more than other two frames. Mostly hinges are formed in beam than in column.


title: Not hating what you see: Self-compassion may protect against negative mental health variables connected to self-objectification in college women

abstract: Abstract Self-objectification is related to maladaptive mental health variables, but little is known about what could ameliorate these associations. Self-compassion, a construct associated with mindfulness, involves taking a non-judgmental attitude toward the self. In this study, 306 college-aged women were recruited; those who were highest ( n = 106) and lowest ( n = 104) in self-compassion were retained for analyses. Levels of body surveillance, body shame, depression, and negative eating attitudes were lower in the high self-compassion group. Furthermore, the fit of a path model wherein body surveillance related to body shame, which, in turn, related to negative eating attitudes and depressive symptomatology was compared for each group, controlling for body mass index. The model fit significantly differently such that the connections between self-objectification and negative body and eating attitudes were weaker in the high self-compassion group. Treatment implications of self-compassion as a potential means to interrupt the self-objectification process are discussed.