title: Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data

abstract: We study the relevance of context-learning for handling asynchrony of annotation.We unite audiovisual and physiological data for continuous affect analysis.We propose multi-time resolution features extraction from multimodal data.The use of context-learning allows to include reaction time delay of raters.Fusion of audiovisual and physiological data performs best on arousal and valence. Automatic emotion recognition systems based on supervised machine learning require reliable annotation of affective behaviours to build useful models. Whereas the dimensional approach is getting more and more popular for rating affective behaviours in continuous time domains, e.g., arousal and valence, methodologies to take into account reaction lags of the human raters are still rare. We therefore investigate the relevance of using machine learning algorithms able to integrate contextual information in the modelling, like long short-term memory recurrent neural networks do, to automatically predict emotion from several (asynchronous) raters in continuous time domains, i.e., arousal and valence. Evaluations are performed on the recently proposed RECOLA multimodal database (27 subjects, 5? min of data and six raters for each), which includes audio, video, and physiological (ECG, EDA) data. In fact, studies uniting audiovisual and physiological information are still very rare. Features are extracted with various window sizes for each modality and performance for the automatic emotion prediction is compared for both different architectures of neural networks and fusion approaches (feature-level/decision-level). The results show that: (i) LSTM network can deal with (asynchronous) dependencies found between continuous ratings of emotion with video data, (ii) the prediction of the emotional valence requires longer analysis window than for arousal and (iii) a decision-level fusion leads to better performance than a feature-level fusion. The best performance (concordance correlation coefficient) for the multimodal emotion prediction is 0.804 for arousal and 0.528 for valence.


title: Health informatics: current issues and challenges

abstract: Health informatics concerns the use of information and information and communication technologies within healthcare. Health informatics and information science need to take account of the unique aspects of health and medicine. The development of information systems and electronic records within health needs to consider the information needs and behaviour of all users. The sensitivity of personal health data raises ethical concerns for developing electronic records. E-health initiatives must actively involve users in the design, development, implementation and evaluation, and information science can contribute to understanding the needs and behaviour of user groups. Health informatics could make an important contribution to the ageing society and to reducing the digital divide and health divides within society. There is a need for an appropriate evidence base within health informatics to support future developments, and to ensure health informatics reaches its potential to improve the health and well-being of patients and the public.


title: Cable-Suspended Robotic Contour Crafting System

abstract: This paper introduces a new concept for a contour crafting construction system. Contour crafting is a relatively new layered fabrication technology that enables automated construction of whole structures. The system proposed here consists of a mobile contour crafting platform driven by a translational cable-suspended robot. The platform includes an extrusion system for laying beads of concrete as well as computer-controlled trowels for forming the beads as they are laid. This system is fully automated and can be used to construct concrete structures rapidly and economically. The novel attributes of this system enable significant improvements over other proposed contour crafting systems, including easier portability, lower cost, and the potential to build much larger structures. This paper presents the kinematics and statics of the proposed system, and uses the reachable workspace of the robot as well as the corresponding cable tensions to approximate the maximum size structure that can be built using this manipulator.Copyright © 2006 by ASME


title: 2007 Special Issue: Optimization and applications of echo state networks with leaky- integrator neurons

abstract: Standard echo state networks (ESNs) are built from simple additive units with a sigmoid activation function. Here we investigate ESNs whose reservoir units are leaky integrator units. Units of this type have individual state dynamics, which can be exploited in various ways to accommodate the network to the temporal characteristics of a learning task. We present stability conditions, introduce and investigate a stochastic gradient descent method for the optimization of the global learning parameters (input and output feedback scalings, leaking rate, spectral radius) and demonstrate the usefulness of leaky-integrator ESNs for (i) learning very slow dynamic systems and replaying the learnt system at different speeds, (ii) classifying relatively slow and noisy time series (the Japanese Vowel dataset - here we obtain a zero test error rate), and (iii) recognizing strongly time-warped dynamic patterns.


title: Kitty: sketching dynamic and interactive illustrations

abstract: We present Kitty, a sketch-based tool for authoring dynamic and interactive illustrations. Artists can sketch animated drawings and textures to convey the living phenomena, and specify the functional relationship between its entities to characterize the dynamic behavior of systems and environments. An underlying graph model, customizable through sketching, captures the functional relationships between the visual, spatial, temporal or quantitative parameters of its entities. As the viewer interacts with the resulting dynamic interactive illustration, the parameters of the drawing change accordingly, depicting the dynamics and chain of causal effects within a scene. The generality of this framework makes our tool applicable for a variety of purposes, including technical illustrations, scientific explanation, infographics, medical illustrations, children's e-books, cartoon strips and beyond. A user study demonstrates the ease of usage, variety of applications, artistic expressiveness and creative possibilities of our tool.


title: Web evolution and Web Science

abstract: This paper examines the evolution of the World Wide Web as a network of networks and discusses the emergence of Web Science as an interdisciplinary area that can provide us with insights on how the Web developed, and how it has affected and is affected by society. Through its different stages of evolution, the Web has gradually changed from a technological network of documents to a network where documents, data, people and organisations are interlinked in various and often unexpected ways. It has developed from a technological artefact separate from people to an integral part of human activity that is having an increasingly significant impact on the world. This paper outlines the lessons from this retrospective examination of the evolution of the Web, presents the main outcomes of Web Science activities and discusses directions along which future developments could be anticipated.


title: Flexible Medical Devices: Review of Controllable Stiffness Solutions

abstract: In the medical field and in soft robotics, flexible devices are required for safe human interaction, while rigid structures are required to withstand the force application and accuracy in motion. This paper aims at presenting controllable stiffness mechanisms described in the literature for applications with or without shape-locking performances. A classification of the solutions based on their working principle is proposed. The intrinsic properties of these adaptive structures can be modified to change their mechanical characteristics from a geometrical point of view or equivalent elastic properties (with internal mechanisms or with a change in material properties). These solutions are compared quantitatively, based on selected criteria linked to the medical field as the stiffness range, the activation time and the working conditions. Depending on the application and its requirements, the most suitable solution can be selected following the quantitative comparisons. Several applications of these tunable stiffness structures are proposed and illustrated by examples of the literature.


title: News Topic Evolution Tracking by Incorporating Temporal Information

abstract: Time stamped texts or text sequences are ubiquitous in real life, such as news reports. Tracking the topic evolution of these texts has been an issue of considerable interest. Recent work has developed methods of tracking topic shifting over long time scales. However, most of these researches focus on a large corpus. Also, they only focus on the text itself and no attempt have been made to explore the temporal distribution of the corpus, which could provide meaningful and comprehensive clues for topic tracking. In this paper, we formally address this problem and put forward a novel method based on the topic model. We investigate the temporal distribution of news reports of a specific event and try to integrate this information with a topic model to enhance the performance of topic model. By focusing on a specific news event, we try to reveal more details about the event, such as, how many stages are there in the event, what aspect does each stage focus on, etc.


title: Structural analysis of chat messages for topic detection

abstract: Purpose – The purpose of this research is to study the characteristics of chat messages from analysing a collection of 33,121 sample messages gathered from 1,700 sessions of conversations of 72 pairs of MSN Messenger users over a four month duration from June to September of 2005. The primary objective of chat message characterization is to understand the properties of chat messages for effective message analysis, such as message topic detection.Design/methodology/approach – From the study on chat message characteristics, an indicative term‐based categorization approach for chat topic detection is proposed. In the proposed approach, different techniques such as sessionalisation of chat messages and extraction of features from icon texts and URLs are incorporated for message pre‐processing. Naive Bayes, Associative Classification, and Support Vector Machine are employed as classifiers for categorizing topics from chat sessions.Findings – Indicative term‐based approach is superior to the traditional documen...


title: Going Deeper in Facial Expression Recognition using Deep Neural Networks

abstract: Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem in computer vision. Despite efforts made in developing various methods for FER, existing approaches lack generalizability when applied to unseen images or those that are captured in wild setting (i.e. the results are not significant). Most of the existing approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where the classifier's hyper-parameters are tuned to give best recognition accuracies across a single database, or a small collection of similar databases. This paper proposes a deep neural network architecture to address the FER problem across multiple well-known standard face datasets. Specifically, our network consists of two convolutional layers each followed by max pooling and then four Inception layers. The network is a single component architecture that takes registered facial images as the input and classifies them into either of the six basic or the neutral expressions. We conducted comprehensive experiments on seven publicly available facial expression databases, viz. MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of our proposed architecture are comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks in both accuracy and training time.


title: A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

abstract: Emergent heterogeneous systems must be optimized for both power and performance at exascale. Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases orders of magnitude and power consumption can easily double. Models have been proposed to isolate power and performance bottlenecks and identify their root causes. However, no current models combine simplicity, accuracy, and support for emergent GPU architectures (e.g. NVIDIA Fermi). We combine hardware performance counter data with machine learning and advanced analytics to model power-performance efficiency for modern GPU-based systems. Our performance counter based approach is simpler than previous approaches and does not require detailed understanding of the underlying architecture. The resulting model is accurate for predicting power (within 2.1%) and performance (within 6.7%) for application kernels on modern GPUs. Our model can identify power-performance bottlenecks and their root causes for various complex computation and memory access patterns (e.g. global, shared, texture). We measure the accuracy of our power and performance models on a NVIDIA Fermi C2075 GPU for more than a dozen CUDA applications. We show our power model is more accurate and robust than the best available GPU power models - multiple linear regression models MLR and MLR+. We demonstrate how to use our models to identify power-performance bottlenecks and suggest optimization strategies for high-performance codes such as GEM, a biomolecular electrostatic analysis application. We verify our power-performance model is accurate on clusters of NVIDIA Fermi M2090s and useful for suggesting optimal runtime configurations on the Keeneland supercomputer at Georgia Tech.


title: Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

abstract: Multimedia content is dominating today's Web information. The nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with a much lower cost than explicit feedback (e.g., product ratings). However, the majority of existing collaborative filtering (CF) systems are not well-designed for multimedia recommendation, since they ignore the implicitness in users' interactions with multimedia content. We argue that, in multimedia recommendation, there exists item- and component-level implicitness which blurs the underlying users' preferences. The item-level implicitness means that users' preferences on items (e.g. photos, videos, songs, etc.) are unknown, while the component-level implicitness means that inside each item users' preferences on different components (e.g. regions in an image, frames of a video, etc.) are unknown. For example, a 'view'' on a video does not provide any specific information about how the user likes the video (i.e.item-level) and which parts of the video the user is interested in (i.e.component-level). In this paper, we introduce a novel attention mechanism in CF to address the challenging item- and component-level implicit feedback in multimedia recommendation, dubbed Attentive Collaborative Filtering (ACF). Specifically, our attention model is a neural network that consists of two attention modules: the component-level attention module, starting from any content feature extraction network (e.g. CNN for images/videos), which learns to select informative components of multimedia items, and the item-level attention module, which learns to score the item preferences. ACF can be seamlessly incorporated into classic CF models with implicit feedback, such as BPR and SVD++, and efficiently trained using SGD. Through extensive experiments on two real-world multimedia Web services: Vine and Pinterest, we show that ACF significantly outperforms state-of-the-art CF methods.


title: The influence of solitary and cooperative violent video game play on aggressive and prosocial behavior

abstract: Research examining the issue of video game violence influences on aggression continues to be debated within the scientific community. Thus far, no consensus has been reached regarding the influence of such games. This study adds to the prior literature by examining how violent video games may promote prosocial or aggressive behavior when played either cooperatively or alone. Results indicated that violent content in video games had no influence on prosocial behavior, aggressive behavior, or self-perceptions of empathy. Playing cooperatively was associated with less aggressive behavior, whether games were violent or not.


title: Hashtag retrieval in a microblogging environment

abstract: Microblog services let users broadcast brief textual messages to people who "follow" their activity. Often these posts contain terms called hashtags, markers of a post's meaning, audience, etc. This poster treats the following problem: given a user's stated topical interest, retrieve useful hashtags from microblog posts. Our premise is that a user interested in topic x might like to find hashtags that are often applied to posts about x. This poster proposes a language modeling approach to hashtag retrieval. The main contribution is a novel method of relevance feedback based on hashtags. The approach is tested on a corpus of data harvested from twitter.com.


title: A PV inverter with charge-pump circuit topology

abstract: This paper proposes a photovoltaic (PV) inverter capable of stepping up voltage and the control algorithm for the inverter. The proposed inverter employs a charge-pump (CP) circuit instead of the commonly used step-up converter of a chopper type. Because the CP circuit does not include any inductor, the proposed inverter can be formed compactly. The CP circuit steps up the dc link voltage of the inverter by superimposing the voltage of the capacitor in the CP circuit on the PV voltage. So that the inverter performs the effective operation of the voltage step-up, the capacitor needs to maintain an appropriate voltage level. The equations modeling the energy variation of the capacitor are derived. Based on the equations, the maximum voltage step-up ratio of the inverter is obtained. The observation of the simulated output voltage waveforms of the inverter introduces two improved topologies of the CP circuit. The experiments using the inverter operated by the described control techniques confirms that the inverter can perform effective step-up operation while executing maximum power point tracking (MPPT) operation successfully.


title: AdWords and generalized online matching

abstract: How does a search engine company decide what ads to display with each query so as to maximize its revenueq This turns out to be a generalization of the online bipartite matching problem. We introduce the notion of a trade-off revealing LP and use it to derive an optimal algorithm achieving a competitive ratio of 1−1/e for this problem.


title: Aggregation and Correlation of Intrusion-Detection Alerts

abstract: This paper describes an aggregation and correlation algorithm used in the design and implementation of an intrusion-detection console built on top of the Tivoli Enterprise Console (TEC). The aggregation and correlation algorithm aims at acquiring intrusion-detection alerts and relating them together to expose a more condensed view of the security issues raised by intrusion-detection systems.


Implementation and Verification of a Nodally-Integrated Tetrahedral Element in FEBio


Remodeling of Alveolar Bone


title: Decentralized leader-follower formation control with obstacle avoidance of multiple unicycle mobile robots

abstract: This work investigates the formation control and obstacle avoidance of multiple differentially driven wheeled mobile robots (WMRs) based on the kinematic model and the leader-follower approach. A combination of a linear model predictive control and input-output feedback linearization is implemented on a team of WMRs in order to accomplish a formation task. The linear model of each robot with nonlinear dynamics is found through feedback linearization, while model predictive control is applied to the linear model to perform the formation control. An obstacle avoidance algorithm also implemented to each robot in formation. The obstacle avoidance strategy is based on generating a virtual force that is considered to make corrections in the linear and angular velocities of each robot in formation. Simulation results are presented in order to demonstrate the performance of a team of WMRs with two formation mission scenarios.


title: A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition

abstract: Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 × 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.


title: Humanoid robot HRP-2

abstract: In this paper, the development of humanoid robot HRP-3 is presented. HRP-3, which stands for Humanoid Robotics Platform-3, is a human-size humanoid robot developed as the succeeding model of HRP-2. One of features of HRP-3 is that its main mechanical and structural components are designed to prevent the penetration of dust or spray. Another is that its wrist and hand are newly designed to improve manipulation. Software for a humanoid robot in a real environment is also improved. We also include information on mechanical features of HRP-3 and together with the newly developed hand. Also included are the technologies implemented in HRP-3 prototype. Electrical features and some experimental results using HRP-3 are also presented.


title: Rope caging and grasping

abstract: We present a novel method for caging grasps in this paper by stretching ropes on the surface of a 3D object. Both topology and shape of a model to be grasped has been considered in our approach. Our algorithm can guarantee generating local minimal rings on every topological branches of a given model with the help of a Reeb graph. Cages and grasps can then be computed from these rings, and physical experimental tests have been conducted to verify the robustness of our approach.


title: An evolving multi-agent scenario generation framework for simulations in preventive medicine education

abstract: We describe the design, implementation and evaluation of a novel multi-agent scenario generation framework for interactive virtual reality simulations towards preventive medicine education. Our scenario generation framework is based on recordings of human movements from a distributed sensor networks deployed in a real-world physical setting. The components of our framework include the generation of unique virtual agent behaviors from the sensor data, and algorithms for the generation of low level or gross movement behaviors such as path determination, directional traffic flows, collision avoidance and overtaking. The framework also includes the generation of high level fine actions for multi-agents such as techniques for interactive activities in pedagogical scenarios based on environment and temporal triggers. We applied our multi-agent scenario generation framework in an interactive simulation for hand hygiene education, and conduct an initial usability study to assess the educational benefits of the simulation to nursing students and evaluated the performance characteristics of our framework. Results of our quantitative and qualitative evaluations suggest that our framework was robust in creating engaging, compelling, and realistic interactive training scenarios with multiple virtual agents in simulated hospital situations.


MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model


title: Low voltage adjustable CMOS Schmitt trigger

abstract: This paper presents a low voltage adjustable CMOS Schmitt trigger using dynamic threshold MOS (DTMOS). Cross-coupled inverter with body control is employed to speed up the switching process, and control the intensity of the feedback. The proposed Schmitt trigger has been designed using 0.18 μm 0.4 V CMOS technology and analyzed using PSPICE with BSIM3V3 device models. The simulation results show rail-to-rail operation and independently adjustable switching voltages for both low-to-high (V T(LH) ) and high-to-low (V T(HL) ) as high as 15 % of the supply voltage. The power dissipation is 0.13 μW.


title: SGM-Nets: Semi-Global Matching with Neural Networks

abstract: This paper deals with deep neural networks for predicting accurate dense disparity map with Semi-global matching (SGM). SGM is a widely used regularization method for real scenes because of its high accuracy and fast computation speed. Even though SGM can obtain accurate results, tuning of SGMs penalty-parameters, which control a smoothness and discontinuity of a disparity map, is uneasy and empirical methods have been proposed. We propose a learning based penalties estimation method, which we call SGM-Nets that consist of Convolutional Neural Networks. A small image patch and its position are input into SGMNets to predict the penalties for the 3D object structures. In order to train the networks, we introduce a novel loss function which is able to use sparsely annotated disparity maps such as captured by a LiDAR sensor in real environments. Moreover, we propose a novel SGM parameterization, which deploys different penalties depending on either positive or negative disparity changes in order to represent the object structures more discriminatively. Our SGM-Nets outperformed state of the art accuracy on KITTI benchmark datasets.


title: Suicide by carbon dioxide.

abstract: Suicides by self-poisoning are common in all parts of the world. Among these intoxications, gases are rarely used, especially carbon dioxide (CO2). Very few cases of self-inflicted and deliberate carbon dioxide poisonings have been reported. This paper presents two uncommon suicides by carbon dioxide intoxication. In one case, a 53-year-old man tightly sealed a small bathroom and locked himself in it likely with dry ice. Warning notices were tagged to the door. In another case, a 48-year-old man working in a restaurant committed suicide by closing himself in a walk-in refrigerator and opening the stored carbon dioxide containers intended for the beverage dispensing equipment. The limited possibilities of proving lethal CO2 intoxications post-mortem necessitate a close cooperation of the involved parties during investigation. Only the synopsis of all findings permits a sound assessment regarding the manner and cause of death.


An Integrated Approach to Rotorcraft Conceptual Design


title: Social Comparison, Envy, and Depression on Facebook: A Study Looking at the Effects of High Comparison Standards on Depressed Individuals

abstract: The co-occurrence of depression and envy is both plausible and empirically established. However, little is known about the mechanisms underlying this correlation. An account is proposed according to which low self-esteem in depressed individuals leads to upward social comparison and thus makes envy more likely. This effect should frequently occur in online social networks like Facebook because they allow for easy impression management and hence provide high comparison standards. In a quasi-experimental online study, depressed and nondepressed participants indicated their self-esteem and were then presented with specifically set up Facebook profiles that were either attractive or unattractive. Participants were asked to compare themselves to the profile owner and to report their resulting feelings of inferiority and envy. Depressed participants were more envious, especially after seeing the attractive profile. Envy was associated with higher self-reported inferiority and also correlated negatively with sel...


title: Spoken Digits Recognition using Weighted MFCC and Improved Features for Dynamic Time Warping

abstract: this paper, we propose novel techniques for feature parameter extraction based on MFCC and feature recognition using dynamic time warping algorithm for application in speaker-independent isolated digits recognition. Using the proposed Weighted MFCC (WMFCC), we achieve low computational overhead for the feature recognition stage since we use only 13 weighted MFCC coefficients instead of the conventional 39 MFCC coefficients including the delta and double delta features. In order to capture the trends or patterns that a feature sequence presents during the alignment process, we compute the local and global features using Improved Features for DTW algorithm (IFDTW), rather than using the pure feature values or their estimated derivatives. The experiments based on TI-Digits corpus demonstrate the effectiveness of proposed techniques leading to higher recognition accuracy of 98.13%.


title: Single camera lane detection and tracking

abstract: In this paper we present a method to detect and track straight lane boundaries, using a forward-looking single camera mounted on-board. The method proved to be robust, even under varying lighting conditions, and in the presence of heavy shadowing cast by vegetation, vehicles, bridges, etc. Moreover, false positive hardly occur. The lane markings are being continuously detected even when the vehicle is performing maneuvers such as excursion or lane change. The performance is achieved by a novel combination of methods, namely, by first finding vanishing point candidates for the lane boundaries, and later by the careful selection of line segments given a vanishing point, and finally by using a smart heuristics to select the whole set of four lane boundaries. Suppression of false positives is further achieved by tracking the vanishing point, and by constraining the lane width based on recent history.


title: Responses of Two Phytoseiid Mites to Pesticides Used in New York Apple Orchards

abstract: The toxicity of a number of orchard pesticides to the predatory mites Amblyseius fallacis (Garman), and Typhlodromus pyri Scheuten, was determined in the laboratory by use of the standard slide-dip technique. Results indicated that azinphosmethyl and endosulfan were non-toxic to both species. Chloropropylate, formetanate hydrochloride, dimethoate and Galecron, however, were extremely toxic to the species. Carbaryl, demeton, and phosalone were highly toxic to A. fallacis , but these materials were nontoxic to T. pyri . Several other pesticides exhibited either no, slight, or moderate toxic effect on A. fallacis and T. pyri . Use of these materials in an integrated pest control program may allow predators to persist in apple orchards and provide satisfactory regulation of phytophagous mites at economic levels.


title: Query Expansion with Freebase

abstract: Large knowledge bases are being developed to describe entities, their attributes, and their relationships to other entities. Prior research mostly focuses on the construction of knowledge bases, while how to use them in information retrieval is still an open problem. This paper presents a simple and effective method of using one such knowledge base, Freebase, to improve query expansion, a classic and widely studied information retrieval task. It investigates two methods of identifying the entities associated with a query, and two methods of using those entities to perform query expansion. A supervised model combines information derived from Freebase descriptions and categories to select terms that are effective for query expansion. Experiments on the ClueWeb09 dataset with TREC Web Track queries demonstrate that these methods are almost 30% more effective than strong, state-of-the-art query expansion algorithms. In addition to improving average performance, some of these methods have better win/loss ratios than baseline algorithms, with 50% fewer queries damaged.


title: Prevalence of external parasites of pigeon in Zabol, southeast of Iran

abstract: External parasites or ectoparasites, are arthropods that live on or in the skin or feathers, using the host as both shelter and food source. Ectoparasites can have significant impacts on animal health and productivity. The aim of this research was to estimate the prevalence and ectoparasites diversity in pigeons of Zabol, southeast of Iran. A total of 264 pigeons were selected randomly (175 males and 89 females) to evaluate the presence of different ectoparasites. Ectoparasites were collected from different parts of the body and from walls and seams of cubbyholes, using hand lens, forceps, and flashlights to increasing the see sight. The samples were preserved in 70 % alcohol and cleared in lactophenol. The overall prevalence of external parasites in pigeon was 78.40 % [female (33.3 %) and male (66.7 %)]. There was no significant difference of infestation of ectoparasites between male and female (P > 0.05). Prevalence was higher in adult pigeons (84.11 %) than pipers (54.0 %). Two species of ectoparasites were identified: Argas reflexus and Columbicola columbae.


title: Android malware attacks and countermeasures: Current and future directions

abstract: Smartphones are rising in popularity as well as becoming more sophisticated over recent years. This popularity coupled with the fact that smartphones contain a lot of private user data is causing a proportional rise in different malwares for the platform. In this paper we analyze and classify state-of-the-art malware techniques and their countermeasures. The paper also reports a novel method for malware development and novel attack techniques such as mobile botnets, usage pattern based attacks and repackaging attacks. The possible countermeasures are also proposed. Then a detailed analysis of one of the proposed novel malware methods is explained. Finally the paper concludes by summarizing the paper.


title: The Xilinx Design Language (XDL): Tutorial and use cases

abstract: With the Xilinx Design Language (XDL), the FPGA vendor Xilinx offers a very powerful interface that provides access to virtually all features of their devices. This includes on one side the generation of complete device descriptions containing information about the FPGA primitives and the routing fabric. On the other side, XDL can be used to constrain systems or to directly implement modules or macros for Xilinx FPGAs. In this paper, we will provide documentation on the language and reveal several use cases for this language.


title: Skeleton-based Human Activity Recognition for Video Surveillance

abstract: Recognizing human activity is one of the important areas of computer vision research today. It plays a vital role in constructing intelligent surveillance systems. Despite the efforts in the past decades, recognizing human activities from videos is still a challenging task. Human activity may have different forms ranging from simple actions to complex activities. Recently released depth cameras provide effective estimation of 3D positions of skeletal joints in temporal sequences of depth maps. In this paper, a system for human activity recognition is proposed. We have considered the task of obtaining a descriptive labeling of the activities being performed through labeling human sub-activities. The activities we consider happen over a long period, and comprise several sub-activities performed in a sequence. The proposed activity descriptor makes the activity recognition problem viewed as a sequence classification problem. The proposed system employs Hidden Markov Models (HMMs) to recognize human activities. The system is evaluated on two benchmark datasets for daily living activity recognition. Experiment results demonstrate that the proposed system outperforms the state-of-the-art methods.


title: Subspace Clustering for Sequential Data

abstract: We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces. Current subspace clustering techniques learn the relationships within a set of data and then use a separate clustering algorithm such as NCut for final segmentation. In contrast our technique, under certain conditions, is capable of segmenting clusters intrinsically without providing the number of clusters as a parameter. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include a new penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral data, video sequences and face images. Our experiments show that our method, OSC, outperforms the state of the art methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR) and SSC.


title: On the implementation of input-feedforward delta-sigma modulators

abstract: This brief addresses some practical issues on the implementation of the input-feedforward delta-sigma modulators. First, the timing constraint imposed by the input-feedforward path is identified and a possible method to relax the constraint is proposed. Second, the drawbacks of the analog adder needed before the quantizer are explained and a method to eliminate the adder is proposed


title: Ultrafast Time-Resolved Emission and Absorption Spectra of meso-Pyridyl Porphyrins upon Soret Band Excitation Studied by Fluorescence Up-Conversion and Transient Absorption Spectroscopy

abstract: A comprehensive study of ultrafast molecular relaxation processes of isomeric meso-(pyridyl) porphyrins (TpyPs) has been carried out by using femtosecond time-resolved emission and absorption spectroscopic techniques upon pumping at 400 nm, Soret band (B band or S2), in 4:1 dichloromethane (DCM) and tetrahydrofuran (THF) solvent mixture. By combined studies of fluorescence up-conversion, time-correlated single photon counting, and transient absorption spectroscopic techniques, a complete model with different microscopic rate constants associated with elementary processes involved in electronic manifolds has been reported. Besides, a distinct coherent nuclear wave packet motion in Qy state is observed at low-frequency mode, ca. 26 cm–1 region. Fluorescence up-conversion studies constitute ultrafast time-resolved emission spectra (TRES) over the whole emission range (430–710 nm) starting from S2 state to Qx state via Qy state. Careful analysis of time profiles of up-converted signals at different emission w...


title: Magnetic Stimulation of the Human Brain with Low-Intensity Field

abstract: The most popular means of depression treatment nowadays are psychotherapy, antide- pressant medication and recently adopted tran- scranial magnetic stimulation (TMS) of the hu- man brain. To overcome their disadvantages we are investigating an application of low-field magnetic stimulation (LFMS) to depression treatment. The 3D electromagnetic models of both TMS and LFMS have been developed in COMSOL Multiphysics. The head is modeled so that various tissues are represented by homoge- neous concentric spheres of different size. Con- currently we are developing a realistic model of the human head with the help of Simpleware software. Obtained LFMS model shows that a current density in the order of 0.1 mA/m 2 can be induced up to 10 mm deep into the brain, what makes it possible to draft a technical specifica- tion on the stimulation device.


Measuring Aesthetics of Game Behavior


title: The Last Mile for IoT Privacy

abstract: The last mile in Internet of Things (IoT) privacy is to give users a unified way to discover and be notified about the privacy properties of IoT services. A conceptual privacy stack that would build a communication bridge between IoT systems and end users is described.


title: Hexapod robot gait stability investigation

abstract: Environmental adaptability, terrain locomotion is what makes walking robots so appealing. Due to a high number of degrees of freedom walking machines tend to be more useful in dangerous or impassible environments. Multi-legged robots have better stability while walking because there are always at least three legs supporting the robot. But having six or more legs also means that choosing or developing most suitable walking patterns is very challenging. In this paper we present a stability investigation of different hexapod robot gaits. Stability of gaits is measured by body tilt during movement. Three different gaits are used: tripod gait, wave gait, and ripple gait. Comparison of each gaits stability with various leg transfer sequences is given. Results show that the most stable gait is wave with the basic leg transfer sequence.


title: Decision Support for IT Investment Projects

abstract: Managerial flexibilities have to be taken into account in ex-ante decision-making on IT investment projects (ITIPs). In many papers of the IS literature, standard financial option pricing models are used to value such managerial flexibilities. Based on a review of the related literature, the paper critically discusses the assumptions of the most frequently used financial option pricing model, namely the Black–Scholes model, arguing for relaxed assumptions that better represent the characteristics of ITIPs. The authors find that existing real option analysis approaches featured in the IS, Finance, and Economics literature are unable to consider more than two of our relaxed assumptions. Consequently, they present their own approach in form of a simulation model for the valuation of real options in ITIPs which offers a better representation of the characteristics of ITIPs by taking the discounted cash-flows and the runtime to be uncertain as well as the market to be incomplete. Based on these modifications of the Black–Scholes model’s assumptions, it is found that the resulting option value contains idiosyncratic risk that has to be taken into account in ITIP decision making. For the realistic case of risk averse decision makers, the consideration of idiosyncratic risk usually leads to a lower risk-adjusted option value, compared to one calculated by means of the Black–Scholes model. This confirms the perception of managers who feel that financial option pricing models frequently overvalue ITIPs and hence may induce flawed investment decisions.


Face Identification with Bilinear CNNs.


title: WiMo: location-based emotion tagging

abstract: In this paper we introduce WiMo, a location-based social networking tool that enables users to share and store their emotional feelings about places. WiMo creates a mobile social network based on common interests and enables users to share not just information but also opinions, experiences and passions. The application uses a geo-emotional tagging system running on a GPS enabled mobile phone to ascribe emotions to places. In this paper we describe the development of an emotion tagging interface and present a case study scenario to situate the application in a real world setting. We then describe work-in-progress on the development of the WiMo prototype interface, and the interaction process for users. We further outline the features of the system and finally discuss next steps in the development of the application.


title: Software-defined caching: managing caches in multi-tenant data centers

abstract: In data centers, caches work both to provide low IO latencies and to reduce the load on the back-end network and storage. But they are not designed for multi-tenancy; system-level caches today cannot be configured to match tenant or provider objectives. Exacerbating the problem is the increasing number of un-coordinated caches on the IO data plane. The lack of global visibility on the control plane to coordinate this distributed set of caches leads to inefficiencies, increasing cloud provider cost. We present Moirai, a tenant- and workload-aware system that allows data center providers to control their distributed caching infrastructure. Moirai can help ease the management of the cache infrastructure and achieve various objectives, such as improving overall resource utilization or providing tenant isolation and QoS guarantees, as we show through several use cases. A key benefit of Moirai is that it is transparent to applications or VMs deployed in data centers. Our prototype runs unmodified OSes and databases, providing immediate benefit to existing applications.


title: Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network

abstract: Electric load forecasting plays a vital role in smart grids. Short term electric load forecasting forecasts the load that is several hours to several weeks ahead. Due to the nonlinear, non-stationary and nonseasonal nature of the short term electric load time series in small scale power systems, accurate forecasting is challenging. This paper explores Long-Short-Term-Memory (LSTM) based Recurrent Neural Network (RNN) to deal with this challenge. LSTM-based RNN is able to exploit the long term dependencies in the electric load time series for more accurate forecasting. Experiments are conducted to demonstrate that LSTM-based RNN is capable of forecasting accurately the complex electric load time series with a long forecasting horizon. Its performance compares favorably to many other forecasting methods.


title: Analysis of a current sensing and reporting tolerance in an inductor DCR sense topology

abstract: This paper shows a method to design an inductor DCR sense circuit that achieves a tight tolerance and good sigma level across all the units in a high volume manufacturing environment. Ultimately, the problem that we are trying to solve is to help designers get rid of the large tolerance seen on the current sensed on many DCR sensed based circuits. The paper presents a novel way to derive the expression of the sensing capacitor voltage, taking into account the resonant circuit time constants mismatch. The expression obtained is shown to be more accurate than what was previously published in the literature. The paper presents a statistical method to compute the total error on the current drawn by a chip, reported to it by the VR controller. Experimental data obtained from a design done using the proposed statistical methods are also shown. The results are shown to yield an overall sigma level that is acceptable industry wide.


A snooping cache coherency protocol for hierarchically organized multiprocessors


title: Snitch: Interactive Decision Trees for Troubleshooting Misconfigurations

abstract: Troubleshooting misconfigurations of modern applications is difficult due to their large and complex state. Snitch is a prototype tool that assists human troubleshooters by finding relationships between application state and subsequent faults. It correlates configuration state and application errors across many machines and users, and across long periods of time. Snitch aids the human expert in extracting patterns from this rich but enormous data set by building decision trees pinpointing potential configuration problems. ::: ::: We applied Snitch to 114 GB of configuration traces from 151 machines over 567 days. We illustrate how Snitch can suggest misconfigurations in case studies of two Windows applications: Messenger and Outlook.


title: VCDB: A Large-Scale Database for Partial Copy Detection in Videos

abstract: The task of partial copy detection in videos aims at finding if one or more segments of a query video have (transformed) copies in a large dataset. Since collecting and annotating large datasets of real partial copies are extremely time-consuming, previous video copy detection research used either small-scale datasets or large datasets with simulated partial copies by imposing several pre-defined transformations (e.g., photometric or geometric changes). While the simulated datasets were useful for research, it is unknown how well the techniques developed on such data work on real copies, which are often too complex to be simulated. In this paper, we introduce a large-scale video copy database (VCDB) with over 100,000 Web videos, containing more than 9,000 copied segment pairs found through careful manual annotation. We further benchmark a baseline system on VCDB, which has demonstrated state-of-the-art results in recent copy detection research. Our evaluation suggests that existing techniques—which have shown near-perfect results on the simulated benchmarks—are far from satisfactory in detecting complex real copies. We believe that the release of VCDB will largely advance the research around this challenging problem.


Virtualization in Cloud Computing


title: Transfer knowledge for high sparsity in deep neural networks

abstract: Deep neural networks have achieve great success in various applications recently. Lots of efforts have been made to reduce their parameters to make these models accessible to portable devices. However, the conflict between model size and performance loss is always crucial when we compress a deep neural network. In this work, we propose a new method to keep the performance of networks when pruning them. For a model to be pruned, we train a teacher model to boost its inference accuracy, so that higher compression ratio can be reached with the same or better performance.


title: Megapixel Size Image Creation using Generative Adversarial Networks

abstract: Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects.


title: Homicides with Mutilation of the Victim’s Body

abstract: Information on homicide offenders guilty of mutilation is sparse. The current study estimates the rate of mutilation of the victim's body in Finnish homicides and compares sociodemographic characteristics, crime history, life course development, psychopathy, and psychopathology of these and other homicide offenders. Crime reports and forensic examination reports of all offenders subjected to forensic examination and convicted for a homicide in 1995-2004 (n = 676) were retrospectively analyzed for offense and offender variables and scored with the Psychopathy Check List Revised. Thirteen homicides (2.2%) involved mutilation. Educational and mental health problems in childhood, inpatient mental health contacts, self-destructiveness, and schizophrenia were significantly more frequent in offenders guilty of mutilation. Mutilation bore no significant association with psychopathy or substance abuse. The higher than usual prevalence of developmental difficulties and mental disorder of this subsample of offenders needs to be recognized. Language: en


title: Face and 2-d mesh animation in MPEG-4

abstract: This paper presents an overview of some of the synthetic visual objects supported by MPEG-4 version-1, namely animated faces and animated arbitrary 2D uniform and Delaunay meshes. We discuss both specification and compression of face animation and 2D-mesh animation in MPEG-4. Face animation allows to animate a proprietary face model or a face model downloaded to the decoder. We also address integration of the face animation tool with the text-to-speech interface (TTSI), so that face animation can be driven by text input.


title: Detecting the financial statement fraud : The analysis of the differences between data mining techniques and experts ’ judgments

abstract: The objective of this study is to examine all aspects of fraud triangle using the data mining techniques and employ the available and public information to proxy variables to evaluate such attributes as pressure/incentive, opportunity, and attitude/rationalization, based on the findings from prior studies in this subject field and also the Statement on Auditing Standards. The second objective is to discuss whether or not the suggestion of the experts agrees with the results obtained from adopting those novel techniques. In specific, this study uses both expert questionnaires and data mining techniques to sort out the different fraud factors and then rank the importance of them. The data mining methods employed in this research include Logistic Regression, Decision Trees (CART), and Artificial Neural Networks (ANNs). Empirically, the ANNs and CART approaches work with the training and testing samples in a correct classification rate of 91.2% (ANNs) & 90.4% (CART) and 92.8% (ANNs) & 90.3% (CART), respectively, which is more accurate than the logistic model that only reaches 83.7% and 88.5% of the correct classification in assessing the fraud presence. In addition, type II error of ANNs drops significantly to 23.9% from 43.3% and 27.8% compared to the ones using CART and logistic models. Finally, the differences between different data mining tools and expert judgments are also compared to provide more insights as a research contribution.


title: Wavelet Compression of ECG Signals Using SPIHT Algorithm

abstract: Abstract — In this paper we present a novel approach for waveletcompression of electrocardiogram (ECG) signals based on the set partitioning in hierarchical trees (SPIHT) coding algorithm. SPIHT algorithm has achieved prominent success in image compression. Here we use a modified version of SPIHT for one dimensional signals. We applied wavelet transform with SPIHT coding algorithmon different records of MIT-BIH database. The results show the high efficiency of this method in ECG compression. Keywords — ECG compression, wavelet, SPIHT. I. I NTRODUCTION LECTROCARDIOGRAM (ECG) signal is a very usefulsource of information for physicians in diagnosing heartabnormalities. With the increasing use of ECG in heart diagnosis, such as 24 hour monitoring or in ambulatorymonitoring systems, the volume of ECG data that should be stored or transmitted, has greatly increased. For example, a 3channel, 24 hour ambulatory ECG, typically has storagerequirement of over 50 MB. Therefore we need to reduce thedata volume to decrease storage cost or make ECG signalsuitable and ready for transmission through commoncommunication channels such as phone line or mobilechannel. So, we need an effective data compression method.The main goal of any compression technique is to achievemaximum data reduction while preserving the significantsignal morphology features upon reconstruction. Datacompression methods have been mainly divided into twomajor categories: 1) direct methods, in which actual signalsamples are analyzed (time domain), 2) transformationalmethods, in which first apply a transform to the signal and dospectral and energy distribution analysis of signals.Examples of direct methods are: differential pulse codemodulation (DPCM), amplitude zone time epoch coding (A TEC), turning point, coordinate reduction time encoding system (CORTES), Fan algorithm, ASEC. Reference [1] is agood review of some direct compression methods used inECG compression.


title: Managed communication and consistency for fast data-parallel iterative analytics

abstract: At the core of Machine Learning (ML) analytics is often an expert-suggested model, whose parameters are refined by iteratively processing a training dataset until convergence. The completion time (i.e. convergence time) and quality of the learned model not only depends on the rate at which the refinements are generated but also the quality of each refinement. While data-parallel ML applications often employ a loose consistency model when updating shared model parameters to maximize parallelism, the accumulated error may seriously impact the quality of refinements and thus delay completion time, a problem that usually gets worse with scale. Although more immediate propagation of updates reduces the accumulated error, this strategy is limited by physical network bandwidth. Additionally, the performance of the widely used stochastic gradient descent (SGD) algorithm is sensitive to step size. Simply increasing communication often fails to bring improvement without tuning step size accordingly and tedious hand tuning is usually needed to achieve optimal performance. This paper presents Bosen, a system that maximizes the network communication efficiency under a given inter-machine network bandwidth budget to minimize parallel error, while ensuring theoretical convergence guarantees for large-scale data-parallel ML applications. Furthermore, Bosen prioritizes messages most significant to algorithm convergence, further enhancing algorithm convergence. Finally, Bosen is the first distributed implementation of the recently presented adaptive revision algorithm, which provides orders of magnitude improvement over a carefully tuned fixed schedule of step size refinements for some SGD algorithms. Experiments on two clusters with up to 1024 cores show that our mechanism significantly improves upon static communication schedules.


title: Blood pressure estimation using video plethysmography

abstract: Remote sensing of vital physiological signs allows for unobtrusive, nonrestrictive and non-contact assessment of individual's health. By using video plethysmogram obtained by digital camera recordings of patient's face or hands, health parameters such as heart rate, respiratory rate and heart rate variability have already been investigated. In this paper, time-domain video plethysmogram from forehead was used for calculation of pulse transit time, which is related to blood pressure. Synchronous measurements of non-contact video plethysmogram, 12-channel electrocardiogram and invasive blood pressure were performed on three subjects. Our results demonstrate that pulse transit time method can be equally efficient with non-contact VPG signal, with error mean and standard deviation of 9.48 ± 7.13 mmHg and 4.48 ± 3.29 mmHg, for systolic and mean arterial pressure, respectively. Additionally our findings show that delay in pulse transit time, calculated from two different VPG signals from forehead and palm, could provide clinically useful measure of changes in systolic blood pressure.


title: A 4 × 4 Butler Matrix for 28 GHz Switched Multi-Beam Antenna

abstract: In this paper, a 4 × 4 Butler matrix (BM) switched-beam patch antenna array is designed and discussed. The design is developed on a single layer of Rogers material; RO 5880 with dielectric constant 2.2 and thickness 0.254mm.This makes the designed antenna low cost and easy of fabrication. The four rectangular patches fed by inset microstrip lines are connected to the outputs of the BM. The results show wide bandwidth that covers from 24.67 GHz to 30.2 GHz in all the four inputs, and the highest array gain performance of 10.8 dBi at 28 GHz. The designed switched beam antenna provides a cost effective approach to implement at 28 GHz wireless communications.


title: Capacitive-Sensing Circuit Technique for Image Quality Improvement on Fingerprint Sensor LSIs

abstract: This paper describes a new capacitive-sensing circuit technique that improves the quality of images captured with capacitive fingerprint sensor LSIs. The quality of the captured image depends on the surface condition of the finger. When the finger is dry, the electrical resistance of the finger surface is high. The finger surface resistance induces a voltage drop in the electrical potential of the finger surface (which should be grounded), which leads to poor image quality. To capture clear images even when the finger is dry, the circuit technique improves the image quality using the series resistance caused at the finger surface. The potential of the finger surface is controlled by an enhancement plate and a voltage control circuit. A test chip implementing this technique was fabricated on a 0.5 ?m CMOS process and a sensor process. The chip captures a clear fingerprint image of a dry finger, confirming the effectiveness of the circuit technique for capturing clear fingerprint images independent of the finger surface condition with the capacitive fingerprint sensor LSIs.


title: Systematic review: probiotics in the management of lower gastrointestinal symptoms in clinical practice – an evidence-based international guide

abstract: BackgroundEvidence suggests that the gut microbiota play an important role in gastrointestinal problems. ::: ::: AimTo give clinicians a practical reference guide on the role of specified probiotics in managing particular lower gastrointestinal symptoms/problems by means of a systematic review-based consensus. ::: ::: MethodsSystematic literature searching identified randomised, placebo-controlled trials in adults; evidence for each symptom/problem was graded and statements developed (consensus process; 10-member panel). As results cannot be generalised between different probiotics, individual probiotics were identified for each statement. ::: ::: ResultsThirty seven studies were included; mostly on irritable bowel syndrome [IBS; 19 studies; treatment responder rates: 18–80% (specific probiotics), 5–50% (placebo)] or antibiotic-associated diarrhoea (AAD; 10 studies). Statements with 100% agreement and ‘high’ evidence levels indicated that: (i) specific probiotics help reduce overall symptom burden and abdominal pain in some IBS patients; (ii) in patients receiving antibiotics/Helicobacter pylori eradication therapy, specified probiotics are helpful as adjuvants to prevent/reduce the duration/intensity of AAD; (iii) probiotics have favourable safety in patients in primary care. Items with 70–100% agreement and ‘moderate’ evidence were: (i) specific probiotics help relieve overall symptom burden in some patients with diarrhoea-predominant IBS, and reduce bloating/distension and improve bowel movement frequency/consistency in some IBS patients and (ii) with some probiotics, improved symptoms have led to improvement in quality of life. ::: ::: ConclusionsSpecified probiotics can provide benefit in IBS and antibiotic-associated diarrhoea; relatively few studies in other indications suggested benefits warranting further research. This study provides practical guidance on which probiotic to select for a specific problem.


title: The validity of the Hospital Anxiety and Depression Scale - An updated literature review

abstract: Objective: To review the literature of the validity of the Hospital Anxiety and Depression Scale (HADS). Method: A review of the 747 identified papers that used HADS was performed to address the following questions: (I) How are the factor structure, discriminant validity and the internal consistency of HADS? (II) How does HADS perform as a case finder for anxiety disorders and depression? (III) How does HADS agree with other self-rating instruments used to rate anxiety and depression? Results: Most factor analyses demonstrated a twofactor solution in good accordance with the HADS subscales for Anxiety (HADS-A) and Depression (HADS-D), respectively. The correlations between the two subscales varied from .40 to .74 (mean .56). Cronbach’s alpha for HADS-A varied from .68 to .93 (mean .83) and for HADS-D from .67 to .90 (mean .82). In most studies an optimal balance between sensitivity and specificity was achieved when caseness was defined by a score of 8 or above on both HADS-A and HADS-D. The sensitivity and specificity for both HADS-A and HADS-D of approximately 0.80 were very similar to the sensitivity and specificity achieved by the General Health Questionnaire (GHQ). Correlations between HADS and other commonly used questionnaires were in the range .49 to .83. Conclusions: HADS was found to perform well in assessing the symptom severity and caseness of anxiety disorders and depression in both somatic, psychiatric and primary care patients and in the general population. D 2002 Elsevier Science Inc. All rights reserved.


title: Statistical Parsing with a Context-Free Grammar and Word Statistics

abstract: We describe a parsing system based upon a language model for English that is, in turn, based upon assigning probabilities to possible parses for a sentence. This model is used in a parsing system by finding the parse for the sentence with the highest probability. This system outperforms previous schemes. As this is the third in a series of parsers by different authors that are similar enough to invite detailed comparisons but different enough to give rise to different levels of performance, we also report on some experiments designed to identify what aspects of these systems best explain their relative performance.


title: High-Efficiency Slim Adapter With Low-Profile Transformer Structure

abstract: This paper presents the implementation of a high-efficiency slim adapter using an resonant converter. A new structure of the slim-type transformer is proposed, which is composed of copper wire as the primary winding and printed-circuit-board winding on the outer layer as the secondary winding. The proposed structure is suitable for a slim and high-efficiency converter because it has advantages of easy utilization and wide conductive cross-sectional area. In addition, the voltage-doubler rectifier is applied to the secondary side due to its simple structure of secondary winding, and a filter is adopted to reduce the output filter size. The validity of this study is confirmed by the experimental results from an 85-W prototype.


title: DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection Under Partial Occlusion

abstract: In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to transfer the learned knowledge to deal with occlusions. This setting alleviates the difficulty in collecting an exponentially large dataset to cover occlusion patterns and is more essential. In this scenario, the proposal-based deep networks, like RCNN-series, often produce unsatisfactory results, because both the proposal extraction and classification stages may be confused by the irrelevant occluders. To address this, [25] proposed a voting mechanism that combines multiple local visual cues to detect semantic parts. The semantic parts can still be detected even though some visual cues are missing due to occlusions. However, this method is manually-designed, thus is hard to be optimized in an end-to-end manner. ::: In this paper, we present DeepVoting, which incorporates the robustness shown by [25] into a deep network, so that the whole pipeline can be jointly optimized. Specifically, it adds two layers after the intermediate features of a deep network, e.g., the pool-4 layer of VGGNet. The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts. We also propose an improved version DeepVoting+ by learning visual cues from context outside objects. In experiments, DeepVoting achieves significantly better performance than several baseline methods, including Faster-RCNN, for semantic part detection under occlusion. In addition, DeepVoting enjoys explainability as the detection results can be diagnosed via looking up the voting cues.


title: On the Reaction to Deprecation of 25,357 Clients of 4+1 Popular Java APIs

abstract: Application Programming Interfaces (APIs) are a tremendous resource—that is, when they are stable. Several studies have shown that this is unfortunately not the case. Of those, a large-scale study of API changes in the Pharo Smalltalk ecosystem documented several findings about API deprecations and their impact on API clients. We conduct a partial replication of this study, considering more than 25,000 clients of five popular Java APIs on GitHub. This work addresses several shortcomings of the previous study, namely: a study of several distinct API clients in a popular, statically-typed language, with more accurate version information. We compare and contrast our findings with the previous study and highlight new ones, particularly on the API client update practices and the startling similarities between reaction behavior in Smalltalk and Java.


title: Face detection and tracking: Using OpenCV

abstract: An application for tracking and detecting faces in videos and in cameras which can be used for multipurpose activities. The intention of the paper is deep study of face detection using open CV. A tabular comparison is performed in order to understand the algorithms in an easier manner. It talks about various algorithms like Adaboost, Haar cascades. This paper aims to help in understanding the best prerequisites for face detection.


title: Security Analysis of Container Images Using Cloud Analytics Framework

abstract: Container technology has become an integral part of today’s major IT services. Although it offers several benefits, it also introduces new challenges for operating and maintaining secure container environments. One such challenge is to retain the ability to detect and address the containers’ vulnerabilities and compliance violations. However, designing an effective solution to enable this capability must be based on the accurate understanding of characteristics observed from actual container images and instances. To contribute toward this objective, we have built a general data processing framework, applying the principles of the state-of-the-art. It is a system that decouples the data collection process from the analysis so as to allow user to focus more on building new analysis logics rather than on the tools for monitoring agents. We applied it to the analysis of container images from the Docker Hub image repository, to learn about their security posture. In this work we present various interesting findings and new insights from analyzing the public image corpus. We have learned that more than 92% of the images contain compliance violations and/or vulnerable packages.


title: A fully matched LTE-A carrier aggregation quadplexer based on BAW and SAW technologies

abstract: An inter-band carrier aggregation (CA) quadplexer allows doubling the download rates and is therefore an important element in upcoming mobile phone systems. In a duplexer design, both Tx and Rx filters should behave like an open-circuit for respective counter-bands at the antenna port. Additionally, they have to attain high suppression in their counter-band to get good in-band isolation. The situation gets more difficult and challenging for a CA quadplexer. Besides an open-circuit requirement for now 3 bands simultaneously, each Tx filter should achieve high suppression in the frequency range of both Rx filters in order to get high in-band and cross-isolation. The same applies for each Rx filter. Generally, it is not sufficient to simply expand the well-known topology concepts of duplexers rather a different design methodology is required. In this paper we will discuss several approaches to realize a CA quadplexer and present an implementation of a B25-B4 CA quadplexer sized 3.6×2.0×0.8mm3 utilizing the company's advanced acoustic wave filtering technologies: bulk acoustic wave (BAW) and surface acoustic wave (SAW).


title: A Comparison Between Data Mining Prediction Algorithms for Fault Detection(Case study: Ahanpishegan co.)

abstract: In the current competitive world, industrial companies seek to manufacture products of higher quality which can be achieved by increasing reliability, maintainability and thus the availability of products. On the other hand, improvement in products lifecycle is necessary for achieving high reliability. Typically, maintenance activities are aimed to reduce failures of industrial machinery and minimize the consequences of such failures. So the industrial companies try to improve their efficiency by using different fault detection techniques. One strategy is to process and analyze previous generated data to predict future failures. The purpose of this paper is to detect wasted parts using different data mining algorithms and compare the accuracy of these algorithms. A combination of thermal and physical characteristics has been used and the algorithms were implemented on Ahanpishegan's current data to estimate the availability of its produced parts. Keywords: Data Mining, Fault Detection, Availability, Prediction Algorithms.


title: Learning haptic affordances from demonstration and human-guided exploration

abstract: In this paper, we present a system for learning haptic affordance models of complex manipulation skills. The goal of a haptic affordance model is to better complete tasks by characterizing what a particular object-action pair feels like. We use learning from demonstration to provide the robot with an example of a successful interaction with a given object. We then use environmental scaffolding to collect grounded examples (successes and unsuccessful "near misses") of the haptic data for the object-action pair, using a force/torque (F/T) sensor mounted at the wrist. From this we build one success Hidden Markov Model (HMM) and one "near-miss" HMM for each object-action pair. We evaluate this approach with five different actions over seven different objects to learn two specific affordances (open-able and scoop-able). We show that by building a library of object-action pairs for each affordance, we can successfully monitor a trajectory of haptic data to determine if the robot finds an affordance. This is supported through cross-validation on the object-action pairs, with four of the seven object-action pairs achieving a perfect F1 score, and with leave-one-object-out testing, where the learned object-actions models correctly identify the specific affordance with an average accuracy of 67% for scoop-able and 53% for open-able.


title: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

abstract: An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.


The quantitative analysis of deoxypentose nucleic acids.


title: Using the Cloud for Parameter Estimation Problems: Comparing Spark vs MPI with a Case-Study

abstract: Systems biology is an emerging approach focused in generating new knowledge about complex biological systems by combining experimental data with mathematical modeling and advanced computational techniques. Many problems in this field are extremely challenging and require substantial supercomputing resources to be solved. This is the case of parameter estimation in large-scale nonlinear dynamic systems biology models. Recently, Cloud Computing has emerged as a new paradigm for on-demand delivery of computing resources. However, scientific computing community has been quite hesitant in using the Cloud, simply because traditional programming models do not fit well with the new paradigm, and the earliest cloud programming models do not allow most scientific computations being efficiently run in the Cloud. In this paper we explore and compare two distributed computing models: the MPI (message-passing interface) model, that is high-performance oriented, and the Spark model, which is throughput oriented but outperforms other cloud programming solutions adding improved support for iterative algorithms through in-memory computing. The performance of a very well known metaheuristic, the Differential Evolution algorithm, has been thoroughly assessed using a challenging parameter estimation problem from the domain of computational systems biology. The experiments have been carried out both in a local cluster and in the Microsoft Azure public cloud, allowing performance and cost evaluation for both infrastructures.


abstract: Title of Thesis: “A Retargetable Optimizing Java-to-C Compiler for Embedded Systems” Degree candidate: Ankush Varma Degree and year: Master of Science, 2003. Thesis directed by: Professor Shuvra S. Bhattacharyya Department of Electrical and Computer Engineering University of Maryland, College Park The Java programming language is achieving greater acceptance in high-end embedded systems such as cellphones and PDAs. However, lowend embedded platforms, such as DSPs or microcontrollers, often have no more than a C compiler, and this prevents Java applications from being run on such systems. Applications must either be re-written in C, or a Java Virtual Machine must be ported to each such system. This paper discusses a compiler that converts portable Java bytecode to C code, allowing applications written in Java to run on embedded systems which may lack a Java Virtual Machine. This is also applicable to barebones embedded systems running without an operating system. We briefly describe code generation strategies, run-time data structures and optimization algorithms used to generate efficient C code. The code size and execution time of the C code were compared with interpreted Java, just-in-time compiled Java, and executables generated directly from Java. On an average, we found the size of the generated stand-alone executable to be over 25 times smaller than that generated by a cutting-edge Javato-native-code compiler, while providing performance comparable to the best of various Java implementation strategies.


title: The role of social capital in virtual teams and organisations : corporate value creation

abstract: The paper systematises the previous research on trust in virtual teams, by examining how trust and other elements of social capital can be created in virtual teams and where the value created by social capital resides. The study is guided by three main questions: What value can social capital create for virtual teams and organisations? How are social capital elements interlinked? What are the difficulties of developing social capital, and especially trust for virtual teams and how can these difficulties affect the process of corporate value creation?


title: MINE: Mutual Information Neural Estimation

abstract: This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings.


title: Tunneling Neural Perception and Logic Reasoning through Abductive Learning

abstract: Perception and reasoning are basic human abilities that are seamlessly connected as part of human intelligence. However, in current machine learning systems, the perception and reasoning modules are incompatible. Tasks requiring joint perception and reasoning ability are difficult to accomplish autonomously and still demand human intervention. Inspired by the way language experts decoded Mayan scripts by joining two abilities in an abductive manner, this paper proposes the abductive learning framework. The framework learns perception and reasoning simultaneously with the help of a trial-and-error abductive process. We present the Neural-Logical Machine as an implementation of this novel learning framework. We demonstrate that--using human-like abductive learning--the machine learns from a small set of simple hand-written equations and then generalizes well to complex equations, a feat that is beyond the capability of state-of-the-art neural network models. The abductive learning framework explores a new direction for approaching human-level learning ability.


title: Recent Advances in Graph Partitioning

abstract: We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions.


title: Improve Cross-Domain Face Recognition with IBN-block

abstract: Domain adaptation is one of the major challenges for face recognition (FR). Most large-scale FR training datasets are built from massive images crawled from the Internet, while in practical applications face images come from specific scenarios. Especially, for applications like ID card verification, registered face images are taken in controlled environments while probe face images are not. There are different distributions between source domain and target domain. In this paper, we propose to use Instance-Batch-Normalization (IBN) block to improve cross-domain FR performance. A million-scale cross-domain test set named IDCard-Scene-1M is used for evaluation. CNN models are trained with an improved loss function which we call L2-ASoftmax Loss. Without using any data from the target domain, IBN-block increased recall rates (@FPR = 10−6) on IDCard-Scene-1M by 1 percentage point for different CNN models. Besides, experiments show that the proposed IBN-CNN models trained with L2-ASoftmax Loss made state-of-the-art performance on MegaFace evaluation.


title: Broadband SIW cavity-backed circularly polarized antenna element and array

abstract: A novel substrate integrated waveguide (SIW) cavity-backed circularly polarized antenna element and 2×2 array is proposed. Taking advantages of low loss and low profile of SIW, the antenna element and array have high efficiency and can be fabricated in the standard print circuit board (PCB) process. By adding SIW cavity around the corner-truncated patch antenna, the bandwidth of the circularly polarized (CP) antenna can be broadened. For the element, an impedance bandwidth of 15% and an axial ratio (AR) bandwidth of 4.3% can be achieved. For the 2×2 array with sequential feeding, the impedance bandwidth and the AR bandwidth can be enhanced to 27% and 13%, respectively.


title: Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues

abstract: AIMS ::: Evaluating expression of the human epidermal growth factor receptor 2 (HER2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER2 is subjective, and consequently prone to interobserver variability. Given the prognostic and therapeutic implications of HER2 scoring, a more objective method is required. In this paper, we report on a recent automated HER2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art artificial intelligence (AI)-based automated methods for HER2 scoring. ::: ::: ::: METHODS AND RESULTS ::: The contest data set comprised digitized whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both haematoxylin and eosin (H&E) and IHC for HER2. The contesting algorithms predicted scores of the IHC slides automatically for an unseen subset of the data set and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple 'Man versus Machine' contest for the scoring of HER2 and show that the automated methods could beat the pathology experts on this contest data set. ::: ::: ::: CONCLUSIONS ::: This paper presents a benchmark for comparing the performance of automated algorithms for scoring of HER2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.


title: Design considerations of HBM stacked DRAM and the memory architecture extension

abstract: Recently, the 3D stacked memory, which is known as HBM (high bandwidth memory), using TSV process has been developed. The stacked memory structure provides increased bandwidth, low power consumption, as well as small form factor. There are many design challenges, such as multi-channel operation, microbump test and TSV connection scan. Various design methodology make it possible to overcome the difficulties in the development of TSV technology. Vertical stacking enables more diverse memory architecture than the flat architecture. The next generation of HBM focuses on not only the bandwidth but also the system performance enhancement by adopting pseudo channel and 8-Hi stacking. The architecture applied to the second generation HBM are introduced in this paper.


title: Serial murder in America: case studies of seven offenders

abstract: This article summarizes and compares information on seven interviewed serial killers in an ongoing project designed to study similarities and differences among these individuals. The aim of this article is to increase our collective knowledge of the dynamics of serial murder by examining the perpetrators' backgrounds, as well as the unique ways in which they view themselves and the world around them. Although qualitative interview research alone is not sufficient to fully understand such behavior, it is useful in many ways. Some of the information discussed based on the seven offenders interviewed is compared with broader epidemiological studies, and the strengths and limitations of each type of research are discussed. Language: en


title: The Impact of Sentiment-driven Feedback on Knowledge Reuse in Online Communities

abstract: Knowledge reuse is of increasing importance for organizations. Despite the extant research, we still do not adequately understand the ways peers are motivated to reuse knowledge with the help of wiki technologies. In this paper, we study the motivation for knowledge reuse in a prominent instance of online social production: Wikipedia. Studying knowledge reuse in Wikipedia is important since Wikipedia has been able to leverage the benefits of efficient knowledge reuse to produce knowledge goods of relatively high quality. Specifically, we explore: 1) how Wikipedia editors (any peer who contributes to developing articles in Wikipedia) communicate their feedback toward each other’s work in peer conversations and 2) to what extent sentiment-driven feedback impacts the level of knowledge reuse in Wikipedia. The results show that displaying sentiment-driven feedback positively influenced the level of knowledge reuse. Our study further shows a significant difference in the level of knowledge reuse between editors who shared mainly positive or mainly negative sentiments. Specifically, displaying mainly positive feedback corresponded to a superior level of knowledge reuse than displaying mainly negative feedback. We contribute to the extant literature of online social production communities in general and Wikipedia in particular by providing a first building block for research on peer feedback’s role in developing and sustaining wiki-based knowledge reuse. We discuss our findings’ implications for theory and practice.


Detection of Web Denial-of-Service Attacks using decoy hyperlinks


Reef : Automating Weak Supervision to Label Training Data


title: CrowdPlanner: A crowd-based route recommendation system

abstract: As travel is taking more significant part in our life, route recommendation service becomes a big business and attracts many major players in IT industry. Given a pair of user-specified origin and destination, a route recommendation service aims to provide users with the routes of best travelling experience according to criteria, such as travelling distance, travelling time, traffic condition, etc. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers. It means travellers' preferences on route selection are influenced by many latent and dynamic factors that are hard to model exactly with pre-defined formulas. In this work we approach this challenging problem with a very different perspective- leveraging crowds' knowledge to improve the recommendation quality. In this light, CrowdPlanner - a novel crowd-based route recommendation system has been developed, which requests human workers to evaluate candidate routes recommended by different sources and methods, and determine the best route based on their feedbacks. In this paper, we particularly focus on two important issues that affect system performance significantly: (1) how to efficiently generate tasks which are simple to answer but possess sufficient information to derive user-preferred routes; and (2) how to quickly identify a set of appropriate domain experts to answer the questions timely and accurately. Specifically, the task generation component in our system generates a series of informative and concise questions with optimized ordering for a given candidate route set so that workers feel comfortable and easy to answer. In addition, the worker selection component utilizes a set of selection criteria and an efficient algorithm to find the most eligible workers to answer the questions with high accuracy. A prototype system has been deployed to many voluntary mobile clients and extensive tests on real-scenario queries have shown the superiority of CrowdPlanner in comparison with the results given by map services and popular route mining algorithms.


title: © 1999 PABST SCIENCE PUBLISHERS Confidence Intervals as an Alternative to Significance Testing

abstract: The article argues to replace null hypothesis significance testing by confidence intervals. Correctly interpreted, confidence intervals avoid the problems associated with null hypothesis statistical testing. Confidence intervals are formally valid, do not depend on apriori hypotheses and do not result in trivial knowledge. The first part presents critique of null hypothesis significance testing; the second part replies to critique against confidence intervals and tries to demonstrate their superiority to null hypothesis significance testing.


title: Utilizing Knowledge-Base Semantics in Graph-Based Algorithms

abstract: Graph-based algorithms convert a knowledge base with a graph structure into one with a tree structure (a join-tree) and then apply tree-inference on the result. Nodes in the join-tree are cliques of variables and tree-inference is exponential in w*, the size of the maximal clique in the join-tree. A central property of join-trees that validates tree-inference is the running-intersection property: the intersection of any two cliques must belong to every clique on the path between them. We present two key results in connection to graph-based algorithms. First, we show that the running-intersection property, although sufficient, is not necessary for validating tree-inference. We present a weaker property for this purpose, called running-interaction, that depends on nonstructural (semantical) properties of a knowledge base. We also present a linear algorithm that may reduce w* of a join-tree, possibly destroying its running-intersection property, while maintaining its running-interaction property and, hence, its validity for tree-inference. Second, we develop a simple algorithm for generating trees satisfying the running-interaction property. The algorithm bypasses triangulation (the standard technique for constructing join-trees) and does not construct a join-tree first. We show that the proposed algorithm may in some cases generate trees that are more efficient than those generated by modifying a join-tree.


title: A Conceptual Definition of Child Neglect

abstract: Although child neglect is the most common form of child maltreatment, considerable confusion remains regarding its definition. A conceptual definition of neglect is presented based on an ecological...


title: Brand Image and Brand Dilution in the Fashion Industry

abstract: We develop a dynamic optimal control model of a fashion designer's challenge of maintaining brand image in the face of short-term profit opportunities through expanded sales that risk brand dilution in the longer-run. The key state variable is the brand's reputation, and the key decision is sales volume. Depending on the brand's capacity to command higher prices, one of two regimes is observed. If the price markups relative to production costs are modest, then the optimal solution may simply be to exploit whatever value can be derived from the brand in the short-run and retire the brand when that capacity is fully diluted. However, if the price markups are more substantial, then an existing brand should be preserved. It may even be worth incurring short-term losses while increasing the brand's reputation, even if starting a new brand name from scratch is not optimal.


Positive Output Super-Lift Luo-Converters


title: Individual alpha neurofeedback training effect on short term memory

abstract: article i nfo Memory performance has been reported to be associated with electroencephalogram (EEG) alpha activity. This study aimed to improve short term memory performance by individual alpha neurofeedback training (NFT). With appropriate protocol designed for NFT, the experimental results showed that the participants were able to learn to increase the relative amplitude in individual alpha band during NFT and short term memory performance was significantly enhanced by 20 sessions of NFT. More importantly, further analysis revealed that the improvement of short term memory was positively correlated with the increase of the relative amplitude in the individual upper alpha band during training. In addition, effective strategies for individual alpha training varied among individuals and the most successful mental strategies were related to positive thinking.


title: X-ray: Automating Root-Cause Diagnosis of Performance Anomalies in Production Software

abstract: Troubleshooting the performance of production software is challenging. Most existing tools, such as profiling, tracing, and logging systems, reveal what events occurred during performance anomalies. However, users of such toolsmust infer why these events occurred; e.g., that their execution was due to a root cause such as a specific input request or configuration setting. Such inference often requires source code and detailed application knowledge that is beyond system administrators and end users. ::: ::: This paper introduces performance summarization, a technique for automatically diagnosing the root causes of performance problems. Performance summarization instruments binaries as applications execute. It first attributes performance costs to each basic block. It then uses dynamic information flow tracking to estimate the likelihood that a block was executed due to each potential root cause. Finally, it summarizes the overall cost of each potential root cause by summing the per-block cost multiplied by the cause-specific likelihood over all basic blocks. Performance summarization can also be performed differentially to explain performance differences between two similar activities. X-ray is a tool that implements performance summarization. Our results show that X-ray accurately diagnoses 17 performance issues in Apache, lighttpd, Postfix, and PostgreSQL, while adding 2.3% average runtime overhead.


title: Penalty logic and its Link with Dempster-Shafer Theory

abstract: Penalty logic, introduced by Pinkas [17], associates to each formula of a knowledge base the price to pay if this formula is violated. Penalties may be used as a criterion for selecting preferred consistent subsets in an inconsistent knowledge base, thus inducing a non-monotonic inference relation. A precise formalization and the main properties of penalty logic and of its associated nonmonotonic inference relation are given in the first part. We also show that penalty logic and Dempster-Shafer theory are related, especially in the infinitesimal case.