nils-herrmann/clean_scirepeval huggingface dataset:
Construction of Abstract State Graphs with PVS

In this paper, we propose a method for the automat ic construction of an abstract s tate graph of an arbi t rary system using the Pvs theorem prover. Given a parallel composition of sequential processes and a par t i t ion of the s ta te space induced by predicates ~ i , ..., ~ on the program variables which defines an abstract s tate space, we construct an abstract s tate graph, start ing in the abstract initial state. The possible successors of a s tate are computed using the Pvs theorem prover by verifying for each index i if ~ or ~ i is a postcondition of it. This allows an abstract s tate space exploration for arbi t rary programs.  abstract interpretation, state graph exploration, theorem proving
Analyzing Business Continuity through a Multi-layers Model

Business Continuity Management (BCM) is a process to manage risks, emergencies, and recovery plans of an organization during a crisis. It results in a document called Business Continuity Plans (BCP) that specifies the methodology and procedures required to backup and recover the functional unit of a disrupted business. Traditionally, the BCP assessment is based only on the continuity of IS infrastructures and does not consider possible relations with the business objectives and business processes. This traditional approach assumes that the risk of business continuity is resulted from the disruption of the IS infrastructures. However, we believe there are situations where the risk emerges even the infrastructures up and running. Moreover, the lack of modeling framework and the aided-tool make the process even harder. In this paper, we propose a framework to support modeling and analysis of BCP from the organization perspective, where risks and treatments are modeled and analyzed along strategic objectives and their realizations. An automated reasoner based on cost-benefit analysis techniques is proposed to elicit and then adopt the most cost-efficient plan. The approach is developed using the Tropos GoalRisk Framework and the Time Dependency and Recovery Model as underlain frameworks. A Loan Originating Process case study is used as a running example to illustrate the proposal.
Determinants of Repurchase Intention in Online Shopping : a Turkish Consumer ’ s Perspective

The aim of this study is to identify Turkish consumers’ online repurchase intention with a specific focus on the influence of e-satisfaction, e-trust and e-loyalty. As there are limited studies that explain online repurchase intention in developing countries, this study conducted in Turkey. An empirical study has been conducted using a questionnaire survey and a total of 389 respondents are used to test the proposed model. The results reveal that esatisfaction, e-rust and e-loyalty have positive influences on intentions to repurchase in online stores. In addition, it was found that trust in an online store is the key determinant of online repurchase intention followed by satisfied digital customers and e-loyalty. The implications of findings are exhaustively discussed in the study.
Information and Influence Propagation in Social Networks

Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? ese are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. is book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.
A circuit failure prediction mechanism (DART) for high field reliability

This paper presents a novel circuit failure prediction mechanism for high field reliability. On-line testing at a power-on/off time of a system detects the circuits' delay degradation that is caused by aging. Dedicated test vectors are applied using BIST architecture. Embedded ring oscillators are utilized to compensate the measured delay values for temperature or voltage shift. The concept and necessary conditions for the mechanism are introduced and some preliminary experimental results show the possible effectiveness of the approach.
Virage Image Search Engine: An Open Framework for Image Management

Until recently, the management of laige image databases has relied exclusively on manually entered alphanumeric annotations. Systems are beginning to emerge in both the research and commercial sectors based on "content-based" image retrieval, a technique which explicitly manages image assets by directly representing their visual attributes. The Virage Image Search Engine provides an open framework for building such systems. The Virage Engine expresses visual features as image "primitives" . Primitives can be very general (such as color, shape, or texture) or quite domain specific (face recognition, cancer cell detection, etc.) . The basic philosophy underlying this architecture is a transformation from the data-rich representation of explicit image pixels to a compact, semantic-rich representation of visually salient characteristics. In practice, the design of such primitives is non-trivial, and is driven by a number of conflicting real-world constraints (e.g. computation time vs. accuracy) . The Virage Engine provides an open framework for developers to "plug-in" primitives to solve specific image management problems. The architecture has been designed to support both static images and video in a unified paradigm. The infrastructure provided by the Virage Engine can be utilized to address high-level problems as well, such as automatic, unsupervised keyword assignment, or image classification.  content-based image retrieval, image databases, visual information management, multimedia databases
A Comparative Study of Polar Code Constructions for the AWGN Channel

We present a comparative study of the performance of various polar code constructions in an additive white Gaussian noise (AWGN) channel. A polar code construction is any algorithm that selects K best among N possible polar bit-channels at the design signal-to-noise-ratio (design-SNR) in terms of bit error rate (BER). Optimal polar code construction is hard and therefore many suboptimal polar code constructions have been proposed at different computational complexities. Polar codes are also non-universal meaning the code changes significantly with the design-SNR. However, it is not known which construction algorithm at what design-SNR constructs the best polar codes. We first present a comprehensive survey of all the well-known polar code constructions along with their full implementations. We then propose a heuristic algorithm to find the best design-SNR for constructing best possible polar codes from a given construction algorithm. The proposed algorithm involves a search among several possible design-SNRs. We finally use our algorithm to perform a comparison of different construction algorithms using extensive simulations. We find that all polar code construction algorithms generate equally good polar codes in an AWGN channel, if the design-SNR is optimized. Bhattacharyya bounds, bit-channels, Gaussian approximation, polar codes
Job performance prediction in a call center using a naive Bayes classifier

This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the
Effects of Heart Rate Variability Biofeedback on EEG Alpha Asymmetry and Anxiety Symptoms in Male Athletes: A Pilot Study.

Heart rate variability biofeedback (HRV-BFB) has been shown as useful tool to manage stress in various populations. The present study was designed to investigate whether the biofeedback-based stress management tool consisting of rhythmic breathing, actively self-generated positive emotions and a portable biofeedback device induce changes in athletes' HRV, EEG patterns, and self-reported anxiety and self-esteem. The study involved 41 healthy male athletes, aged 16-21 (mean 18.34 ± 1.36) years. Participants were randomly divided into two groups: biofeedback and control. Athletes in the biofeedback group received HRV biofeedback training, athletes in the control group didn't receive any intervention. During the randomized controlled trial (days 0-21), the mean anxiety score declined significantly for the intervention group (change-4 p < 0.001) but not for the control group (p = 0.817). In addition, as compared to the control, athletes in biofeedback group showed substantial and statistically significant improvement in heart rate variability indices and changes in power spectra of both theta and alpha brain waves, and alpha asymmetry. These changes suggest better self-control in the central nervous system and better flexibility of the autonomic nervous system in the group that received biofeedback training. A HRV biofeedback-based stress management tool may be beneficial for stress reduction for young male athletes.
Finding Communities in Dynamic Social Networks

Communities are natural structures observed in social networks and are usually characterized as "relatively dense" subsets of nodes. Social networks change over time and so do the underlying community structures. Thus, to truly uncover this structure we must take the temporal aspect of networks into consideration. Previously, we have represented framework for finding dynamic communities using the social cost model and formulated the corresponding optimization problem , assuming that partitions of individuals into groups are given in each time step. We have also presented heuristics and approximation algorithms for the problem, with the same assumption [32]. In general, however, dynamic social networks are represented as a sequence of graphs of snapshots of the social network and the assumption that we have partitions of individuals into groups does not hold. In this paper, we extend the social cost model and formulate an optimization problem of finding community structure from the sequence of arbitrary graphs. We propose a semi definite programming formulation and a heuristic rounding scheme. We show, using synthetic data sets, that this method is quite accurate on synthetic data sets and present its results on a real social network.
Removing ECG noise from surface EMG signals using adaptive filtering

Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularization factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions.
Retrieving Web Pages Using Content, Links, URLs and Anchors

For this year’s web track, we concentrated on the entry page finding task. For the content-only runs, in both the ad-hoc task and the entry page finding task, we used an information retrieval system based on a simple unigram language model. In the Ad hoc task we experimented with alternatieve approaches to smoothing. For the entry page task, we incorporated additional information into the model. The sources of information we used in addition to the document’s content are links, URLs and anchors. We found that almost every approach can improve the results of a content only run. In the end, a very basic approach, using the depth of the path of the URL as a prior, yielded by far the largest improvement over the content only results.
Low-Level Software Security: Attacks and Defenses

This tutorial paper considers the issues of low-level software security from a language-based perspective, with the help of concrete examples. Four examples of low-level software attacks are covered in full detail; these examples are representative of the major types of attacks on C and C++ software that is compiled into machine code. Six examples of practical defenses against those attacks are also covered in detail; these defenses are selected because of their effectiveness, wide applicability, and low enforcement overhead.
Single-Fed Low-Profile High-Gain Circularly Polarized Slotted Cavity Antenna Using a High-Order Mode

In this letter, a single-fed low-profile high-gain circularly polarized slotted cavity antenna using a high-order cavity mode, i.e., TE440 mode, is proposed. The proposed antenna has a simple structure that consists of a radiating linearly polarized slotted cavity antenna and a linear-to-circular polarization converter. An antenna prototype operating at WLAN 5.8-GHz band fabricated with low-cost standard printed circuit board (PCB) process is exemplified to validate the proposed concept. Measured results compared to their simulated counterparts are presented, and a good agreement between simulation and measurement is obtained.
Modeling wrinkles on smooth surfaces for footwear design

We describe two new shape operators that superimpose wrinkles on top of a smooth NURBS surface. Previous research studying wrinkles focused mostly on cloth modeling or in animations, which are driven more by visual realism, but allow large elastic deformations. Our operators generate wrinkle-shaped deformations in a region of a smooth surface along a given boundary based on a few basic parametric inputs such as wrinkle magnitude and extent (these terms will be defined in the paper). The essential geometric transformation to map the smooth surface to a wrinkled one will be defined purely in terms of the geometry of the surface and the input parameters. Our model is based on two surface properties: geodesic offsets and surface energy. Practical implementation of the operators is discussed, and examples presented. Finally, the motivation for the operators will be given through their application in the computer-aided design and manufacture of footwear.
Face recognition using 3D local geometrical features: PCA vs. SVM

Thirty local geometrical features extracted from 3D hitman face surfaces have been used to model the face for face recognition. They are the most discriminating ones selected from a set of 86. We have experimented with 420 3D-facial meshes (without texture) of 60 individuals. There are 7 images per subject including views presenting fight rotations and facial expressions. The HK algorithm, based in the signs of the mean and Gaussian curvatures, has been used for region segmentation. Experiments under controlled and non-controlled acquisition conditions, considering pose variations and facial expressions, have been achieved to analyze the robustness of the selected characteristics. Success recognition results of 82.0% and 90.16% were obtained when the images are frontal views with neutral expression using PCA and SVM, respectively. The recognition rates only decrease to 76.2% and 77.9% using PCA and SVM matching schemes respectively, under gesture and light face rotation.
Polyps auto-detection in Wireless Capsule Endoscopy images using improved method based on image segmentation

Wireless Capsule Endoscopy (WCE) is a noninvasive instrument that widely used in screening the whole intestine and it has been utilized as a model especially for the examination of gastrointestinal (GI) diseases. However, it is numerous images of the detecting result produced by WCE that always burdens the physicians. To solve this problem, it is necessary to combine the manual diagnosis with the image segmentation technology. In this paper we proposed a feasible method by using K-means clustering and localizing region-based active contour segmentation for polyps auto-detection in WCE images. Experimental results shows the method is promising and efficient.
BIER — Boosting Independent Embeddings Robustly

Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. This leverages large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increases retrieval accuracy of the embedding. Our method does not introduce any additional parameters and works with any differentiable loss function. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-ofthe- art methods on the CUB-200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets by a significant margin.
Approximation Theory of the Mlp Model in Neural Networks

In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models. Mathematically it is also one of the simpler models. Nonetheless the mathematics of this model is not well understood, and many of these problems are approximation-theoretic in character. Most of the research we will discuss is of very recent vintage. We will report on what has been done and on various unanswered questions. We will not be presenting practical (algorithmic) methods. We will, however, be exploring the capabilities and limitations of this model. In the rst two sections we present a brief introduction and overview of neural networks and the multilayer feedforward perceptron model. In Section 3 we discuss in great detail the question of density. When does this model have the theoretical ability to approximate any reasonable function arbritrarily well? In Section 4 we present conditions for simultaneously approximating a function and its derivatives. Section 5 considers the interpolation capability of this model. In Section 6 we study upper and lower bounds on the order of approximation of this model. The material presented in Sections 3{6 treats the single hidden layer MLP model. In Section 7 we discuss some of the diierences that arise when considering more than one hidden layer. The lengthy list of references includes many papers not cited in the text, but relevant to the subject matter of this survey.
Comprehensively and efficiently protecting the heap

The goal of this paper is to propose a scheme that provides comprehensive security protection for the heap. Heap vulnerabilities are increasingly being exploited for attacks on computer programs. In most implementations, the heap management library keeps the heap meta-data (heap structure information) and the application's heap data in an interleaved fashion and does not protect them against each other. Such implementations are inherently unsafe: vulnerabilities in the application can cause the heap library to perform unintended actions to achieve control-flow and non-control attacks.Unfortunately, current heap protection techniques are limited in that they use too many assumptions on how the attacks will be performed, require new hardware support, or require too many changes to the software developers' toolchain. We propose Heap Server, a new solution that does not have such drawbacks. Through existing virtual memory and inter-process protection mechanisms, Heap Server prevents the heap meta-data from being illegally overwritten, and heap data from being meaningfully overwritten. We show that through aggressive optimizations and parallelism, Heap Server protects the heap with nearly-negligible performance overheads even on heap-intensive applications. We also verify the protection against several real-world exploits and attack kernels.