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IEEE/ACM International Workshop on Complex Faults and Failures in Large software systems (COUFLESS)

作者： Aurelien Delaitre Bertrand Stivalet Elizabeth Fong Vadim Okun Dept. of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Software and Systems Division IT Laboratory National Institute of Standards and Technology Gaithersburg MD USA

software static analysis is one of many options for finding bugs in software. Like compilers, static analyzers take a program as input. This paper covers tools that examine source code - without executing it - and output bug reports. Static analysis is a complex and generally undecidable problem. Most tools resort to approximation to overcome these obstacles and it sometimes leads to incorrect results. Therefore, tool effectiveness needs to be evaluated. Several characteristics of the tools should be examined. First, what types of bugs can they find? Second, what proportion of bugs do they report? Third, what percentage of findings is correct? These questions can be answered by one or more metrics. But to calculate these, we need test cases having certain characteristics: statistical significance, ground truth, and relevance. Test cases with all three attributes are out of reach, but we can use combinations of only two to calculate the metrics. The results in this paper were collected during Static Analysis Tool Exposition (SATE) V, where participants ran 14 static analyzers on the test sets we provided and submitted their reports to us for analysis. Tools had considerably different support for most bug classes. Some tools discovered significantly more bugs than others or generated mostly accurate warnings, while others reported wrong findings more frequently. Using the metrics, an evaluator can compare candidates and select the tool that aligns best with his or her objectives. In addition, our results confirm that the bugs most commonly found by tools are among the most common and important bugs in software. We also observed that code complexity is a major hindrance for static analyzers and detailed which code constructs tools handle well and which impede their analysis.

关键词： software Measurement Production Complexity theory computer bugs Java NIST

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An incremental turn-taking model for task-oriented dialog systems

arXiv

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arXiv 2019年

作者： Coman, Andrei C. Yoshino, Koichiro Murase, Yukitoshi Nakamura, Satoshi Riccardi, Giuseppe Signals and Interactive Systems Lab Department of Information Engineering and Computer Science University of Trento Italy Augmented Human Communication Laboratory Graduate School of Information Science Nara Institute of Science and Technology Japan

In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are able to timely decide when to interrupt the speaker for competitive or non-competitive reasons. In state-of-the-art turn-by-turn dialog systems the decision on the next dialog action is taken at the end of the utterance. In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of the user utterance. To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario. The re-labeling consists of assigning a binary value to each token in the user utterance that allows to identify the appropriate point for taking the turn. Finally, we implement an incremental Turn Taking Decider (iTTD) that is trained on these new labels for the turn-taking decision. We show that the proposed model can achieve a better performance compared to a deterministic handcrafted turn-taking algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Fast wide-area P-SAR/ISAR performance prediction

Fast wide-area P-SAR/ISAR performance prediction

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IEEE National Conference on Radar

作者： William C. Barott Braham Himed Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach FL Air Force Research Laboratory Wright-Patterson Air Force Base Dayton OH

ISBN: (纸本)9781509008643

This paper presents techniques for predicting the resolution of the radar imaging point spread function (PSF). These techniques are useful for predicting the performance of multistatic passive synthetic aperture radars (P-SARs), and passive inverse SARs (P-ISARs), for which the PSF depends on a multitude of factors. Greedy algorithms are used in combination with the fast techniques to optimize the PSF resolution for both incoherent and coherent multistatic imaging scenarios, subject to constraints on combining methods, sparsity, and target isotropy.

关键词： Image resolution Radar imaging Imaging Passive radar Transmitters Geometry

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A embedding model for text classification

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Expert systems 2019年第6期36卷 e12460-e12460页

作者： Liu, Wenfeng Liu, Peiyu Yang, Yuzhen Yi, Jing Zhu, Zhenfang School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Heze University Heze China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Computer Science and Technology Shandong Jianzhu University Jinan China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China

Existing word embeddings learning algorithms only employ the contexts of words, but different text documents use words and their relevant parts of speech very differently. Based on the preceding assumption, in order to obtain appropriate word embeddings and further improve the effect of text classification, this paper studies in depth a representation of words combined with their parts of speech. First, using the parts of speech and context of words, a more expressive word embeddings can be obtained. Further, to improve the efficiency of look-up tables, we construct a two-dimensional table that is in the format to represent words in text documents. Finally, the two-dimensional table and a Bayesian theorem are used for text classification. Experimental results show that our model has achieved more desirable results on standard data sets. And it has more preferable versatility and portability than alternative models. © 2019 John Wiley & Sons, Ltd

关键词： Embeddings

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Rating protocol design for extortion and cooperation in the crowdsourcing contest dilemma

arXiv

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arXiv 2017年

作者： Lu, Jianfeng Xin, Yun Zhang, Zhao Tang, Shaojie Yan, Songyuan Tang, Changbing Department of Computer Science and Engineering Zhejiang Normal University Jinhua Zhejiang China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Information Systems University of Texas at Dallas RichardsonTX75080 United States

Crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks. However, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Hence, incentive mechanisms are of great importance to overcome the inefficiency of the socially undesirable equilibrium. Existing incentive mechanisms are not effective in providing incentives for cooperation in crowdsourcing competitions due to the following features: heterogeneous workers compete against each other in a crowdsourcing platform with imperfect monitoring. In this paper, we take these features into consideration, and develop a novel game-theoretic design of rating protocols, which integrates binary rating labels with differential pricing to maximize the requester's utility, by extorting selfish workers and enforcing cooperation among them. By quantifying necessary and sufficient conditions for the sustainable social norm, we formulate the problem of maximizing the revenue of the requester among all sustainable rating protocols, provide design guidelines for optimal rating protocols, and design a low-complexity algorithm to select optimal design parameters which are related to differential punishments and pricing schemes. Simulation results demonstrate how intrinsic parameters impact on design parameters, as well as the performance gain of the proposed rating protocol. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting

arXiv

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arXiv 2023年

作者： Zhu, Huilin Yuan, Jingling Zhong, Xian Yang, Zhengwei Wang, Zheng He, Shengfeng School of Computer Science and Artificial Intelligence Wuhan University of Technology China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computing and Information Systems Singapore Management University Singapore

Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. However, existing domain adaptation methods tend to focus on inter-dataset differences while overlooking the intra-differences within the same dataset, leading to additional learning ambiguities. These domain-agnostic factors, e.g., density, surveillance perspective, and scale, can cause significant in-domain variations, and the misalignment of these factors across domains can lead to a drop in performance in cross-domain crowd counting. To address this issue, we propose a Domain-agnostically Aligned Optimal Transport (DAOT) strategy that aligns domain-agnostic factors between domains. The DAOT consists of three steps. First, individual-level differences in domain-agnostic factors are measured using structural similarity (SSIM). Second, the optimal transfer (OT) strategy is employed to smooth out these differences and find the optimal domain-to-domain misalignment, with outlier individuals removed via a virtual "dustbin" column. Third, knowledge is transferred based on the aligned domain-agnostic factors, and the model is retrained for domain adaptation to bridge the gap across domains. We conduct extensive experiments on five standard crowd-counting benchmarks and demonstrate that the proposed method has strong generalizability across diverse datasets. Our code will be available at: https://***/HopooLinZ/DAOT/. © 2023, CC0.

关键词： Alignment

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Probability Distributions on Partially Ordered Sets and Network Interdiction Games

arXiv

引用

arXiv 2018年

作者： Dahan, Mathieu Amin, Saurabh Jaillet, Patrick School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States

This article poses the following problem: Does there exist a probability distribution over subsets of a finite partially ordered set (poset), such that a set of constraints involving marginal probabilities of the poset’s elements and maximal chains is satisfied? We present a combinatorial algorithm to positively resolve this question. The algorithm can be implemented in polynomial time in the special case where maximal chain probabilities are affine functions of their elements. This existence problem is relevant for the equilibrium characterization of a generic strategic interdiction game on a capacitated flow network. The game involves a routing entity that sends its flow through the network while facing path transportation costs, and an interdictor who simultaneously interdicts one or more edges while facing edge interdiction costs. Using our existence result on posets and strict complementary slackness in linear programming, we show that the Nash equilibria of this game can be fully described using primal and dual solutions of a minimum-cost circulation problem. Our analysis provides a new characterization of the critical components in the interdiction game. It also leads to a polynomial-time approach for equilibrium computation. Copyright © 2018, The Authors. All rights reserved.

关键词： Probability distributions

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Comparative analysis of time series model and machine testing systems for crime forecasting

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Neural Computing and Applications 2020年第17期33卷 10621-10636页

作者： Jha, Sudan Yang, Eunmok Almagrabi, Alaa Omran Bashir, Ali Kashif Joshi, Gyanendra Prasad School of Computer Science and Engineering Lovely Professional University Phagwara India School of Software Soongsil University Seoul South Korea Department of Information Systems King Abdul Aziz University Jeddah Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University Manchester UK School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Computer Science and Engineering Sejong University Seoul South Korea

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

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Auto-Encoder based Structured Dictinoary Learning

Auto-Encoder based Structured Dictinoary Learning

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IEEE Workshop on Multimedia Signal Processing

作者： Deyin Liu Yuanbo Lin Wu Liangchen Liu Qichang Hu Lin Qi School of Information Engineering Zhengzhou University China School of Computer Science and Information Engineering Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province China School of Information Technology and Electrical Engineering The University of Queensland Australia Motovis Australia Pty Ltd Adelaide Australia

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

Dictionary learning and deep learning are two popular representation learning paradigms, which can be combined to boost the classification task. However, existing combination methods often learn multiple dictionaries embedded in a cascade of layers, and a specialized classifier accordingly. This may inattentively lead to overfitting and high computational cost. In this paper, we present a novel deep auto-encoding architecture to learn only a dictionary for classification. To empower the dictionary with discrimination, we construct the dictionary with class-specific sub-dictionaries, and introduce supervision by imposing category constraints. The proposed framework is inspired by a sparse optimization method, namely Iterative Shrinkage Thresholding Algorithm, which characterizes the learning process by the forward-propagation based optimization w.r.t the dictionary only, reducing the number of parameters to learn and the computational cost dramatically. Extensive experiments demonstrate the effectiveness of our method in image classification.

关键词： Dictionaries Signal processing algorithms Optimization methods Signal processing Computational efficiency Task analysis Image classification

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

引用

Proceedings of the 37th International Conference on Neural information Processing systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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