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Proceedings of the 37th IEEE/ACM international conference on Automated Software engineering

作者： Yi Song Xiaoyuan Xie Xihao Zhang Quanming Liu Ruizhi Gao School of Computer Science Wuhan University China Sonos Inc. USA

ISBN: (纸本)9781450394758

Failures that are not related to a specific fault can reduce the effectiveness of fault localization in multi-fault scenarios. To tackle this challenge, researchers and practitioners typically cluster failures (e.g., failed test cases) into several disjoint groups, with those caused by the same fault grouped together. In such a fault isolation process that requires input in a mathematical form, ranking-based failure proximity (R-proximity) is widely used to model failed test cases. In R-proximity, each failed test case is represented as a suspiciousness ranking list of program statements through a fingerprinting function (i.e., a risk evaluation formula, REF). Although many off-the-shelf REFs have been integrated into R-proximity, they were designed for single-fault localization originally. To the best of our knowledge, no REF has been developed to serve as a fingerprinting function of R-proximity in multi-fault scenarios. For better clustering failures in fault isolation, in this paper, we present a genetic programming-based framework along with a sophisticated fitness function, for evolving REFs with the goal of more properly representing failures in multi-fault scenarios. By using a small set of programs for training, we get a collection of REFs that can obtain good results applicable in a larger and more general scale of scenarios. the best one of them outperforms the state-of-the-art by 50.72% and 47.41% in faults number estimation and clustering effectiveness, respectively. Our framework is highly configurable for further use, and the evolved formulas can be directly applied in future failure representation tasks without any retraining.

关键词： Fault isolation Clustering Parallel debugging Failure proximity Search-based software engineering

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Attack against UAV Infrared Object Detection Based on Gradient Descent Method

Attack against UAV Infrared Object Detection Based on Gradie...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Mingkang Xu Keyu Chen Haoli Xu Xing Yang Jun Lu Bin Qu Haoqi Gao Zhiyang Hu Mengjiang Wu Jun Chen State Key Laboratory of Pulsed Power Laser College of Electronic Engineering National University of Defense Technology Hefei China National University of Defense Technology Hefei China College of Computer Science and Software Enginering Hohai University Nanjing China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

At present, infrared intelligent detection technology has been widely used in various fields. Due to the development of its technology, how to interfere with important targets such as intelligent recognition of vehicles has become a hot issue in the current technical field. therefore, this study is based on the near-infrared band. We generate vehicle target confrontation samples to interfere with infrared detector devices. this paper proposes to first collect the single-band image of the protection target through the infrared UAV, and aligns it to align the position of the protection target in the single-band image to obtain the salient features of the protection target. then, based on the loss function calculated by machine learning method, the adversarial samples of single-band images are generated by using the basic iterative method. the constraint function is used to jointly optimize the loss function, so that the attack of the adversarial samples on the single-band detection system of the infrared UAV can reach the target value, and the adversarial samples of the infrared image in this state are obtained. then, it is arranged in multiple positions on the target vehicle to complete the interference to the infrared detection system. We first need to complete the simulation attack experiment in the digital domain, and then complete the confrontation with the infrared detector by using a special material that meets the requirements in the physical domain. the experimental results show that the attack success rate of this method has a certain effect. this paper has a certain reference for the future research of vehicle infrared reconnaissance and anti-reconnaissance.

关键词： Infrared detectors Accuracy Target recognition Autonomous aerial vehicles Linear programming Stability analysis Protection Optimization thermal stability Convergence

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Detection of Financial Fraudulent Activities with Machine Learning:A Case Study of Detecting Potential Tax and Invoice Fraud 23

Detection of Financial Fraudulent Activities with Machine Le...

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Proceedings of the 2023 7th international conference on Computer science and artificial Intelligence

作者： Maohong Tian Jian Liang Dequan Zhang Xintong Zhang Zuo Wang Hualin Li ChongQing Institute of Engineering China and Intelligent Application of Financial Big Data Chongqing Colleges and Universities Engineering Research Center China and Shu Yi Xin Credit Management CoLTD China ChongQing Institute of Engineering China Shu Yi Xin Credit Management Co LTD China

ISBN: (纸本)9798400708688

Financial fraud is a widespread problem that can cause significant economic losses. Traditional fraud detection methods often rely on manual audits and rules-based systems, which can be time-consuming and error-prone. In recent years, machine learning methods have emerged as a promising approach to automating fraud detection by leveraging large-scale data analysis. this article explores the use of machine learning methods to detect financial fraud by using tax, invoice, and big data. We first introduce the challenges and opportunities of using these data sources for fraud detection, and then survey various machine learning techniques that have been applied to this problem. We also discuss the evaluation metrics and case studies of these methods, and highlight the potential benefits and limitations of using machine learning for fraud detection. Finally, we identify some future research directions and challenges in this area. this article aims to provide a comprehensive method of the state-of-the-art in using machine learning methods for financial fraud detection, and to inspire further research and development in this important field.

关键词： big data financial fraud fraud detection. invoice data machine learning tax data

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Efficient Real-Time Dense Pedestrian Detector Based on Improved YOLOv7-tiny 23

Efficient Real-Time Dense Pedestrian Detector Based on Impro...

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Proceedings of the 2023 7th international conference on Advances in Image Processing

作者： Xuezhi Li Yiping Shi Ziyun Zhao Shaojie Shi Shanghai University Of Engineering Science China

ISBN: (纸本)9798400708275

the state-of-the-art YOLO framework strikes an excellent balance between speed and accuracy and has become one of the most effective object detection algorithms. However, when performing dense pedestrian detection tasks, the network is computationally intensive, real-time is insufficient, and it is difficult to be applied to edge devices with weak computational power in practical use. therefore, we propose a detection algorithm based on YOLOv7-tiny improvement. Based on the GhostNetv2 network, we use the parameter-free attention mechanism SimAM to construct the GhostNet-tiny structure and propose the C3Ghost-tiny module to be combined with the YOLO network in order to reduce the network computational parameters. We propose a new neck network structure to extract richer information with a bi-directional weighted pyramid structure and weighted connectivity of features at different scales. We also use WIoU as a coordinate loss function to improve the network's attention to small targets. We tested the effectiveness of the proposed method on the public dataset CrowdHuman, and the results show that our method can effectively reduce the network parameters while improving the accuracy. Notably, compared to the YOLOv7-tiny model, our model improves the accuracy value by 2.13%, reduces the amount of network parameters by 5.9%, reduces the amount of floating-point calculations by 60.3%, reduces the size of the network model file by 27.7%, and improves the detection speed by up to 46.64%.

关键词： YOLOv7 attention mechanism dense scene efficient

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ICEBAR: Feedback-Driven Iterative Repair of Alloy Specifications 22

ICEBAR: Feedback-Driven Iterative Repair of Alloy Specificat...

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Proceedings of the 37th IEEE/ACM international conference on Automated Software engineering

作者： Simón Gutiérrez Brida Germán Regis Guolong Zheng Hamid Bagheri thanhvu Nguyen Nazareno Aguirre Marcelo Frias Department of Computer Science University of Rio Cuarto Argentina and CONICET Argentina Department of Computer Science University of Rio Cuarto Argentina University of Nebraska-Lincoln United States of America George Mason University United States of America Department of Software Engineering Buenos Aires Institute of Technology Argentina and CONICET Argentina

ISBN: (纸本)9781450394758

Automated program repair (APR) techniques have shown great success in automatically finding fixes for programs in programming languages such as C or Java. In this work, we focus on repairing formal specifications, in particular for the Alloy specification language. As opposed to most APR tools, our approach to repair Alloy specifications, named ICEBAR, does not use test-based oracles for patch assessment. Instead, ICEBAR relies on the use of property-based oracles, commonly found in Alloy specifications as predicates and assertions. these property-based oracles define stronger conditions for patch assessment, thus reducing the notorious overfitting issue caused by using test-based oracles, typically observed in APR contexts. Moreover, as assertions and predicates are inherent to Alloy, whereas test cases are not, our tool is potentially more appealing to Alloy users than test-based Alloy repair tools. At a high level, ICEBAR is an iterative, counterexample-based process, that generates and validates repair candidates. ICEBAR receives a faulty Alloy specification with a failing property-based oracle, and uses Alloy’s counterexamples to build tests and feed ARepair, a test-based Alloy repair tool, in order to produce a repair candidate. the candidate is then checked against the property oracle for overfitting: if the candidate passes, a repair has been found; if not, further counterexamples are generated to construct tests and enhance the test suite, and the process is iterated. ICEBAR includes different mechanisms, with different degrees of reliability, to generate counterexamples from failing predicates and assertions. Our evaluation shows that ICEBAR significantly improves over ARepair, in both reducing overfitting and improving the repair rate. Moreover, ICEBAR shows that iterative refinement allows us to significantly improve a state-of-the-art tool for automated repair of Alloy specifications without any modifications to the tool.

关键词： Formal Specification Alloy Automated Repair

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COViT: Convolutions and ViT based Deep Learning Model for Covid19 and Viral Pneumonia Classification using X-ray Datasets 23

COViT: Convolutions and ViT based Deep Learning Model for Co...

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Proceedings of the 2023 7th international conference on Computational Biology and Bioinformatics

作者： Athar Shahzad Fazal Somaiya Khan Ali Khan School of Science and Technology Nottingham Trent University UK School of Electronics Engineering Beijing University of Posts and Telecommunications China School of Computer Science and Technology Zhejiang Normal University China

ISBN: (纸本)9798400716331

artificial Intelligence based Covid19 through X-ray scans has revolutionized early diagnosis and treatment since the outbreak. there have been remarkable achievements in the research of Covid19 from Normal or other Pneumonia X-ray image classification using a convolutional neural network (CNN). CNN alone face problems in describing low-level features and can miss important information. Moreover, accurate diagnosis is important in the medical field with minimum false alarms. To answer the issue, the researchers of this paper have turned to self-attention mechanism inspired by the ViT, which has displayed state-of-the-art performance in the classification task. the proposed COViT method uses convolutions of 3 × 3 instead of patch embedding as in ViT, then alternate self-attention and MLP with hardswish function are added, and finally, MLP head is proposed which has average pooling, fully connected (FC) layer with ReLU function and kernel L2 as a classifier which improves the accuracy. Exhaustive experiments are carried out on three datasets. We have only considered Covid19 and Viral Pneumonia classes for our problem. the proposed model has achieved 98.98% classification accuracy on dataset1, 99.50% on dataset2 and 99.18% on dataset3, which validates the efficiency of COViT the proposed COViT which uses self-attention and CNN shows superiority over other SOTA models and has better accuracy than the methods in the literature.

关键词： COViT Classification Covid19 Deep Learning Viral Pneumonia X-ray dataset

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer science (including subseries Lecture Notes in artificial Intelligence and Lecture Notes in Bioinformatics) 2020年 12071 LNCS卷 vi页

作者： Méry, Dominique Raschke, Alexander Houdek, Frank Institute of Software Engineering and Programming Languages Ulm University Ulm Germany LORIA Campus Scientifique Université de Lorraine Vandoeuvre-les-Nancy France Research and Development Mercedes-Benz AG Sindelfingen Germany

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On Cognitive Level Classification of Assessment-items Using Pre-trained BERT-based Model 23

On Cognitive Level Classification of Assessment-items Using ...

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Proceedings of the 2023 7th international conference on Natural Language Processing and Information Retrieval

作者： Adnan Saif Dipto Md. Mahmudur Rahman Limon Fatima Tanjum Tuba Md Mohsin Uddin M. Saddam Hossain Khan Rashedul Amin Tuhin Department of Computer Science and Engineering East West University Bangladesh

ISBN: (纸本)9798400709227

Outcome based education (OBE) is gaining popularity nowadays due to its effectiveness in preparing learners for their future roles as active participants. Bloom's taxonomy is a well-known educational model as it helps implement the OBE. It offers teachers a tool to encourage progressive thinking and intellectual development in students. Bloom's Taxonomy helps categorize cognitive skills and learning objectives, while the cognitive level classification of assessment items determines the complexity needed to complete the assessment. By using both the cognitive level classification and Bloom's Taxonomy, educators can create assessments that accurately measure students' abilities and target specific learning outcomes. this paper proposes a model that can accurately categorize assessment items according to their cognitive complexity. the paper utilizes the cognitive domains of Bloom's Taxonomy and focuses specifically on the first four levels: remembering, understanding, applying, and analysing. Some previous attempts have been made in this field but, to our best knowledge, this is the first time the Bidirectional architecture of Deep Learning (DL) algorithms and BERT-based Transformer model have been used. By comparing several machine learning (ML) algorithms, DL algorithms with Bidirectional layers and pre-trained BERT-based Transformer model, it has been found that the BERT model scores the highest among all the other ML and DL algorithms. the performance is evaluated based on accuracy, precision, recall, and F1-score. the BERT-based Transformer model has an accuracy of 89%, where some of the other algorithms performed considerably well (BiLSTM: 84%, LSTM: 83%, BiGRU: 83%, RF: 83%). Also, compared to the state-of-the-art model, the transformer model scored higher. this suggests that the model can be used to deploy as the classification model. the developed model will help educators with the exact assessment items to implement OBE. Overall, this research contributes to

关键词： BERT model Bidirectional Bloom's Taxonomy Cognitive Level Deep Learning Machine Learning Natural Language Processing (NLP) Outcome Based Education (OBE) Transformer

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