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SSRN 2023年

作者： Jia, Xibin Zhang, Wang Wang, Luo Yang, Zhenghan Yang, Dawei Hong, Min Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Friendship Hospital The Capital University of Medical Sciences Beijing100050 China Department of Computer Software Engineering Soonchunhyang University Asan31538 Korea Republic of

Semi-supervised learning methods have gained significant attention in recent years due to the challenge of obtaining a large amount of labeled data. While leveraging unlabeled data has shown promise in improving segmentation performance, accurate predictions in ambiguous boundary regions remain a challenge in medical image segmentation. To address this issue and effectively leverage unlabeled data, we propose a unified cross-task and cross-model consistency (CTCMC) regularization framework for boundary-aware semi-supervised medical image segmentation. Our framework consists of two identical sub-models, each performing the main foreground segmentation task and an auxiliary boundary segmentation task to obtain a foreground map and a boundary map, respectively. To promote consistency between two tasks and enable effective exploitation of unlabeled data, we introduce a novel differential implementation of the morphological gradient to convert boundary map to foreground map, which serves as the basis for the cross-task consistency loss. Furthermore, the cross-model consistency is put forward to regularize the two identical sub-models, where two types of pseudo labels from one perturbed sub-model are used to supervise the other sub-model with corresponding tasks, and vice versa. The joint training of boundary segmentation, cross-task consistency and cross-model consistency facilitates discriminative representation learning, enhancing boundary information, and ultimately improving medical image segmentation performance. We conduct comprehensive experiments on two public datasets to evaluate the effectiveness of our proposed method. The experimental results demonstrate that our method outperforms current state-of-the-art semi-supervised medical image segmentation methods and achieves better segmentation performance with more accurate boundaries. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

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An Empirical Study on Low Code Programming using Traditional vs Large Language Model Support

arXiv

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arXiv 2024年

作者： Liu, Yongkun Chen, Jiachi Bi, Tingting Grundy, John Wang, Yanlin Yu, Jianxing Chen, Ting Tang, Yutian Zheng, Zibin School of Software Engineering Sun Yat-sen University China The The University of Western Australia Australia The Faculty of Information Technology Monash University Australia The School of Artificial Intelligence Sun Yat-sen University China The School of Computer Science and Engineering University of Electronic Science and Technology of China China University of Glasgow United Kingdom

Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has began to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools, and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers’ discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLM-based LCP share common primary usage scenarios, they significantly differ in scope, limitations and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering. Copyright © 2024, The Authors. All rights reserved.

关键词： Life cycle

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Research on deep learning image recognition technology in garbage classification

Research on deep learning image recognition technology in ga...

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Communications technology and Computer Science (ACCTCS), Asia-Pacific Conference on

作者： Qiang Guo Yuliang Shi Shikai Wang School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665447034

Nowadays, with the rapid development of industry, people's daily life is becoming more and more rich and diverse. However, while enjoying the pleasure brought by material life, a large number of garbage are produced. They are various, and people lack the knowledge related to garbage classification, which leads to the difficulty of manual or robot classification. This paper studies a garbage classification algorithm model based on deep learning convolutional neural network Efficientnet to help identify garbage classification. In this research, data augmentation and normalization are carried out to solve the problem of small amount of data sets and different sizes of pictures. Efficientnet is used to extract the features of images. In order to solve the problem that BN has no obvious effect on small batches in the network, we replace BN with group normalization (GN). In order to prevent some irrelevant information in the image from affecting the training of the model, we add attention mechanism after the output of Efficientnet to emphasize or select the important information of the target processing object, and suppress some irrelevant details, so that the model can focus on the key features and better identify the image; according to the above process, we use softmax to classify the spam image and divide it into four categories (Recyclables, Kitchen garbage, Hazardous garbage, Other garbage) The results show that the model can effectively extract the features of the input garbage image, and get accurate judgment, and identify the types of garbage. The experimental results show that the average accuracy of the algorithm model is high, and has good classification performance and robustness. In the practical significance of the research, this reliable model can help people quickly know the type of garbage, or can be applied to robot sorting, to help detect the types of garbage for robot judgment and sorting, so it has very important application scenarios and significanc

关键词： Deep learning Training Service robots Feature extraction Robustness Classification algorithms Mobile applications

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TF-SASM: Training-free Spatial-aware Sparse Memory for Multi-object Tracking

arXiv

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arXiv 2024年

作者： Nguyen-Quang, Thuc Tran, Minh-Triet Software Engineering Lab John von Neumann Institute University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam Faculty of Information Technology Software Engineering Lab John von Neumann Institute University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam

Multi-object tracking (MOT) in computer vision remains a significant challenge, requiring precise localization and continuous tracking of multiple objects in video sequences. The emergence of data sets that emphasize robust reidentification, such as DanceTrack, has highlighted the need for effective solutions. While memory-based approaches have shown promise, they often suffer from high computational complexity and memory usage due to storing feature at every single frame. In this paper, we propose a novel memory-based approach that selectively stores critical features based on object motion and overlapping awareness, aiming to enhance efficiency while minimizing redundancy. As a result, our method not only store longer temporal information with limited number of stored features in the memory, but also diversify states of a particular object to enhance the association performance. Our approach significantly improves over MOTRv2 in the DanceTrack test set, demonstrating a gain of 2.0% AssA score and 2.1% in IDF1 score. © 2024, CC BY-NC-SA.

关键词：

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Predictive Analysis of Graduate Students' Employability Using Machine Learning Techniques

Predictive Analysis of Graduate Students' Employability Usin...

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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Endalkachew Hiwot Tamene Ayodeji Olalekan Salau Satvik Vats Keshav Kaushik Tibebu Legesse Molla Ting Tin Tin Department of Software Engineering Wollo University Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Graphic Era Deemed to be University Dehradun Uttarakhand India Amity School of Engineering and Technology Amity University Mohali Punjab India Department of Information Technology Wollo University Ethiopia Faculty of Data Science and Information Technology INTI International University Negeri Sembilan Malaysia

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Machine Learning evaluates datasets, finds patterns, and makes judgments based on data, it is essential for tackling a wide range of problems across multiple areas. The main objective of this study is to know, understand and analyze undergraduate student's employability status in Kombolcha Institute of technology Wollo University. We prepared and cleaned the collected data to develop the predictive models. We used Random Forest and Gradient Boost ensemble Machine Learning methods. In order to explain the results of the experiment that classified students who are employed and unemployed. The study used different metrics to measure the performance of the developed model. Random Forest model accuracy is 96.7% and Gradient Boost model accuracy is 91.4%. Random Forest algorithm was discovered to perform better than other models.

关键词： Measurement Accuracy Machine learning algorithms Employment Predictive models Prediction algorithms Stakeholders Predictive analytics Random forests Monitoring

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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Advances in Computing Research on Science engineering and technology (ACROSET), International Conference on

作者： Nikhilesh Pramod Mankar Tushar Mahure Anushka A. Joshi Sagar Dhanraj Pande Aditya Khamparia Ankita Shrikrushna Nathe Fadi Al Turjman School of Engineering and Technology Pimpri Chinchwad University (PCU) Pune India Independent Researcher Amravati India Department of Computer Science Babasaheb Bhimrao Ambedkar University Amethi India Department of Information Technology Kavikulguru Institute of Technology and Science Nagpur India Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI

关键词： Cyberbullying Entertainment industry Machine learning Gain measurement Natural language processing Internet Bayes methods Computer crime

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Heterogeneous Multi-commodity Network Flows over Time 1

引用

17th International Computer Science Symposium in Russia, CSR 2022

作者： Li, Yifen Bei, Xiaohui Qiao, Youming Tao, Dacheng Chen, Zhiya School of Traffic and Transportation Engineering Central South University Changsha China School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Centre for Quantum Software and Information University of Technology Sydney Ultimo Australia UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

ISBN: (数字)9783031095740

ISBN: (纸本)9783031095733

In the 1950’s, Ford and Fulkerson introduced dynamic flows by incorporating the notion of time into the network flow model (Oper. Res., 1958). In this paper, motivated by real-world applications including route planning and evacuations, we extend the framework of multi-commodity dynamic flows to the heterogeneous commodity setting by allowing different transit times for different commodities along the same edge. We first show how to construct the time-expanded networks, a classical technique in dynamic flows, in the heterogeneous setting. Based on this construction, we give a pseudopolynomial-time algorithm for the quickest flow problem when there are two heterogeneous commodities. We then present a fully polynomial-time approximation scheme when the nodes have storage for any number of heterogeneous commodities. The algorithm is based on the condensed time-expanded network technique introduced by Fleischer and Skutella (SIAM J. Comput., 2007). © 2022, Springer Nature Switzerland AG.

关键词： Polynomial approximation

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on Computer engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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