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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Yongqi Zhao, Jitao He, Dongxiao Jin, Di Huang, Yuxiao Wang, Zhen College of Intelligence and Computing Tianjin University Tianjin China Department of Data Science George Washington University WashingtonDC United States School of Cybersecurity Northwestern Polytechnical University Xi’an China

ISBN: (纸本)157735897X

Graph Contrastive Learning (GCL) aims to self-supervised learn low-dimensional graph representations, primarily through instance discrimination, which involves manually mining positive and negative pairs from graphs, increasing the similarity of positive pairs while decreasing negative pairs. Drawing from the success of Contrastive Learning (CL) in other domains, a consensus has been reached that the effectiveness of GCLs depends on a large number of negative pairs. As a result, despite the significant computational overhead, GCLs typically leverage as many negative node pairs as possible to improve model performance. However, given that nodes within a graph are interconnected, we argue that nodes cannot be treated as independent instances. Therefore, we challenge this consensus: Does employing more negative nodes lead to a more effective GCL model? To answer this, we explore the role of negative nodes in the commonly used InfoNCE loss for GCL and observe that: (1) Counterintuitively, a large number of negative nodes can actually hinder the model’s ability to distinguish nodes with different semantics. (2) A smaller number of high-quality and non-topologically coupled negative nodes are sufficient to enhance the discriminability of representations. Based on these findings, we propose a new method called GCL with Effective and Efficient Negative samples, E2Neg, which learns discriminative representations using only a very small set of representative negative samples. E2Neg significantly reduces computational overhead and speeds up model training. We demonstrate the effectiveness and efficiency of E2Neg across multiple datasets compared to other GCL methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems

A Fast Feature Selection for Interpretable Modeling Based on...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Andrea Tangherloni Paolo Cazzaniga Nicolò Stranieri Francesca M. Buffa Marco S. Nobile Department of Computing Sciences Bocconi Institute for Data Science and Analytics Bocconi University Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Computing Sciences Bocconi University Milan Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Large datasets are often beneficial for the generation of predictive models using machine learning approaches. However, it is often the case that not all variables in the dataset contain useful information. In fact, some variables might be useless, redundant, misleading, or even harmful to performance, both in terms of accuracy and computational effort. Because of that, Feature Selection (FS) is one of the most delicate and important steps in machine learning. This is even more relevant in the case of interpretable models based on Fuzzy Inference Systems (FIS). The reasons are two-fold: on the one hand, FIS are generally built on top of a data partitioning based on clustering, which can suffer from high dimensionality; on the other hand, the knowledge base of the FIS, to be concretely understandable, should not contain rules involving too many variables. FS can be performed using multiple approaches, most notably filter and wrapper methods. The latter are often based on evolutionary algorithms, where a population of candidate solutions (each representing a possible set of selected variables) evolves towards the optimal selection. Although wrapper methods can be effective, they are, in general, computationally expensive. In this work, we propose a completely different – and more computationally effective – algorithm based on Random Forest (RF) models. Specifically, we exploit RFs to rank variables according to their importance. Then, we use that information to perform a statistical analysis and determine the minimal set of features necessary to build an accurate FIS. We show the effectiveness of our approach by using two (semi)synthetic datasets built on real-world datasets, and we validate our approach by applying the FS method to a medical dataset.

关键词： Radio frequency Fuzzy logic Accuracy Statistical analysis Computational modeling Predictive models Feature extraction Inference algorithms Noise measurement Random forests

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A data-Driven Approach Towards Software Regression Testing Quality Optimization 25

A Data-Driven Approach Towards Software Regression Testing Q...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moubayed, Abdallah Alhindawi, Nouh Alsakran, Jamal Injadat, MohammadNoor Kanan, Mohammad King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Arizona State University School of Computing and Augmented Intelligence GilbertAZ United States Jadara University Department of Software Engineering Irbid Jordan Fairleigh Dickinson University Canada Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees. © 2024 IEEE.

关键词： Software testing

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A Two-Stage Emission Mismatch Penalty Game to Facilitate Carbon and Electricity Market Interaction 6

A Two-Stage Emission Mismatch Penalty Game to Facilitate Car...

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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Xu, Gaoyuan Shi, Jian Jiang, Wenqian Wu, Chenye Wang, Dan Han, Zhu The Chinese University of HongKong School of Data Science Shenzhen China Technology University of Houston Department of Engineering United States The Chinese University of HongKong School of Science and Engineering Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong University of Houston Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350347159

The electricity sector is undergoing a low carbon transition to combat the looming threats caused by climate change. Given the urgency of meeting the climate goals set by the Intergovernmental Panel on Climate Change (IPCC), the cap-and-trade carbon emission market has emerged as an instrumental tool for policy-makers to impose emission restrictions on generation companies (Gencos). In this paper, we seek to model the sophisticated interactions among the CMO-carbon market operator, ESO-electricity system operator, and Gencos in a coupled electricity and carbon market. We propose a two-stage structure in which CMO conducts a uniform price auction to allocate the gross emission allowance to each Genco in the form of hourly allowance allowances in the first stage. Then, in the next stage, ESO conducts an emission-aware economic dispatch whereas the Gencos may further adjust the hourly schedules based on the dispatch outcomes. The coupling of the carbon market and the electricity market is characterized by an emission mismatch penalty mechanism, and we develop an iterative solving algorithm to reach the equilibrium. Through numerical studies, we highlight that the proposed mechanism is effective in emission reduction and market management. © 2022 IEEE.

关键词： Power markets

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： V. Sathiyapriya K. Nethra Devi S. Deepa M. Marimuthu P. Manikandan S. Prabu Department of Computer Science and Engineering Knowledge Institute of Technology Kakapalayam India Department of Artificial Intelligence and Data Science Gnanamani College of Technology Namakkal India School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches.

关键词： Image segmentation Accuracy Soft sensors Pancreatic cancer Medical services Feature extraction Prediction algorithms Whale optimization algorithms Classification algorithms Ensemble learning

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Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling

arXiv

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arXiv 2025年

关键词： Contrastive Learning

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Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： A Siva Kumar Pv Gopirajan Jayaraj Ramasamy Beulah Jackson Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of IT Faculty of Engineering and Technology Botho University Botswana-Africa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver’s face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver’s eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety.

关键词： Support vector machines Face recognition Machine learning Feature extraction Fatigue Real-time systems Vectors

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DIRECT DISTRIBUTIONAL OPTIMIZATION FOR PROVABLE ALIGNMENT OF DIFFUSION MODELS

arXiv

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arXiv 2025年

作者： Kawata, Ryotaro Oko, Kazusato Nitanda, Atsushi Suzuki, Taiji Department of Mathematical Informatics University of Tokyo Japan Center for Advanced Intelligence Project RIKEN Japan Department of ECCS UC Berkeley United States Singapore College of Computing and Data Science Nanyang Technological University Singapore

We introduce a novel alignment method for diffusion models from distribution optimization perspectives while providing rigorous convergence guarantees. We first formulate the problem as a generic regularized loss minimization over probability distributions and directly optimize the distribution using the Dual Averaging method. Next, we enable sampling from the learned distribution by approximating its score function via Doob’s h-transform technique. The proposed framework is supported by rigorous convergence guarantees and an end-to-end bound on the sampling error, which imply that when the original distribution’s score is known accurately, the complexity of sampling from shifted distributions is independent of isoperimetric conditions. This framework is broadly applicable to general distribution optimization problems, including alignment tasks in Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), and Kahneman-Tversky Optimization (KTO). We empirically validate its performance on synthetic and image datasets using the DPO objective. © 2025, CC BY.

关键词： Probability distributions

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Deep Neural Network Approach on Phishing Websites Detection, Identification and Proactive Prevention

Deep Neural Network Approach on Phishing Websites Detection,...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Soundarya K Siddaraju H S Hitesh Tekchandani Navodit Bhardwaj Pooja Rani Pallavi Joshi Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Researcher at Food and Drug Administration MD USA

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Phishing is a prevalent type of cyberattack that involves posing as a trustworthy source in an email message to fraudulently attempt to get personal information such as usernames, passwords, and bank account information. Strong detection and prevention methods are required to address the phishing issue. The objective is to develop efficient algorithms that use machine learning (ML) and data mining methods to accurately detect and categorize phishing URLs. This initiative aims to support ongoing initiatives to strengthen cybersecurity and shield consumers from the risks associated with phishing scams. It can be observed from the results that the Deep neural network model gives 95% of accuracy and 96% of precision for classification and detection of phishing websites.

关键词： Uniform resource locators Accuracy Recurrent neural networks Phishing Computational modeling Prevention and mitigation Transfer learning Feature extraction Convolutional neural networks Long short term memory

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Extremal maximal entanglement

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Physical Review A 2025年第5期111卷 052410-052410页

作者： Wanchen Zhang Yu Ning Fei Shi Xiande Zhang School of Mathematical Sciences University of Science and Technology of China Hefei 230026 China Hefei National Laboratory University of Science and Technology of China Hefei 230088 China Department of Computer Science School of Computing and Data Science University of Hong Kong Hong Kong 999077 China

A pure multipartite quantum state is called absolutely maximally entangled if all reductions of no more than half of the parties are maximally mixed. However, an n-qubit absolutely maximally entangled state exists only when n equals 2, 3, 5, and 6. A natural question arises when it does not exist: Which n-qubit pure state has the largest number of maximally mixed ⌊n/2⌋-party reductions? We denote this number by Qex(n). It was shown that Qex(4)=4 by Higuchi and Sudbery [Phys. Lett. A 273, 213 (2000)] and Qex(7)=32 by Huber et al. [Phys. Rev. Lett. 118, 200502 (2017)]. In this paper, we give a general upper bound of Qex(n) by linking the well-known Turán problem in graph theory and provide lower bounds using constructive and probabilistic methods. In particular, we show that Qex(8)=56, which is the third known value for this problem.

关键词： Quantum entanglement

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