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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cheng, Minjie Luo, Dixin Xu, Hongteng Gaoling School of Artifcial Intelligence Renmin University of China China School of Computer Science and Technology Beijing Institute of Technology China Beijing Key Laboratory of Big Data Management and Analysis Methods China

ISBN: (纸本)157735897X

As a fundamental problem of graph analysis, graph visualization aims to embed a set of graphs in a low-dimensional (e.g., 2D) space and provide insights into their distribution and clustering structure. Focusing on this problem, we propose a novel Wasserstein t-distributed embedding (WatE) method, leading to an information-enriched graph visualization paradigm. Our method learns a graph neural network to represent each graph as the mean and covariance of its node embedding distribution. Accordingly, our method can visualize each graph as an ellipse (determined by the mean and the covariance) rather than a single point. The positions of different ellipses reveal the relations among different graphs as traditional visualization methods do, while the size and shape of an ellipse preserve the node-level structural information of the corresponding graph. We propose a regularized t-distributed stochastic neighbor embedding (Rt-SNE) framework to learn the visualization model, deriving a Wasserstein distance-based Student’s t-distribution of graph pairs and fitting the distribution to the data distribution under regularization. Both subjective and objective evaluations demonstrate that WatE achieves encouraging performance in various graph visualization and clustering tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Flow visualization

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Heuristic Algorithm for Solving Travelling Salesman Problem: Adding the Endpoints of the Longest Edges

Heuristic Algorithm for Solving Travelling Salesman Problem:...

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International Conference on Problems of Cybernetics and Informatics (PCI)

作者： Fidan Nuriyeva Department of Computer Science Dokuz Eylul University Izmir Turkey Laboratory of Recognition Identification and Methods of Optimal Solutions Institute of Control Systems Baku Azerbaijan

This study proposes, a new heuristic algorithm based on insertion heuristic to solve the symmetric Travelling Salesman Problem. In the proposed algorithm, firstly the two longest edges are selected and the starting tour passing through their endpoints is constructed, then the endpoints of the next longest edges are added to this tour and the solution passing through all the vertices is found. This problem is known to be NP-Hard. The proposed method was successfully tested on different-sized problems. The experimental results show that the proposed algorithm outperforms many existing methods.

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Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?

arXiv

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arXiv 2023年

作者： Corsini, Andrea Yang, Shanchieh Jay Department of Science and Methods for Engineering University of Modena and Reggio Emilia Modena Italy Department of Computer Engineering Rochester Institute of Technology Rochester United States

Machine learning (ML) has become increasingly popular in network intrusion detection. However, ML-based solutions always respond regardless of whether the input data reflects known patterns, a common issue across safety-critical applications. While several proposals exist for detecting Out-Of-Distribution (OOD) in other fields, it remains unclear whether these approaches can effectively identify new forms of intrusions for network security. New attacks, not necessarily affecting overall distributions, are not guaranteed to be clearly OOD as instead, images depicting new classes are in computer vision. In this work, we investigate whether existing OOD detectors from other fields allow the identification of unknown malicious traffic. We also explore whether more discriminative and semantically richer embedding spaces within models, such as those created with contrastive learning and multi-class tasks, benefit detection. Our investigation covers a set of six OOD techniques that employ different detection strategies. These techniques are applied to models trained in various ways and subsequently exposed to unknown malicious traffic from the same and different datasets (network environments). Our findings suggest that existing detectors can identify a consistent portion of new malicious traffic, and that improved embedding spaces enhance detection. We also demonstrate that simple combinations of certain detectors can identify almost 100% of malicious traffic in our tested scenarios. © 2023, CC BY.

关键词： Embeddings

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Tackling Non-stationarity in Decentralized Multi-Agent Reinforcement Learning with Prudent Q-Learning 19th

Tackling Non-stationarity in Decentralized Multi-Agent Rein...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Wei, Jianan Wang, Liang Tao, Xianping Hu, Hao Wu, Haijun State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China National Experimental Teaching Demonstration Center of Computer Science Technology and Software Engineering Nanjing University Nanjing210023 China

ISBN: (纸本)9783031203084

Multi-Agent Reinforcement Learning (MARL) is challenging due to the non-stationary issue of an agent’s learning environment caused by multiple co-evolving agents, i.e., the uncertainty rises with multiple agents learning and evolving simultaneously. Though centralized learning can solve the problem to some degree, it is hard to expand into numerous agents or real-world applications. We focus on the native property of the non-stationary environment and use a independent method to alleviate its harmful influence. Here are the two main contributions of this article: 1) We formalize the non-stationary environment by supposing it follows a pessimistic transition, and we explain the existence of a lower bound of accumulated reward expectation that the agent can learn. 2) We devise a scheme called Prudent Q-Learning (PQL) based on independent learning for controlling multi-agent systems in non-stationary environments. We present both tabular and Deep Q-Learning based implementations of the proposed Prudent Q-Learning scheme. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Remote Sensing-Based Agricultural Damage Assessment: A Semantic Segmentation Method for Analyzing Corn Field Changes 25

Remote Sensing-Based Agricultural Damage Assessment: A Seman...

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Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Meng Wu Zeyu Li Yu Wang Yi Wei Ying Chen Xiaoyue Tu School of Information Science and Technology Sanda University Shanghai China Computer Science and Artificial Intelligence Department Leiden University Leiden Holland Teaching Department Kede Future (Shanghai) Education Technology Co. Ltd. Shanghai China

ISBN: (纸本)9798400713231

The increasing impact of natural disasters on agricultural production highlights the vital need for accurate estimation of changes in crop cultivation areas as pertains to agricultural recovery and formulation of food security strategies. In this study, the authors apply an integrated approach of remote sensing data and semantic segmentation technology by taking the June 2023 event in Illinois, termed the derecho event, as a case study. DeepCBAMNet is applied to analyze the pre-and post-disaster remote sensing images, yielding an overall model accuracy of 93.39% and a mean Intersection over Union (mIoU) of 81.88% on the training dataset. The study reports an area affected by the disaster of 69,352.01 acres with a 91.98% accuracy rate when compared with that reported by the United States Department of Agriculture (75,401.60 acres). The methodology is a key advancement in the assessment of agricultural disasters, as it gives a new technical approach to the rapid and accurate assessment of disaster impact, thus allowing government authorities to put in place more productive post-disaster recovery strategies and agricultural policies.

关键词： Agricultural Disaster Monitoring

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Machine Learning for Energy Load Prediction and its Interpretation 11

Machine Learning for Energy Load Prediction and its Interpre...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Charytanowicz, Malgorzata Olwert, Anna Radziszewska, Weronika Jarnicka, Jolanta Gajowniczek, Krzysztof Zabkowski, Tomasz Brozyna, Jacek Mentel, Grzegorz Matejko, Grzegorz Research Institute Pas Center for Methods of Data Analysis Systems Warsaw Poland Research Institute Pas Department of Computer Science Systems Warsaw Poland Research Institute Pas Center for Computer Modelling Systems Warsaw Poland Warsaw University of Life Sciences Institute of Information Technology Warsaw Poland Rzeszow University of Technology Department of Quantitative Methods Rzeszów Poland Polskie Towarzystwo Cyfrowe Lublin Poland

ISBN: (纸本)9781665456562

This paper refers to real-life data on energy consumption from three companies, connected to a medium voltage grid. The companies use single-, two-, or three-zone tariffs and the general characteristic of their activity is comparable. The input dataset contains hourly electricity consumption recorded from January 2020 to December 2020. The independent variables used for the study include weather, time-dependent factors, and aggregated energy factors. We perform the prediction of consumption using three methods: naive, multiple regression, and extreme gradient boosting regression. The performance of the methods is compared based on the MSE, RMSE, MAE, and MAPE metrics. The study shows that the scores achieved for the extreme gradient boosting method are higher than for the multiple regression. To gain the interpretability of the energy load forecasting and feature analysis the Shapley additive explanations method is used for the best model. As a result, a ranking of the most important factors is created. It shows the importance of aggregated energy factors and weather conditions while placing daylight and season at the bottom of the ranking. © 2022 IEEE.

关键词： Energy utilization

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Special Session: Amplifying Voices Through Animaker Animation: Results from a Longitudinal Study with Latino/a/x Engineering Students 54

Special Session: Amplifying Voices Through Animaker Animatio...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Mejia, Joel Alejandro Hernandez, Sofia Paul, Neethu University of Texas at San Antonio Dept. of Biomedical & Chemical Eng. San AntonioTX United States University of Texas at San Antonio Dept. of Computer Science San AntonioTX United States University of Texas at San Antonio Dept. of Int. Learning & Teaching San Antonio United States

ISBN: (纸本)9798350351507

This special session explores the application of Chicana Feminist Epistemology methodologies in engineering education research with a focus on Latino/a/x students at Hispanic Serving Institutions. By utilizing Platicas and Testimonios as forms of data and data collection approaches in engineering education research, this session aims to foster a more inclusive and empowering research approach while challenging traditional Westernized paradigms. Through the analysis of Animaker videos created from vignettes emerging from these Platicas and Testimonios, participants will have a better understanding of the experiences of Latino/a/x engineering students, emphasizing asset-based methodologies and the creation of multimodal artifacts for research purposes. The research approaches presented in this session seek to amplify marginalized voices, promote critical consciousness, and challenge traditional ways of knowing and doing in engineering education research, encouraging attendees to integrate these methodologies into their research and teaching practices effectively to support diverse student populations. © 2024 IEEE.

关键词： Students

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Large language models meet user interfaces: The case of provisioning feedback

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computers and Education: Artificial Intelligence 2024年 7卷

作者： Pozdniakov, Stanislav Brazil, Jonathan Abdi, Solmaz Bakharia, Aneesha Sadiq, Shazia Gašević, Dragan Denny, Paul Khosravi, Hassan School of Electrical Engineering and Computer Science The University of Queensland St LuciaQLD4072 Australia Institute for Teaching and Learning Innovation The University of Queensland St LuciaQLD4072 Australia Centre for Learning Analytics Faculty of Information Technology Monash University MelbourneVIC3800 Australia School of Computer Science University of Auckland 38 Princes Street Auckland1010 New Zealand

Incorporating Generative Artificial Intelligence (GenAI), especially Large Language Models (LLMs), into educational settings presents valuable opportunities to boost the efficiency of educators and enrich the learning experiences of students. A significant portion of the current use of LLMs by educators has involved using conversational user interfaces (CUIs), such as chat windows, for functions like generating educational materials or offering feedback to learners. The ability to engage in real-time conversations with LLMs, which can enhance educators' domain knowledge across various subjects, has been of high value. However, it also presents challenges to LLMs' widespread, ethical, and effective adoption. Firstly, educators must have a degree of expertise, including tool familiarity, AI literacy and prompting to effectively use CUIs, which can be a barrier to adoption. Secondly, the open-ended design of CUIs makes them exceptionally powerful, which raises ethical concerns, particularly when used for high-stakes decisions like grading. Additionally, there are risks related to privacy and intellectual property, stemming from the potential unauthorised sharing of sensitive information. Finally, CUIs are designed for short, synchronous interactions and often struggle and hallucinate when given complex, multi-step tasks (e.g., providing individual feedback based on a rubric on a large scale). To address these challenges, we explored the benefits of transitioning away from employing LLMs via CUIs to the creation of applications with user-friendly interfaces that leverage LLMs through API calls. We first propose a framework for pedagogically sound and ethically responsible incorporation of GenAI into educational tools, emphasizing a human-centred design. We then illustrate the application of our framework to the design and implementation of a novel tool called Feedback Copilot, which enables instructors to provide students with personalized qualitative feedback on their

关键词： Students

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Multyperson Health Monitoring Wireless Network 30

Multyperson Health Monitoring Wireless Network

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30th Telecommunications Forum, TELFOR 2022

作者： Shevchuk, Bohdan Ivakhiv, Orest Kochan, Roman National Academy of Sciences of Ukraine Glushkov Institute of Cybernetics Department of Numerical Methods Optimization Kyiv03187 Ukraine Lviv Polytechnic National University Computer Technology Automation and Metrology Institute Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine University of Bielsko-Biala Department of Computer Science and Automatics Bielsko-Biala Poland

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Methodological and algorithmic bases of the local-regional and global wireless networks operation for functional states of people long-term monitoring, processing, encoding, encrypting and transmitting samples of monitoring signals and video data frames using a signaling approach are proposed. The technology is focused on evidence-based monitoring of the condition of operators of various human-machine complexes, athletes and healthy people in order to prevent various diseases. © 2022 IEEE.

关键词： Wireless networks

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2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion

arXiv

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arXiv 2024年

作者： Wang, Dongsheng Feng, Xiaoqin Liu, Zeming Wang, Chuan The Department of Science and Technology Teaching China University of Political Science and Law Beijing102249 China Mobvoi AI Lab Beijing100044 China School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Information Engineering CAS Beijing100085 China

Named entity recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities in sentences into pre-defined types. It plays a crucial role in various research fields, including entity linking, question answering, and online product recommendation. Recent studies have shown that incorporating multilingual and multimodal datasets can enhance the effectiveness of NER. This is due to language transfer learning and the presence of shared implicit features across different modalities. However, the lack of a dataset that combines multilingualism and multimodality has hindered research exploring the combination of these two aspects, as multimodality can help NER in multiple languages simultaneously. In this paper, we aim to address a more challenging task: multilingual and multimodal named entity recognition (MMNER), considering its potential value and influence. Specifically, we construct a large-scale MMNER dataset with four languages (English, French, German and Spanish) and two modalities (text and image). To tackle this challenging MMNER task on the dataset, we introduce a new model called 2M-NER, which aligns the text and image representations using contrastive learning and integrates a multimodal collaboration module to effectively depict the interactions between the two modalities. Extensive experimental results demonstrate that our model achieves the highest F1 score in multilingual and multimodal NER tasks compared to some comparative and representative baselines. Additionally, in a challenging analysis, we discovered that sentence-level alignment interferes a lot with NER models, indicating the higher level of difficulty in our dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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