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34th ACM Web Conference, WWW 2025

作者： Lin, Xixun Cao, Yanan Sun, Nan Zou, Lixin Zhou, Chuan Zhang, Peng Zhang, Shuai Zhang, Ge Wu, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China Academy of Mathematics and Systems Science CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Academy of Mathematics and Systems Science CAS Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400712746

Graph-level out-of-distribution (OOD) detection, which attempts to identify OOD graphs originated from an unknown distribution, is a vital building block for safety-critical applications in Web and society. Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. In CGOD, we develop a new aggregated non-conformity score function based on the proposed adaptive data augmentation. Through the guidance from two designed metrics, i.e., score consistency and representation diversity, our augmentation strategy can generate multiple non-conformity scores, and aggregating these generated non-conformity scores together is robust to the misleading information. Meanwhile, our score function can perceive the subsequent process of conformal inference, enabling the aggregated non-conformity score to be adaptive to different input graphs and deriving a more accurate score estimation. We conduct experiments on multiple real-world datasets with different empirical settings. Extensive results and model analyses demonstrate the superior performance of our approach over several competitive baselines. © 2025 Copyright held by the owner/author(s).

关键词： Network theory (graphs)

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Advancing Carriers Mobility in MnSb2Te4 Thermoelectrics via Tailored Textures and Vacancy Modification

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Advanced Energy Materials 2025年第0期

作者： Xu, Xiao Mao, Dasha Liedke, Maciej Oskar Butterling, Maik Cui, Juan Wang, Jinghan Luo, Yiyuan Ge, Zhenhua Hirschmann, Eric Wagner, Andreas He, Jiaqing Nielsch, Kornelius He, Ran Institute for Metallic Materials Leibniz Institute for Solid State and Materials Research Dresden01069 Germany Department of Physics Southern University of Science and Technology Shenzhen518055 China Institute of Radiation Physics Helmholtz-Zentrum Dresden-Rossendorf Dresden01328 Germany Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China Faculty of Materials Science and Engineering Kunming University of Science and Technology Kunming650093 China

Enhancing carrier mobility plays a crucial role in significantly improving thermoelectric performance. However, due to the lack of a systematic strategy, achieving high mobility remains an elusive goal for most compounds. In this study, the hot-forging method is applied to polycrystalline MnSb₂Te₄, achieving a remarkable 300% improvement in carrier mobility. Through electron backscattering diffraction microstructural analysis, it is demonstrated how optimizing textures can accelerate carrier movement in MnSb₂Te₄ bulk materials. Moreover, theoretical calculations, combined with experimental positron annihilation spectroscopy, reveal that Te vacancies help counteract intrinsic cation defects, leading to a simultaneous increase in carrier mobility. As a result, the hot-forged MnSb₂Te₄ specimen, with a diameter of 15 mm, reaches a record-high maximum figure of merit (ZT) value of 1.3 at 773 K and an impressive average ZT of 0.7 between 323 and 773 K. The experimental output efficiency of 4.6%, observed at 773 K on the MnSb₂Te₄-based single-leg module, further confirms that the improved transport properties are due to the enhanced carrier mobility. This comprehensive study offers valuable insights into mobility enhancement in MnSb₂Te₄ and provides a promising direction for exploring similar improvements in other thermoelectric materials. © 2025 The Author(s). Advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Positron annihilation

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Optimization of Crop Yields in Sustainable Agriculture: Application of Big Data Analytics and Artificial Intelligence

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 375-386页

作者： Nimma, Divya Dhanke, Jyoti A. Murthy, G.S.N. Khandekar, Sachin Dadu Hymavathi, J. Jangir, Pradeep Singh, Mahesh Department of Computational Science University of Southern Mississippi Hattiesburg 39406 MS United States Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Maharashtra Pune India Department of CSE Aditya University Surampalem India Department of Civil Engineering Sinhgad College of Engineering Maharashtra Pune India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Robotics and Automation Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

Making informed economic decisions based on agricultural data is challenging without proper crop management. Data has become the single most important part of modern farming, and its rapid evolution is a major contributor to the rise of “smart farming.” Using sensors to gather objective data with the end aim of increasing productivity and sustainability has several advantages. Information that might boost productivity while decreasing resource waste and pollution levels is essential for managed farms. The cornerstone of future sustainable farming will be data-driven agriculture using robotic solutions based on AI-based techniques. This paper examines the current state of advanced farm management systems, discussing topics like data acquisition in crop fields and variable rate applications, to assist farmers in saving money, protecting the environment, and transforming food production so that it can sustainably keep up with the expected increase in population. The advent of machine learning, big data technologies, and high-performance computers has opened up new avenues for data-intensive research in the interdisciplinary subject of agro technology. These avenues of exploration have been aided by the development of more capable computers. A comprehensive literature review on the issue of the applications of machine learning in agricultural manufacturing is provided in this research. Machine learning has shown its utility in agriculture via two applications: filtering and categorizing products. The use of machine learning on sensor data is transforming conventional farm management systems into AI-enabled programs. These services provide farmers instantaneous, complete access to ideas and insights that may guide their decisions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Big data analytics Optimize crop yields Sustainable agriculture

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Cognitive Large Language Model in Social Media with Local Memory

Cognitive Large Language Model in Social Media with Local Me...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Vigneshwaran N A. Vijayaraj V. P. Murugan R. Jebakumar N. Mageshkumar R. Megavannan Department of IT R.M.K. Engineering College Chennai Department of Mathematics Panimalar Engineering College Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Computer Science and Technology Madanapalle Institute of Technology & Science Andhra Pradesh Faculty of Management SRM Institute of Science and Technology

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Enter the era of social media reimagined! This research taps into an interesting combination of advanced Artificial Intelligence, with large language models such as ChatGPT and GEMINI your favorite social platform. Personal information of each user is armed with voice commands and LLM. But this is the first. LLM talk to each other, exchange information. face recognition adds an extra layer in the mass layer the excitement of your Chatbot sharing only the right information Your account security level private, reserved or public. When it comes to security. We got you covered Choose between playing low key or just sharing just the right number of juicy details. Plus, all the classic features It's still fun, like scrolls and scrolls.

关键词： Social networking (online) Large language models Face recognition Oral communication Chatbots Security

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Machine Learning-powered Job Offer Verification

Machine Learning-powered Job Offer Verification

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Vigneshwaran N A. Vijayaraj V.P. Murugan Jebakumar Jebakumar N. Mageshkumar R. Megavannan Department of IT R.M.K. Engineering College Chennai Department of Mathematics Panimalar Engineering College Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Computer Science and Technology Madanapalle Institute of Technology & Science Andhra Pradesh Faculty of Management SRM Institute of Science and Technology

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In the modern world, technological developments and inventions have given us the ability to handle practically each aspect of our lives, including keeping track of money, education, job appearances, and security. But this reliance on technology has also made it easier for con artists to defraud people and gain quick money. Fake work notices are a specific approach that has recently become more common. Unsuspecting people submit applications for these fake job openings, handing over their personal information and paying the application fees to the con artists as though they were going to fall for the fraud and waste their hard-earned money. In this paper, the Natural Language Processing (NLP) approach is used to identify bogus job postings. We collected relevant information from job advertisements, after that we applied the random forest classifier to train and evaluate our model. The results we obtained show that our method can recognize fake job advertisements with a 97% accuracy rate.

关键词： Technological innovation Accuracy Jobs listings Employment Feature extraction Natural language processing Fraud Classification algorithms Security Random forests

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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A BROKEN HARDY INEQUALITY ON FINITE ELEMENT SPACE AND APPLICATION TO STRAIN GRADIENT ELASTICITY

arXiv

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arXiv 2025年

作者： Liao, Yulei Ming, Pingbing Department of Mathematics Faculty of Science National University of Singapore 10 Lower Kent Ridge Road Singapore119076 Singapore LSEC Institute of Computational Mathematics and Scientific/Engineering Computing AMSS Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

We illustrate a broken Hardy inequality on discontinuous finite element spaces, blowing up with a logarithmic factor with respect to the meshes size. This is motivated by numerical analysis for the strain gradient elasticity with natural boundary conditions. A mixed finite element pair is employed to solve this model with nearly incompressible materials. This pair is quasi-stable with a logarithmic factor, which is not significant in the approximation error, and converges robustly in the incompressible limit and uniformly in the microscopic material parameter. Numerical results back up that the theoretical predictions are nearly optimal. Moreover, the regularity estimates for the model over a smooth domain have been proved with the aid of the Agmon-Douglis-Nirenberg theory. Copyright © 2025, The Authors. All rights reserved.

关键词： Set theory

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A Comunication Framework for Compositional Generation

arXiv

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arXiv 2025年

作者： Elberg, Rafael Petrache, Mircea Parra, Denis Department of Computer Science PUC Chile Centro Nacional de Inteligencia Artificial CenIA Chile Department of Mathematics PUC Chile Institute for Mathematical and Computational Engineering PUC Chile

Compositionality and compositional generalization—the ability to understand novel combinations of known concepts—are central characteristics of human language and are hypothesized to be essential for human cognition. In machine learning, the emergence of this property has been studied in a communication game setting, where independent agents (a sender and a receiver) converge to a shared encoding policy from a set of states to a space of discrete messages, where the receiver can correctly reconstruct the states observed by the sender using only the sender’s messages. The use of communication games in generation tasks is still largely unexplored, with recent methods for compositional generation focusing mainly on the use of supervised guidance (either through class labels or text). In this work, we take the first steps to fill this gap, and we present a self-supervised generative communication game-based framework for creating compositional encodings in learned representations from pre-trained encoder-decoder models. In an Iterated Learning (IL) protocol involving a sender and a receiver, we apply alternating pressures for compression and diversity of encoded discrete messages, so that the protocol converges to an efficient but unambiguous encoding. Approximate message entropy regularization is used to favor compositional encodings. Our framework is based on rigorous justifications and proofs of defining and balancing the concepts of Eficiency, Unambiguity and Non-Holisticity in encoding. We test our method on the compositional image dataset Shapes3D, demonstrating robust performance in both reconstruction and compositionality metrics, surpassing other tested discrete message frameworks. © 2025, CC BY.

关键词： Encoding (symbols)

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Augmented reality for area measurement reasoning of elementary students

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Educational Technology Research and Development 2025年 1-35页

作者： Flavin, Eunhye Chung, Moon Hwang, Sunghwan Flavin, Matthew T. Center for Education Integrating Science Mathematics and Computing Georgia Institute of Technology 505 10th St NW Atlanta 30332 GA United States Department of Education Studies Stonehill College 320 Washington Street North Easton 02322 MA United States Department of Mathematics Education Chuncheon National University of Education 126 Gongji-ro Gangwon-do Chuncheon-si South Korea School of Electrical and Computer Engineering Georgia Institute of Technology 777 Atlantic Dr NW Atlanta 30309 GA United States

Area measurement is a foundational component across STEM fields. The area formula offers limited insight for children trying to develop their area measurement reasoning skills, particularly when dealing with composite shapes. Spatial structuring, where children explore structural units of a 2D space, is an alternative approach. However, poor manipulation of physical units can reinforce misconceptions about area measurement. To address this issue, we developed MeoGeo, an augmented reality (AR) smartphone application. MeoGeo allows children to readily create virtual structural arrays with units of varying sizes, superimposing them on their surroundings in real-time. We conducted an exploratory multiple-case study over a 7-week program to examine the effect of AR activities. Data included written responses from three elementary students on paper-and-pencil tests, along with in situ videos recordings. Our findings indicate that engagement in AR activities facilitated reasoning skills in area measurement for both basic and composite shapes. Furthermore, students maintained their reasoning skills beyond the intervention period. Our study underscores the importance of developing AR systems that align explicitly with the developmental progression of each child, and it highlights the critical role of the instructor in effective execution of AR activities. © The Author(s) 2025.

关键词： Area measurement Augmented reality Elementary education Intelligent system mathematics education mathematics reasoning

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