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FeBT: A Feature Balancing Transformer for Corporate ESG Forecasting

IEEE Transactions on Knowledge and Data engineering 2025年

作者： Li, Yawen Zhuang, Mengyu Ye, Guanhua Li, Yan Wang, Junheng Zhou, Jinyi Zhang, Pengfei Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Beijing University of Posts and Telecommunications School of Computer Science National Pilot School of Software Engineering Beijing China University of Electronic Science and Technology of China School of Management and Economics Chengdu China University of Science and Technology Beijing School of Economics and Management Beijing China The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia

Environmental, social, and governance (ESG) serves as a crucial indicator for evaluating firms in terms of sustainable development. However, the existing ESG evaluation systems suffer from limitations, such as narrow coverage, subjective bias, and lack of timeliness. Therefore, there is a pressing need to leverage machine learning methods to predict the ESG performance of firms using their publicly available data. Traditional machine learning models encounter the feature imbalance problem due to the heterogeneity in ESG-related features. Common approaches typically involve unfolding all features, thereby granting highdimensional folding features greater exposure and accessibility to downstream models, which results in the neglect of lowdimensional features. To fill the research gap regarding fully using the heterogeneous features of enterprises to enhance AIbased ESG prediction performance, we propose the Feature Balancing Transformer (FeBT), a model based on autoencoders and Transformer blocks. FeBT incorporates a novel feature balancing technique that compresses and enhances high-dimensional features from imbalanced data into low-dimensional representations, thereby ensuring a more balanced impact of high-dimensional and low-dimensional features on the model's performance in the downstream ESG forecasting module. Extensive experiments verified the superior performance of FeBT compared with stateof-the-art methods in real-world ESG-related datasets and evidenced that our feature balancing module provides significant insights from high-dimensional folding features. © 1989-2012 IEEE.

关键词： Data Masking ESG Forecasting Time Series Forecasting

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Big Data-Intelligence Analytics for Energy Optimization in IoT-Enabled Smart Home Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Yihong Song, Qiang Zhaoqing University Big Data Engineering Technology Center China Zhaoqing University School of Computer Science and Software China

This article explores the integration of Artificial Intelligence (AI) and Big Data Analytics to optimize energy consumption in IoT-enabled smart home devices. It presents a robust analytical framework that leverages Variational Autoencoders (VAEs) for feature extraction and Differential Evolution (DE) for optimizing energy management parameters. Data was gathered from various IoT devices, including energy usage patterns, occupancy data, and environmental conditions. The results show a notable 40% reduction in energy consumption, leading to annual cost savings of up to $300 per household. Moreover, user satisfaction increased by 25%, with participants reporting heightened awareness and engagement in energy conservation. The study highlights how the proposed framework efficiently identifies common usage patterns and optimizes energy distribution while preserving user comfort. These findings reinforce the potential of AI-driven analytics in improving energy efficiency in smart homes, demonstrating that advanced algorithms not only support energy conservation but also promote active user participation in sustainability efforts. © 2025 IEEE.

关键词： Energy utilization

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A self-supervised graph convolutional model for recommendation with exponential moving average

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Neural Computing and Applications 2025年第9期37卷 6777-6793页

作者： Chen, Rui Pang, Kangning Wang, Zonglin Liu, Qingfang Tang, Cundong Chang, Yanshuo Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China Information Center Shijiazhuang Posts and Telecommunications Technical College Shijiazhuang050021 China School of Information Science and Technology Northwest University Xi’an710127 United States Insititute for Silk Road Research Xi’an University of Finance and Economics Xi’an710100 China

Recommendation algorithms based on graph convolutional networks can integrate user and item node information along with their interaction topology, better capturing the intricate relationships between users and items, thereby enhancing the accuracy of recommender systems. However, existing methods often overlook the impact of noise in user behavior data on recommendation performance. Additionally, when there are too many convolutional layers in the graph, the node representations tend to smoothing, resulting in an inability to accurately distinguish user preferences. To address these issues, we propose a self-supervised graph convolutional model for recommendation with exponential moving average (SGCERec). Specifically, we first employ exponential moving average (EMA) techniques from the field of time-series analysis to denoise the raw user interaction data. Then, by applying layer filtering technique to update the propagation of information and the representation of nodes within the graph convolutional network, we effectively deepen the model hierarchy, enabling the model to gain a deeper understanding of the features and structures of the graph data, thereby improving the performance and effectiveness of the recommender systems. Finally, experimental results on three real datasets show that SGCERec outperforms state-of-the-art recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Recommender systems

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Fed RDP: A Robust Federated Learning Framework for Multi Party Decision-Making

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IEEE Transactions on Network and Service Management 2025年

作者： An, Jian Tang, Siyu Ping, Ruyuan Li, Ran He, Xin Jin, Xiaolong Xi’an Jiaotong University School of Computer Science and Technology China Shaanxi Province Key Laboratory of Computer Network Xi’an Jiaotong University Xi’an710049 China Xi’an Jiaotong University School of Computer Science and Technology Xi’an710049 China Henan University School of Software Kaifeng475001 China Rocket Force University of Engineering School of Combat Supporting Xi’an710000 China

In the field of intelligent manufacturing, the vast and heterogeneous nature of data across different departments poses significant challenges for collaborative decision-making. Direct data sharing often leads to severe privacy and security concerns. Although federated learning offers a promising solution, issues such as participant selection and privacy protection remain inadequately addressed. During model training, it is crucial to minimize the influence of low-quality participants and prevent the inference of sensitive information through gradient analysis, which could threaten model performance or data privacy. To address these challenges, this paper proposes a robust federated learning model, Fed-RDP, based on participant contribution evaluation and personalized differential privacy. The model leverages blockchain technology for secure data flow and storage, implemented through smart contracts. Historical parameters are submitted to evaluate contributions based on the concept of the least core, with real-time updates to reputation scores. When participants upload their local models, the scores are used as weights to aggregate and update the global model. Additionally, personalized differential noise is added to the uploaded gradients based on participant scores, preserving privacy while maximizing the utility of the data. Experimental results demonstrate that this approach effectively identifies low-quality participants, optimizes evaluation time, and protects privacy through personalized differential noise. © 2004-2012 IEEE.

关键词： Federated learning

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Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Qiu, Houming Zhu, Kun Luong, Nguyen Cong Niyato, Dusit Jiangxi University of Finance and Economics School of Software and IoT Engineering Jiangxi 330032 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. Firstly, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Secondly, there are few existing works considering security issues in data transmission of distributed computing systems/or coded distributed machine learning systems. Thirdly, the number of required results for which need to wait increases with the degree of decoding functions. In this paper, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches. © 2013 IEEE.

关键词： Parallel processing systems

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Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis

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computers in Biology and Medicine 2025年 187卷 109791-109791页

作者： Ejiyi, Chukwuebuka Joseph Cai, Dongsheng Fiasam, Delali Linda Adjei-Arthur, Bonsu Obiora, Sandra Ayekai, Browne Judith Asare, Sarpong K. Jonathan, Anto Leoba Qin, Zhen College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Leeds Business School Leeds Beckett University LeedsLS1 3HB United Kingdom School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

The variability in image modalities presents significant challenges in medical image classification, as traditional deep learning models often struggle to adapt to different image types, leading to suboptimal performance across diverse datasets. This is critical in the diagnosis of conditions like cataracts and cancers, where imaging data spans various modalities, including visible eye images for cataracts and histopathological images for cancers among others. Cataracts, a leading cause of blindness, and lung and breast cancers, major contributors to cancer-related deaths, require early detection for effective intervention. However, many existing models fall short in handling modality differences, limiting their performance. To address this, we propose ResoMergeNet (RMN), designed to handle multi-modal medical image classification. RMN integrates transfer learning and advanced techniques – the ResBoost framework and ConvMergeNet, enabling the model to effectively extract relevant features from both visible eye images and histopathological images. The model's architecture emphasizes both global and local feature extraction while minimizing the influence of irrelevant data, thus improving classification performance across modalities. Evaluated on the cataract dataset (binary classification), RMN achieved an accuracy of 99.17 %. For lung cancer (3-class classification), it attained 100 % accuracy, while on the BreakHis (8-class classification) dataset, RMN reached 99.24 % accuracy at 100× magnification and 98.28 % at 200× magnification. These results demonstrate RMN's robustness and adaptability to varying image modalities, highlighting its potential as a reliable diagnostic tool in medical settings. Through its versatility, RMN offers a promising solution for improving early diagnosis and healthcare outcomes in the fight against cataracts, lung, and breast cancers. © 2025 Elsevier Ltd

关键词： Lung cancer

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Variational Autoencoders for Spatio-Temporal Dynamic Graph Data 24

Variational Autoencoders for Spatio-Temporal Dynamic Graph D...

引用

Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Yanhui Peng Sicheng Xi School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798400709753

Most of the existing graph variational autoencoders primarily focus on static graphs, and fail to fully consider the temporal characteristics of dynamic networks, which are difficult to capture the characteristics of real-world network evolution over time, resulting in poor prediction accuracy of downstream tasks. The traditional encoder is improved by introducing the cloud model, the forward cloud generator model is used to extend the normal distribution to the Gaussian cloud distribution, and the idea of grey prediction is combined to serialize each snapshot to expand the downstream link prediction task. By fusing spatial and temporal features, experiments are carried out on three real dynamic network datasets. The results show that the proposed model is superior to the baseline model in link prediction.

关键词： cloud model

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Trauma-Informed Insights from Co-Design of Self-Disclosure Robots with Domestic Abuse Survivors 25

Trauma-Informed Insights from Co-Design of Self-Disclosure R...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Nyomi Carrine Morris Tom Williams Ben Jelen MIRRORLab Colorado School of Mines Golden CO USA Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre Haute IN USA

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Survivors of domestic abuse face significant challenges securing recovery resources, such as housing, mental health care, and social connections. Accessing these resources requires survivors to disclose their status as survivors of domestic abuse; a process that can be traumatic and emotionally burdensome. In this work, we consider how social robots might help domestic abuse survivors to address challenges surrounding self-disclosure. To do so, we conducted a three-session co-design workshop involving 8 participants from a domestic abuse shelter. Our results provide three key contributions: (1) new insights into the benefits and barriers to self-disclosure; (2) new insights into the ways that social robots can help survivors to better achieve those benefits and overcome those barriers before, during, and after disclosure, including key design recommendations associated with each of these phases; and (3) observations into how a trauma-informed computing perspective may enable more effective design work in Human-Robot Interaction.

关键词： Conferences Social robots Mental health Faces

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SolSearch: An LLM-Driven Framework for Efficient SAT-Solving Code Generation

arXiv

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arXiv 2025年

作者： Sheng, Junjie Lin, Yanqiu Wu, Jiehao Huang, Yanhong Shi, Jianqi Zhang, Min Wang, Xiangfeng School of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China

The Satisfiability (SAT) problem is a core challenge with significant applications in software engineering, including automated testing, configuration management, and program verification. This paper presents SolSearch, a novel framework that harnesses large language models (LLMs) to discover and optimize SAT-solving strategies automatically. Leveraging a curriculum-based, trial-and-error process, SolSearch enables the LLM to iteratively modify and generate SAT solver code, thereby improving solving efficiency and performance. This automated SAT-solving paradigm has the advantage of being plug-and-play, allowing integration with any SAT solver and accelerating the development or design process of new SAT solvers (new methods). Our preliminary experimental results are encouraging by demonstrating that the LLM-powered paradigm improves state-of-the-art SAT solvers on general SAT benchmarks and significantly enhances the performance of the widely used Z3 solver (11% on PAR-2 score). These results highlight the potential for using LLM-driven methods to advance solver adaptability and effectiveness in real-world software engineering challenges. Future research directions are discussed to further refine and validate this approach, offering a promising avenue for integrating AI with traditional software engineering tasks. © 2025, CC BY.

关键词： Application programs

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Safeguarding Privacy in Real-Time Trajectory Releases for AI-Driven Consumer Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Cai, Sujin Tian, Wei Lv, Taizhi Wang, Xiangchen Li, Xin Jiangsu Maritime Institute School of Information and Engineering Nanjing211106 China Guizhou University of Commerce College of Computer and Information engineering Guiyang550014 China Southern University of Science and Technology College of Computer Science Shenzhen518055 China Hohai University College of Computer Science and Software Engineering Nanjing211106 China

Intelligent consumer applications optimize product recommendations by analyzing users' real-time trajectory information and mobility patterns to gain insights into their consumption preferences. However, privacy concerns often cause individuals to hesitate when sharing their locations. This paper introduces a real-time trajectory release scheme called Dynamic Trajectory Releasing based on Geo-indistinguishability (DTRG) that ensures client-side data protection. A significant contribution of this work is the introduction of a novel concept, w-event Geo-indistinguishability, which extends the measurement of privacy for real-time trajectories. Building upon the theoretical foundation of w-event Geo-indistinguishability, we design a dynamic privacy budget allocation strategy that doesn't require pre-setting trajectory length. This strategy recycles unused privacy budgets and progressively reallocating them in a backward cumulative manner, enhancing the utilization efficiency of the privacy budget. By incorporating users' travel information for prediction, DTRG adaptively perturbs real locations based on prediction results. Through the collaborative operation of the Prediction Mechanism (PM), Detection Mechanism (DM) and Noisy Mechanism (NM), this approach further optimizes the utility of the budget. Experimental results demonstrate that DTRG significantly improves data availability, achieving up to a 36% increase compared to some competing algorithms while maintaining the desired level of privacy. © 1975-2011 IEEE.

关键词： Differential privacy

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