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arXiv 2025年

作者： Wang, Shiqi Zhang, Zhibo Fang, Libing Nguyen, Cam-Tu Li, Wenzhon School of Artificial Intelligence Nanjing University Jiangsu Nanjing China School of Computer Science and Technology Nanjing University Jiangsu Nanjing China School of Management and Engineering Nanjing University Jiangsu Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China

Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN);and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. To handle such challenges, we propose a novel graph-based method, namely, Knowledge-enhanced GCN with Robust Two-stage Learning (KeGCNR), which leverages Knowledge Graph Embeddings to mitigate the information overload and effectively learns rich representations. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Extensive experimental results not only confirm the importance of interactions but also show the superiority of KeGCNR over a number of strong baselines in terms of fraud detection effectiveness and robustness. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph embeddings

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yankun Yuan Lin Wang Chonghui Xiao Zijuan Liu Fan Dang Xu Wang Haitian Zhao Xin Miao School of Information Science and Engineering Yanshan University National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Automation and BNRist Tsinghua University Global Innovation Exchange Tsinghua University School of Software and BNRist Tsinghua University

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications.

关键词： Wireless communication Wireless sensor networks Energy consumption Data centers Time-frequency analysis Time series analysis Smart homes Quality of experience Data communication Optimization

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Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

arXiv

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arXiv 2023年

作者： Sheng, Junjie Li, Wenhao Jin, Bo Zha, Hongyuan Wang, Jun Wang, Xiangfeng School of Computer Science and Technology East China Normal University Shanghai China School of Data Science The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Tongji University Shanghai China

Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) also have been found suffering relative over-generalization (RO) as people do and stuck to sub-optimal cooperation. Recent methods have shown that assigning reasoning ability to agent can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods. This paper first proves that RO can be avoided when the MARL method satisfies a consistent reasoning requirement under certain conditions. Then we introduce a novel reasoning framework, called negotiated reasoning, that first builds the connection between reasoning and RO with theoretical justifications. After that, we propose an instantiated algorithm, Stein variational negotiated reasoning (SVNR), which uses Stein variational gradient descent to derive a negotiation policy that provably avoids RO in MARL under maximum entropy policy iteration. The method is further parameterized with neural networks for amortized learning, making computation efficient. Numerical experiments on many RO-challenged environments demonstrate the superiority and efficiency of SVNR compared to state-of-the-art methods in addressing RO. © 2023, CC BY.

关键词： Multi agent systems

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Intelligent New Energy Vehicle Battery Swap Scheme based on Blockchain Technology 23

Intelligent New Energy Vehicle Battery Swap Scheme based on ...

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Proceedings of the 2023 5th International Conference on Blockchain Technology

作者： Ya Han Jianbo Gao Zhi Guan Zhong Chen Lifeng Ren School of Computer Science Peking University China and Peking University Chongqing Research Institute of Big Data China National Engineering Research Center for Software Engineering Peking University China Peking University Chongqing Research Institute of Big Data China

ISBN: (纸本)9798400708930

With the development of electric vehicle technology, electric vehicles surpass fuel vehicles in terms of power performance and intelligence, and the cruising range has also increased to more than 400km with the increase of battery energy density and power consumption. However, the overall is still facing the problem of energy refueling anxiety, and the fuel vehicle refueling time is 5 minutes, and the current fast charge takes at least 60 minutes. The waiting time for charging during peak hours has also been further lengthened. Increasing the speed of energy replenishment has become an urgent need for new energy vehicle owners. The way electric vehicles are supplied with energy is fundamentally different from that of traditional cars. The battery swap mode naturally separates the vehicle and electricity, providing the possibility for novel business models (vehicle electricity separation, battery sharing). Let the battery return to its "energy carrier" use attribute, realize the sharing of batteries, create conditions for battery financialization, carry out full life cycle value management of batteries, implement battery gradient utilization, and provide a feasible path for future energy storage business. However, in battery swap mode, there are many divisions of responsibilities. For example, if the battery burns the car, should I find the main engine factory or the power exchange company/high-voltage electrical failure after the power exchange, especially when the responsibility is not clear, who will check and repair. This paper proposes a new energy vehicle monitoring platform based on blockchain technology, which can manage the whole process life cycle of new energy batteries through blockchain traceability technology. At the same time, according to the current responsibility division system, the transparent division of responsibilities is realized in combination with blockchain smart contracts.

关键词： Battery swap mode Blockchain New energy Responsibility division

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

关键词：

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NLOS/LOS Classification by Constructing Indirect Environment Interaction From GNSS Receiver Measurements Using a Transformer-Based Deep Learning Model

NLOS/LOS Classification by Constructing Indirect Environment...

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2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Li, Zhenni Zeng, Kungan Wang, Lujia Kan, Xie Yuan, Rong Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Software and Engineering Sun Yat-sen University Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Techtotop Microelectronics Technology Co. Ltd Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

ISBN: (纸本)9781713871378

The blockage of the direct signal by high-rise buildings degrades the positioning performance of the global navigation satellite system (GNSS) in urban canyons. Traditional methods usually distinguish the line-of-sight (LOS), and non-line-of-sight (NLOS) signals using the satellite’s elevation angle or signal strength to mitigate the issue, which cannot obtain satisfactory classification accuracy in complex urban environments. Recently, machine learning-based methods using GNSS measurements have been developed to improve GNSS signal classification. However, dynamic changes in environments and the limited features from the measurements raise a challenge to the classification performance of algorithms. To address the above issues, a transformer-based deep learning model in this paper is proposed for LOS and NLOS signal classification. The innovation of our proposed model is using the self-attention mechanism to construct the indirect environment interaction for learning the representation of environmental information. First, the measurements of captured satellites in the same epoch will be fed into the model. The model’s feature extraction layer can learn the fundamental representation. Then, the self-attention module can construct the indirect environment interaction by computing the relevance between the satellites. Finally, the module’s output is transferred to the output layer for classification. A comparative experimental study is conducted with real-world data collected in urban canyons. The results demonstrate that our model can keep more than 90% classification accuracy while support vector machine (SVM) and multilayer perception (MLP) decline to about 80% with the sites increasing. © 2023 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM.

关键词： Satellites

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Design approach centered on the activity or the learning situations: for a learner-centered learning environment (LCLE)

Design approach centered on the activity or the learning sit...

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IEEE Education engineering (EDUCON)

作者： Aminata Wague Mamadou Bousso Laurence Capus Sustainable Development and Society Doctoral School Iba Der Thiam University of Thies Thies Senegal Department of Computer Science Iba Der Thiam University of Thies Thies Senegal Department of Computer Science and Software Engineering Laval University Quebec Canada

This article deals with the design of TELE (Technology-Enhanced Learning Environment), specifically LCLE (Learner-Centered Learning Environment). It suggests adopting the activity-centered approach in favour of the non-formal learning situation to design an LCLE. After demonstrating that this approach only considers formal learning situations, we describe the different stages of its implementation process. Subsequently, we develop a comparative table of the activity paradigm's formal and non-formal learning situations, allowing us to validate the proposition in question.

关键词： Reflection engineering education

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Prompting Continual Person Search

arXiv

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arXiv 2024年

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin Ning, Xin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University Beijing China School of Computing Macquarie University Sydney Australia Institute of Semiconductors Chinese Academy of Sciences Beijing China

The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn from increasing real-world data and adaptively process input from different domains. To this end, this work introduces the continual person search task that sequentially learns on multiple domains and then performs person search on all seen domains. This requires balancing the stability and plasticity of the model to continually learn new knowledge without catastrophic forgetting. For this, we propose a Prompt-based Continual Person Search (PoPS) model in this paper. First, we design a compositional person search transformer to construct an effective pre-trained transformer without exhaustive pre-training from scratch on large-scale person search data. This serves as the fundamental for prompt-based continual learning. On top of that, we design a domain incremental prompt pool with a diverse attribute matching module. For each domain, we independently learn a set of prompts to encode the domain-oriented knowledge. Meanwhile, we jointly learn a group of diverse attribute projections and prototype embeddings to capture discriminative domain attributes. By matching an input image with the learned attributes across domains, the learned prompts can be properly selected for model inference. Extensive experiments are conducted to validate the proposed method for continual person search. The source code is available at https://***/PatrickZad/PoPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Deep-learning assisted Cross-Layer Routing in Multi-hop Wireless Network 7

Deep-learning assisted Cross-Layer Routing in Multi-hop Wire...

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： Regis, Paulo Alexandre Bhunia, Suman Patra, Amar Nath Sengupta, Shamik Southeastern Louisiana University Department of Computer Science HammondLA United States Miami University Department of Computer Science and Software Engineering OxfordOH United States Radford University School of Computing and Information Sciences RadfordVA United States University of Nevada Department of Computer Science and Engineering RenoNV United States

ISBN: (纸本)9781665444316

Wireless ad hoc networks rely on exchanging information among the nodes to find a feasible route between source and destination. Routing protocols require periodically flooding the entire system with the updated state of the network. Routing protocols define the rules and semantics of how this information is spread throughout the network and can be classified into proactive and reactive protocols, both of which eventually sends out control packets to the network. However, with the increase of the scale of the network, the amount of packets exchanged uses resources that could be otherwise used for application messages. In this paper, we explore the use of machine learning techniques to restrict the flooding radius of control packets. We define the problem objective as a regression problem and use neural networks to model it. The model can be used in networks that share similar attributes, suggesting that transfer learning can be used to re-purpose the initial model. The results show that using machine learning reduces the amount of resources used to maintain the network state up to date, which could be subsequently used to improve the overall performance of the networked system. We anticipate machine learning to be the starting point for more sophisticated models of cross-layer routing mechanisms. © 2021 IEEE.

关键词： Routing protocols

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Evaluating and improving the interpretability of item embeddings using item-tag relevance information

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Frontiers of computer science 2020年第3期14卷 143-158页

作者： Tao LIAN Lin DU Mingfu ZHAO Chaoran CUI Zhumin CHEN Jun MA College of Data Science Taiyuan University of TechnologyJinzhong 030600China Software College Shandong UniversityJinan 250101China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 101408China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent ***,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation ***,the item-item co-occurrence information,constructed from the sparse and long-tail distributed user-item interaction matrix,is over-estimated for rare items,which could lead to bias in learned item *** this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance ***,we design two metrics to quantitatively evaluate the interpretability of item embeddings from different viewpoints:interpretability of individual dimensions of item embeddings and semantic coherence of local neighborhoods in the latent *** also propose a tag-informed item embedding(TIE)model that jointly factorizes the user-item interaction matrix,the item-item co-occurrence matrix and the item-tag relevance matrix with shared item embeddings so that different forms of information can co-operate with each other to learn better item *** on the MovieLens20M dataset demonstrate that compared with other state-of-the-art MF methods,TIE achieves better top-N recommendations,and the relative improvement is larger when the user-item interaction matrix becomes *** leveraging the itemtag relevance information,individual dimensions of item embeddings are more interpretable and local neighborhoods in the latent space are more semantically coherent;the bias in learned item embeddings are also mitigated to some extent.

关键词： recommender system matrix factorization item embedding item-tag relevance interpretability

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