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9th International Conference on Smart City Applications, SCA 2024

作者： Ledmaoui, Younes Raif, Mourad El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. Through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. The seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Battery management systems

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Seismic multiple attenuation based on improved U-Net

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Applied Geophysics 2024年第4期21卷 680-696,879页

作者： Quan Zhang Xiao-yu Lv Qin Lei Bo Peng Yan Li Yao-wen Zhang State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University) Chengdu 610500China School of Computer Science and Software Engineering Southwest Petroleum UniversityChengdu 610500China

Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl *** the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuation and high computational requirements,particularly in complex geological *** multiple attenuation methods rely on prior geological information and involve extensive *** deep neural networks for multiple attenuation can effectively reduce manual labor costs while improving the efficiency of multiple *** study proposes an improved U-net-based method for multiple *** conventional U-net serves as the primary network,incorporating an attentional local contrast module to effectively process detailed information in seismic *** is placed on distinguishing between seismic multiples and *** improved network is trained using seismic data containing both multiples and primaries as input and seismic data containing only primaries as *** eff ectiveness and stability of the proposed method in multiple attenuation are validated using two horizontal layered velocity models and the Sigsbee2B velocity *** learning is employed to endow the trained model with the capability to suppress multiples across seismic exploration areas,eff ectively improving multiple attenuation efficiency.

关键词： Multiple suppression U-net Attentional local contrast

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A Comprehensive Review of Protein Language Models

arXiv

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arXiv 2025年

作者： Wang, Lei Li, Xudong Zhang, Han Wang, Jinyi Jiang, Dingkang Xue, Zhidong Wang, Yan School of Software Engineering Huazhong University of Science and Technology China School of Life Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

At the intersection of the rapidly growing biological data landscape and advancements in Natural Language Processing (NLP), protein language models (PLMs) have emerged as a transformative force in modern research. These models have achieved remarkable progress, highlighting the need for timely and comprehensive overviews. However, much of the existing literature focuses narrowly on specific domains, often missing a broader analysis of PLMs. This study provides a systematic review of PLMs from a macro perspective, covering key historical milestones and current mainstream trends. We focus on the models themselves and their evaluation metrics, exploring aspects such as model architectures, positional encoding, scaling laws, and datasets. In the evaluation section, we discuss benchmarks and downstream applications. To further support ongoing research, we introduce relevant mainstream tools. Lastly, we critically examine the key challenges and limitations in this rapidly evolving field. Copyright © 2025, The Authors. All rights reserved.

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. The main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. The study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. The ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R2 score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. This significant R2 score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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Dynamic Behavior Pattern: Mining the Fraudsters in Telecom Network 24

Dynamic Behavior Pattern: Mining the Fraudsters in Telecom N...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Dengshi Zeng, Lu Hu, Ruimin Huang, Zijun Liang, Xiaocong Zang, Yilong School of Artificial Intelligence Jianghan University Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798350319934

In telecom network fraud, the fraud method of the fraudsters varies significantly in different periods. However, the traditional method of telecom fraud is to train the telecom fraud mining model through the user's long-term telephone data. So they can not detect the new fraud method of fraudsters and can not identify those with only have short-term telephone data. To increase the identification precision of telecom fraudsters, the dynamic changes in social structure and behavior pattern features of fraudsters were studied in this paper. Specifically, from the perspective of social structure change, the social structure of fraudsters changes rapidly over time. In contrast, the social structure of normal users is stable over time. From the perspective of user behavior pattern features, we found that the behavior patterns of fraudsters and normal users were very different. The behavior pattern of fraudsters in the different periods also changes significantly. Therefore, a telecom network fraud detection framework based on the social relationship evo-lution model was proposed. We designed the feature extraction method of dynamic telephone social behavior patterns based on time slices. The method combines the features of social structure and behavior patterns of users. And the Telephone Network Fraud Detection(TNFD) model is built using LSTM. We set up a sliding time window to enable the model to identify users with only short-term telephone data. This time window dynamically takes the features of the user's telephone social behavior pattern as the input of the TNFD model. Experiments on real dataset show that the precision of TNFD is 7% higher than that of the latest method. Our research may reference public security organs in telecom fraud cases. © 2022 IEEE.

关键词： Crime

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios

A Method Based on Genetic Programming to Automatically Const...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yan Ma Changsheng Zhang Yan Gao Ying Guo School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated.

关键词： Industries Genetic programming Prediction algorithms Knowledge discovery Boosting Decision trees Stock markets Random forests Fuzzy systems

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Learning Intrinsic Invariance within Intra-Class for Domain Generalization

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Chaoyang Wang, Zengmao Du, Bo Wuhan University School of Computer Science Wuhan430072 China Wuhan University School of Computer Science Artificial Intelligence Institute of Wuhan University National Engineering Research Center for Multimedia Software China Hubei LuoJia Laboratory China

Deep learning methods often struggle with the domain shift problem, leading to poor generalization on out-of-domain (OOD) data. To address the problem, domain generalization (DG) has been proposed to leverage the source domains to train a model that can generalize to OOD data. Existing domain generalization methods primarily focus on learning domain invariance, but they fail to ensure proximity among samples within the same category when domains are aligned for domain-invariant learning. Consequently, their generalization performance remains suboptimal. In this paper, we propose a novel approach to address this issue by iteratively approximating the category domain-invariant distribution from all domains. Our method involves an iterative loop where we initially estimate the domain-invariant distribution for each category by averaging the statistical characteristics across all domains. Then the adversarial perturbation alignment is adopted to keep each sample close to its corresponding category domain-invariant distribution. With the iterative loop, the deep network is optimized for robust domain invariance learning. Extensive experiments demonstrate that our proposed method consistently outperforms state-of-the-art approaches across various scenarios. © 2025 IEEE.

关键词： Adversarial machine learning

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars 24

Towards Planning Package Delivery Paths by Exploiting the Cr...

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作者： Nie, Hejing Wen, Yiping Li, Tiancai Ouyang, Sipeng Tian, Muyang School of Computer Science and Engineering Hunan University of Science and Technology China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China

ISBN: (纸本)9798350319934

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method. © 2022 IEEE.

关键词： Cost effectiveness

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KGCODE-Tab Results for SemTab 2022

KGCODE-Tab Results for SemTab 2022

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2022 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2022

作者： Li, Xinhe Wang, Shuxin Zhou, Wei Zhang, Gongrui Jiang, Chenghuan Hong, Tianyu Wang, Peng School of Computer Science and Engineering Southeast University China College of Software Engineering Southeast University China Chien-Shiung Wu College Southeast University China

This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in three tasks of the content: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). The specific techniques used by KGCODE-Tab will be introduced briefly. The strengths and weaknesses of KGCODE-Tab will also be discussed. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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Effective Image Representation using Double Colour Histogram for Content-Based Image Retrieval

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Informatica (Slovenia) 2021年第7期45卷 97-105页

作者： Martey, Ezekiel Mensah Lei, Hang Li, Xiaoyu Appiah, Obed School of Information & Software Engineering University of Electronic Science and Technology of China China Department of Computer Science and Informatics University of Energy and Natural Resource Ghana

Image representation is critical to the successful realisation of Content-Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik, Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Image representation

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