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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Guo, Jijin Guo, Feng Zhang, Han Zhao, Qian Liu, Yuankai School of Computer Science and Engineering Linyi University Linyi China School of Information Science and Engineering Linyi University Linyi China

ISBN: (纸本)9798400718199

The complexity and importance of the electricity market has driven the continuous development of electricity price forecasting research. This study uses measured data from the Polish electricity market, and after a thorough exploration of the electricity price data, it is found that the data exhibit significant autocorrelation, seasonality, intra-weekly trends and intra-day trends. In order to better enable the model to learn these properties, improvement work was done in terms of feature engineering and model structure. First, multimodal training features were constructed by dealing with lagged values of electricity prices, energy demand and wind energy generation values. Second,Given the more time-series nature of power data, by improving the traditional recurrent neural network (RNN) model so that it consists of multiple RNN units, the LSTM and GRU models are trained simultaneously for performance comparison, and the results show that the optimized RNN model performs well in all evaluation metrics, demonstrates the best prediction accuracy and interpretation ability, and further improves the prediction performance of electricity price data. It provides an effective reference for the stable operation of the electricity market, the optimal utilization of resources, the planning of electricity consumption by users and economic decision-making [1]. © 2024 ACM.

关键词： Power markets

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A Vulnerability Detection Method for Smart Contracts Based on Dynamic Meta Optimizer

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IEEE Internet of Things Journal 2025年第13期12卷 24904-24915页

作者： He, Daojing Gong, Wei Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

With the increasingly complex blockchain technology environment and emerging security threats, the detection and prevention of vulnerabilities in blockchain smart contracts have become crucial for ensuring the healthy development of blockchain technology and avoiding substantial economic losses. Recently developed vulnerability detection methods for smart contracts suffer from the drawbacks of insufficient feature extraction and inadequate multitask detection. This paper proposes a multifaceted learning model, DLR, based on a dual-loop architecture of meta learning to address these issues by adopting model-agnostic meta-learning techniques. This model employs a syntax analyzer for targeted feature extraction, with graph information used as a supporting tool during the initial stage of feature engineering. Combined with an improved optimizer algorithm in the inner loop, the model can effectively learn and adjust based on the specific requirements of each task. In the outer loop, the model achieves efficient learning rate adjustment for multi-task learning by integrating the adaptive learning rate of the Adam optimizer with a global adjustment using simulated annealing strategy, thereby enhancing performance across diverse tasks. Experimental results demonstrate significant improvements in detection accuracy over state-of-the-art methods for three types of vulnerabilities, with our method achieving detection accuracies of 94.40%, 93.36%, and 94.33% for reentrancy, timestamp dependence, and integer overflow vulnerabilities, respectively. © 2014 IEEE.

关键词： Contrastive Learning

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Core-Competitiveness in Partially Observable Networked Market 24

Core-Competitiveness in Partially Observable Networked Marke...

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33rd ACM Web Conference, WWW 2024

作者： Li, Bin Hao, Dong School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798400701719

In auction theory, a core is a stable outcome where no subgroup of participants can achieve better results for themselves. Core-competitive auctions aim to generate revenue that is achievable in a core. They are particularly important because they not only generate optimized revenue for the seller, but also provide an efficient and stable environment for participants. We generalize the design of core-competitive auctions to encompass partially observable networked markets (PONM). Unlike traditional auctions, which often deal with scenarios of limited trading activity, our approach to core-competitive auctions for PONM captures the nature of real-world transaction markets, which is a large linking world for the economic entities and commodities circulate among the entities in the market. Our generalizing the auction market to PONM can much improve the liquidity of the auction, and is especially meaningful for the web economics. Specifically, we quantify the upper and lower bounds of the minimum core revenue in PONM, and further prove that there does not exist any truthful auction for PONM which is efficient and core-competitive. Governed by this impossible result, we identify the criteria that the allocation rule for PONM should meet. Based on these criteria, we propose a new class of auction mechanisms for PONM that is individually rational, incentive-compatible, and core-competitive. © 2024 ACM.

关键词： Competition

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Hybrid Water Quality Prediction with Multimodal Low-rank Fusion and Localized Attention

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IEEE Internet of Things Journal 2025年第12期12卷 21158-21169页

作者： Bi, Jing Li, Yibo Yuan, Haitao Wang, Mengyuan Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Energy and Power Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

Water quality prediction methods forecast the short-or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality's nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates ConvNeXt V2, Multimodal bottleneck transformer, Low-rank multimodal fusion, ITransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy. © 2025 IEEE.

关键词： Rain

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DeepFake Videos Detection Using Crowd Computing

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4547-4564页

作者： Salini, Yalamanchili HariKiran, J. School of Computer Science and Engineering VIT-AP University Amaravati 522237 India

In recent times, the widespread dissemination of misinformation through realistic artificially generated images and deep faked videos has become a common issue, particularly prevalent on social media platforms targeting popular personalities. Detecting such deep fake content has become increasingly challenging as the technology behind it continues to advance, making it difficult for existing algorithms to avoid false positives. This paper focuses on deepfake detection and makes two key contributions: We propose automated deep fake detection as a supplement to human cognitive abilities, addressing the lack of studies quantifying human capabilities in identifying faked videos from multi-dimensional perspectives. To fill this gap, we conducted experiments with individuals from diverse age groups and professions, comparing their evaluations against automated approaches using state-of-the-art deep fakes. Employing various machine learning algorithms, we predict an individual's ability to correctly classify a video as genuine or fake. By combining human cognitive abilities with video statistical metadata, we achieved an impressive accuracy of 98.3% in predicting participant decisions on video authenticity. To our knowledge, this marks the first attempt to blend these two factors in the context of deepfake detection. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Deep fake detection Deep fake generation Deep fakes GANs

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Beyond boundaries: Unifying classification and segmentation in wildfire detection systems

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Multimedia Tools and Applications 2024年 1-33页

作者： Singh, Swapnil Vazirani, Vidhi Singhania, Sanvika Suroth, Vaishnavi Singh Soni, Vaibhav Biwalkar, Ameyaa Krishnan, Deepa Computer Science Department Virginia Tech BlacksburgVA United States Computer Science Department Northwestern University EvanstonIL United States Computer Engineering Department Mukesh Patel School of Technology Management & ampEngineering NMIMS University Maharashtra Mumbai India

Forest fires, a dangerous natural phenomenon, cause large-scale destruction in forests and nearby communities. In this paper, we leverage the capabilities of classification and fast prediction of machine learning and deep learning to detect forest fires by proposing a dual-stage approach of identification and segmentation of burnt/burning regions. The initial identification step uses simple classification by harnessing custom convolutional neural network and AlexNet to detect satellite images displaying signs of burning, followed by the next step of segmentation, which employs UNet and SegNet architectures to precisely localize the burnt/burning areas. We chose UNet considering its ability of efficient feature preservation, and SegNet prominent use for precise boundary delineation. For training our models, we have curated a dataset using Google Earth Engine, querying latitude, longitude, and dates. Image processing techniques facilitated the extraction of segmentation masks, bounding box images, and segmented images. The post-training results portray that CNN outperforms AlexNet with an 88.19% accuracy for classification, while in segmentation, UNet excelled over SegNet with a dice score of 0.6869. If deployed, this study stands to benefit forest officials, local communities, and biodiversity. Moreover, it aids afforestation teams in identifying burnt zones for targeted reforestation efforts, ensuring a more effective response to forest fires. © The Author(s) 2024.

关键词： Landsat

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Hybridization of covalent organic frameworks and photosensitive metal-organic rings:A new strategy for constructing supramolecular Z-scheme heterostructures for ultrahigh photocatalytic hydrogen evolution

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Aggregate 2024年第2期5卷 239-249页

作者： Xin-Ao Li Zi-Zhan Liang Ye-Cheng Zhou Jian-Feng Huang Xiao-Lin Wang Li-Min Xiao Jun-Min Liu The Key Laboratory of Low-Carbon Chemistry&Energy Conservation of Guangdong Province School of Materials Science and EngineeringSun Yat-sen UniversityGuangzhouChina School of Computer Science and Engineering Beihang UniversityBeijingChina

The rational design of Z-scheme heterojunction photosystems based on covalent organic frameworks(COFs)is a promising strategy for harnessing solar energy for hydrogen ***,a direct Z-scheme single-atom photocatalyst based on COF and metal-organic ring has been constructed through the supramolecular interactions of coral-like COF(S-COF)and photosensitized Pd2L2 type metal-organic ring(MAC-FA1).The MAC-FA1/S-COF heterojunction exhibits good light absorption,efficient charge separation and transfer,slow electron-hole recombination,and highly dispersed Pd active sites,enabling an efficient and stable H_(2) evolution *** optimized 4%MAC-FA1/S-COF achieves an H_(2) evolution rate of 100 mmol g^(−1)h^(−1)within 5 h and obtains a total accumulated turn-over number relative to Pd(TON_(Pd))of 437,685 within 20 h,far superior to S-COF,MAC-FA1,M-5/S-COF,Pd/S-COF,and M-5/Pd/S-COF,which is one of the highest records among COF-based photocatalysts for solar-driven H_(2)*** is the first work to incorporate photosensitized metal-organic rings/cages into porous crystalline COFs to form a supramolecular Z-scheme heterojunction,which has significant potential as a high-performance photocatalyst for solar-driven H_(2)production.

关键词： covalent organic framework metal-organic ring single-atom catalyst visible-light-driven H2 production Z-scheme heterojunction

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A METHOD FOR IMPROVING THE ACCURACY OF SFM 3D MEASUREMENTS BY RANDOM PATTERN PROJECTION

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AIJ Journal of Technology and Design 2023年第71期29卷 533-537页

作者： Sugano, Miki Kakizaki, Ken'ichi Tominaga, Yasumasa Araki, Shunsuke Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

SfM can reconstruct the 3D shape of an object with high accuracy if there are many feature points on the surface to be measured. However, in the field of architecture, there is a problem that building surfaces are beautifully preserved and the shape cannot be reconstructed because of the small number of feature points. In this study, we proposed a method to improve the measurement accuracy of SfM by projecting a random pattern on a wall surface and distributing a large number of feature points. Experiments confirmed that the measurement accuracy was as good as that of a laser scanner. © 2023 Architectural Institute of Japan. All rights reserved.

关键词： 3D measurement Measurement accuracy Pattern projection Point cloud Random pattern SfM

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An In-Depth Review of Machine Learning, Blockchain, and Deep Learning Models for Intelligent Security and Resilience Enhancement in Smart Cities

An In-Depth Review of Machine Learning, Blockchain, and Deep...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Lanjewar, Umesh Khekare, Ganesh Wahi, Amitabh Bhagwant University Department of Computer Science & Engineering Ajmer India School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331517953

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract) © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

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Chinese Journal of Electronics 2022年第4期31卷 612-625页

作者： ZHOU Wei MA Yanfang PAN Haiyu School of Computer Science and Technology Huaibei Normal University School of Computer Science and Information Engineering Changzhou Institute of Technology School of Computer Science and Information Security Guilin University of Electronic Technology

Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

关键词： Component-based software Trustworthiness Weight User feedback Measurement

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