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共 13410 条记录，以下是191-200 订阅

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Edge Energy Orchestration 20th

Edge Energy Orchestration

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20th international conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Kumar, Vijay Valizadeh, Nima Petri, Ioan Rana, Omer Nag, Ambarish Zisman, Sagi Tripp, Charles Paul, Shuva School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Engineering Cardiff University Cardiff United Kingdom Colorado United States Colorado United States

ISBN: (纸本)9783031812255

Edge computing devices have increased in number and capability over recent years. The ability to process data and execute machine learning in proximity to data generation and collection sources provides several advantages over using cloud- based data centers. We describe an orchestration mechanism that enables edge devices to make more effective use of energy resources in their proximity – a technique we refer to as "edge energy orchestration". A software "orchestrator" can take account of renewable generation to alter how task execution on edge devices is carried out. An application scenario is used to illustrate the use of the orchestrator in practice, followed by a discussion about how this approach can be generalized to a broader set of applications © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clean energy

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FedZipper: A Layer-wise Quantization Compression Framework for Federated learning with Statistical Heterogeneity 20th

FedZipper: A Layer-wise Quantization Compression Framework f...

引用

20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Du, Haizhou Peng, Yan Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9789819628636

Quantization is a common compression scheme for reducing traffic in federated learning that converts data from floating-point numbers to a low-precision representation. Existing quantization approaches typically apply fixed degrees of model parameter quantization on non-IID data at a coarse-grained level. It hurts the whole accuracy of the training model and fails to improve the efficiency of the federated learning process. In the paper, we propose a layer-wise quantization compression framework for federated learning with statistical heterogeneity, namely FedZipper. FedZipper can identify and quantify the critical layers of the learning model on each client. Moreover, FedZipper adopts an adaptive mechanism to aggregate different transmitted layers from different clients. Our extensive experiments show up to 38.8% improvement in the communication data volume for the whole training process while less decline in model accuracy. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning

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Span-Based Chinese Few-Shot NER with Contrastive and Prompt learning 13th

Span-Based Chinese Few-Shot NER with Contrastive and Prompt ...

引用

13th international conference on Natural Language Processing and Chinese computing

作者： Ye, Feiyang Lai, Peichao Yang, Sanhe Zhang, Zhengfeng Wang, Yilei Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

For Chinese Named Entity Recognition (NER) tasks, achieving better performance with fewer training samples remains a challenge. Previous works primarily focus on enhancing model performance in NER by incorporating additional knowledge to construct entity features. These approaches neglect the semantic information of entity labels and the information of entity boundaries. Moreover, conventional methods typically treat NER as a sequence labeling task, which makes them inadequate for addressing the issue of nested entities. We propose a new span-based approach by using contrastive learning and prompt learning to address these problems. By pulling similar entities closer together, pushing dissimilar entities further apart, and leveraging entity label information, we improve model performance in few-shot scenarios effectively. Experimental results demonstrate that our method achieves significant performance improvements on a sampled Chinese nested medical dataset and several other flattened datasets, providing a new insight into addressing challenges in few-shot NER tasks.

关键词： Named Entity Recognition Contrastive learning Few-shot learning

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Modeling Comparative Logical Relation with Contrastive learning for Text Generation 13th

Modeling Comparative Logical Relation with Contrastive Learn...

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13th international conference on Natural Language Processing and Chinese computing

作者： Dan, Yuhao Tian, Junfeng Zhou, Jie Yan, Ming Zhang, Ji Chen, Qin He, Liang East China Normal Univ Lab Artificial Intelligence Educ Shanghai Peoples R China ECNU Shanghai Inst Artificial Intelligence Educ Shanghai Peoples R China ECNU Sch Comp Sci & Tecluiol Shanghai Peoples R China Xiaohongshu Inc Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819794393;9789819794409

data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works mainly focus on describing the superficial associative relations among entities, while ignoring the deep comparative logical relations, such as A is better than B in a certain aspect with a corresponding opinion, which is quite common in our daily life. In this paper, we introduce a new D2T task named comparative logical relation generation (CLRG). Additionally, we propose a Comparative Logic (CoLo) based text generation method, which generates texts following specific comparative logical relations with contrastive learning. Specifically, we first construct various positive and negative samples by fine-grained perturbations in entities, aspects and opinions. Then, we perform contrastive learning in the encoder layer to have a better understanding of the comparative logical relations, and integrate it in the decoder layer to guide the model to correctly generate the relations. Noting the data scarcity problem, we construct a Chinese Comparative Logical Relation dataset (CLRD), which is a high-quality human-annotated dataset and challenging for text generation with descriptions of multiple entities and annotations on their comparative logical relations. Extensive experiments show that our method achieves impressive performance in both automatic and human evaluations.

关键词： Natural language processing data-to-text generation Contrastive learning dataset construction

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Image Based Cattle Monitoring System 4th

Image Based Cattle Monitoring System

引用

4th international conference on Smart Multimedia, ICSM 2024

作者： Zala, Bhagyashree Gandhi, Ritik Yang, Fan Valente, Tiago Cheng, Irene Department of Computing Science University of Alberta Edmonton Canada Department of Agricultural Food and Nutritional Sciences University of Alberta Edmonton Canada

ISBN: (纸本)9783031824746

Cattle detection and stock monitoring in open fields continue to pose a challenge in smart agriculture due to the low efficiency of acquiring the data and expensive human labour. With the advancement of computer vision technology, it is now possible to apply multi-modal data analysis to address the issues and implement the image based cattle monitoring applications. In this study, we take advantage of baseline object detection methods and transfer learning to detect a large herd in open fields. We also built a cattle dataset with annotated thermal images captured from a drone flying about 30 m above the ground. We use machine learning regression models to estimate the live weight of the cattle based on several manual measurements as well as image based segmentation instances. Meanwhile, the cattle detection and weight estimation have been integrated into the image based cattle monitoring system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Risk Analysis of Labor Contracts Based on BCBR Model 24

Risk Analysis of Labor Contracts Based on BCBR Model

引用

8th international conference on computing and data Analysis, ICCDA 2024

作者： Chen, Fen Wang, Baosong Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400710445

In recent years, with the rise of artificial intelligence technologies such as deep learning, natural language processing (NLP) has become a hot topic. However, research on contractual texts, particularly employment contracts, remains scarce due to challenges such as the limited availability of contract data. This paper pioneers a study on the classification and risk assessment of employment contract texts. Considering the unique characteristics of employment contract texts, we propose a fusion model that leverages the strengths of the BERT, CNN, and LSTM models, termed the BCBR model. This model integrates BERT’s self-attention mechanism to capture semantic relationships between words, CNN’s ability to extract local spatial features, and LSTM’s capacity to learn time-series dependencies. Initially, the self-attention mechanism in BERT is utilized to grasp the semantic relations among words. Subsequently, CNN is employed to extract textual features, while LSTM captures long-term dependencies. The classification results from BERT-CNN and BERT-LSTM are then linearly weighted. For comparative analysis, we benchmark the BCBR model against several others, including BERT-CNN, BERT-LSTM, TEXTCNN, TEXTRNN, and BERT. The results demonstrate that the BCBR model achieves the highest F1 scores, which is the harmonic mean of precision and recall, at 0.9168, 0.9143, and 0.9136, respectively. Based on these findings, we conduct a risk analysis of the contracts and present our conclusions along with future prospects. © 2024 Copyright held by the owner/author(s).

关键词： Risk analysis

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machine learning-Powered Intrusion Detection for Network Security 12th

Machine Learning-Powered Intrusion Detection for Network Sec...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Nandish, H.D. Rahul, R. Dasari, Hari Charan Kumaran, U. Umah, B. Nagarajan, Senthil Murugan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg

ISBN: (纸本)9789819788354

In today's digital age, network security is paramount. This paper introduces a robust web application designed to swiftly identify network attacks and real-time anomalies, enabling users to protect their networks effectively. By leveraging cutting-edge technologies and machine learning, our system provides insights into network accuracy, F1-score, and precision, allowing users to gauge their network's safety. Our intrusion detection system classifies attacks into four categories: Denial of Service (DoS), Probe, Remote-to-User (R2L), and User-to-Root (U2R). Positioned strategically within the network, it monitors data traffic from all connected devices and promptly responds to suspicious activity, ensuring network security. Users receive instant alerts to take immediate action. Key technologies include data preprocessing (LabelEncoder and MinMaxScaler), user-friendly web interface (MERN stack), and enhanced security with Google Earth 2.0 for authentication and MongoDB for secure data storage. The Python-based backend operates on the NSL-KDD dataset, facilitating effective intrusion detection evaluation. To enhance accuracy, our system employs four machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Random Forest, and Long Short-Term Memory (LSTM), offering comprehensive protection against network threats. In summary, this paper presents a potent network intrusion detection system for real-time threat identification and security assessment. By combining advanced technologies and machine learning, it ensures accuracy and reliability, serving as a valuable tool for network administrators and security professionals in safeguarding digital networks from evolving threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Application with Digital Currencies Trading Using machine learning 18th

Application with Digital Currencies Trading Using Machine L...

引用

18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Zheng, Tengjiao Ning, Li Zhao, Yanming Yiu, Siu Ming The University of Hong Kong Pok Fu Lam Hong Kong University of Chinese Academy of Science ShenZhen China

ISBN: (纸本)9783031714634

At present, with the rapid development of blockchain technology and digital currencies, digital assets have gradually become a crucial component of investment portfolios, with Bitcoin standing out as the most prominent. Our aim is to reduce the investment risk for stakeholders in the highly volatile Bitcoin market by expanding predictions beyond Bitcoin and into other digital currencies. Through the application of machine learning models tailored for time series data and deep learning models, we have discovered methods for predicting Bitcoin prices with a high degree of accuracy. This article centers around predicting the closing price of Bitcoin using attributes from various categories of digital currencies, including Bitcoin itself, Ethereum and stablecoins. We employ multiple evaluation techniques to assess the performance of our models. Our findings indicate that using Bitcoin’s attributes or stablecoins pegged to Bitcoin can achieve accuracy rates of up to 98%, with Ethereum closely following, boasting accuracy rates exceeding 90%. In the practical application domain, we innovatively employed a stablecoin model pegged to Bitcoin for backtesting Bitcoin returns. This model not only filled a significant gap in this field but also surpassed models trained solely on Bitcoin itself, yielding returns that exceeded 40 times. This approach underscores the potential of diversifying predictive analytics in digital asset investments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Oracle Bone Inscriptions Recognition Based on Transfer learning and Improved ResNet50 3rd

Oracle Bone Inscriptions Recognition Based on Transfer Learn...

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3rd international conference on machine learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Li, Zengming He, Xuansen Lu, Haihui Mo, Jianming Wen, Guanjin Huang, Ziyun Zhang, Wenjian Guangzhou College of Commerce Guangzhou511363 China College of Information Science and Engineering Hunan University Changsha410082 China

ISBN: (纸本)9789819624676

Oracle Bone Inscriptions (OBI) are the earliest Chinese characters found so far, they are the treasures of Chinese traditional culture and have high research value. However, the machine recognition of OBI is a difficult problem. To address this challenge, this paper presents a new method of OBI recognition based on transfer learning and improved ResNet50 network. Firstly, the deep convolutional neural network structure of ResNet50 is used to obtain rich feature representations by learning on large general dataset, and then it is transferred to the recognition of OBI, so as to effectively extract the key features of OBI. On this basis, the parameters of ResNet50 model are fine-tuned on a small data set of a specific OBI topology to improve the recognition rate of OBI. The experiment utilized rubbed images of the original OBI as the dataset. The findings demonstrated that the proposed method achieved superior recognition accuracy, reaching 95.641% compared to alternative methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Domain Adaptation for Chinese Offensive Language Detection 13th

Domain Adaptation for Chinese Offensive Language Detection

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13th international conference on Natural Language Processing and Chinese computing

作者： Ying, Hao Ou, Qiongrong Fan, Chengjun Mei, Lin Zhang, Shuyu Xu, Xu Fudan Univ Sch Informat Sci & Technol Inst Elect Light Sources State Key Lab Photovolta Sci & Technol Shanghai 200433 Peoples R China DTH Serv China Adm Ctr Beijing 100866 Peoples R China Donghai Lab Zhoushan 316021 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Accurate detection of offensive language is crucial for maintaining harmony on social media platforms. However, the lack of well-annotated datasets makes it challenging to classify semantically Chinese offensive language using deep learning. To this end, we have studied how to transfer rich corpus knowledge from other languages to Chinese, exploring the impact of data from different cultural backgrounds on the detection of offensive language in Chinese under various conditions. We found that when enough Chinese corpus and labeling information are available, domain adaptation can prevent negative transfers caused by cultural differences while utilizing rich corpus knowledge to enhance detection performance. In a zero-shot learning environment, domain adaptation allows the effective transfer of corpus knowledge from specific languages to Chinese language detection tasks based on the model's linguistic background, thereby enhancing the performance of monolingual models in cross-lingual tasks. Our research indicates that domain adaptation plays a positive role in cross-cultural transfer detection.

关键词： Offensive Language Detection Transfer learning Domain Adaptation

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