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共 68658 条记录，以下是141-150 订阅

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A Novel Key Generation Algorithm Utilizing Lattice-Based Cryptography Principles 3rd

A Novel Key Generation Algorithm Utilizing Lattice-Based Cry...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Arul, Rajakumar Sivaselvam, B. Choudhary, Ankush Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India Department of Computer Science and Engineering IIITDM Kancheepuram Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819726707

technology advancement is inevitable, and it has created a revolution in all the sectors creating huge opportunities. Artificial intelligence and machine learning have boosted the advancement of all the sectors making the life easier and comfortable. On the other hand, these advancements highly depend on the connectivity through which all the terminals are communicating. Thus, all the technology developments depend on the communication paradigm, particularly on the wireless medium. Securing the information and infrastructure is crucial, and it demands highly complex problems that cannot be breached with high computation machines like quantum computer. The emergence of quantum computer has made all the existing cryptographic algorithms vulnerable creating a space for advanced algorithms that are safe from quantum computer attacks. Key generation is the foundational process of any cryptographic algorithm, and in this research, we propose a novel key generation mechanism based on lattice using mathematical functions. The proposal is evaluated using theoretical assumptions, and it is found to be safe and secure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Evaluation of Station Route Allocation Scheme of Train Dispatching Command System

Evaluation of Station Route Allocation Scheme of Train Dispa...

引用

international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Shuting Wu, Daohua School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Collaborative Innovation Center of Railway Traffic Safety National Research Center of Railway Safety Assessment Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819639564

The volume of passenger and freight transportation in railways has been steadily increasing year by year. To meet growing demands and improve transportation efficiency, it is desirable to operate as many trains as possible within stations with limited infrastructure capacity. The train dispatching command system is a key equipment in improving transportation efficiency while ensuring safety. This paper proposes using Colored Petri Nets (CPNs) to model the train dispatching command system for evaluating and analyzing the efficiency of route establishment and track utilization within the station yard under different scenarios. The results indicate that the Through Route Priority scheme is suitable for station systems requiring high reliability and low latency. The Adaptive Allocation scheme, by dynamically adjusting route priorities, can enhance overall efficiency. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Freight transportation

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An Array of Silicon Dual Microneedle Electrodes Integrated With mini-LEDs for Electrophysiological Recording And Simultaneous Application of Electrical and Optical Stimuli to the Retina for Artificial Vision 38

An Array of Silicon Dual Microneedle Electrodes Integrated W...

引用

38th IEEE international conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chung, Seung-Han Kim, Chaesung Kim, Yong-Kweon Lee, Seung-Ki Park, Jae-Hyoung Im, Maesoon Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of College of Engineering Korea University School of Electrical Engineering Seoul Korea Republic of Dankook University Department of Electronics and Electrical Engineering Yongin Korea Republic of Seoul Korea Republic of Division of Bio-Medical Science and Technology Seoul Korea Republic of Kyung Hee University KHU-KIST Department of Converging Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798331508890

We report a novel hybrid neural interface device that not only enables simultaneous application of electrical and optical stimuli but also offers electrophysiological recording capability. A 6×6 silicon dual microneedle electrode array was fabricated and each pair consisted of recording and stimulating electrodes. A mini-LED matrix was also integrated underneath the electrode array to deliver optical stimulation. Spiking activities of retinal ganglion cells of an ex-vivo retina were successfully recorded in response to electrical and optical stimuli applied from the fabricated device. © 2025 IEEE.

关键词： Light emitting diodes

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LSTM Economic Forecasting Model Based on Adam Optimization 24

LSTM Economic Forecasting Model Based on Adam Optimization

引用

5th international conference on computer science and Management technology, ICCSMT 2024

作者： Liu, Sixing Lan, Quanxiang School of Mathematics and Computer Science Panzhihua University Sichuan Panzhihua China

ISBN: (纸本)9798400709999

In contemporary economic theory, inflation is regarded as a pivotal factor, with a close correlation to the Consumer Price Index (CPI). Consequently, fluctuations in consumer prices can give rise to inflationary pressures, which in turn can precipitate global economic turbulence. The objective of this study is to develop a consumer price forecasting model for the global economy. The complexity and acceleration of changes in the CPI may result in the ARIMA model exhibiting significant forecasting errors with regard to consumer prices. This paper puts forth the use of a Long Short-Term Memory (LSTM) model for the purpose of forecasting the consumer price index. This approach is proposed as a means of addressing the shortcomings of the ARIMA model in terms of forecasting accuracy. In order to enhance the precision of the model, the Adam optimization algorithm is employed. The model was employed to forecast the consumer price index for 20 countries around the globe for the 13-year period following 2023. The outcomes were then compared and analyzed with those of the ARIMA model. The findings demonstrate that the LSTM model, based on Adam optimization, exhibits enhanced stability and prediction accuracy. The root mean square errors (RMSE) are all below 4, the mean absolute percentage errors (MAPE) are all below 12%, and the R2 value is nearly 0.99. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inflation

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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The Design of a Wildlife Video Data Storage System Based on Hbase 24

The Design of a Wildlife Video Data Storage System Based on ...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Zhou, Xiaojian Zou, Jingnan Zhang, Zhen College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China Sanya racing Communication service Co. LTD Hainan Sanya China

ISBN: (纸本)9798400710094

Wildlife is a valuable natural resource and an important component of biodiversity and natural ecosystems. This design focuses on wildlife video data and proposes an automated deployment tool for the HBase database environment, quickly establishing a data foundation for training models. The design primarily includes using Shell scripts to automate the deployment of Hadoop and HBase clusters, simplifying the deployment process and improving efficiency. It also involves using Python to serialize wildlife videos and store them in the created or selected HBase tables. After performing data cleansing and serialization on the video data, it is stored in the corresponding table. This design introduces automated deployment and storage technology in the context of wildlife conservation, enhancing the efficiency of environment setup, reducing manual intervention errors, and improving reliability. © 2024 Copyright held by the owner/author(s).

关键词： Storage efficiency

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Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models 31

Rethinking Vocabulary Augmentation: Addressing the Challenge...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Lin, Nankai Zeng, Peijian Zheng, Weixiong Jiang, Shengyi Zhou, Dong Yang, Aimin School of Information Science and Technology Guangdong University of Foreign Studies China School of Computer Science and Technology Guangdong University of Technology China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Intelligence Education Lingnan Normal University China

ISBN: (纸本)9798891761964

The performance of multilingual language models (MLLMs) is notably inferior for low-resource languages (LRL) compared to high-resource ones, primarily due to the limited available corpus during the pre-training phase. This inadequacy stems from the under-representation of low-resource language words in the subword vocabularies of MLLMs, leading to their misidentification as unknown or incorrectly concatenated subwords. Previous approaches are based on frequency sorting to select words for augmenting vocabularies. However, these methods overlook the fundamental disparities between model representation distributions and frequency distributions. To address this gap, we introduce a novel Entropy-Consistency Word Selection (ECWS) method, which integrates semantic and frequency metrics for vocabulary augmentation. Our results indicate an improvement in performance, supporting our approach as a viable means to enrich vocabularies inadequately represented in current MLLMs. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Class-Aware Contrastive Learning for Fine-Grained Skeleton-Based Action Recognition 17th

Class-Aware Contrastive Learning for Fine-Grained Skeleton-...

引用

17th Asian conference on computer Vision, ACCV 2024

作者： Bian, Xinyu Chang, Dongliang Yang, Yuqi He, Zhongjiang Liang, Kongming Ma, Zhanyu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China China Telecom Artificial Intelligence Technology Co. Ltd. Beijing China

ISBN: (纸本)9789819608843

Graph convolutional networks have significantly advanced skeleton-based action recognition by efficiently processing non-mesh skeleton sequences. However, existing methods struggle with fine-grained action recognition due to the high similarity of samples across categories. In this paper, we propose a class-aware contrastive learning framework designed to emphasize subtle motion feature differences. Our method enhances discriminative capability for fine-grained action recognition by refining negative sample selection in contrastive learning to prioritize samples from similar categories. Furthermore, our framework incorporates global context from multiple sequences during the graph learning process and utilizes memory banks to store rich instance information, enriching cross-sequence context understanding. Our method achieves remarkable performance compared to state-of-the-art methods on the NTU RGB+D, NW-UCLA, and FineGym datasets. Codes are available at: https://***/PRIS-CV/Class-Aware-Contrastive-Learning-for-A-ction-Recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework 3rd

Deciphering Diabetes: Illuminating Prognostic Insights with ...

引用

3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rony, Mohammad Abu Tareq Islam, Mohammad Shariful Saha, Pritom Johora, Fatama Tuz Ahammad, Mejbah Alam, Shah Md Nazmul Rahman, Md Saifur Nag, Anindya Department of Statistics Noakhali Science and Technology University Noakhali3814 Bangladesh Computer Science and Telecommunication Engineering Noakhali Science and Technology University NoakhaliNoakhali-3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering University of Chittagong Chittagong Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University Tempe United States Strategic Management University UniversityoftheCumberlands Williamsburg United States Computer Science and Engineering Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819783540

Diabetes is a chronic disease that impacts millions of people globally. Early detection and management of diabetes can help mitigate potential complications and enhance patient outcomes. Machine learning (ML) algorithms have become more prevalent for predicting diabetes in recent years. The research paper proposed a machine learning-based approach for predicting diabetes utilizing the Diabetes Health indicator dataset and compared the comprehensive performance of multiple ML algorithms. The study used the LIME (Local Interpretable Model-Agnostic Explanations) package to provide interpretable predictions and acquire insights into the factors contributing to the model’s predictions. Although the ML models were interpretable, they also delivered the highest accuracy 86% demonstrated by the XGBoost classifier in prediction. The LIME produces local explanations for individual predictions, emphasizing the most significant features contributing to the prediction. The study found that, for the XGB model, BMI has a negative influence, while Genhlth positively impacts the outcome. Moreover, Age and Income have a positive impact on the outcome of diabetes. The findings of this research can potentially improve patient outcomes and alleviate the global burden of diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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computer network information security monitoring system 2

Computer network information security monitoring system

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Lin, Hongkai Wuhan Business University Hubei Wuhan430056 China

ISBN: (纸本)9781510689053

Although traditional protection methods can improve the security of the network to a certain extent, its effect is often limited in the face of complex network environment and various attacks. Therefore, it is an important problem to be solved in the field of communication network security to find a more efficient and intelligent communication network link data security protection method. This paper proposes a computer network information security monitoring system combined with knowledge mapping technology, which aims to improve the data security protection effect of computer communication network. this paper designs and implements an information security situation awareness system, which can find and deal with network security threats in time by monitoring and analyzing network data traffic and security event information in real time, and improves the efficiency and level of information security protection. The results show that the information security situational awareness system based on artificial intelligence technology has high detection accuracy and strong real-time response capability, and can effectively deal with various network security threats. It has a good application prospect in data security protection of communication network link. © 2025 SPIE.

关键词： Network security Data communications computer security Defense and security Information security Data modeling Machine learning computer networks Evolutionary algorithms Education and training

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