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LLaMA-LoRA Neural Prompt engineering:A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains

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Data Intelligence 2024年第2期6卷 375-408页

作者： Songlin Chen Weicheng Wang Xiaoliang Chen Peng lu Zaiyan Yang Yajun Du School of Computer and Software Engineering Xihua Universitychengdu 610039P.R.china Department of Computer Science and Operations Research University of MontrealMontrealQC H3C3J7Canada College of Artificial intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP ***,existing large language models have limitations in comprehending and reasoning in *** paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource *** propose LLaMA-LoRA,a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation(LoRA)of Large Language Models technique for ***-of-Thought(CoT)are crucial for generating intermediate reasoning chains in language models,but their effectiveness can be limited by isolated language *** reasoning resulting from conventional prompts negatively impacts model *** prompts are introduced to encourage reasoning chain generation and accurate answer *** the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning *** LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks,surpassing benchmark performance achieved by related language models such as GPT-3.5,Chat-GLM,and OpenAssistant,delivering accurate,comprehensive,and professional *** availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.

关键词： Chinese natural language processing Neural prompt engineering Large language models Low-Rank adaptation Chain-of-thought

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Medium-Term AQI Prediction in Selected Areas of Bangladesh Based on Bidirectional GRU Network Model

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SN computer science 2024年第5期5卷 1-13页

作者： Mandal, Ashis Kumar Sen, Rikta Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technoloy University Rangpur Dinajpur 5200 Bangladesh Department of Computer Science University of Saskatchewan Saskatoon S7N5C9 SK Canada Graduate School of Software and Information Science Iwate Prefectural University Sugo Iwate Takizawa 020-0693 Japan

Currently, Bangladesh is among the countries with the highest levels of air pollution in the world. Major cities of Bangladesh experience severe air pollution, significantly impacting public health and the environment. The Air Quality Index (AQI) serves as a convenient measure to assess air quality in specific locations. Accurate AQI forecasting is crucial as it allows policymakers to prepare and implement strategies to mitigate air pollution, thereby safeguarding human health and the ecosystem. The primary aim of this paper is to develop an effective model capable of forecasting AQI on a medium-term basis for four selected locations in Bangladesh. To achieve this, we propose a Bidirectional Gated Recurrent Unit (BiGRU)-based model to forecast the AQI for each location up to seven days in advance (i.e., medium-term AQI prediction). We collected daily AQI data from four distinct locations in Bangladesh from March 1, 2014, to December 31, 2020. To validate the performance of the model, we used various statistical measures, including Root Mean Square Error (RMSE), Normalized Root Mean Square Error (nRMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²) score. We also compared the performance of the proposed model with three related models: Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Convolutional Neural Network (CNN). Simulation results demonstrate that the BiGRU model outperforms other approaches, showcasing an RMSE ranging from 27.42 to 43.27 for medium-term AQI prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air pollution Air Quality Index (AQI) Deep learning Gated Recurrent Unit (GRU) Time series analysis

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An Improved Bi-LSTM EEG Emotion Recognition Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 623-639页

作者： Ma, Shuai Cui, Jianfeng Chen, Chin-Ling Xiao, Weidong Liu, Lijuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

EEG emotion signal feature extraction is computationally intensive and the classification accuracy of the model is not high. Therefore, it can seriously affect the overall performance of classification algorithms. In order to better apply deep learning methods to EEG emotion recognition classification. To address the above problems, this paper uses the Fast Fourier Transformation algorithm to extract richer and more complete multi-channel feature information in feature extraction. At the same time, for the problem of low classification accuracy, an improved Bi-LSTM classification model is proposed. Finally, the model proposed in this paper is verified by 32-channel EEG signal data. The experimental results show that the classification method proposed improves the accuracy of Arousal to 93.35% and Valence to 93.09%. Compared with other models, the improved Bi-LSTM model has an excellent performance in EEG emotion recognition. Meanwhile, this paper explores the brain emotion mechanism by the accuracy of the improved model for different brain region channels, showing that the brain regions with the highest emotional relevance are the parietal and frontal lobes in the brain. © 2022 Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Emotion Recognition

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Rumor Detection Based on Macro-Micro Public Opinion Modeling and Sentiment Scores

Rumor Detection Based on Macro-Micro Public Opinion Modeling...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Li Hu, Jingyang Fu, Shihao Xing, Xin Zhou, Wei School of Big Data and Software Engineering Chongqing University Chongqing400030 China School of Computer Science and Mathematics Anyang University Henan Province 455000 China Chongqing Academy of Metrology and Quality Inspection China

ISBN: (纸本)9781665410205

Existing rumor detection methods have not ade-quately considered which features should be addressed at the macroscopic or micro levels. Furthermore, the fundamental manifestation of news dissemination-public opinion-has received insufficient attention. We propose a macro-micro based public opinion (MMPO) modeling method to address these gaps. This approach enables a better understanding of news dissemination on social media platforms. Firstly, we incorporate post sentiment as a crucial feature. Secondly, we examine how the propagation at the micro-level influences sub-posts. Next, we investigate the temporal variations in public opinion from a macro perspective. Lastly, we integrate the acquired general propagation information with textual data to obtain more distinct representations of news propagation. By leveraging the macro perspective, the proposed model effectively captures the fine-grained characteristics of news propagation and helps smooth out the noise in user comments. The experimental evaluation showcases the superiority of our model in rumor detection compared to existing methods. © 2024 IEEE.

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Research on Pedestrian Detection Algorithm Based on Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第4期50卷 1446页

作者： Wang, Ying Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Pedestrian detection model based on deep learning has been widely used in various fields. However, its capabilities are limited when operating in complex environments due to high false alarm rates and low detection accuracy. To address these limitations, this paper presents an improved model called YOLOv5s-FCC. This enhanced model retains the distinctive features of the original model while improving several aspects such as the loss function, feature extraction, feature output, and anchor frames. The model's performance is evaluated on the CrowdHuman and WiderPerson datasets, which consist of high pedestrian density and significant obstacles, making detection challenging. To overcome these challenges, the datasets are first re-clustered using the K-Means++ clustering technique to obtain optimal anchor frames. Additionally, the Focal-EIOU loss function is employed to accelerate convergence speed and improve regression results. A coordinate convolution layer is fused before the output of different-scale features to provide more informative content. Lastly, a new pyramid pool layer of CSPCSPPF space is used to extract feature information, which reduces the repetition of gradient information and realizes more accurate detection. Comparative experiments using testing sets from both datasets demonstrate an improvement in Mean Average Precision of 2.2% and 1.8%, respectively, along with a reduction in the miss rate of 4.6% and 3.7%. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Object detection

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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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Exploring the Power of Entangled Data in Quantum Machine Learning

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Wuhan University Journal of Natural sciences 2024年第3期29卷 193-194页

作者： WANG Xinbiao DU Yuxuan TU Zhuozhuo LUO Yong YUAN Xiao TAO Dacheng Institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia Software Wuhan UniversityWuhan 430072HubeiChina JD Explore Academy Beijing 101111China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Computer Science Faculty of EngineeringUniversity of SydneyNSW 2008Australia Center on Frontiers of Computing Studies Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China

Quantum entanglement is a key resource for achieving superiority of quantum ***,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning *** successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML *** entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum ***,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.

关键词： quantum specified integrate

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A Hybrid Approach for Developer Recommendation Based on Social Network 2

A Hybrid Approach for Developer Recommendation Based on Soci...

引用

2nd International Conference on Electronic Information Technology, EIT 2023

作者： Liang, Huilin Wei, Qingjie School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

Bug fixing requires collaboration among developers, however, most of current studies recommend developers based on the textual content and similarity of bug reports, ignoring the implicit social relationships generated by developers when they collaborate on fixing tasks. This paper proposes a hybrid approach for bug developer recommendation to address this problem. The method constructs a developer social network, extracts developer social relationships implicitly modeled in bug report history records and comment, and uses Graph Convolutional Neural Network (GCN) to learn the closeness between developers. In addition, Convolutional Neural Networks (CNN) are used to learn the bug report text content, suitable developers are recommended by fusing the collaborative relationships between developers and the textual content of bug reports. According to experimental findings, the method's accuracy is significantly better than that of other methods. © 2023 SPIE.

关键词： Convolutional neural networks

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Cable: Design of a Transmission-Type Snake Rescue Robot 4

Cable: Design of a Transmission-Type Snake Rescue Robot

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Zhang, Kefeng Wang, Jiawei Zhang, Hao Jiang, Jichao Dou, Xinrui Zhao, Wenyi Tian Gong University School Of Electronics&Information Engineering China Tian Gong University School Of Aeronautics And Astronautics China Tian Gong University School of Computer Science and Technology China Tian Gong University School of Software China

ISBN: (纸本)9798350364798

The advancement of robotics holds great promise for enhancing rescue missions. Existing rescue robots often suffer from poor collaboration. In response to this challenge, we present the design of Cable, a snake rescue robot with docking capabilities. The docking mechanism enhances the robot's redundancy, enabling it to perform more intricate movements and provide essential services, such as sound, light, and power, in confined spaces for the rescued individuals. One notable feature is the integration of a visual system that collaborates seamlessly with rescue personnel, facilitating comprehensive surveys of the rescue area. This visual feedback not only aids in environmental assessment but also empowers the robot to autonomously select optimal movement modes. This paper begins by introducing the system architecture design and visual algorithms, followed by an analysis of docking strategies and kinematic processes. Finally, the feasibility of the robot design is validated through experiments. The results demonstrate the robot's collaborative advantages in complex rescue environments. © 2024 IEEE.

关键词： Machine design

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Stress detection system for social media users 7

Stress detection system for social media users

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Febriansyah, Mochamad Rizky Nicholas Yunanda, Rezki Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency - inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM. © 2022 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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