检索结果-内蒙古大学图书馆

Internet of Brain, Thought, Thinking, and Creation

Chinese Journal of Electronics 2022年第6期31卷 1025-1042页

作者： ZHANG Zhimin YIN Rui NING Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Engineering Research Center for Cyberspace Data Analysis and Applications

Thinking space came into being with the emergence of human civilization. With the emergence and development of cyberspace, the interaction between those two spaces began to take place. In the collision of thinking and technology, new changes have taken place in both thinking space and cyberspace. To this end, this paper divides the current integration and development of thinking space and cyberspace into three stages, namely Internet of brain(IoB), Internet of thought(IoTh), and Internet of thinking(IoTk). At each stage, the contents and technologies to achieve convergence and connection of spaces are discussed. Besides, the Internet of creation(IoC) is proposed to represent the future development of thinking space and cyberspace. Finally, a series of open issues are raised, and they will become thorny factors in the development of the Io C stage.

关键词： thinking space IoTk Internet of thinking IoTh Internet of thought cyberspace development Internet of Things Internet of brain cyber-physical systems IoB human civilization emergence

来源：评论

学校读者我要写书评

暂无评论

Household Waste Classification Algorithms Based on Various Convolutional Neural Networks: A Review of Performance Optimization 21

Household Waste Classification Algorithms Based on Various C...

引用

21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Contamination 5

Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Co...

引用

5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Sanath, G. Deekshitha, S. Shravya, H. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Computer Science and Engineering Bengaluru India Cambridge Institute of Technology Information Science and Engineering Bengaluru India Senior Data Science Trainer Dhaarini Academy Bengaluru India

ISBN: (纸本)9798331528539

Effective monitoring of water quality remains a problem, in real-time contamination detection and prediction. Despite advancements in the sector, many existing approaches continue to rely on labor-intensive laboratory testing, which limits their capacity to give rapid and accurate results. By leveraging advanced ML/DL models, it is possible to build a more robust and adaptable platform for real-time analysis, prediction, and decision support. Approaches based on models such as Random Forest, XGBoost, CNN, RNN, LSTM, and GRU have been good with the accuracy of water quality predictions. They help identify pollution sources and diagnose water quality issues, providing essential information to stakeholders in sustainable water resource management. The ML and DL approaches help improve water quality monitoring and boost future research in environmental sustainability and natural resource management. © 2024 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Visual abstraction of dynamic network via improved multi-class blue noise sampling

引用

Frontiers of computer science 2023年第1期17卷 171-185页

作者： Yanni PENG Xiaoping FAN Rong CHEN Ziyao YU Shi LIU Yunpeng CHEN Ying ZHAO Fangfang ZHOU School of Computer Science and Engineering Central South UniversityChangsha 410083China Institute of Big Data Hunan University of Finance and EconomicsChangsha 410205China Rail Data Research and Application Key Laboratory of Hunan Province Changsha 410083China

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.

关键词： dynamic network visualization massive sequence view multi-class blue noise sampling visual abstraction

来源：评论

学校读者我要写书评

暂无评论

A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval

A Framework of Knowledge Graph-Enhanced Large Language Model...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Yading Song, Dandan Zhou, Changzhi Tian, Yuhang Wang, Hao Yang, Ziyi Zhang, Shuhao School of Computer Science and Technology Beijing Institute of Technology China School of Cyberspace Science and Technology Beijing Institute of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Knowledge graphs (KGs) can provide explainable reasoning for large language models (LLMs), alleviating their hallucination problem. Knowledge graph question answering (KGQA) is a typical benchmark to evaluate the methods enhancing LLMs with KG. Previous methods on KG-enhanced LLM for KGQA either enhance LLMs with KG retrieval in a single round or perform multi-hop KG reasoning in multiple rounds with LLMs. Both of them conduct retrieving and reasoning based solely on the whole original question, without any processing to the question. To tackle this limitation, we propose a framework of KG-enhanced LLM based on question decomposition and atomic retrieval, called KELDaR. We introduce question decomposition tree as the framework for LLM reasoning. This approach extracts the implicit information of reasoning steps within complex questions, serving as a guide to facilitate atomic retrieval on KG targeting the atomic-level simple questions at leaves of the tree. Additionally, we design strategies for atomic retrieval, which extract and retrieve question-relevant KG subgraphs to assist the few-shot LLM in answering atomic-level questions. Experiments on KGQA datasets demonstrate that our framework outperforms existing reasoning-based baselines. And in a low-cost setting without additional training or fine-tuning, our framework achieves competitive or superior results compared to most existing training-based baselines. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Unveiling Early Autism Spectrum Disorder Detection: A Comprehensive Study of Machine Learning and Deep Learning Approaches 6

Unveiling Early Autism Spectrum Disorder Detection: A Compre...

引用

6th IEEE Pune Section International Conference, PuneCon 2023

作者： Tarai, Trilochan Parhi, Manoranjan Mishra, Debahuti Shaw, Kailash University Department of Computer Science and Engineering Odisha Bhubaneswar India University Centre of Data Science Department of Computer Science and Engineering Odisha Bhubaneswar India Department of Aiml Pune India

ISBN: (纸本)9798350324204

According to research by the world health organization (WHO), approximately 0.63% of children are diagnosed with autism spectrum disorder (ASD). ASD commonly emerges during childhood and persists through adolescence and adulthood. Early detection of ASD is crucial as it can lead to effective interventions and improved outcomes. Machine learning and deep learning techniques have gained significant recognition for their efficacy in various fields including biomedicine and medicine. These models offer valuable tools for data interpretation in clinical diagnostics and decision-making processes. Consequently, researchers emphasize the utility of both machine learning and deep learning methods for disease screening. This study aims to leverage both machine learning and deep learning techniques to classify ASD cases using a dataset sourced from the UCI repository. The dataset encompasses pediatric cases. The classification task employed k-nearest neighbors' algorithm (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Among these, CNN outperformed SVM and k-NN, achieving a remarkable accuracy of 99.71%. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Factor Analysis: Uncovering Factor Structure in the Indonesian Teacher Engagement Index (ITEI) Survey Results data 11

Factor Analysis: Uncovering Factor Structure in the Indonesi...

引用

11th International Conference on computer, Control, Informatics and its Applications, IC3INA 2024

作者： Cam, Grace Shirley Sasmoko Indrianti, Yasinta Manalu, Sonya Rapinta Moniaga, Jurike V. Data Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Primary Teacher Education Department Faculty Humanities Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Podomoro University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

This study aims to explore the latent structure of the Indonesian Teacher Engagement Index (ITEI) based on the ITEI survey results of primary school teachers in various regions of Indonesia. Exploration of the ITEI structure is obtained by identifying the main factors hidden in the ITEI survey data with machine learning. The machine learning method used is Exploratory factor analysis (EFA) with Principal Component Analysis (PCA). The ITEI dataset consists of 10,365 records, covering 44 items from 6 ITEI dimensions. The machine learning analysis yielded 4 main factors that became new dimensions, explaining the variation of 29 items. The four new factors found are summarized into the dimensions of Values and Ethics in Education, Personal and Social Development in Education, Character and Leadership in Education, and Social Competence and Justice in Education. This exploration was conducted to show the latent structure of the ITEI that matches the real situation and provide a deeper understanding of the main components that make up the teacher engagement index in Indonesia, so that conclusions can be drawn accurately from the survey results. The implications of these findings can be used to improve understanding and development of more effective education policies in the future. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graphs for Competency-Based Education

Knowledge Graphs for Competency-Based Education

引用

2023 IEEE International Conference on Big data, Bigdata 2023

作者： Linxen, Andrea Endel, Florian Opel, Simone Beecks, Christian FernUniversität in Hagen Chair of Data Science Hagen Germany FernUniversitäat in Hagen Department for Computer Science Hagen Germany

ISBN: (纸本)9798350324457

The project Knowledge Graphs for competency-based Education (KG4CBE) conducts educational data science research to establish competency-based instruction in higher-education programs. In this paper, we propose the design of a knowledge graph to examine the impact of instructional design on student-teacher interaction to facilitate complex learning. For this purpose, the knowledge graph will incorporate the components of an online introductory data science course, including educational materials and learning tasks created with the Four Component Instructional Design (4C/ID) model. Furthermore, the knowledge graph will incorporate data recording the behaviors, interactions and assessments of participating students. To study the competency-based instruction process, the proposed knowledge graph must be scalable to the big data quantities common in educational settings. Therefore, the knowledge graph will be deployed as a tool with accompanying routines to acquire, simulate and load educational data. Furthermore, this tool will provide methods to interact with and visualize the stored information. As future research, we aim to evaluate the proposed knowledge graph in a large-scale educational design research study, to examine the impact of monitoring, forecasting and recommendations in complex learning settings. © 2023 IEEE.

关键词： educational big data educational data science instructional design knowledge graph

来源：评论

学校读者我要写书评

暂无评论

A Background Knowledge Revising and Incorporating Dialogue Model

A Background Knowledge Revising and Incorporating Dialogue M...

引用

作者： Zhao, Xinyan Feng, Xiao Chen, Huanhuan University of Science and Technology of China School of Data Science Hefei230027 China University of Science and Technology of China School of Computer Science and Technology Hefei230027 China

Currently, dialogue systems have attracted increasing research interest. In particular, background knowledge is incorporated to improve the performance of dialogue systems. Existing dialogue systems mostly assume that the background knowledge is correct and comprehensive. However, low-quality background knowledge is common in real-world applications. On the other hand, dialogue datasets with manual labeled background knowledge are often insufficient. To tackle these challenges, this article presents an algorithm to revise low-quality background knowledge, named background knowledge revising transformer (BKR-Transformer). By innovatively formulating the knowledge revising task as a sequence-to-sequence (Seq2Seq) problem, BKR-Transformer generates the revised background knowledge based on the original background knowledge and dialogue history. More importantly, to alleviate the effect of insufficient training data, BKR-Transformer introduces the ideas of parameter sharing and tensor decomposition, which could significantly reduce the number of model parameters. Furthermore, this work presents a background knowledge revising and incorporating dialogue model that combines the background knowledge revision with response selection in a unified model. Empirical analyses on real-world applications demonstrate that the proposed background knowledge revising and incorporating dialogue system (BKRI) could revise most low-quality background knowledge and substantially outperforms previous dialogue models. © 2012 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Time and performance comparison on suicide detection using various feature engineering and machine learning models 15

Time and performance comparison on suicide detection using v...

引用

15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Thongsi, Kittisak Booncherd, Nannaphas Songmuang, Pokpong Thammasat Univesity Department of Data Science and Technology Pathumthani Thailand Thammasat Univesity Department of Computer Science Pathumthani Thailand

ISBN: (纸本)9781665477123

Today more people use social media to express their opinion and their emotions. There are many types of text in social media including text that convey a tendency to be depressed or suicidal. We use sentiment analysis to detect suicidal texts, because if detected, it could save many lives and many families. In this research, we have an objective to explore a method that is both high performance and less time-using. We design experiments that have 30 combinations between five machine learning models with six feature engineering methods. All experiments use accuracy and total time for model generation as metrics. We use deep neural networks with glove embedding as a comparator because this combination performed well in this dataset on Kaggle competition. From the experimental results, we find that the suitable combination that generates fast and has good accuracy is Random Forest with TF-IDF with 0.897 and 145 seconds. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：