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10th International Conference on Information Technology and Quantitative Management, ITQM 2023

作者： Yao, Tongsheng Shi, Yong Chen, Ming School of Economics and Management University of Chinese Academy Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Management and Economics Beijing Institute of Technology Beijing100081 China

Research in the carbon field has experienced rapid growth in recent years and has attracted widespread academic attention. This paper conducts a comprehensive analysis of the carbon field using bibliometric methods. By collecting a large number of academic publications related to carbon and utilizing bibliometric indicators, We performed statistical and descriptive analyses on the development trends, research areas, research institutions, and authors in the field of carbon economy literature. Additionally, we employed co-citation analysis and keyword co-occurrence analysis to identify the current research focuses in the carbon field and explore the temporal changes in research hotspots. Ultimately, we analyzed the future development trends of the carbon field, providing valuable insights for scholars to conduct further research in this field. These research findings are of great significance in promoting the development and application of the carbon economy field. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative Management.

关键词： Carbon

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Improved Method for Intersection Multi-Events Traffic Signal Induction Control in Intelligent Connected New Energy Vehicles 1

Improved Method for Intersection Multi-Events Traffic Signal...

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1st International Conference on Intelligent Transportation and Smart Cities, ICITSC 2024

作者： Wang, Ruihan Liang, Zijun Li, Yuqi Zhan, Xuejuan Wang, Ruipeng Deng, Ran Niu, Yiran Kumari, Saru School of Urban Construction and Transportation Hefei University Hefei230601 China Anhui Provincial Key Laboratory of Urban Rail Transit Safety and Emergency Management Hefei China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory Hefei China Department of Mathematics Ch. Charan Singh University Meerut India

ISBN: (纸本)9781643685205

Traditional fixed signal timing often leads to issues such as green light empty and residual queue at intersections. Traffic signal induction control can automatically adjust signal timing based on vehicle arrivals, but it still struggles to handle immediate control of special traffic events like long queue, exit overflow and intersection deadlock. Furthermore, it does not account for the impact of intelligent connected new energy vehicles (ICNEVs). So we propose an improved method for intersection multi-events traffic signal induction control in ICNEVs. The method involves deploying various types of detectors at intersections to identify events like green light empty, long queue, exit overflow, and intersection deadlock. Establish the induction control logic rules corresponding to different events, and consider the permeability optimization control parameters of ICNEVs at intersection. Finally, the proposed method is validated using real intersection data through the VISSIM traffic simulation software. The results demonstrate that the method effectively reduces the average queue length and delay at intersections. It also reveals that the higher the permeability of ICNEVs, the better the performance of intersection induction control. © 2024 The Authors.

关键词： Traffic signals

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Preparation of Uniform SiO2Insulating Layer on the Inner Wall of TSV by Thermal Oxidation 29

Preparation of Uniform SiO2Insulating Layer on the Inner Wal...

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29th International Symposium on Semiconductor Manufacturing, ISSM 2022

作者： Fengjie, Guo Yang, Ran Jing Shuo, Wang Kui, Ma Shun, Yang Fa Guizhou Provincial Key Laboratory of Micro and Nano Electronics and Software Technology College of Big Data and Information Engineering Guizhou University Guizhou Province Guiyang City China

ISBN: (纸本)9781665471336

SiO2 insulating layer is an indispensable part of a TSV. In the current process, the SiO2 insulating layer is commonly deposited on the inner wall of the TSV based on deep trench sputtering method. The thickness at different position (neck, middle, bottom) of the SiO2 insulating layer, deposited by deep trench sputtering, is non-uniform. In this paper, the thickness uniformity of SiO2 insulating layer prepared on the inner wall of TSV based on CVD&PVD process and thermal oxidation method is comparatively studied. The experimental results show that, based on the CVD&PVD process, the average thickness of the SiO2 insulating layer at middle and bottom position of the TSV has changed by - 54.02% and - 58.30% compared with that at the top position, respectively. Based on the thermal oxidation method, the average thickness of the SiO2 insulating layer at middle and bottom position of the TSV has changed by 1.17% and 0.26% compared with that at the top position, respectively. The thermal oxidation method can realize the SiO2 insulating layer with uniform thickness on the inner wall of TSV. © 2022 IEEE.

关键词： Silica

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Real Estate with AI: An agent based on LangChain 11

Real Estate with AI: An agent based on LangChain

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Haurum, Kasper Raupach Ma, Ruiqi Long, Wen Business School Aalborg University Aalborg9220 Denmark Sino-Danish College University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Recent developments in large language models (LLMs) have opened new avenues for the real estate industry. These models not only understand language but also function as intelligent agents, engaging with investors through open-ended conversations and influencing their decision-making. Utilizing unstructured data from a professional Danish real estate website, we developed a real estate AI agent in both English and Danish using LangChain and Pinecone. Through testing and evaluation, our agent has demonstrated superior professional and concise outputs compared to other LLMs like Doubao and ChatGPT 4 and shown excellent performance and effectiveness. Our work serves as a reference for AI in real estate investment-related research and proposes new solutions to the "unprofessional foundation " and " expensive consulting fee" problems encountered by ordinary investors in their investment decisions. © 2024 The Authors.

关键词： Chatbots

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StyleMI: An Image Processing-Based Method for Detecting Unauthorized Style Mimicry in Fine-Tuned Diffusion Models in a More Realistic Scenario

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IEEE Access 2025年 13卷 97364-97375页

作者： Xinjun Zhang Shixin Zuo Yu Sang School of Electronics and Information Engineering Liaoning Technical University Huludao China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao Liaoning China

The rapid development of diffusion models and model fine-tuning methods have enabled widespread applications in artistic style mimicry while also leading to significant concerns about copyright infringement. Existing approaches protect artists from unauthorized style mimicry by introducing adversarial examples, watermarks, or membership inference attacks. However, the former two are vulnerable to image pre-processing and unable to be applied to published artworks, while the latter requires access to the target model, which is typically inaccessible in real-world scenarios. To this end, the task of detecting unauthorized style mimicry without access to the target model is formulated into a novel membership inference problem. The key insight is that images generated by a diffusion model inherently carry information of its training dataset. A style comparison-based method is proposed, which consists of a LoRA-based style transfer module and a CLIP-based style extraction and comparison module. The style transfer module first learns the artist’s artistic style, then removes the style of the given image to avoid its influence, and finally transfers the artist’s style to the given image. The style extraction and comparison module utilizes a diffusion model to extract the content information of an image and then remove it in a latent semantic space with the pre-trained CLIP model to acquire style-related features and compare them. Experiments on Stable Diffusion demonstrate the effectiveness of the proposed method. The best result achieved a True Positive Rate of 85%, a False Positive Rate of 0%, an Attack Success Rate of 99.29%, a Precision of 100%, and an Area Under the Curve of 1.

关键词： Diffusion models Training Feature extraction data mining Image synthesis data models Copyright protection Computational modeling Watermarking Perturbation methods

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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Electronic Medical Record Sharing System Based on Hyperledger Fabric and InterPlanetary File System 2021

Electronic Medical Record Sharing System Based on Hyperledge...

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5th International Conference on Compute and data Analysis, ICCDA 2021

作者： Li, Lei Yue, Zhengxiang Wu, Gongqing Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781450389112

Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With the continuous development of data sharing, big data applications and other technologies, new requirements are proposed for the storage of medical privacy data. At present, medical data have the characteristics of obstructed sharing and exchange, difficult data tracing and data sensitivity, which increases the cost and difficulty of each diagnosis. Moreover, patients lack the right to know and control their own medical data, which is easy to cause data fraud or data leakage. As a decentralized, tamper proof and traceable distributed ledger system, blockchain brings new solutions to these problems. This paper designs a medical data fusion and distributed privacy management system based on the Hyperledger Fabric blockchain development platform and InterPlanetary File System. It can not only solve the problem of mutual distrust in the traditional system, but also realize the safe data storage and the safe sharing of medical record data between medical organizations which is a beneficial attempt to overcome the disadvantages of traditional hospital data separation. At the same time, according to the requirements of different levels and types of medical users or groups for data security and personal privacy protection, the access control technology of users is studied to ensure personal privacy data. © 2021 ACM.

关键词： Blockchain

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Community partition based source localization in early time 9

Community partition based source localization in early time

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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Shi, Jinchen Fang, Yang Tan, Zhen Yang, Hui Zhao, Xiang National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China National University of Defense Technology Changsha China The 30th Research Institute of China Electronics Technology Group Corporation Chengdu China National University of Defense Technology Laboratory for Big Data and Decision Changsha China

ISBN: (纸本)9798350391367

Since the widespread transmission of viruses, especially the outbreak of COVID-19, the identification of the source has become a crucial research direction. Due to the inability to acquire the states of all nodes and the significant cost of obtaining the state of each node in large networks, utilizing a limited number of sensors to monitor the entire network has become an important research focus. Previous methods simply fused observation results in various ways, while a recent model ESL based on expert support scores was proposed to enhance the model flexibility and improve the computational process. However, this method only improved the calculation process as it deploys the sensors randomly without effective information acquisition process. Therefore, we chose a sensor deployment method suitable for evidence theory and combined it with evidence theory to further enhance the accuracy and transferability of source localization using evidence theory. The results have been confirmed with real network data, demonstrating the effectiveness of our approach. © 2024 IEEE.

关键词： Sensor nodes

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