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arXiv 2024年

作者： Yang, Jing Zhao, Yu Yang, Linyao Wang, Xiao Chen, Long Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410000 China Zhejiang Lab Hangzhou311121 China School of Artificial Intelligence Anhui University Hefei266114 China Department of Computer and Information Science University of Macau 999078 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems. However, existing methods still struggle with limited and unevenly distributed annotated data. Therefore, inspired by the abundant global knowledge stored within pre-trained language models (PLMs), we propose a multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt tuning and contrastive learning to tackle these issues. To elicit more effective prompts for PLMs, we introduce a task-oriented prompt construction approach that thoroughly takes the myriad factors of TRE into consideration for automatic prompt generation. In addition, we design temporal event reasoning in the form of masked language modeling as auxiliary tasks to bolster the model’s focus on events and temporal cues. The experimental results demonstrate that TemPrompt outperforms all compared baselines across the majority of metrics under both standard and few-shot settings. A case study on designing and manufacturing printed circuit boards is provided to validate its effectiveness in crowdsourcing scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

arXiv

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arXiv 2024年

作者： Xu, Xusheng Cui, Jiangyu Cui, Zidong He, Runhong Li, Qingyu Li, Xiaowei Lin, Yanling Liu, Jiale Liu, Wuxin Lu, Jiale Luo, Maolin Lyu, Chufan Pan, Shijie Pavel, Mosharev Shu, Runqiu Tang, Jialiang Xu, Ruoqian Xu, Shu Yang, Kang Yu, Fan Zeng, Qingguo Zhao, Haiying Zheng, Qiang Zhou, Junyuan Zhou, Xu Zhu, Yikang Zou, Zuoheng Bayat, Abolfazl Cao, Xi Cui, Wei Li, Zhendong Long, Guilu Su, Zhaofeng Wang, Xiaoting Wang, Zizhu Wei, Shijie Wu, Re-Bing Zhang, Pan Yung, Man-Hong MindSpore Quantum Special Interest Group Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Department of Physical Chemistry University of the Basque Country UPV/EHU Apartado 644 Bilbao48080 Spain School of Physics and Astronomy Sun Yat-sen University Zhuhai519082 China Key Laboratory of Quantum Physics and Photonic Quantum Information Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China CAS Key Laboratory for Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Shenzhen International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack-a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance. © 2024, CC BY.

关键词： Quantum computers

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Event Stream-Based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline

Event Stream-Based Visual Object Tracking: A High-Resolution...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiao Wang Shiao Wang Chuanming Tang Lin Zhu Bo Jiang Yonghong Tian Jin Tang School of Computer Science and Technology Anhui University Hefei China University of Chinese Academy of Sciences Beijing China Institute of Optics and Electronics CAS Chengdu China Beijing Institute of Technoloay Beijing China Peng Cheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Tracking with bio-inspired event cameras has garnered increasing interest in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The former incurs higher inference costs while the latter may be susceptible to the impact of noisy events or sparse spatial resolution. In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multimodal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals. Specifically, a teacher Transformer-based multimodal tracking framework is first trained by feeding the RGB frame and event stream simultaneously. Then, we design a new hierarchical knowledge distillation strategy which includes pairwise similarity, feature representation, and response maps-based knowledge distillation to guide the learning of the student Transformer network. In particular, since existing event-based tracking datasets are all low-resolution (346 × 260), we propose the first large-scale high-resolution (1280 × 720) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians, vehicles, UAVs, ping pong, etc. Ex-tensive experiments on both low-resolution (FE240hz, Vi-sEvent, COESOT), and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method.

关键词： Knowledge engineering Training Visualization Pedestrians Self-supervised learning Transformers Pattern recognition

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Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach

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IEEE Transactions on Knowledge and Data engineering 2025年

作者： Li, Xiaoyu Zhang, Yitian Long, Guodong Hu, Yupeng Lu, Wenpeng Chen, Meng Zhang, Chengqi Gong, Yongshun Shandong University School of Software Jinan250100 China McGill University Electrical and Computer Engineering MontrealQCH3A 0G4 Canada University of Technology Sydney School of Computer Science SydneyNSW2007 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250100 China

Traffic forecasting plays a crucial role in establishing an Intelligent Transportation System (ITS) by providing essential insights. Existing traffic forecasting relies on the assumption that there is a hidden invariant spatial-temporal pattern in the large-scale dataset. However, the traffic patterns are easily influenced by many unpredictable external factors, such as policy interventions and climate changes. Due to the dynamic nature of these exogenous factors, the traffic network's spatial-temporal patterns are also changed, thus impacting the performance of traffic forecasting models. Thus, there is an urgent need to rethink the traffic forecasting model in a fast-adaptive manner. To solve this challenge, this paper proposes an Adaptive Spatio Temporal Context Learning framework named ASTCL, which achieves desired forecasting accuracy using daily basis traffic data collected from dozens of sensors. ASTCL constructs adaptive spatio-temporal contexts for target locations in the traffic network and generates dynamic sequence graphs based on semantic similarities. The adaptive contexts aggregate valuable information from available data, while the graphs reveal dynamic trends in traffic properties. Further, ASTCL introduces a joint convolution and attention mechanism to model intricate spatio-temporal relationships from multiple perspectives. Extensive experiments conducted on four real-world datasets demonstrate that ASTCL achieves remarkable fast adaptability and outperforms other state-of-the-art methods by a significant margin. © 1989-2012 IEEE.

关键词： Global warming

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Exploring the Integration of Wearable Sensors Technologies in Professional Sports for Enhanced Performance and Injury Prevention

Exploring the Integration of Wearable Sensors Technologies i...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Rumman Ahmad Pavan P.S. Monali Ravindra Borade N. Thangarasu Aakash Sharma Prakash Jadhav Indian Jamia Millia Islamia School of Engineering and Technology Civil Engineering JAIN (Deemed to be University) Bangalore Karnataka India AI & Ds Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Visvesvaraya Technological University Karnataka India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This report discusses how wearable monitor technologies might improve and prevent injury in professional sports. Method phases include cleaning sensor data, dimensionality reduction, grouping, and machine learning. Fourier changes, wavelet compression, and statistical feature extraction can clean, consistent, and make data usable. By combining and decreasing data dimensions, the strategy discovers relevant patterns that influence forecast models. Neural networks are more accurate and effective for assessing sports success and injury risk using machine learning. On data accuracy, latency, power efficiency, signal stability, and wearability, it compares favorably to other techniques. The proposed strategy is the best for real-time sports applications since it excels in all areas. The strategy enhances performance and makes smart tech secure and pleasant, making gamers more eager to use it. In the end, this research helps players make informed choices that increase their ability, prevent injury, and improve professional sports achievements.

关键词： Dimensionality reduction Accuracy Prevention and mitigation Current measurement Predictive models Real-time systems Reliability Injuries Monitoring Sports

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Big Data in Agricultural and Environmental Assessment: Opportunities and Difficulties

Big Data in Agricultural and Environmental Assessment: Oppor...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Mrinmoy Kumar Chini Sipra Sagar D. Bhanu Vishal Sharma M. Vigenesh Santosh Kumar Department of Chemistry TMU Department of Applied Sciences Invertis University Bareilly Uttar Pradesh Department of Information Technology Karpagam Institute of Technology Coimbatore School of Media Film & Television Studies IIMT University Meerut Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore AI & Ds Vishwakarma Institute of Information Technology Pune

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Big Data in Agriculture and Ecological Analysis: Possibilities and Challenges The same capabilities that gave rise to the Internet and are revamping medicines are now being used as agriculture stands on the verge of a digitalization. In general, the cheap cost of gathering growth, together with data from temperature readings and data gathered by aircraft and satellites, is what is driving this digital agrarian economy. These technologies promise to generate more food with less resources, less land, less labor, and a lower environmental impact. The inability to gather and analyses data in a manner that produces usable farmers, as well as the need to teach farmers how to utilize such tools, are now obstacles to fulfilling this promise. The promise and obstacles to fulfilling to change agriculture are reviewed in this essay.

关键词： Natural resources Temperature distribution Costs Satellites Production Pricing Big Data Agriculture Manufacturing Monitoring

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Hierarchical Mutual Information Analysis: Towards Multi-View Clustering in the Wild

Hierarchical Mutual Information Analysis: Towards Multi-View...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiatai Wang Zhiwei Xu Xuewen Yang Xin Wang Li Tao Haihe Lab of ITAI Tianjin China College of Data Science and Application Inner Mongolia University of Technology Huhhot China Institute of Computing Technology Chinese Academy of Sciences Beijing China InnoPeak Technology Inc Palo Alto California USA Department of Electrical and Computer Engineering Stony Brook University New York U.S.A.

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multi-view clustering (MVC) can explore common semantics from multiple views and has been extensively used to support management with unsupervised training data. However, the issue of spatio-temporal asynchronism often leads to multi-view data being missing or unaligned in the real world. This limit poses significant challenges in learning consistent representations. This paper proposes a deep MVC framework where data recovery and alignment are fused hierarchically from an information-theoretic perspective, maximizing the mutual information among different views and ensuring the consistency of their latent spaces. To address the issue of missing views, we use dual prediction for instance-level alignment. While leveraging contrastive reconstruction enhances the mutual information of features within the same class for class-level alignment. This could be the first attempt to view recovery and alignment can be solved simultaneously in a unified theoretical framework. Extensive experiments show that our method outperforms baseline methods even in the cases of missing and unaligned views.

关键词： Semantics Noise Neural networks Decision making Training data Feature extraction Entropy

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Global Challenge for Safe and Secure LLMs Track 1

arXiv

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arXiv 2024年

作者： Jia, Xiaojun Huang, Yihao Liu, Yang Tan, Peng Yan Yau, Weng Kuan Mak, Mun-Thye Sim, Xin Ming Ng, Wee Siong Ng, See Kiong Liu, Hanqing Zhou, Lifeng Yan, Huanqian Sun, Xiaobing Liu, Wei Wang, Long Qian, Yiming Liu, Yong Yang, Junxiao Zhang, Zhexin Lei, Leqi Chen, Renmiao Lu, Yida Cui, Shiyao Wang, Zizhou Li, Shaohua Wang, Yan Goh, Rick Siow Mong Zhen, Liangli Zhang, Yingjie Zhao, Zhe Nanyang Technological University Singapore AI Singapore Singapore Hangzhou Innovation Institute Beihang University China School of Computer Science and Technology Anhui University China Qiyuan laboratory China IHPC Agency for Science Technology and Research Singapore CoAI Group Tsinghua University China Singapore Institute of Information Engineering Chinese Academy of Sciences China RealAI China

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models. Copyright © 2024, The Authors. All rights reserved.

关键词： Public administration

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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