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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yang, Zhihao Zhang, Yunqi Li, Songda Chen, Qinhui Zhao, Hui Cai, Wei Lin, Xi Software Engineering Institute East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai China Electronics Technology Group Corporation The 51st Research Institue of China Beijing China

ISBN: (纸本)9783031306778

Online social media has lots of moving object information. Extracting movement information is a challenging work. It suffers from spatio-temporal information extraction, vague time, and vague location. Previous information extraction methods merely focus on the trajectory extraction. In this demonstration, we develop a web-based application, TrajTrace, to track moving objects. TrajTrace extracts triple from social media text employing our proposed span-level joint entity and relation extraction model, OMLer. OMLer casts joint extraction as a token pair multi-categories classification task. It predicts the triple list corresponding to the input sequence. We employ BERT to encode the input sentence word by word. The self-attention mechanism and BiLSTM are applied to learn sequence features. Then, an order-first time matching algorithm is designed to solve the lacking temporal information problem in the extracted triples. Utilizing the proposed TF-IDF based clustering algorithm, we make the vague time accurate. The vague geographic location is converted to accurate latitude and longitude using the Bezier geodetic coordinate conversion algorithm. Toward aircraft and ships, besides the keyword search and the trajectories visualization, TrajTrace provides the historical activity area search of a specific object and the spatio-temporal distribution of moving objects at a given time or location. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Construction of Index System of International Economic Cooperation Innovation Model in Digital Era-based on TIF Theory and DIKWP Model 25

Construction of Index System of International Economic Coope...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Shanqi, Zhan Tang, Fuliang Li, Yongmei Qiurong, Mei Zining, Lu Xing-Sen, Li Biyun, Zhang School of Software and Microelectronics Peking University Beijing China Dikwp Research Group School of Computer and Technology Hainan University China College of Engineering Peking University China Center of Chinese and Foreign Think Tanks China Beijing Financial Holdings Group China Research Institute of Extenics and Innovation Methods Guangdong University of Technology China China

ISBN: (纸本)9798350330014

With the deepening of globalization, international economic cooperation has become a key factor in promoting economic growth and development. However, the rapid changes and technological advances brought about by the digital age are changing the rules and methods of international economic cooperation. In this digital age, data is no longer just a resource, it is more like a new economic currency, and the development of information technology has made it easier to move data across borders, providing unprecedented opportunities for all kinds of economic activities. However, international economic cooperation in the digital age also comes with a series of challenges. Data privacy and security issues are more prominent, the digital divide persists, and regulatory and legal issues become more complex. The issues of fairness, fairness, efficiency and dialectical openness of digital platforms have become major issues that attract the attention of all countries. Therefore, in these turbulent times, we need innovative methods and indicators to assess and guide international economic cooperation to ensure that it leads to sustainable, beneficial, and balanced growth that benefits everyone around the world. © 2023 IEEE.

关键词： Economics

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections 12

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Wang, Yifan Zeng, Wei Li, Guanyu Xiong, Chenglong Wang, Zicheng Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (纸本)9798331511074

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate. © 2024 IEEE.

关键词： Markov processes

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Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

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Computers, Materials & Continua 2023年第2期74卷 3727-3741页

作者： Md Zahid Hasan Ontor Md Mamun Ali Kawsar Ahmed Francis M.Bui Fahad Ahmed Al-Zahrani S.M.Hasan Mahmud Sami Azam Department of Software Engineering(SWE) Daffodil International University(DIU)SukrabadDhaka1207Bangladesh Group of Biophotomatiχ Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonS7N5A9SKCanada Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia Department of Computer Science American International University-Bangladesh(AIUB)KuratoliDhaka1229Bangladesh College of Engineering IT and EnvironmentCharles Darwin UniversityCasuarina0909NTAustralia

Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing *** this many women have ***,it is curable if it can be diagnosed and detected at an early stage and taken proper *** the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early *** solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell *** system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning *** build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and *** the YOLOv5 models were applied to the labeled dataset to train the *** versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early *** of the model’s variations performed *** model can effectively detect cervical cancerous cell,according to the findings of the *** the medical field,our study will be quite *** can be a good option for radiologists and help them make the best selections possible.

关键词： Cervical cancer pap-smear deep learning cancerous cell YOLOv5 model

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Pre-training Retnet of Simulating Entities and Relations as Sentences for Knowledge Graph Reasoning

Pre-training Retnet of Simulating Entities and Relations as ...

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Information engineering (ACIE), IEEE Asia Conference on

作者： Jun Cheng Tao Meng Xiao Ao Xiaohua Wu Midea Building Technology Midea Group (Shanghai) Co. Ltd Shanghai China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering YiBin Park of UESTC Chengdu China

Knowledge graphs have diverse applications, but a common challenge across these scenarios is the issue of missing triples. In this paper, we present a knowledge reasoning model based on the Retnet framework, employing a pre-training and fine-tuning approach. During the pre-training phase, we enhance the model's inference capabilities by replacing entities with sentence descriptions as inputs. This novel strategy aims to address the prevalent problem of missing triples in knowledge graphs. For various downstream tasks, we can fine-tune it to meet specific requirements. We conducted extensive experiments on several public datasets, covering triple classification, relationship prediction, and link prediction. The results consistently showcase the exceptional performance of our model. Particularly, in link prediction experiments on the FB15K-237 dataset, we achieved an impressive Mean Reciprocal Rank (MRR) of .356 and a Hits@10 score of 52.4.

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TOWARDS PRAGMATIC INTEROPERABILITY: A CONCEPTUAL FRAMEWORK FOR AN ENERGY IOT PLATFORM IN THE GERMAN ENERGY SECTOR

TOWARDS PRAGMATIC INTEROPERABILITY: A CONCEPTUAL FRAMEWORK F...

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CIRED 2024 Vienna Workshop

作者： Karmann, Alexandra Kilthau, Maximilian Derksen, Christian Strobel, Florian Bittermann, Lukas Korotkiewicz, Kamil Ostmeyer, Jonas Fay, Alexander Institute of Automation Technology Helmut-Schmidt-University Hamburg Germany Research Group for Business Informatics and Software Engineering University Duisburg-Essen Essen Germany Institute of Electrical Power and Energy Technology University of Technology Hamburg Hamburg Germany PSI GridConnect GmbH Karlsruhe Germany PSI FLS Fuzzy Logik & Neuro Systeme GmbH Dortmund Germany Ruhr-University Bochum Germany

ISBN: (纸本)9781837242672

This paper introduces an innovative conceptual framework for an Energy Internet of Things (eIoT) platform designed to address challenges related to grid congestion, voltage limit violations, and the growing demands on low-voltage grids due to increased electrification in the energy and heating sectors. The proposed platform seamlessly integrates cutting-edge technologies including advanced data collection, state estimation algorithms, AI-driven decision-making processes, and grid control functionalities. Its primary objective is to facilitate proactive measures aimed at enhancing energy distribution efficiency and effectively managing consumption devices while ensuring compliance with regulatory standards such as §14a of the Energy Industry Act (EnWG) in Germany. © The Institution of engineering & technology 2024.

关键词： Internet of Energy

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

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Intelligent Automation & Soft Computing 2023年第8期37卷 1587-1608页

作者： Lei Yin Chang Sun Ming Gao Yadong Fang Ming Li Fengyu Zhou School of Control Science and Engineering Shandong UniversityJinan250061ShandongChina School of Software Shandong UniversityJinan250101ShandongChina Academy of Intelligent Innovation Shandong UniversityShunhua RoadJinan250101ShandongChina Inspur Cloud Information Technology Co. Ltd.Inspur GroupJinan250101ShandongChina

The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling ***,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such *** this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task ***,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic ***,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling ***,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving *** with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.

关键词： Task scheduling cloud computing hyper-heuristic algorithm makespan optimization

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ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks 24

ReForm-Eval: Evaluating Large Vision Language Models via Uni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Zejun Wang, Ye Du, Mengfei Liu, Qingwen Wu, Binhao Zhang, Jiwen Zhou, Chengxing Fan, Zhihao Fu, Jie Chen, Jingjing Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Software College Northeastern University Liaoning Shenyang China Alibaba Group Zhejiang Hangzhou China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities to perceive visual signals and perform visually grounded reasoning. However, the capabilities of LVLMs have not been comprehensively and quantitatively evaluated. Most existing multi-modal benchmarks require task-oriented input-output formats, posing great challenges to automatically assess the free-form text output of LVLMs. To effectively leverage the annotations available and reduce the manual efforts required for constructing new benchmarks, we propose to re-formulate existing benchmarks into unified LVLM-compatible formats. Through systematic data collection and reformulation, we present ReForm-Eval benchmark, offering substantial data for evaluating various capabilities of LVLMs. Through extensive experiments and analysis in ReForm-Eval, we demonstrate the comprehensiveness and reliability of ReForm-Eval in assessing various LVLMs. Our benchmark and evaluation framework is now available at https://***/FudanDISC/ReForm-Eval © 2024 ACM.

关键词： Visual languages

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Architecting Digital Twins for Intelligent Transportation Systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation Computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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