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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Tanjim Mahmud Imran Hasan Mohammad Tarek Aziz Taohidur Rahman Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå Sweden

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue—fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue *** confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our *** a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age.

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R-CTM: A data-driven macroscopic simulation model for heterogeneous traffic

R-CTM: A data-driven macroscopic simulation model for hetero...

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作者： Liu, Jingyao Deng, Zhigang Mao, Tianlu Wang, Zhaoqi Beijing Key Lab of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Computer Science University of Houston HoustonTX United States

There is a well-known trade-off between computational efficiency and computational accuracy in the field of traffic simulation. In this article, we propose a novel recurrent neural network based model with an integrated attention mechanism, called R-CTM, to simulate heterogeneous traffic flow with multiple types of vehicles. It can effectively extract the traffic flow patterns of spatial and temporal changes from training traffic data, which can be real-world traffic data or synthetic traffic data via microscopic simulation models. Through experiments and comparisons, we show that it can significantly outperform the state of the art methods in terms of simulation accuracy. Besides accuracy, we also demonstrate its scalability: its runtime consumption does not linearly increase with respect to the spatial extent. © 2022 John Wiley & Sons, Ltd.

关键词： Recurrent neural networks

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Towards Prevention of Sportsmen Burnout:Formal Analysis of Sub-Optimal Tournament Scheduling

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computers, Materials & Continua 2022年第1期70卷 1509-1526页

作者： Syed Rameez Naqvi Adnan Ahmad S.M.Riazul Islam Tallha Akram M.Abdullah-Al-Wadud Atif Alamri Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia

Scheduling a sports tournament is a complex optimization problem,which requires a large number of hard constraints to *** the availability of several such constraints in the literature,there remains a gap sincemost of the new sports events pose their own unique set of requirements,and demand novel *** talking of the strictly time bound events,ensuring fairness between the different teams in terms of their rest days,traveling,and the number of successive games they play,becomes a difficult task to resolve,and demands *** this work,we present a similar situation with a recently played sports event,where a suboptimal schedule favored some of the sides more than the *** introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the ***,we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire *** latter is crucial,since successively playing a large number of games may lead to sportsmen burnout,which must be prevented.

关键词： Sports scheduling optimization constraint programming fatigue analysis

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RDladder: Resolution-Duration Ladder for VBR-encoded Videos via Imitation Learning 42

RDladder: Resolution-Duration Ladder for VBR-encoded Videos ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Jia, Lianchen Zhou, Chao Huang, Tianchi Li, Chaoyang Sun, Lifeng Tsinghua University Department of Computer Science and Technology China Beijing Kuaishou Technology Co. Ltd. China BNRist China Tsinghua University Key Laboratory of Pervasive Computing Ministry of Education China

ISBN: (纸本)9798350334142

With the rapid development of the streaming system, a large number of videos need to be transcoded to multiple copies according to the encoding ladder, which significantly increases the storage overhead than before. This scenario demonstrates the prosperity of streaming media but also presents new challenges about achieving the balance between better quality for users and less storage cost. In our work, we observe two significant points. The first one is selecting proper resolutions under certain network conditions can reduce storage costs while maintaining a great quality of experience. The second one is the segment duration is critical, especially in VBR-encoded videos. Considering these points, we propose RDladder, a resolution-duration ladder for VBR-encoded videos via imitation learning. We jointly optimize resolutions and duration using neural networks to determine the combination of these two metrics considering network capacity, video information, and storage cost. To get more faithful results, we use over 500 videos, encoded to over 2,000,000 chunks, and collect real-world network traces for more than 50 hours. We test RDladder in simulation, emulation, and real-world environments under various network conditions, and our method can achieve near-optimal performance. Furthermore, we discuss the influence between the RDladder and ABR algorithms and summarize some characteristics of RDladder. © 2023 IEEE.

关键词： Ladders

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Adaptive Multi-modal Data Fusion Method for Dermatosis Diagnosis

Adaptive Multi-modal Data Fusion Method for Dermatosis Diagn...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Yaning Yang, Xiaodong Chen, Yiqiang Chen, Shubai Zeng, Bixiao Cai, Hong Chinese Academy of Sciences The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing China University of Jinan College of Information Science and Engineering Shandong China Shandong Academy of Intelligent Computing Technology Shandong China Air Force Medical Center of the PLA Department of Dermatology Beijing China

ISBN: (纸本)9798350337488

Meta-data, e.g., gender, medical history, etc., is of vital importance for dermatosis diagnosis clinically, while most existing AI-based methods ignore this information and only employ dermoscopy images. Due to the modality disequilibrium of image and meta-data, there would be unequal optimization during the modal fusion, resulting in the weak meta-data modality being suppressed and losing their contributions to the classification. To address this issue, we propose an Adaptive Multi-modal Fusion (AMF) method for dermatosis diagnosis, which reweights the fusion factor of image and meta-data by evaluating their optimization discrepancy through the Euclidean norm. What's more, the Co-Attention mechanism is employed to align the two modalities implicitly. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on multi-modal dermatosis public datasets including ISIC 2019, PDA-UFES-20, and HAM10000. The results demonstrate that the proposed method can achieve better performance in comparison to the state-of-the-art methods. © 2023 IEEE.

关键词： Deep Learning Dermatosis Diagnosis Multi-modal Fusion Transformer

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DSWHAR: A Dynamic Sliding Window Based Human Activity Recognition Method 19

DSWHAR: A Dynamic Sliding Window Based Human Activity Recogn...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Sun, Li Yang, Xiaodong Hu, Chunyu School of Computer Science and Technology Jinan250353 China Shandong Academy of Intelligent Computing Technology Jinan250101 China Institute of Computing Technology Chinese Academy of Sciences The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China

ISBN: (纸本)9798350346558

Sensory data has been widely used for human activity recognition (HAR), where sliding window (SW) is one of the typical methods to segment continuous signals. Most existing HAR methods select fixed-length sliding windows for different activities. However, with complex and diverse human activities, a single fixed sliding window is not suitable for acquiring features of different activities. Some studies have dynamically adjusted the window size based on the activity of feature difference segmentation, but the continuous occurrence of the activity can lead to an oversized window for segmentation and easily introduce additional noise leading to degradation of classification performance. To tackle the above problem, we propose a novel dynamic sliding window based human activity recognition method, called DSWHAR. In the training phase, DSWHAR firstly explores the relationship between the optimal sliding window length and the signal spectrum of each target activity, then a candidate window set is built for the target activity and multiple classifiers are trained using different windows. During the recognition phase, the latest prediction probability is used to dynamically select a suitable window for the next prediction, and the corresponding classifier is used for the final recognition. We compare DSWHAR with the method with fixed sliding windows and dynamic sliding window algorithm on the MHEALTH dataset, respectively, and the experimental results show that DSWHAR not only achieves better overall HAR accuracy but also has unbiased performance on different activities. © 2022 IEEE.

关键词： Pattern recognition

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The Odyssey Journey: Top-Tier Medical Resource Seeking for Specialized Disorder in China 25

The Odyssey Journey: Top-Tier Medical Resource Seeking for S...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Chan, Ka I Hu, Siying Wang, Yuntao Xu, Xuhai Lu, Zhicong Shi, Yuanchun Institute Tsinghua University Beijing China Department of Computer Science City University of Hong Kong Hong Kong Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Technology Qinghai University Xining China Google New York CityNY United States Department of Computer Science George Mason University FairfaxVA United States Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Qinghai University Xining China

ISBN: (纸本)9798400713941

It is pivotal for patients to receive accurate health information, diagnoses, and timely treatments. However, in China, the significant imbalanced doctor-to-patient ratio intensifies the information and power asymmetries in doctor-patient relationships. Health information-seeking, which enables patients to collect information from sources beyond doctors, is a potential approach to mitigate these asymmetries. While HCI research predominantly focuses on common chronic conditions, our study focuses on specialized disorders, which are often familiar to specialists but not to general practitioners and the public. With Hemifacial Spasm (HFS) as an example, we aim to understand patients' health information and top-tier1 medical resource seeking journeys in China. Through interviews with three neurosurgeons and 12 HFS patients from rural and urban areas, and applying Actor-Network Theory, we provide empirical insights into the roles, interactions, and workflows of various actors in the health information-seeking network. We also identified five strategies patients adopted to mitigate asymmetries and access top-tier medical resources, illustrating these strategies as subnetworks within the broader health information-seeking network and outlining their advantages and challenges. © 2025 Copyright held by the owner/author(s).

关键词： Diagnosis

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Dual-View Interaction-Aware Lane Change Prediction for Autonomous Driving 39

Dual-View Interaction-Aware Lane Change Prediction for Auton...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yuhuan Zhang, Zhen Bai, Rufan Liu, Han Wang, Wei Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China Department of Computer and Information Science University of Macau China Department of Computer Science and Engineering Southeast University China School of Software Dalian University of Technology China School of Medical Technology Beijing Institute of Technology China

ISBN: (纸本)157735897X

As artificial intelligence techniques evolve, we are approaching a critical moment for the widespread deployment of autonomous vehicles. Subsequently, the emergence of mixed-autonomy traffic environments presents formidable challenges to autonomous vehicles, especially for the accurate prediction of lane change intentions of their surrounding human-driven vehicles, which is crucial for ensuring the safety of autonomous vehicles. Existing lane change prediction models mainly focus on capturing the temporal variations in the movement dynamics of individual vehicles. However, the neglect to consider inter-vehicle interactions hinders their capability in complex lane change scenarios, resulting in suboptimal prediction performance. Moreover, current interaction-aware approaches for autonomous driving fail to explicitly model future interactions between vehicles, leading to unreasonable prediction results that can cause collisions between vehicles. To address the above issues, we propose to incorporate the concept of perceived safety into future interaction modeling and design a dual-view interaction-aware lane change prediction model. We evaluate the proposed model on two real-world datasets and experimental results show that the proposed model achieves average improvements of 11.7-12.4% in classification ability and 75.6-95.7% in forecast ability over the best-performing baselines across the two datasets. The ablation study and investigation into future interaction modeling demonstrate that our model has advantages in interpreting lane change scenarios from a driving safety perspective. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Generating Post-healing Images of Skin Diseases Based on an Adversarial Self-coding Generator 2nd

Generating Post-healing Images of Skin Diseases Based on an ...

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2nd International Conference on Image, Vision and Intelligent Systems, ICIVIS 2022

作者： Li, Yaning Chen, Yiqiang Yang, Xiaodong Cai, Hong University of Jinan Shandong Jinan250024 China Shandong Academy of Intelligent Computing Technology Shandong Jinan250102 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Science Beijing100086 China Department of Dermatology Air Force Medical Center PLA Beijing100142 China

ISBN: (纸本)9789819909223

Goal: In recent years, the rapid development of computer vision and deep learning has driven the improvement of new technologies in medical imaging, while applications in various aspects have emerged. Regarding dermatology, which is an important branch of medical imaging technology, the techniques surrounding it are currently focused on lesion segmentation, identification, and classification of benign and malignant cells in pathological sections. we explore a post-healing image generation method based on dermatological diseases to fill the current situation where the field is relatively under-researched in terms of post-healing. Methods: In this work, the ISIC 2018 dataset is used as our object to be recovered, and the U-net neural network is used as the network model of the segmentation module to segment the healthy skin cell region and the focal cell region by setting the threshold region. The erosion module is used to obtain the area to be filled for this lesion, thus simulating the different stages of healing. Finally, Images are generated by a VAE/GAN-based adversarial autoencoder. Conclusion: Our proposed method can generate more realistic post-healing images of skin diseases compared with the VAE and GAN methods. Significance: This work explores the application of AI-aided diagnosis in the generation of post-healing images in medical imaging, which can help doctors and patients understand the healing process after surgery to a certain extent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image reconstruction

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Bilateral Pose Transformer for Human Pose Estimation 22

Bilateral Pose Transformer for Human Pose Estimation

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4th International Symposium on Signal Processing Systems, SSPS 2022

作者： Yen, Chia Chen Pin, Tao Xu, Hongmin Department of Computer Science and Technology Tsinghua University China Key Laboratory of Pervasive Computing Ministry of Education Tsinghua University China Oppo Inc. China

ISBN: (纸本)9781450396103

Human Pose is a well-defined fundamental task researched by the computer vision community for years. Previous Convolutional Neural Network (CNN) based works have achieved significant success in the human pose. Recently, Vision Transformer (VT) has shown superior performance on computer vision tasks. However, current VT methods emphasize local information less and often focus on only a single scale feature that may not be suitable for dense image prediction tasks, which essentially requires multi-scale representations. In this paper, we propose a novel Bilateral Pose Transformer (BPT) framework to handle the human pose. Specifically, BPT consists of an innovated bilateral branch encoder and a multi-scale integrating decoder. The bilateral branch encoder contains a Context Branch (CB) and Spatial Branch (SB). The CB involves a VT-based backbone to capture the context clues and produce multi-scale context features. The CNN-based SB maintains high-resolution representations containing rich spatial information to introduce the local spatial information that supplements the CB explicitly. About the decoder, a Mixed Feature Module consisting of local attention CNN is proposed to integrate the various-scale context and spatial features effectively. Experiments demonstrate that our approach achieves competitive performances in human pose estimation. Specifically, compared to the HRNet [1], the BPT saves 43% GFLOPs and drops only 0.1 points AP, achieving 75.7% AP with 9.0 GFLOPs, on the COCO keypoints dataset. © 2022 ACM.

关键词： Convolutional neural networks

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