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

作者： Chen, Weihao Shi, Yuanchun Wang, Yukun Shi, Weinan Chen, Meizhu Gao, Cheng Mei, Yu Zhu, Yeshuang Zhang, Jinchao Yu, Chun Department of Computer Science and Technology Tsinghua University Beijing China Qinghai University Qinghai Xining China School of Architecture Tsinghua University Beijing China Pattern Recognition Center WeChat AI Tencent Inc. Beijing China

ISBN: (纸本)9798400713941

Text entry is a fundamental and ubiquitous task, but users often face challenges such as situational impairments or difficulties in sentence formulation. Motivated by this, we explore the potential of large language models (LLMs) to assist with text entry in real-world contexts. We propose a collaborative smartphone-based text entry system, CATIA, that leverages LLMs to provide text suggestions based on contextual factors, including screen content, time, location, activity, and more. In a 7-day in-the-wild study with 36 participants, the system offered appropriate text suggestions in over 80% of cases. Users exhibited different collaborative behaviors depending on whether they were composing text for interpersonal communication or information services. Additionally, the relevance of contextual factors beyond screen content varied across scenarios. We identified two distinct mental models: AI as a supportive facilitator or as a more equal collaborator. These findings outline the design space for human-AI collaborative text entry on smartphones. © 2025 Copyright held by the owner/author(s).

关键词： Ambient intelligence

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Integrating virtual reality technology into architecture education: the case of architectural history courses

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Open House International 2021年第4期46卷 498-509页

作者： Ibrahim, Anwar Al-Rababah, Amneh Ibrahim Bani Baker, Qanita Department of Architecture Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Jordan University of Science and Technology Irbid Jordan

Purpose: Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in architectural history courses as well. This paper aims to examine the effect of using VR technology on the students’ learning abilities of history of architecture. Design/methodology/approach: The experimental approach was used. Two experiments were designed by creating virtual environments for two selected architectural examples from the Modern architecture course. The participants who were students of Modern architecture class had to complete two questionnaires, one for each example. The first one was based on Bloom’s taxonomy, and the other was prepared to test the participants’ analytical and critical skills. Besides, participants had to fill out satisfaction and ease-of-use survey on a five-step Likert scale. Findings: Participants in the VR condition achieved better grades in knowledge gain compared to those in the traditional conditions. Their analytical and critical thinking skills were improved in the VR conditions. Gender has a significant impact on analytical and critical thinking skills. Participants recorded a high level of satisfaction;however, male students were more satisfied than female students who reported concerns about the weight of the used tools and nausea symptoms. Research limitations/implications: This study informs architecture education and provides insights into the potentials of using advanced technology in architectural history education. Teaching the various history of architecture courses will be improved, shifted toward a more student-centered curriculum, and may acquire more excitement and conscious curiosity. Originality/value: Using VR in architectural education is rigorous in architectural design courses and students’ design projects’ presentations. This research expands architectural education research by examining ot

关键词： Architectural education Bloom’s taxonomy Built environment tectonics and technologies History of architecture VR technology

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A Trusted and Decentralized Federated Learning Framework for IoT devices in Smart City 17

A Trusted and Decentralized Federated Learning Framework for...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Wang, Sheng Chen, Chun Han, Bing Zhu, Jun Nanjing Construction Market Supervision Station Nanjing China Jiangsu Public Works Co.Ltd Nanjing China Nanjing University of Aeronautics and Astronautics Department of College of Computer Science and Technology Nanjing China City University of Hong Kong Department of Architecture and Civil Engineering Hong Kong

ISBN: (纸本)9798350351637

Smart cities, through investment in human and social capital as well as traditional and modern communications infrastructure, drive sustainable economic growth and high quality of life. IoT devices, as crucial components of smart cities, leverage the Federated Learning (FL) paradigm to contribute significantly to the advancement of smart cities by providing the value of data while safeguarding local data. However, existing federated learning frameworks for smart cities face several issues, including excessive centralization of decision-making and data, unbalanced resource allocation, and security and privacy concerns. To address these challenges, This paper aims to propose a blockchain-based federated learning framework for IoT devices in smart cities. We combine multiple security and privacy-preserving techniques such as differential privacy and Trusted Execution Environments (TEE) with federated learning to establish a framework that facilitates secure and trustworthy data exchange and FL requirements for IoT devices in smart cities. Moreover, we offload the local training computation from the limited IoT devices to the edge server and execute trust aggregation on the blockchain smart contracts. We build a prototype of our designed system and conduct experiments on diverse federated learning datasets. Experimental results demonstrate that our scheme achieves high efficiency while ensuring security and privacy. Through this work, we provide a viable solution for federated learning in smart cities, thereby advancing the sustainable development and intelligence of smart cities. © 2024 IEEE.

关键词： Federated learning

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When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features

When Do Neuromorphic Sensors Outperform cameras? Learning fr...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Daniel Deniz Eduardo Ros Cornelia Fermller Francisco Barranco Dep. of Computer Architecture and Technology University of Granada Spain Perception and Robotics Group University of Maryland USA

Visual event sensors only output data when changes in the scene happen at very high frequency. This allows for smartly compressing the scene and thus, enabling real-time operation. Despite these advantages, works in the literature have struggled to show a niche for these event-driven approaches compared to conventional sensors, especially when focusing on accuracy performance. In this work, we show a case that fully exploits event sensor advantages: for manipulation action recognition, learning events achieves superior accuracy and time performance. The recognition of manipulation actions requires extracting and learning features from the hand pose and trajectory and the interaction with the object. As shown in our work, approaches based on event sensors are the best fit for extracting these dynamic features contrarily to conventional approaches based on full frames, which mostly extract spatial features and need to reconstruct the dynamics from sequences of frames. Finally, we show how using a tracker to extract the features to be learned only around the hand, we obtain an approach that is scene- and almost object-agnostic and achieves good time performance with a very limited impact in accuracy.

关键词： Visualization Neuromorphics Neural networks Human-robot interaction Sensor phenomena and characterization Feature extraction Real-time systems

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An enhanced scheme for mutual authentication for healthcare services

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Digital Communications and Networks 2022年第2期8卷 150-161页

作者： Salman Shamshad Muhammad Faizan Ayub Khalid Mahmood Saru Kumari Shehzad Ashraf Chaudhry Chien-Ming Chen Department of Computer Science COMSATS University IslamabadSahiwal Campus57000Pakistan Department of Mathematics Ch.Charan Singh UniversityMeerutUttar Pradesh250004India Department of Computer Engineering Faculty of Engineering and ArchitectureIstanbul Gelisim UniversityIstanbulTurkey College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Riphah School of Computing&Innovation(RSCI) Riphah International UniversityLahore CampusLahore 55150Pakistan Department of Mathematics University of Padua35131 PaduaItaly

With the advent of state-of-art technologies,the Telecare Medicine Information System(TMIS)now offers fast and convenient healthcare services to patients at their ***,this architecture engenders new risks and challenges to patients'and the server's confidentiality,integrity and *** order to avoid any resource abuse and malicious attack,employing an authentication scheme is widely considered as the most effective approach for the TMIS to verify the legitimacy of patients and the ***,several authentication protocols have been proposed to this *** recently,Chaudhry et *** that there are vulnerabilities of impersonation attacks in Islam et al.'s ***,they introduced an improved protocol to mitigate those security ***,Qiu et *** that these schemes are vulnerable to the man-in-the-middle,impersonation and offline password guessing ***,they introduced an improved scheme based on the fuzzy verifier techniques,which overcome all the security flaws of Chaudhry et al.'s ***,there are still some security flaws in Qiu et al.'s *** this article,we prove that Qiu et al.'s protocol has an incorrect notion of perfect user anonymity and is vulnerable to user impersonation ***,we introduce an improved protocol for authentication,which reduces all the security flaws of Qiu et al.'s *** also make a comparison of our protocol with related protocols,which shows that our introduced protocol is more secure and efficient than previous protocols.

关键词： Authentication protocol Security protocol Anonymous protocol Impersonation attack TMIS

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Single Beacon Cruise Positioning Algorithm in Wireless Sensor Networks

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Chinese Journal of Electronics 2023年第3期22卷 558-562页

作者： ZHU Darong HAN Jianghong OU Jian MIN Jie School of Computer and Information Hefei University of Technology Hefei China School of Electronics and Information Engineering Anhui University of Architecture Hefei China Department of Mathematics and Physics Anhui University of Architecture Hefei China

Node localization is important in WSNs (Wireless sensor networks) applications. We present a new cruise positioning algorithm, where a single beacon node cruises an entire network, broadcasting its position periodically with fixed signal strength. Meawhile, an unknown node obtains the location and the corresponding distance of the beacon node. Two possible position coordinate sets for the unknown node can then be calculated, and the coordinate mean of the point set with a small variance is an unbiased estimator of the unknown node's coordinate. The positioning algorithm's computation is simple, positioning accuracy is not limited by a physical distance measuring tool, and there are no restrictions on the path of motion, which makes it more realistic. An algorithm simulation is presented to compare the original and cruise location algorithm. Numerical results show that the cruise localization algorithm can achieve good positioning accuracy.

关键词： Wireless communication Location awareness Wireless sensor networks Position measurement Broadcasting Distance measurement Hardware

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Fatigue crack propagation across grain boundary of Al-Cu-Mg bicrystal based on crystal plasticity XFEM and cohesive zone model

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Journal of Materials Science & technology 2022年第31期126卷 275-287页

作者： Qi Zhao Magd Abdel Wahab Yong Ling Zhiyi Liu School of Materials Science and Engineering Hubei University of Automotive TechnologyShiyan 442002China Faculty of Mechanical-Electrical and Computer Engineering School of Engineering and TechnologyVan Lang UniversityHo Chi Minh CityVietnam Soete Laboratory Faculty of Engineering and ArchitectureGhent UniversityTechnologiepark Zwijnaarde 903B-9052 ZwijnaardeBelgium School of Materials Science and Engineering Central South UniversityChangsha 410083China

In this paper,a methodology integrating crystal plasticity(CP),the eXtended finite element method(XFEM)and the cohesive zone model(CZM)is developed for an Al-Cu-Mg alloy to predict fatigue crack propagation(FCP)across grain boundary(GB)of Al-Cu-Mg alloy during stageІІ.One GB model is incor-porated into FCP constitutive law to describe grain interaction at GB.A bicrystal containing GB is built up to simulate FCP behavior through L participated *** features including GB characteristic,cumulative plastic strain(CPS)distribution and crystal slipping evidence can be *** numer-ical results are compared with published experimental data to check the accuracy of *** work demonstrates that the combination of CP containing GB constitutive laws,XFEM and CZM is a promising methodology in predicting twist angle-controlled crack deflection through GBs.

关键词： Grain boundary Twist angle Crystal plasticity Extended finite element method Fatigue crack propagation Cumulative plastic strain

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Enhanced Federated Learning for Iot Systems

SSRN

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SSRN 2023年

作者： Mora-Gimeno, Francisco José Elouali, Aya Mora, Higinio Mora Szymanski, Julian Department of Computer Science Technology and Computation University of Alicante Alicante Spain Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

Federated learning consists on training several local models on edge devices where data is collected. These models are then shared with the cloud where the general model is calculated and transmitted back to the edge. This process is repeated several times until the general model achieves the desired accuracy. This technique permits training a model on the largest possible quantity of data without having to share the device’s private data with other devices or the cloud. However, the main drawback of this method is transmitting big size models multiple time on the network from and to every edge device participating in the training. In this paper, we propose a data transmission reduction method in federated learning context which, based on data similarity, reduces the amount of weights to be transmitted to the cloud. Several experiments has been performed to prove the effectiveness of the method and define the best components to use it such as data representation method and change calculation method. The performed experiments revealed that the proposed solution reduces data transmission up to 75% while having only 0.02% accuracy deterioration © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Efficient adaptation of large vision transformer via adapter re-composing 23

Efficient adaptation of large vision transformer via adapter...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Wei Dong Dawei Yan Zhijun Lin Peng Wang College of Information and Control Engineering Xi'an University of Architecture and Technology and Xi'an Hypersonic Measurement Technology Co. Ltd. College of Information and Control Engineering Xi'an University of Architecture and Technology School of Computer Science Northwestern Polytechnical University School of Computer Science and Engineering University of Electronic Science and Technology of China

The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained models. Consequently, effectively adapting large pre-trained models to downstream tasks in an efficient manner has become a prominent research area. Existing solutions primarily concentrate on designing lightweight adapters and their interaction with pre-trained models, with the goal of minimizing the number of parameters requiring updates. In this study, we propose a novel Adapter Re-Composing (ARC) strategy that addresses efficient pre-trained model adaptation from a fresh perspective. Our approach considers the reusability of adaptation parameters and introduces a parameter-sharing scheme. Specifically, we leverage symmetric down-/up-projections to construct bottleneck operations, which are shared across layers. By learning low-dimensional re-scaling coefficients, we can effectively re-compose layer-adaptive adapters. This parameter-sharing strategy in adapter design allows us to further reduce the number of new parameters while maintaining satisfactory performance, thereby offering a promising approach to compress the adaptation cost. We conduct experiments on 24 downstream image classification tasks using various Vision Transformer variants to evaluate our method. The results demonstrate that our approach achieves compelling transfer learning performance with a reduced parameter count. Our code is available at https://***/DavidYanAnDe/ARC.

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FlatProxy: A DPU-centric Service Mesh architecture for Hyperscale Cloud-native Application

arXiv

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arXiv 2023年

作者： Li, Ming Lu, Wenyan Lin, Hanyue Wu, Jingya Zhang, Yu Yan, Guihai State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Service mesh is a fundamental technology for building cloud-native applications, which ensures the stable running of a large number of services by an intermediate layer that governs communication between services. However, service mesh is not well suited for high-performance scenarios. The root cause is that the current service mesh is not suitable for the evolution of cloud-native applications. On the one hand, the service mesh built on CPU cannot listen to communication bypassing the CPU. On the other hand, service mesh includes many I/O-intensive and computationally-intensive tasks that can overload CPU cores as traffic grows beyond CPU performance. Therefore, we propose a data-centric service mesh that migrates the proxy of the service mesh to the entrance of the network. Moreover, we also design the DPU-centric FlatProxy, a data-centric service mesh based on DPU. There are three advantages to the DPU-centric service mesh. Firstly, it takes over all traffic flow in and out of the node, which expands the sense scale of the service mesh from container to node. Secondly, it improves communication performance and reduces host resource usage by offloading some functions and optimizing communication. Thirdly, it minimizes performance and security issues through the physical isolation of business services and cloud infrastructure. Compared with Envoy, the current mainstream service mesh implementation, FlatProxy reduces latency by 90% and improves throughput by 4x in Gbps and 8x in qps, and it only occupies a small amount of CPU resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Mesh generation

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