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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Henrique Souza Rossi Karan Mitra Christer Åhlund Irina Cotanis Niclas Ögren Per Johansson Department of Computer Science Electrical and Space Engineering Mobile and Pervasive Computing Luleå University of Technology Skellefteå Sweden Infovista Skellefteå Sweden

Quality of Experience (QoE) subjective assessment often demands setting up expensive lab experiments that involve controlling several software programs and services. In addition, these experiments may pose significant challenges regarding man-agement of testbed software components as they may have to be synchronized for efficient data collection, leading to human errors or loss of time. Further, maintaining error-free repeatability between subsequent subjective tests and comprehensive data collection is essential. Therefore, this paper proposes, develops and validates ALTRUIST, a multi-platform tool that assists the experimenter in conducting subjective tests by controlling external applications, facilitates data collection and automates test execution for conducting repeatable subjective tests in broad application areas.

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Model Trip: Enhancing Privacy and Fairness in Model Fusion Across Multi-Federations for Trustworthy Global Healthcare

Model Trip: Enhancing Privacy and Fairness in Model Fusion A...

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International Conference on Data Engineering

作者： Qian Chen Yiqiang Chen Bingjie Yan Xinlong Jiang Xiaojin Zhang Yan Kang Teng Zhang Wuliang Huang Chenlong Gao Lixin Fan Qiang Yang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Peng Cheng Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Webank Hong Kong University of Science and Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Federated Learning has emerged as a revolutionary innovation in the evolving landscape of global healthcare, fostering collaboration among institutions and facilitating collaborative data analysis. As practical applications continue to proliferate, numerous federations have formed in different regions. The optimization and sustainable development of federation-pretrained models have emerged as new challenges. These challenges primarily encompass privacy, population shift and data dependency, which may lead to severe consequences such as the leakage of sensitive information within models and training samples, unfair model performance and resource burdens. To tackle these issues, we propose FairFusion, a cross-federation model fusion approach that enhances privacy and fairness. FairFusion operates across federations within a Model Trip paradigm, integrating knowledge from diverse federations to continually enhance model performance. Through federated model fusion, multi-objective quantification and optimization, FairFusion obtains trustworthy solutions that excel in utility, privacy and fairness. We conduct comprehensive experiments on three public real-world healthcare datasets. The results demonstrate that FairFusion achieves outstanding model fusion performance in terms of utility and fairness across various model structures and subgroups with sensitive attributes while guaranteeing model privacy.

关键词： Training Privacy Data privacy Technological innovation Collaboration Medical services Data models

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Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images 26

Explainable AI-Based Humerus Fracture Detection and Classifi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Barua, Koushick Mahmud, Tanjim Barua, Anik Sharmen, Nahed Basnin, Nanziba Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Hossain, Sazzad Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Maa-O-Shishu Hospital Medical College Dept. of Obstetrics and Gynecology Chittagong4100 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Chittagong4202 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden Department of Computer Science and Engineering Dhaka1207 Bangladesh

ISBN: (纸本)9798350359015

The human skeletal framework relies heavily on bones, and one such crucial component is the 'Humerus.' Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available 'MURA' dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)"and "Ensemble-2 (DenseNet121 and DenseNet169),"which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model's predictions, providing valuable insights for medical practitioners. © 2023 IEEE.

关键词： Convolutional neural network Explainable AI (XAI) Humerus X-ray diagnosis

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An Efficient Spatial-Temporal Representation Method for EEG Emotion Recognition 19

An Efficient Spatial-Temporal Representation Method for EEG ...

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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： Weng, Weining Gu, Yang Chen, Yiqiang Wang, Guoqiang Shi, Nianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China School of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China

ISBN: (纸本)9798350346558

Affective computing based on electroencephalogram (EEG) has been widely studied due to the high correlation between the brain and emotions. Neurological research shows that emotions are generated by the interaction of neural circuits separated in different brain regions. Recently, many deep learning methods have been demonstrated to recognize different emotions. However, these methods extract features at the data level, ignoring the brain's neural mechanisms of emotion generation, which may result in the features' weak ability to express emotion. To solve this problem and extract more discriminant features according to the neural mechanism of emotion generation, this article proposes an attention based spatial-temporal representation method (ASTR) for emotion recognition. The ASTR method constructs spatial-temporal representations of emotion from neural circuits' activity in different brain regions. In the model, the cascade attention quantifies the multi-granularity correlation and information exchange pattern between brain regions as the spatial features, which measure neural circuits' activation and information interaction in different brain regions. The one-way transformer introduces temporal order in spatial features, calculates temporal variation features from the long-term activity of neural circuits and generates the emotion representation. The representation incorporates spatial-temporal features of neural circuits' activity during emotion generation, which is highly related to emotion. We conducted experiments on SEED and DEAP datasets. The experimental results show that the emotion recognition accuracy of our model is about 3.0% higher than that of the SOTA algorithms, reaching more than 98%. The results also visualize the emotion generation process, revealing how the brain works under different emotions. © 2022 IEEE.

关键词： Emotion Recognition

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A Cost-Effective Approach for NDN-Based Internet of Medical Things Deployment

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computers, Materials & Continua 2022年第1期70卷 233-249页

作者： Syed Sajid Ullah Saddam Hussain Abdu Gumaei Mohsin SAlhilal Bader Fahad Alkhamees Mueen Uddin Mabrook Al-Rakhami Department of Information Technology Hazara University MansehraKPK21120Pakistan Research Chair of Pervasive and Mobile Computing Department of Information SystemsKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Faculty of Applied SciencesTaiz UniversityTaiz6803Yemen Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Digital Science Faculty of ScienceUniversiti Brunei DarussalamJln Tungku linkGadongBruneiBE1410Darussalam

Nowadays,healthcare has become an important area for the Internet of Things(IoT)to automate healthcare facilities to share and use patient data anytime and anywhere with Internet *** present,the host-based Internet paradigm is used for sharing and accessing healthcare-related ***,due to the location-dependent nature,it suffers from latency,mobility,and *** this purpose,Named Data Networking(NDN)has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet ***,the novel breed lacks a secure framework for *** article constructs an NDN-Based Internet of Medical Things(NDN-IoMT)framework using a lightweight certificateless(CLC)*** adopt the Hyperelliptic Curve Cryptosystem(HCC)to reduce cost,which provides strong security using a smaller key size compared to Elliptic Curve Cryptosystem(ECC).Furthermore,we validate the safety of the proposed scheme through *** cost-efficiency,we compare the designed scheme with relevant certificateless signature *** final result shows that our proposed scheme uses minimal network ***,we deploy the given framework on NDN-IoMT.

关键词： Internet of Medical Things healthcare Named Data Networking

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A Secure NDN Framework for Internet of Things Enabled Healthcare

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computers, Materials & Continua 2021年第4期67卷 223-240页

作者： Syed Sajid Ullah Saddam Hussain Abdu Gumaei Hussain AlSalman IT Department Hazara UniversityMansehra21120Pakistan Research Chair of Pervasive and Mobile Computing Department of Information SystemsCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Taiz UniversityTaizYemen Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Healthcare is a binding domain for the Internet of Things(IoT)to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the *** current IP-based Internet architecture suffers from latency,mobility,location dependency,and *** Named Data Networking(NDN)has been projected as a future internet architecture to cope with the limitations of IP-based ***,the NDN infrastructure does not have a secure framework for IoT healthcare *** this paper,we proposed a secure NDN framework for IoTenabled Healthcare(IoTEH).In the proposed work,we adopt the services of Identity-Based Signcryption(IBS)cryptography under the security hardness Hyperelliptic Curve Cryptosystem(HCC)to secure the IoTEH information in *** HCC provides the corresponding level of security using minimal computational and communicational resources as compared to bilinear pairing and Elliptic Curve Cryptosystem(ECC).For the efficiency of the proposed scheme,we simulated the security of the proposed solution using Automated Validation of Internet Security Protocols and Applications(AVISPA).Besides,we deployed the proposed scheme on the IoTEH in NDN infrastructure and compared it with the recent IBS schemes in terms of computation and communication *** simulation results showed the superiority and improvement of the proposed framework against contemporary related works.

关键词： Named data networking healthcare identity-based signcryption

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E3Sym: Leveraging E(3) Invariance for Unsupervised 3D Planar Reflective Symmetry Detection

E3Sym: Leveraging E(3) Invariance for Unsupervised 3D Planar...

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International Conference on computer Vision (ICCV)

作者： Ren-Wu Li Ling-Xiao Zhang Chunpeng Li Yu-Kun Lai Lin Gao Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Informatics Cardiff University

Detecting symmetrical properties is a fundamental task in 3D shape analysis. In the case of a 3D model with planar symmetries, each point has a corresponding mirror point w.r.t. a symmetry plane, and the correspondences remain invariant under any arbitrary Euclidean transformation. Our proposed method, E3Sym, aims to detect planar reflective symmetry in an unsupervised and end-to-end manner by leveraging E(3) invariance. E3Sym establishes robust point correspondences through the use of E(3) invariant features extracted from a lightweight neural network, from which the dense symmetry prediction is produced. We also introduce a novel and efficient clustering algorithm to aggregate the dense prediction and produce a detected symmetry set, allowing for the detection of an arbitrary number of planar symmetries while ensuring the method remains differentiable for end-to-end training. Our method also possesses the ability to infer reasonable planar symmetries from incomplete shapes, which remains challenging for existing methods. Extensive experiments demonstrate that E3Sym is both effective and robust, outperforming state-of-the-art methods.

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Explainable AI for Tomato Leaf Disease Detection: Insights into Model Interpretability

Explainable AI for Tomato Leaf Disease Detection: Insights i...

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International Conference on computer and Information Technology (ICCIT)

作者： Tanjim Mahmud Koushick Barua Anik Barua Nanziba Basnin Sudhakar Das Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Leeds Beckett University UK Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå Sweden

Plant diseases pose a significant threat to global food security by limiting access to safe and abundant food sources while impacting agricultural productivity and food safety. To address this challenge, innovative disease detection techniques are crucial. This paper presents a novel approach to tomato leaf disease detection grounded in deep transfer learning. We employ convolutional neural networks (CNNs) to recognize and categorize diseases, leveraging pre-trained models such as EfficientNetB3, Xception, and mobileNetV2. Our experimental results highlight the efficacy of these models, with the CNN achieving an accuracy of 0.92, mobileNetV2 at 0.93, Xception at 0.94 and EfficientNetB3 at 0.993. By combining these machine learning techniques with image processing methods, we create a system capable of diagnosing tomato leaf diseases promptly. The system employs image processing techniques to facilitate disease recognition, enabling automated disease identification as soon as symptoms manifest on leaves or plants. This empowers farmers with timely information and appropriate treatment options, ultimately leading to precise disease management. Furthermore, our research delves into model interpretability by employing Saliency maps and Grad-CAM techniques, providing valuable insights into the decision-making process of our deep learning models. This transparency enhances the trustworthiness of the AI-driven disease detection system, fostering its practical application in agriculture.

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Energy Efficient Probabilistic Semantic Communication over SAGIN

Energy Efficient Probabilistic Semantic Communication over S...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Gan, Xu Huang, Chongwen Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Hangzhou China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Southeast University National Mobile Communications Research Laboratory Nanjing China

ISBN: (纸本)9798350351255

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Detecting human-object interaction with multi-level pairwise feature network

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Computational Visual Media 2021年第2期7卷 229-239页

作者： Hanchao Liu Tai-Jiang Mu Xiaolei Huan Key Laboratory of Pervasive Computing Ministry of EducationBNRistDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China College of Information Sciences and Technology Pennsylvania State UniversityUniversity ParkPA 16802USA.

Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an *** studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the *** argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the *** thus propose a multi-level pairwise feature network(PFNet)for detecting human–object *** network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action *** experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.

关键词： human–object interaction detection pairwise feature network deep learning multi-level object instance

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