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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： Nguyen, Minh Yan, Wei Qi Auckland University of Technology Department Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350330014

This paper presents an innovative Smart Traffic Light System designed to assess Emotional Expression in enclosed settings such as corporate offices and educational facilities. The system combines artificial intelligence, super-resolution algorithms, and facial recognition to provide both real-time and longitudinal insights into the collective emotional atmosphere. Utilizing a three-tier light indicator, the system visualizes emotional trends in the short-term (immediate), medium-term (daily or weekly), and long-term (semi-annual or annual) contexts, employing a color-coded scheme to denote positive or negative emotional states. This augments situational awareness and contributes to the emotional well-being of individuals in these settings. The underlying technology leverages closed-circuit television (CCTV) footage and introduces a state-of-the-art temporal expression recognition mechanism to capture a broad spectrum of human emotions. A notable innovation is the incorporation of facial super-resolution techniques to enhance the quality of low-resolution facial data, thereby boosting the system's overall accuracy and robustness. This enhancement is accomplished while maintaining adherence to ethical standards for data collection and usage. This system has implications that reach far beyond simple emotional recognition. It serves as a cornerstone for the development of emotionally intelligent smart cities. Providing both immediate feedback and historical data on community emotional states, it enables more informed decision-making, streamlined resource allocation, and ultimately, an improvement in the overall quality of life. This research contributes to the evolving discussion on how smart cities can foster communities that are not just more efficient, but also more emotionally resilient. © 2023 IEEE.

关键词： Smart city

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Performance Optimization of SAC-OCDMA Network Based on 2-D CS Code Utilizing Two Light Sources 6

Performance Optimization of SAC-OCDMA Network Based on 2-D C...

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6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024

作者： Alayedi, Mohanad Jaradat, Ahmad M. Elgammal, Ziad Malkawi, Malek Haliç University Department of Software Engineering Istanbul Turkey University of Detroit Mercy Department of Electrical and Computer Engineering and Computer Science MI United States Istanbul Medipol University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331529987

This study introduces a novel 2-dimensional (2D) spectral/temporal code, termed 2D-cyclic shift (2D-CS) code, optimized for non-coherent Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) systems. Computational results demonstrate that the 2D-CS code has fostered the SAC-OCDMA system's performance whence spectral efficiency (SE), phase induced intensity noise (PIIN), and cardinality. Specifically, the 2D-CS code can increase cardinality by up to 117%, 89%, and 61% compared to one-dimensional CS (1D-CS), 2D-multi service (2D-MS), and 2D-diluted perfect difference (2D-DPD) codes, respectively. The system's performance was evaluated using Optisystem software, showing significant improvements in quality factor (QF) and bit error rate (BER) with LED and laser light sources for four active users at 115 dBm transmitted power and 20,000 m distance. © 2024 IEEE.

关键词： Code division multiple access

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Variational Collective Graph AutoEncoder for Multi-behavior Recommendation 23

Variational Collective Graph AutoEncoder for Multi-behavior ...

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23rd IEEE International Conference on Data Mining, ICDM 2023

作者： Liu, Yang Rao, Qianzhen Pan, Weike Ming, Zhong Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen China

ISBN: (纸本)9798350307887

Variational autoencoder (VAE) is known as a classic and effective method in modeling users' homogeneous behaviors in recommender systems. In recent years, graph neural networks (GNNs) have achieved promising performance in learning users' preferences by modeling complex relationships between users and items. However, most VAE- and GNN-based methods are for single-behavior recommendation, rather than the more prevalent counterpart in real-world applications, i.e., multi-behavior recommendation. This motivates us to leverage VAE and GNNs to address the more important and challenging problem of multi-behavior recommendation. Traditional multi-behavior recommendation models have not captured the complex transition relationships across different types of behaviors well. Moreover, they often ignore the varying semantic strength of different types of behaviors. To tackle the above two challenges, we propose a novel multi-behavior recommendation framework named VCGAE (short for Variational Collective Graph AutoEncoder), which inherits the advantages of VAE and GNNs to fully exploit the multi-behavior data. Specifically, we design a behavior transition network that considers different transition relationships across different types of behaviors in order to learn users' personal preferences. In addition, we introduce a behavior contrastive regularization module that extracts different correlations between users' auxiliary behaviors and target behavior. By doing so, we gain a deeper understanding of the semantic strength of different behaviors, thereby improving the overall recommendation performance. Extensive experiments on three real-world datasets clearly demonstrate the effectiveness of our VCGAE compared with the state-of-the-art methods. © 2023 IEEE.

关键词： Graph neural networks

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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GPU Accelerated Modelling and Real-time Rendering of Fluid Motion 38

GPU Accelerated Modelling and Real-time Rendering of Fluid M...

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Valentine, William Mukundan, Ramakrishnan University of Canterbury Dept. Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes a fluid rendering pipeline that uses OpenGL-4 shaders to employ the parallel processing capabilities of the GPU. The fluid's surface mesh is produced using tessellation shader stages where the input patches are assigned tessellation levels based on the fluid heightmap's curvature. The curvature is stored using a texture buffer object which allows access by shaders, thus allowing the tessellation calculations to be carried out in parallel. Use of this adaptive tessellation method increases both the simulation's framerate as well as its capacity to handle a greater number of primitives. Furthermore, it more optimally distributes the mesh's vertices to effectively increase the level of detail without using more primitives. Polygon culling using the geometry shader further optimises the number of primitives used to define the fluid surface. The Phong-Blinn model is used for surface lighting. We propose two GPU-based fluid surface flow visualisation methods. Texture buffer objects can be used to store and update a surface texture. Alternatively, particle positions are updated each frame using the geometry shader and stored in a buffer object using transform feedback. These flow visualisation techniques are particularly effective for communicating the swirling motion of vortices. © 2023 IEEE.

关键词： Graphics processing unit

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Navigating the Digital Twin Network landscape:A survey on architecture,applications,privacy and security

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High-Confidence Computing 2024年第4期4卷 129-149页

作者： Akshita Maradapu Vera Venkata Sai Chenyu Wang Zhipeng Cai Yingshu Li Department of Computer and Information Sciences Towson UniversityTowson21252USA Department of Software Engineering and Game Development Kennesaw State UniversityMarietta30060USA Department of Computer Science Georgia State UniversityAtlanta30303USA

In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity *** has led to the development and commercialization of Digital Twin(DT)*** widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical *** create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT *** high volume of user data and the ubiquitous communication systems in DTNs come with their own set of *** most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data ***,currently,there is not enough literature that focuses on privacy and security issues in DTN *** this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their ***,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective *** then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the *** also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the ***,we provide some open research issues and problems in the field of DTN privacy and security.

关键词： Digital Twin Networks Network architecture Privacy and security Federated learning Blockchain Internet of Vehicles 6G Supply chain

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Federated Social Recommendation with Rényi Differential Privacy 3

Federated Social Recommendation with Rényi Differential Pri...

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3rd International Symposium on computer Technology and Information science, ISCTIS 2023

作者： Wang, Tian Jiao Wu, Jie School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China

ISBN: (纸本)9798350325386

A social recommendation system based on graph neural networks is a system that uses social relationships between users to generate personalized recommendations. To improve recommendation accuracy, it is usually necessary to collect a large amount of user behavior data, which may lead to user privacy breaches and data misuse. Existing privacy protection methods often sacrifice recommendation effectiveness or increase computational costs. Therefore, this article proposes a federated social recommendation system (FRSRec) based on Rényi differential privacy. By combining hash encryption technology with Rényi differential privacy, user data privacy can be protected while ensuring recommendation effectiveness and computational efficiency. This article conducted experimental evaluations on three real datasets. The experimental results show that the FRSRec model achieves optimal results in both MAE and RMSE, demonstrating that the FRSRec model can effectively achieve privacy protection while improving recommendation quality. © 2023 IEEE.

关键词： Recommender systems

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SkeMo: Sketch Modeling for Real-Time Model Component Generation 26

SkeMo: Sketch Modeling for Real-Time Model Component Generat...

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26th ACM/IEEE International Conference on Model Driven engineering Languages and Systems, MODELS 2023

作者： Chapai, Alisha Sharma Rapos, Eric J. Miami University Department of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350324808

software modeling is a powerful tool in the design and implementation of high-quality software systems. Models can be used from high-level design to formal code generation, with various applications in between. Often, software models are initially created informally, by sketching on a whiteboard or paper during the early design phase of the system, and eventually converted into formal models using advanced modeling tools. The formalization of sketches into actual model components can be time-consuming, error-prone, and laborious. To address these shortcomings, we present SkeMo, an environment for real-Time model component generation from sketch-based inputs. We curated a sketch dataset of 3000 images of various class diagram components and implemented a powerful Convolution Neural Network to classify input sketches as model components. We integrated our sketch classifier into an existing web-based model editor and added a touch interface to support sketch-based modeling. We evaluated the SkeMo environment in two ways: Through ten-fold cross-validation of the image classifier and collection of metrics and feedback from a 20-participant user study. Based on our results, sketch-based modeling demonstrates significant promise as an intuitive interface that is both easy to use and allows for faster model creation among most users. © 2023 IEEE.

关键词： Deep neural networks

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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RealDriftGenerator: A Novel Approach to Generate Concept Drift in Real World Scenario

RealDriftGenerator: A Novel Approach to Generate Concept Dri...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Borong Huang, Chao Zhu, Xiaohui Jin, Nanlin School of Advance Technology Xi'an Jiaotong-Liverpool University Department of Computer Science and Software Engineering SuZhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9781665410205

Concept drift refers to the probability distribution of data generation changes over time in a data stream environment. In recent years, there has been an increasing interest in drift detection models. However, due to the lack of labeled concept drift datasets, most researchers tend to using synthetic drift data generators for model training. These generators only have relatively simple feature distributions, which fail to capture the complexity found in real-world scenarios. This paper introduces a real scenario concept drift label generator (RealDriftGenerator). This generator aims to preserve the complexity and temporal correlation of real-world scenario while generating concept drifts with user defined drift positions and drift widths. The validation result show that the temporal correlation coefficients of RealDriftGenerator is significantly higher than benchmark drift generators. Additionally, the ability of RealDriftGenerator to capture the complexity in real-world scenarios is 20% higher than benchmark drift generators(measured by model performance). The source code of RealDriftGenerator has been published on https://***/sniperrifle71/realDriftGenerator. © 2024 IEEE.

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