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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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Augmenting Images with a Mid-Processing Unit to Enhance Classification Accuracy 13

Augmenting Images with a Mid-Processing Unit to Enhance Clas...

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13th international symposium on Communication systems, networks and Digital Signal Processing (CSNDSP)

作者： Johnson, Gordon Argyriou, Vasileios Politis, Christos Kingston Univ Comp Sci & Math London England

ISBN: (数字)9781665410441

ISBN: (纸本)9781665410441

Expression recognition is a challenging task. This paper aims to improve upon the accuracy of an existing Machine Learning classification system, with no-retraining of the existing model, by augmenting the images to improve the classification accuracy. A Mid-Processing Unit is used to manipulate data from the first pass of the classifier, this enhances the original image and improves the overall accuracy result. Three, dimensional reduction algorithms are explored as methods to augment the images;Principal Component Analysis, T-distributed Stochastic Neighbour Embedding, and Non-Negative Matrix Factorisation. Facial Landmarks are also explored as an additional data source. Two phased testing was used;1. to identify which method combination most improved accuracy, and 2. to fine tune the applied weight to the original images. The final results showed that T-distributed Stochastic Neighbour Embedding in combination with a weight set to 0.024, achieved an almost 1% increase in classifier accuracy.

关键词： artificial intelligence machine learning expression recognition t-distributed stochastic neighbour embedding classification

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Forti-Ins: A Blockchain Based Framework to Automate Healthcare Insurance Processing in Smart Cities 9

Forti-Ins: A Blockchain Based Framework to Automate Healthca...

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9th IEEE international symposium on Smart Electronic systems, iSES 2023

作者： Alruwaill, Musharraf N. Mohanty, Saraju P. Kougianos, Elias University of North Texas Department of Computer Science and Engineering United States University of North Texas Department of Electrical Engineering United States

ISBN: (纸本)9798350383249

Individuals with health insurance are protected from financial risk and have access to critical medical treatments. However, traditional healthcare insurance has issues such as complexity, availability, claim processing time, fraudulent claims, and double claims. These problems can lead to issues such as patient funds being held, lengthy claim processing, and fraudulent claims. The claim process is handled manually in order to verify each medical treatment, validate the claim, and ensure that it complies with the coverage policy. Significant advantages of an automated system for healthcare insurance procedures include a reduction in time, increased accuracy, and improved insurance service quality. However, the centralized nature of such systems may present vulnerabilities, such as a single point of failure, loss of transparency, and integrity. The proposed Forti-Ins system integrates blockchain technology, smart contracts, and a distributed file system to facilitate automated claim processing, prevent double claims, increase transparency, reduce administrative costs, and strengthen system robustness, all within a secure framework. Smart contracts facilitate the automated handling of healthcare insurance procedures in a secure manner, while distributed file systems provide cost-effective management of large volumes of files. © 2023 IEEE.

关键词： Transparency

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Security-Reliability Tradeoff for Multiple Friendly Jammers Aided Relay Selection in Wireless networks 16

Security-Reliability Tradeoff for Multiple Friendly Jammers ...

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16th international Conference on Cyber-Enabled distributed Computing and Knowledge Discover, CyberC 2024

作者： Xiao, Tianle Zou, Ronggui Chinese Academy of Sciences Institute of Information Engineering BeiJing China China Mobile Zijin Innovation Institute NanJing China

ISBN: (纸本)9798331506896

In this paper, we consider a cooperative relay network consisting of a source, multiple intermediates, and a destination in the face of an eavesdropper. We propose a multiple friendly jammers (FJs) aided relay selection (MFJRS) scheme where an intermediate that maximizes the main channel capacity is chosen as the best relay, and the remaining intermediates act as FJs which can emit artificial noises to confuse the eavesdropper. We derive closed-form expressions of both outage and intercept probabilities and analyze the security-reliability tradeoff (SRT) performance for the MFJRS scheme. Numerical results show that the MFJRS scheme performs better than a conventional relay selection (CRS) and one FJ aided relay selection (OFJRS) schemes in terms of their SRT performance, indicating the effectiveness of multiple FJs in enhancing transmission security and reliability. It is also found that optimizing the power allocation factor between the best relay and multiple FJs further enhances the SRT performance. © 2024 IEEE.

关键词： Relay control systems

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A distributed user-oriented IoT-based Air Pollution Monitoring 28

A distributed user-oriented IoT-based Air Pollution Monitori...

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IEEE 28th international Workshop on computer-Aided Modeling and Design of Communication Links and networks (CAMAD)

作者： Figueroa, Jose Jorge Figueroa Shen, Hao Ahmadi, Hamed Canberk, Berk Univ York Sch Phys Engn & Technol York N Yorkshire England Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9798350303490

Environmental health services are paramount for ensuring the Quality of Life (QoL) in cities, with urban design playing a pivotal role. The rapid urbanization of cities globally has exacerbated air pollution, posing severe health risks to residents. Air quality is closely tied to human health, and elevated pollutant levels can lead to various illnesses, particularly affecting vulnerable populations. Indoor and outdoor air pollution is a leading cause of premature deaths in some regions. To address these challenges, governments and organizations are investing in air quality monitoring. This paper presents an IoT-based Air Pollution Monitor (APMIoT) comprising Raspberry Pico W embedded wireless systems and multiple sensors. These APMIoT modules transmit real-time data to a cloud server architecture, enabling the computation of pollutant indices and an Air Quality Index (AQI). Users can monitor and compare air quality data through an intuitive dashboard interface. The APMIoT includes key sensor modules for measuring particle matter, humidity, temperature, GPS coordinates, total volatile organic compounds (TVOC), and CO2 levels. It utilizes FreeRTOS for modular programming, ensuring independent handling of various processes. ThingSpeak serves as the cloud server, collecting, analysing, and visualizing data from single and multiple APMIoT devices. Custom analytics provide in-depth pollutant analysis and AQI representation. Testing demonstrates system reliability and alignment with external AQI data sources. This paper underscores the importance of open-source, customizable air quality monitoring systems for both indoor and outdoor settings. It offers a robust tool for environmental monitoring and air quality assessment, essential for managing urban health and well-being effectively.

关键词： Real-Time Insights Air Pollution Monitoring ThingSpeak Adaptability MQTT Collective IoT

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OSN Rumor Control Model Based on Community Immunization 22

OSN Rumor Control Model Based on Community Immunization

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Dong, Ming Sun, Zhiqiang Tan, Zhenhua Zhang, Bin Northeastern University School of Computer Science and Engineering Shenyang China Northeastern University Software College Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Ministry of Education China

ISBN: (纸本)9798331509712

This paper proposes a rumor control model based on community immunization. Based on the community division and the trust network inference algorithm, the model redefines the standard to measure the importance of nodes in the network. First, the model uses the Louvain clustering algorithm based on the Ochiai coefficient to discover the network community and then presents the trust network inference algorithm. By analyzing the key factors that affect trust transfer between nodes, the trust evaluation between unfamiliar nodes is inferred, and important nodes with a high degree of trust in the network community are calculated. Finally, combined with the characteristics of inner degree and outer degree centrality of nodes in the network community, five types of important nodes in the network are screened out. To avoid repeated selection of nodes, this paper identifies a group of key nodes in the network community for local immunization by means of deduplication and taking intersection, so as to realize effective control of rumors in the network. © 2024 IEEE.

关键词： Clustering algorithms

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Data-Driven distributed Autonomous Architecture for 6G networks 1

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1st international Artificial Intelligence Conference, IAIC 2023

作者： Li, Pengyu Chen, Xinyu Sun, Zhenqiang Xing, Yanxia Zhou, Jianfeng Fan, Wanpeng 6G Research Center China Telecom Research Institute Beijing China Wireless Product Operation ZTE Corporation Nanjing China

ISBN: (数字)9789819712809

ISBN: (纸本)9789819712793

Currently, the industry has put forward new scenarios, including ISAC (Integrated sensing and communication), computing and network coordination, and ubiquitous intelligence, for potential application to 6G networks in the future. Addressing the systematic and organic integration of data, computing, and communication becomes a primary concern. Meanwhile, the commercialization of 5G networks on a large scale has gradually revealed some new issues, such as network reliability, flexibility, rapid deployment, and service customization, all of which can benefit from further improvement. In this paper, we propose a new intelligent and simplified architecture for 6G networks. We detail its functionalities, key features and procedures, and illustrate how to implement a new service in the future based on this architecture. This preliminarily verifies the capability of the architecture to enhance the existing networks while meeting future service requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： 5G mobile communication systems

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NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator 51

NeuraChip: Accelerating GNN Computations with a Hash-based D...

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ACM/IEEE 51st Annual international symposium on computer Architecture (ISCA)

作者： Shivdikar, Kaustubh Agostini, Nicolas Bohm Jayaweera, Malith Jonatan, Gilbert Abellan, Jose L. Joshi, Ajay Kim, John Kaeli, David Northeastern Univ Boston MA 02115 USA Korea Adv Inst Sci & Technol Daejeon South Korea Univ Murcia Murcia Spain Boston Univ Boston MA USA

ISBN: (纸本)9798350326598;9798350326581

Graph Neural networks (GNNs) are emerging as a formidable tool for processing non-euclidean data across various domains, ranging from social network analysis to bioinformatics. Despite their effectiveness, their adoption has not been pervasive because of scalability challenges associated with large-scale graph datasets, particularly when leveraging message passing. They exhibit irregular sparsity patterns, resulting in unbalanced compute resource utilization. Prior accelerators investigating Gustavson's technique adopted look-ahead buffers for prefetching data, aiming to prevent compute stalls. However, these solutions lead to inefficient use of the on-chip memory, leading to redundant data residing in cache. To tackle these challenges, we introduce NeuraChip, a novelGNN spatial accelerator based on Gustavson's algorithm. NeuraChip decouples the multiplication and addition computations in sparse matrix multiplication. This separation allows for independent exploitation of their unique data dependencies, facilitating efficient resource allocation. We introduce a rolling eviction strategy to mitigate data idling in on-chip memory as well as address the prevalent issue of memory bloat in sparse graph computations. Furthermore, the compute resource load balancing is achieved through a dynamic reseeding hash-based mapping, ensuring uniform utilization of computing resources agnostic of sparsity patterns. Finally, we present NeuraSim, an open-source, cycle-accurate, multi-threaded, modular simulator for comprehensive performance analysis. Overall, NeuraChip presents a significant improvement, yielding an average speedup of 22.1x over Intel's MKL, 17.1x over NVIDIA's cuSPARSE, 16.7x over AMD's hipSPARSE, and 1.5x over prior state-of-the-art SpGEMM accelerator and 1.3x over GNN accelerator. The source code for our open-sourced simulator and performance visualizer is publicly accessible on GitHub1.

关键词： Decoupled Computations Graph Neural networks (GNN) Hardware-software co-design On-chip Memory Sparse Matrix Multiplication (SpGEMM) Spatial Accelerators

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Vehicle Identification Using RESNET-50: CNN approach 3

Vehicle Identification Using RESNET-50: CNN approach

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3rd IEEE international Conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Singh, Kartikey Patil, Netra Mohite, Sagar Ganapati Jadhav, Swati Mohite, Snehal Gayakwad, Milind Joshi, Rahul College of Engineering Department of Computer Engineering Pune India BARTC's Vishwakarma Institute of Technology Department of Computer Engineering Pune India College of Engineering Cse Department Pune India College of Engineering Department of It Pune India Symbiosis Institute of Technology Cse Department Pune India

ISBN: (纸本)9798350354348

Vehicle This paper proposes a method for vehicle brand identification using a Convolution Neural Network (CNN) with improved Accuracy. The proposed method utilizes static image datasets and live CCTV surveillance data for training and real-time vehicle detection and recognition. This system can detect moving vehicles and recognize their brands, count them, and verify permits. Leveraging the advancements in deep learning, the proposed model builds upon classical CNNs with specific enhancements. This improved model is trained on a benchmark vehicle detection dataset and demonstrates superior performance in identifying vehicles on the road. The primary objective of this study is to develop a cost-effective and efficient solution for identifying vehicle brands across various applications. A deep learning-based classification technique is employed to achieve this goal. The model utilizes the popular ResNet-50 architecture combined with transfer learning techniques. Initial training yielded an accuracy of 34%. However, with further training, the model improved significantly, reaching an accuracy of 95.31%. A training-to-testing data split ratio of 43:7 was used. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Office Solutions: Monitoring and Enhancing Environmental Quality for Optimal Workplace Performance

Smart Office Solutions: Monitoring and Enhancing Environment...

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2024 international symposium on networks, computers and Communications, ISNCC 2024

作者： Gatea, Aseel Habeeb Al-Ibadi, Alaa Ali, Musaab Adil Computer engineering University of Basrah Basrah Iraq

ISBN: (纸本)9798350364910

Smart gadgets are a big part of our life since they make a lot of work easier and simpler, which saves us a lot of time and effort. With time, a lot of employees have put in lengthy workdays and a lot of effort at the office. As a result, sleep deprivation and work stress cause psychological and physical tension, which lowers output. In order to streamline work and shorten working hours, a smart office system has to be designed. The smart office system is made up of intelligent sensors that scan office data and connect it to a smartphone so that it may be managed remotely rather than by pressing. It is made up of a number of systems that increase system security, like the ones that regulate the lighting and temperature and who is allowed to enter the office. Traditional workplaces have been turned into extremely intelligent and efficient spaces through the integration of Artificial Intelligence (AI) into smart offices. This study of the literature explores the development, uses, advantages, difficulties, and potential future developments of AI-powered smart workplaces. It offers a thorough analysis of the current state of the field and highlights significant developments at the nexus of artificial intelligence and office environments. © 2024 IEEE.

关键词： Office automation

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