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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Cost-balanced Adaptive Replica Server Cache Management under...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Rohit Kumar Gupta Arka Prokash Mazumdar Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. this method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. the experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

关键词： Cloud computing Adaptation models Costs Content distribution networks Machine learning Predictive models Servers Resource management Time-varying systems

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RADAR: Robust Anomaly Detection and Recognition for IoT Botnet Attacks Using Hyper-Dimensional Models

RADAR: Robust Anomaly Detection and Recognition for IoT Botn...

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international conference on Signal Processing and Communication (ICSC)

作者： Abdullah M Alqarni Faiz S Alshehri Majed S Alharbi Nitin Talreja Sajal Bhatia School of Computer Science and Engineering Sacred Heart University Fairfield USA Principal Data Engineer Optum Services Inc. (United Health Group) Hartford USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

As our world continues to embrace digital technology, cyber threats have become increasingly prevalent, especially botnet attacks. Ensuring cybersecurity is a significant concern, and to address this, we need Intrusion Detection systems (IDS) that can automatically adapt to new threats using advanced machine learning (ML) and deep learning (DL) techniques. Our project aims to develop an advanced anomaly detection and recognition system for IoT botnet attacks using hyper-dimensional models namely Random Forest, Decision Tree, Gradient Boosting, Multi-layer Perceptron (MLP), and K-Nearest Neighbor (KNN) and makes uses of hyperparameter tuning for performance optimization. By integrating an ensemble voting classifier that combines these five algorithms using a soft-voting mechanism, our results indicate an improved detection accuracy and adaptability. the proposed ensemble-based model is tested on a publicly available dataset which offers comprehensive coverage of botnet attack patterns and outperforms all individual classifiers, achieving an accuracy of 99.88% and an ROC AUC score of 0.999. the proposed approach highlights the advantages of combining hyper-dimensional classification models in effectively detecting IoT botnet attacks.

关键词： Adaptation models Accuracy Botnet Intrusion detection Signal processing algorithms Nearest neighbor methods Radar signal processing Anomaly detection Tuning Random forests

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QCI Robot with Generative AI Knowledge Graph for Taiwanese/Japanese Co-Learning Model Application 29th

QCI Robot with Generative AI Knowledge Graph for Taiwanese/J...

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29th international conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

作者： Lee, Chang-Shing Wang, Mei-Hui Lee, Yu-Hsiang Kubota, Naoyuki Sato-Shimokawara, Eri Obo, Takenori Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Mechanical Systems Engineering Tokyo Metropolitan University Hachioji Japan Department of Computer Science Tokyo Metropolitan University Hachioji Japan

ISBN: (纸本)9789819645954

this paper proposes a Quantum Computational Intelligence (QCI) robot with a Generative AI (GAI) knowledge graph (KG) for Taiwanese and Japanese co-learning model applications. During the 2024 ieee CIS Summer School on QCI at Tokyo Metropolitan University (TMU) in Japan, we organized lectures and a hands-on workshop on QCI for young students to learn and experience QCI using the QCI&AI-FML learning tool and robot. Learners first observe, study, research, utilize, understand, and explain what they have learned in the heart-sutra-based human and machine co-learning model. We transcribed the collected multimodal data using the OpenAI Whisper models into English or Japanese texts. the GAI Knowledge Graph (GAIKG) agent generates the knowledge graph with concepts, relations, and communities for the transcribed texts by providing prompts with Large Language Models (LLMs), such as Trustworthy AI Dialogue Engine (TAIDE). the Sentence BERT (SBERT) similarity agent computes the similarity between the collected texts and the golden standard provided by the English/Japanese domain experts. Next, CI domain experts constructed the CI model based on the generated data from these two agents to infer the performance of the generated knowledge graph and deployed the model to the QCI robot. Finally, the QCI robot evaluates how well the generative knowledge graph leverages human and machine learning according to the constructed CI model. In addition, the QCI robot evaluates the performance of the generative knowledge graph in co-learning English/Japanese based on the constructed CI model and quantum fuzzy inference engine. In the future, we will extend the QCI robot to more countries for young students to co-learn CI, QCI, and Taiwanese/Japanese languages with smart machines. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer aided instruction

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Privacy-Preserving Model for Cyber threat Intelligence, Sharing Across Multi-Organizational Platforms

Privacy-Preserving Model for Cyber Threat Intelligence, Shar...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Shridhar Pandey H. Azath Rizwan Ur Rahman Hemraj Lamkuche School of Computing Science Engineering and Artificial Intelligence (SCAI) VIT Bhopal University Bhopal India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

the research presents a privacy-preserving structure for secure Cyber threat Intelligence (CTI) exchanging, using cutting-edge AI techniques to increase cooperative IT delicacy while keeping secret information safe. We propose a system based on Federated Learning, Differential Privacy and Homomorphic Encryption to model training in multiple organizations without exchanging the raw data. Using these privacy-preserving techniques together, organizations can collaborate to enhance their overall threat detection and response capabilities while maintaining the confidentiality of sensitive data. the implemented results indicate significant advancements with a 50%-55% detection accuracy overall for role intention measurement, model accuracy increase to 82% (predicting the next role intention), 30% computational overhead improvement, and a 10% improvement in novel threat detection. It provides 85% compliance against regulatory standards including GDPR and NIST. the applicability of the proposed solution on real-world data in the financial and healthcare domains demonstrates the proposed solution achieves a balance between privacy protection and CTI sharing still providing a powerful method for distributed, scalable and efficient collaboration in secure cybersecurity .

关键词： Differential privacy Privacy Accuracy Federated learning Collaboration Organizations threat assessment Cyber threat intelligence Homomorphic encryption computer security

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Blockchain Enabled Data Communication Scheme for Vehicular Named Data Networks

Blockchain Enabled Data Communication Scheme for Vehicular N...

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international conference on Signal Processing and Communication (ICSC)

作者： Anu Kaushik Rasmeet S Bali Computer Science & Engineering Department Chandigarh University India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

With the tremendous increase in the number of vehicles they exchange of information among them has also increased specifically aimed at road safety. Mostly, the information traveling in this network is important and personal, which brings security and privacy an important aspect during the information sharing. Here, Named Data Networking emerges as a futuristic network architecture that aims to focus on the content rather than the address of the content. Along with this, it provides in-networking caching, security, and adaptive data forwarding features for proving a Quality of Service (QoS) to the network. However, when this is applied on the vehicles termed as Vehicular Named Data Networking, a lot of challenges arise because of highly mobile vehicles. these challenges specifically raised concern towards the QoS and security aspects. the use of a decentralized ledger is required where every data packet can be sent from a verified vehicle only. this ensures that only authenticated and authorized data sources are contributing in the network, thus preventing malicious attacks. In this paper we have proposed a Blockchain enabled Data communication scheme for Vehicular Named Data Networks (BD-VNDN). this scheme provides an authenticated data packet to every interest packet and has been designed for vehicle-to-vehicle and vehicle-to-infrastructure-based communication. To verify the efficiency of the proposed scheme, extensive simulations have been performed on the ndnSim simulator. Different scenarios have been evaluated for the scheme and the results have been analyzed in terms of such as throughput, interest satisfaction delay, and interest success rate.

关键词： Adaptive systems Soft sensors Vehicular ad hoc networks Quality of service Signal processing throughput Road safety Blockchains Security Data communication

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Cafeteria Self-Service Billing System based on YOLOv11 and HSV threshold Segmentation Algorithm

Cafeteria Self-Service Billing System Based on YOLOv11 and H...

引用

Consumer Electronics and computer engineering (ICCECE), 2021 ieee international conference on

作者： Hailun Wang Wensheng Wang Junyao He College of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In this paper, a cafeteria self-service billing system is designed to address the problems of low recognition speed, weak anti-interference ability, and poor accuracy, etc. this design flexibly combines the YOLOv11 target detection algorithm and the HSV threshold segmentation algorithm, and the YOLOv11 algorithm improves the backbone and neck architecture, which greatly improves the feature extraction capability. A new convolutional mechanism C3k2 and C2PSA, as well as the depth separable convolution DWConv are introduced, which successfully realizes high-precision recognition of dish types as well as faster recognition speed and stronger robustness, which can improve the user experience and system efficiency. And through experiments, the average recognition accuracy and confidence of YOLO v11 are 2.8% and 2.4% higher than YOLOv10, and 3.8% and 4.4% higher than YOLOv8, respectively, and the HSV threshold segmentation algorithm can also identify the color of the dinner plate very well. In this paper, the HSV threshold segmentation algorithm is also tested in different environments, and the experimental results can also prove that the recognition accuracy is still above 80% even in the case of strong reflection. the hardware hardware part of the system to Raspberry Pi 4b as the motherboard, selected PN532 near-field communication module and thus the use of NFC for the interaction of the comprehensive test results show that the cafeteria self-service billing system is stable, can identify the type of food and the color of the dinner plate and calculate the total price, the accuracy is high, but also has a very good robustness, anti-interference ability, real-time is good, and has a certain degree of theoretical research and practical application value.

关键词： Accuracy Target recognition Heuristic algorithms Color Feature extraction Robustness Reflection Real-time systems Hardware Self-service

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Transformer-based Encoder Method for Classification of Heart Rate Variability (HRV)

Transformer-Based Encoder Method for Classification of Heart...

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10th international conference on Health Informatics and Medical systems, HIMS 2024 and 10th international conference on Biomedical engineering, BIOENG 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Tang, Shijun Department of Computer Science Schreiner University KerrvilleTX78028 United States

ISBN: (纸本)9783031859076

Transformer models can be used to process a sequence-to-sequence information and effectively capture dependencies among words or tokens in the sequence. ECG signals are the time sequence data. the transformer model is well suited for classification of ECG signals. In this paper, we use the encoder architecture of the transformer model to detect heart rate variability and classify electrocardiogram (ECG) data from the PhysioNet. the results show that our model can achieve 91% accuracy, 94% recall, 92% precision and 93% F1 Score. the experimental results demonstrate that the proposed method achieves a good classification performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Classification ECG Encoder architecture Heart Rate Variability (HRV) Transformer-based Encoder Method

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Classification of Heart Rate Variability (HRV) based on Attention Mechanism of Transformer Model

Classification of Heart Rate Variability (HRV) Based on Atte...

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作者： Tang, Shijun Department of Computer Science Schreiner University KerrvilleTX78028 United States

ISBN: (纸本)9783031859076

In this paper, a novel ECG classification method based on attention mechanism of transformer model and feature extraction was proposed to detect normal and abnormal ECG signals. RR intervals, half width of R peak as well as their distributions were taken as features of ECG signals. then, the transformer-based neural network was trained to classify imbalanced ECG signals. this research shows how to detect heart rate variability and classify electrocardiogram (ECG) data from the PhysioNet using transformer-based neural network model. the experimental results demonstrate that the proposed method achieves a good classification performance. the proposed method can be applied to distinguish between different cardiovascular diseases or autonomic nervous system disorders and to aid in early detection and diagnosis of conditions such as arrhythmias, heart failure, or diabetic autonomic neuropathy. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrocardiography

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Off-line CNN based Finger Number Gesture Recognition System Using Raspberry Pi 4

Off-line CNN Based Finger Number Gesture Recognition System ...

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作者： Batgerel, Gerelbat Kwon, Chunki Soonchunhyang University Chungcheongnam-do Asan31538 Korea Republic of

ISBN: (纸本)9783031859076

In this study, we implemented off-line wearable CNN based finger number gesture recognition system on Raspberry pi. OpenBCI’s bio-sensing board was used to collect sEMG training signals and sEMG test signals collected from the forearm of a subject when finger number gesture is performed. Using training data, the CNN model was trained on the Google Colaboratory platform, and the resulting model was deployed and executed on the Raspberry Pi board. In experimental study, CNN model on Raspberry pi recognizes finger numbers from one to five with an accuracy of 91.3%. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN finger number gesture recognition off-line Raspberry pi

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Dynamic systems development: Towards continuity, growth and evolution 10

Dynamic systems development: Towards continuity, growth and ...

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10th ieee international conference and workshop on the engineering of computer-based systems

作者： Dalcher, D Middlesex Univ Software Forens Ctr London N14 4YZ England

ISBN: (纸本)0769519172

the dynamic nature of knowledge and software evolution and usage present a pervasive challenge to system developers. Discrete attempts to create such systems often lead to a mismatch between system, expectation and a changing reality. the rationale for a Dynamic Feedback Model stems from the need to focus on a continuous and long-term perspective of development and growth in change-intensive environments. this paper makes the case for a learning and knowledge-driven view of software development and presents such a model in a way that accounts for the long-term survival, growth and evolution of software-intensive systems.

关键词： Programming Project management Knowledge management Forensics Feedback Software engineering Monitoring Quality management Software systems Entropy

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