International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 5, No. 4, April 2024 | pp. 1–8

DOI: 10.46882/2024/IJCST/000232

Article Type: Original Research Paper

Title: Graph-Based Anomaly Detection in Decentralized Identity Management Systems Using Temporal Tracking

Names of Authors: Elena Rostova¹, Yuki Tanaka²

Authors’ Affiliations: ¹Data Engineering Laboratory, NordTech University, Oslo, Norway; ²Advanced VLSI Systems Laboratory, Tokyo Institute of Technology, Tokyo, Japan

Abstract: Abstract: Decentralized identity frameworks allow web users to control their personal credentials without relying on centralized single sign-on systems. However, malicious users can exploit these networks by creating multiple fake accounts to skew voting metrics or bypass security limits. This paper presents an automated anomaly isolation framework that maps decentralized identity validation logs using a Temporal Graph Neural Network (TGNN). The model builds real-time structural graphs tracking interaction speeds, link patterns, and credential verification histories. A dynamic edge-weighting algorithm identifies abnormal account groups and separates suspicious nodes based on localized graph changes. We validated this network defense layer using a verification dataset of 12,000 decentralized verification logs under active stress. The system isolated suspicious identity patterns with a 96.4% classification rate. The detection pipeline executed its analysis within a strict timeframe of 32 ms per verification request, proving its viability for live application tracking.

Keywords: Decentralized Identity, Graph Neural Networks, Anomaly Detection, Sybil Attacks, Network Security, Token Verification

Manuscript Timeline: Received: September 12, 2023; Revised: November 20, 2023; Accepted: January 18, 2024; Published: April 12, 2024

Citation: Rostova, E., & Tanaka, Y. (2024). Graph-Based Anomaly Detection in Decentralized Identity Management Systems Using Temporal Tracking. International Journal of Computer Science and Technology, 5(5), 1–8. DOI: 10.46882/2024/IJCST/000232

International Journal of Computer Science and Technology | Vol. 5, No. 3, March 2024 | pp. 1–8

DOI: 10.46882/2024/IJCST/000230

Article Type: Original Research Paper

Title: Quantifying Carbon Emission Footprints in Distributed Cloud Architectures via Runtime Telemetry Tracking

Names of Authors: Elena R. Rostova¹, Jean-Pierre Dubois²

Authors’ Affiliations: ¹Cloud Metrics Division, Global Tech Systems, Stockholm, Sweden; ²Department of Computer Science, Swiss Federal Institute of Technology, Lausanne, Switzerland

Abstract: Large-scale cloud data centers consume significant electrical energy, contributing to global carbon emissions. However, existing sustainability metrics estimate carbon impacts using static server configurations, failing to capture dynamic load variations or localized grid energy mixes. This paper presents an automated runtime monitoring framework named Eco-Track that quantifies carbon emission footprints at the individual container level. Eco-Track tracks server hardware activities, including CPU instruction counts, memory cycles, and thermal profiles. It cross-references this real-time consumption data with regional grid carbon intensity tracking feeds. The framework automatically moves low-priority processing tasks to data center zones operating on green energy sources. We tested the tracking tool across a multi-region distributed cloud infrastructure. The tracking results show that Eco-Track accurately measures individual software task carbon footprints with a margin of error under ±2.5%. Implementing our automated task migration loop reduced total operational carbon impacts by 18.6% without degrading application performance metrics.

Keywords: Green Computing, Cloud Data Centers, Carbon Footprint Tracking, Runtime Telemetry, Container Metrics, Sustainable Software

Manuscript Timeline: Received: August 25, 2023; Revised: October 30, 2023; Accepted: December 12, 2023; Published: March 06, 2024

Citation: Rostova, E. R., & Dubois, J. -P. (2024). Quantifying Carbon Emission Footprints in Distributed Cloud Architectures via Runtime Telemetry Tracking. International Journal of Computer Science and Technology, 5(3), 1–8. DOI: 10.46882/2024/IJCST/000230

International Journal of Computer Science and Technology | Vol. 5, No. 2, February 2024 | pp. 1–8

DOI: 10.46882/2024/IJCST/000229

Article Type: Original Research Paper

Title: Decentralized Cross-Chain Liquidity Routing Pools via Cryptographic Time-Lock Commitments

Names of Authors: Liam O'Donnell¹, Sophia Rossi²

Authors’ Affiliations: ¹School of Computer Science, Trinity College Dublin, Dublin, Ireland; ²FinTech Infrastructure Lab, Bocconi University, Milan, Italy

Abstract: The rapid expansion of decentralized financial architectures has resulted in fragmented liquidity ecosystems isolated across independent, non-compatible blockchain networks. Existing cross-chain bridge mechanisms rely heavily on centralized wrapped tokens, creating single points of failure vulnerable to multi-million dollar exploits. This study introduces a completely decentralized cross-chain liquidity routing protocol utilizing Hash Time-Locked Contracts (HTLCs) combined with threshold signatures. This design allows users to swap digital assets across independent chains without relying on intermediary custody agents. The protocol uses an automated market maker function to balance pool ratios dynamically, minimizing asset slippage across cross-network transactions. We tested the system by processing transactions across independent Ethereum, Avalanche, and Polygon test networks. The tracking evaluations show that cross-chain swaps were finalized within an average duration of 48 seconds. The framework reduced overall transaction cost structures by 28.3% compared to classic bridge implementations while maintaining structural transaction safety.

Keywords: Blockchain, Cross-Chain Bridges, Hash Time-Locked Contracts, Automated Market Makers, Cryptographic Protocols, Liquidity Pools

Manuscript Timeline: Received: August 20, 2023; Revised: October 22, 2023; Accepted: December 05, 2023; Published: February 14, 2024

Citation: O'Donnell, L., & Rossi, S. (2024). Decentralized Cross-Chain Liquidity Routing Pools via Cryptographic Time-Lock Commitments. International Journal of Computer Science and Technology, 5(2), 1–8. DOI: 10.46882/2024/IJCST/000229

International Journal of Computer Science and Technology | Vol. 5, No. 1, January 2024 | pp. 1–8

DOI: 10.46882/2024/IJCST/000228

Article Type: Original Research Paper

Title: Mitigating Epistemic Uncertainty in Autonomous Vehicle Trajectory Planning via Bayesian Neural Networks

Names of Authors: Klaus Meyer¹, Rachel Green²

Authors’ Affiliations: ¹Autonomous Systems Engineering Center, Fraunhofer Institute, Karlsruhe, Germany; ²Robotics Intelligence Group, Apex Auto Labs, Detroit, USA

Abstract: Autonomous vehicle systems operating within highly dynamic urban settings struggle with sensor noise and unpredictable pedestrian activities. Standard deterministic trajectory planning models fail to quantify model uncertainty, occasionally resulting in unsafe maneuvers under novel traffic scenarios. This paper introduces an adaptive trajectory planning framework that uses Bayesian Neural Networks (BNNs) to estimate epistemic uncertainty fields in real time. The model estimates variance boundaries across its structural network paths, outputting a localized confidence metric alongside each path prediction. If tracking confidence falls below a pre-configured target, the navigation layer selects a defensive path option. We verified this framework on an autonomous driving test platform navigating complex urban simulations. The validation results show that our Bayesian model decreased localized near-miss incidents by 41.2% compared to standard deterministic planning networks. The system executed its processing iterations within an average latency window of 22 ms, satisfying real-time safety requirements.

Keywords: Autonomous Vehicles, Trajectory Planning, Bayesian Neural Networks, Epistemic Uncertainty, Safety-Critical Systems, Robotics

Manuscript Timeline: Received: August 12, 2023; Revised: October 15, 2023; Accepted: November 28, 2023; Published: January 08, 2024

Citation: Meyer, K., & Green, R. (2024). Mitigating Epistemic Uncertainty in Autonomous Vehicle Trajectory Planning via Bayesian Neural Networks. International Journal of Computer Science and Technology, 5(1), 1–8. DOI: 10.46882/2024/IJCST/000228

International Journal of Computer Science and Technology | Vol. 4, No. 12, December 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000227

Article Type: Review Paper

Title: Neuromorphic Computing Architecture Paradigms and Silicon Implementations: A Comparative Analysis

Names of Authors: Kofi Mensah¹, Yuki Tanaka²

Authors’ Affiliations: ¹Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; ²Advanced VLSI Systems Laboratory, Tokyo Tech, Tokyo, Japan

Abstract: Von Neumann processing architectures encounter severe energy bottlenecks and memory speed limitations when running modern deep learning model structures. Neuromorphic computing presents an alternative framework by designing silicon architectures that mimic biological neural networks. This review paper provides a comparative analysis of modern neuromorphic silicon implementations, including Intel Loihi, IBM TrueNorth, and SpiNNaker. We evaluate each hardware platform across operational vectors such as synaptic density, energy dissipation, and on-chip learning efficiency. The study aggregates performance datasets compiled from 40 experimental research papers published over the past six years. Our findings show that neuromorphic designs cut power usage by up to 1,000x compared to classic GPU architectures when processing sparse temporal streams. However, configuring non-von Neumann systems requires specialized programming tools and remains limited by hardware constraints during dense matrix calculations. This paper presents a taxonomical framework to guide hardware designers in matching neuromorphic platforms with specific edge applications.

Keywords: Neuromorphic Computing, Non-Von Neumann Architecture, Spiking Neural Networks, Silicon Implementations, Energy Efficiency, VLSI

Manuscript Timeline: Received: March 20, 2023; Revised: May 12, 2023; Accepted: June 28, 2023; Published: December 10, 2023

Citation: Mensah, K., & Tanaka, Y. (2023). Neuromorphic Computing Architecture Paradigms and Silicon Implementations: A Comparative Analysis. International Journal of Computer Science and Technology, 4(12), 1–8. DOI: 10.46882/2023/IJCST/000227

International Journal of Computer Science and Technology | Vol. 4, No. 11, November 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000226

Article Type: Original Research Paper

Title: Context-Aware Document Summarization Architecture Using Transformer-Based Attention Weighting

Names of Authors: Anna Kowalski¹, Hans-Jürgen Integrated²

Authors’ Affiliations: ¹Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland; ²Institute for Cognitive Systems Research, University of Stuttgart, Stuttgart, Germany

Abstract: Automated document summarization frameworks frequently generate disconnected output strings or omit critical context when processing long technical texts. This paper introduces an abstractive document summarization model that uses a context-aware transformer architecture. The framework features a targeted attention-weighting layer that highlights domain-specific technical terminology and structural document headings. This layer prevents the model from dropping high-impact semantic connections across paragraphs. We integrated a pointer-generator network layer to copy unique named entities and numerical metrics accurately from source text fields into the summaries. The neural model was trained and validated using standard CNN/DailyMail and arXiv technical text corpora. The validation results show a ROUGE-L score improvement of 4.35 points over baseline text-summarization models. Human evaluation tracking confirmed that the generated summaries maintained high context consistency, minimizing factual hallucination rates to less than 1.8% across tested articles.

Keywords: Natural Language Processing, Document Summarization, Transformers, Attention Mechanism, Factual Consistency, Text Mining

Manuscript Timeline: Received: March 15, 2023; Revised: May 08, 2023; Accepted: June 22, 2023; Published: November 15, 2023

Citation: Kowalski, A., & Integrated, H. -J. (2023). Context-Aware Document Summarization Architecture Using Transformer-Based Attention Weighting. International Journal of Computer Science and Technology, 4(11), 1–8. DOI: 10.46882/2023/IJCST/000226