International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 2, No. 6, June 2021 | pp. 41–48

Research Article

Title: Enhancing Named Entity Recognition in Financial Text Mining Using Domain-Specific Bidirectional Encoder Representations

Names of Authors: John Smith¹, Emily Johnson², and Robert Williams³

Authors’ Affiliations:
¹Department of Computer Science, Stanford University, Stanford, California, United States
²Department of Linguistics, University of California, Berkeley, Berkeley, California, United States
³Center for Data Science, New York University, New York, New York, United States

Abstract: Extracting actionable insights from corporate financial statements, earnings call transcripts, and market news reports requires accurate Named Entity Recognition (NER) models. Financial text mining is complicated by highly specialized vocabularies, ambiguous abbreviation patterns, and semantic constructs that differ sharply from general language corpora. General-purpose pre-trained language models like standard BERT often fail to extract domain-specific entities such as asset tickers, corporate acquisitions, fiscal regulatory terms, and quantitative financial values. This paper introduces FinBERT-NER, an enhanced language model tailored for financial named entity recognition tasks. The model adapts the Bidirectional Encoder Representations from Transformers (BERT) architecture by performing domain-specific continuous pre-training on a massive corporate dataset comprising 8.5 billion words sourced from SEC filings and financial news. A specialized tokenization dictionary is integrated to prevent excessive sub-word fragmentation of financial terminology. Experimental evaluations on manually annotated financial benchmark datasets indicate that FinBERT-NER achieves a precision of 94.2%, a recall of 93.6%, and an F1-score of 93.9%, outperforming base BERT and standard LSTM-CRF models by a wide margin. The model improves extraction accuracy for complex multi-word corporate entities, serving as a reliable foundation for automated market sentiment profiling, regulatory compliance tracking, and quantitative investment algorithms.

Keywords: Named entity recognition, Natural language processing, Financial text mining, BERT, Transformer models, Information extraction

Manuscript Timeline: Received: March 20, 2021; Revised: April 26, 2021; Accepted: May 18, 2021; Published: June 1, 2021

Citation: Smith, J., Johnson, E., & Williams, R. (2021). Enhancing named entity recognition in financial text mining using domain-specific bidirectional encoder representations. International Journal of Computer Science and Technology, 2(6), 41–48. DOI: 10.46882/2021/IJCST/000018

International Journal of Computer Science and Technology | Vol. 2, No. 5, May 2021 | pp. 33–40

Research Article

Title: A Decentralized Identity Management Framework Using Smart Contracts and Zero-Knowledge Proofs

Names of Authors: Amir Hassan¹, Laila Mansour², and Tarek Fadel³

Authors’ Affiliations:
¹Department of Computer Engineering, Cairo University, Giza, Egypt
²Department of Information Technology, Alexandria University, Alexandria, Egypt
³Department of Computer Science, American University in Cairo, Cairo, Egypt

Abstract: Traditional centralized identity management systems pose significant risks, including single points of data breach, corporate data harvesting, and a lack of user control over personal information. Self-Sovereign Identity (SSI) frameworks address these issues by empowering individuals to manage their digital credentials independently. However, deploying SSI on public blockchains introduces tracking issues and data leakage vectors due to the transparent nature of distributed ledgers. This study proposes a decentralized, privacy-preserving identity management framework that integrates permissioned smart contracts with zero-knowledge proofs (ZKPs), specifically utilizing zk-SNARKs. The proposed architecture separates identity verification from credential disclosure, allowing users to generate cryptographic proofs verifying attributes (such as age thresholds or financial capacities) without exposing their underlying personally identifiable information (PII). Smart contracts act as a root of trust, validating these cryptographic proofs and verifying issuer public keys on-chain while storing zero raw user data. Prototyping evaluations conducted on a Hyperledger Fabric network demonstrate that proof generation takes 1.2 seconds on standard smartphones, and verification executes in less than 45 ms on the ledger. The framework successfully mitigates identity fraud, guarantees complete user anonymity, and complies fully with data privacy mandates, making it viable for secure e-governance and decentralized finance applications.

Keywords: Self-sovereign identity, Blockchain, Smart contracts, Zero-knowledge proofs, Privacy preservation, Identity management

Manuscript Timeline: Received: February 18, 2021; Revised: March 24, 2021; Accepted: April 22, 2021; Published: May 1, 2021

Citation: Hassan, A., Mansour, L., & Fadel, T. (2021). A decentralized identity management framework using smart contracts and zero-knowledge proofs. International Journal of Computer Science and Technology, 2(5), 33–40. DOI: 10.46882/2021/IJCST/000017

International Journal of Computer Science and Technology | Vol. 2, No. 4, April 2021 | pp. 25–32

Research Article

Title: Dynamic Task Offloading and Resource Scheduling in Mobile Edge Computing Using Deep Deterministic Policy Gradient

Names of Authors: Santiago Gomez¹, Valeria Rodriguez², and Mateo Lopez³

Authors’ Affiliations:
¹Department of Systems Engineering, National University of Colombia, Bogotá, Colombia
²Faculty of Engineering, University of the Andes, Bogotá, Colombia
³Department of Computer Science, University of Buenos Aires, Buenos Aires, Argentina

Abstract: Mobile Edge Computing (MEC) reduces computational strain on mobile devices by offloading resource-intensive tasks to local edge nodes. However, highly dynamic environments—characterized by unpredictable user mobility, varying task arrival rates, and fluctuating wireless channel qualities—complicate offloading decisions. Static or heuristic scheduling rules cannot adapt to continuous, multi-dimensional system states, resulting in excessive task drop rates and battery depletion. This paper proposes an autonomous, continuous-action task offloading and resource scheduling framework utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm. The MEC ecosystem maps into a continuous state-action space where the actor network determines fractional offloading ratios and local CPU scaling frequencies, while the critic network evaluates the resulting system reward. The target multi-objective reward function minimizes both processing latency and device energy consumption under strict task completion deadline constraints. The model incorporates continuous changes in transmission bandwidth measured in megabits per second (Mbps) and local CPU workloads measured in cycles per second. Simulation experiments demonstrate that the proposed DDPG-based controller converges efficiently, reducing average task execution delay by 34.8% and device battery consumption by 29.1% relative to conventional genetic and Q-learning approaches. The framework ensures reliable execution for interactive edge applications under non-stationary traffic conditions.

Keywords: Mobile edge computing, Task offloading, Deep reinforcement learning, Continuous action space, Resource scheduling, Energy efficiency

Manuscript Timeline: Received: January 15, 2021; Revised: February 22, 2021; Accepted: March 19, 2021; Published: April 1, 2021

Citation: Gomez, S., Rodriguez, V., & Lopez, M. (2021). Dynamic task offloading and resource scheduling in mobile edge computing using deep deterministic policy gradient. International Journal of Computer Science and Technology, 2(4), 25–32. DOI: 10.46882/2021/IJCST/000016

International Journal of Computer Science and Technology | Vol. 2, No. 3, March 2021 | pp. 17–24

Research Article

Title: Optimizing Deep Neural Network Inference on Heterogeneous Edge Devices Using Adaptive Model Quantization

Names of Authors: Chen Wei¹, Li Na², and Zhang Wei³

Authors’ Affiliations:
¹Department of Computer Science and Technology, Tsinghua University, Beijing, China
²State Key Laboratory of Computer Architecture, Institute of Computing Technology, CAS, Beijing, China
³School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

Abstract: Deploying state-of-the-art deep neural networks on edge devices enables real-time, low-latency computer vision and natural language applications. However, modern models possess millions of parameters, causing severe memory footprint and power consumption challenges on resource-constrained embedded hardware. Model compression via uniform quantization reduces precision from 32-bit floating-point (FP32) to 8-bit integers (INT8), but often incurs unacceptable accuracy drops in complex network topologies. This study proposes an adaptive, mixed-precision model quantization framework designed to optimize deep learning inference on heterogeneous edge processors. The system uses a layer-wise sensitivity analysis based on Hessian trace estimation to determine the structural tolerance of individual network layers to precision loss. Layers displaying high sensitivity maintain 8-bit or 16-bit precisions, while noise-tolerant components compress to ultra-low 4-bit or 2-bit formats. A multi-objective optimization engine balances model accuracy, execution latency, and memory consumption across target hardware architectures. Hardware experiments on NVIDIA Jetson Nano and Raspberry Pi 4 platforms show that the mixed-precision framework reduces model memory size by 73.5% and accelerates inference execution speed by 2.4 times compared to baseline FP32 implementations. Crucially, top-1 accuracy degradation on the ImageNet dataset is restricted to less than 0.65%, making it highly effective for real-time edge intelligence.

Keywords: Deep learning inference, Edge computing, Model quantization, Mixed-precision, Hardware acceleration, Computer vision

Manuscript Timeline: Received: December 05, 2020; Revised: January 18, 2021; Accepted: February 15, 2021; Published: March 1, 2021

Citation: Wei, C., Na, L., & Wei, Z. (2021). Optimizing deep neural network inference on heterogeneous edge devices using adaptive model quantization. International Journal of Computer Science and Technology, 2(3), 17–24. DOI: 10.46882/2021/IJCST/000015

International Journal of Computer Science and Technology | Vol. 2, No. 2, February 2021 | pp. 9–16

Research Article

Title: An Automated Microservice Decomposition Approach for Monolithic Applications Using Evolutionary Clustering

Names of Authors: Liam Walker¹, Olivia Davies², and Ethan Taylor³

Authors’ Affiliations:
¹School of Computer Science, University of Manchester, Manchester, United Kingdom
²Department of Software Engineering, University of Bristol, Bristol, United Kingdom
³Department of Computer Science, University of Edinburgh, Edinburgh, United Kingdom

Abstract: Modern software engineering favors microservice architectures over monolithic structures to improve scalability, maintainability, and continuous deployment velocity. However, migrating legacy monolithic applications to microservices is a labor-intensive, error-prone manual task that requires deep domain knowledge. Automatic decomposition techniques help, but traditional static code analysis often misinterprets runtime structural dependencies, creating highly coupled services. This paper introduces an automated framework for monolithic application migration that combines static metric extraction with runtime business logic tracing. The methodology maps a legacy system as a multi-layered dependency graph where classes represent nodes, and edges model static method calls, database foreign keys, and shared data structures. A multi-objective evolutionary clustering algorithm based on NSGA-II partitions this graph, maximizing internal service cohesion while minimizing inter-service coupling. Empirical evaluations on three open-source monolithic enterprise applications demonstrate that the framework produces microservice boundaries with an average cohesion improvement of 31.4% and a coupling reduction of 27.2% compared to traditional K-means and hierarchical clustering techniques. The resulting service topologies reduce network communication overhead and respect data transactional boundaries. This approach streamlines legacy software modernization workflows by reducing manual architectural redesign efforts.

Keywords: Microservices migration, Software architecture, Monolithic decomposition, Evolutionary clustering, Multi-objective optimization, Static analysis

Manuscript Timeline: Received: November 15, 2020; Revised: December 20, 2020; Accepted: January 14, 2021; Published: February 1, 2021

Citation: Walker, L., Davies, O., & Taylor, E. (2021). An automated microservice decomposition approach for monolithic applications using evolutionary clustering. International Journal of Computer Science and Technology, 2(2), 9–16. DOI: 10.46882/2021/IJCST/000014

International Journal of Computer Science and Technology | Vol. 2, No. 1, January 2021 | pp. 1–8

Research Article

Title: A Quantum-Resistant Lattice-Based Cryptographic Protocol for Secure Vehicle-to-Everything Communications

Names of Authors: Hans Müller¹, Lukas Schmidt², and Emma Fischer³

Authors’ Affiliations:
¹Department of Mathematics and Computer Science, Technical University of Berlin, Berlin, Germany
²Department of Information Security, Karlsruhe Institute of Technology, Karlsruhe, Germany
³Institute of Computer Science, University of Göttingen, Göttingen, Germany

Abstract: Vehicle-to-Everything (V2X) communication networks enable real-time coordination among autonomous vehicles, roadside infrastructure, and pedestrians to optimize traffic flow and prevent collisions. Securing these safety-critical messages requires high-speed digital signatures to verify sender authenticity and message integrity within milliseconds. However, current V2X security architectures rely heavily on Elliptic Curve Digital Signature Algorithms (ECDSA), which are vulnerable to cryptanalytic attacks by future quantum computers using Shor's algorithm. This paper proposes a quantum-resistant V2X authentication protocol based on the Ring Learning With Errors (R-LWE) hard mathematical problem. The protocol introduces an optimized lattice-based digital signature scheme tailored for the constrained computational environments of On-Board Units (OBUs) in vehicles. To accelerate execution, the Number Theoretic Transform (NTT) is utilized to optimize polynomial multiplication operations. Experimental evaluations conducted on an automotive-grade ARM Cortex-A72 processor reveal that the proposed lattice scheme verifies safety messages in 0.42 ms, well within the 2 ms real-time V2X threshold constraint. The transmission payload increases signature sizes to 1216 bytes, but remains highly manageable over dedicated short-range communications channels. The security analysis proves the scheme achieves existential unforgeability under chosen-message attacks, offering a secure transition path toward post-quantum intelligent transportation systems.

Keywords: Vehicle-to-everything, Post-quantum cryptography, Lattice-based cryptography, Ring learning with errors, Digital signatures, Automotive security

Manuscript Timeline: Received: October 12, 2020; Revised: November 15, 2020; Accepted: December 10, 2020; Published: January 3, 2021

Citation: Müller, H., Schmidt, L., & Fischer, E. (2021). A quantum-resistant lattice-based cryptographic protocol for secure vehicle-to-everything communications. International Journal of Computer Science and Technology, 2(1), 1–8. DOI: 10.46882/2021/IJCST/000013