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All Journal Scientific Journal of Engineering Research Scientific Journal of Computer Science Methods in Science and Technology Studies
Atta Ur Rahman
Nanjing University of Information Science and Technology
Computer Science & IT Engineering

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Software Ecosystem Architectural Challenges and Mitigation Strategies: A Systematic Literature Review Inayat Ur Rahman; Atta Ur Rahman; Sara Shahzad; Sajid Ur Rahman
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December (Article in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.456

Abstract

Software ecosystems (SECO) play a crucial role in modern software development by enabling accelerated innovation, collaboration among multiple stakeholders, and efficient utilization of shared resources and technologies. However, achieving these benefits requires robust, adaptable, and well-structured architectural design and management. Despite their importance, SECO architectures face several critical challenges, including interface instability, security vulnerabilities, scalability limitations, governance complexity, sustainability concerns, and evolving ecosystem dynamics. Although prior studies have explored individual aspects of SECO, there is a clear research gap in providing a comprehensive and systematic synthesis of architectural challenges and their corresponding mitigation strategies. In particular, no systematic literature review (SLR) has thoroughly examined these issues in an integrated manner. To address this gap, this study aims to systematically identify, categorize, and analyze architectural challenges in SECO and evaluate existing mitigation techniques. A structured SLR methodology is employed to collect, assess, and synthesize relevant literature, leading to the development of a conceptual framework that organizes both challenges and solutions. The findings reveal that key mitigation strategies—such as modularization, variability management, custom design approaches, and sandboxing—can significantly improve architectural stability, scalability, and sustainability. These results provide valuable insights for both researchers and practitioners by offering a consolidated understanding of SECO architectural issues and practical guidance for designing more resilient and sustainable software ecosystems.
A Study of Loss Weight Balance in Lightweight Self-Distilled Crowd Counting Muhammad Raza; Atta Ur Rahman; Pandula Pallewatta; Inayat Ur Rahman; Sahib Bahadar
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.493

Abstract

Lightweight crowd counting is important for real-time surveillance and resource-constrained deployment, where both computational efficiency and effective supervision are required. Although teacher-free self-distillation can improve lightweight density-regression models by guiding intermediate representations without an external teacher, the influence of composite loss weights in such frameworks has not been sufficiently analyzed. This paper presents a focused coefficient-wise loss-weight analysis within the Lightweight Self-Knowledge Distillation framework for single-image crowd counting. Instead of proposing a new architecture, the study investigates how the coefficients α, β, γ, and λ₂ affect optimization behavior and counting accuracy under a fixed experimental setup on ShanghaiTech Part B. Specifically, α controls intermediate feature alignment, β controls consistency supervision, γ controls direct density-regression supervision, and λ₂ controls the structural similarity term in the regression loss. The results show that moderate values of α and β improve performance by providing useful internal regularization, while excessive auxiliary weighting can slightly degrade accuracy. The analysis also indicates that γ should remain dominant because direct density-map regression is the primary learning signal. The best observed configuration is α = 6.0, β = 2.0, γ = 13.0, and λ₂ = 0.2, achieving 8.94 MAE and 11.51 RMSE on ShanghaiTech Part B. These findings highlight the importance of balanced supervision design within the evaluated LSKD framework on ShanghaiTech Part B.
Trust–Privacy-Based User Acceptance Model for Location-Based Mobile Navigation Services: Conceptual Development and Research Propositions Atta Ur Rahman; Fuyang Ke; Muhammad Raza
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.522

Abstract

The two most important factors for location-based mobile navigation services are navigation performance and trusted data governance, especially when location data is used repeatedly. Previous studies have tended to focus on privacy concerns, perceived risk, trust, transparency, control, and service quality separately or in settings other than consumer navigation, as is the case in this study. As a result, the interaction between privacy risk perceptions and provider assurances in influencing mobile navigation acceptance remains poorly defined. This conceptual study develops a trust- and privacy-based user acceptance framework by synthesizing the literature through a structured procedure and incorporating the Technology Acceptance Model, Privacy Calculus Theory, and Trust Theory. The framework posits that privacy concern increases perceived risk, while privacy concern and perceived risk reduce trust. In contrast, transparency, perceived control over location data, and service reliability can enhance trust. The roles of perceived usefulness and user acceptance intention in the pathway to user acceptance remain unchanged, suggesting that trust is an antecedent to perceived usefulness and user acceptance intention. The framework fills the theoretical gap between privacy-risk assessment and trust-building service attributes, and between trust in technology acceptance and repeated disclosure of the location setting. It also recognizes the need for transparent data practices, meaningful location control, and reliable service performance as provisional design priorities, which require future empirical validation. The propositions are to be used as a starting point for further testing on platforms, providers, user groups, cultures, and regulatory frameworks.
LSKD: Lightweight Self-Knowledge Distillation Framework for Fast and Robust Crowd Counting Muhammad Raza; Miaogen Ling; Atta Ur Rahman; Pandula Pallewatta; Aboubakar Abdinur Hersi; Shehan Maxwell Beruwalage; Deshan Sachintha Kannangara
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.436

Abstract

Crowd counting plays an important role in the surveillance of the safety of the people, traffic, and intelligent surveillance systems. However, the exact density estimations remain hard to achieve in highly congested scenes due to the tough occlusion, large-scale variance, and complicated background. Although the recent deep-learning methods have high performance, several of them do not need computationally efficient underlying backbone networks, and rather, they employ an external teacher-student distillation architecture, which can limit their use in resource-constrained applications. To avoid this problem, we introduce LSKD, a lightweight self-knowledge distillation network that is density map regression-specific. Unlike other conventional teacher-dependent processes, LSKD can also independently carry out internal multi-level feature alignment within a single small network that is not in need of an external teacher model. The structure integrates a Feature Matching Block (FMB) and a Context Fusion (CoFuse) block to enhance the hierarchical match of features and global awareness of context. The large experiments demonstrate that LSKD obtain competitive performance using the number of parameters as 2.65 million and GFLOPs as 10.23. Particularly, it has 63.17 MAE on ShanghaiTech Part A, 8.94 on ShanghaiTech Part B, 143.7 on UCF-QNRF, and 223.88 on UCF-CC-50, which is a good ratio between the accuracy and the efficiency of the calculations. Such results indicate that LSKD has an implementable and efficient solution to the real-time counting of crowds at the edge devices.
Co-Authors Aboubakar Abdinur Hersi Deshan Sachintha Kannangara Fuyang Ke Inayat Ur Rahman Miaogen Ling Muhammad Raza Pandula Pallewatta Sahib Bahadar Sajid Ur Rahman Sara Shahzad Shehan Maxwell Beruwalage