Factors Affecting on-time Delivery in Hybrid Software Project Management in the Context of Bangladesh Software Firms

Md Mahbub Alam, Sabrina Islam Prit and Kanij Fatema, Department of Software Engineering, Independent University , Dhaka, Bangladesh

ABSTRACT

This study examines the critical factors influencing the timely completion of software projects within Bangladesh's software industry. It identifies several challenges, including skill gaps, communication barriers, inconsistent project lifecycles, budget constraints, inadequate capacity planning, cultural resistance, and regulatory compliance issues. The study highlights the agility of small firms in adopting hybrid methodologies and the significant impact of project delays on their operations. To address these challenges, it proposes solutions such as advanced time estimation techniques, adaptive project management frameworks, enhanced stakeholder management tools, and improved resource management software. The research aims to provide actionable insights and practical guidelines to enhance the efficiency and effectiveness of hybrid project management practices, ensuring on-time delivery and better project outcomes in the Bangladesh software sector.

Keywords

Hybrid Project Management, Project Lifecycle, Budget Constraints, Cultural Resistance, Agile Methodologies, Stakeholder Management,Time Estimation Techniques.

Challenges with Securing Digital Identity

Nikhil Ghadge, Software Architect, Workforce Identity Cloud, Okta.Inc

ABSTRACT

Ensuring the security of digital identities has become increasingly critical in today's interconnected world. This paper examines the multifaceted challenges associated with securing digital identities, spanning technological, human, legal, and regulatory aspects. Key technological hurdles include vulnerabilities in authentication mechanisms, risks associated with biometric data, issues with multi-factor authentication, and challenges in implementing secure hardware. Human factors like social engineering threats, lack of awareness and education, insider threats, and psychological impacts of identity theft further complicate the landscape. Legal and regulatory hurdles such as compliance with data protection laws, cross-border data security issues, establishing digital identity standards, and balancing privacy and security concerns pose additional obstacles. The paper highlights the severe implications of unsecured digital identities and provides recommendations for enhancing security through a multi-pronged approach involving technological advancements, educational initiatives, and ethical considerations. A call to action emphasizes the urgent need for future research and collaborative efforts to bolster encryption, authentication protocols, and policy enforcement mechanisms in the digital domain.

Keywords

Digital Identity, Multi Factor Authentication, Security, Threat Detection, Identity Theft.

Safety and Security Management System Using Facial Recognition and Deep Convolutional Neural Network (DCNN)

Benjamin L. Cornelio Jr, College of Information and Communication Technology, Iloilo State University Science and Technology, Dingle Campus, Dingle Iloilo Philippines

ABSTRACT

This study presents a comprehensive assessment of a computer vision-based system designed for security and safety applications, particularly in distinguishing between authorized and unauthorized users. Through meticulous analysis, it was found that while the system effectively identifies faces and detects facial recognition, concerns regarding functionality and maintainability arise, primarily attributed to suboptimal plug-and-play camera quality. However, the system's capacity to deliver text notifications and alarms upon user detection received commendable ratings for usability and portability. The overall evaluation based on ISO 25010 criteria indicates a commendable performance. Conclusions drawn from the study highlight the critical impact of camera quality, processor performance, and hardware components on the system's operational efficacy. Practical recommendations advocate for the adoption of high-performance servers and high-definition cameras connected to a network to streamline operations. Additionally, suggestions for rigorous testing of superior datasets and the integration of multi-factor authentication mechanisms are proposed to fortify security measures. In essence, this study underscores the system's potential for real-world implementation across diverse settings, acknowledging both its strengths and areas for improvement in addressing contemporary security challenges.

Keywords

Face Recognition, Computer Vision, Face Detection, Security Alarm, Deep Convolutional Neural Network, Django, Twilio.