arXiv subjects

Mohd Anwar

Publications and source records attributed to Mohd Anwar.

7 recordsLinked to original sources

arXiv2026

Reversible double cyclic codes over a chain ring

In this paper, we study the structure of double cyclic codes of length $(\gamma,\delta)$ over $\mathbb F_q+u\mathbb F_q, u^2=0$. We also study the dual of double cyclic code of length $(\gamma,\delta)$ and give a minimal spanning set of double cyclic codes. Moreover, we study the necessary and sufficient conditions for a double cyclic code to be reversible and reversible-complement double cyclic code and with the help of these codes, we constructed DNA codes over $\mathbb F_4+u\mathbb F_4, u^2=0$. We also constructed some optimal codes to support our results.

cs.IT↗

arXiv2024

Security Implications of User Non-compliance Behavior to Software Updates: A Risk Assessment Study

Software updates are essential to enhance security, fix bugs, and add better features to the existing software. While some users accept software updates, non-compliance remains a widespread issue. While some users accept software updates, non-compliance remains a widespread issue. End users' systems remain vulnerable to security threats when security updates are not installed or are installed with a delay. Despite research efforts, users' noncompliance behavior with software updates is still prevalent. In this study, we explored how psychological factors influence users' perception and behavior toward software updates. In addition, we investigated how information about potential vulnerabilities and risk scores influences their behavior. Next, we proposed a model that utilizes attributes from the National Vulnerability Database (NVD) to effectively assess the overall risk score associated with delaying software updates. Next, we conducted a user study with Windows OS users, showing that providing a risk score for not updating their systems and information about vulnerabilities significantly increased users' willingness to update their systems. Additionally, we examined the influence of demographic factors, gender, on users' decision-making regarding software updates. Our results show no statistically significant difference in male and female users' responses in terms of concerns about securing their systems. The implications of this study are relevant for software developers and manufacturers as they can use this information to design more effective software update notification messages. The communication of the potential risks and their corresponding risk scores may motivate users to take action and update their systems in a timely manner, which can ultimately improve the overall security of the system.

cs.SE↗

arXiv2022

Inclusive Privacy Design for Older Adults Living in Ambient Assisted Living

Ambient assisted living (AAL) environments support independence and quality of life of older adults However, in an AAL environment, privacy-related issues (e.g., unawareness, information disclosure, and lack of support) directly impact older adults and bystanders (e.g., caregivers, service providers, etc.). We explore the privacy challenges that both older adults and bystanders face in AAL. We call for inclusive privacy design and recommend following areas of improvement: consent, notification, and consideration for cultural differences.

cs.CY↗

arXiv2022

Privacy Threats on the Internet of Medical Things

The Internet of Medical Things (IoMT) is a frequent target of attacks -- compromising both patient data and healthcare infra-structure. While privacy-enhanced technologies and services (PETS) are developed to mitigate traditional privacy concerns, they cannot be applied without identifying specific threat models. Therefore, our position is that the new threat land-scape created by the relatively new and underexplored IoMT domain must be studied. We briefly discuss specific privacy threats and threat actors in IoMT. Furthermore, we argue that the privacy policy gap needs to be identified for the IoMT threat landscape.

cs.CY↗

arXiv2022

Mitigating shortage of labeled data using clustering-based active learning with diversity exploration

In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data. ALCS employs a density-based clustering approach to explore the cluster structure from the data without requiring exhaustive parameter tuning. A bi-cluster boundary-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes. Additionally, we developed an effective diversity exploration strategy to address the redundancy among queried samples. Our experimental results justified the efficacy of the ALCS approach.

cs.LG↗

arXiv2021

Surveillance of COVID-19 Pandemic using Social Media: A Reddit Study in North Carolina

Coronavirus disease (COVID-19) pandemic has changed various aspects of people's lives and behaviors. At this stage, there are no other ways to control the natural progression of the disease than adopting mitigation strategies such as wearing masks, watching distance, and washing hands. Moreover, at this time of social distancing, social media plays a key role in connecting people and providing a platform for expressing their feelings. In this study, we tap into social media to surveil the uptake of mitigation and detection strategies, and capture issues and concerns about the pandemic. In particular, we explore the research question, "how much can be learned regarding the public uptake of mitigation strategies and concerns about COVID-19 pandemic by using natural language processing on Reddit posts?" After extracting COVID-related posts from the four largest subreddit communities of North Carolina over six months, we performed NLP-based preprocessing to clean the noisy data. We employed a custom Named-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA) method for topic modeling on a Reddit corpus. We observed that 'mask', 'flu', and 'testing' are the most prevalent named-entities for "Personal Protective Equipment", "symptoms", and "testing" categories, respectively. We also observed that the most discussed topics are related to testing, masks, and employment. The mitigation measures are the most prevalent theme of discussion across all subreddits.

cs.SI↗

arXiv2021

Music Embedding: A Tool for Incorporating Music Theory into Computational Music Applications

Advancements in the digital technologies have enabled researchers to develop a variety of Computational Music applications. Such applications are required to capture, process, and generate data related to music. Therefore, it is important to digitally represent music in a music theoretic and concise manner. Existing approaches for representing music are ineffective in terms of utilizing music theory. In this paper, we address the disjoint of music theory and computational music by developing an opensource representation tool based on music theory. Through the wide range of use cases, we run an analysis on the classical music pieces to show the usefulness of the developed music embedding.

cs.SD↗