Impact of computer training on professional library activities in Pakistan

Report on a survey of alumni of the Pakistan Library Association’s Computer Training Center in Lahore to determine the impact of the Certificate in Library Automation (CLA) on librarianship in Pakistan. The survey provided information on: the profile of the respondents; their participation in computer training before and after taking the CLA; the impact of their training on their success in job seeking; their access to and use of computers; their participation in library automation projects and other auto-mation related activities; and suggestions regarding further courses and the improvement of the Training Centre. The results showed that the courses were attended mainly by comparatively young professional librarians, most of whom were working in Lahore. Their computer training contributed a lot to their success in getting new jobs. Most of them had access to computer facilities in their offices and use them daily, and have participated significantly in automation activities in their libraries. Many also applied their computing knowledge in writing books, articles, delivering lectures and providing consultancy services. Many suggestions were made for improving the courses

How economists cite literature: citation analysis of two core Pakistani economic journals

Selected volumes of the Pakistan Development Review (PDR) and the Pakistan Economic and Social Review (PESR) were analysed to find the citation pattern of their articles. Eight volumes of each journal were selected, two volumes representing a decade. The results revealed that the PDR has been the most cited journal. The mean score of citations per article remained insignificantly different in the two core journals. More than 50 per cent of the citations from both journals were single-authored. More than 50 per cent of the citations were from non-journal sources, mainly books. Although citations from online sources were seen, it was a negligible number. About 47 per cent of the total citations of the PDR were up to five years old compared with the citations of the PESR, where only 25 per cent fell into this category. Most of the authors used foreign books as citations. There is a significant similarity in the top most cited journals in both cases. Most of the frequently cited journals were from the USA

Proposed framework for end-of-life vehicle recycling system implementation in Malaysia

Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 187–193.Normally in Malaysia, vehicles are being used extensively regardless of its age or condition. This situation is not only in rural areas but exists in major cities. Vehicle manufacturers expected their vehicles to last in 15 years, hence vehicles exceeding this limit are considered as End-of-Life Vehicle (ELV). The extensive usage of ELV may lead to vehicle failure which threatens the safety of its user as well as other road users. ELV usage also contributes to environmental pollution. In order to overcome this, a framework for ELV management needs to be developed. Prior to that, a survey was done to study the current practice being applied in Malaysia. This paper also study the existing framework applied by other countries as adaptation for Malaysian ELV recycling implementation framework. This framework is expected to assist the government in drafting new ELV related policies

Adversarial Robustness Through Artifact Design

Adversarial examples arose as a challenge for machine learning. To hinder them, most defenses alter how models are trained (e.g., adversarial training) or inference is made (e.g., randomized smoothing). Still, while these approaches markedly improve models' adversarial robustness, models remain highly susceptible to adversarial examples. Identifying that, in certain domains such as traffic-sign recognition, objects are implemented per standards specifying how artifacts (e.g., signs) should be designed, we propose a novel approach for improving adversarial robustness. Specifically, we offer a method to redefine standards, making minor changes to existing ones, to defend against adversarial examples. We formulate the problem of artifact design as a robust optimization problem, and propose gradient-based and greedy search methods to solve it. We evaluated our approach in the domain of traffic-sign recognition, allowing it to alter traffic-sign pictograms (i.e., symbols within the signs) and their colors. We found that, combined with adversarial training, our approach led to up to 25.18\% higher robust accuracy compared to state-of-the-art methods against two adversary types, while further increasing accuracy on benign inputs

Personal Information Sharing Behavior Using Social Media: A Bibliometric Analysis from 2007-2024

This study explores personal information sharing behavior publication patterns and trends on social media from 2007-2024 with an aim to highlight the annual growth of personal information sharing behavior (PISB) on social media platforms, key patterns in the PISB literature in terms of frequently cited authors, countries, institutions, sources, highly cited papers, collaboration and authorship patterns, thematic evolution, keyword and key factor analysis (such as countries, sources, and keywords). We used Scopus database for data extraction, and 1020 pertinent records were chosen. The data was evaluated with Microsoft Excel, Access, Biblioshiny and VOSviewer software. The United States of America is leading in a top authors, organizations and as a country on PISB literature. This authorship pattern trends revealed that the authors on PISB literature prefer to work with two, three, four or as a single author and they give low preference to work with more than four authors. The country-level collaboration trend revealed that the collaboration between United States and China are the most frequently occurring among the rest of other countries. Thematic evolution identified that some themes become obsolete, and some emerge with the passage of time. However, notably, the recent period (2021-2024) is mainly connected with various social media issues and challenges. The three-factor (keywords, countries and sources) revealed that the researchers of top countries used mostly six keywords (self-disclosure, social media, privacy, Facebook, social networking sites, and social support) and they preferably published in two major sources. This finding shows that the authors from the top ten countries mainly published their work in highly selected journals

Personal information sharing behavior using social media: A bibliometric analysis from 2007-2024

This study explores personal information sharing behavior publication patterns and trends on social media from 2007-2024 with an aim to highlight the annual growth of personal information sharing behavior (PISB) on social media platforms, key patterns in the PISB literature in terms of frequently cited authors, countries, institutions, sources, highly cited papers, collaboration and authorship patterns, thematic evolution, keyword and key factor analysis (such as countries, sources, and keywords). We used Scopus database for data extraction, and 1020 pertinent records were chosen. The data was evaluated with Microsoft Excel, Access, Biblioshiny and VOSviewer software. The United States of America is leading in a top authors, organizations and as a country on PISB literature. This authorship pattern trends revealed that the authors on PISB literature prefer to work with two, three, four or as a single author and they give low preference to work with more than four authors. The country-level collaboration trend revealed that the collaboration between United States and China are the most frequently occurring among the rest of other countries. Thematic evolution identified that some themes become obsolete, and some emerge with the passage of time. However, notably, the recent period (2021-2024) is mainly connected with various social media issues and challenges. The three-factor (keywords, countries and sources) revealed that the researchers of top countries used mostly six keywords (self-disclosure, social media, privacy, Facebook, social networking sites, and social support) and they preferably published in two major sources. This finding shows that the authors from the top ten countries mainly published their work in highly selected journals

Does privacy have any impact on self-disclosure? A systematic review of relevant studies

The study aims to collect and review (systematically) literature that investigates the impact of privacy on self-disclosure using Facebook. Another purpose of the review is to identify the theories/ models applied/tested, the software used for data analysis in the reviewed research, the quality of the reviewed studies, and the countries leading in terms of publishing on the topic. The research also intends to shed light on the privacy-related aspects covered in the published studies. The researchers searched four databases: Scopus, Web of Science, Library, Information Science and Technology Abstracts (LISTA), and Google Scholar to collect and review the literature on the topic. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were used to conduct this review. The findings confirm that most reviewed studies found a negative impact of various privacy aspects on self-disclosure on social media sites. The review identified that Privacy Calculus Theory was the most frequently tested in the reviewed studies. The majority of the reviewed literature quality scores ranged between 12 to 13. It was also found that Privacy Concerns were the most discussed in the research reviewed. It was found that most literature was produced in the years 2023 and 2018 and that more than 85% of the studies were published collaboratively with two and more than two authors. This is the first review of its kind that collected and reviewed (systematically) the literature that investigated the impact of privacy on self-disclosure on social networking sites. This is also the first systematic review identifying the most used theories in the reviewed research. This is a unique study that identified the privacy aspect frequently discussed in the literature in the area. This review may get the attention of social media providers to offer various personalized and customized features by empowering users with better control over their privacy and access to personal information, which is likely to mitigate user privacy concerns and improve their trust in social media sites

Finding common support and assessing matching methods for causal inference

Doctor of PhilosophyDepartment of StatisticsMichael J. HigginsThis dissertation presents an approach to assess and validate causal inference tools to es- timate the causal effect of a treatment. Finding treatment effects in observational studies is complicated by the need to control for confounders. Common approaches for controlling include using prognostically important covariates to form groups of similar units containing both treatment and control units or modeling responses through interpolation. This disser- tation proposes a series of new, computationally efficient methods to improve the analysis of observational studies. Treatment effects are only reliably estimated for a subpopulation under which a common support assumption holds—one in which treatment and control covariate spaces overlap. Given a distance metric measuring dissimilarity between units, a graph theory is used to find common support. An adjacency graph is constructed where edges are drawn between similar treated and control units to determine regions of common support by finding the largest connected components (LCC) of this graph. The results show that LCC improves on existing methods by efficiently constructing regions that preserve clustering in the data while ensuring interpretability of the region through the distance metric. This approach is extended to propose a new matching method called largest caliper matching (LCM). LCM is a version of cardinality matching—a type of matching used to maximize the number of units in an observational study under a covariate balance constraint between treatment groups. While traditional cardinality matching is an NP-hard, LCM can be completed in polynomial time. The performance of LCM with other five popular matching methods are shown through a series of Monte Carlo simulations. The performance of the simulations is measured by the bias, empirical standard deviation and the mean square error of the estimates under different treatment prevalence and different distributions of covariates. The formed matched samples improve estimation of the population treatment effect in a wide range of settings, and suggest cases in which certain matching algorithms perform better than others. Finally, this dissertation presents an application of LCC and matching methods on a study of the effectiveness of right heart catheterization (RHC) and find that clinical outcomes are significantly worse for patients that undergo RHC