The Role of Emerging Digital Technologies in the Apparel Industry of Pakistan

Abstract Purpose: Current study examines the relationship of industry 4.0 on firm performance through process mediation of employee involvement and mass customization capabilities. The motivation to conduct the current study was driven by the decreasing trend of Pakistan apparel exports and the inconsistent findings in the literature on the relationship among variables. Design/Methodology/Approach: Population of the study are the export members firms of HS code 61 and HS code 62 apparel manufacturing firms. From 1564 firms 10000 senior and middle management identified as a population. Structural equation modeling (SEM) was used to test the research model. Findings: The results reveal that industry 4.0 technologies positively impacted firm performance. Study findings also show the process mediation of employee involvement and mass customization capability between big data and firm performance. Implication/Originality/Value: To the best author's knowledge, this is the first attempt to examine the process mediation of employee involvement and mass customization capability on the relationship between industry 4.0 and Pakistan apparel firm performance

How has Outsourcing Human Resource (HR) Services within National Health Services (NHS) Impacted upon Staff Turnover and Wider Local Economy in UK?

This paper critically investigates how outsourcing human resource (HR) services within National Health Services (NHS) have affected staff turnover and wider local economy of UK. Interviews were structured with the view to investigate this issue. The research shows that despite the pressure of outsourcing by Department of Health (DoH), NHS acute trusts are reluctant to outsource their HR functions. Those who outsourced have done this with some strategic planning. Reverse trend of outsourcing has also been observed. Outsourcing HR functions have not lost as many jobs as it was expected. No direct relationship was observed in the wider local economy and outsourcing in NHS trusts. According to findings of the research HR outsourcing has not drastic effects on wider local economy if observed on small scale

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Mitigating man-in-the-middle attacks on mobile devices by blocking insecure http traffic without using vpn

Mobile devices are constantly connected to the Internet, making countless connections with remote services. Unfortunately, many of these connections are in cleartext, visible to third-parties while in transit. This is insecure and opens up the possibility for man-in-the-middle attacks. While there is little control over what kind of connection running apps can make, this paper presents a solution in blocking insecure HTTP packets from leaving the device. Specifically, the proposed solution works on the device, without the need to tunnel packets to a remote VPN server, and without special privileges such as root access. Speed tests were performed to quantify how much network speed is being impacted while filtering. To investigate how blocking HTTP traffic can affect day-to-day usage, common tasks were put to the tests, tasks such as browsing, searching, emailing, instant messaging, social networking, consuming streaming content, and gaming. The results from the tests are interesting, websites that do not support HTTPS were exposed, apps that do not fully support HTTPS were also being uncovered. One surprisingly, and arguably pleasant, side effect was discovered – the filtering solution blocks out advertisements in all of the games being tested, hence contributing to an improved gaming experience