A framework for the governance of social media in the workplace

Social media is fast becoming an ever-increasingly significant part of the world of business and a phenomenon which cannot be evaded. The advent of social media in the workplace compels organisations to acclimatise to the transformation emanating from employees‟ adoption of these technologies (Hanaki & Casella, 2008). Approximately seventy percent of organisations do not have a social media governance framework in place (Fink et al., 2011). Social media governance in organisations is very disjointed; companies have varying stances as to social media strategy, the risks, benefits and business use of social media (Thompson et al., 2011). The growth of social media and its use in the business environment will see a more standardised approach to social media governance (Thompson et al., 2011). Being at the forefront of technology development in Africa, and in certain areas, globally (Government of the Republic of South Africa, 2012), places added emphasis on IT organisations in South Africa to set the standard as it relates to social media governance. The diversity and depth of the human and technology resources within these organisations, creates an environment conducive to establishing and pioneering sound social media governance structures. The treatise consists of a study on the governance of social media and the successive development of two frameworks; an integrated framework for the governance of social media in the workplace, as well as integrated framework for a social media policy within an IT organisation. These frameworks are empirically evaluated amongst employees, within the context of Information Technology (IT) organisations, in South Africa. Several recommendations are proposed by the author in relation to the adoption of the proposed frameworks

A framework for the governance of social media in the workplace

Social media is fast becoming an ever-increasingly significant part of the world of business and a phenomenon which cannot be evaded. The advent of social media in the workplace compels organisations to acclimatise to the transformation emanating from employees‟ adoption of these technologies (Hanaki & Casella, 2008). Approximately seventy percent of organisations do not have a social media governance framework in place (Fink et al., 2011). Social media governance in organisations is very disjointed; companies have varying stances as to social media strategy, the risks, benefits and business use of social media (Thompson et al., 2011). The growth of social media and its use in the business environment will see a more standardised approach to social media governance (Thompson et al., 2011). Being at the forefront of technology development in Africa, and in certain areas, globally (Government of the Republic of South Africa, 2012), places added emphasis on IT organisations in South Africa to set the standard as it relates to social media governance. The diversity and depth of the human and technology resources within these organisations, creates an environment conducive to establishing and pioneering sound social media governance structures. The treatise consists of a study on the governance of social media and the successive development of two frameworks; an integrated framework for the governance of social media in the workplace, as well as integrated framework for a social media policy within an IT organisation. These frameworks are empirically evaluated amongst employees, within the context of Information Technology (IT) organisations, in South Africa. Several recommendations are proposed by the author in relation to the adoption of the proposed frameworks

Intelligent Human-input-based Blockchain Oracle (IHiBO)

The advent of Distributed Ledger Technologies (DLTs) has paved the way for a new paradigm of traceability in all information systems areas. In the context of decision-making processes, however, DLTs are generally used only to trace the end results. In this work we argue that a reasoning system can be put in place for making these decisions, in order to enhance auditability, transparency, and finally to provide explainability. We propose the Intelligent Human-input-based Blockchain Oracle (IHiBO), a cross-chain oracle that enables the execution and traceability of formal argumentation and negotiation processes, involving the intervention of human experts. We take as reference the decision-making processes of fund managements, as trust is of crucial importance in such ``trust services''. The architecture and implementation of IHiBO are based on leveraging two-layer DLTs, smart contracts, argumentation and negotiation in a multi-agent setup. Finally, we provide some experimental results that support our discussion, namely that in the use-case we have considered our methodology can increase trust from principals to trusted services

Efficient Estimation of the Local Robustness of Machine Learning Models

Machine learning models often need to be robust to noisy input data. Real-world noise (such as measurement noise) is often random and the effect of such noise on model predictions is captured by a model's local robustness, i.e., the consistency of model predictions in a local region around an input. Local robustness is therefore an important characterization of real-world model behavior and can be useful for debugging models and establishing user trust. However, the na\"ive approach to computing local robustness based on Monte-Carlo sampling is statistically inefficient, especially for high-dimensional data, leading to prohibitive computational costs for large-scale applications. In this work, we develop the first analytical estimators to efficiently compute local robustness of multi-class discriminative models. These estimators linearize models in the local region around an input and compute the model's local robustness using the multivariate Normal cumulative distribution function. Through the derivation of these estimators, we show how local robustness is connected to such concepts as randomized smoothing and softmax probability. In addition, we show empirically that these estimators efficiently compute the local robustness of standard deep learning models and demonstrate these estimators' usefulness for various tasks involving local robustness, such as measuring robustness bias and identifying examples that are vulnerable to noise perturbation in a dataset. To our knowledge, this work is the first to investigate local robustness in a multi-class setting and develop efficient analytical estimators for local robustness. In doing so, this work not only advances the conceptual understanding of local robustness, but also makes its computation practical, enabling the use of local robustness in critical downstream applications

FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

Political participation and e-petitioning an analysis of the policy-making impact of the Scottish Parliament\u27s e-petition system

Worldwide, representative democracies have been experiencing declining levels of voter turnout, lower membership levels in political parties, and apathy towards their respective political systems and their ability to influence the political process. E-democracy, and specifically E-petitioning, have been touted as a possible solution to this problem by scholars of electoral systems. In 1999, the Scottish Parliament reconvened for the first time in nearly three hundred years, and quickly set out to change the way politics were handled in Scotland by launching the world\u27s first online E-petition system. Analyzing the Scottish Parliament\u27s E-petition system, and assessing the extent to which it fulfilled the aspiration and goals of its designers serves as a litmus test to see whether it is an effective medium to increase public political participation, and whether it could be replicated in other democratic countries. Data was collected from the Scottish Parliament\u27s E-petitioning website, which hosts all the E-petitions and details of who signed them, each E-petition\u27s path through the Parliament, who sponsored the petition, and other important information. Since success of an E-petition is highly subjective due to the original petitioner\u27s own desired goals, three case studies of E-petitions and a data analysis were utilized to evaluate the system. Results suggest that the Scottish Parliament\u27s E-petition system has engaged Scots in the political process, given them a medium to participate in meaningful policy formulation, and produced tangible changes in policy through E-petitions

How WEIRD is Usable Privacy and Security Research? (Extended Version)

In human factor fields such as human-computer interaction (HCI) and psychology, researchers have been concerned that participants mostly come from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) countries. This WEIRD skew may hinder understanding of diverse populations and their cultural differences. The usable privacy and security (UPS) field has inherited many research methodologies from research on human factor fields. We conducted a literature review to understand the extent to which participant samples in UPS papers were from WEIRD countries and the characteristics of the methodologies and research topics in each user study recruiting Western or non-Western participants. We found that the skew toward WEIRD countries in UPS is greater than that in HCI. Geographic and linguistic barriers in the study methods and recruitment methods may cause researchers to conduct user studies locally. In addition, many papers did not report participant demographics, which could hinder the replication of the reported studies, leading to low reproducibility. To improve geographic diversity, we provide the suggestions including facilitate replication studies, address geographic and linguistic issues of study/recruitment methods, and facilitate research on the topics for non-WEIRD populations.Comment: This paper is the extended version of the paper presented at USENIX SECURITY 202

Privacy-Preserving Crowd-Sourcing of Web Searches with Private Data Donor

Search engines play an important role on the Web, helping users find relevant resources and answers to their questions. At the same time, search logs can also be of great utility to researchers. For instance, a number of recent research efforts have relied on them to build prediction and inference models, for applications ranging from economics and marketing to public health surveillance. However, companies rarely release search logs, also due to the related privacy issues that ensue, as they are inherently hard to anonymize. As a result, it is very difficult for researchers to have access to search data, and even if they do, they are fully dependent on the company providing them. Aiming to overcome these issues, this paper presents Private Data Donor (PDD), a decentralized and private-by-design platform providing crowd-sourced Web searches to researchers. We build on a cryptographic protocol for privacy preserving data aggregation, and address a few practical challenges to add reliability into the system with regards to users disconnecting or stopping using the platform. We discuss how PDD can be used to build a flu monitoring model, and evaluate the impact of the privacy-preserving layer on the quality of the results. Finally, we present the implementation of our platform, as a browser extension and a server, and report on a pilot deployment with real users

LAOps : Learning Analytics with Privacy-aware MLOps

The intake of computer science faculty has rapidly increased with simultaneous reductions to course personnel. Presently, the economy is recovering slightly, and students are entering the working life already during their studies. These reasons have fortified demands for flexibility to keep the target graduation time the same as before, even shorten it. Required flexibility is created by increasing distance learning and MOOCs, which challenges students’ self-regulation skills. Teaching methods and systems need to evolve to support students’ progress. At the curriculum design level, such learning analytics tools have already been taken into use. This position paper outlines a next-generation, course-scope analytics tool that utilises data from both the learning management system and Gitlab, which works here as a channel of student submissions. Gitlab provides GitOps, and GitOps will be enhanced with machine learning, thereby transforming as MLOps. MLOps that performs learning analytics, is called here LAOps. For analysis, data is copied to the cloud, and for that, it must be properly protected, after which models are trained and analyses performed. The results are provided to both teachers and students and utilised for personalisation and differentiation of exercises based on students’ skill level.publishedVersionPeer reviewe