The digital glass house - Social networking and privacy

Since the explosion of the Internet age, nearly 2 billion people are connected to the World Wide Web, creating seemingly limitless opportunities for communication and collaboration including social networking. Communication is virtually instantaneous and vast amounts of information are available at the touch of a key.In such an open digital environment, we take it for granted that almost any information can be sourced online by anyone with Internet access.The rapid growth of the social networking sites (SNS) such as Facebook, which reaches 500 million users recently, has coincided with an increasing concern over personal privacy.This study examines how Facebook users' perceptions of privacy, frequency of use, and the disclosure of their personal information with other users. This study was guided by two research questions: What are the Facebook users' perceptions of privacy and what is the personal information they disclose to other users? Does the Facebook users' frequency of use affect their disclosure of personal information? 149 respondents from the researcher's own Facebook profile filled up a Web-based questionnaire in August 2010. The data was analyzed using descriptive and inferential statistics.The research hypothesized that higher levels of privacy perception will result in less disclosure of personal information and the more active a user is on Facebook, the greater will be the user's likelihood of maintaining a private profile. The results of chi-square tests and correlation analysis found significant positive relationships between privacy perception and the disclosure of personal information, and no significant relationships between frequency of use and disclosure of personal information. Recommendations for future researchers were also included

The Impact Of Technology Trust On The Acceptance Of Mobile Banking Technology Within Nigeria

With advancement in the use of information technology seen as a key factor in economic development, developed countries are increasingly reviewing traditional systems, in various sectors such as education, health, transport and finance, and identifying how they may be improved or replaced with automated systems. In this study, the authors examine the role of technology trust in the acceptance of mobile banking in Nigeria as the country attempts to transition into a cashless economy. For Nigeria, like many other countries, its economic growth is linked, at least in part, to its improvement in information technology infrastructure, as well as establishing secure, convenient and reliable payments systems. Utilising the Technology Acceptance Model, this study investigates causal relationships between technology trust and other factors influencing user’s intention to adopt technology; focusing on the impact of seven factors contributing to technology trust. Data from 1725 respondents was analysed using confirmatory factor analysis and the results showed that confidentiality, integrity, authentication, access control, best business practices and non-repudiation significantly influenced technology trust. Technology trust showed a direct significant influence on perceived ease of use and usefulness, a direct influence on intention to use as well as an indirect influence on intention to use through its impact on perceived usefulness and perceived ease of use. Furthermore, perceived ease of use and perceived usefulness showed significant influence on consumer’s intention to adopt the technology. With mobile banking being a key driver of Nigeria’s cashless economy goals, this study provides quantitative knowledge regarding technology trust and adoption behaviour in Nigeria as well as significant insight on areas where policy makers and mobile banking vendors can focus strategies engineered to improve trust in mobile banking and increase user adoption of their technology

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

NOTION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE - AN EMPIRICAL INVESTIGATION FROM A USER\u27S PERSPECTIVE

The growing attention on artificial intelligence-based decision-making has led to research interest in the explainability and interpretability of machine learning models, algorithmic transparency, and comprehensibility. This renewed attention on XAI advocates the need to investigate end user-centric explainable AI, due to the universal adoption of AI-based systems at the root level. Therefore, this paper investigates user-centric explainable AI from a recommendation systems context. We conducted focus group interviews to collect qualitative data on the recommendation system. We asked participants about the end users\u27 comprehension of a recommended item, its probable explanation and their opinion of making a recommendation explainable. Our finding reveals end users want a non-technical and tailor-made explanation with on-demand supplementary information. Moreover, we also observed users would like to have an explanation about personal data usage, detailed user feedback, authentic and reliable explanations. Finally, we proposed a synthesized framework that will include end users in the XAI development process

M-health review: joining up healthcare in a wireless world

In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

Governing Networks and Rule-Making in Cyberspace

The global network environment defies traditional regulatory theories and policymaking practices. At present, policymakers and private sector organizations are searching for appropriate regulatory strategies to encourage and channel the global information infrastructure (“GII”). Most attempts to define new rules for the development of the GII rely on disintegrating concepts of territory and sector, while ignoring the new network and technological borders that transcend national boundaries. The GII creates new models and sources for rules. Policy leadership requires a fresh approach to the governance of global networks. Instead of foundering on old concepts, the GII requires a new paradigm for governance that recognizes the complexity of networks, builds constructive relationships among the various participants (including governments, systems operators, information providers, and citizens), and promotes incentives for the attainment of various public policy objectives in the private sector

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Enhancing User Authentication with Facial Recognition and Feature-Based Credentials

This research proposes a novel and trustworthy user authentication method that creates individualized and trusted credentials based on distinctive facial traits using facial recognition technology. The ability to easily validate user identification across various login methods is provided by this feature. The fundamental elements of this system are face recognition, feature extraction, and the hashing of characteristics to produce usernames and passwords. This method makes use of the OpenCV library, which is free software for computer vision. Additionally, it employs Hashlib for secure hashing and Image-based Deep Learning for Identification (IDLI) technology to extract facial tags. For increased security and dependability, the system mandates a maximum of ten characters for users and passwords. By imposing this restriction, the system increases its resilience by reducing any possible weaknesses in its defense. The policy also generates certificates that are neatly arranged in an Excel file for easy access and management. To improve user data and provide reliable biometric authentication, this study intends to create and implement a recognition system that incorporates cutting-edge approaches such as face feature extraction, feature hashing, and password creation. Additionally, the system has robust security features using face recognition