INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2021-04-14T07:08:30+00:00 Editorial Office Open Journal Systems The Effect of ‘Datafication’ on Knowledge Diffusion: A Topological Framework 2021-03-19T05:59:03+00:00 Ayse Kok Arslan <p>This paper begins by distinguishing between data infrastructure, data entry and data points as three distinct, but interrelated situations. Data practices are understood in the general sense of the word here, i.e., such as actions, actions, and consequences, of introducing data-generating technologies for knowledge codification. This paper will investigate both the generics and specificities of data practices to explore the disentanglement of the liveness of data practices, i.e. how such practices are happening with regard to knowledge codification. Within this regard, this study seeks to account for the ‘fluid and heterogeneous ontology’ of such practices. In other words, the framework conceptualizes data processing as correlational, and aims to provide a technique to explore they disentanglement of these relationships.</p> 2021-04-14T00:00:00+00:00 Copyright (c) 2021 Ayse Kok Arslan Developing a Conversational Agent to Explore Machine Learning Concepts 2021-03-19T05:56:20+00:00 Ayse Kok Arslan <p>This study aims to introduce a discussion platform and curriculum designed to help people understand how machines learn. Research shows how to train an agent through dialogue and understand how information is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy based on existing research and integrates a wide range of different subject documents into a set of key AI literacy skills to develop a user-centered AI. This functionality and structural considerations are organized into a conceptual framework based on the literature. Contributions to this paper can be used to initiate discussion and guide future research on AI learning within the computer science community.</p> 2021-04-14T00:00:00+00:00 Copyright (c) 2021 Ayse Kok Arslan AN EMPIRICAL MODEL FOR EXPLORING AI IN GOVERNMENT: PUTTING SOCIO-TECHNOLOGICAL SYSTEMS PERSPECTIVES INTO USE 2021-03-19T05:47:17+00:00 Ayse Kok Arslan <p>This study explores the evolution of global AI dynamics by discussing its role in government with a focus on aspects of development and governance of social and technological systems (STS). This document reports three research questions, including the extent of the analysis: (1) theories regarding the concept of AI in the public sector; (2) expectations regarding the development of AI in the public sector; and, (3) the challenges and opportunities of AI in the public sector. This experimental study provides an experimental framework for a comprehensive approach to measuring the magnitude of AI policy that allows for the methods of evaluating different governance practices and policy priorities in different countries. The study sheds light onto areas of policy that have the potential to implement AI programs and strategies; administrative functions open to the acceptance of AI applications and strategies; and the challenges / risks that community managers may face in defining AI policies and projects in the public sector including how to deal with cyber-troops.</p> 2021-04-14T00:00:00+00:00 Copyright (c) 2021 Ayse Kok Arslan Code Generation from Simulink Models with Task and Data Parallelism 2021-03-22T07:05:41+00:00 Pin Xu Masato Edahiro Kondo Masaki <p>In this paper, we propose a method to automatically generate parallelized code from Simulink models, while exploiting both task and data parallelism. Building on previous research, we propose a model-based parallelizer (MBP) that exploits task parallelism and assigns tasks to CPU cores using a hierarchical clustering method. We also propose a<br />method in which data-parallel SYCL code is generated from Simulink models; computations with data parallelism are expressed in the form of S-Function Builder blocks and are executed in a heterogeneous computing environment. Most parts of the procedure can be automated with scripts, and the two methods can be applied together. In the evaluation, the data-parallel programs generated using our proposed method achieved a maximum speedup of approximately 547 times, compared to sequential programs, without observable differences in the computed results. In addition, the programs generated while exploiting both task and data parallelism were confirmed to have achieved better performance than those exploiting either one of the two.</p> 2021-04-14T00:00:00+00:00 Copyright (c) 2021 Pin Xu, Masato Edahiro, Kondo Masaki