Oct'18
Welcome to the IUP Journal of Computer Sciences
Focus
The introduction of the term "open source software" was a deliberate effort to make this field of endeavor more understandable to newcomers and to business, which was viewed as necessary to its spread to a broader community of users. The problem with the main earlier label, "free software," was not its political connotations, but that�to newcomers�its seeming focus on price is distracting. A term was needed that focuses on the key issue of source code and that does not immediately confuse those new to the concept. The first term that came along at the right time and fulfilled these requirements was rapidly adopted: open source.
How I Coined the Term, 2018
The theory of Diversity Computing proposes diversity computing devices that will invite people who are different from each other to participate in an active and reflective process of meaning-making. Here diversity refers to the infinite variety in interpersonal settings, rather than to a set of quantifiable or observable characteristics. Differences between people may operate on known diversity dimensions�for example, gender identity or race�but can also depend on mood, health, recent experiences and moving away from a stance that one group represents the norm against which others are measured. It therefore requires going beyond creating shared meanings to participating in meaning-making, including via constructive disagreements, which are unavoidable.
Diversity Computing requires specific methodology innovations in disciplines spanning the humanities, social sciences and life sciences, as well as those disciplines more traditionally associated with computing innovations. Adopting a fundamental position of acceptance of difference, it would enhance inclusion, facilitate mutual understanding and enable individuals to find their own way in social situations.
At the outset, Diversity Computing required a combination of philosophical and cognitive theory, participatory methodology and digital innovation. The theoretical basis is that people co-create meanings by participating in each other�s sense-making activities. Self-reflection and reflecting with each other will be a core part of both the creation and implementation of Diversity Computing.
Robert D Atkinson in his paper, "�It Is Going to Kill Us!� and Other Myths About the Future of Artificial Intelligence," removes many existing myths about Artificial Intelligence (AI) which are bothering the minds of both common man and the elite. This paper is educative for aspiring folks of computer science.
In the other paper, "Detection of Intruder in Wireless Sensor Networks", the authors, Ashwini G Sapkal and G S Ambadkar, have suggested a new approach to the problem of detection of intruder in Wireless Sensor Networks (WSNs). The simulation results have showed better performance parameters like packet delivery ratio, end-to-end delay and throughput.
Consulting Editor
"It Is Going to Kill Us!" and Other Myths About the Future of Artificial Intelligence
When it comes to Artificial Intelligence (AI), myths are spreading faster than the technology itself is advancing. Left unchecked, they could inspire fears that undermine the technology�s progress to the detriment of economic growth and social progress. AI is a branch of computer science that overlaps with other areas of study, including robotics, natural language processing, and computer vision. While AI has become commonplace, the public still has a poor understanding of the technology. As a result, a diverse cast of critics, driven by fear, opportunism or ignorance, has jumped into the intellectual vacuum to warn policymakers that, sooner than we think, AI will produce a parade of horribles. Indeed, these voices have grown so loud that their narratives may soon be accepted as truth. Needless to say, when AI is so demonized, there is a real risk that policymakers will seek to retard its progress. The truth is that AI systems are tools in the service of humans, and we can use them to make our lives vastly better. Given the promise that innovation in AI holds for economic growth and societal advancement, it is critical that policymakers actively support its further development and use. The paper provides a primer on AI and debunks five prevailing myths that, if accepted, threaten to slow or undermine its progress.
Detection of Intruder in Wireless Sensor Networks
Various attacks observed in Wireless Sensor Networks (WSNs) result in limiting or spoiling the ability of the networks in carrying out the expected work. WSNs have finite resources, can be deployable in uncontrollable environments and can be smoothly accessed by an intruder or rather attacker. When an attacker attacks a network layer, it can also affect the other layers. In suggested algorithm, local sensor action on many layers is supervised and evaluated to detect the possible attacker. A general approach of an anomaly-based Intrusion Detection System (IDS) is modified (mIDS). mIDS uses the OTP (One-Time Password) concept, which can find out the presence of the attacker inside the network. The results show that the suggested approach has better end-to-end delay, throughput and packet delivery ratio.