Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Decision Model for Decentralized Autonomous Organization Platform Selection: Three Industry Case Studies
170
0
0.0
(
0
)
Added by
Elena Baninemeh
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.
rate research
Read More
Administering standardized examinations is a challenging task, especially for those universities for which colleges affiliated to it are geographically distributed over a wide area. Some of the challenges include maintaining integrity and confidentiality of examination records, preventing mal-practices, issuing unique identification numbers to a large student population and managing assets required for the smooth conduct of examinations. These challenges aggravate when colleges affiliated to universities demand academic and administrative autonomy by demonstrating best practices consistently over a long period. In this chapter, we describe a model for decentralized and autonomous examination system to provide the necessary administrative support. The model is based on two emerging technologies of Blockchain Technology and Internet of Things (IoT). We adopt a software architecture approach to describe the model. The prescriptive architecture consists of {em architectural mappings} which map functional and non-functional requirements to architectural elements of blockchain technology and IoT. In architectural mappings, first, we identify common use-cases in administering standardized examinations. Then we map these use-cases to the core elements of blockchain, i.e. distributed ledgers, cryptography, consensus protocols and smart-contracts and IoT. Such kind of prescriptive architecture guide downstream software engineering processes of implementation and testing
Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning. However, AI models require a large amount of sensitive training data and are usually computationally intensive to build. The commercial value of such models motivates attackers to mount various attacks. Adversaries can launch model extraction attacks for monetization purposes or step-ping-stone towards other attacks like model evasion. In specific cases, it even results in destroying brand reputation, differentiation, and value proposition. In addition, IP laws and AI-related legalities are still evolving and are not uniform across countries. We discuss model extraction attacks in detail with two use-cases and a generic kill-chain that can compromise autonomous cars. It is essential to investigate strategies to manage and mitigate the risk of model theft.
The expansion of artificial intelligence (AI) and autonomous systems has shown the potential to generate enormous social good while also raising serious ethical and safety concerns. AI technology is increasingly adopted in transportation. A survey of various in-vehicle technologies found that approximately 64% of the respondents used a smartphone application to assist with their travel. The top-used applications were navigation and real-time traffic information systems. Among those who used smartphones during their commutes, the top-used applications were navigation and entertainment. There is a pressing need to address relevant social concerns to allow for the development of systems of intelligent agents that are informed and cognizant of ethical standards. Doing so will facilitate the responsible integration of these systems in society. To this end, we have applied Multi-Criteria Decision Analysis (MCDA) to develop a formal Multi-Attribute Impact Assessment (MAIA) questionnaire for examining the social and ethical issues associated with the uptake of AI. We have focused on the domain of autonomous vehicles (AVs) because of their imminent expansion. However, AVs could serve as a stand-in for any domain where intelligent, autonomous agents interact with humans, either on an individual level (e.g., pedestrians, passengers) or a societal level.
Autonomous parking technology is a key concept within autonomous driving research. This paper will propose an imaginative autonomous parking algorithm to solve issues concerned with parking. The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) for planning a feasible trajectory from a given start point to a parking lot, and a path smoothing module for optimizing the efficiency of parking tasks. Our algorithm is based on a real kinematic vehicle model; which makes it more suitable for algorithm application on real autonomous cars. Furthermore, due to the introduction of the imagination mechanism, the processing speed of our algorithm is ten times faster than that of traditional methods, permitting the realization of real-time planning simultaneously. In order to evaluate the algorithms effectiveness, we have compared our algorithm with traditional RRT, within three different parking scenarios. Ultimately, results show that our algorithm is more stable than traditional RRT and performs better in terms of efficiency and quality.
How to attribute responsibility for autonomous artificial intelligence (AI) systems actions has been widely debated across the humanities and social science disciplines. This work presents two experiments ($N$=200 each) that measure peoples perceptions of eight different notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful difference in how people perceived these agents moral responsibility; human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these findings, such as the need for explainable AI in high-stakes scenarios.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
