Future cities and autonomous vehicles: analysis of the barriers to full adoption

The inevitable upcoming technology of autonomous vehicles (AVs) will affect our cities and several aspects of our lives. The widespread adoption of AVs repose at crossing distinct barriers that prevent their full adoption. This paper presents a critical review of recent debates about AVs and analyse the key barriers to their full adoption. This study has employed a mixed research methodology on a selected database of recently published research works. Thus, the outcomes of this review integrate the barriers into two main categories; (1) User/Government perspectives that include (i) Users' acceptance and behaviour, (ii) Safety, and (iii) Legislation. (2) Information and Communication Technologies (ICT) which include (i) Computer software and hardware, (ii) Communication systems V2X, and (iii) accurate positioning and mapping. Furthermore, a framework of barriers and their relations to AVs system architecture has been suggested to support future research and technology development

cii Student Papers - 2022

In this collection of papers, we, the Research Group Critical Information Infrastructures (cii) from the Karlsruhe Institute of Technology, present eight selected student research articles contributing to the design, development, and evaluation of critical information infrastructures. During our courses, students mostly work in groups and deal with problems and issues related to sociotechnical challenges in the realm of (critical) information systems. Student papers came from five different cii courses, namely Emerging Trends in Internet Technologies, Emerging Trends in Digital Health, Digital Health, Critical Information Infrastructures, and Selected Issues on Critical Information Infrastructures: Collaborative Development of Innovative Teaching Concepts in summer term of 2021 and the winter term of 2021/2022

Understanding Organizations’ Artificial Intelligence Journey: A Qualitative Approach

Background: With growth in Artificial Intelligence (AI) adoption, challenges and hurdles are also becoming evident. Organizations implementing AI are challenged to find ways to leverage AI to produce optimum results and benefits for the organization. Understanding other organizations’ AI implementation journeys will help them start and implement AI. By understanding the different facets of AI implementation, they can strategize AI to gain business value. Though several studies have examined AI adoption, there are few studies on how firms implement it. We close this gap by studying AI adoption and implementations in various firms. Method: Using a qualitative approach of semi-structured interviews, we studied twenty global organizations of various sizes that have implemented AI. Results: The study categorizes the results into four major themes – facilitators, barriers, trends, and strategies for implementing AI. Our study reinforces the relevance of the TOE framework and Roger’s DOI theory in studying AI adoption. Organizational factors such as top management support, strategic roadmap, availability of skilled resources, and corporate culture influenced AI adoption. Their lack of data or poor data quality is a primary challenge. The privacy laws concerning data, as well as regulatory bottlenecks, further exacerbate this problem. We also identified and mapped the standard AI implementations to their AI technologies. We found that most of them exploit AI’s image and natural language processing capabilities to automate their processes. Regarding implementation, firms work with partners to obtain customer data and use federated learning. Conclusion: Understanding firms’ AI implementation journey will help us promote further adoption and experimentation. Organizations can identify areas where they can leverage AI to enhance value, prepare themselves for the future, start and proceed with AI implementation efforts and overcome barriers they might encounter

A review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi criteria analysis challenges and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore the uncertainty and supply chain performance lead to improved users confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance while development performance can be under development.Comment: Revie

Agents and Robots for Reliable Engineered Autonomy

This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Universidad inteligente: una visión de la adopción de la tecnología

Smart University is an emerging concept, strongly anchored to smart technologies and considered by different authors in the literature. Organizations, including universities, need to incorporate smart technologies to take advantage of their capabilities to transform their processes and drive them toward new organizational models. A Smart University focuses on improving its technological infrastructure for achieving its quality educational goals. This paper presents the integration of the key factors for adopting four smart technologies: Cloud Computing, Big Data, Artificial Intelligence, and the Internet of Things. This characterization and integration allow us to conclude on the need to align digital technologies with the organization's processes, requiring greater interaction with the company’s senior management.Universidad inteligente es un concepto emergente, fuertemente anclado a las tecnologías inteligentes, y considerado por diferentes autores en la literatura. Las organizaciones, incluidas las universidades, necesitan incorporar las tecnologías inteligentes para aprovechar las capacidades que proporcionan para transformar sus procesos e impulsarlas hacia nuevos modelos organizativos. Una universidad inteligente se centra en la mejora de su infraestructura tecnológica para lograr sus objetivos educativos de calidad. Este trabajo presenta la integración de los factores clave para la adopción de cuatro tecnologías inteligentes: Computación en la nube, Big Data, Inteligencia Artificial, e Internet de las Cosas. Esta caracterización e integración nos permite concluir sobre la necesidad de alineación de las tecnologías digitales con los procesos de la organización, exigiendo una mayor interacción con la alta dirección de la empresa

Applications of Emerging Smart Technologies in Farming Systems: A Review

The future of farming systems depends mainly on adopting innovative intelligent and smart technologies The agricultural sector s growth and progress are more critical to human survival than any other industry Extensive multidisciplinary research is happening worldwide for adopting intelligent technologies in farming systems Nevertheless when it comes to handling realistic challenges in making autonomous decisions and predictive solutions in farming applications of Information Communications Technologies ICT need to be utilized more Information derived from data worked best on year-to-year outcomes disease risk market patterns prices or customer needs and ultimately facilitated farmers in decision-making to increase crop and livestock production Innovative technologies allow the analysis and correlation of information on seed quality soil types infestation agents weather conditions etc This review analysis highlights the concept methods and applications of various futuristic cognitive innovative technologies along with their critical roles played in different aspects of farming systems like Artificial Intelligence AI IoT Neural Networks utilization of unmanned vehicles UAV Big data analytics Blok chain technology et

The effect of perceived security, perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in digital payment platform

The effect of perceived security perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in the digital payment platfor

UTAUT Model, Smart Exhibition Sorted by Relevance: Word Cloud Visualization Review

The aim of the paper is to introduce a visualization method with word cloud visualization to illustrate evolution of the smart exhibition, other relevant exhibition modes with virtual presentation and the articles in the application of UTAUT model in a set of documents. The relationship between UTAUT model and smart area, and the smart exhibition and some other smart areas is to be presented rapidly and evidently. This article provides interactive visual analysis of smart exhibition sorted by relevance and the industries or fields in the application of the UTAUT Model by a set of key words, at different time points based on the presentation of D2 or D3 to highlight the core word to make the trend of the smart exhibition clearly understood