The proceedings of the International Conference on Automation and Computation 2022 (AUTOCOM-22) consist of complete research articles that were presented at the conference. Each of the research articles was double-blind reviewed by the experts of the corresponding domain. The book contains a blend of problems and respective solutions related to computer-based automation & computation to highlight the recent technological developments in computer-based automation. It serves as an environment for researchers to showcase the latest research results on Data Science & Engineering, Computing Technologies, Computational Intelligence, Communication & Networking, Signal & Image Processing, Intelligent Control Systems & Optimization, Robotics and Automation, Power, Energy & Power Electronics, Healthcare & Computation, AI for human interaction, etc. It aims to give deep insight into the current trends of research in science and technology and shall introduce the reader to the new problems and respective approaches toward the solution and shall enlighten the researchers, students and academicians about the research being carried out in the field.

This book examines the growing importance of algorithms and automation—including emerging forms of artificial intelligence—in the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on ‘reporting algorithms’, such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematise computational journalism by, for example, pointing out some of the challenges inherent in applying artificial intelligence to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner. The chapters in this book were originally published as a special issue of Digital Journalism.

From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. An expert in computer science and media explains the present and future of a world in which news is created by algorithm. Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time. Yet the wide-scale automation of the news itself has largely escaped attention. In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories—increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism. Newsbots converse with social media audiences, distributing stories and receiving feedback. Online media has become a platform for A/B testing of content, helping journalists to better understand what moves audiences. Algorithms can even draft certain kinds of stories. These techniques enable media organizations to take advantage of experiments and economies of scale, enhancing the sustainability of the fourth estate. But they also place pressure on editorial decision-making, because they allow journalists to produce more stories, sometimes better ones, but rarely both. Automating the News responds to hype and fears surrounding journalistic algorithms by exploring the human influence embedded in automation. Though the effects of automation are deep, Diakopoulos shows that journalists are at little risk of being displaced. With algorithms at their fingertips, they may work differently and tell different stories than they otherwise would, but their values remain the driving force behind the news. The human–algorithm hybrid thus emerges as the latest embodiment of an age-old tension between commercial imperatives and journalistic principles.

Neurocomputing for Design Automation provides innovative design theories and computational models with two broad objectives: automation and optimization. This singular book: Presents an introduction to the automation and optimization of engineering design of complex engineering systems using neural network computing Outlines new computational models and paradigms for automating the complex process of design for unique engineering systems, such as steel highrise building structures Applies design theories and models to the solution of structural design problems Integrates three computing paradigms: mathematical optimization, neural network computing, and parallel processing The applications described are general enough to be applied directly or by extension to other engineering design problems, such as aerospace or mechanical design. Also, the computational models are shown to be stable and robust - particularly suitable for design automation of large systems, such as a 144-story steel super-highrise building structure with more than 20,000 members. The book provides an exceptional framework for the automation and optimization of engineering design, focusing on a new computing paradigm - neural networks computing. It presents the automation of complex systems at a new and higher level never achieved before.

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

Contents:A New Way to Acquire Knowledge (H-Y Wang)An SPN Knowledge Representation Scheme (J Gattiker & N Bourbakis)On the Deep Structures of Word Problems and Their Construction (F Gomez)Resolving Conflicts in Inheritance Reasoning with Statistical Approach (C W Lee)Integrating High and Low Level Computer Vision for Scene Understanding (R Malik & S So)The Evolution of Commercial AI Tools: The First Decade (F Hayes-Roth)Reengineering: The AI Generation — Billions on the Table (J S Minor Jr)An Intelligent Tool for Discovering Data Dependencies in Relational DBS (P Gavaskar & F Golshani)A Case-Based Reasoning (CBR) Tool to Assist Traffic Flow (B Das & S Bayles)A Study of Financial Expert System Based on Flops (T Kaneko & K Takenaka)An Associative Data Parallel Compilation Model for Tight Integration of High Performance Knowledge Retrieval and Computation (A K Bansal)Software Automation: From Silly to Intelligent (J-F Xu et al.)Software Engineering Using Artificial Intelligence: The Knowledge Based Software Assistant (D White)Knowledge Based Derivation of Programs from Specifications (T Weight et al.)Automatic Functional Model Generation for Parallel Fault Design Error Simulations (S-E Chang & S A Szygenda)Visual Reverse Engineering Using SPNs for Automated Diagnosis and Functional Simulation of Digital Circuits (J Gattiker & S Mertoguno)The Impact of AI in VLSI Design Automation (M Mortazavi & N Bourbakis)The Automated Acquisition of Subcategorizations of Verbs, Nouns and Adjectives from Sample Sentences (F Gomez)General Method for Planning and Rendezvous Problems (K I Trovato)Learning to Improve Path Planning Performance (P C Chen)Incremental Adaptation as a Method to Improve Reactive Behavior (A J Hendriks & D M Lyons)An SPN-Neural Planning Methodology for Coordination of Multiple Robotic Arms with Constrained Placement (N Bourbakis & A Tascillo) Readership: Computer scientists, artificial intelligence practitioners and robotics users. keywords:

This book collects recent theoretical advances and concrete applications of learning automata (LAs) in various areas of computer science, presenting a broad treatment of the computer science field in a survey style. Learning automata (LAs) have proven to be effective decision-making agents, especially within unknown stochastic environments. The book starts with a brief explanation of LAs and their baseline variations. It subsequently introduces readers to a number of recently developed, complex structures used to supplement LAs, and describes their steady-state behaviors. These complex structures have been developed because, by design, LAs are simple units used to perform simple tasks; their full potential can only be tapped when several interconnected LAs cooperate to produce a group synergy. In turn, the next part of the book highlights a range of LA-based applications in diverse computer science domains, from wireless sensor networks, to peer-to-peer networks, to complex social networks, and finally to Petri nets. The book accompanies the reader on a comprehensive journey, starting from basic concepts, continuing to recent theoretical findings, and ending in the applications of LAs in problems from numerous research domains. As such, the book offers a valuable resource for all computer engineers, scientists, and students, especially those whose work involves the reinforcement learning and artificial intelligence domains.