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Aiming at the problem of poor music source files and sound quality in the process of AI music creation, this paper proposes an automatic text generation algorithm to assist the analysis of AI music creation. Firstly, for the text generation task of independent multi-source syntactic structure diagram data, the relationship between multi-source input documents is modeled from semantic association and syntactic dependence, so as to realize the generation of final music writing text. Secondly, considering the problem of difficult to locate related work in massive music texts. Finally, the actual effect of AI music is comprehensively judged. The results show that this paper proposes a text automatic generation algorithm, which can optimize the multimodal encoder, make overall judgment on the internal data, network data, and graph structure of music text, and improve the encoding rate of information and semantics. Therefore, the text automatic generation algorithm can control the music unit, identify the characteristics of multiple modes in music creation, and improve the effect of AI music creation. [ABSTRACT FROM AUTHOR]

The paper explores how the daily lives of people with vision impairments are changed by artificial intelligence. They suffer a great deal in circumstances they are not aware of. When they go alone in town, people are worried about their safety. The overall aim of the system is to provide low-cost navigation assistance to blind people that give a sense of artificial vision by informing people of the artificial intelligence environment of objects. An ultrasound sensor is used to detect the distance between objects to the blind person to guide voice and vibration, which can be heard and felt by the blind person. The software can help identify objects in the world by using the voice command, conduct text analysis and recognize the document's text on paper. It can be an important way for blind people to communicate and encourage blind people to live independently. [ABSTRACT FROM AUTHOR]

This paper describes a computational infrastructure used to support creative design in detecting emergent shapes in the specific context of shape grammar implementation. Shape grammars have been used to represent the knowledge behind the creative work of architects, designers and artists. This kind of grammars are inherently visual and they allow the implementation of computational mechanisms to either synthesize or analyze designs of visual languages, including the detection of emergent sub-shapes languages. They have obvious applications to design, including for marketing. The infrastructure presented, together with the algorithm to which it gives support, the latter proposed in another, twin, paper, is a core component of a system, described in our past work, that allows users to build their own shape grammars and experiment with and use them. [ABSTRACT FROM AUTHOR]

Generative AI algorithms that are able to generate creative output are progressing at tremendous speed. This paper presents a research agenda for Generative AI-based support for designers. We present examples of existing applications and thus illustrate the possible application space of Generative AI reflecting the current state of this technology. Furthermore, we provide a theoretical foundation for AI-supported design, based on a typology of design knowledge and the concept of evolutionary creativity. Both concepts are discussed in relation to the changing roles of AI and the human designer. The outlined research agenda presents 10 research opportunities for possible AI-support to augment the designer of the future. The results presented in this paper provide researchers with an introduction to and overview of Generative AI, as well as the theoretical understanding of potential implications for the future of the design discipline. [ABSTRACT FROM AUTHOR]

Current research in artificial intelligence has been primarily focused on machine learning as applied to specific computer and engineering problems. For example, facial recognition or malware detection. This aspect of artificial intelligence has seen numerous advances and continues to advance. Research regarding synthetic consciousness has not progressed on par with research into other sub domains of artificial intelligence. While there has been speculation regarding the eventuality of some level of consciousness being developed via artificial intelligence, no specific research modalities have been adequately developed. This paper focuses on two synthetic consciousness issues. The first issue is to provide a basis for developing synthetic consciousness. The second issue is outlining specific research modalities that have a significant probability of achieving synthetic consciousness. The premise of this paper is to outline how to most effectively create algorithmic systems that have a high probability of leading to the emergent property of consciousness. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is an essential component in modern games. With AI, players can feel the challenges in the game and the game will feel more real. AI has several branches, one of which is path-finding. Pathfinding is a way of finding the shortest path between two points. The main problem in path-finding is how to perform a path-finding accurately and requires fewer computational resources (CPU and memory). This paper describes the results of research that tested the A* and Hierarchical Pathfinding A* algorithms using the Unity 3D platform and the C# programming language. The graph or space to be used is a graph with 8 branch nodes. While the benchmarks used are the path generated and the processing time of an algorithm. This paper results in a conclusion that the path generated by the A* algorithm is shorter than the Hierarchical Pathfinding A* algorithm. The number of paths processed for the A* algorithm is more than the Hierarchical Pathfinding A* algorithm. The total execution time of the A* Algorithm is smaller than that of the A* Hierarchical Pathfinding Algorithm. The Hierarchical Pathfinding A* algorithm experiences spikes in execution time more often than the A* algorithm. However, if the total spike time, the Hierarchical Algorithm is less than the A* Algorithm. [ABSTRACT FROM AUTHOR]

The goal to promote human limits is for Artificial Intelligence (AI). It takes a posture on public administrations, represents the increasing availability of regaining clinical data and the rapid creation of intelligent strategies. The need to stress the need to use AI in the fight against the COVID-19 crisis. The paper outlines the main role played by Ai technologies in this unprecedented war and introduces a survey of AI methods used for multiple purposes in the fight against the outbreak of COVID-19. This paper also explains how the body temperature and coughing of the incoming person are assessed and whether the incoming person has not a protective facial mask. Should either of the above tests disqualify the participant, an alarming device invokes the local officials; the entrant may otherwise enter the premises after his/her hand has been sanitized. [ABSTRACT FROM AUTHOR]

This paper describes a simple two stage algorithm for finding emergent sub-shapes in shapes, in the context of shape grammar systems. Matching the shape in the left side of a rule of a shape grammar with parts of a shape in a design in process to decide if the rule is applicable, is its main purpose. Shape grammars have been used to represent the knowledge behind the creative work of architects, designers and artists and allow the implementation of computational mechanisms to analyze and synthesize designs of visual languages, with obvious applications to design, including for marketing. Their computational mechanisms can include the detection of emergent sub-shapes. The algorithm we propose performs this task and is a core component of a system, described in our past work, that allows users to build their own shape grammars and use them. [ABSTRACT FROM AUTHOR]

Social media curation algorithms can raise problems in terms of distortion of reality and mindless news consumption behaviors. This paper proposes algorithmic awareness as a plausible tool towards instilling mindful news consumption on social media platforms. Specifically, the paper investigates the effects of an Algorithmic Awareness (AA) intervention on 1) users perceived awareness about algorithmic curation and filter bubble effect and 2) news consumption behaviors in social media platforms. Based on concepts from information processing and mindfulness, we propose that imparting algorithmic awareness can coerce social media users to make more mindful decisions about whether to believe news posts and perform activities that contribute to their spread (e.g., read, share, fact check, customizing feed). To this end, we design an explanation-based intervention and propose to conduct a between subject's online experiment. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence (AI) algorithms raises serious concerns about its implications for public safety. A prominent example is sentiment analysis, which attempts to infer people's emotions from their expression in text, audio, and video. Sentiment analysis is a subfield of natural language processing, which combines advances in machine learning and computational linguistics. The ability to read human emotions has a wide range of application, from commercial use to politically motivated social engineering. While sentiment analysis brings great benefits via capturing societal trends, it may likewise entail the risks of malicious use of AI (MUAI). In this case, a threat to psychological security or even political stability at the national or global level may arise. Some of the most apparent examples of MUAI using sentiment analysis relate to the assessment of people's emotional triggers, which can be used for large-scale antisocial information campaigns. In practical terms, this might lead to a higher level of adaptation of disinformation, social engineering, hate speech, or phishing attacks to a specific audience. However, there are even more sophisticated ways of applying sentiment analysis in the context of MUAI. Soon, intelligence analytical systems may appear, which can (based on human emotions and public sentiments recognition) develop scenarios of targeted psychological impact on particular segments of almost the entire society. In this regard, the author of this paper aims to assess the potential dangers of using sentiment analysis concerning public safety. This paper focuses on the types and forms of malicious use of sentiment analysis, as well as the ways to mitigate its antisocial use. The outlined problems will be considered through scenario and system analysis. Both methods are capable of displaying the set of factors influencing the MUAI and its results. The relevance of the study derives from the need for a clear understanding of the potential threats arising from advanced technologies. The overall results can be useful for researchers of AI ethical problems and security professionals. [ABSTRACT FROM AUTHOR]

The field of robotics research has been well developed with the combination of computers and machinery. With the advancement of artificial intelligence, mobile robots are also attracting more attention today, and path planning is a crucial foundational technology for mobile robots to complete transportation requirements and reach pertinent target areas. In an efficiency-conscious society, the single-goal planning of transmission is gradually failing to meet the needs of enterprises and factories for efficient operations, path planning that can simultaneously plan the optimal methods and reach many target points is increasingly replacing the conventional single-goal path planning. However, there are more factors to be considered in the real complex environment to face various complex road conditions, and for this reason, various single or hybrid algorithms are being optimized and solved for this kind of problem. This paper summarizes the main methods of path planning to simulate the scale of obstacles and environmental scene maps in various conditions, focusing on several basic algorithms and their hybrid algorithms for solving multi-objective path planning problems in global and local path planning, as well as their improvements and innovations on the basic algorithms. This paper's primary idea is to divide the multi-path planning process into various components and substitute each part into a suitable algorithm and model to solve it separately to accomplish the task of reaching multiple target points efficiently. [ABSTRACT FROM AUTHOR]

Most of the mobile robot path planning is estimated to reach its predetermined aim through the shortest path and avoiding the obstacles. This paper is a survey on path planning algorithms of various current research and existing system of Unmanned Ground Vehicles (UGV) where their challenging issues to be intelligent autonomous robot. The focuses are some short reviews on individual papers for UGV in the known environment. Methods and algorithms in path planning for the autonomous robot had been discussed. From the reviews, we obtained that the algorithms proposed are appropriate for some cases such as single or multiple obstacles, static or movement obstacle and optimal shortest path. This paper also describes some pros and cons for every reviewed paper toward algorithms improvement for further work. [ABSTRACT FROM AUTHOR]

Narratives about intelligent artefacts have influenced both the public's imaginary and the actual development of the AI field since its foundation. Yet, in times where the field seems to be flourishing on the one hand, but rushing into an AI winter on the other, factual narratives about AI applications and advancements are more essential than ever. What is the gap between the actual capabilities of today's AI and the vocabulary used to report about them? In particular, what is the AI lingua used in official, legal documents in business? To find out, we analysed leading share index companies' annual reports from a representative fraction of the German economy (DAX 30), as a starting step in this direction. In this paper, we present a fact-based methodology for systematically assessing the true state of enterprise AI of those companies. Our initial empirical investigation covers only the annual reports of leading listed German enterprises in the DAX 30 as of May 2021 (i.e. before the DAX's expansion to 40 members). For this concrete example, we collected their annual reports from 2010 to 2020 (N=312). We then built upon previous work by extending natural language processing (NLP) algorithms we developed for these purposes. The idea is to systematically process and automatically detect the use of AI-related terminology in those annual reports. Such a terminology is part of a classification schema we introduce for differentiating concrete types of Air-elated terms. We also compare different NLP libraries regarding their suitability and speculate on the reasons behind the poor performance of some of them. Furthermore, we look at relevant AI keywords and phrases, thereby conducting a human-based semantic analysis of the context -- tasks that machines still cannot do effectively. We also give guidance on how to proceed in similar studies, i.e. on how to extend our methodology and the key findings to other national economies. This way, we are contributing not only to an informed perception about the state of enterprise AI, but also to filling the gap between the narratives it uses and the actual state of AI development. [ABSTRACT FROM AUTHOR]

In the era of machine intelligence, dynamic gesture recognition is assumed to be a difficult and challenging problem because it involves the human element. Moreover, the gestures formed for a particular problem vary from person to person. Therefore, the accuracy of classification sometimes becomes inappropriate. Also, gesture recognition is affected by lag in detection; however, a negative lag is assumed to be favorable. In this paper, an attempt has been made to control the system volume through hand gesture recognition which may help in the remote operation of a device and differently-abled people. The YOLO algorithm will deep learning approach is used in this paper, which detects hand movement and its direction. Based on the direction of hand movement, the volume can be increased or decreased. [ABSTRACT FROM AUTHOR]

Recently, there has been increased research on the topic of artificially intelligent programs having the capability of developing advanced systems that are presently used by governments and organizations to analyze highly complex structures across sectors in ways not possible with conventional information technology. While some AI is subject to rigid testing and ethical reviews, other applications raise questions as to what governance structures are in place to control the risks to humanity and long term harmful economic and social consequences. This paper raises awareness about how governments and private industry face an unprecedented challenge in managing these complex systems that include regulators, markets, and special interests that all play a role in influencing the development of AI in different contexts without a full appreciation of the impact of AI on human rights and other consequences. The research focuses on three primary areas: (1) How AI technologies have evolved; (2) What are the major ethical and human rights issues evolving from the use of AI in the public and business environment; and (3) how can we improve our frameworks and governance structure for AI regulation. Through empirical evidence this paper explores the legal implications including the rights and duties of the government and private industry in protecting against unlawful intrusions into people's lives, while at the same time advancing recommendations for accountability frameworks and regulations essential to ensure safety and security in advancing artificially intelligent systems. [ABSTRACT FROM AUTHOR]

The Artificial Intelligence (AI) revolution continues to engage with the engineering and computing education world. A machine learning algorithm, or AI application itself, does not always cater to human ideals or ethical considerations. There is a need to be aware of this lack of contextual knowledge in order to design models accordingly. When considering our modern world and striving for diversity, equity, and inclusion, it is essential to ensure that technology works for all. Even though there is an excitement for the advancement of AI, there is also a need to enhance our understanding and consideration of the ethical implications of AI to inform future generations and future AI technology. The education system has a significant role in molding the minds of future AI pioneers and engineers. Therefore, it is vital to understand the attitudes and beliefs of undergraduate and graduate students who will play a pivotal role in the ethical implications of AI advancements. This work-in-progress paper focuses on a survey analysis to examine engineering and computing students' perspectives on ethics in AI before and after taking a course that includes AI and ethics within the syllabus. The following research questions will guide this study: What are the attitudes of engineering and computing students before and after taking a course that covers AI and ethics? In addition, how do their attitudes vary by demographics such as age, gender, and experience? Our goal is to present our current research and survey instrument to the American Society for Engineering Education (ASEE) audience to receive insight and feedback before finalizing the Institutional Review Board (IRB) and distributing it on the target campus. This work-in-progress closes out with the next steps, future work, implications, and concluding thoughts. [ABSTRACT FROM AUTHOR]

This paper introduces a new concept for the automation of the assembly planning process, to enable Design for Assembly (DfA). The approach involves the application of reinforcement learning (RL) to assembly sequence planning (ASP) based on a 3D-CAD model. The ASP algorithm determines assembly sequences through assembly by disassembly. The assembly sequence is then used for the generation of subassemblies by considering the product contact information. The approach aims to support the creation of the manufacturing bill of materials (MBOM) by automating the assembly planning process. [ABSTRACT FROM AUTHOR]

Kidney is one of the significant organs in the human body which performs filtering out blood, balances fluid, removes the waste, maintains the level of electrolytes and hormone levels. So, any disorder or dysfunction in kidney needs to be detected on time in order to preserve life. Segmentation on kidney tumor in medical field is a critical task and many conventional methods have been employed for early prediction of kidney abnormalities but with limitations such as high cost, extended time for computation and analysis with huge amount of data. Due to all such problems, the prediction rate and accuracy has reduced considerably. In order to overcome the challenges, Artificial Intelligence (AI) technology has penetrated into the field of medicine particularly in the renal department. The evolution of AI in kidney therapies improve the process of diagnosis through several Machine Learning (ML) and Deep Learning (DL) algorithms. It has the capability of improving and influencing on the status with its capacity of learning from the massive data and apply them accordingly to differentiate on the circumstances. The storage of larger data and segmentation with AI assistance are highly helpful for the analysis of occurrence of the disease. AI algorithms have predicted the severity of tumor stages with effective accuracies. Hence, this paper provides a critical review of different AI based algorithms being used in the kidney tumor prognostication. Its numerous benefits in field of segmentation have been researched from the existing works and provides an insight on the contribution of AI in the kidney disease prediction. [ABSTRACT FROM AUTHOR]

The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms. [ABSTRACT FROM AUTHOR]

AI-based predictive data mining algorithms play an important role in the prediction-role of the two-faceted roles of data mining (classification as the other role). Having a rigid predictive algorithm is tantamount to a successful and near-human predictive decision in knowledge discovery among databases being implemented among government, commercial and educational sectors. In this paper, three (3) of the most commonly used AI-based predictive data mining algorithms, namely: instance-based (nearest neighbor), statistical (naive Bayes), and Bayesian networks were evaluated using academic admission data in order to assess which predictive algorithm is best suited for predicting an ideal collegiate course for a student based on the student's high school grades and historical admission records. This study sought also to find out which among the four predictive algorithms can be used in terms of volume of historical data and number of data attributes. [ABSTRACT FROM AUTHOR]

Conducting an orchestra had been a privilege reserved for professional conductors, until there aremusic conducting systems that can follow the user's lead. The music playing and beat tracking components of conducting systems have been well developed, but the beat-following algorithm has room for improvement. In this paper we present a mobile conducting app with a novel beat-following algorithm, which can switch between different behaviours to accommodate different musical situations and user preferences, and allow the user to make their own interpretations of classic orchestral works. [ABSTRACT FROM AUTHOR]

Accurate identification of the QRS complex is critical to analyse heart rate variability (HRV), which is linked to various adverse outcomes in premature infants. Reliable and accurate extraction of HRV characteristics at a large scale in the neonatal context remains a challenge. In this paper, we investigate the capabilities of 15 state-of-the-art QRS complex detection implementations using two real-world preterm neonatal datasets. As an attempt to improve the accuracy and reliability, we introduce a weighted ensemble-based method as an alternative. Obtained results indicate the superiority of the proposed method over the state of the art on both datasets with an F1-score of 0.966 (95% CI 0.962-0.97) and 0.893 (95% CI 0.892-0.894). This motivates the deployment of ensemble-based methods for any HRV-based analysis to ensure robust and accurate QRS complex detection. [ABSTRACT FROM AUTHOR]

Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t > 4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems. [ABSTRACT FROM AUTHOR]

In parallel with the increased use of Artificial Intelligence (AI) in recruitment processes, there is also an ongoing discussion on the dehumanisation in automated recruitment. On one hand AI-based recruitment has the potential to reduce human bias, on the other hand there are parts of the process that still need human judgement. Another concern is that the identified dehumanisation could harm the relationship between employees and employers. Research indicates that AI-based technologies definitely have the potential to increase the efficiency of the recruitment process by replacing humans in time-consuming tasks. Less research has been conducted on the human perceptions about AI-based recruitment. In a time when AI-based recruitment tools are used in a rapidly increasing number of companies and organisations, it is important to better explore the human side of the process. Therefore, this paper investigates: What are the perceptions of the job candidate conditions in automatised and AI-based recruitment processes? This study was conducted with a qualitative approach with data gathered from candidates and recruiters that all had experiences from AI-based recruitment processes. Four candidates and two recruiters were chosen with the idea of a purposive sampling. Answers from six audio recorded semi-structured interviews were categorised in a deductive thematic analysis. The theoretic lens for the study was the Model of Applicant Reaction to Selection. Findings showed that the informants had a negative attitude towards the dehumanised recruitment process. The most obvious finding was the general critique towards the AI-based assessment of candidates' social skills. At the same time, the majority of the informants agreed that AI-based recruitment tools have the potential to make time-consuming administrative tasks more efficient. Only one informant was willing to go through a completely AI-based recruitment process, and all informants pointed out different ways in which the recruitment tools need to be improved. The conclusion is that the AI-based recruitment tools must be made more transparent and used as a support for decision-making rather than being the decision maker. The recommendation is a hybrid solution, where AI-based tools are used to assist and create the basis for well-informed human decisions. [ABSTRACT FROM AUTHOR]

In this six-part paper, the authors first define fake science as a concept and identify at a high level the problems and consequences of fake science dissemination especially where fake science is published as grey literature and/or disseminated across social media platforms. In addition, they identify factors contributing to the creation of fake science from the "the replication crisis" in scientific research to the impact of technologies such as Artificial Intelligence. Part 2 moves into the United States Legal Landscape and considers US policy around fake science and related issues illustrated through a detailed discussion of applicable statutes and case law. Specifically, the authors discuss ISP immunity under 47 U.S.C. § 230 and the Constitutional implications of the United States v. Alverez, 132 S. Ct. 2537 (2012) and the decision and the applications of Central Hudson Gas & Electric Corp. v. Public Service Commission of New York, 447 U.S. 557 (1980). There will also be consideration of fake grey data as commercial speech or as a deceptive trade practice. Part 3 addresses the European Legal Landscape through a discussion of applicable laws and legal precedents in a similar manner to part 2. Part 4, Comparisons of the United States and European Legal Landscapes looks at the similarities and differences between the United States and Europe in addressing their shared concerns over the creation, use and dissemination of fake scientific information. Part 5, Prevention and Deterrence considers measures and actions which help to reduce the creation of fake science or that mitigate the problems it creates. These measures and actions are presented and incorporated into the fake science lifecycle presented in Part 1, Problem Definition. In Part 6, the authors make recommendations including technology driven solutions designed to ferret out fake science and in turn reducing the serious problems fake science presents. Recommendations include Facebook and other social media AI tools; manually flagging fake data; and the creation of truth seeking algorithms. [ABSTRACT FROM AUTHOR]

Artificial Intelligence(AI) is simulation of human intelligence in machines. It is programmed such that it can think as human and perform actions or take appropriate decision. In current covid-19 pandemic, its important to diagnosis more asymptomatic people to save their life. There various diseases related to thorax such as pneumonia, lung cancer, COPD(Chronic Obstructive Pulmonary Disease). Its leading death cause in world. Even fetus are also effected by pneumonia from birth times. The remote area people also can be saved by proper diagnosis on time by using CAD(Computer Assisted Detection). There is some challenges in training of algorithm in AI to give more accuracy. In this paper those issues such as class imbalance, multi task and data size are discussed with solutions for each problem. Different diseases, which look similar by radiologist can be detected in early stage. The pre-processing and finetuning of thorax x-ray is done before applying to CNN(convolutional neural network). Loss functions are calculated with proper weightage value. So that algorithm work even in small training set. [ABSTRACT FROM AUTHOR]

With technological developments in artificial intelligence, algorithms are increasingly capable to perform tasks that were considered to be unique for humans. However, literature suggests that although algorithms are often superior in performance, users are reluctant to interact with algorithms instead of human agents - a phenomenon known as algorithm aversion. But, as algorithm aversion is attracting scientific attention, empirical findings are inconclusive and papers find the opposite effect of algorithm appreciation. With this literature review, we synthesize evidence from 29 publications with 84 distinct experimental studies to investigate how algorithm characteristics and human agents' characteristics influence algorithm aversion. We show how algorithm agency, performance, perceived capabilities and human involvement as well as human agents' expertise and social distance, influence whether users develop algorithm aversion, i.e., choose humans over algorithms, utilize humans' support more often and evaluate humans' actions more favourable. Furthermore, we provide a systematic conceptualization of aversion as a biased assessment and develop propositions for future research. With our work, we contribute to algorithm aversion literature and the contemporary discussion on the impact of algorithmic agents on the future of work. We indicate that the emerging literature stream on algorithm aversion is worth considering for human-computer interaction researchers. [ABSTRACT FROM AUTHOR]

Megaprojects are continuing to capture worldwide attention from India’s Smart Cities, to the $4.75 billion Hadron Collider at CERN, to the US $150 billion International Space Station, and Europe’s largest railway infrastructure project, Crossrail in London. The Organization for Economic Cooperation and Development (OECD) estimates global investment needs of $6.3 trillion per year from 2016-2030 (Mirabile, et al. 2017). To meet this growing demand, there has been a recent call, within the megaproject scholarship, for a better understanding of “what goes on in megaprojects – how they are managed and organized, from within, by the managers who are tasked with bringing them to fruition.” (Söderlund, et al. 2017). Studies about megaprojects have generally concentrated on the cost conflicts between the stakeholders and cost overrun issues (Adam et al., 2014; Flyvbjerg, 2017), however these have been superseded by more important issues in recent years such as project security, protecting the health and safety of its workers, project sustainability and value creation, and managing the impact of climate change and presently a global pandemic. All of these require a more agile approach to project management and a more sophisticated intelligence that can be generated through Algorithms that are used to generate artificially intelligent systems. Through an analysis of the AI and Project Management literature, ethnographic studies, and semi-structured interviews with project management professionals, this paper explores the growing use of Artificial Intelligence to manage megaprojects including the obligations of private industry, and the government as the guardian of the public interest, while at the same time exploring the technical, managerial and ethical considerations in the deployment of AI. [ABSTRACT FROM AUTHOR]

Design processes managed by algorithms provide solutions and improvements in terms of efficiency, performance, choice of materials, and cost optimization. It is a whole new approach to industrial design in which artificial intelligence participates directly in the design processes. The paper aims to investigate the way we design through algorithms, and consequent changes in thoughts, approaches, and generation of ideas that are rising determining new ways of defining things and their relations. [ABSTRACT FROM AUTHOR]

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study. [ABSTRACT FROM AUTHOR]

Researchers increasingly acknowledge that algorithms can exhibit bias, but artificial intelligence (AI) is increasingly integrated into the organizational decision-making process. How does biased AI shape human choices? We consider a sequential AI-human decision that mirrors organizational decisions; an automated system provides a score and then a human decides a score using their discretion. We conduct an AMT survey and ask participants to assign one of two types of scores: a subjective, context-dependent measure (Beauty) and objective, observer-independent measure (Age). Participants are either shown the AI score, shown the AI score and its error, or not shown the AI score. We find that participants without knowledge of the AI score do not exhibit bias; however, knowing the AI scores for the subjective measure induces bias in the participants' scores due to the anchoring effect. Although participants' scores do not display bias, participants who receive information about the AI error rates devalue the AI score and reduce their error. This study makes several contributions to the information systems literature. First, this paper provides a novel way to discuss artificial intelligence bias by distinguishing between subjective and objective measures. Second, this paper highlights the potential spillover effects from algorithmic bias into human decisions. If biased artificial intelligence anchors human decisions, then it can induce bias into previously unbiased scores. Third, we examine a method to encourage participants to reduce their reliance on the artificial intelligence, reporting the error rate, and find evidence that it is effective for the objective measure. [ABSTRACT FROM AUTHOR]

Metaheuristic methods have many design parameters, and fine-tuning of that parameters can improve these algorithms performance. In this paper, a sampling based algorithm configuration approach is proposed and applied it to the Differential Ant-Stigmergy Algorithm (DASA)'s five control parameters. Performance of a large parameter set of DASA, obtained by Latin Hypercube Hammersley Sampling (LHHS) method, used to solve the Sphere function and compared it with another tuned version. The results of our experiment demonstrated that LHHS method found better performing configurations than the default parameter value of the DASA and also than another proposed tuned version DASA*. And the results demonstrated that three parameter configurations obtained with LHHS found better result than the best configuration obtained with Sobol Sequence Sampling method (DASA*) for function dimension 20, and five parameter configuration for function dimension 40. According to the results, it can be said that usage of LHHS for initialization of other state-of-art algorithm configuration methods instead of other sampling methods is worth investigating. [ABSTRACT FROM AUTHOR]

As a general rule, for pecuniary and regulatory reasons, commercial banks assiduously manage the credit risk of their loan portfolios. Algorithmic decision-making (ADM) systems may enable lenders to arrive at credit decisions that previously would not have been possible. However, the point at which utilizing ADM is optimized is still open for debate. To help illuminate the issues, a systematic literature review is conducted to investigate the following questions: How does algorithmic decision-making (ADM) contribute to the effectiveness of credit risk assessment (CRA)? And, what, if anything, can be done to improve the contribution of ADM? The review indicates that ADM’s contributions have largely been through enhanced human decision-making under uncertainty. In addition, the review underscores the importance of organizational arrangements to the successful deployment of ADM systems. Furthermore, the technology’s contribution to CRA can be improved by addressing algorithmic bias and transparency issues. [ABSTRACT FROM AUTHOR]

Rapidly expanding application of algorithms in the workplace and our everyday lives has led to emerging new challenges related to their scrutiny and accountability. Today organizations face legal, ethical and brand reputation consequences caused by algorithmic bias and other impacts of algorithmic systems usage. This research-inprogress seeks to contribute to IS literature by proposing a set of design principles for improving algorithmic accountability as a part of an organizational IT strategy. Drawing on accountability and ethically aligned design theories, this study utilizes action design research methodology based on the data gathered within the context of an immersive practice-based project. This paper presents a synopsis of the first project cycle by investigating barriers to improving algorithmic accountability within an organization. [ABSTRACT FROM AUTHOR]

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms. [ABSTRACT FROM AUTHOR]

In Computer Science, the Boolean Satisfiability Problem(SAT) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. SAT is one of the first problems that was proven to be NP-complete, which is also fundamental to artificial intelligence, algorithm and hardware design. This paper reviews the main algorithms of the SAT solver in recent years, including serial SAT algorithms, parallel SAT algorithms, SAT algorithms based on GPU, and SAT algorithms based on FPGA. The development of SAT is analyzed comprehensively in this paper. Finally, several possible directions for the development of the SAT problem are proposed. [ABSTRACT FROM AUTHOR]

The industrial sector globally is at the cusp of fourth industrial revolution. "Industry 4.0" first introduced in 2013, focuses on the concept of smart factory and cyber physical systems thus integrating advanced technologies such as automation, cloud computing, internet of things, 3-D printing, blockchain and big data analytics. As its extension in one of the prominent service sectors, the soul of "Healthcare 4.0" lies in the integration and convergence of real and virtual worlds, wherein manual appointments, in-person examinations and treatment systems can be integrated with software, algorithms and people. Emerging technology applications such as wearables coupled with the artificial intelligence and predictive diagnostics capabilities have already started getting traction, globally. However, the policy interventions by governments to adapt the structure of medical policies in view of evolving technologies and data security concerns are still lacking. The need of the policy alignment becomes imperative especially in case of India, which is emerging as a medical tourism hub. This paper presents a concept note while capturing the emerging trends in Healthcare 4.0 from an Indian perspective. Also, the mapping of possible health applications till date has been done to analyze the evolution of the area across multiple disciplines. Current research issues and challenges for the implementation are further highlighted. The research trend in this domain is expected to increase at least for the next decade. [ABSTRACT FROM AUTHOR]

The artificial neural network provides us a new theoretical method on the stability classification of roadway surrounding rock. The surrounding rock is a complex dielectrics, it exhibits a nonlinear relationship between influencing factors and the stability of roadway surrounding rock, for such problems, BP neural networks have higher modeling capabilities, can truly portray nonlinear relationship between the question and influencing factors, therefore, the paper uses BP neural networks to build the prediction model for the stability classification of roadway surrounding rock, and uses VC++ 6.0 to design and implementation of system. Through the training and testing of samples, the results show that the system is reliable. [ABSTRACT FROM AUTHOR]

Bayesian networks (BN) have step by step emerge as one of the essential successes in Artificial Intelligence (AI) investigation. Founding an operative BN structure is the core of the mastering and foundation and submission of BN. In these network SL, the vintage approach of using expert to build the community shape is regularly substituted with the aid of the facts gaining knowledge of structure technique. However, because of the massive quantity of possible community systems, the quest area is much big. The technique of BN studying over training facts commonly has the troubles of precision is low or complexity is high, which make the SL of fluctuate substantially from that of truth, which has a terrific stimulus on the reason and sensible application of BN. In order to overcome this difficult. In this paper, Novel Whale Optimization Algorithm for BN Structure Learning (SL) is (WO-BNS) introduced. The proposed WO-BNS has examined in reliability evaluation and fault prognosis of complex structures with actual case packages. In extra, fault prognosis is studied for figuring out essential components, essential reasons, and diagnosis routes. Experimental simulation effects display that the proposed WO-BNS SL algorithm has better shape and convergence. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is the study and the usage of the mathematical algorithms which can improve their performance without the need for human interaction. These algorithms are considered as a subset of Artificial Intelligence (AI). Machine learning algorithms use past data as input and produce new predicted values as an output. Machine learning algorithms have been used in many areas for solving an innumerable number of tasks. However, the various tasks need applying of different machine learning algorithms for obtaining maximum accuracy of the target results. In this paper, an analysis with consideration of the advantages, disadvantages, and different areas of applications in the real world are made for each of the four ML algorithm groups - supervised, unsupervised, semi-supervised, and reinforcement learning. After the comparative analysis is done, the ensemble methods boosting, stacking, and bagging are introduced, described, and compared. Emphasis is done on defining the accuracy of which ML algorithms can be improved and which ensemble methods can be used for that. Machine Learning algorithms combined with ensemble methods are highly competitive and provide the best results in most cases where they are applicable. [ABSTRACT FROM AUTHOR]

Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms. In this paper, we present findings on what 13 musician/developer teams, a total of 61 users, needed when co-creating a song with AI, the challenges they faced, and how they leveraged and repurposed existing characteristics of AI to overcome some of these challenges. Many teams adopted modular approaches, such as independently running multiple smaller models that align with the musical building blocks of a song, before re-combining their results. As ML models are not easily steerable, teams also generated massive numbers of samples and curated them post-hoc, or used a range of strategies to direct the generation, or algorithmically ranked the samples. Ultimately, teams not only had to manage the "flare and focus" aspects of the creative process, but also juggle them with a parallel process of exploring and curating multiple ML models and outputs. These findings reflect a need to design machine learning-powered music interfaces that are more decomposable, steerable, interpretable, and adaptive, which in return will enable artists to more effectively explore how AI can extend their personal expression. [ABSTRACT FROM AUTHOR]

With the rapid growth of data volume and the development of artificial intelligence technology, deep-learning methods are a new way to model land subsidence. We utilized a long short-term memory (LSTM) model, a deep-learning-based time-series processing method to model the land subsidence under multiple influencing factors. Land subsidence has non-linear and time dependency characteristics, which the LSTM model takes into account. This paper modelled the time variation in land subsidence for 38 months from 2011 to 2015. The input variables included the change in land subsidence detected by InSAR technology, the change in confined groundwater level, the thickness of the compressible layer and the permeability coefficient. The results show that the LSTM model performed well in areas where the subsidence is slight but poorly in places with severe subsidence. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) technology suffers from several issues such as blockage by solid material and absorption by oxygen or rain. These issues cause degradation in TCP performance, where many packets are dropped. Retransmission for the packet drops will be required to receive the whole segments of data. This paper focuses on the side effects of congestion and link errors over TCP performance in the mmWave environment. This study proposes two mechanisms to enhance the conventional TCP Congestion Control Algorithm (CCA) through Additive Increase/Multiplicative Decrease (AIMD) approach. Firstly, the proposed method exploits the Additive Increase (AI) phase to stay in a stable state as long as possible. Secondly, the proposed method uses a Multiplicative Decrease (MD) phase to prevent the congestion window (cwnd) to be reduced. The proposed TCP enhancement will be compared with the default TCP-CCA. The evaluation of the proposed TCP enhancement is conducted in ns-3. The simulation results confirm that the proposed method refines cwnd up to 14% of in comparison with default TCP. [ABSTRACT FROM AUTHOR]

We aim to improve the teaching of the principles of optimization, including computational intelligence (CI), to a mixed audience of business and computer science students. Our students do not always have sufficient programming or mathematics experience and may be put off by the expected difficulty of the course. In this context we are testing the potential of games in teaching. We deployed a game prototype (design probe) and found out that the prototype led to increased student motivation, intuitive understanding of the principles of optimization, and strong interaction in a team. Ultimately, with the future work we sketch out, this novel approach could improve the learning and understanding of optimization algorithms and CI in general, contributing to the future of Explainable AI (XAI). [ABSTRACT FROM AUTHOR]

Design is increasingly influenced by digitalisation yet differs largely across domains. We present synergies between the works of UX designers and data scientists. We can utilise personas to represent users and their behaviours, or synthetic populations to represent agent groups. Despite sharing characteristics, their synergies have not been explored so far. We propose a workflow and test it in the urban mobility context to link a synthetic population of Paris with a set of contextual personas. This builds the basis for an integrated approach for designing urban mobility across fields. [ABSTRACT FROM AUTHOR]

A new image classification method based on texture feature and improved BP neural network is introduced in the paper. By this method, image matching can be limited to the specific category, which can greatly reduce the retrieval time complexity. Comparisons are given between the improved BP neural network and the genetic BP, the experimental results show that the new method got higher efficiency in texture image classification than the traditional methods. [ABSTRACT FROM AUTHOR]

Optimizers are approaches or algorithms dependent to enhance the characteristics of the Neural Network (NN) like weights and learning rate in order to decrease the loss rate, On the other hand, Siamese Neural Network (SNN) are two identical sub-networks, they work in parallel and they are sharing parameters and weight, SNN uses for indicate similarity. In this research, we study the effect of optimizers Siamese Neural Network, using Digits handwritten (MINST) dataset, the effects is studied for Adam, Nadam, Adadelta and SGD optimizers with respect to process time and accuracy, the accuracy is 97%, 97%, 79% and 92%. [ABSTRACT FROM AUTHOR]

In this paper we present a concept for an Intelligent System for Radiogram Analysis (ISAR) for welds quality inspection. Both, hardware and software solutions have been introduced in the system. The software operates with variety of scanner standards. It contains preliminary image processing (linear and nonlinear filtering algorithms) and some specialized functions, like Sauvola's tresholding or IQI detection. The aim of the ISAR system is to support a radiologist in his work. [ABSTRACT FROM AUTHOR]