Search

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

The growth of artificial intelligence (AI) has significantly changed the field of education and is revolutionizing the ways trainees learn. AI has the capability to completely transform the way we teach and learn, improving accessibility, individualization, and effectiveness in the classroom. Intelligent tutoring systems (ITS) development is one of the primary areas where AI is having an impact. These systems analyze student data using algorithms and offer tailored feedback and direction depending on each student's unique needs and learning preferences. Another illustration of how technology is changing education is Chatbots that run on AI. These Chatbots give students immediate feedback, assist trainers in tracking students' development, and evaluate and pinpoint areas where students might be having challenges. Overall, the application of AI in education is changing how we teach and learn and improving access to, personalization of, and effectiveness of education. This research at Elmergib University focuses on methodologies to incorporate AI Chatbots in chemical engineering challenging disciplines ensuring that the technologies support student learning rather than replacing the role of critical thinking or human interaction. The incorporation of AI technology tools as instructing topics of process optimization, data analysis, safety, and communication. There are remarkable possibilities for AI technologies to expand significantly. [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]

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]

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]

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 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]

The fight during pandemic has been very difficult, and the only effective prevention was to maintain Social Distancing. Maintaining social distancing allowed the disease to spread at a slower rate. Social Distancingis basically meant for people to avoid contact and interaction in public. Even in the past Artificial Intelligence proved to give some promising results to avoid common life problems. The desired prototype will be provided with some detailed information on using python, CV, deep learning to keep a check on people and their distancing. In order to maintain social distancing on streets this tool will be subjected, thus it will allow a safe distance between people. Observing People at work environments, processing plants, shops we can coordinate this device to theirsurveillance camera frameworks and can screen whether individuals are maintaining a protected separation fromone another or not. [ABSTRACT FROM AUTHOR]

The article analyzes the use of artificial neural networks in modern cryptography and cryptoanalysis. The theoretical basis of artificial intelligence is briefly described. Sources for the use of artificial neural networks in block symmetric key encryption algorithms have been studied. As an example, the structure and operation of the SM4 block encryption algorithm are described in detail. The constant values of SM4 are shown in the tables. The components of the block encryption algorithm are described in detail. The components of artificial neurons and their computational processes are shown in graphs and formulas. For the first round of the SM4 block encryption algorithm, research was conducted using artificial neural networks. The results of the experiment are given in tabular form. Based on the results of this article, artificial neural networks can be used in cryptanalysis. In performing these calculations, it is important to correctly select the activation functions and the loss functions. Optimization should also be considered. By using artificial neural networks in cryptanalysis, new methods can be developed and results obtained. [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]

Recently, the world has a treasure of data; health data is one of them. Artificial intelligence (AI), especially machine learning (ML), is required to analyze these data and construct the associated innovative applications intelligently. There are several kinds of algorithms, including unsupervised, supervised, reinforcement and semi-supervised learning. Deep learning, which is component of a wider family of machine learning technologies, can also examine massive volumes of data successfully. This study aims to review various different healthcare disease prediction strategies such as single techniques, hybrid methods, and hybrid ensemble approaches in machine learning and deep learning. [ABSTRACT FROM AUTHOR]

Informed search algorithms in Artificial intelligence are applied to execute the arrangement in cloud. The Best First Search (BFS) is used to allocate the job to Practical Machine (PM) and Virtual Vehicle (VV). In the BFS the best one as first priority assortment of job is used which may relegate the concert of cloud for minimization of Operational Completion Time (OCT). In the cloud computing environment most noteworthy problem is task scheduling to augment the concert and effectiveness of cloud it is an important move The best job selection is enhanced with job assortment, PM selection and VV algorithms. The performance of BFS compared with other task scheduling algorithms. [ABSTRACT FROM AUTHOR]

The best job selection is enhanced with job assortment, PM selection and VV algorithms. The performance of Machine Utilization Algorithm (MUA) compared with other task scheduling algorithms In the cloud computing environment most noteworthy problem is task scheduling to augment the concert and effectiveness of cloud it is an important move. Informed search algorithms in Artificial intelligence are applied to execute the arrangement in cloud. The MUA is used to allocate the job to Practical Machine (PM) and Virtual Vehicle (VV). In the MUA the best one as first priority assortment of job is used which may relegate the concert of cloud for minimization of mean flow time with given process time and travel in the example problem [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]

We theorize why some artificial intelligence (AI) algorithms unexpectedly treat protected classes unfairly. We hypothesize that mechanisms by which AI assumes agencies, rights, and responsibilities of its stakeholders can affect AI bias by increasing complexity and irreducible uncertainties: e.g., AI’s learning method, anthropomorphism level, stakeholder utility optimization approach, and acquisition mode (make, buy, collaborate). In a sample of 726 agentic AI, we find that unsupervised and hybrid learning methods increase the likelihood of AI bias, whereas “strict” supervised learning reduces it. Highly anthropomorphic AI increases the likelihood of AI bias. Using AI to optimize one stakeholder’s utility increases AI bias risk, whereas jointly optimizing the utilities of multiple stakeholders reduces it. User organizations that co-create AI with developer organizations instead of developing it in-house or acquiring it off-the-shelf reduce AI bias risk. The proposed theory and the findings advance our understanding of responsible development and use of agentic AI. [ABSTRACT FROM AUTHOR]

The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. While explainable artificial intelligence (XAI) methods have been proposed, little research has focused on the agreement between these methods and expert clinical knowledge. This study applies current state-of-the-art explainability methods to clinical decision support algorithms developed for Electronic Medical Records (EMR) data to analyse the concordance between these factors and discusses causes for identified discrepancies from a clinical and technical perspective. Important factors for achieving trustworthy XAI solutions for clinical decision support are also discussed. [ABSTRACT FROM AUTHOR]

The filter bubble phenomenon and its negative societal effects have been extensively explored in the literature in the past decade. However, the ability of modern AI‐based systems to create personalized information bubbles, that is, to classify similar contents and users into clusters according to their interests and behavior, can actually be quite beneficial if utilized and managed properly and ethically. In this article we present ongoing research that aims to refine such bubble‐building smart systems by adopting an ethical, multi‐perspective approach that allows for linking isolated bubbles into a consolidated bubblesphere and offering users a choice to explore diverse bubbles related to their topics of interest. To implement the proposed approach, content matching should be based on diverse similarity, which can be derived from a multi‐viewpoint KOS. In addition, the study explores how such a multi‐viewpoint KOS and bubblesphere can be constructed using Wikidata's ranks and qualifiers. [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]

This article provides a complete classification of attacks using artificial intelligence. Three main identified sections were considered: attacks on information systems and computer networks, attacks on artificial intelligence models (poisoning attacks, evasion attacks, extraction attacks, privacy attacks), attacks on human consciousness and opinion (all types of deepfake). In each of these sections, the mechanisms of attacks were identified and studied, in accordance with them, the methods of protection were set. In conclusion, a specific example of an attack using a pretrained model was analyzed and protected against it using the method of modifying the input data, namely, image compression in order to get rid of extraneous noise. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has pervaded contemporary societies in almost every way as an externalized, fragmented, and optimized form of rationality. I call this externalized mode of thought "algorithmic rationality" and to the ideology favoring it, "algorithmic reason". Although algorithmic reason original goal was to facilitate the arising of a highly participative and global Culture, fitting all citizens in a dynamic democratic society, History has it that AI technology would be ceased by consumer logic and computational propaganda. I discuss the economical, epistemological, and political implementation of algorithmic reason, introducing three cases. I argue that such implementation comprises a cybernetic loop, involving a centralized AI and its instrumentalized users. Commenting upon the ubiquity of such loop, I introduce three limitations of algorithmic reason. The first two are of a computational nature. The third owes its presence to a cybernetic loop, producing a steering effect on the human mind and promoting a cultural flattening effect. This, I conclude, may result in the impoverish of creativity, critical thought, and intellectual curiosity. [ABSTRACT FROM AUTHOR]