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Interactive and immersive technologies can significantly enhance the fruition of museums and exhibits. Several studies have proved that multimedia installations can attract visitors, presenting cultural and scientific information in an appealing way. In this article, we present our workflow for achieving a gaze-based interaction with artwork imagery. We designed both a tool for creating interactive “gaze-aware” images and an eye tracking application conceived to interact with those images with the gaze. Users can display different pictures, perform pan and zoom operations, and search for regions of interest with associated multimedia content (text, image, audio, or video). Besides being an assistive technology for motor impaired people (like most gaze-based interaction applications), our solution can also be a valid alternative to the common touch screen panels present in museums, in accordance with the new safety guidelines imposed by the COVID-19 pandemic. Experiments carried out with a panel of volunteer testers have shown that the tool is usable, effective, and easy to learn.

Background Last-mile delivery (LMD) is becoming more and more demanding due to an increasing number of users and traffic problems in cities. Besides, medical crises (like the COVID-19 outbreak) and air pollution represent additional motives for the transition from traditional to socially and environmentally sustainable LMD mode. An emerging problem for companies in the postal and logistics industry is how to determine the best LMD mode in a multi-criteria setting under uncertainty. Method For the first time, an extension of the Weighted Aggregated Sum Product ASsessment (WASPAS) method under the picture fuzzy environment is presented to solve the LMD mode selection problem. The introduced picture fuzzy set (PFS) based multi-criteria decision-making (MCDM) method can be highly beneficial to managers who are in charge of LMD since it can take into account the neutral/refusal information and efficiently deal with high levels of imprecise, vague, and uncertain information. The comparative analysis with the existing state-of-the-art PFS-based MCDM methods approved the high reliability of the proposed picture fuzzy WASPAS method. Its high robustness and consistency are also confirmed. The presented method can be used to improve LMD in urban areas worldwide. Besides, it can be applied to solve other emerging MCDM problems in an uncertain environment. Findings A real-life case study of Belgrade is presented to fully illustrate the potentials and applicability of the picture fuzzy WASPAS method. The results show that postomates are the best mode for LMD in Belgrade, followed by cargo bicycles, drones, traditional delivery, autonomous vehicles, and tube transport.

Para-consistent logic is a non-classical logic whose foundations allow the treatment of contradictions without invalidating the conclusions. This paper presented an attempt of using Annotated Para-consistent Analysis (APA) for supporting eXplained Artificial Intelligence (XAI) on Neural-Networks. For the study case, it was presented a situation where a binary classification model was able to correctly recognize one label but not the other with the form of the principle of explosion (p\(\wedge \lnot \)p). By plotting the linear output max-min scaled data into the paraconsistent reticulate, it was possible to properly understand that the selected architectures were being able to predict. This result is early support for using APA for Neural-Network XAI, corroborating with this paper hypothesis.

Distributed manufacturing systems have become consensus, particularly networked manufacturing systems (NMS) owing to its flexible and adaptable nature in response to customized requirements. Highlighting of functions in NMS and their integration with recent key enabling technologies i.e., Artificial intelligence, Machine learning, Internet of Things, Block chain technology, and Agent-based techniques, etc. are of utmost importance to identify problems and increase its efficiency. Hence, besides the above-mentioned approaches, this paper surveyed and analysed various articles systematically related to networked manufacturing in the context of knowledge creation and information, security, interoperability, and reliability. To identify the most related papers, the search has been conducted with Web of Science and Scopus databases. Subsequently, after evaluation 30 most related papers were selected and analyzed that further extended by identifying the issues and gaps in the existing empirical knowledge. Finally, the paper presents a roadmap for future research directions and developments.

This paper presents an analysis of the state of the art of artificial intelligence applications in the textile industry. A review of the existing literature was performed. This article presents three methods of analyzing textile yarn. Some techniques, used in textile fabric inspection, are presented throughout this paper, as well as the use of artificial intelligence on improving the performance of productive systems using neural networks and artificial vision. The preliminary results demonstrate that the techniques covered are an asset in obtaining defects in textile fabrics at the industrial level. Taking into account the various methods of inspection and analysis of textile yarn, all present pros and cons in applicability in the textile area. In terms of advantages, all allow a better analysis of the textile yarn and defect detection with high quality, but with applicability in more complex systems. As a disadvantage, they present the fact that they do not have an already standardized algorithm that can be used, which makes its use more complex. Some possible future applications are also described.

In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

In this paper, we develop and submit a novel model to the Neureka 2020 challenge11https://neureka-challenge.com/ that can predict seizures from an EEG signal. This challenge places emphasis on limiting the number of false positives, or reducing the places in which background signal is erroneously labeled as a seizure. As a result, our model is intended to act as a tool to aid neurologists as opposed to supplanting them completely. The goal is to allow a doctor to move more quickly through EEG data, enabling the doctor to treat more patients and devote more time toward treatment. Our model is a transformer based neural network architecture. We call it TABS, after Transformer Based Seizure detector. TABS scored a sensitivity of 9.03% and a false alarm rate of 31.21 per 24 hours when evaluated using the Time-Aligned Event Score.

The way students learn changed significantly over the past two years, due to the current pandemic. However, this change was neither desired not planed beforehand. As a result, in many cases, it may have been undertaken without the appropriate care. In this paper we propose a framework for online education tailored for Computer Science degrees. Its goals are twofold: to avoid disruptive changes by providing a familiar and supportive structure for teaching/learning activities, and to motivate Students to learn autonomously, despite their reduced contact with their peers or the Teacher .

Cybersecurity is relevant to everyone, as cyberthreat concerns individuals and whole societies, and a precise cyberattack targeted at critical infrastructure may pose danger to millions of citizens. At a European level, several initiatives have aimed at protecting CI, one of them being InfraStress. This paper presents a part of the InfraStress architecture, the ST.CD.2 component, which is dedicated to cyberthreat detection. The question of handling missing data, the used dataset and experimental setup have been discussed. The ST.CD.2 component has been demonstrated in industrially relevant environment; the results of this experiment have been provided. When compared with the results achieved by means of the traditional ways of handling missing data, the proposed method proves superior. Then, the final conclusions are given and future work is briefly described.

The area of Automatic Speech Emotion Recognition has been a hot topic for researchers for quite some time now. The recent breakthroughs on technology in the field of Machine Learning open up doors for multiple approaches of many kinds. However, some concerns have been persistent throughout the years where we highlight the design and collection of data. Proper annotation of data can be quite expensive and sometimes not even viable, as specialists are often needed for such a complex task as emotion recognition. The evolution of the semi supervised learning paradigm tries to drag down the high dependency on labelled data, potentially facilitating the design of a proper pipeline of tasks, single or multi modal, towards the final objective of the recognition of the human emotional mental state. In this paper, a review of the current single modal (audio) Semi Supervised Learning state of art is explored as a possible solution to the bottlenecking issues mentioned, as a way of helping and guiding future researchers when getting to the planning phase of such task, where many positive aspects from each piece of work can be drawn and combined.

Industrial processes produce a considerable volume of data and thus information. Whether it is structured sensory data or semi- to unstructured textual data, the knowledge that can be derived from it is critical to the sustainable development of the industrial process. A key challenge of this sustainability is the intelligent management of the generated data, as well as the knowledge extracted from it, in order to utilize this knowledge for improving future procedures. This challenge is a result of the tailored documentation methods and domain-specific requirements, which include the need for quick visibility of the documented knowledge. In this paper, we utilize the expert knowledge documented in chip-design failure reports in supporting user access to information that is relevant to a current chip design. Unstructured, free, textual data in previous failure documentations provides a valuable source of lessons-learned, which expert design-engineers have experienced, solved and documented. To achieve a sustainable utilization of knowledge within the company, not only the inherent knowledge has to be mined from unstructured textual data, but also the relations between the lessons-learned, uncovering potentially unknown links. In this research, a knowledge graph is constructed, in order to represent and use the interconnections between reported design failures. A search engine is developed and applied onto the graph to answer queries. In contrast to mere keyword-based searching, the searchability of the knowledge graph offers enhanced search results beyond direct matches and acts as a mean for generating explainable results and result recommendations. Results are provided to the design engineer through an interactive search interface, in which, the feedback from the user is used to further optimize relations for future iterations of the knowledge graph.

This paper deals with multi-objective location-routing problems involving distribution centres and a set of customers. It proposes a new two-stage solution method that comprehends the concept of sectorization. Distribution centres are opened, and the corresponding opening cost is calculated. A subset of customers is assigned to each of them and, in this way, sectors are formed. The objective functions in assigning customers to distribution centres are the total deviation in demands of sectors and the total deviation in total distance of customers from centroid of sectors, which must be minimized. Afterward, a route is determined for each sector to meet the demands of customers. At this stage, the objective function is the total distance on the routes in the sectors, that must be minimized. Benchmarks are defined for the problem and the results acquired with the two-stage method are compared to those obtained with NSGA-II. It is observed that NSGA-II can achieve many non-dominated solutions.

Spatio-temporal data streams are often related in complex ways, for example, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors. Some of these factors are controllable such as engine inputs or the airframe’s angle of attack, while others contextual, such as air density, or turbulence. It is therefore critical to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity, a malfunctioning pitot-static system, or other abnormal flight conditions. In this paper, we extend our PILOTS programming language [1] to support machine learning techniques that will help data scientists: (1) create parameterized failure models from data and (2) continuously train a statistical model as new evidence (data) arrives. The linear regression approach learns parameters of a linear model to minimize least squares error for given training data. The Bayesian approach classifies operating modes according to supervised offline training and can discover new statistically significant modes online. As shown in Tuninter 1153 simulation result, dynamic Bayes classifier finds discrete error states on the fly while the error signatures approach requires every error state predefined. Using synthetic data, we compare the accuracy, response time, and adaptability of these machine learning techniques. Future dynamic data driven applications systems (DDDAS) using machine learning can identify complex dynamic data-driven failure models, which will in turn enable more accurate flight planning and control for emergency conditions.