Ebook: Novelties in Intelligent Digital Systems
Artificial intelligence and intelligent digital systems have become indispensible to many areas of modern life.
This book presents the proceedings of the 1st International Conference on Novelties in Intelligent Digital Systems (NIDS2021), held in Athens, Greece, from 30 September to 1 October 2021. The conference took place as a virtual event due to COVID-19 restrictions. The NIDS conference lays special emphasis on the novelties of intelligent systems and on the interdisciplinary research which enables, supports, and enhances Artificial Intelligence (AI) in software development. It promotes high-quality research, creating a forum for the exploration of challenges and new advances in AI, and addresses experts, researchers and scholars in the fields of artificial and computational intelligence in systems and in computer sciences in general, enabling them to learn more about pertinent, strongly related and mutually complementary fields. The conference promotes an exchange of ideas, reinforcing and expanding the network of researchers, academics, and market representatives.
The 30 accepted papers included here have each been reviewed rigorously by two or three reviewers through a double-blind process which reflects the commitment of the IIS academic community to make NIDS a top-flight, selective and high-quality conference. They are grouped in 6 sections, and cover the topics of Learning; Extended Reality; Data Mining and Machine Learning; Health and Environment; Brain Assessment and Reasoning; and Computer Vision Describing some very significant research and reflecting many interesting new ideas, the book will be of interest to all those working in the field.
The 1st International Conference on Novelties in Intelligent Digital Systems (NIDS2021) was held in Athens, Greece, under the auspices of the Institute of Intelligent Systems (IIS). The Hosting Institution of the conference was the University of West Attica (Greece). NIDS2021 was implemented virtually due to COVID-19 restrictions, on the scheduled dates, that is from September 30 to October 1, 2021.
NIDS lays special emphasis on the novelties of intelligent systems and on interdisciplinary research which enables, supports, and enhances Artificial Intelligence (AI) in software development. It promotes high-quality research, creating a forum for the exploration of challenges and novel advancements in AI.
NIDS addresses experts, researchers and scholars in the fields of artificial and computational intelligence in systems and in computer sciences in general, enabling them to learn more about pertinent, strongly related and mutually complementary fields. It triggers an exchange of ideas, reinforcing and expanding the network of researchers, academics, and market representatives.
Topics within the scope of NIDS series include, but are not limited to:
Cognitive Systems Collaborative Learning
Data Mining and Knowledge Extraction
Decision Making Systems
Educational Data Mining
Genetic Algorithm Applications
Intelligent Information Systems
Natural Language Processing
Personalized Systems and Services
Pervasive Multimedia Systems
Semantic Web Applications
Social Media Applications
Social Network Analytics
The Call for Scientific Papers solicited work presenting substantive new research results in using advanced computer technologies and interdisciplinary research for enabling, supporting and enhancing intelligent systems. The Posters Track provided an interactive forum for authors to present research prototypes and work in progress to the conference participants.
The international Program Committee consisted of 50 leading members of the Intelligent Systems community, as well as of highly promising younger researchers. The Conference (General) Chairs were Cleo Sgouropoulou and Ioannis Voyatzis from the University of West Attica (Greece), whereas the Program Committee Chairs were Claude Frasson from the University of Montreal (Canada), Katerina Kabassi from Ionian University (Greece), and Athanasios Voulodimos from the University of West Attica (Greece).
Scientific papers were reviewed rigorously by two and in some cases by three reviewers (one of which was senior) through a double-blind process, thus reflecting the commitment of the IIS academic community to make NIDS a top-flight, selective, high- quality conference. We believe that the chosen full papers describe some very significant research, the short papers reflect some very interesting new ideas, while the posters present research in progress that deserves close attention. In the review process, the reviewers’ evaluations were generally respected. The management of the review process and the preparation of the proceedings were handled through EasyChair.
We would like to thank all those who have contributed to the conference: the authors, the Program Committee members and the Organization Committee with its chair, Kitty Panourgia, as well as the Institute of Intelligent Systems. Special thanks to Christos Troussas and Akrivi Krouska to have launched and carefully followed this conference.
We are also thankful to our conference sponsor, the Entropy journal (MDPI) for their financial contribution to the Best Paper Award.
During the COVID-19 period, education has been required to reform and adapt to a modern, imposed paradigm in which all of its activities must be delivered in a completely digital format. As a result, in light of this pressing need, educational institutions should either repurpose existing facilities, such as learning management systems, or develop new online education options in a timely manner. In view of the above, this paper proposes an alternative educational tool over Facebook by exploiting the technology of social networking. This Facebook-based application was used as the sole platform for supporting asynchronous learning of high school students in the tutoring of mathematics during the COVID-19 lockdown. This modern educational approach through Facebook encompasses interactive software which involves an e-class environment with social characteristics, multimedia-based learning material delivery and learning activities and assessment of different types. The presented Facebook-based interactive educational software has been evaluated by school students with very encouraging results.
Reinforcement Learning methods such as Q Learning, make use of action selection methods, in order to train an agent to perform a task. As the complexity of the task grows, so does the time required to train the agent. In this paper Q Learning is applied onto the board game Dominion, and Forced ε-greedy, an expansion to the ε-greedy action selection method is introduced. As shown in this paper the Forced ε-greedy method achieves to accelerate the training process and optimize its results, especially as the complexity of the task grows.
Scientific gait analysis methods aim to offer objective measurements, to assist physicians towards an accurate diagnosis or pre-diagnosis of ailments before they actually manifest through noticeable symptoms. This paper reviews selected gait analysis system technologies, trends, applications and discusses errors and precision in spatial and angular readings. Furthermore, we propose a novel test and calibration method using a biomimetic rig. To illustrate this, we conduct three tests on an optical single-camera gait analysis system based on a mobile android smart-phone with specially developed software.
The COVID-19 pandemic struck humanity in February 2020. Closures of educational institutions, worldwide, resulted to the creation of emergency remote teaching environments as a substitute to face to face learning. The disruption caused in the academic community has stimulated innovative learning methods within all levels of the educational sector. New parameters affecting knowledge transmission are getting involved while students follow courses apart on a common virtual learning environment. This research is based on a first-semester Mechanical Engineering CAD module in tertiary education. A learning strategy has been applied by reforming the traditional face-to-face leaning mode to a fully remote learning environment. The methods applied have been tested using statistical analysis and have shown to contribute significantly in students’ spatial perception in 2-Dimentional Drawings. The outcomes of this research reveal a novel teaching strategy that improved students’ academic achievements in CAD during the lockdown. Specific aspects can be considered sustainable on their return back to normality.
This paper presents a novel framework for developing educational hypermedia systems incorporating adaptation techniques and tailored feedback. In particular, the adaptation techniques refers to the content presentation; where the system hides/displays information according to students’ knowledge level and learning goals, and to the navigation design; where the system proposes the learning path that is better to be followed based on their profile. Finally, the framework embodies a diagnostic model that analyzes the students’ misconceptions and provides tailored feedback and advices on bridging students’ knowledge gap. This framework aims to enhance the effectiveness of learning process, increasing student engagement through the adaptive content and navigation and improving student performance through the tailored feedback.
Nowadays human activities are incoming at a digitalization stage. The introduction of information technology along with new forms of communication, influence a variety of forms of human action and focus mainly on the integration and the convergence of the digital and physical worlds. The use of more intelligent – electronic solutions, improves the lives of people around the world, according to studies carried out on the ingress of new smart technologies. Artificial and Ambient intelligence nowadays getting more and more attention about the development of smart, digital environments. The Smart Cities designed for All must aim to arrange the disparity in cities through smart technology, making cities both smart and accessible to a range of users regardless of their abilities or disabilities. The birth of “Artificial Intelligence” (AI) has facilitated the complex computations for reality simulation the new communication era of wireless 5G, all combined have given the hope for a new and better future, to reverse disability to empower the humans with more capabilities, to be faster than they can ever be, stronger than they can ever dream. This paper provides an overview of Ambient Intelligence and smart environments, as well as how technological advancements will benefit everyday usage by devices in common spaces such as homes or offices, and how they will interact and serve as a part of an intelligent ecosystem by bringing together resources such as networks, sensors, human-computer interfaces, pervasive computing, and so on.
Parkinson’s Disease is a progressive, irreversible disease that is only slowed down with the use of medications and therapy. These are the only way to slow down the progression of the disease to more severe situation. Currently, virtual reality is being use on games and entertainment. Some doctors prefer the use of virtual reality like Wii to help supplement the therapy done on rehabilitation. However, devices to cater virtual reality for mid-class to lower-class patients cannot afford such devices. An alternative device, usually accessible to everyone is proposed to cater these virtual reality applications that can help in therapy of Parkinson’s patients. With that, the capabilities of VR can be accessible to more patients who cannot avail expense medication and devices for virtual reality.
Subjective cognitive decline is an early state of Alzheimer’s Disease which affects almost 10 million people every year. It results from negative emotions such as frustration which are more present than healthy adults. For this reason, our work focuses on relaxing subjective cognitive decline patients using virtual reality environments to improve their memory performance. We proposed in our previous work a neurofeedback approach which adapts the virtual environment to each patient according to their emotions using a Neural Agent. We found that the Neural Agent can adapt the environment to each participant but have limitations. This work is a continuation of our approach in which we propose a Limbic Agent able to monitor the interactions between the Neural Agent and patients’ emotional reactions, learn from these interactions, and modify the Neural Agent in order to enhance the adaptation to each patient with an Intelligent Cognitive Control System. Our goal is to create a system able to support the Limbic System which is the main area in charge of controlling emotions and creating memory in the human brain. We used data collected form our previous work to train the Limbic Agent and results showed that the agent is capable of modifying the weight of existing rules, generating new intervention rules, and predicting if they will work or not.
Augmented Reality has been integrated in educational settings in the field of engineering. Prior research has examined the learning outcomes and the pedagogical affordances of this technology. However, training undergraduate engineers, from diverse knowledge level, requires customized training approach, tailored to the individual learning pace. In this paper, we present PARSAT (Personalized Augmented Reality Spatial Ability Training), which is a mobile Augmented Reality application for the enhancement of students’ spatial visualization skills. The application takes into account the theoretical contents of engineering design, deployed through video tutorials, and student-computer interaction with 3D objects. Students interpret different views of a 3D object, which are represented on their mobile screen. PARSAT efficaciously strengthens students’ recognition of spatial structures and views, adjusted to the fulfillment of their personal needs. In terms of personalization, PARSAT consists of different levels, which do not follow a linear flow, as each student takes part in a different sequence of activities, according to their time spent in the 3D object manipulation, and their assessment scores at the end of each level. Furthermore, an agent is used to analyze students’ knowledge level, and send them feedback. The system reduces unnecessary cognitive load and, at the same time, improves students learning experience in learning engineering drawing.
There is an increasing number of people with Alzheimer’s disease. Negative emotions are not only one of the symptoms of AD, but also the accelerator of the disease. Animal therapy can have a positive impact on the negative emotions of patients, but it has strict requirements for both environments and animals. In this study, we aim to explore the effectiveness of using virtual animals and their impact on the reduction of patients’ negative emotions to improve the user’s cognitive functions. This approach has been implemented in the Zoo Therapy project, which presents an immersive 3D virtual reality animal environment, where the impact on the patient’s emotion is measured in real-time by using electroencephalography (EEG). In addition to creating highly realistic virtual animals, the innovation of Zoo Therapy is also in its communication mechanism as it implements bidirectional human-computer interaction supported by 3 interaction methods: 3D buttons, speech instruction, and Neurofeedback. Patients can actively interact with virtual animals through 3D buttons or speech instructions. The Neurofeedback system will guide the animal to actively interact with the patients according to their real-time emotional changes to reduce their negative emotions. Experiments and preliminary results show that it is possible to interact with virtual animals in Zoo Therapy, and the Neurofeedback system can intervene in Zoo VR environment when the emotional value goes down and might reduce patients’ negative emotions.
Traditional learning methods frequently fail to provoke students’ interest, stimulate their enjoyment and encourage them to participate in learning activities, resulting in discomfort, distractions, and disengagement, if not quitting. Education’s goal is to improve the quality and effectiveness of teaching and learning methods. This paper aims to present a framework based on Virtual Reality (VR) technology and contemporary Head Mounted Displays, that incorporates game-based techniques and adaptive design according to the student’s profile. As a result, this paper analyzes the relevant literature, the VR apparatus, the importance of VR, as well as gamification, personalization and adaptive design in education, which are the learning foundations on which the framework is based. Finally, the framework’s modules and structure are presented, taking into account all of the previously mentioned parameters. This novel framework aspires to serve as a basis for educational applications that use immersive Virtual Reality technologies to transform learning procedures into entertaining, engaging, enjoyable, and effective experiences.
Augmented Reality (AR) is an emerging technology thriving in recent years. The implementation of AR in education offers great opportunities to enhance educational environments achieving better learning outcomes. As students with learning disabilities struggle with reading comprehension, an AR learning environment provides them support to better understand texts they actually read. Even though few studies have tried to explore the impact of AR technology to reading comprehension for students with learning disabilities in Secondary Education, there is a lack of research grounded in the incorporation of learning theories and personalization technologies. The goal of this paper is to present an AR educational environment capable of supporting meaningful learning outcomes by taking into consideration each student special educational needs and learning style. The novelty of this study lies in the student-centered and personalized design, which leads to improved understanding, student interaction and self-learning.
The study aims to explore VR Serious Games as a form of therapy for people with dementia. It seeks to establish the utility of VR-based interventions with the application of Montessori Method. This study also serves as a basis for researchers, healthcare professionals, and developers who plan to incorporate VR therapy with other therapeutic approaches and to create a system that may be replicated for other illnesses via telemedicine to address the most vulnerable sectors. The main beneficiaries of this study are people with dementia and those who directly interact with them such as their doctors, caregivers, and family members of the patient.
The work presents a new approach to the study of problems associated with the initial definition of the concept of “intelligence”. In work, intelligence is a property that ensures, at some level, the successful interaction of a subject or system with their environment, which is specified in the form of a subject area. For this, the subject area is modeled (smart modeling). Modeling is the process of representing a domain in which the subject must solve the corresponding problems.
The domain model is presented as a body of knowledge covering the constituent elements of this domain. Knowledge is a means of describing this model and is determined using a language that includes the logic of describing the corresponding entities of the subject area. Language properties are used to model some abstract formal structure in the form of a mathematical representation. The modeling process consists in a sequential transition from the description of the subject area to an interconnected structure of knowledge, associated with the constituent elements of this area, which are highlighted in the modeling process.
Faced with the disruption generated by the COVID-19 pandemic, the advent of enforced and exclusive online learning presented a challenging opportunity for researchers worldwide, to quickly adapt curricula to this new reality and gather electronic data by tracking students’ satisfaction after attending online modules. Many researchers have looked into the subject of student satisfaction to discover if there is a link between personal satisfaction and academic achievement. Using a set of data, filtered out of a statistical analysis applied on an online survey, with 129 variables, this study investigates students’ satisfaction prediction in a first-semester Mechanical Engineering CAD module combined with the evaluation and the effectiveness of specific curriculum reforms. A hybrid machine learning model that has been created, initially consists of a Generalized Linear Model (GLAR), based on critical variables that have been filtered out after a correlation analysis. Its fitting errors are utilized as an extra predictor, that is used as an input to an artificial neural network. The model has been trained using as a basis the 70% of the population (consisting of 165 observations) to predict the satisfaction of the remaining 30%. After several trials and gradual improvement, the metamodel’s architecture is produced. The trained hybrid model’s final form had a coefficient of determination equal to 1 (R = 1). This indicates that the data fitting method was successful in linking the independent variables with the dependent variable 100 percent of the time (satisfaction prediction).
In this paper, we scrutinize the effectiveness of various clustering techniques, investigating their applicability in Cultural Heritage monitoring applications. In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images. A total of 6 different clustering approaches have been evaluated over a set of 14 different orthorectified hyperspectral images. Experimental setup in this study involves K-means, Spectral, Meanshift, DBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate its performance by the use of performance metrics such as Calinski-Harabasz, Davies-Bouldin indexes and Silhouette value. In this approach, we evaluate the outcomes of the clustering methods by comparing them with a set of annotated images which denotes the ground truth regarding the decomposition and/or corrosion area of the original images. The results depict that a few clustering techniques applied on the given dataset succeeded decent accuracy, precision, recall and f1 scores. Eventually, it was observed that the deterioration was detected quite accurately.
Efforts toward COVID-19 proximity tracking in closed environments focus on efficient proximity identification by combining it with indoor localization theory for location activity monitoring and proximity detection. But these are met with concerns based on existing considerations of the localization theory like costly infrastructure, multi-story support, and over-reliance on sensor networks. Semantic location identities (SLI), or location data stored with additional meaningful context, has become a feasible localizing factor especially in locations that have multiple spaces with different usage from each other. There is also a novel method of classification framework, called hierarchical classification, that leverages the hierarchical structure of the labels to reduce model complexity. The research aims to provide a solution to proximity analysis and location activity monitoring considering guidelines released in a Philippine context that addresses concerns of indoor localization and handling of geospatial data by implementing a hybrid hierarchical indoor semantic location identity classification that focuses on observable events within context-unique locations.
Instructional materials, internet accessibility, student involvement and communication have always been integral characteristics of e-learning. During the transition from face-to-face to COVID-19 new online learning environments, the lectures and laboratories at universities have taken place either synchronously (using platforms, like MS Teams) or asynchronously (using platforms, like Moodle). In this study, a case study of a Greek university on the online assessment of learners is presented. As a testbed of this research, MS Teams was employed and tested as being a Learning Management System for evaluating a single platform use in order to avoid disruption of the educational procedure with concurrent LMS operations during the pandemic. A statistical analysis including a correlation analysis and a reliability analysis has been used to mine and filter data from online questionnaires. 37 variables were found to have a significant impact on the testing of tasks’ assignment into a single platform that was used at the same time for synchronous lectures. The calculation of Cronbach’s Alpha coefficient indicated that 89% of the survey questions have been found to be internally consistent and reliable variables and sampling adequacy measure (Bartlett’s test) was determined to be good at 0.816. Two clusters of students have been differentiated based on the parameters of their diligence, communication abilities and level of knowledge embedding. A hierarchical cluster analysis has been performed extracting a dendrogram indicating 2 large clusters in the upper branch, three clusters in the lower branch and an ensuing lower branch containing five clusters.
The increasing amounts of data have affected conceptual modeling as a research field. In this context, process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution. While automatic algorithms for process mining and analysis are needed to filter out irrelevant data and to produce preliminary results, visual inspection, domain knowledge, human judgment and creativity are needed for proper interpretation of the results. Moreover, a process discovery on an event log usually results in complicated process models not easily comprehensible by the business user. To this end, visual analytics has the potential to enhance process mining towards the direction of explainability, interpretability and trustworthiness in order to better support human decisions. In this paper we propose an approach for identifying bottlenecks in business processes by analyzing event logs and visualizing the results. In this way, we exploit visual analytics in the process mining context in order to provide explainable and interpretable analytics results for business processes without exposing to the user complex process models that are not easily comprehensible. The proposed approach was applied to a manufacturing business process and the results show that visual analytics in the context of process mining is capable of identifying bottlenecks and other performance-related issues and exposing them to the business user in an intuitive and non-intrusive way.
A widespread practice in machine learning solutions is the continuous use of human intelligence to increase their quality and efficiency. A common problem in such solutions is the requirement of a large amount of labeled data. In this paper, we present a practical implementation of the human-in-the-loop computing practice, which includes the combination of active and transfer learning for sophisticated data sampling and weight initialization respectively, and a cross-platform mobile application for crowdsourcing data annotation tasks. We study the use of the proposed framework to a post-event building reconnaissance scenario, where we utilized the implementation of an existing pre-trained computer vision model, an image binary classification solution built on top of it, and max entropy and random sampling as uncertainty sampling methods for the active learning step. Multiple annotations with majority voting as quality assurance are required for new human-annotated images to be added on the train set and retrain the model. We provide the results and discuss our next steps.
The analysis of disease occurrence over the smallest unit of a region is critical in designing data-driven and targeted intervention plans to reduce health impacts in the population and prevent spread of disease. This study aims to characterize groups of local communities that exhibit the same temporal patterns in dengue occurrence using the Fuzzy C-means (FCM) algorithm for clustering spatiotemporal data and investigate its performance in clustering data on dengue cases aggregated yearly, monthly and weekly. In particular, this study investigates similar patterns of Dengue cases in 129 barangays of Baguio City, Philippines recorded over a period of 9 years. Results have shown that the FCM has promising results in grouping together time series data of barangays when using data that is aggregated weekly.