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The reform of tertiary education teaching promotes teachers to adjust timely teaching plans based on students’ learning feedback in order to improve teaching performance. Thefore, learning score prediction is a key issue in process of the reform of tertiary education teaching. With the development of information and management technologies, a lot of teaching data are generated as the scale of online and offline education expands. However, a teacher or educator does not have a comprehensive dataset in practice, which challenges his/her ability to predict the students’ learning performance from the individual’s viewpoint. How to overcome the drawbacks of small samples is an open issue. To this end, it is desirable that an effective artificial intelligent tool is designed to help teachers or educators predict students’ scores well. We propose a boosting and decision-tree-regression-based score prediction (BDTR-SP) model, which relies on an ensemble learning structure with base learners of decision tree regression (DTR) to improve the prediction accuracy. Experiments on small samples are conducted to examine the important features that affect students’ scores. The results show that the proposed model has advantages over its peer in terms of prediction correctness. Moreover, the predicted results are consistent with the actual facts implied in the original dataset. The proposed BDTR-SP method aids teachers and students to predict students’ performance in the on-going courses in order to adjust the teaching and learning strategies, plans and practices in advance, enhancing the teaching and learning quality. Therefore, the integration of information technology and artificial intelligence into teaching and learning practices is able to push forward the reform of tertiary education teaching. Full article

Photovoltaic (PV) cells are a technology of choice for providing power to self-sufficient Internet of Things (IoT) devices. These devices’ declining power demands can now be met even in indoor environments with low light intensity. Correspondingly, light simulation systems need to cover a wide spectrum of irradiance intensity to emulate a PV cell’s working conditions while meeting cost targets. In this paper, we propose a method for calculating the irradiance distribution for a given number and position of LED sources to meet irradiance and uniformity requirements in LED-based light simulators. In addition, we establish design guidelines for minimizing non-uniformity under specific constraints and utilize a function to evaluate the degree of non-uniformity and determine the optimal distance from the illuminated surface. We demonstrate that even with a small number of low-cost LED sources, high levels of irradiance can be achieved with bounded non-uniformities. The presented guidelines serve as a resource for designing tailored, low-cost light simulators that meet users’ area/intensity/uniformity specifications. Full article

Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application. Full article

Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases. Full article

The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It is then necessary to build models analyzing their decisions when interacting, while taking into account the specificities of these interactions. This paper, therefore, aimed to propose an explicit model of such interactions based on game theory, taking into account, not only environmental characteristics (e.g., criticality), but also human characteristics (e.g., workload and experience level) for the intervention (or not) of agents, to help the latter. Game theory is a well-known approach to studying such social interactions between different participants. Existing works on the construction of game matrices required different ad hoc descriptors, depending on the application studied. Moreover, they generally focused on the interactions between agents, without considering human beings in the analysis. We show that these descriptors can be classified into two categories, related to their effect on the interactions. The set of descriptors to use is thus based on an explicit combination of all interactions between agents and humans (a weighted sum of 2-player matrices). We propose a general model for the construction of game matrices based on any number of participants and descriptors. It is then possible to determine using Nash equilibria whether agents decide (or not) to intervene during the tasks concerned. The model is also evaluated through the determination of the gains obtained by the different participants. Finally, we illustrate and validate the proposed model using a typical scenario (involving two agents and two humans), while describing the corresponding equilibria. Full article

Nowadays, the Internet of Things (IoT) has become widely used at various places and for various applications. To facilitate this trend, we have developed the IoT application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time), which offers standard features for collecting, displaying, and analyzing sensor data. An edge device is usually installed to connect sensors with the server, where the interface configuration, the data processing, the communication protocol, and the transmission interval need to be defined by the user. In this paper, we proposed an edge device framework for SEMAR to remotely optimize the edge device utilization with three phases. In the initialization phase, it automatically downloads the configuration file to the device through HTTP communications. In the service phase, it converts data from various sensors into the standard data format and sends it to the server periodically. In the update phase, it remotely updates the configuration through MQTT communications. For evaluations, we applied the proposal to the fingerprint-based indoor localization system (FILS15.4) and the data logging system. The results confirm the effectiveness in utilizing SEMAR to develop IoT application systems. Full article

In this paper, we propose a novel attack on image encryption for privacy-preserving deep neural networks (DNNs). Although several encryption schemes have been proposed for privacy-preserving DNNs, existing cipher-text-only attacks (COAs) have succeeded in restoring visual information from encrypted images. Image encryption using the Vision Transformer (ViT) is known to be robust against existing COAs due to the operations of block scrambling and pixel shuffling, which permute divided blocks and pixels in an encrypted image. However, the correlation between blocks in the encrypted image can still be exploited for reconstruction. Therefore, in this paper, a novel jigsaw puzzle solver-based attack that utilizes block correlation is proposed to restore visual information from encrypted images. In the experiments, we evaluated the security of image encryption for privacy-preserving deep neural networks using both conventional and proposed COAs. The experimental results demonstrate that the proposed attack is able to restore almost all visual information from images encrypted for being applied to ViTs. Full article

The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective. Full article

Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts’ personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector. Full article

In the past decade, technological advancements in mental health care have resulted in new approaches and techniques. The proliferation of mobile apps and smartphones has significantly improved access to psychological self-help resources for individuals. In this paper, a narrative review offers a comprehensive overview of recent developments in mental health mobile apps, serving as a foundation to introduce the design and development of “Safer”. Safer is a mobile application that targets the transdiagnostic process of emotion dysregulation. The review outlines the theoretical framework and design of Safer, an mHealth app grounded in the Dialectical Behavior Therapy (DBT) model, aimed at fostering emotion regulation skills. Full article

An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through various channels, including messaging platforms, websites, and mobile applications. This paper presents a retrieval system that evaluates the similarity between a user’s query and a given question. The system uses natural language processing (NLP) algorithms to interpret user input and associate responses by addressing the problem as a semantic search similar question retrieval. Although a common approach to question and answer (Q&A) retrieval is to create labelled Q&A pairs for training, we exploit an unsupervised information retrieval system in order to evaluate the similarity degree between a given query and a set of questions contained in the knowledge base. We used the recently proposed SBERT model for the evaluation of relevance. In the paper, we illustrate the effective design principles, the implemented details and the results of the conversational system and describe the experimental campaign carried out on it. Full article

Item response theory (IRT) models are factor models for dichotomous or polytomous variables (i.e., item responses). The symmetric logistic or probit link functions are most frequently utilized for modeling dichotomous or polytomous items. In this article, we propose an IRT model for dichotomous and polytomous items using the asymmetric generalistic logistic link function that covers a lot of symmetric and asymmetric link functions. Compared to IRT modeling based on the logistic or probit link function, the generalized logistic link function additionally estimates two parameters related to the asymmetry of the link function. To stabilize the estimation of item-specific asymmetry parameters, regularized estimation is employed. The usefulness of the proposed model is illustrated through simulations and empirical examples for dichotomous and polytomous item responses. Full article

Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differences in the writing style of alphabets and digits. This study aims to discuss the steps needed to create an adaptive framework for OCR models, with the intent of exploring a reasonable method to customize an OCR solution for a unique dataset of English language numerical digits were developed for this study. We develop a digit recognizer by training our model on the MNIST dataset with a convolutional neural network and contrast it with multiple models trained on combinations of the MNIST and custom digits. Using our methods, we observed results comparable with the baseline and provided recommendations for improving OCR accuracy for localized or personalized handwritten text. This study also provides an alternative perspective to generating data using conventional methods, which can serve as a gold standard for custom data augmentation to help address the challenges of scarce data and data imbalance. Full article

In this study we propose a measure of the entropy of the norm (R, S) for q-row orthopair fuzzy sets (qROFS). The proposed entropy measure is validated both theoretically and practically to ensure validity. We also propose a simple methodology for the purpose of solving a multi-criteria decision-analysis problems using the introduced entropy measure. This method takes into account different circumstances of criteria weights, such as unknown weights, as well as other cases when the weights are not fully known. Finally, a demonstration with numerical examples for the proposed entropy has been provided to show how to apply the novel methodologies. Full article

The tremendous increase in documents available on the Web has turned finding the relevant pieces of information into a challenging, tedious, and time-consuming activity. Text summarization is an important natural language processing (NLP) task used to reduce the reading requirements of text. Automatic text summarization is an NLP task that consists of creating a shorter version of a text document which is coherent and maintains the most relevant information of the original text. In recent years, automatic text summarization has received significant attention, as it can be applied to a wide range of applications such as the extraction of highlights from scientific papers or the generation of summaries of news articles. In this research project, we are focused mainly on abstractive text summarization that extracts the most important contents from a text in a rephrased form. The main purpose of this project is to summarize texts in German. Unfortunately, most pretrained models are only available for English. We therefore focused on the German BERT multilingual model and the BART monolingual model for English, with a consideration of translation possibilities. As the source of the experiment setup, took the German Wikipedia article dataset and compared how well the multilingual model performed for German text summarization when compared to using machine-translated text summaries from monolingual English language models. We used the ROUGE-1 metric to analyze the quality of the text summarization. Full article

This article is devoted to the study of the entropic orderliness of road safety systems of various dimensionalities. The author’s methodology for quantitative assessment of the quality of the road safety systems is based on the use of information entropy analysis, the essence of which is to assess the significance (or “weights”) of various information-technological stages of the road traffic accident rate formation process. The main emphasis in this paper is on the philosophical interpretation of the results of entropic evaluation of the orderliness of urban road safety systems. The article aimed to philosophically understand the reasons for the diversity in the results of assessing the entropy of road safety (RS) in Russian cities. Within the framework of this goal, the results of the analysis of the state of the issue, ideological approaches and methods for assessing the relative entropy of urban road safety systems were presented. The study was based on analyzing statistics that characterize the processes of the formation of road traffic accidents in Russian cities classified into three groups based on population size. The experimental results obtained were explained from the point of view of human psychology. Rather, results were explained from the perspective of human psychology. The final results of the study once again illustrated the objectivity of Hegel’s dialectical laws and, perhaps, once again shattered illusions about the possibility of achieving high levels of road safety in cities by building rigid systems to regulate the actions of traffic participants. In the author’s opinion, the results of the presented philosophical analysis will be useful to managers specializing in the management of complex systems (not only transport but also other fields) to comprehend the contradictions of the complex nature of humans and the paradoxes of their behavior when their freedom of action is restricted through external control. Full article

This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO₂), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO₂ monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video Magnification (EVM) technique to enhance minor variations in skin pixel intensity in particular facial regions. More specifically, the emphasis in this study is in the utilization of infrared cameras, in order to explore the possibility of contactless SpO₂ monitoring under low-light or night-time conditions. Many different methods were employed for regression. A study of machine learning regression methods was performed, including a Generalized Additive Model (GAM) and an Extra Trees Regressor, based on 12 novel features extracted from the extracted amplified photoplethysmography (PPG) signal. Deep learning methods were also explored, including a 3D Convolution Neural Network (CNN) and a Video Vision Transformer (ViViT) architecture on the amplified forehead/cheeks video. The estimated SpO₂ values of the best performing method reach a low root mean squared error of 1.331 and an $R^2$ score of 0.465 that fall within the acceptable range for these applications. Full article

Currently, we are developing the user-PC computing (UPC) system based on the master-worker model as a scalable, low-cost, and high-performance computing platform. To run various application programs on personal computers (PCs) with different environments for workers, it adopts Docker technology to bundle every necessary software as one image file. Unfortunately, the Docker file/image are manually generated through multiple steps by a user, which can be the bottleneck. In this paper, we present a web-based Docker image assistant generation (DIAG) tool in the UPC system to assist or reduce these process steps. It adopts Angular JavaScript for offering user interfaces, PHP Laravel for handling logic using RestAPI, MySQL database for storing data, and Shell scripting for speedily running the whole program. In addition, the worker-side code modification function is implemented so that a user can modify the source code of the running job and update the Docker image at a worker to speed up them. For evaluations, we collected 30 Docker files and 10 OpenFOAM jobs through reverse processing from Docker images in Github and generated the Docker images using the tool. Moreover, we modified source codes for network simulations and generated the Docker images in a worker five times. The results confirmed the validity of the proposal. Full article

The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. The methodological approach of applying MOO algorithms to simulation-enabled models has established itself as a standard. Despite increasing in computational power, the effectiveness and efficiency of such algorithms, i.e., their ability to identify as many Pareto-optimal solutions as possible with as few simulation samples as possible, plays a decisive role. However, the question of which class of MOO algorithms is most effective or efficient with respect to which class of problems has not yet been resolved. To tackle this performance problem, hybrid optimization algorithms that combine multiple elementary search strategies have been proposed. Despite their potential, no systematic approach for selecting and combining elementary Pareto search strategies has yet been suggested. In this paper, we propose an approach for designing hybrid MOO algorithms that uses reinforcement learning (RL) techniques to train an intelligent agent for dynamically selecting and combining elementary MOO search strategies. We present both the fundamental RL-Based Hybrid MOO (RLhybMOO) methodology and an exemplary implementation applied to mathematical test functions. The results indicate a significant performance gain of intelligent agents over elementary and static hybrid search strategies, highlighting their ability to effectively and efficiently select algorithms. Full article