Reviewerly/exHarmony huggingface dataset:
Gaze-Based Monitoring in the Classroom

In the roles of lecturers we have to present certain topics to students by means of Powerpoint slides, videos, animations, and the like. The content itself but also the presentation speed play crucial roles to make a lecture understandable for the students. Since each student has a different experience level, personal mood, or might be distracted by other scenarios the lecturers cannot keep track of all the individual students in a course but has to present the content more or less independently of the audience. In this paper we introduce work-in-progress that focuses on monitoring the students’ behavior in a course including the understanding of the presentation in terms of presentation speed. To reach this goal we make use of mobile eye tracking devices that permanently track the eye movements of the students paying visual attention to the lecturer’s slides. In the current state of the developed system we record two options, whether an individual student could follow and understand the slides or not while the summed up students’ feedback is reflected and displayed to the teacher in real-time, providing some kind of overview and avoiding asking questions permanently about the understanding of the slides’ contents. Finally, we will discuss challenges and limitations of our gaze-based monitoring system.
Towards Developing an Animation Kit for Functional Vision Screening with Eye Trackers

Developing accessible tools that support the identification of functional vision problems with reliable measurements of eye movements is a common interest. To develop such an open source tool considering eye tracker data needs automatic processing of eye data, handling time stamps, dynamic data generation, and parameter adaptation. This paper illustrates the possibility of such a tool for saccadic, smooth pursuit and circular eye movements and discusses future steps.
Discussing the importance of calibration in low-cost gaze estimation solutions

Calibration of gaze estimation systems has proven to be a key process in high-end systems. Now that the research effort is focused on low-cost systems, which are closer to the end-user and their day-to-day life, it is necessary to rethink a theoretical framework adapted to this new reality. This document details the proposal of a doctoral thesis focused on the dissection of the calibration process in low-cost systems, its importance and working line.
Analysis of Eye Tracking Data: Supporting Vision Screening with Eye Tracking Technologies

Interpreting gaze measurements for individual tasks, such as saccades, fixations, and smooth pursuits can be challenging for vision screening tasks on a computer screen. To address this challenge, we propose a data analysis pipeline by aligning recent approaches from vision research and computation. A pipeline gets raw eye-tracking data and provides comprehensive gaze metrics measurements and visual representation (scan path and heatmap) to give insights into ocular behaviour for vision screening tasks. Our preliminary studies have focused on current trends, challenges, and eye-tracking technologies for supporting the vision screening of children. This approach is important for providing a conceptual framework for gaze analysis aiming to support vision experts.
The design and evaluation of a digital learning-based English chatbot as an online self-learning method

A majority of government and international organisations have chosen English as their official language since it is the only language in the world with the status of a global language and a world language. One of the key components of learning a language is oral communication; however, students frequently overlook the value of oral communication, which reduces the effectiveness of language acquisition. To enhance the motivation and effectiveness of non-native English speakers to learn and practise English, this study aims to develop an interactive self-learning and self-improvement approach. A well-designed and chatbot-assisted learning environment is designed, using a variety of concepts such as informal learning, language learning, mobile learning, and educational mobile application, and evaluated.
Aplikasi Mobile Untuk Menekan Biaya Penjualan Produk Pertanian Lokal

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Research aim: This mobile application development aims to directly connect local agricultural producers to their buyers without any intermediary party in order to keep the selling costs minimum. &#x0D; Design/Methode/Approach: Rapid Application Development method with User Oriented are used in this research, which allows a relatively short development time and supports a more intense interaction with the related stakeholders. &#x0D; Research Finding: This research finds that simplification of transactions and more exposure through this application is very important.&#x0D; Theoretical contribution/Originality: This application has unique functions and interfaces that are designed as simple as possible to be easily operated by local agricultural producers, even with minimum digital literacy level. &#x0D; Practitionel/Policy implication: This implicated on application speed factor, data transmission size, and minimum hardware requirements are also the main criterias on which this application development is based. The result is a new and unique mobile application which is easy to use, does not consume too much internet quota, and supports older smartphones which are very beneficial for stakeholder. &#x0D; Research limitation: However system limitations such as integration with digital payment, rating systems, and recommendation systems still not covered yet. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Information technologies in the educational process of higher educational institutions

Informatization of the educational process, in general, and the educational system of the higher educational establishments, in particular, is one of the most significant tasks of restructuring and shaping the education system in accordance with society requirements. Educational institutions nowadays have an opportunity to disseminate new material in a way that responds to the unique needs of each student thanks to using computer networks and online technologies. The purpose of the academic paper is to identify and provide a brief assessment of the primary trends in implementing information technologies in higher educational institutions (HEIs) in terms of the specifics of their application in the educational process. Methodology. In the course of the research, analytical and bibliographic methods. At the same time, induction, deduction, analysis, synthesis of information, system-structural, comparative, logical-linguistic methods, abstraction, and idealization were applied to study and process data. By the way, the research authors also conducted a questionnaire in online mode. Results. Based on the research results, the primary and most significant theoretical aspects of using information tools in higher educational institutions, as well as the standpoints of scientists and heads of departments of higher educational institutions on key aspects of the issue outlined.
Outlier detection toward high-dimensional industrial data using extreme tensor-train learning machine with compression

Outlier detection in a high-dimensional dataset is a significant but challenging task in a number of applications. Extreme learning machine (ELM) is a powerful modeling tool for identifying outlier in an underlying dataset. However, when dealing with outliers in high-dimensional industry data, ELM brings huge storage and computational cost. To address this issue, we propose ELM based on a tensor-train format (ETFLM). Specifically, a tensor-train layer is builded with tensor-train decomposition. The fully connected layers of a neural network are replaced with tensor-train layers. Based on tensor-train layers and ELM, ETFLM is proposed in this study and its training algorithm is further presented. The experimental results show that ETFLM achieves high compression rate on low-dimensional data, and detection accuracy is slightly decreased. However, on high-dimensional data, ETFLM achieves more than 60%, whereas traditional algorithms achieve less than 40%.
Python-Based Desktop Assistant with Voice Recognition

Voice assistants offer a more intuitive and efficient means of interacting with computers, reducing the need for input devices like keyboards and mice. By incorporating natural language processing technology, they can understand complex user requests and perform a wide range of tasks. Adding emotion recognition and personalization features can tailor responses to users' preferences and moods. Multi-language support and continuous listening further enhance user experience. Voice assistants have come a long way in terms of development, from the first digital laptop to current devices with impressive processing capabilities. However, there is still potential for further improvement. Voice assistants offer an opportunity to reduce hardware costs and the space required to accommodate input devices. They are a significant breakthrough in human-computer interaction and will continue to become more powerful and efficient as technology evolves. The development of voice assistants reflects the advancements in science and technology. Their applications can extend beyond basic input control and offer users an improved computing experience. Voice assistants will undoubtedly become a key component of our daily lives
Robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization

Non-negative matrix factorization (NMF) has been widely used in machine learning and data mining fields. As an extension of NMF, non-negative matrix tri-factorization (NMTF) provides more degrees of freedom than NMF. However, standard NMTF algorithm utilizes Frobenius norm to calculate residual error, which can be dramatically affected by noise and outliers. Moreover, the hidden geometric information in feature manifold and sample manifold is rarely learned. Hence, a novel robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization (RCHNMTF) is proposed. First, a robust capped norm is adopted to handle extreme outliers. Second, dual hyper-graph regularization is considered to exploit intrinsic geometric information in feature manifold and sample manifold. Third, orthogonality constraints are added to learn unique data presentation and improve clustering performance. The experiments on seven datasets testify the robustness and superiority of RCHNMTF.
Immersive Virtual Reality Content Supporting a Wide and Free Viewpoint Made With a Single 360° Camera

Light field (LF)-based virtual reality (VR) systems have been proposed to tackle the problem of 360° panoramic image based VR not being able to support viewpoint movements. LF-based approaches still have shortcomings in that the user’s movement range is narrow and an extensive setup and high cost are required for data acquisition. This paper proposes lawn mowing pattern LF (LMP-LF) structure and presents a wide and free VR system utilizing an LMP-LF. The contributions of this paper are as follows. First, this paper proposes an LMP-LF in which several three-dimensional LFs (3DLFs) are arranged at uniform intervals. This simplifies the data acquisition process and widens the range of viewpoint movements. Second, this paper defines a light ray pair between two adjacent 3DLFs of the LMP-LF. The relationship between two light rays forming a light ray pair is expressed only with one-dimensional (1D) disparity, which greatly reduces the amount of disparity and the estimation complexity. Third, a real-time VR system using the LMP-LF is implemented and demonstrated. The proposed VR system achieves a high rendering speed of 125 frames-per-second (fps). In the prototype system, wide and free rendering performance was verified by building an LMP-LF for an automobile showroom at a size of 50m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
REAL-X—Robot Open-Ended Autonomous Learning Architecture: Building Truly End-to-End Sensorimotor Autonomous Learning Systems

Open-ended learning is a core research field of developmental robotics and AI aiming to build learning machines and robots that can autonomously acquire knowledge and skills incrementally as infants. The first contribution of this work is to highlight the challenges posed by the previously proposed benchmark ‘REAL competition’ fostering the development of truly open-ended learning robots. The benchmark involves a simulated camera-arm robot that: (a) in a first ‘intrinsic phase’ acquires sensorimotor competence by autonomously interacting with objects; (b) in a second ‘extrinsic phase’ is tested with tasks, unknown in the intrinsic phase, to measure the quality of previously acquired knowledge. The benchmark requires the solution of multiple challenges usually tackled in isolation, in particular exploration, sparse-rewards, object learning, generalisation, task/goal self-generation, and autonomous skill learning. As a second contribution, the work presents a ‘REAL-X’ architecture. Different systems implementing the architecture can solve different versions of the benchmark progressively releasing initial simplifications. The REAL-X systems are based on a planning approach that dynamically increases abstraction and on intrinsic motivations to foster exploration. Some systems achieves a good performance level in very demanding conditions. Overall, the REAL benchmark represents a valuable tool for studying open-ended learning in its hardest form.
Multi-population cooperative teaching–learning-based optimization for nonlinear equation systems

Abstract Solving nonlinear equation systems (NESs) requires locating different roots in one run. To effectively deal with NESs, a multi-population cooperative teaching–learning-based optimization, named MCTLBO, is presented. The innovations of MCTLBO are as follows: (i) two niching technique (crowding and improved speciation) are integrated into the algorithm to enhance population diversity; (ii) an adaptive selection scheme is proposed to select the learning rules in the teaching phase; (iii) the new learning rules based on experience learning are developed to promote the search efficiency in the teaching and learning phases. MCTLBO was tested on 30 classical problems and the experimental results show that MCTLBO has better root finding performance than other algorithms. In addition, MCTLBO achieves competitive results in eighteen new test sets.
Mean-reverting schemes for solving the CIR model

A family of methods for the numerical solution of the CIR model reproducing the mean-reversion property of the exact solution is presented. The convergence of the methods in the strong and weak senses is established. In addition, a method that captures exactly the first and second long term moments of the CIR process is found.
An Active Learning Algorithm for Image Classification Based on Difficulty and Competence

Training a high-quality image classification model often requires a large number of labeled datasets. However, in real-world business scenarios or production environments, the cost of obtaining labeled samples can be prohibitively high. Therefore, finding ways to obtain valuable labeled data at a lower cost is essential to improve the effectiveness of the algorithm. In this paper, we propose an effective and novel image selection strategy for active learning called Competence-based Active Learning. Our approach selects informative source images to strengthen the model’s generalization ability while minimizing the cost of manual labeling. Our experiments on MNIST, FashionMNIST, CIFAR10, and CIFAR100 demonstrate that our selection approach outperforms random selection and conventional selection methods. Moreover, Competence-based Active Learning not only enhances the generalization ability of the model but also reduces the training time.
Aspect-Context Level Information Extraction via Transformer Based Interactive Attention Mechanism for Sentiment Classification

Aspect-context sentiment classification aims to classify the sentiments about an aspect that corresponds to its context. Typically, machine learning models considers the aspect and context separately. They do not execute the aspect and context in parallel. To model the contexts and aspects separately, most of the methods with attention mechanisms typically employ the Long Short Term Memory network approach. Attention mechanisms, on the other hand, take this into account and compute the parallel sequencing of the aspects-context. The interactive attention mechanism extracts features of a specific aspect regarding its context in the sequence, which means aspects are considered when generating context sequence representations. However, when determining the relationship between words in a sentence, the interactive attention mechanism does not consider semantic dependency information. Moreover, the attention mechanisms did not capture the polysemous words. Normally conventional embedding models, such as GloVe word vectors, have been used. In this study, transformers are embedded into the attention mechanism approaches to overcome the semantic relationship problem. For this reason, the BERT pre-train language model is used to capture the relationship among the words in a sentence. The interactive attention mechanism is then applied to the model’s distribution of that word. The final sequence-to-sequence representation in terms of context and aspect is used into general machine learning classifiers for aspect-level sentiment classification. The proposed model was evaluated on the two datasets, i.e., Restaurant and Laptop review. The proposed approach has state-of-the-art results with all attention mechanisms and attained significantly better performance than the existing ones.
A Hierarchical Authentication System for Access Equipment in Internet of Things

With the development of Internet of Things (IoT) technology, massive heterogeneous equipment involving all walks of life has been connected to networks. However, many current authentication methods have poor robustness and cannot ensure the safety of access equipment due to the complexity and uncertainty of the network environment. To deal with this problem, a hierarchical authentication system was proposed in this paper. First, an equipment identification method (EIM), using stack denoising autoencoders (SDAs), is developed to weaken the uncertainty to recognize the type of access equipment. Second, an equipment authentication method (EAM), based on the identification result and similarity theory, is designed to improve the verification accuracy to guarantee the credibility of access equipment. Third, the proposed hierarchical authentication system was tested in a real access platform. The experimental results demonstrated the satisfactory performance of this proposed hierarchical authentication system.
MPClan: Protocol Suite for Privacy-Conscious Computations

Abstract The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical solutions, the literature has relied on secure multiparty computation techniques. However, recent research over rings has mostly focused on the small-party honest-majority setting of up to four parties tolerating single corruption, noting efficiency concerns. In this work, we extend the strategies to support higher resiliency in an honest-majority setting with efficiency of the online phase at the centre stage. Our semi-honest protocol improves the online communication of the protocol of Damgård and Nielsen (CRYPTO’07) without inflating the overall communication. It also allows shutting down almost half of the parties in the online phase, thereby saving up to 50% in the system’s operational costs. Our maliciously secure protocol also enjoys similar benefits and requires only half of the parties, except for one-time verification towards the end, and provides security with fairness. To showcase the practicality of the designed protocols, we benchmark popular applications such as deep neural networks, graph neural networks, genome sequence matching, and biometric matching using prototype implementations. Our protocols, in addition to improved communication, aid in bringing up to 60–80% savings in monetary cost over prior work.
Impacts on the Mental Health of Professionals in a Prisonal System in Alagoas During the Covid-19 Pandemic

Introduction: In the fight against the new coronavirus, prison system workers around the world have faced difficulties, including high risk of contamination, excessive working hours. inadequate and sometimes with ineffective protective measures against virus infection, generating frustration and exhaustion. Factors such as these contribute to emotional exhaustion and an increased risk of psychiatric illness. Based on the scientific evidence, with little to very little research on public prison workers, there are indications to speculate that the mental health condition of prison workers may also be affected during the COVID-19 outbreak. Objective: To evaluate the impacts of the pandemic generated by COVID-19 on the mental health of professionals in the prison system in the city of Maceió/Alagoas. Methodology: This is a prospective descriptive quantitative research. The research was carried out online with the research subjects being workers in the prison system in the city of Maceió. Inclusion criteria include any worker in the prison system in Maceió/Alagoas. Professionals who do not have internet access via cell phone, computer or tablet will be excluded from the survey; professionals with impaired cognitive capacity that prevents them from answering the questionnaire and illiterate professionals. A questionnaire was carried out to characterize the sample with questions about age, biological sex, ethnicity, income, profession, workload, whether you have a diagnosis of anxiety/depression or whether you are undergoing or have undergone psychiatric treatment, who you live with, whether you have /had a family member infected, if he was infected and, finally, if he had to isolate himself from his family for some reason. Results: it is observed that, of the 38 participants, 71.1% (n =27) do not have a diagnosis of anxiety/depression, while 26.3% (n=10) have such a diagnosis. Regarding psychiatric treatment, 73.7% (n=28) do not or have never had it and 23.7% (n=9) have or have already had this treatment. The presence of some degree of anxious mood was answered by 84.2% (n=32), while 15.8% (n=6) denied having this symptom. In addition, symptoms such as tension, insomnia, intellectual difficulties and depressed mood were reported by more than 60% (n=23) of study participants. Conclusion: The effects of the pandemic are perceived even more sensitively in the prison system. The need for confrontation and resolutive measures in the face of the negative repercussions on the mental health of health professionals in the prison system is essential.