Reviewerly/exHarmony huggingface dataset:
Eye Tracking as a Research and Training Tool for Ensuring Quality Education

According to the Sustainable Development Goal “Ensuring quality education for all is fundamental to creating peace and prosperity in the world”, the use of modern methods in education acquires special relevance. Eye tracking as a research and training tool in education could expand the limits of the information to adjust not only the communication strategy of a university, but also the content of educational process, to embed a new research tool as a part of lectures and classes, and to enhance students’ engagement into research activities within their specialism. The article presents the results of pilot study on the basis of eye tracking during a master class for the students of the first grade of the Master Programme “Advertising and PR in International Communications”. A total of 38 students participated in the master class and the pilot study of a social advertisement. The nonverbal research was expanded with follow-up interview to take stock of the eye tracking applicability within educational process and research activities. Eye tracking research can help to further verify and enrich educational theories for better understanding how learners process information. The basic directions of eye tracking research in education are demonstrated: imbedding eye tracking in the content of research disciplines for media communication training areas; the implementation of eye tracking research results and recommendations concerning teaching materials, websites, e-books and courses, presentations and media; using the eye tracking research results in the university communication activities; Involving the students specializing in media communication into active studies.
Predicting Bug-Fixing Time Using the Latent Dirichlet Allocation Model with Covariates

The expected bug-fixing resolution time is one of the most important factors in bug triage, as an accurate prediction of bug-fixing times of newly submitted bugs helps to support both resource allocation and the triage process. Our approach treats the problem of bug-fix time estimation as a text categorization problem. To address this problem, we used Latent Dirichlet Allocation (LDA) model, a hierarchical statistical model based on what are called topics. Formally, a topic is a probability distribution over terms in a vocabulary. Such topic models provide useful descriptive statistics for a collection, which facilitates tasks like classification. Here we build a classification model on latent Dirichlet allocation (LDA). In LDA, we treat the topic proportions for a bug report as a draw from a Dirichlet distribution. We obtain the words in the bug report by repeatedly choosing a topic assignment from those proportions, then drawing a word from the corresponding topic. In supervised latent Dirichlet allocation (SLDA), we add to LDA a response variable associated with each document. Finally, we consider the supervised latent Dirichlet allocation with covariates (SLDAX) model, a generalization of SLDA, that incorporates manifest variables and latent topics as predictors of an outcome. We evaluated the proposed approach on a large dataset, composed of data gathered from defect tracking systems of five well-known open-source systems. Results show that SLDAX provides a better recall than those provided by topic models LDA-based.
Future educational environment – Identification of smart campus topic trends using text mining

Purpose Recent attention to the developments focusing on the educational services has been noteworthy, with the educational environment specifically the smart campus emerging both as a domain and as an opportunity. As a domain worthy of exploration, a number of research efforts are being conceptualized around smart campus initiatives. The existing bouquet of research publications on smart campus provides a testimony of the enthusiasm and also exposes the heterogeneous attempts the domain has witnessed to date. The available evidence is still inadequate to provide clarity on the thrust areas of research around smart campus. Design/methodology/approach Given the understanding, this study intends to decode the domain to get an early impression of the focus of the research concentration around smart campus. Thereby the study resorts to an automated text-mining approach using Python on contents shortlisted systematically, and published between the period 2010 and May 2022, from select databases. Findings Based on the analysis it was possible to identify eight themes (i.e. smart campus characteristics, smart campus stakeholders, smart campus frameworks, smart campus technologies, smart campus infrastructure, smart campus evaluation, smart learning environment and smart campus applications) characterizing research efforts within the smart campus literature. Originality/value The themes around the smart campus showcase the thrust areas receiving attention. These characterize extant research endeavours in the smart campus domain and can offer useful pointers to researchers going forward. This awareness can also be beneficial to institutional leadership and technology providers intending to implement smart campus initiatives, contributing to the development of the educational environment.
How Do Programming Students Read and Act upon Compiler Error Messages?

The research on understanding how programmers read and comprehend error messages is still limited. Through an eye-tracking study, we hope to add to what is known about how student programmers read and act upon error messages. In this paper, we elaborate on the methodology that we plan to use in the hopes of eliciting feedback from the research community that can help us improve upon our research design. We propose the study to enable us to collect data about whether student programmers read error messages, how they parse the code to find the source of the error, and how students with different ability and confidence levels vary in the way they process the errors. We will be examining where the participants fixate and how they read through the code to find the error. We are also interested in comparing the scan patterns of students who are proficient versus less proficient as well as those with high and low self-efficacy. We hope that the findings will contribute to the discourse about how student programmers read and parse code for errors, as well as to the discourse about the design of better error messages.
Multi-ledger Coordinating Mechanism by Smart Contract for Individual-Initiated Trustworthy Data Sharing

With the development of the Internet of Things (IoT) and sensors, personal health data can be collected by wearable devices. However, one of the biggest concerns about the storage and use of sensitive personal health data is privacy. To address this issue, we propose a new Multi-Ledger Coordinating Mechanism (MLCM) with blockchain for trustworthy data sharing under the control of each individual data owner. MLCM, enabled by smart contracts, lets an individual user control and manage all his/her own data in a flexible way and enhances data security and privacy preservation in user authentication and management. Also, all activities related to data access are monitored and further recorded in blockchain. The evaluation experiment is designed to demonstrate the feasibility of the proposed mechanism, and the result shows that the prototype implementation of one smart contract based on Hyperledger Fabric is stable.
Users’ Opinions When Drawing with an Eye Tracker

Eye tracking is interwoven with the research in the field of Human-Computer Interaction as a tool that examines the user’s visual attention in their interactions with computing devices and can reveal behaviors and factors that could otherwise remain unnoticed. In this paper, we take a novel approach by examining the use of an eye tracking system as an input method by developing a drawing application that the users can draw with their gaze. The application was developed to address the need for drawing software accessible to all individuals using commercially available and affordable hardware. An evaluation experiment (n = 25) was conducted, with results showing that although participants felt drawing with their gaze had a degree of difficulty, they enjoyed the experience and thought that drawing with their gaze was an exciting and fun experience. Furthermore, comments were made that could constitute a meaningful improvement for future application iterations. Finally, a longitudinal study showed that users’ drawing skills markedly improved each time they used the application while also feeling more inclined to use it daily.
Iterative Improvement of Automatically Generated Practice with the Content Improvement Service

The Content Improvement Service (CIS) is a platform-level adaptive system that monitors millions of automatically generated formative practice questions that are available to students as a study feature in thousands of textbooks. The CIS was designed to do what was not possible by humans—use real-time data to monitor question performance at enormous scale and determine if a change is required. The CIS was designed to use multiple types of data and analyses to make its decisions, including question difficulty (mean score) and student feedback (ratings). In this paper, we outline the decisions made by the CIS for both methods. We also show how human investigation of these analyses can identify trends and insights useful to automatic question generation systems.
Intentional Microgesture Recognition for Extended Human-Computer Interaction

As extended reality becomes more ubiquitous, people will more frequently interact with computer systems using gestures instead of peripheral devices. However, previous works have shown that using traditional gestures (pointing, swiping, etc.) in mid-air causes fatigue, rendering them largely unsuitable or long-term use. Some of the same researchers have promoted “microgestures”—smaller gestures requiring less gross motion—as a solution, but to date there is no dataset of intentional microgestures available to train computer vision algorithms for use in downstream interactions with computer systems such as agents deployed on XR headsets. As a step toward addressing this challenge, we present a novel video dataset of microgestures, classification results from a variety of ML models showcasing the feasibility (and difficulty) of detecting these fine-grained movements, present a demonstration of a novel keyframe detection method as a way to increase recognition accuracy, and discuss the challenges in developing robust recognition of microgestures for human-computer interaction.
Unsupervised Saliency Detection via kNN Mechanism and Object-Biased Prior

In recent years, some researchers have put forth the compactness hypothesis, which suggests that similar colours tend to accumulate in the salient region as opposed to the non-salient region in image saliency detection. We discovered that the k-nearest neighbour (kNN) mechanism assumes the presence of similar objects in close proximity. As the kNN method is a supervised learning approach, we introduced an unfixed k value and combined it with the clustering idea of k-means to develop a novel algorithm called kNN clustering. We proposed an object-biased prior and an improved boundary and background prior based on a given compact matrix. Our algorithm was extensively tested on five publicly available datasets. The experimental results demonstrated that it outperformed eight existing top unsupervised models in producing high-quality saliency maps at full resolution. The improved prior methods were particularly effective when employed with existing algorithms lacking prior knowledge, especially with low-performing models.
Evaluating Explainable AI (XAI) in Terms of User Gender and Educational Background

Artificial intelligence (AI) and machine learning (ML) have become ubiquitous tools in the modern era. As more technologies become dependent on AI and ML, there is a greater push to understand how to use Explainable AI (XAI) to help human users understand the underlying decision processes of the technology. We report on a laboratory-controlled experiment focused on trust and understanding comparing different types of XAI explanations with a recommendation system while controlling for participant gender and educational background. We found statistically significant interactions of both gender and educational background. As a result, we conclude that there is not one particular way to show explanations that is best for all audiences because both gender and the person’s education and professional background play a part in determining their trust and understanding in the system. In addition, although a number of publications have promoted visual word clouds as a way to show XAI, our participants rejected that type of explanation and preferred textual explanations.
Co-creating User Journey Map – A Systematic Approach to Exploring Users’ Day-to-Day Experience in Participatory Design Workshops

Mutual learning scaffolds co-realization and knowledge exchange between the users and designers to facilitate effective collaborative creation and decision processes in participatory design. Two common challenges recognized in mutual learning processes are how to efficiently uncover users’ experience and immerse designers or engineers in users’ know-how of their everyday experience. In this study, we intend to introduce a collaborative user journey map creation process among users, designers, and system engineers. Unlike other user journey maps created by the researchers and served as a reference for workshop discussion, in this study, we had the older participants, the engineers, and the designers to co-create the user journey map during the workshop to enhance mutual learning. The new user journey map building process included using LEGO® to build the participants’ life space representations, interviewing the participants about their typical day experience with the living space representation and event sheets, building up the user journey map with event sheets’ information and advanced interviews, and identifying the life challenges within groups. The post-interview analysis showed that the chronological and visual features of the user journey map effectively revealed older participants’ life experiences and challenges with other workshop members. The new way of making user journey maps engaged all the workshop participants in the journey map co-creation activity and enhanced designers’ or engineers’ commitment to the mutual learning process.
AN ASSESSMENT OF ONLINE COURSES FOR SUPPORTING GREEK HIGHER EDUCATION TEACHERS’ DIGITAL COMPETENCE

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 6965-6969ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1826Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
STUDYING CHALLENGES, TASKS AND BENEFITS OF USING VIRTUAL SPACES FOR EDUCATIONAL, LEARNING AND SHOWCASING PURPOSES

STUDYING CHALLENGES, TASKS AND BENEFITS OF USING VIRTUAL SPACES FOR EDUCATIONAL, LEARNING AND SHOWCASING PURPOSES
AUTOMATIZED CONGRUENCE OF REVIEWERS' ASSESSMENTS AND RAW DATA FOR EDUCATIONAL OBJECTS IN COMPARATIVE QUALITY EVALUATION

AUTOMATIZED CONGRUENCE OF REVIEWERS' ASSESSMENTS AND RAW DATA FOR EDUCATIONAL OBJECTS IN COMPARATIVE QUALITY EVALUATION
Trademark Detection Algorithm Based on Artificial Intelligence

In the field of computer vision, the performance of deep learning in target detection task has exceeded the past and reached the highest level in history. The trademark detection algorithm is based on artificial intelligence. Artificial intelligence based algorithms are used to detect trademarks of specific products. Its working principle is to compare the images of products with those of other products with similar images in the database. Different from the target detection task in the open field, the trademark detection task has the characteristics of large target scale span, complex scene change, serious target rotation deformation and a large number of small targets. At present, the mainstream target recognition algorithm has low accuracy in the recognition task in the trademark field. The trademark detection algorithm compares the images and finds the similarity between them. If there is any similarity, it will be marked as a trademark item. The algorithm is developed using artificial intelligence and machine learning technology to detect trademarks in images or video frames captured from different angles.
Image encryption algorithm based on 2D hyper-chaotic system and central dogma of molecular biology

Abstract With the widespread use of images, image security has received much attention. Image security can be guaranteed by encrypting the plain image and transmitting the corresponding cipher image. This paper proposes an image encryption algorithm based on the novel two-dimensional (2D) hyper-chaotic system, bidirectional diagonal crossover transformations (BDCTs) and central dogma of molecular biology. Firstly, six chaotic sequences are generated using the proposed hyper-chaotic system, which are used in the permutation and diffusion processes. Secondly, the 5th–8th bit-planes of plain image are permuted using the designed BDCTs. Thirdly, the permuted image is dynamically encoded into a quaternary DNA sequence, and then the quaternary DNA sequence is transcribed into a quaternary RNA sequence. After that, the quaternary RNA sequence is diffused using quaternary RNA operations. Finally, the diffused RNA sequence is translated into a codon sequence, and then the designed codon-level multipoint crossover scheme is used to further improve the diffusion effect. Experiment results and security analyses demonstrate that our algorithm has high security and efficiency. In other words, our algorithm is quite suitable for real-time image cryptosystems.
Accurate Gradient Selective Based Histogram Bin Shifting for Reversible Data Hiding

A good prediction Error Expansion (PEE) based reversible data hiding method demands an effective pixel prediction strategy. There are numerous pixel prediction techniques in the literature. The current pixel can be predicted using gradients. In order to integrate more data in the current pixel, several researchers have suggested using gradient estimation to predict the current pixel. In this paper First, a 3 × 4 neighbourhood context-based Accurate Gradient Selective Prediction with gradients of the image has been proposed. Later, to more accurately anticipate the current pixel, the Causal pixel method has been utilised in place of linear predictors. In accordance with the pixel’s local complexity, adaptive histogram bin shifting has been employed to insert more data with less distortion. TThe experimental study demonstrates that the suggested strategy performs better than a number of other published methods.
Binocular Matching Method for Detecting and Locating Marine Resources Using Binocular Cameras and Deep Learning

It is well known that Artificial Intelligence and underwater robots play a significant role in energy exploration. This paper proposes a novel method using binocular cameras to detect, locate, and track marine resources. The method combines the YOLOv5 object detection method with the BM algorithm binocular ranging method. The method then uses a neural network to identify and detect the position of the target in the image, matches the detection framework with the depth chart, and employs a weighted average model to calculate the relative distance between the target and the object detected by the camera. Finally, underwater robots can use the relative distance to locate and track marine resources.
Nighttime Vehicle Detection Algorithm Enhanced by NightvisionGAN

To solve the problem of low vehicle detection accuracy caused by inconspicuous nighttime vehicle features and complex environmental lighting interference, In this paper, a nighttime vehicle detection algorithm based on NightvisionGAN is proposed. The generator coding and decoding process are connected in corresponding layers, and the residual module is used to realize the conversion of features of different scales, and the CBAM attention mechanism is introduced to make the generator pay attention to vehicle features adaptively, improve the generation quality of details such as body outline color and avoid the loss of small target vehicles; the L1 regularization is introduced to control the sparsity of generator model channels to realize channel pruning, distillation loss is added in the pruning process to dynamically adjust the number of channels of the student network, constructing the lightweight model NightvisionGAN_light to improve the detection speed; the proposed image enhancement method is jointly trained with YOLOv3 to improve the detection accuracy. Verified by the BDD dataset, the proposed algorithm can effectively reduce the leakage rate and false detection rate of nighttime vehicle detection.
Stereo Matching Algorithm Based on Improved Census Transform

Aiming at the problem that the traditional Census transform is too dependent on the center pixel, which is easily affected by noise, this paper proposed a stereo matching algorithm based on improved Census transform. The weighted average calculation is introduced into the traditional Census transform. The weights of the pixels in the window are reasonably assigned, and the weighted average of all the pixels in the window is used to replace the central pixel to calculate the matching cost, which improves the robustness of the algorithm to noise. The Winner-Take-All algorithm is used to calculate the matching cost. Finally, the left-right consistency check and median filtering are used to complete the disparity optimization. The proposed algorithm is tested on the data set provided by the public test platform. The results show that the proposed algorithm improves the matching accuracy compared with the original algorithm.
Image stitching technology based on SIFT, FLANN, and RPOSAC algorithms

For the traditional SIFT algorithm extracts a large number of feature points, it takes a lot of time for each matching in the image stitching process, and the matching effect is not good when there is a scale change or rotation change in the image. An image stitching method based on SIFT, FLANN and PROSAC algorithms is proposed. Firstly, the collected images are preprocessed, and the SIFT algorithm is used to extract the features. The FLANN fast nearest neighbor search package is used to match the nearest neighbor feature points, and the progressive consistent sampling ( PROSAC ) algorithm is used to eliminate the mismatched feature points. Finally, the gradual-in and gradual-out method is used for image fusion to obtain the mosaic image. Experiments show that this method realizes the stitching of multiple images and achieves good results.