Reviewerly/exHarmony huggingface dataset:
Predictive Evaluation of Artificial Intelligence Functionalities of Software: A Study of Apps for Children’s Learning of English Pronunciation

This paper is based on a study that developed and used a predictive evaluation method to evaluate educational software with embedded Artificial Intelligence (AI) functionalities for children’s learning of English pronunciation. The approach built on Squires and Preece’s [1] heuristics which used a social constructivist view of learning but were developed before AI functionalities were commonplace in educational software. Three AI-powered English learning apps were selected for the predictive evaluation. The evaluation enabled a comparison between the AI-powered English-learning apps based on their potential for learning. The study is the first to predictively evaluate pedagogical values of AI functionalities for improving ESL pronunciation and may provide an approach to developing predictive evaluation more widely for educational software that embeds AI functionalities.
Arguing for a Quantum Computing Curriculum: Lessons from Australian Schools

This chapter makes an argument for the development of a quantum computing curriculum. Building from a general look at innovation adoption, it presents survey data from 307 schools on the implementation of a new Digital Technologies subject in Australia. It was found that states and territories were taking 6–11 years to report student learning in the subject to parents. On a national basis, the analysis finds implementation is taking nearly 10 years from when the subject was framed, until full delivery. Despite this trajectory, the curriculum was radically revised after only 7 years. This rate of revision makes it difficult for schools to keep up with curriculum policy. With binary digit (bit) storage sizes predicted to fall to atomic levels in the next three decades, it is argued that quantum computing will become increasingly important. A future curriculum for this new computing paradigm therefore needs to be developed as a matter of urgency, so schools can anticipate innovation instead of lagging behind it. The chapter proposes a quantum curriculum for schools be developed as part of an international effort with the scope of recent Informatics frameworks.
A Workshop of a Digital Kamishibai System for Children and Analysis of Children’s Works

The authors have developed a digital kamishibai system, for authoring and performing kamishibai is said to have good educational effects on children. The system is implemented so that even children can operate it easily. The characteristics of the system are authoring with drag-and-drop operation and performance with ad-lib animation. Through the experiment, the authors made sure that children over six years old could handle the system. In this paper, the authors carried out a workshop in which children authored and performed digital kamishibai. Forty-eight groups participated. From the questionnaire, both children and their parents were mostly favorable to the system. The effects mentioned above were observed. Through the workshop, various kamishibai works were made. The authors showed these works to a staff of a children’s hall (an expert of kamishibai) and had review them. He pointed out children’s comprehension of stories, characteristics and style of representation, and structures and originality of the works. One of the future works is a detailed analysis of the works.
Typical English Teaching Methods Used in Secondary Level

This paper strived to portray the typical English teaching Methods applied by English Secondary teachers. To find the answer, there were twenty-two participants involved in this study, they were from public and private schools in East Java. The study employed qualitative methods with questionnaires, interviews and observations as the instruments. To gain the data, the researchers put all possible teaching activities or techniques taken from the theory of teaching English as a foreign language in the questionnaire. Then, the observations were done to see the actual teaching and learning process in the classroom. At last, the interview was employed to enrich the data. This current study found that mostly teachers under the study claimed to use various teaching activities in the teaching and learning process in their teaching. However, during the observations, teaching activities or techniques mainly were done with the Grammar Translation Method.
Towards a Theory of Interoperability of Software Systems

Interoperability is a property of software quality that is related to the cooperation between software systems for exchanging information. However, this concept is not well explained or understood. A theory would be useful to explain interoperability in terms of its essential elements and propositions. Theoretical contributions of interoperability are intended to formalize this concept by using common frameworks, models, and meta-models. However, tentative contributions developed in the past have failed to propose a theory of interoperability due to four reasons: (1) a disunified vocabulary is used, (2) the essential elements for describing interoperability are not well identified, (3) only a single level of interoperability is assessed, and (4) interoperability principles are not well formalized. This paper tentatively proposes an axiomatic theory of interoperability as a complementary approach to the existing knowledge. The proposed theory seeks to better formalize the concepts of interoperability and suggest actions aimed at establishing interoperability. After a brief review of related works and the state of the art, a set of axioms and propositions is presented. This theory is evaluated by a group of experts, and an example is presented to illustrate its use. Conclusions and future works are outlined at the end of the paper.
Adapting to Remote Learning during COVID-19: An Engineering Education Approach

With a focus on the problems faced by the education sector, this article shows the main technological tools used by teachers for remote teaching in an engineering education program during the first year of the COVID-19 pandemic. Through a case study in a Latin American university positioned in the QS World University Ranking, the tools most frequently used for communication, storing information, messaging, and sharing learning videos are described. This study examines engineering’s areas of knowledge, considering that learning them poses a challenge for teaching in virtual environments due to the need to carry out laboratory practices. Using statistical methods, the relationships between the areas of the education program, the teachers’ sociodemographic variables, and the technological tools used to offer online classes during the lockdown are described. The results show that, from the perspective of the teacher and for the field of engineering, the tools with the most significant effect, in sometimes essential for teaching during the pandemic, were those related to the Google platforms, which were used in 75% of the cases for video communication and storage. Meanwhile, with 44 and 56% WhatsApp and Moodle, were the most used for messaging and sharing contents and learning materials.
A lightweight neural network designed for fluid velocimetry

Abstract We devise a novel lightweight image matching architecture (), which is designed and optimized for particle image velocimetry (PIV). is a convolutional neural network (CNN) that performs symmetric image matching and employs an iterative residual refinement strategy, which allows us to optimize the total number of refinement steps to balance accuracy and computational efficiency. The network is trained on kinematic datasets with a loss function that penalizes larger gradients. We consider a six-level () and a four-level () version of the network and demonstrate that they are considerably leaner and faster than a state-of-the-art network designed for optical flow. reconstructs the velocity field from synthetic and experimental PIV images with an accuracy comparable or superior both to existing CNNs as well as to state-of-the-art cross-correlation methods (i.e., a commercial implementation of ). Although less accurate, allows a significant reduction of the computational costs with respect to any other method considered. All CNNs prove more robust than with respect to particle loss and allow effective error reduction by increasing the particle seeding density. Thanks to reduced computational cost and memory requirement, we envision the deployment of on low-cost devices to provide affordable, real-time inference of the flow field during PIV measurements.
Virtual Reality in education: supporting new learning experiences by developing self-confidence of Postgraduate Diploma in Education (PGDE) student-teachers

Emerging evidence has demonstrated that Virtual Reality (VR) supported lessons are able to enhance positive emotions and engagement as well as more memorable experiences, when compared to more traditional instructional tools such as readings from textbooks and videos.However, teachers' self-efficacy while teaching with VR technologies, VR safety in the classroom, the need for technical support and costs have been identified as potential obstacles for Pre-Service Teachers (PSTs) who are interested in the effective use of VR in the classroom.This study aims to explore how we may begin to address these obstacles by integrating VR supported lessons in Initial Teacher Education (ITE) courses.The originality of this study lies in the implementation of a short intervention in an already crowded Post graduate Diploma in Education (PGDE) curriculum and in analysing the impact on PSTs' self-confidence as a factor to initiate sustainable development.Participants were 198 PGDE Primary School studentteachers, who attended two-hour VRsupported sessions a week apart in February 2020.Findings suggest that engagement with VR lessons have encouraged participanting PSTs to explore this technology for their future practice across different subjects, impacting positively on their selfconfidence.
A Benchmark Dataset to Distinguish Human-Written and Machine-Generated Scientific Papers

As generative NLP can now produce content nearly indistinguishable from human writing, it is becoming difficult to identify genuine research contributions in academic writing and scientific publications. Moreover, information in machine-generated text can be factually wrong or even entirely fabricated. In this work, we introduce a novel benchmark dataset containing human-written and machine-generated scientific papers from SCIgen, GPT-2, GPT-3, ChatGPT, and Galactica, as well as papers co-created by humans and ChatGPT. We also experiment with several types of classifiers—linguistic-based and transformer-based—for detecting the authorship of scientific text. A strong focus is put on generalization capabilities and explainability to highlight the strengths and weaknesses of these detectors. Our work makes an important step towards creating more robust methods for distinguishing between human-written and machine-generated scientific papers, ultimately ensuring the integrity of scientific literature.
Fast Globally Optimal Catalog Matching using MIQCP

Abstract We propose a novel exact method to solve the probabilistic catalog matching problem faster than previously possible. Our new approach uses mixed integer programming and introduces quadratic constraints to shrink the problem by multiple orders of magnitude. We also provide a method to use a feasible solution to dramatically speed up our algorithm. This gain in performance is dependent on how close to optimal the feasible solution is. Also, we are able to provide good solutions by stopping our mixed integer programming solver early. Using simulated catalogs, we empirically show that our new mixed integer program with quadratic constraints is able to be set up and solved much faster than previous large linear formulations. We also demonstrate our new approach on real-world data from the Hubble Source Catalog. This paper is accompanied by publicly available software to demonstrate the proposed method.
A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

The satellite navigation system of Unmanned Aerial Vehicles (UAVs) is susceptible to external interference in a complex environment, resulting in the loss of their own geodetic coordinate information. A spatial registration method for multi-UAVs based on a cooperative platform in a geodesic coordinate information-free environment is proposed to solve this problem. The mutual observation information between UAVs is approximated by the observation information of the cooperative platform. Indirect observation information of the target can be obtained on account of mutual observation. On the basis of this, a close-range spatial registration algorithm without the geodetic coordinate information of UAVs is designed by means of the right-angle translation method. Finally, the Kalman filtering technique is used to track maritime targets. In this paper, the proposed method is verified by a simulation experiment and a practical experiment. The proposed method is 90% effective in reducing systematic errors. The tracking accuracy after alignment is significantly better than that of the original trajectory.
Development and Validation of a Positive-Item Version of the Visual Aesthetics of Websites Inventory: The VisAWI-Pos

Despite its importance, few validated scales exist to measure aesthetics in HCI. One notable exception, the Visual Aesthetics of Websites Inventory (VisAWI), has never been validated in English. Furthermore, the VisAWI contains negatively formulated items, which adversely impact the psychometric quality of survey scales. Consequently, this paper’s aim was the development of a positive-item version of the VisAWI, the VisAWI-pos, as a viable alternative to the original scale. Positive alternatives for the negative items of the VisAWI scale were developed and evaluated in a first study (N = 41). Afterward, a pre-registered second study (N = 966) was conducted to validate the VisAWI-pos. In addition, the VisAWI’s English version was formally validated for the first time. Results showed that the English VisAWI has comparable psychometric qualities to validated versions in other languages. Furthermore, the VisAWI-pos provided matching results to the original VisAWI while exhibiting equal or improved psychometric quality.
Broad Learning Model with a Dual Feature Extraction Strategy for Classification

The broad learning system (BLS) is a brief, flat neural network structure that has shown effectiveness in various classification tasks. However, original input data with high dimensionality often contain superfluous and correlated information affecting recognition performance. Moreover, the large number of randomly mapped feature nodes and enhancement nodes may also cause a risk of redundant information that interferes with the conciseness and performance of the broad learning paradigm. To address the above-mentioned issues, we aim to introduce a broad learning model with a dual feature extraction strategy (BLM_DFE). In particular, kernel principal component analysis (KPCA) is applied to process the original input data before extracting effective low-dimensional features for the broad learning model. Afterwards, we perform KPCA again to simplify the feature nodes and enhancement nodes in the broad learning architecture to obtain more compact nodes for classification. As a result, the proposed model has a more straightforward structure with fewer nodes and retains superior recognition performance. Extensive experiments on diverse datasets and comparisons with various popular classification approaches are investigated and evaluated to support the effectiveness of the proposed model (e.g., achieving the best result of 77.28%, compared with 61.44% achieved with the standard BLS, on the GT database).
An Exploratory Study of Helping Undergraduate Students Solve Literature Review Problems Using Litstudy and NLP

(1) Many undergraduate students struggle to produce a good literature review in their dissertations, as they are not experienced, do not have sufficient time, and do not have the required skills to articulate information. (2) Subsequently, we deployed Litstudy and NLP tools and developed a recommendation system to analyze articles in an academic database to help the students produce literature reviews. (3) The recommendation system successfully performed three levels of analysis. The elementary-level analysis provided demographic statistical analysis to the students, helping them understand the background information of the selected articles they would review. The intermediate-level analysis provided visualization of citations in network graphs for the students to understand the relationships of the articles’ authors, regions, and institutes so that the flow of ideas, development, and similarity of the selected articles can be better analyzed. The advanced level of analysis provided topic modeling functions for the students to understand the high-level themes of the selected articles to improve productivity as they read through them and simultaneously boost their creativity. (4) The three levels of analysis successfully analyzed the selected articles to provide innovative results and triggered the students to handle literature reviews in a new way. Further enhancement opportunities were identified in integrating the NLP technologies with large language models to facilitate the generation of research ideas/insights. This would be an exciting opportunity to have AI/NLP integrated to help the students with their research.
Image synthesis from an ethical perspective

Abstract Generative AI has gained a lot of attention in society, business, and science. This trend has increased since 2018, and the big breakthrough came in 2022. In particular, AI-based text and image generators are now widely used. This raises a variety of ethical issues. The present paper first gives an introduction to generative AI and then to applied ethics in this context. Three specific image generators are presented: DALL-E 2, Stable Diffusion, and Midjourney. The author goes into technical details and basic principles, and compares their similarities and differences. This is followed by an ethical discussion. The paper addresses not only risks, but opportunities for generative AI. A summary with an outlook rounds off the article.
Fast image encryption algorithm with random structures

AbstractBlock encryption algorithms are among the most preferred applications in cryptographic systems. Block ciphers should have accomplished some requirements for a secure communication system. They should be evaluated in terms of cryptanalysis methods for widespread usage. The aim of this paper is to introduce a new secure and fast block encryption algorithm for images. For this purpose, a new block cipher, which offers an innovative encryption structure for key generation systems and can use S-boxes with different methods, is proposed. A Dynamic S-Box is used in the algorithm for both substitution and key generation purposes. Linear and differential cryptanalysis methods were performed successfully. UACI and NPCR tests show that the proposed symmetric block cipher algorithm is compatible with image encryption systems. The 512-bit key length provides the highest security for block encryption. Additionally, information entropy test, correlation coefficients, mean-squared error, and peak signal-to-noise ratio analyses were concluded successfully. The novelty of the paper is building a cryptanalysis attack-resistant block cipher algorithm that presents a lightweight cryptographic solution for image encryption systems.KEYWORDS: Block ciphersymmetric encryptionS-BoxNPCR and UACIimage processing AcknowledgementsThis study has been produced from the doctoral dissertation of Taha Etem. Authors’ contributions: T.E. conceived and designed the analysis, collected the data, contributed analysis tools, and wrote the paper. T.K. edited the paper, controlled the analysis, and supervised.Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialData sharing is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsTaha EtemTaha Etem received the B.Sc. degree in Electrical-Electronics Engineering from Firat University, Elazig, Turkey, in 2013, and the M.Sc. degree in Electrical-Electronics Engineering from Inonu University, Malatya, Turkey, in 2017, and received the Ph.D. degree in Electrical-Electronics Engineering from Firat University, Elazig, Turkey, in 2022. He was with Cankiri Karatekin University, Cankiri, Turkey, as a Faculty Member. He is currently an Assistant Professor in the Computer Engineering Department. His research interests include encryption systems, random number generators and radio-frequency systems.Turgay KayaTurgay Kaya was born in Elazig, Turkey, in 1982. He received the B.Sc., M.Sc. and Ph.D. degrees in electrical-electronics engineering from the Firat University in 2003, 2006 and 2011, respectively. From 2004 to 20013, he was a Research Assistant at department of electrical-electronics engineering, Firat University, Elazig, Turkey. Since 2013, he has been an Associate Professor same department. His research interests include digital and analog filter design, biomedical signal processing, signal and image processing, microprocessing, artificial intelligence, heuristic optimization.
Service Recommendation of Industrial Software Components Based on Explicit and Implicit Higher-Order Feature Interactions and Attentional Factorization Machines

In the context of the rapid advancement of the Industrial Internet and Urban Internet, a crucial trend is emerging in the realization of unified, service-oriented, and componentized encapsulation of IT and OT heterogeneous entities underpinned by a service-oriented architecture. This is pivotal for achieving componentized construction and development of extensive industrial software systems. In addressing the diverse demands of application tasks, the efficient and precise recommendation of service components has emerged as a pivotal concern. Existing recommendation models either focus solely on low-order interactions or emphasize high-order interactions, disregarding the distinction between implicit and explicit aspects within high-order interactions as well as the integration of high-order and low-order interactions. This oversight leads to subpar accuracy in recommendations. Real-world data exhibit intricate structures and nonlinearity. In practical applications, different interaction components exhibit varying predictive capabilities. Therefore, in this paper we propose an EIAFM model that fuses explicit and implicit higher-order feature interactions and introduce an attention mechanism to identify which low-level feature interactions contribute more significantly to the prediction results. This approach leads to increased interpretability, combining both generalization and memory capabilities. Through comprehensive experiments on authentic datasets that align with the characteristics of the Service Recommendation of Industrial Software Components problem, we demonstrate that the EIAFM model excels compared to other cutting-edge models in terms of recommendation effectiveness, with the evaluation metrics for the AUC and log-loss reaching values of 0.9281 and 0.3476, respectively.