Reviewerly/exHarmony huggingface dataset:
Multimedia-Based Informal Learning in Museum Using Augmented Reality

Many studies have demonstrated that augmented reality (AR) learning environments could effectively enhance learning efficiency in formal education compared to real learning environments. Furthermore, multimedia learning in AR has been shown to significantly improve learning performance and reduce cognitive load. However, few studies have explored the impact of AR environments on cognitive load and learning performance in informal learning, such as museum education. This study aims to fill this gap and investigate the impact of the relationship between the visual and auditory channels on knowledge acquisition and cognitive load in AR, as quantified by various indicators including heart rate variability, knowledge questionnaire score, user preference, and NASA-TLX. The results of within-group experiments suggested that the combination of visual and auditory information can substantially improve learning performance and reduce the task load compared to single-channel information delivery.
Digital construction of higher education management based on multimodal machine database

Abstract In this paper, we first study the multimodal interaction activities in higher education management from the two points of multimodal interaction types and multimodal interaction activity evaluation and propose the perfect way of digital construction of multimodal higher education management from the perspective of students, teachers and higher education institutions. Then, the higher education management system is constructed based on semi-supervised fusion feature learning and homogeneous multimodal features of multimodal machines, and the system architecture and database design are explained in detail. Next, the research literature statistics and system performance of ‘Digitalization of Higher Education Management’ are analyzed through experimental research. The results show that the periodicals’ literature statistics show a rapid increase in articles, mainly from 2003 to 2004 and 2006-2007. The higher education management system maintained an accuracy rate of 80.49% regarding system performance. It is confirmed that the management system constructed in this study can predict the learners’ status more accurately and predictably, contributing to the development of intelligent education management in higher education institutions.
With a Few Square Roots, Quantum Computing is as Easy as Π

Rig groupoids provide a semantic model of \PiLang, a universal classical reversible programming language over finite types. We prove that extending rig groupoids with just two maps and three equations about them results in a model of quantum computing that is computationally universal and equationally sound and complete for a variety of gate sets. The first map corresponds to an $8^{\text{th}}$ root of the identity morphism on the unit $1$. The second map corresponds to a square root of the symmetry on $1+1$. As square roots are generally not unique and can sometimes even be trivial, the maps are constrained to satisfy a nondegeneracy axiom, which we relate to the Euler decomposition of the Hadamard gate. The semantic construction is turned into an extension of \PiLang, called \SPiLang, that is a computationally universal quantum programming language equipped with an equational theory that is sound and complete with respect to the Clifford gate set, the standard gate set of Clifford+T restricted to $\le 2$ qubits, and the computationally universal Gaussian Clifford+T gate set.
DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions

General large language models (LLMs) such as ChatGPT have shown remarkable success, but it has also raised concerns among people about the misuse of AI-generated texts. Therefore, an important question is how to detect whether the texts are generated by ChatGPT or by humans. Existing detectors are built on the assumption that there is a distribution gap between human-generated and AI-generated texts. These gaps are typically identified using statistical information or classifiers. In contrast to prior research methods, we find that large language models such as ChatGPT exhibit strong self-consistency in text generation and continuation. Self-consistency capitalizes on the intuition that AI-generated texts can still be reasoned with by large language models using the same logical reasoning when portions of the texts are masked, which differs from human-generated texts. Using this observation, we subsequently proposed a new method for AI-generated texts detection based on self-consistency with masked predictions to determine whether a text is generated by LLMs. This method, which we call DetectGPT-SC. We conducted a series of experiments to evaluate the performance of DetectGPT-SC. In these experiments, we employed various mask scheme, zero-shot, and simple prompt for completing masked texts and self-consistency predictions. The results indicate that DetectGPT-SC outperforms the current state-of-the-art across different tasks.
Comparison of confidence regions for quantum state tomography

Abstract The quantum state associated to an unknown experimental preparation procedure can be determined by performing quantum state tomography. If the statistical uncertainty in the data dominates over other experimental errors, then a tomographic reconstruction procedure must express this uncertainty. A rigorous way to accomplish this is via statistical confidence regions (CRs) in state space. Naturally, the size of this region decreases when increasing the number of samples, but it also depends critically on the construction method of the region. We compare recent methods for constructing CRs as well as a reference method based on a Gaussian approximation. For the comparison, we propose an operational measure with the finding, that there is a significant difference between methods, but which method is preferable can depend on the details of the state preparation scenario.
Student's Cybersecurity Awareness in Post Covid-19 Pandemic

The study used the Theory of Planned Behavior to investigate the relationship between undergraduate accounting students' knowledge of internal control and cyber security and how it impacts their decision-making and behavior. The study was carried out on Indonesian students, and the data was analyzed by regression analysis. The results showed that students' attitudes and perceived behavioral control positively influenced their intention to practice cybersecurity. However, the effect of subjective norms and education on cyber security was insignificant. The four variables studied accounted for 41.2% of the total variance in behavioral intentions. At the same time, other factors contributed to the remaining variance. The findings suggest a need to assess the effectiveness of current cyber security education practices to achieve educational goals better. The negligible impact of the educational variable highlights the necessity of re-evaluating the current approach to cybersecurity education.
Learning by Successful or Unsuccessful Experiences?

Humans learn from both successful and unsuccessful experiences, because useful information about how to solve complex problems can be gleaned not only from success but also from failure. In this paper, we propose a method for investigating this difference by applying Preference based Inverse Reinforcement Learning to Double Transition Models built from replays of StarCraft II. Our method provides two advantages: (1) the ability to identify integrated reward distributions from computational models composed of multiple experiences, and (2) the ability to discern differences between learning by successes and failures. Our experimental results demonstrate that reward distributions are shaped depending on the trajectories utilized to build models. Reward distributions based on successful episodes were skewed to the left, while those based on unsuccessful episodes were skewed to the right. Furthermore, we found that players with symmetric triple reward distributions had a high probability of winning the game.
Toward Developing Attention-Based End-To-End Automatic Speech Recognition

In recent decades, significant research has been conducted in the field of Automatic Speech Recognition (ASR). Machine learning in the field of Natural Language Processing (NLP) is an innovative area of research and has become a popular topic for researchers. This paper aims to examine various Automatic Speech Recognition systems developed over the past decades. Antiquated ASR systems consist of acoustic model, language model, pronunciation model, Weighted Finite-State Transducer (WFST)-based decoder, and text-normalizer. These components are separately trained and assembled. We will discuss various end-to-end (E2E) structures such as Connectionist Temporal Classification (CTC), Recurrent Neural Network-Transducer (RNN-T), and the attention-based model. We will also explore some of the modern attention-based architectures such as Jasper, Transformer, and Conformer model.
The Impact of Virtual Reality on Online Education

Virtual reality technology was proposed and developed by Mr. Jaron Lanier, founder of VPL in the United States, in 1984, which is the first popular virtual reality device in the world. However, due to equipment, functions, etc., the technology has been in a slow development stage. Especially when the epidemic breaks out, many students were forced to study online at home, at which time, software such as classic Zoom burst out one after another, but students are isolated from each other and only face a computer when they have classes at home. Therefore, students cannot timely communicate with teachers about their doubts in the class, at the same time, teachers cannot get feedback from students in time. For this reason, this paper makes a specific analysis of the impact of virtual reality technology, on online education through using eye tracking technology to judge whether the virtual reality classroom is conducive to students' learning by comparing students' concentration and immersion. This study finds that there are still some shortcomings in virtual reality education, and it is not feasible to completely replace offline education. However, as an auxiliary tool for reality education, it is currently the main development direction. The significance of this study lies in discovering the advantages and disadvantages of current virtual reality education and providing direction for the development of virtual reality education in the future.
Advanced file carving: ontology, models and methods

File carving techniques are important in the field of digital forensics. At the same time, the rapid growth in the amount and types of data requires the development of file carving methods in terms of capabilities, accuracy, and computational efficiency. However, most of the methods are developed to solve specific tasks and are based on a certain set of assumptions and a priori knowledge about the files to be recovered. There is a lack of research that systematizes methods and structures approaches to identify gaps and determine perspective directions for development, considering the latest advances in information technology and artificial intelligence. The subject matter of this article is the structure, factors, efficiency criteria, methods, and tools of file carving, as well as the current state and tendencies of development of file carving methods. The goal of this study is to systematize knowledge about advanced file carving methods and identify perspective directions for their development. The tasks to be solved are as follows: to identify the main stages of file carving and analyze approaches to their implementation; to build an ontological scheme of file carving; and to identify perspective directions for the development of carving methods. The methods used were literature review, systematization, and summarization. The obtained results are as follows. An ontological scheme for the file carving concept is constructed. The scheme includes the principles, properties, phases, techniques, evaluation criteria, tools used, and factors influencing file carving. The features, limitations, and fields of application of the data recovery methods are provided. It was established that the most widespread approach to file reconstruction is still a manually detailed analysis of the internal structure of files and/or their contents, identifying specific patterns that allow reassembling the sequence of data fragments in the correct order. However, most of the methods do not provide one hundred percent guaranteed results. This article analyzes the current state and prospects of using artificial intelligence methods in the field of digital forensics, particularly for identifying data blocks, clustering, and reconstructing files, as well as restoring the contents of media files with damaged or lost headers. The necessity of having priori information about the file structure or content for successfully carving fragmented data is determined. Conclusions. The scientific novelty of the obtained results is as follows: for the first time, advanced file carving methods are systematized and analyzed by directions of development and the perspectives of using artificial intelligence for identifying data blocks, clustering, and file content restoration; for the first time, an ontological scheme of file carving is constructed, which can be used as a roadmap for developing new advanced systems in the digital forensics field.
Ky Fan norm application for video segmentation

This article presents results of applying the KyFan norm in the context of solving the problem of video segmentation. Since the task of video analysis can be considered as analysis of the sequence of images, it was decided to find a way to formalize the description of the video frame using the mathematical apparatus of non-square matrices. When choosing a method, particular attention was paid precisely to universality with respect to the dimension of the initial data due to the technical characteristics and nature of the video data -video frames are matrices of arbitrary dimension. The ability to skip the step of matrix transformation to square dimension, or vectorization using some descriptor allows you to reduce computational costsrequired for this transformation. It was decided to use the value of the Ky Fan norm as an image descriptor, since it is built on top of matrix singular values. As it is known, singular values are calculated during the singular decomposition of the matrix and can be used, among other features, to reduce the dimension of the source data. A singular decomposition does not impose restrictions on either the dimension or the character of the elements of the original matrix. In addition, it can be used to derive other matrix decompositions with required characteristics. A comparative analysis of the effectiveness of the obtained descriptor was carried out in the case of using the k-norm and 1-norm, which showed that the 1-norm allows us to identify the most significant changes in the scene, while k -norm is able to detect minor. In other words, depending on the context of the source video data and the scope of the developed application, it is possible to configure the sensitivity of the application to a change in the scene by varying thenumber of singular values involved. The decision about the presence of changes in the context of video scene is made based on a comparison of descriptors of two consecutive images, that is, the values of the Ky Fan norm.
IMPLEMENTATIONOFARBITRARYBITNESSPERMUTATIONS IN ONE OF THE CLASSES OF LINEAR STRUCTURES

Speed of transformation and simplicity of implementation are one of the key contributors in permutation researches. The paper reviews the implementation of arbitrary bitness permutation in the field of computer engineering on one of the classes of combination structures of linear complexity from the number of variables –one-dimensional cascades of structural units.The fact that the reflectionformed by the specified linear structure is completely the same asthe reflectionof the corresponding Mealyfinite state machine as a prototype of the structural module of the cascadeis used.This allowed us to explore the properties of structural units and the cascade as a whole in the context of the concepts of the theory of digital automata.The implementation of arbitrary bitnesspermutationsis based on usage of the connected graphs for state table and on usage of unique combinations without repeats for each row of output table. The purpose of this permutation is to convert large volumes of data in fastand simplewayusing hardware or software with the ability to be used in multiple areas of researches. The study of providing the bijectivity of the reflection and the equivalence analysis of permutations was performed. The algorithm of constructionof finite-statemachines for implementationof direct and invertedpermutationsis shown, as well as examples ofstateand outputtablesconstruction. Examplesofhardware implementationusingfield-programmable gate arraysare given. The sizeof stateand output tables for the software implementation is estimated. The number of unique bijective reflections and amount of key information for the investigated permutation in cryptographic transformations has been estimated. The theoretical speed of transformations of the bijective reflection is estimated for both field-programmable gate arrays and software implementation according to the modern indicators of types of computing devices memory speed. The practicalverification of processingspeed is made with software implementation. Areas of application of the investigatedarbitrary bitnesspermutation are proposed.
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval

We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., 'a list of ice cream shops in San Diego'). In the past, such queries were considered to be tasks that could only be solved via web-search or knowledge bases. More recently, large language models (LLMs) have demonstrated initial emergent abilities in this task. However, many current retrieval benchmarks are either saturated or do not measure constraint satisfaction. Motivated by rising concerns around factual incorrectness and hallucinations of LLMs, we present KITAB, a new dataset for measuring constraint satisfaction abilities of language models. KITAB consists of book-related data across more than 600 authors and 13,000 queries, and also offers an associated dynamic data collection and constraint verification approach for acquiring similar test data for other authors. Our extended experiments on GPT4 and GPT3.5 characterize and decouple common failure modes across dimensions such as information popularity, constraint types, and context availability. Results show that in the absence of context, models exhibit severe limitations as measured by irrelevant information, factual errors, and incompleteness, many of which exacerbate as information popularity decreases. While context availability mitigates irrelevant information, it is not helpful for satisfying constraints, identifying fundamental barriers to constraint satisfaction. We open source our contributions to foster further research on improving constraint satisfaction abilities of future models.
Attribute-based Access Control for Secure Firmware Over-The-Air Updates in Vehicles

The vulnerability of modern connected vehicles stems from the fact that firmware updates are frequently downloaded wirelessly. By taking advantage of flaws in the update procedure, malicious actors can compromise vehicle systems and put the lives of drivers and passengers at risk. To secure communications between vehicles, this research details a method for employing Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Secure and adaptable data sharing in decentralized settings is made possible by CP-ABE, a cryptographic technique. The goals of this study is to improve the state of the art for vehicular communication systems with regards to key revocation, attribute revocation, and efficient encryption and decryption performance. The proposed method can be broken down into three stages: publishing updates, choosing updates, and retrieving updates. The paper describes CP-ABE and discusses how it can be used for in-car communication. Current CP-ABE-based access control and location privacy schemes for vehicular communication are also discussed. The proposed methodology employs optimized algorithms and techniques to solve the problems of key revocation, attribute revocation, and efficient encryption and decryption performance. Furthermore, for the evaluation of the effectiveness of the proposed method the AVISPA simulation tool is used that verify the correctness of the proposed scheme, proving its resilience to attacks.
Bullyproof Sentinel: Fortifying Online Spaces Against Bullying using Machine Learning

In the current social media landscape, cyberbullying and online harassment remain pressing issues. Therefore, we planned a cyberbullying detection system for social media platforms. It can protect online communities from toxic behaviors and make a global impact by fostering a safer and more constructive online environment. We are mainly focused on developing an improved technique to detect bot profiles on social media, developing a mechanism to detect abusive content and sarcasm, developing a mechanism to detect defamatory comments on social media, and developing an effective method for detecting offensive and abusive content in image-format reviews on social media using machine learning algorithms.
The effect of flipbook-based digital books on elementary school students’ interest in learning

In school, interest plays a crucial role in students' learning activities because when students have a high interest in learning, it contributes to their happiness. This study aimed to examine the impact of flipbooks on students' learning interests in elementary schools. The research employed a pre-experimental design known as the One-Group Pretest-Posttest Design. This research was conducted at two schools in Indonesia, that is, State Elementary School 1 Tumenggungan Lamongan and Mulyoagung I State Elementary School Tuban. The data collection technique employed was a questionnaire, and the data analysis was conducted using the Paired Samples T-Test. The findings revealed that students' interest in learning was already good before using flipbooks. However, their interest significantly improved to a very good level after incorporating the use of flipbooks. These results indicate a strong and significant relationship between using flipbooks and students' learning interests. Keywords: Digital book; flipbook; Interest in learning; learning; students