Reviewerly/exHarmony huggingface dataset:
The integration of mobile learning in STEM-PjBL for Physics learning: a systematic literature review

Abstract Mobile learning (m-learning) has emerged as a promising educational approach that utilizes mobile devices to enhance learning experiences. In the field of STEM education, the integration of m-learning with PjBL as means to foster student engagement and facilitate deep learning. This Systematic Literature Review (SLR) aims to examine the existing research on integrating m-learning in STEM-PjBL specifically focused on physics learning. The research method used is an SLR collected 214 articles and then selected 50 articles based on themes about m-learning integration, STEM-PjBL, and physics learning from scientific international literature indexed by Google Scholar, Scopus, Research Gate, Elsevier, ERIC, Springer, Taylor &amp; Francis Group. The synthesis of existing studies informs about 1) the effectiveness of integration m-learning in STEM for kinematics, magnetism, and other physics topics; 2) the benefits obtained are knowing the media for learning, increasing students’ motivation, HOTS, problem-solving skills, and science process skills; and 3) the challenges of technological development in education are very fast and associated with this instructional approach. With 18 articles or 36% of the entire article, devoted to the quantitative method, those three primary topics are the device/m-learning, PjBL, and learning models. Based on the findings of this literature review, m-learning materials for physics instruction, m-learning that is integrated with different learning models, and STEM-PjBL implementation for improving students’ 21st-century skills.
Mathematical Foundation for Data-Centric AI

Mathematics provide better tools in order to understand and analyze the underlying data. In data analysis, mathematics is crucial. Some of the fundamental quantitative ideas and methods that are applied in data analysis are listed below.
Optimized FPGA Implementation of an Artificial Neural Network Using a Single Neuron

Since its emergence in the early 1940s as a connectionist approximation of the functioning of neurons in the brain, artificial neural networks have undergone significant development. The trend of increasing complexity is steadily exponential and includes an ever-increasing variety of models. This is due on the one hand to the achievements in microelectronics, and on the other to the growing interest and development of the mathematical apparatus in the field of artificial intelligence. It can be assumed however that overcomplicating the structure of the artificial neural network is no guarantee of success. Following this reasoning, the paper proposes a continuation of the author’s previous research to create an optimized neural network designed for use on resource-constrained hardware. The new solution aims to present a design procedure for building neural networks using only a single hardware neuron by using context switching and time multiplexing by the aid of an FPGA device. This would lead to significant reduction in computational requirements and the possibility of creating small but very efficient artificial neural networks.
Revealing inherent quantum interference and entanglement of a Dirac particle

Abstract Although originally predicted in relativistic quantum mechanics, Zitterbewegung can also appear in some classical systems, which leads to the important question of whether Zitterbewegung of Dirac particles is underlain by a more fundamental and universal interference behavior without classical analogs. We here reveal such an interference pattern in phase space, which underlies but goes beyond Zitterbewegung , and whose nonclassicality is manifested by the negativity of the phase space quasiprobability distribution, and the associated pseudospin-momentum entanglement. We confirm this discovery by numerical simulation and an on-chip experiment, where a superconducting qubit and a quantized microwave field respectively emulate the internal and external degrees of freedom of a Dirac particle. The measured quasiprobability negativities agree well with the numerical simulation. Besides being of fundamental importance, the demonstrated nonclassical effects are useful in quantum technology.
Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data

Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional (FR) and non-functional (NFR). Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual identification of these classes is a timewaster, and it needs to be automated. Methodically, machine learning (ML) approaches are applied to address this problem. In this study, twenty ML algorithms, such as Naïve Bayes, Rotation Forests, Convolutional Neural Networks, and transformers such as BERT, were used to predict FR and NFR. Any ML algorithm requires a dataset for training. For this goal, we generated a unique Turkish dataset having collected the requirements from real-world software projects with 4600 samples. The generated Turkish dataset was used to assess the performance of the three groups of ML algorithms in terms of F-score and related statistical metrics. In particular, out of 20 ML algorithms, BERTurk was found to be the most successful algorithm for discriminating FR and NFR in terms of a 95% F-score metric. From the FR and NFR identification problem point of view, transformer algorithms show significantly better performances.
Computing Model for Real-Time Online Fraudulent Identification

Existing improvement within the communication over systems and e-commerce area have led to significant rise within the utilization of credit cards for all sorts of exchanges, counting those conducted online and indeed in conventional stores. But deceptive credit card communications have consistently expanded, causing commercial teachers to lose a parcel of cash on annually premise which diminishes the turn over. The creation of efficient extortion discovery procedures is basic to play down these misfortunes; however, doing so is troublesome because it is greatly uneven in nature since of the larger part of credit card datasets. Besides, utilizing conventional information mining calculations for credit card false uncovering is incapable owing to its engineering, which involves a settled mapping of factors from input sets with the yield set of vectors. Employing an Outfits of Neural Arrange (NN) classifiers and hybridized data re-sampling procedure, this research presents a conspiracy that's both compelling and productive for recognizing false utilization of credit cards. The gathering classifier is created utilizing Upgraded Bolster Vector Information Circle (ESVDS) and Stochastic Particle Swarm Optimization (SPSO) demonstrated as the fundamental learner within the cat boosting technique. By combining the SMOTE-Synthetic Minority Over-sampling Method with the Altered Closest Neighbor (ENN) method, the crossover re-sampling is fulfilled. Proposed show surpasses other calculations in tests utilizing information from Brazilian banks and UCSD-FICO. Since the issue of information error was unravelled employing a cross breed approach, making it more vigorous in distinguishing imperceptibly false exchanges.
Ontology and Machine Learning: A Two-Way Street to Improved Knowledge Representation and Algorithm Accuracy

The advent of machine learning has revolutionized how we interpret data and information. In this process, ontology is crucial since it defines the relationships between the data elements. On the other hand, machine learning may be helpful in ontology engineering by automatically generating ontologies from data. This paper explores how ontologies can be utilized in machine learning to improve algorithms’ accuracy and how machine learning can facilitate the ontology engineering process.
X-Space: Interaction design of extending mixed reality space from Web2D visualization

Mixed reality offers a larger visualization space and more intuitive means of interaction for data exploration, and many works have dedicated on combining 2D visualizations on screen with mixed reality. However, for each combination, we need to customize the implementation of the corresponding mixed reality 3D visualization. It is a challenge to simplify this development process and enable agile building of mixed reality 3D visualizations for 2D visualizations. In addition, many existing 2D visualizations do not provide interfaces oriented to immersive analytic, so how to extend the mixed reality 3D space from existing 2D visualizations is another challenge. This work presents an agile and flexible approach to interactively transfer visualizations from 2D screens to mixed reality 3D spaces. We designed an interactive process for spatial generation of mixed-reality 3D visualizations, defined a unified data transfer framework, integrated data deconstruction techniques for 2D visualizations, implemented interfaces to immersive visualization building tool-kits, and encapsulated these techniques into a tool named X-Space. We validated that the approach is feasible and effective through 2D visualization cases including scatter plots, stacked bar charts, and adjacency matrix. Finally we conducted expert interviews to discuss the usability and value of the method.
Academic Achievements and Tasks of the Korean Society for the Study of Moral Education

본 연구에서는 한국도덕교육학회의 창립 이후 현재까지의 학문적 성과를 밝히고 향후의 과제를 제시하고자 하였다. 한국도덕교육학회 연차학술대회(1993-2023) 주제와 성경재 강연회(2006-2023) 주제로부터 ‘성리학, 심성 함양, 메타프락시스’를 주요 주제로 선정하여 학술지 <도덕교육연구>에 수록된 총 622편의 논문 중에서 175편의 연관 논문을 확인하였다. 한국도덕교육학회는 초창기부터 도덕교육의 동양적 전통에 관한 학술적 관심을 지속적으로 추구하여 왔으며, 성리학의 교육이론에 관한 집중적 탐구는 한국도덕교육학회의 이론적 성과를 대표하고 있다. ‘메타프락시스’는 심성 함양을 위한 실천을 바로 그 실천에 의하여 설명하는 담론 형식을 가리키며, 성리학은 심성 함양 이론의 메타프락시스적 성격을 집약적으로 예시한다는 데에 그 중요한 의의가 있다. 한국도덕교육학회는 훌륭한 사람을 기르는 일을 가리키는 넓은 의미의 도덕교육에 관심을 두고 교육의 본질과 성격에 관한 이론적 해명을 위하여 노력해 왔다. 교육, 종교, 정치를 심성 함양의 실천이라는 포괄적 관점에서 해석하는 메타프락시스 3부론(metapraxis triology)과 그것이 담고 있는 매체 이론에 관한 연구는 한국도덕교육학회의 주요 향후 과제가 되고 있다.This study attempted to reveal the academic achievements of the Korean Society for the Study of Moral Education(KSSME) since its founding and to suggest major research tasks. Three key-words, 'Neo-Confucianism, cultivation of mind-nature, metapraxis' were selected as the main topics from the titles of KSSME's annual academic conferences (1993-2023) and the themes of Seongkeongjae's lecture conferences (2006-2023). As a result of the analysis, 175 articles were identified as related to those key-words, among 622 papers included in the volumes of the Journal of Moral Education(1993-2023) published by KSSME. KSSME has continuously pursued academic interest in the Eastern tradition of moral education since its early days, and its exploration of the educational theory of Neo-Confucianism is prominent. ‘Metapraxis’ refers to a form of discourse that explains the practice for cultivating mind-nature through the very practice itself, and significance of Neo-Confucianism lies in that it is an intensive example of educational theory as metapraxis. KSSME has been focused on moral education in a most broad sense, and has worked to provide theoretical explanations on the essential nature of education itself. As an academic task to be further developed by KSSME, it needs to pay attention to the metapraxis trilogy, which interprets education, religion, and politics from a comprehensive perspective of the practice for cultivating mind-nature, especially the media theory it contains.
Behavioural Analysis of Deaf and Mute People Using Gesture Detection

Deaf and mute people have unique communication and social challenges that make it hard to express their thoughts, needs, and ideas. Understanding people's behavior is more important to protect them and help them integrate into society. This study discusses the critical need for behavioral analysis on deaf and mute people and introduces the Automatic Behavioral Analysis Employing Gesture Detection Framework (ABA-GDF). Gesture detection technology has gained popularity recently. This emphasis may be due to its ability to overcome communication hurdles and illuminate nonverbal communication. Current methods have various challenges, including limited accuracy and adaptability. The ABA-GDF architecture comprises three phases: dataset collection, modeling, and deployment. The data collection technique includes hand signals used by deaf and quiet people. The material is then processed to partition and normalize the hand area for consistent analysis. During Modelling, feature descriptor attributes are developed to extract relevant motion information. A classifier learns and predicts using the feature vectors, enabling the framework to recognize and interpret motions and actions. Large-scale simulations of ABA-GDF showed promising results. The ABA-GDF framework achieved 92% gesture recognition accuracy on the dataset. The system's robustness is demonstrated by its capacity to understand non-verbal messages. The research showed a 15% reduction in false positives compared to earlier methods, demonstrating its real-world usefulness.
Determination of tin in geological materials using LA-ICP-MS: Seemingly simple analysis?

Laser ablation inductively coupled plasma mass spectrometry is a unique technique designated for in situ analysis of a wide variety of solid samples. Nowadays, LA-ICP-MS is almost routinely used to study chemical and isotopic composition of geological materials. This paper reviews our experience with the analysis of tin in geological samples and comprehensively summarizes the limitations that may lead to inaccurate and imprecise outcomes. Experimental results show the extent of cross-contamination given by the ablation of materials containing tenth, and tens of %m/m of Sn, and elucidate the impact of contamination on a background progress. Moreover, an analysis is also jeopardized by the occurrence of doubly charged ions of uranium as well as by the matrix effect amplified with operating conditions. Finally, the inhomogeneity of NIST SRM 614 is highlighted via 2D mapping especially within the rim area where tin content reaches up to 8 mg kg−1. Furthermore, a real quartz sample is subjected to LA-ICP-MS analysis to demonstrate the manifestation of discussed negative effects. The true tin content in quartz sample and NIST 614 is verified via GF-AAS. The result of NIST 614 bulk analysis is in a good agreement with GeoReM value.
A Critical Discussion of "Competency-Based Curriculum" Arguments: Focusing on Their Viewpoints of Knowledge

이 논문은 이른바 “역량 기반 교육과정”이 도입되는 데 주요한 역할을 했을 것이라고 생각되는 교육학 담론들―역량론―을 그것이 상정하고 있는 지식관에 비추어 비판적으로 논의하는 것을 목적으로 한다. 본론은 세 장으로 이루어진다. 첫째 장에서는 역량론자들이 지식 교육의 문제로 지적하고 있는 ‘무기력한 지식 교육’이란 무엇이며, ‘역량 교육’은 그것의 대안이 될 수 있는지를 분석한다. 연구자는 지식은 행위와 분리되어 있다는 역량론자들의 관점은 지식을 보는 올바른 관점이 아니라고 주장한다. 둘째 장에서는 앎의 종류, 곧 지식의 종류와 각각의 지식에 따른 ‘하는 것’의 종류를 아리스토텔레스를 참조하여 제시한다. 역량론에서는 지식 교육으로는 학생들에게 ‘하는 것’을 가르치지 못한다고 주장하지만, 연구자는 지식은 원래부터 그 주체의 행위를 위한 것이라고 주장한다. 본론의 셋째 장은 ‘역량’ 개념의 논리적 오류를 고찰하는 내용으로 이루어진다. 전통적으로 인간의 지적 능력을 가리키는 다른 개념들―기억력, 상상력, 창의력, 논리적 사고력 등―과 마찬가지로, ‘역량’이라는 개념 또한 형식도야설의 오류를 범할 위험이 있다. 결론에서 연구자는 교육은 그 자체가 지식을 가르치고 배우는 일로 이루어지며 그 점에서 교육의 문제는 올바른 지식 교육은 어떻게 할지의 문제로 접근해야 한다고 주장한다.The purpose of this study is to critically discuss the arguments that are thought to influence on the introduction of the so-called "competency-based curriculum", chiefly with regard to their viewpoints concerning knowledge. In chapter Ⅱ the author analyses the meaning of 'inert knowledge education' which the competency arguments mention, although they never clarify, as the main problem of existing knowledge education and the possibility of 'competency education' to the solution of the problem. The author argues that it is hard to be valid to see what one knows as something separate from what one does as the competency arguments see as it is. In chapter Ⅲ the author shows the kinds of knowledge and accordingly the kinds of act, with reference to Aristotelian conceptions. While the competency arguments say that knowledge education cannot teach students 'what they do', the author says that knowledge is by its nature meant to make people do the relevant act. The chapter Ⅳ consists of author's exposure of logical fallacies that the competency arguments commit. Just as other traditional concepts such as memory, imagination, creativity, logical thinking, etc., the concept of competency is also prone to the fallacy that the old formal discipline theory committed. Finally in conclusion the author argues that education is itself knowledge education and therefore problems of education ought to be approached as those of what is the right way to do knowledge education.
Difference-Making Conditionals and Connexivity

Abstract Today there is a wealth of fascinating studies of connexive logical systems. But sometimes it looks as if connexive logic is still in search of a convincing interpretation that explains in intuitive terms why the connexive principles should be valid. In this paper I argue that difference-making conditionals as presented in Rott ( Review of Symbolic Logic 15, 2022) offer one principled way of interpreting connexive principles. From a philosophical point of view, the idea of difference-making demands full, unrestricted connexivity, because neither logical truths nor contradictions or other absurdities can ever ‘make a difference’ (i.e., be relevantly connected) to anything. However, difference-making conditionals have so far been only partially connexive. I show how the existing analysis of difference-making conditionals can be reshaped to obtain full connexivity. The classical AGM belief revision model is replaced by a conceivability-limited revision model that serves as the semantic base for the analysis. The key point of the latter is that the agent should never accept any absurdities.
Signcryption-Based Encrypted Traffic Detection Scheme for Fast Establishing Secure Connections

The popularity of Internet traffic encryption has made encrypted traffic detection becoming a hot topic in the present academic community. Due to its fine-grained detection strategy that avoids the influence of traffic shaping, the encrypted payload detection method based on deep packet inspection (DPI) can obtain better detection results when compared to machine learning detection methods based on traffic features. Yet, existing DPI-based methods will cause non-negligible delays due to encryption in real-time scenarios. Signcryption can improve the delay in DPI-based detection scenarios. However, existing signcryption schemes fail to resolve the conflict between the correctness of detection results and message confidentiality. This paper proposes an encrypted traffic detection scheme based on signcryption that can effectively reduce latency. Furthermore, in the high concurrency scenario, the gateway is introduced to replace the client to complete the preprocessing protocol with the middle agent, avoiding the execution of preprocessing protocols between each client and the middle agent, thus reducing the latency caused by excessive calculation. In this paper, the algorithms of rule signcryption, preprocessing protocol, client processing packet, and traffic detection are designed for this scheme. The confidentiality and unforgeability of the scheme are analyzed by the random oracle model. Finally, through simulation experiments, our scheme significantly improves the delay problem in the preprocessing phase compared with the state-of-the-art methods.
Efficient Zero-Knowledge Arguments for Matrix Relations over Rings and Non-malleable Enhancement

Various matrix relations widely appeared in data-intensive private computations, as a result their efficient zero-knowledge proofs/ arguments are indispensable in such applications. In the first part of this paper, we concretely establish efficient zero-knowledge arguments for linear matrix relation $$\textbf{A} \textbf{U}=\textbf{B}$$ over the residue ring $$\textrm{Z}_m$$ with logarithmic message complexity. We take a direct, matrix-oriented (rather than vector-oriented in usual) approach to such establishments on basis of the elegant commitment scheme over finite ring recently established by Attema et al. (2022). The commit-and-proof protocol is public-coin and in c.r.s paradigm (c.r.s used only as the public-key of the commitment scheme), suitable for matrices in any size and significantly outperforms the protocols constructed in usual approach with smaller-sized c.r.s.(e.g., decreased by a factor of d where d is the extension degree of Galois ring and n is the order of the witness square), fewer rounds (decreased by a fraction $$>\log d / 2 \log n$$ ) and lower message complexity (e.g., number of ring elements decreased by a fraction $$>\log d / \log n$$ ) for large-size squares. The on-line computational complexities are almost the same in both approaches. In the second part, on basis of the simulation-sound tag-based trapdoor commitment scheme we establish a general compiler to transform any public coin proof/argument protocol into the one which is concurrently non-malleable with unchanged number of rounds, slightly increased message and computational complexity. Such enhanced protocols, e.g., the version compiled from the construction in the first part of this work, can run in parallel environment while keeping all their security properties, particularly resisting man-in-the-middle attacks.
Improved Key-Recovery Attacks Under Imperfect SCA Oracle for Lattice-Based KEMs

Plaintext-checking (PC) oracle-based side-channel-attacks (SCAs) is a major class of key recovery chosen-ciphertext SCAs on lattice-based key encapsulation mechanisms (KEMs). In practice, the PC oracle derived in SCAs is usually imperfect. In this paper, we propose an improved SCA for lattice-based KEMs with lower sample complexity (i.e. the required number of traces) when the PC oracle is imperfect. The basic idea of our method is to roughly recover the secret key and detect the erroneous position in the secret key, later correct the problematic entries with various known attacks for LWE (e.g., primal attack). We experimentally verified our method on Kyber512, and the reduction of sample complexity is significant. For example, it can reduce the sample complexity by $$54.06\%$$ when the accuracy is 0.890, for higher accuracy 0.950, the reduction of sample complexity is $$35.81\%$$ . We also estimate the concrete security of Kyber512 after the side-channel-attack for different accuracy levels, including the primal attack, dual attack, decoding approach and the hybrid attack.
Linicrypt in the Ideal Cipher Model

We extend the Linicrypt framework for characterizing hash function security as proposed by McQuoid, Swope, and Rosulek (TCC 2018) to support constructions in the ideal cipher model. In this setting, we give a characterization of collision- and second-preimage-resistance in terms of a linear-algebraic condition on Linicrypt programs, and present an efficient algorithm for determining whether a program satisfies the condition. As an application, we consider the case of the block cipher-based hash functions proposed by Preneel, Govaerts, and Vandewall (Crypto 1993), and show that the semantic analysis of PGV given by Black et. al. (J. Crypto. 2010) can be captured as a special case of our characterization.
Management Mathematics: The Audacity of BOPE

Abstract Accepted by: Aris Syntetos Can mathematical research from a business school professor that is published in academic journals also substantially improve actual industrial practice in global supply chains? Yes. Can mathematics and operations management help improve fairness in access to organ transplants? Yes, again. Can mathematics developed for business applications facilitate the development of new types of quantum hardware? Also, yes. I have three main messages in this invited essay: (1) free yourself from self-imposed constraints and explore a wider range of new problems and arcane mathematics; (2) be bold and imaginative in developing novel solutions and (3) implement your ideas in practice, scaling them with a channel partner.