Reviewerly/exHarmony huggingface dataset:
A Study on Role of Artificial Intelligence in Education

Artificial intelligence (AI) has revolutionized the field of education, leveraging advancements in computing and information processing technologies. The application of AI in education (AIED) has brought forth a multitude of possibilities, opportunities, and challenges, profoundly impacting educational practices across various domains. Within the realm of learning, instruction, and administration, AI has gained considerable acceptance and utilization in educational institutions. Educators have leveraged the AI tools to enhance the quality of instructional methods and streamline administrative tasks. With AI-powered systems, tasks such as evaluating and grading learner assignments can be accomplished more efficiently and expeditiously. Moreover, the use of cognitive computing and customization has facilitated the individualization and tailoring of syllabus and materials to meet the unique needs of learners. This personalized approach has fostered increased absorption, engagement, and an enriched learning environment for students overall. The impact of AI in education extends beyond traditional boundaries, permeating multiple areas of educational practice. From personalized learning experiences to efficient administrative processes, AI has revolutionized how knowledge is disseminated and acquired. As AI continues to advance, it holds immense potential for further innovation and transformation within the education sector, propelling us into a new era of enhanced educational outcomes and opportunities for students worldwide. This paper provides the information about intelligent education tools, examines the transformative impact of AI in diverse areas of education, and addresses the challenges associated with its implementation. After examining the existing literature on impact of AI in different areas of education, future research directions are also recommended in this paper.
Practice of Scientific Research Feedback Teaching in the Course of "Valve Testing Technology" in Higher Vocational Education

Teaching and research complement each other in the process of talent cultivation in universities. Conducting research feedback teaching for the core course of valve design and manufacturing, "Valve Testing Technology," integrates the research content and achievements of new weld non-destructive testing technology into the teaching of this course, which is beneficial for students to understand the forefront of the profession and master the course content. It can not only enhance students' engineering practice ability, but also cultivate their professional innovation and team awareness.
A novel phase-shifting segmented phase coding method for absolute phase retrieval

The phase-coding method is widely used during 3D measurement for attaining absolute phase retrieval. However, traditional the phase-coding method still suffers from low accuracy and insufficient reliability. To address these issues, a phase-shifting segmented phase coding method was proposed in this work for absolute phase retrieval in complex object measurements, which specifically improved upon the segmented method of phase coding. Coding patterns with different phase-shifting were obtained by embedding the codeword into the phase-shifting phase. These patterns were also combined and arranged to create novel segmented coding patterns. In addition, this segmented encoding can be self-connected based on its different phase-shifting information to obtain the complete fringe order, achieving thus absolute phase retrieval of over 36 codewords. To solve the problem of the periodic misalignment of the wrapped phase and fringe order, the complementary phase coding method was encoded by the bidirectional coding method, and the separation method of the bidirectional coding was improved. From experimental results, it was demonstrated that the proposed method has high measurement accuracy and reliability.
Explainable AI for the Olive Oil Industry

Understanding Machine Learning results for the quality assessment of olive oil is hard for non-ML experts or olive oil producers. This paper introduces an approach for interpreting such results by combining techniques of image recognition with knowledge representation and reasoning. The Design Science Research strategy was followed for the creation of the approach. We analyzed the ML results of fluorescence spectroscopy and industry-specific characteristics in olive oil quality assessment. This resulted in the creation of a domain-specific knowledge graph enriched by object recognition and image classification results. The approach enables automatic reasoning and offers explanations about fluorescence image results and, more generally, about the olive oil quality. Producers can trace quality attributes and evaluation criteria, which synergizes computer vision and knowledge graph technologies. This approach provides an applicable foundation for industries relying on fluorescence spectroscopy and AI for quality assurance. Further research on image data processing and on end-to-end automation is necessary for the practical implementation of the approach.
Report on the exact methods for finding minimum-sized DFA

Abstract This paper presents four state-of-art methods for the finite-state automaton inference based on a sample of labeled strings. The first algorithm is Exbar, and the next three are mathematical models based on ASP, SAT and SMT theories. The potentiality of using multiprocessor computers in the context of automata inference was our research’s primary goal. In a series of experiments, we showed that our parallelization of the exbar algorithm is the best choice when a multiprocessor system is available. Furthermore, we obtained a superlinear speedup for some of the prepared datasets, achieving almost a 5-fold speedup on the median, using 12 and 24 processes.
The Wigner-Vlasov formalism for time-dependent quantum oscillator

Abstract This paper presents a comprehensive investigation of the problem of a harmonic oscillator with time-depending frequencies in the framework of the Vlasov theory and the Wigner function apparatus for quantum systems in the phase space. A new method is proposed to find an exact solution of this problem using a relation of the Vlasov equation chain with the Schrödinger equation and with the Moyal equation for the Wigner function. A method of averaging the energy function over the Wigner function in the phase space can be used to obtain time-dependent energy spectrum for a quantum system. The Vlasov equation solution can be represented in the form of characteristics satisfying the Hill equation. A particular case of the Hill equation, namely the Mathieu equation with unstable solutions, has been considered in details. An analysis of the dynamics of an unstable quantum system shows that the phase space square bounded with the Wigner function level line conserves in time, but the phase space square bounded with the energy function line increases. In this case the Vlasov equation characteristic is situated on the crosspoint of the Wigner function level line and the energy function line. This crosspoint moves in time with a trajectory that represents the unstable system dynamics. Each such trajectory has its own energy, and the averaging of these energies by the Wigner function results in time-dependent discreet energy spectrum for the whole system. An explicit expression has been obtained for the Wigner function of the 4th rank in the generalized phase space <?CDATA $\left\{x,p,\dot{p},\ddot{p}\right\}.$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close="}" open="{" separators=""> <mml:mrow> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:mi>p</mml:mi> <mml:mo>,</mml:mo> <mml:mover accent="true"> <mml:mi>p</mml:mi> <mml:mo>̇</mml:mo> </mml:mover> <mml:mo>,</mml:mo> <mml:mover accent="true"> <mml:mi>p</mml:mi> <mml:mo>̈</mml:mo> </mml:mover> </mml:mrow> </mml:mfenced> <mml:mo>.</mml:mo> </mml:math>
Geomagnetic reference map super-resolution using convolutional neural network

Abstract Geomagnetic matching navigation technology, as an important assisted navigation method, has gradually been wildly used in the navigation field, in which the quality of geomagnetic reference map is of great significance on improving the guidance and positioning accuracy. However, the existing geomagnetic reference map reconstruction methods can hardly satisfy the actual needs. Considering the popularity of deep learning methods in the image super-resolution (SR) field, a geomagnetic reference map SR method using a convolution neural network is proposed. First, rectangular harmonic analysis theory is used to construct the training dataset, which is based on data from the United States Geological Survey and the International Geomagnetic Reference Field. Then, a multi-channel convolutional neural network, which integrates all the three independent components in the geomagnetic reference field, is proposed of reference map SR reconstruction. Experimental results show the effectiveness of our proposed method over other state-of-the-art methods.
FedSiM: a similarity metric federal learning mechanism based on stimulus response method with Non-IID data

Abstract Federal learning based on parameter sharing under the assumption that the data obey independent identical distribution (IID has already achieved good results in areas such as fault diagnosis. Data collected by the decentralized devices often do not obey IID. However, when faced with the scenario of client data obeying Non-IID distribution, its diagnostic accuracy is usually weak. Based on this, we did an investigation on the mechanism causing this phenomenon and found that it was attributed to the weight shift of the network. Therefore, based on the stimulus response principle, we investigated the network similarity of federal clients under different data distributions and explain the reasons for the weight shift. Firstly, it was pointed out that there are differences in the regions where the network is activated when performing different classification tasks. Then, similarity metric federal learning (FedSiM) was proposed based on the principle that there are differences between the activated regions. Finally, experiments were designed on the Case Western Reserve University bearing failure dataset for different degrees of IID cases. The results show that FedSiM can improve the diagnostic accuracy by 15.8 percentage points in the case of Non-IID, and a few shared FedSiM methods to further improve the accuracy were also given.
Sphericity measurement based on telecentric imaging mechanism with image distortion correction and eccentricity error compensation

Abstract Sphericity is an essential geometric parameter for determining the processing manufacturing quality of spherical components. This study presents a sphericity measurement method based on telecentric imaging mechanism with image distortion correction and eccentricity error compensation. A sphericity measurement system is designed, and the projection images of the cross-section on the equatorial plane of the measured sphere are acquired using a line scan camera with bilateral telecentric lenses. An analytical camera model for this sphericity measurement system is established by considering major sources of image distortion. The subpixel edge points of the projection images are extracted and converted to cross-sectional profiles on the equatorial plane according to the camera model. An eccentricity error compensation strategy for contour matching of the obtained cross-sectional profile on the equatorial plane is also developed to obtain the three-dimensional contour point coordinates of the sphere. Sphericity is then calculated according to the least square criterion. The effectiveness of the proposed method is verified by measuring two steel balls with diameter in 20 mm and 30 mm. The sphericity measurement error is reduced by 0.3383 mm and 0.4653 mm respectively after image distortion correction and is reduced by 0.2268 mm and 0.0795 mm respectively after eccentricity error compensation. The proposed method can be extended to form error measurements for opaque components with rotary structure on the outer contour.
Sistem Informasi e-Tourism Ekowisata Hutan Mangrove Se-bagai Media Promosi Pariwisata Bali Berbasis Android

The province of Bali has many exotic and interesting tourist areas to be visited by domestic and foreign tourists. One example is Taman Raya Hutan (Mangrove) one of the ecotourism tourist destinations in the Province of Bali. Based on data from the Bali Provincial Statistics Center, tourist arrivals to Indonesia have decreased by 7.62% due to the COVID-19 pandemic. One of the impacts affected by the decrease is the object Mangrove ecotourism in Ngurah Rai Bali Tahura which is only able to attract 1,000 visits in 2020 based on the results of interviews that have been conducted. From the results of these problems, it is necessary to build an e-tourism information system as a means of providing information and as a forum for the promotion of mobile technology-based Grand Forest Park (Mangrove) tourism objects, which is an effort to develop a strategy to promote Ngurah Rai Grand Forest Park (Mangrove) ecotourism as an effort to restore the tourism industry. This information system contains text, images, audio, and video and is supported by an attractive and user-friendly display design. This system development method uses the Software Development Life Cycle which consists of the concept stage, application design, the testing stage. This system testing phase uses the whitebox testing method and a direct questionnaire survey with 10 respondents in the test, The results of testing this system get a value of 81.43% which indicates that the system is running well and is acceptable. Through the results of the test data, it is hoped that it can become an alternative promotional medium to increase the number of tourists or customers to visit the Ngurah Rai Bali mangrove forest.
Implementation of Al-Miftah Lil'ulum Method in Improving the Quality of Reading The Kitab Kuning on Students of Madrasah Tarbiyah Islamiyah Canduang

This research uses a qualitative descriptive with a case study type. Collecting data using interviews, observation, and documentation. In analyzing the data, this research uses the data analysis model "Miles and Huberman" which consists of three stages, namely data reduction, data presentation and drawing conclusions. In checking the validity of the data, researchers used methods, namely increasing the persistence of observations, using appropriate reference materials, and reading book tests. The process of implementing the Al-Miftah Lil 'Ulum Method at Madrasah Tarbiyah Islamiyah - Canduang, Agam Regency was carried out through 3 stages, namely preparation, implementation, and evaluation. Besides the stages carried out, there are several supports in the learning process as well as challenges (obstacles) in making it happen
An Efficient Storage Optimization Scheme for Blockchain Based on Hash Slot

Blockchain has developed tremendously in recent years with its own features such as anonymity and immutability. The advent of the big data era has caused the size of data on blockchain systems to increase exponentially, which leads to a great challenge to the storage capacity of nodes. Therefore, this paper proposes a storage optimization scheme based on hash slot algorithm to reduce the storage volume of nodes. We propose a model to evaluate the liveness of each block and adopt different storage strategies for blocks of different liveness to reduce the storage overhead of each node and the additional communication cost when querying ledger data. With this scheme, the ledger data can be distributed more reasonably on each node according to the storage capacity of each node by allocating the hash slots using the hash slot allocation algorithm we proposed. And experiment results show that our scheme can reduce the storage volume overhead of nodes by 78% and cause negligible additional time when accessing the ledger data.
A Relation Extraction Model for Enhancing Subject Features and Relational Attention

In existing relation extraction methods, there are often issues such as Error propagation or insufficient attention, which limits the improvement of extraction performance. To this end, this paper proposes a relation extraction model that enhances subject features and relational attention (SFRARE). SFRARE uses the pre-trained model for word embedding, while using the LSTM neural network to enhance the semantic features of the subject to reduce the impact of error propagation on extraction performance, and uses multi-head attention mechanism to make the model pay more attention to the relation related to the subject. The experimental results of the model are better than the baseline model CasRel on two English public datasets NYT and WebNLG, and also better than the baseline model in complex contexts. Finally, all the experimental results show that the proposed model SFRARE outperforms the current mainstream models on both Chinese and English public data sets, proving that the model has certain generalization ability and alleviates the problems of error propagation and insufficient attention to relations.
Vulnerability Detection Based on Unified Code Property Graph

As the number of source codes grows rapidly, detecting source code vulnerabilities in current software has become an important study. Most current deep learning-based vulnerability detection technologies treat source code as a sequence, which loses the source code's structural information, leading to many false positives in the detection results. We propose a novel source code vulnerability detection model, named UCPGVul, based on the Unified Code Property Graph (UCPG). A new graph representation, UCPG, is proposed to extract semantic features from the source code. By extracting features from UCPG, our proposed UCPGVul model can capture more vulnerability features. Experimental results on a publicly available dataset show that UCPGVul can achieve more accurate and stable detection results compared to five state-of-the-art methods.
An Analysis of the Rust Programming Practice for Memory Safety Assurance

Memory safety is a critical concern in software development, as related issues often lead to program crashes, vulnerabilities, and security breaches, leading to severe consequences for applications and systems. This paper provides a detailed analysis of how Rust effectively addresses memory safety concerns. The paper first introduces the concepts of ownership, reference and lifetime in Rust, highlighting how they contribute to ensuring memory safety. It then delves into an examination of common memory safety issues and how they manifest in popular programming languages. Rust's solutions to these issues are compared to those of other languages, emphasizing the benefits of using Rust for enhanced memory safety. In conclusion, this paper offers a comprehensive exploration of prevalent memory safety issues in programming and demonstrates how Rust effectively addresses them. With its encompassing mechanisms and strict rules, Rust proves to be a reliable choice for developers aiming to achieve enhanced memory safety in their programming endeavors.
Short Repudiable Ring Signature: Constant Size and Less Overhead

Ring signature, introduced by Rivest, allows the signer to make valid signatures without the knowledge of the signer. But unconditional anonymity also protects malicious users, so that the reputation of the group can be affected. Lin et al. proposed a rejectable signature scheme, which solved this problem well, but it has a drawback of increasing the signature's size with the number of ring members. To address the problem, a new scheme is proposed in combination with the existing short associated ring signature scheme. This proposed scheme utilizes a dynamic accumulator to accumulate public keys and knowledge signatures for rapid identity verification without revealing one's private key or identity. By doing so, in our scheme we can maintain a constant signature length and minimize the signature overhead. Additionally, in this paper we prove that the resulting signature is both unforgeable and anonymous while also being able to repudiate. Furthermore, related experiments are conducted to demonstrate the effectiveness of this approach. Experimental results show reduced time overhead and shorter signal length compared to theoretical calculations, reinforcing the feasibility of our scheme.
Secure Mutual Aid Service Scheme Based on Blockchain and Attribute-Based Encryption in Time Bank

Time bank is a new model intended to supplement traditional pension systems and alleviate social pension pressure. However, the existing time bank system only considers service demands from the elderly as public, ignoring the need for privacy protection. There is low public participation due to the lack of trust among users. To address these issues, we propose a scheme based on blockchain and attribute-based encryption in time bank, enabling fine-grained access control for shared service demands and promoting secure mutual aid service. We design a multi-authority authorization model to avoid the security implications of a central authority. In order to quickly respond to the secure mutual aid service, we design an outsourced decryption mechanism to reduce the computation overhead on the user side, making it more suitable for resource-constrained devices (e.g., cell phones, iPads). In addition, we introduce an anonymization mechanism to protect the identity privacy of users while regulating the circulation of time coins. Finally, the effectiveness of our proposed scheme is proved by relevant experiments.
A Suitability Assessment Framework for Medical Cell Images in Chromosome Analysis

The process of chromosome karyotype analysis is a highly time-consuming and error-prone task heavily relying on the experience of the cytogeneticists and influenced by factors such as fatigue and decrease of attention. Many efforts have dedicated to automatic chromosome karyotype analysis using various computer vision techniques based on geometric morphology and deep learning. However, few of them have paid attention to selections of high-suitability medical cell images for chromosome karyotype analysis. High-suitability cell images not only can significantly decrease the difficulty of manual chromosome karyotype analysis, but also can boost the analysis performance of automatic chromosome karyotype analysis algorithms. This paper proposes a suitability assessment framework for evaluating the suitabilities of cell images to address the issue of selecting high-suitability medical cell images for the inputs of chromosome karyotype analysis. The quantitative experimental results show that using the proposed suitability assessment framework to select suitable inputs can significantly boost chromosome segmentation performance by 5.06 percentage points of mAP, 2.4 percentage points of $$AP^{50}$$ , and 3.58 percentage points of $$AP^{75}$$ . The qualitative experiments with a group of cell images show that the corresponding suitability results evaluated by the proposed framework are highly in accordance with results evaluated by the experienced analysts, demonstrating the effectiveness of the proposed method to address the selection issue of suitable medical cell images.