Reviewerly/exHarmony huggingface dataset:
The Use of Lexical Approach in Enhancing Vocabulary Skills among Upper Primary Learners

This study explored the impact of a lexical approach on enhancing the vocabulary skills of upper primary pupils in an ESL classroom in Malaysia. The research involved 30 elementary school pupils in Johor and used a mixed-method research design. Data collection methods included pre and post-tests, an observational checklist, and a focus group interview. The results indicated a significant improvement in the students' vocabulary skills after implementing the lexical approach. Additionally, this approach positively affected students' motivation and changed their negative perceptions about vocabulary. The paper concludes with recommendations and implications for educators and researchers, considering its limitations.
Dynamic Security Aspects of Onion Routing

An anonymous communication network (ACN) is designed to protect the identities of two parties communicating through it, even if an adversary controls or observes parts of the network. Among the ACNs, Tor represents a practical trade-off between offering a reasonable level of anonymity and, simultaneously, an acceptable transmission delay. Due to its practical impact, there is abundant literature on the performance of Tor concerning both communication and security aspects. Recently, a static framework was suggested for evaluating and comparing, in a quantifiable way, the effect of different scenarios (attacks, defence mechanisms, and other protocol changes). Although a static model is useful, many scenarios involve parameters and stochastic variables that change or evolve over time, or that may be influenced by active and malicious adversaries. In this paper, we propose a dynamic framework for evaluating such scenarios. We identify several scenarios where this framework is applicable, and illustrate our framework by considering the guard node mechanism in Tor. We evaluate and compare variations on the guard node concept suggested in the literature with respect to relevant performance metrics and, using the framework, support our evaluation with a theoretical analysis.
Efficient Secure Two Party ECDSA

Distributing the Elliptic Curve Digital Signature Algorithm (ECDSA) has received increased attention in past years due to the wide range of applications that can benefit from this, particularly after the popularity that the blockchain technology has gained. Many schemes have been proposed in the literature to improve the efficiency of multiparty ECDSA. Most of these schemes either require heavy homomorphic encryption computation or multiple executions of a functionality that transforms Multiplicative shares to Additive shares (MtA). Xue et al. (CCS 2021) proposed a 2-party ECDSA protocol secure against malicious adversaries and only requires one execution of MtA, with an online phase that consists of only one party sending one field element to the other party with a computational overhead dominated by the verification step of the signature scheme. We propose a novel protocol, based on the assumption that the Computational Diffie-Hellman problem is hard, that offers the same online phase performance as the protocol of Xue et al., but improves the offline phase by reducing the computational cost by one elliptic curve multiplication and the communication cost by two field elements. To the best of our knowledge, our protocol offers the most efficient offline phase for a two-party ECDSA protocol with such an efficient online phase.
Identity-Based Threshold Signatures from Isogenies

The identity-based signature, initially introduced by Shamir [26], plays a fundamental role in the domain of identity-based cryptography. It offers the capability to generate a signature on a message, allowing any user to verify the authenticity of the signature using the signer’s identifier information (e.g., an email address), instead of relying on a public key stored in a digital certificate. Another significant concept in practical applications is the threshold signature, which serves as a valuable tool for distributing the signing authority. The notion of an identity-based threshold signature scheme pertains to the distribution of a secret key associated with a specific identity among multiple entities, rather than depending on a master secret key generated by a public key generator. This approach enables a qualified group of participants to jointly engage in the signing process. In this paper, we present two identity-based threshold signature schemes based on isogenies, each of which addresses a different aspect of security. The first scheme prioritizes efficiency but offers security with abort, while the second scheme focuses on robustness. Both schemes ensure active security in the quantum random oracle model. To build these identity-based threshold signatures, we begin by modifying the identity-based signature scheme proposed by Shaw and Dutta [27], to accommodate the CSI-SharK signature scheme. Subsequently, we leverage the resulting identity-based signature and build two threshold schemes within the CSIDH (Commutative Supersingular Isogeny Diffie-Hellman) framework. Our proposed identity-based threshold signatures are designed based on CSI-SharK and can be easily adapted with minimal adjustments to function with CSI-FiSh.
Feistel Ciphers Based on a Single Primitive

We consider Feistel ciphers instantiated with tweakable block ciphers (TBCs) and ideal ciphers (ICs). The indistinguishability security of the TBC-based Feistel cipher is known, and the indifferentiability security of the IC-based Feistel cipher is also known, where independently keyed TBCs and independent ICs are assumed. In this paper, we analyze the security of a single-keyed TBC-based Feistel cipher and a single IC-based Feistel cipher. We characterize the security depending on the number of rounds. More precisely, we cover the case of contracting Feistel ciphers that have $$d\ge 2$$ lines, and the results on Feistel ciphers are obtained as a special case by setting $$d=2$$ . Our indistinguishability security analysis shows that it is provably secure with $$d+1$$ rounds. Our indifferentiability result shows that, regardless of the number of rounds, it cannot be secure. Our attacks are a type of a slide attack, and we consider a structure that uses a round constant, which is a well-known counter measure against slide attacks. We show an indifferentiability attack for the case $$d=2$$ and 3 rounds.
Zero-Knowledge Systems from MPC-in-the-Head and Oblivious Transfer

Zero-knowledge proof or argument systems for generic NP statements (such as circuit satisfiability) have typically been instantiated with cryptographic commitment schemes; this implies that the security of the proof system (e.g., computational or statistical) depends on that of the chosen commitment scheme. The MPC-in-the-Head paradigm (Ishai et al., JoC 2009) uses the same approach to construct zero-knowledge systems from the simulated execution of secure multiparty computation protocols. This paper presents a novel method to construct zero-knowledge protocols which takes advantage of the unique properties of MPC-in-the-Head and replaces commitments with an oblivious transfer protocol. The security of the new construction is proven in the Universal Composability framework of security and suitable choices of oblivious transfer protocols are discussed together with their implications on the security properties and computational efficiency of the zero-knowledge system.
FopLAHD: Federated Optimization Using Locally Approximated Hessian Diagonal

Federated optimization or Federated learning (FL) is a novel decentralized machine learning algorithm, where a server model or global model is trained collaboratively without collecting local data from the sources or clients. However, the data heterogeneity across the ever-growing data sources affects the FL optimization in terms of slow convergence of the global model, which requires large number of communication rounds between the server and the clients to achieve a targeted performance from the global model. In this paper, we propose FopLAHD, that aims to decrease this communication rounds by accelerating global model convergence. To accelerate convergence, FopLAHD uses locally approximated Hessian diagonal vectors along with local gradients while finding the global update direction. Experimental results on highly heterogeneous data partitions with partial participation of the total clients show that FopLAHD outperforms existing state-of-the-art FL algorithms such that FedProx, SCAFFOLD, DONE and GIANT in terms of communication rounds while reaching to a certain precision of the global model convergence.
Sentiment Analysis of ChatGPT Tweets Using Transformer Algorithms

This study explores the application of the Transformer model in sentiment analysis of tweets generated by ChatGPT. We used a Kaggle dataset consisting of 217,623 instances labeled as "Good", "Bad", and "Neutral". The Transformer model demonstrated high accuracy (90%) in classifying sentiments, particularly predicting "Bad" tweets. However, it showed slightly lower performance for the "Good" and "Neutral" categories, indicating areas for future research and model refinement. Our findings contribute to the growing body of evidence supporting deep learning methods in sentiment analysis and underscore the potential of AI models like Transformers in handling complex natural language processing tasks. This study broadens the scope for AI applications in social media sentiment analysis.
Data Storage and Sharing Scheme Based on Hyperledger Fabric

As academic research becomes more and more international, close cooperation between different research institutions has become a key factor in promoting the development of science and technology. Research teams and institutions in different countries urgently need an effective and secure online platform for collaboration and data sharing. To solve the above problems, this paper proposes a scientific research data storage and sharing scheme based on Hyperledger Fabric, which combines blockchain technology with interplanetary file system and effectively relieves the storage pressure of blockchain network. The multi-channel architecture is designed to protect scientific research data based on physical isolation of different channel ledger data. In addition, through proxy re-encryption, a fine-grained permission control scheme for user data is designed and implemented. The data receiver can decrypt the ciphertext without obtaining the key of the sender. In this way, the private key and plaintext of the sender are not leaked during data sharing, which improves data privacy and achieves data sharing. Finally, Docker technology was used to build a test environment, and the system was tested for function and performance, which verified the feasibility of the scheme system.
Enhancing Cultural Education through Integrating Restoration and VR Storytelling Exploration

Redtory was a historical symbol that emerged from an old industrial site in Guangzhou and transformed into a contemporary creative park. However, it was unfortunately demolished in May 2019. The study aims to leverage the immersive and interactive advantages of VR technology to design a gamified storytelling experience while restoring the historical memory of Redtory. The project incorporates VST theory to design the interactive storyline. To achieve this, the VR platform covered various scenes, including the starting scene, factory buildings, historical square, and the creative corridor. Interactive elements such as landmarks and interactive art devices can enhance tourists’ curiosity and engagement throughout the tour. The findings of this study provide insights and potential directions for future research in creating immersive and educational experiences.
A Recommender System for Reinforcement Learning Based on Label Value Distribution

As a very important application scenario, the combination of reinforcement learning and recommendation system has been closely concerned by all sectors of society. In this paper, we propose a Contextual quantile regression reinforcement learning model by using value distribution reinforcement learning and label distribution learning methods, adopting nonlinear reinforcement learning methods and combining the personalized requirements of the recommendation system; And from the perspective of the system, the relevant characteristics of the improved model are analyzed and the overall design is carried out; Finally, the simulation experiment is carried out by simulating the real recommendation environment, and different models are used for comparative analysis. Through experimental research and comparative analysis, it can be seen that the accuracy, data utilization, robustness, model convergence speed and stability of the recommendation system with nonlinear expectation idea have been greatly improved.
Fourier-Convolutional PaDiMによる異常検知

Anomaly detection aims to detect unusual patterns and samples in a training distribution. In this domain, many researchers have paid attention to anomaly detection models using ImageNet-pretrained weights. Among them, PaDiM is a promising approach that detects anomalies based on the feature distribution. While such approaches have achieved significant results, they tend to overlook global information due to the texture bias caused by ImageNet-pretrained convolutional models. Therefore, in this paper, we propose incorporating Fast Fourier Convolution, which can extract global information in the frequency domain, into PaDiM. This proposed model is named Fourier-Convolutional PaDiM (FC-PaDiM). Our FC-PaDiM is able to extract global features from frequency space and local features from feature space for more accurate anomaly detection. In our experiments, we demonstrated that our proposed FC-PaDiM allowed for extracting local and global features compared to PaDiM. Moreover, our additional analysis revealed the robustness of perturbations in frequency bands in the MVTecAD dataset.
Sistem Informasi Manajemen Pegawai Berbasis Desktop (Studi Kasus: LPP RRI Jayapura)

Human Resources Management Information System (HRMIS) is a critical element in organizations or institutions to ensure the efficiency and effectiveness of employee data management. This research discusses the development of a desktop-based HRMIS at LPP RRI Jayapura to overcome challenges in employee data processing, such as the lack of user understanding of the existing system and issues in personnel administration. The research method used is the waterfall approach with steps including requirement analysis, system design, implementation, and testing. The research location focuses on the HR and General Affairs department of LPP RRI Jayapura. Data collection is done through observation and interviews with the Head of the Administrative Division. The results of the research include system design, database table structure, user interface, and black box testing. This system features the management of job positions, ranks, employees, spouses, children, promotion, mutation, and report generation. Black box testing indicates that the system functions well. The conclusion of this research is that the development of a desktop-based HRMIS can improve the efficiency and effectiveness of employee data management at LPP RRI Jayapura. This system is expected to provide solutions to existing challenges and enhance the performance of personnel management in the institution.
Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Abstract The purpose of this paper is to present a pedagogical model for mapping subject knowledge in the field of music education. The model includes two aspects: the extraction of entities and inter-entity relationships within the subject domain and the construction of a subject ontology. In the construction process of subject knowledge mapping, this paper proposes a remote supervised music subject knowledge extraction method based on a convolutional neural network combined with an attention mechanism, which realizes the extraction of entities in the music subject domain and the relations between corresponding entities. This paper proposes a keyword extraction method for obtaining the set of discipline concepts in ontology construction. To improve performance, this paper proposes a framework structure for knowledge fusion that includes three aspects: data preprocessing, similarity calculation, and knowledge fusion. As a result of the study, the teaching model can improve the performance of music majors on average by 13.3. In terms of self-efficacy, on average reached 3.58 (SD = 0.535), which is at a good level. The results demonstrated the effectiveness of the music teaching model based on subject knowledge mapping.
Research on Intelligent and Innovative Teaching Mode of Dance Discipline in Colleges and Universities under the Background of Informatization

Abstract In this paper, we first count the dance action motion information and compute different lengths of dance action videos through the threshold of segmentation as the input of a convolutional neural network. Combined with the two-layer level optimization function, the dance action trajectory values can be solved. The dance action trajectory values are compared with the standard action in terms of point distance, which suggests the location of the error for dance teaching assistance training. Finally, based on the three-dimensional data capture to establish a three-dimensional dance teaching model, the dance student’s various parts of the movement error results only between 0.25-2.21, the movement trajectory and the Kinect standard movement trajectory curve is highly fitted, and the dance amplitude, dance strength, dance consistency, dance standardization degree of performance is excellent.
Diversified Curriculum Innovation for Japanese Language Education in Colleges and Universities under the Deep Learning Model

Abstract This paper constructs a diversified Japanese language course recommendation model based on the autoencoder network model using deep learning, which includes stack autoencoder, self-attention mechanism encoder, and relevance decoder. A system for evaluating the quality of Japanese language education was constructed using hierarchical analysis. The correlation between the Japanese listening test and the degree of innovation of diversified Japanese language education courses and the influence of the innovation of diversified Japanese language education courses on students’ Japanese language performance were analyzed, respectively. The study showed that the correlation coefficient between the Japanese listening test score and the diversified Japanese language education program was 0.865, and the students who received the diversified Japanese language education program scored 7 points higher than the students who received the traditional Japanese language education program.
English Translation Stylistic Features and Syntax Translation with Application of Knowledge Mapping

Abstract Aiming at the problems of excessive instantiation complexity, poor interpretability and low generalization ability of knowledge reasoning techniques, this paper unites inductive logic programming ILP with HET neural network to construct a hybrid logic rule and neural network knowledge graph reasoning model - HETIL model. The ILP is utilized to quantify the first-order logic rules, and the multi-layer rule space is constructed by arranging and combining the rules through logic symbols. The rules are instantiated and fed into the HET network, and the attention coefficients aggregate the node features to complete the end-to-end training and generate the rule learning model. Finally, the validity of the model is verified by machine translation experiments, and the results show that the accuracy of the HETIL model in syntactic structure types is more than 0.8 overall. The accuracy in terms of phrase structure reaches 0.879. The average BLEU value of the HETIL model can reach 29.24, which is 1.51 BLEU points higher than the benchmark model. Therefore, the effect of English translation by applying a knowledge graph is better than traditional machine translation.
The Development Trend of Innovative Teaching of Dance in Colleges and Universities Based on Clustering Algorithm in the Context of Big Data

Abstract This paper establishes a model for teaching dance innovation in colleges and universities based on big data. Using a clustering algorithm to obtain the characteristics of dance teaching using affiliation assignment calculation to maximize the fuzzy indicators, the dance innovation teaching indicators are divided to improve the assessment accuracy. The optimization of dance teaching assessment indicators is normalized to complete the data processing and clustering in the teaching system and combined with the user preference to divide the student group based on similar characteristics. The results show that 45.00% of the student’s performance ability of physical balance is excellent, and the dance teaching classroom question score is 93. Big data technology can effectively integrate and categorize the existing dance teaching resources in colleges and universities, thus improving the quality of dance teaching.
Medical Image Segmentation Based on Clustering Feature Information Enhancement

Medical image segmentation provides reliable basis for clinical diagnosis and pathology research, and has important medical application value. However, it remains difficult to obtain a large number of accurate annotation data in medical image segmentation. Recently, some progress has been made in a study based on metric learning, which adopts self-supervised learning on prototype network. In this work, an adaptive local prototype pooling method was plugged into prototype network to solve the common challenge of foreground–background imbalance in medical image. However, this study ignored the influence of foreground–background imbalance and intra-class imbalance on query image, which would lead to intra-class differences in query features extracted from the query branch. Therefore, the differences of intra-class similarity score would be generated when calculating the similarity between the support prototypes and the query features. In order to solve the problem, we proposed a clustering feature information enhancement method that aims to fuse prior information obtained through clustering into the segmentation network. We optimized the similarity score through an information interaction module, and introduced Gaussian filter to smooth the clustering noises. We conducted experiments on the ABD-MRI dataset, Which is from ISBI 2019 Combined Healthy Abdominal Organ Segmentation Challenge. Our results demonstrate the effectiveness of fusing the clustering feature information, and the mean Dice coefficient has been improved by more than 5%.