Reviewerly/exHarmony huggingface dataset:
Investigation of starting conditions in generative processes for the design of engineering structures

Engineering design has traditionally involved human engineers manually creating and iterating on designs based on their expertise and knowledge. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are used to ex-plore a much larger design space that may not have ever been considered by human engineers. However, for complex systems, the designer is often required to start the EvoDevo process with an initial design (seed) which the development process will optimise. The question is: will a good starting seed yield a good set of design solutions for the given problem? This paper considers this question and suggests that sub-optimal seeds can provide, up to certain limits, better design solutions than relatively more optimal seeds. In addition, this paper highlights the importance of designing the appropriate seed for the appropriate problem. In this paper, the problem analysed is the structural performance of a Warren Truss (bridge-like structure) under a single load. The main conclusion of this paper is that up to a limit sub-optimal seeds provide in general better sets of solutions than more optimal seeds. After this limit, the performance of sub-optimal seed starts to degrade as parts of the phenotype landscape become inaccessible.
Recoverable Active Protection Framework for Neural Network Models

To protect the intellectual property (IP) of neural network models, many active protection methods for models based on parameter obfuscation have been proposed. However, the obfuscated models obtained by parameter obfuscation may easily attract the attention of attackers and be maliciously tampered. When the parameters of obfuscated model are tampered, the obfuscated model cannot be used normally, even if the authorized user has the secret keys. In this paper, we propose a general framework of recoverable active protection for neural network models (FRANN), which can locate and recover tampered parameters, and thus allow authorized users to use the model normally. Specifically, a hash function and the reference-sharing mechanism are first employed to generate hash-bits and recovery-bits, which can be utilized to locate and recover tampered parameters, respectively. Then, important parameters of the model are selected and obfuscated to obtain an obfuscated model. Experimental results show that our framework is suitable for parameter obfuscation-based model active protection methods, which ensures that authorized users can recover the tampered model with the secret keys as long as the model suffers a tampering rate not greater than 0.45.
Motion-Based Convolutional Neural Networks for Super-Resolution from Compressed Videos

In this paper, an improved super-resolution method is proposed for compressed videos, which integrates the multi-image super-resolution (MISR) method into standard video decoders such as H.26x and AV1 to employ the reference frame (RF) and motion vector (MV) information from video decoder to gain better super-resolution efficiency and performance. The proposed motion vector-based multi-image super-resolution (MVSR) is trained by the convolutional neural networks (CNN) model, which consists of a series of convolution layers and is learned to identify the relationship of image features between the ground truth high-resolution (HR) and super-resolution (SR), and generates the SR frames that could improve the quality of the final output frame. The experimental results have shown that the proposed MVSR is capable of enhancing the SR quality over 1dB in terms of peak-to-noise ratio (PSNR) when compared to other similar approaches.
Anonymity on the internet versus identification of offenders

More and more people choose to act anonymously on the Internet. This fact generates many benefits, for example, it encourages the expression of critical opinions. On the other hand, there is also a “dark side” to this phenomenon. Specifically, perpetrators can opt to commit crimes on the Internet anonymously. This article argues that anonymity on the Internet should not be an obstacle to finding out the truth and, respectively, to the administration of justice. So, Internet privacy must yield when the imperative of identifying offenders and reparations to victims is at stake. However, this must be done with safeguards in mind, not arbitrarily.
Efforts to Improve the Quality of Schools Based on the Analysis of Educational Reports

The objective of this study was to examine school quality report cards in order to enhance the standard of education. This study employs descriptive qualitative methodologies, which involve analysing data or documentation, doing observations, and conducting interviews. Data collecting methods encompassed interviews, observation, and documentation. The participants in this study included the principal, vice principal, school treasurer, three teachers, and five studentss, who provided information. In addition, the employed data analysis technique is comprised of data reduction, data collecting, data display, and conclusion. The findings of this study suggest that the implementation of school report cards or education report cards can enhance the overall quality of schools. The indicators on the quality report card that can enhance the quality of school education include the quality and pertinence of student learning outcomes, which are categorised as satisfactory and progressing in relation to students' reading skills, numeracy abilities, and character dimensions. Consequently, there is a positive correlation between pupils' proficiency in literacy and the overall quality of education, including numeracy and character development. The school's quality is directly proportional to the pupils' level of numeracy skills and character. Schools must consistently aim for high-quality student learning outcomes, necessitating changes in all areas that contribute to student achievement.
High-Quality Evaluation for Invisible Watermarking Based on Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD)

. In this research, we propose an innovative approach that integrates Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) to enhance the quality and security of digital images. The purpose of this technique is to embed imperceptible watermarks into images, preserving their integrity and authenticity. The integration of DCT allows for an efficient transformation of image data into frequency components, forming the basis for embedding watermarks that are nearly invisible to the human eye. In this context, SVD offers an advantage by separating singular values and corresponding vectors, facilitating a more sophisticated watermarking process. The quality evaluation using metrics such as MSE, PSNR, UQI, and MSSIM demonstrates the effectiveness of this approach. Low average MSE values, ranging from 0.0058 to 0.0064, indicate minimal distortion in the watermarked images. Additionally, high PSNR values, ranging from 67.20 dB to 67.22 dB, affirm the high image quality achieved after watermarking. These results validate that the integration of DCT and SVD provides a high level of security while maintaining optimal visual quality in digital images. This approach is highly relevant and effective in addressing the challenges of image protection in this digital era.
Formant Analysis in Native and Non-Native Speakers

This research wants to find out how the voice formants of people who speak English as their first language differ from the voices of people who speak English as a second language. This research uses a computer program called Praat to help with the research. The people to be studied are those who grew up speaking English and those who learned it later in life. The samples in this research were taken 1 male voice and 1 female voice non-native speaker and 2 native speaker voice sample Male and Female. The findings of the analysis show that there are not many differences between native and non-native speakers' pronunciations of each monophthong, especially when they relate to the monophthong /ə/, which is difficult for non-native speakers to pronounce correctly.
Enhancing Recommender Systems through Hybrid Fusion of SVD/SVD++ and k-Nearest Neighbors

In this paper, we propose an innovative hybrid recommender system that combines the strengths of both SVD and kNN methodologies to construct a comprehensive movie recommendation system. The SVD algorithm is designed to capture latent factors and unveil user preferences, while kNN leverages similarity between user interactions and past likes to provide suggestions. Our novel approach involves merging the outputs of the SVD and kNN models using a weighted sum. These weights are learned through optimization, enabling us to offer personalized recommendations that deliver impressive performance. Experimental results using a movie dataset showcase the superiority of our proposed method over individual SVD and kNN models, especially concerning accuracy and diversity of recommendations. This underscores the advantages and effectiveness of fusing these two techniques: collaborative filtering and K-Nearest Neighbors.
Enhanced Password Security through Multi-Split Encryption and Recovery

This paper introduces a novel approach to password management using Shamir’s Secret Sharing scheme. The methodology involves generating and distributing private key shares, enhancing password security by requiring multiple shares to reconstruct the original key. The system ensures secure password storage through encryption and facilitates robust password resets. By leveraging Shamir’s Secret Sharing, compromised entities cannot breach user privacy, offering a paradigm shift in digital trust. This paper explores implementation intricacies, security considerations, and the potential to reshape password security.
AI Based Human Computer Interactions for Specially-Abled

Artificial intelligence (AI) plays a crucial role in supporting people with special needs. People who are blind, deaf, or possibly disabled require ongoing extra care and attention when it comes to education because they have difficulties with everything from keeping track of attendance to completing tests. They cannot use computers for their needs; hence, they are dependent on someone. The use of a computer requires physical contact or interaction, such as using a mouse, keyboard, or touch screen. We have created a touch less sketching interface utilizing the pattern recognition method to overcome these problems. In pattern recognition, machine learning algorithms are used to automatically identify patterns and regularities in data that is given as words, pictures, sounds, or other recognizable components. Others include a virtual mouse that may be operated with the eyeball for those who are unable to move their hands. To quantify eye movement, common eye tracking algorithms analyze the separation between infrared LEDs projected onto the eye and the pupil of the eye. The mediapipe framework, the tensorflow model, and the openCV library are required for these two algorithms, which enable blind individuals to hear news, music, and other information without having to browse the internet.
Contextual Transaction Verification System

The project introduces an innovative contextual-based verification system tailored for payment solutions. The system operates by capturing and analyzing payment transaction data, in real-time, to facilitate the seamless execution or termination of transactions. Key parameters under scrutiny encompass location data, device specifics, transaction history, user conduct, environmental factors, biometric data, user demographics, transaction contexts, and user interaction patterns. The user’s device serves as a pivotal data acquisition tool within this framework. Furthermore, the system’s deployment is versatile, accommodating cloud environments such as AWS and Azure, or alternatively, dedicated server configurations. This system exemplifies a novel approach to enhancing the security and efficiency of payment transactions through multifaceted contextual analysis.
Implementing Convolutional Neural Networks in Low-Resolution Consumer Imaging Devices for Eye-Gaze Forecasting

For developing technological consumer electronic systems like monitoring systems for drivers and cutting-edge interfaces for users, precise and effective eye gaze estimation is essential. In this process, the eye gaze is located in every frame image or video stream. In this research, a CNN model has been proposed to classify eye-gaze images into four classes. The Eye Gaze dataset has been employed to train the model. The model has been tuned twice by employing Adam and RMSprop optimizer. With both the optimizers the model has outperformed in the classification task. The model has shown a very good accuracy of 96% and a validation accuracy of 90% with Adam Optimizer. The technology in this study can be utilized as an innovative approach in the field of autonomous vehicles and in healthcare by utilizing this approach for medically retarded persons to know their gestures by their eye-gaze.
Translator Stylistics: A Corpus Analysis of Two English Translations of Traditional Chinese Medicine Classic Da Sheng Bian

Da Sheng Bian, a significant work on obstetrics and gynecology that emerged in the early Qing Dynasty, was initially published as “A Treatise on Midwifery” in 1842 by William Lockhart, a British missionary to China. In 1894, John G. Kerr, an American missionary, translated the text as “The Tat Shang Pin”. This paper conducts a comparative study of the two English translations using a self-constructed English-Chinese parallel corpus of Da Sheng Bian. The study explores the translation styles of the two translators by examining the token-types ratio and frequency at the lexical level, mean sentence length at the syntactic level, and the use of conjunctions at the discourse level. The observed differences in translation styles between the two translations are analyzed in relation to the translators’ backgrounds and translation strategies.
Cryptography: Recent Advances and Research Perspectives

Cryptography is considered as a branch of both mathematics and computer science, and it is related closely to information security. This chapter explores the earliest known cryptographic methods, including the scytale, Caesar cipher, substitution ciphers, and transposition ciphers. Also, explains the evolution of these methods over time. The development of symmetric and asymmetric key cryptography, hash functions, and digital signatures is also discussed. The chapter highlights major historical events and technological advancements that have driven the need for stronger and more efficient encryption methods. In addition, the chapter explores the potential for integrating artificial intelligence tools with cryptographic algorithms and the future of encryption technology.
A Road Map for the Development of Teaching Material on Scandinavian Prosody

In two recent Nordplus projects, we have been working on how to teach Scandinavian prosody.One aim was to familiarize young speakers of Danish, Swedish and Norwegian with the speech melodies of their "neighboring languages" since they have increasingly less contact with the other Scandinavian languages and the mutual comprehensibility of the neighboring languages is decreasing.Another, related aim was to find a way to teach prosody to secondlanguage learners of Scandinavian languages.In this paper, we present what we see as a road map to the development of comprehensive teaching materials for Scandinavian prosody: the aspects that need to be addressed and the resources we believe are needed for the development of a teaching material that will appeal to both language teachers and language learners.
Mind Maps: An Alternative to Improve Quality and Communication During the Requirements Engineering Process

This work-in-progress paper presents a proposal for eliciting and specifying software requirements using the technique of mind maps. The objective of the study was to investigate whether the application of mind maps improves the understanding and documentation of user and system requirements. To achieve this objective, a case study was conducted in an industry with the participation of students from a Computer Science course, where students had the opportunity to observe the requirements gathering process for a real software. The students were organized into groups, with two experienced requirements engineering professors acting as mentors. Evaluation of the study included a questionnaire to assess the satisfaction level of the key and indirect users involved in the research and the average satisfaction score was very positive, indicating a high degree of satisfaction with the employed mind mapping technique. Additionally, a comprehensive evaluation was conducted to gather qualitative and quantitative data on the students' learning experience, including their opinions on the case study. The results revealed a high level of overall satisfaction among the students, with a majority expressing appreciation for the case study approach and its positive impact on their skills development in requirements engineering. Our findings highlight the benefits of incorporating mind maps in software engineering education, particularly in enhancing communication, comprehension, and collaboration among stakeholders. The study's case study approach, combined with industry engagement, provided students with practical experience and valuable insights into real-world requirements engineering challenges.
VALIDITY AND EFFECTIVENESS OF DEVELOPING PROCEDURE TEXT ITEMS USING WORDWALL LEARNING MEDIA FOR GRADE VII STUDENTS OF 12TH STATE JUNIOR HIGH SCHOOL IN MAKASSAR

The validity and effectiveness of the development of procedural text questions using Wordwall learning media in grade VII students at SMP Negeri 12 Makassar is a research aimed at determining the validity and effectiveness of the use of Wordwall media as an evaluation media in the form of questions in procedural text learning for grade VII students of SMP Negeri 12 Makassar, specifically referring to the independent curriculum. The research is a research and development (R&amp;D) using quantitative descriptive analysis method. The 4D model is used, which includes the stages of define, design, development, and disseminate. The subjects of the study are Indonesian language material experts, media experts, students, and educators using questionnaires and evaluation sheets. The data collection techniques used in this study are observation, interviews, and questionnaires. The sample determination in this study uses the Slovin model. The results of this study indicate that the implementation of procedural text questions on Wordwall learning media is considered valid by validators selected by the researcher and has met the qualifications to assess learning media, stating that 96% of the questions in this media are very feasible, while the media assessment is also very feasible with a percentage of 100%, so it is recommended to be used in the evaluation process of Indonesian language learning, especially in the procedural text sub-material for grade VII students of SMP Negeri 12 Makassar from various aspects. The effectiveness of the media is assessed based on the scores obtained by students on the questions provided in Wordwall media, which can be answered with an average score of 17 out of 19 questions or 89% of students get a satisfactory score, so it is considered very effective. In addition, the Worwall learning media is considered very interesting with a percentage of 92.7% so it can be used as an alternative learning media. The 4D model used has been carried out and fulfilled as a whole
Bwjoin: A Blockwise GPU-based Algorithm for Set Similarity Joins

Set similarity joins play a pivotal role in diverse fields, ranging from modern database management systems to near-duplicate detection and even galaxy cluster analysis in cosmology. However, due to their quadratic nature, these operations have been associated with substantial computational costs. To tackle this challenge, parallel solutions have been developed in recent years, spanning algorithms for distributed and shared memory architectures, as well as massively parallel systems like GPU accelerators. In this paper, we propose a new GPU-based algorithm, using the prefix-filtering technique, that harnesses the power of blockwise parallelism, achieving better performance than its competitors, especially for high threshold similarity joins in big datasets.
A Simplified Variant for Graph Convolutional Network Based Knowledge Graph Completion Model

Knowledge graphs (KGs) serve as useful resources for various applications including machine learning, data mining, and artificial intelligence. Knowledge Graph Completion (KGC) aims at reasoning over known (observed) facts and infer the missing (unobserved) relationships, to improve KG's coverage. Many recent works have laid stress on leveraging Graph Convolutional Networks (GCNs) for KGC task, which obtain good representation learning ability for graph-structured data, and have achieved satisfactory results. However, GCN-based researches continue to improve these lines of models by introducing an increasingly complex feature space. Especially, the hyper-parameter space of conventional GCN-based KGC model is too huge, resulting in low computational efficiency and excessive storage pressure. To overcome this problem, this paper investigates a simplified and generic variant for GCN-based KGC model, and shows that simple changes in the architecture of the underlying architecture (with a Subtraction Operator and an Addition Operator), can compte with state-of-the-art GCN-based models. Therefore, this paper proposes a easy-to-accomplish plug-in unit, which could be loaded into any current GCN-based KGC architectures, and help them maximize the maintenance of native model's original performance while significantly reducing parameter scale.
A data encryption scheme combined symmetric encryption algorithm with asymmetric encryption algorithm

In order to explore the research of computer network data security encryption technology. The author proposes a data encryption scheme that combines symmetric encryption algorithms with asymmetric encryption algorithms, by combining AES algorithm with RSA algorithm and ECC algorithm, improved hybrid encryption algorithms AES-RSA and AES-ECC are obtained. In order to verify the correctness of the scheme, a Hadoop experimental platform was built and tested using Hadoop Eclipse, the time required for different algorithms to encrypt the same size of data was measured. Through the analysis of the obtained experimental data and the safety of the experimental plan, the experimental results indicate that, the AES-RSA algorithm takes slightly longer to encrypt the same data than the AES-ECC algorithm, this is because in these two hybrid encryption algorithms, the RSA algorithm defaults to using a 1024 bit key, while the ECC algorithm selects a key length of 160 bits. A 160 bit ECC key can provide security equivalent to a 1024 bit RSA key, so based on the advantage of small key bits, under the same security strength, the AES-ECC algorithm has higher encryption efficiency than the AES-RSA algorithm. It has been proven that the improved encryption algorithm has the advantages of efficiency and high security, and can achieve secure storage of data in cloud computing environments.