Reviewerly/exHarmony huggingface dataset:
Application of Graph Theory in the Analysis of Query Similarity and Complexity in Relational Databases

Information Systems constant changes and refactoring processes eventually result in design debts, one of them being related to the database management. Redundancy and complexity are often found in databases and eventually affect the overall system performance. In this study, the analysis conducted was based on graph analysis, which is an essential technique in several fields demanding data management. The investigation involved the study of relationships and connections in a realworld financial organization database, between SQL queries represented as nodes and edges in a graph structure. By analyzing the graph structure and properties, it was possible to identify important nodes, detect clusters of related data, and uncover hidden relationships and redundancy. The results indicate that 50% of the database queries had medium to high similarity in subgraphs, which allows the organization to gain valuable insights into their data, make informed decisions, and optimize their database performance.
Isolated Words Speech Recognition System for Brahvi Language using Recurrent Neural Network

Speech recognition is an interdisciplinary field drawing from computer science, linguistics, and computer engineering. Many modern devices have speech recognition functions in them to allow for easier. The demand for speech recognition in new languages corresponds to cultural preservation, accessibility, market growth, research, enhanced efficiency, innovation, and empowerment, rendering it a significant pursuit within the realm of language technology. Machine learning-based speech-to-text conversion has advanced significantly in the modern era. Brahvi, one of the most spoken languages in Balochistan, is used by approximately 4 million people in Pakistan. In this paper, a cutting-edge model for converting isolated Brahvi words from speech to text using recurrent neural networks (RNN) is proposed. Firstly, we introduced processing techniques and algorithms that can be applied to the speech recognition of single words in the Brahvi language. Seventy regularly used Brahvi language words, encompassing a variety of dialects and voice frequencies, were collected to create a pioneering dataset for this language. The specific RNN model proposed for word speech recognition and its validation on a data set in which the proposed model demonstrates 91 % accuracy.
Research on Gradient Selection Grid Data Federation Learning System Based on Top-K Multi-Secret Sharing

The Top-K asymptotic combined with secret sharing to design a federated learning protocol, which can protect users' privacy and reduce communication costs. First, the algorithm uses the Top-K gradient selection algorithm to filter the required input gradients to reduce the number of required input gradients. A candidate quantization scheme and index fusion algorithm suitable for multiple boundaries are designed. The noisy data is distributed to multiple servers using a key-sharing mechanism. While ensuring the security of information transmission, balancing multiple servers' configurations maximizes the entire network's operational efficiency. The configuration of multiple servers maximizes the operational efficiency of the entire network. The experimental results show that the method studied in this project ensures the confidentiality of user information and improves its accuracy and execution speed.
Design And Implementation Of A Block Chain Enabled E-Voting With IoT

This study presents a comprehensive five-year analysis of a pioneering Blockchain-enabled E-Voting system, augmented with Internet of Things (IoT) integration. The research focused on evaluating the system's performance, security measures, user experience, and societal impact over the extended period. The results reveal a commendable average voter turnout of 82%, signifying a sustained level of civic engagement and trust in the electronic voting process. The implementation of advanced security measures, including encryption protocols and regular penetration testing, led to a substantial reduction in security incidents, thereby safeguarding the integrity of votes. Furthermore, the continuous enhancements in the user interface and the incorporation of enhanced accessibility features resulted in a consistent increase in user satisfaction ratings. This demonstrates the system's dedication to inclusivity, providing an intuitive platform for citizens of diverse backgrounds and abilities. The integration of Blockchain and IoT technologies proved to be a powerful synergy, enhancing the transparency, security, and accessibility of the e-voting system. The system exhibited resilience against potential threats and maintained robust performance throughout the five-year study period. This research underscores the potential transformative impact of Blockchain-enabled E-Voting systems with IoT integration on democratic processes worldwide. It provides a secure and transparent platform for citizens to exercise their voting rights, ultimately cultivating trust and confidence in electronic voting systems..
Non-Autoregressive Speech Synthesis by Fusion of CoordConv and Sound Quality

Non-autoregressive text-to-speech (TTS) models, such as FastSpeech, have demonstrated the capability to synthesize speech at significantly faster speeds compared to previous autoregressive models, all while maintaining comparable quality. The key to effectively training the FastSpeech model lies in leveraging an autoregressive teacher model for duration prediction, thereby enriching the input with additional information. In non-autoregressive speech synthesis models, speech generation occurs in parallel and simultaneously, resulting in fast generation speed. However, due to the lack of dependency between preceding and following frames, the generated speech is slightly less natural compared to autoregressive speech synthesis models. This paper proposes an enhancement to the classic end-to-end non-autoregressive speech synthesis framework that does not rely on the teacher model. This paper proposes an enhancement to the classic end-to-end non-autoregressive speech synthesis framework to improve the naturalness of the generated speech. The proposed improvements involve two specific measures. Firstly, sound quality features of the input unit are estimated and utilized as additional input along with the text. This approach aims to provide the model with information regarding the desired sound quality, thereby enhancing the naturalness of the synthesized speech. Secondly, to improve the model's ability to perceive position, including frame number and frequency band, the generated Mel map can be more effectively decoded through the utilization of CoordConv, which replaces the standard convolution in the CBHG module. The introduction of CoordConv helps enhance the decoding process performed by the vocoder. Our experimental evaluation on the LJSpeech dataset demonstrates that the utilization of the proposed method leads to enhancements in MOS scores, speaker similarity indicators, and significantly improves the naturalness of synthesized speech
BigFive: A Chinese Textual Dataset Supporting Psychology Knowledge Graph Construction

Obtaining the personalities of users conveyed by their published short texts has a wide and important range of applications, from detecting abnormal behavior of online users to accurately customization recommendation. Advancement in this area can be improved using large-scale datasets with coarse- and fine-grained typologies, adaptable to multiple downstream tasks, i.e., psychology knowledge graph construction and intelligence collection of psychological questionaries. Although the LIWC lexicon helps personality prediction, the larger volume of textual dataset related to Big Five personality types still cannot meet the requirements of researches and applications, especially for tasks in Chinese. Therefore, this paper introduces BigFive, a large, high quality Chinese textual dataset manually annotated by the psychological experts. BigFive contains 13,478 Chinese phrases that belong to five categories (coarse-grained) and 30 categories (fine-grained). The reliability of five categories grouped by personality level and 30 categories grouped by dimension level is demonstrated via a detailed data analysis. In addition, a strong baseline is build based on a fine-tuning BERT model. Our BERT-based model achieves an average F1-score of. 33 (std=.24) in terms of 30 categories and an average F1-score of. 66 (std=.05) in terms of five categories. The experimental results suggest that there is much room for improvement.
Investigations on Scientific Literature Meta Information Extraction Using Large Language Models

The meta information in scientific literature including article title, author, institutions, year, journal, etc., plays a critical role in providing useful information to research peers. Traditional meta information extraction methods usually rely on rules and templates. Recently, due to the booming of Large Language Models (LLMs), its application in scientific literature meta-information extraction has drawn more and more attention. This paper aims to explore and evaluate the effects of meta information extraction for scientific literature using large language models. First, datasets consisting of the publications in given academic areas are built for the experiments. Then, the task definition and evaluation metric (i.e., accuracy rate) are described. Various large language models as well as the traditional methodology are used in experiments to execute the task of meta information extraction. The results are analyzed and compared among the use of various LLMs.
GAFM: Learning the Weights of Feature Interaction via Graph Attentional Factorization Machine

Click-through rate prediction, which refers to predicting the probability of a user clicking on an ad or item based on input features, is critical in the development of recommendation systems. Traditional machine learning algorithms are less effective, while deep learning-based models that capture higher-order feature interactions can yield better results. However, these models ignore the capture of feature dependencies. In this work, we propose a new model, GAFM, which is based on a graph attention network, trained to obtain attention scores between features and other features. Then updates the features to capture feature dependencies flexibly on the one hand, and to filter out features with higher reusability for input into the Factorization Machines for feature interaction on the other. Our method undergoes rigorous evaluation on four real-world datasets, conclusively demonstrating its ability to boost CTR prediction accuracy. By considering feature dependencies and selecting pertinent features for interaction modeling, GAFM emerges as a promising approach to enhance recommendation system performance in CTR prediction. Additionally, this research advances deep learning techniques in recommendation systems and sheds light on the significance of feature dependencies and selection for precise predictive outcomes.
Implementation of C4.5 and Support Vector Machine (SVM) Algorithm for Classification of Coronary Heart Disease

Coronary Heart Disease (CHD) is a chronic disease that is not contagious and can cause heart attacks. This makes CHD one of the diseases that cause the highest mortality globally. CHD can be caused by the main factor, namely an unhealthy lifestyle, so that in an effort to identify and deal with CHD, many studies have been conducted, one of which is the use of information technology. With so many CHD patient data, data mining can be used using. classification methods include C4.5 algorithm and Support Vector Machine (NBC). The C4.5 algorithm is a decision tree-like algorithm that groups attribute values into classes so that it resembles a tree, while SVM is an algorithm that separates data with a hyperplane. This study aims to classify the CHD dataset by comparing the C4.5 and SVM algorithms. So that the best accuracy value for this data is produced, namely the SVM algorithm of 64.51% and followed by the C4.5 algorithm of 64.30%.
KOMPARASI ALGORITMA DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MENENTUKAN KUALITAS UDARA DI PROVINSI DKI JAKARTA

Air quality in DKI Jakarta Province refers to the state and cleanliness of the air in the area at a given time. The type and concentration of air pollutants are among the indicators used to assess air quality. DKI Jakarta's air pollution is severe, causing respiratory irritation, respiratory illnesses, and long-term health issues such as cardiovascular disease and lung cancer. Air pollution can also harm the environment by limiting visibility and harming ecosystems. The problem with the research is that no appropriate and relevant features for predicting air quality were used. The goal of this study is to identify and compare algorithms with the highest accuracy between decision trees. In determining air quality, naive Bayes and k-nearest neighbor are used. According to the findings of the K-5fold evaluation process performed with the RapidMiner tool, the accuracy of the Decision Tree algorithm was 95.89%, the accuracy of the Nave Bayes algorithm was 93.15%, and the accuracy of the K-NN algorithm was 91.78%. Based on these findings, the decision tree method has the greatest or best accuracy when compared to the Nave Bayes and K-NN algorithms.
Application of Adaptive Neighborhood Denoising Algorithm in Image Enhancement

As the basis of image processing, image denoising has always been a research hotspot in the field of computer vision. The purpose of image denoising is to suppress noise from the distorted image and restore the image as much as possible, so that the restored surround image is closer to the original image in quality. Aiming at the Neigh Shrink method used in wavelet image denoising, this paper proposes an effective image denoising algorithm to protect the image edge. Mainly improving the fixed neighborhood range in the Neighbor Shrink method, adaptively segmenting the image into different neighborhoods for denoising based on its own properties; And further combining the correlation within the wavelet layer, fixed windows were added to each irregular neighborhood, and coefficients with geometric distances closer and within the same irregular neighborhood were selected to improve the Neigh Shrink method. We studied image enhancement based on wavelet transform, first analyzed the basic methods of image enhancement, and then put image enhancement into the wavelet domain for research. We proposed a new adaptive image enhancement algorithm using wavelet transform, and verified its feasibility and superiority in the environment.
PERANCANGAN SISTEM PREDIKSI KEBUTUHAN STOK DARAH BAGI ORGANISASI NON PROFIT MENGGUNAKAN METODE FUZZY MAMDANI

A non-profit organization is a nonprofit organization formed with the aim of providing assistance, and it does not seek profit in its activities. In addition to organizing blood donation campaigns, non-profit organizations often help hospitals in need of blood by providing the necessary blood supply through the Indonesian Red Cross (PMI). However, the problem faced is that the availability of blood stock is often depleted during urgent situations, resulting in the inability to fulfill the hospitals' blood requests. Therefore, as a solution to address the challenges faced by non-profit organizations, the development of an information system for predicting blood stock requirements is necessary to determine whether there is a need to replenish the blood stock in the following months. In this research, the developed blood stock prediction system implements the Fuzzy Mamdani method. The research results indicate that the Fuzzy Mamdani method, in predicting blood stock requirements, has achieved a satisfactory level of accuracy. Although a limited number of rules, specifically 24 rules, were processed, this method was able to provide predictions that closely approximated the actual requirements. However, it should be noted that the accuracy level can be further improved by adding more comprehensive rules and using a wider dataset.
S2TGAN: A GAN-Based Mobile Video Streaming Generation Model for Stalling Detection

Due to the complex and dynamic nature of the wireless network environment, data-driven model often requires a significant amount and diversity of training data, leading to a challenging difficulty of stalling detection with high performance, especially at the client side. To address this issue, a novel Generative Adversarial Network (GAN) based data generation model Seq2Seq-TCN GAN (S2TGAN) is proposed in this paper for both chunk-based and stream-based video streaming sequences in order to further improve the performance of stalling detection of video on demand (VoD) and live video streaming (LVS). Resorting to Seq2Seq, the generator is constructed to generate video streaming sequences under the control of stalling information, and the discriminator adopts Temporal Convolutional Network (TCN) to extract more discriminative temporal features of video streaming sequences. Meanwhile, Iman-Conover method is used to incorporate domain knowledge to guide the generation of video streaming and improve the fidelity of synthetic data. A LVS dataset collected from HuYa and DouYu is carefully prepared to verify the generation performance of proposed model, as well as a public RequetDataSet. Extensive experimental results indicate that the proposed model can outperform the other state-of-the-arts in terms of maximum mean discrepancy (MMD) and Train on synthetic, Test on Real (TSTR) evaluation methods of stalling detection.
Online Learning Preparation of College Students in Higher Vocational Education

Information technology has led to changes in traditional teaching concepts and approaches. In order to meet the new generation's pursuit of new learning forms, university teaching reform is imminent, and blended teaching, which combines the advantages of traditional teaching and online teaching, has undoubtedly become the first choice for university teaching reform in the era of technological innovation. In the face of rapid reform, how to cope with it is the primary issue for teachers and students in universities to consider. This study adopts research methods such as the questionnaire survey method and literature research method to investigate the readiness of teachers and students to promote blended teaching reform in a university in Shanxi Province. Firstly, the relevant literature of the last ten years was combed and analyzed to shed light on the research ideas of blended teaching readiness, and then it was decided to measure the readiness of teachers and students through questionnaires, teacher-student interviews, and classroom observations. In the early stages of the research practice, some teachers and students were interviewed to understand the educational background of the study participants, and a questionnaire was developed to investigate the readiness of teachers and students for blended teaching, which was divided into two parts: a teacher questionnaire and a student questionnaire. At the same time, we went into the teaching classroom to examine the real teaching situation in the field and record the real responses of teachers and students in the classroom. Next, the data obtained from the questionnaires, interviews, and observations were collected, collated, and analyzed in detail. Finally, the findings of this study are discussed in the context of the questionnaire, interview and observation results, and research implications are drawn. The study found that teachers were competent and relatively well prepared for the requirements of blended learning, but the actual implementation was less optimistic due to practical factors such as the difficulty of the subject, lack of time, and technical difficulties, and some teachers continued to teach using traditional lecture methods. Students generally lack self-awareness and have poor study management and self-discipline. First-year students were significantly more competent in all areas and more motivated to learn than students in other years. Reflections and analysis of the study findings yielded some insights, (1) Explore appropriate teaching styles based on the nature of the subject; (2) Concentrating on teachers' and students' lack of time; (3) Providing technical assistance; (4) Determining appropriate teaching styles for students.
Moderating Effect of Dominant Logic on the Relationship between Innovation Capability and SMEs Performance in Abuja

Performance of SMEs in today's dynamic business environment is crucial for the development of any economy. However, there are still obstacles hindering the expected optimal performance of the sector. The objective of this study therefore, is to examine the moderating role of dominant logic on the relationship between product innovation, marketing innovation and firm performance among SMEs in Three local governments (Abaji, AMAC and Kuje) in Abuja. A sample of 235 questionnaires was analyzed using Structural Equation Modelling. The result indicated that, in the direct relationship, Marketing Innovation and Dominant Logic have significant effect on SMEs performance, while the direct relationship between Product Innovation and Performance was found not to be significant. It was further found that Dominant Logic significantly moderates the relationship between Product Innovation and Firm Performance. On the other hand, Dominant Logic does not significantly moderate the relationship between Marketing Innovation and SMEs performance. The study therefore recommends that businesses should ensure managerial cognition are well defined and communicated at all levels, so as to improve product innovation. Also, there is need to emphasize the idea behind new trends so as to come up with a befitting marketing innovation that makes their business stand out by delivering quality service in the most cost effective manner which will in turn enhance their performance.
What Factors Affect College Students' Learning Satisfaction? - A Research on Online Learning During Covid-19 in Zhumadian Vocational and Technical College

During the 2020 epidemic in China, online teaching was promoted, but fewer studies have investigated the learning satisfaction of college students who have been taught online on a large scale for an extended period of time. The factors that affect learning satisfaction mainly include students' basic attributes, learning resources, learning platforms and learning interactions. According to the results of the questionnaire survey, course content, course resources, platform technology, platform design, teacher-student interaction, student-student interaction, and interaction mode are the main factors affecting students' basic attributes. The survey results also showed that course content and interaction mode significantly affect students' learning satisfaction. Based on these findings, teachers should enrich course content to provide students with more practical, rich, and vivid teaching experiences. Additionally, teachers should pay close attention to their communication with students, offer timely guidance to students' learning, and provide feedback to any questions they ask.