Reviewerly/exHarmony huggingface dataset:
Research on Rockdb knob Configuration based on LightGBM and NSGA2

RocksDB is an exemplary database engine in terms of performance. However, its performance optimization necessitates manual adjustment of numerous intricate knob configuration, which poses challenges for database users. In order to address this predicament, this paper proposes an automated method for tuning RocksDB knob configuration. Firstly, we employ a neural network algorithm to extract pivotal features of the knob configuration. These features are acquired through profound analysis and learning from RocksDB, enabling precise capture of the parameters' impact on performance. Subsequently, we introduce the Non-dominated Sorting Genetic Algorithm II (NSGA2), a multi-objective evolutionary algorithm, to optimize the rotary configurations and select the most promising configuration scheme based on the maximized fitness values. This algorithm adeptly balances multiple performance metrics, thus discovering the optimal knob configuration scheme. Lastly, we construct an adaptive function model based on Light Gradient Boosting Machine (LightGBM) to evaluate and select the knob configuration schemes. To validate the efficacy of our approach, we conducted comparative experimental studies between our proposed method and three alternative approaches under six different workloads. The experimental results demonstrate the ability of our method to enhance performance across various workload scenarios. Through this research, we present an automated method for tuning RocksDB knob configuration, which bestows convenience and efficiency upon database users.
HealthBeat traceability platform based on blockchain technology

Data privacy entails safeguarding users' control over information access, while data accessibility aims to ensure unrestricted availability of information. Inevitably, conflicts between privacy and accessibility arise, particularly within the healthcare sector. To tackle this challenge, we propose in this paper leveraging blockchain technology. By utilizing a peer-to-peer distributed network with a secure and shared ledger, blockchain provides an excellent solution for tracking and tracing records effectively.
Tibetan Handwritten Character Generation Based on CGAN Model

Tibetan handwritten text recognition is very important in the digitized construction of Tibetan literature. However, training Tibetan handwritten text recognition network requires a large number of Tibetan handwritten datasets, and handwritten text image generation is one of the effective methods to solve this problem. However, the research of Tibetan handwritten text image generation based on deep learning is still in its infancy and the research needs further advancement. Based on the Tibetan handwritten character dataset and the two structures of conditional generative adversarial networks, this paper realizes the automatic generation of Tibetan handwritten character images, FID values reached 50.946 and 36.856, which verified the validity of the Tibetan handwritten text synthesis method.
YOLOv5 UAV Feature Detection with Swin-Transformer

Unmanned Aerial Vehicle (UAV) has the characteristics of "low, slow and small" and complex flight environment. Using existing target recognition algorithms to identify UAV faces high model complexity, large parameter amount, and is prone to missed detection and false detection. Therefore, this paper proposes a special object detection method of YOLOv5 UAV based on Swin-Transformer, namely ST-YOLO. ST-YOLO uses Swin Transformer to integrate into the skeleton network of this improved model and uses other improvements to improve the performance of the model on visual tasks such as object detection. Compared with the original network, ST-YOLO has the parameter amount reduced by 10.70%, the calculation amount reduced by 10.13%, the FPS is basically the same, and the mAP is improved by 3.83%. Therefore, the proposed ST-YOLO has better results in terms of object detection accuracy, calculation amount and model scale.
Exploring the Limits of Large Language Models for Word Definition Generation: A Comparative Analysis

In this paper, we explore the ability of large language models (LLMs) to generate word definitions for newly invented words in Spanish through the task of Unknown Definition Modeling. The main goal of our study is to determine the extent to which LLMs can abstract meaning from context and compare the performance of different models for this task. To conduct our analysis, we created a dataset of 20 made-up words, usage examples, and their definitions in Spanish. We then evaluated several LLMs, including OpenAI GPT-3.5-turbo, OpenAI GPT-3, and Google Flan-T5, using automatic evaluation based on cosine similarity of sentence embeddings and qualitative human evaluation on a 4-point Likert scale. Our findings indicate that larger models tend to generate better definitions than smaller models, with the performance of the models generally aligning with their size. This study contributes to our understanding of LLMs' strengths and weaknesses in generating definitions for unknown words, and offers valuable insights for future research and applications in natural language processing.
A Performance Evaluation of Basic HotStuff in Private ConsenSys GoQuorum Networks

Private blockchains are uncompetitive with the throughput of distributed databases. HotStuff(RS) is a recent BFT-SMR consensus protocol that is supposedly a faster alter-native to the established IBFT and QBFT protocols. Despite its promise, HS remains untested in private Ethereum blockchains. Previous work evaluates HS as a standalone protocol but never in a blockchain environment due to the difficulty of implementing BFT-SMR protocols. Our work addresses this gap by implementing and evaluating Basic HotStuff(BHS), a HS variant that trades latency for simplicity, against IBFT and QBFT in a private Ethereum blockchain. We create a consensus module for BHS in GoQuorum 22.7.4 and measure the performance of BHS, IBFT, and QBFT under varying (i) network sizes, (ii) transaction loads, and (iii) network conditions. Our work shows that, at scale, BHS significantly outperforms IBFT and QBFT under low-bandwidth conditions and has the lightest network traffic under all network conditions.
Cross-modality Complementary Interaction Network

Salient Object Detection (SOD) aims to identify the most conspicuous objects and regions in the human vision. This paper presents the Cross-Modality Complementary Interaction Network(CCINet), a novel approach for obtaining complementary features between different modalities. We introduce feature complementarity Module (FCM) to capture inter-modality complementary features. Subsequently, the Multi-Scale Aggregation Module (MAM) is utilized to acquire multilevel information across different resolutions. Finally, we employ a decoder to generate saliency maps. Experimental results demonstrate the superiority of our model over previous methods, as it outperforms them across four evaluation indicators on five challenging datasets.
An Integrating Semantic and Structural Features Method for Knowledge Graph Embedding

Knowledge graph embedding is a method of projecting entities and relations into continuous low-dimensional embeddings, which is commonly used for inferring and predicting missing entities or relations in the knowledge graph. However, most existing embedding methods do not fully consider the rich semantic and structural features contained in the knowledge graph, leading to poor feature expression and affecting the accuracy of downstream tasks. To address this problem, we propose a knowledge graph embedding method ISSKE that integrates semantic and structural features. First, we use the ALBERT pre-training model to embed the textual description information of entities and relations. Then, we propose a relation-weighted graph convolutional neural network model R-WGCN to learn the embedding representation of entities and relations simultaneously. Finally, we put forward Conv-Internal as the decoder, which uses internal embedding to construct 2D convolutional filters. Conv-Internal can not only promote the interaction between entities and relations but also significantly reduce the number of parameters and improve the training and inference efficiency. The experimental results show that the performance of this model on tasks such as triple classification and link prediction is better than that of baseline methods. In the link prediction task on FB15k-237 and WN18RR datasets, compared with KG-BERT, the Hits@10 indicator increased by 23.8% and 17.0%, respectively.
Sentiment Analysis Based on Text About President Candidate 2024 in Indonesia Using Artificial Intelligence with Parameter Optimization Algorithm

Indonesia as a democratic country, is set to hold the election for choose the next President and Vice President every five years. As is customary during election periods, political campaigns are conducted using various methods, ranging from traditional to modern, including social media. This has resulted in a significant increase in the use of social media platforms. As a result, social media data has become a valuable source of information, including public opinion about the candidates. The purpose of the research is to make the improvement for sentiment analysis on election prediction in Indonesia using Artificial Intelligence and optimization algorithm. Also, the purposed of this research is to make the approach of do the survey about the public response towards president candidate so it can improve their engagement and outreach their strategies. The sentiment analysis does in the research is take the data from Twitter using several keywords and it will be labeled using Valence Aware Dictionary for Sentiment Reasoning (VADER) method and the performance of it will be test using three algorithms such as Naive Bayes, K - Nearest Neighbor (KNN) and Support Vector Machine (SVM). The result of research show that VADER method combined with SVM and Grid Search CV for parameter optimization get the best result where it gets 91.93% of accuracy, 91.76% of recall, 97.94% of precision and 91.84% of f1 - score with test size 0.1, c value 0.1 and RBF kernel for SVM Parameter. Compared with traditional surveys, this proposed method can provide the first understanding of preference patterns based on data and also can be an additional tool for analyzing the preferences of presidential candidates.
Text Proofreading Method for Tibetan Sound Similarity Syllables Based on Soft-Masked BERT

Error detection and error correction are two very important elements in the field of text proofreading research. In this paper, we propose an algorithm to construct a Tibetan sound similarity syllables confusion set, which generates a large amount of tagged data by adding noise to the correct Tibetan text, and use the soft-masked technique to construct a model for Tibetan text proofreading, which consists of an error detection network and an error correction network. Experimental results on two different test sets show that the method can effectively detect and correct the wrong syllables in the text.
A Residual Graph Networks for 3D object detection in point clouds

The text describes a 3D point cloud object detection model based on a graph neural network. Due to the disorder, sparsity and overlap of LiDAR point cloud, it leads to problems in the detection accuracy of small objects. To solve this problem, a 3D small object detection algorithm based on residual graph neural network is proposed. The algorithm improves performance through two key steps. First, the original point cloud is sampled, and the sampled point cloud is framed within a fixed radius. Second, residual connections are introduced to enhance the feature extraction capability of graph networks. Finally, the aggregation of node features is completed on the deep feature map, and the features of the two stages are spliced. Experimental results show that on the KITTI dataset, the proposed method achieves significant performance improvements in point cloud small object detection tasks with higher accuracy and discrimination. By employing residual connections, the model can better capture important features in point clouds, thus improving the feature extraction capability of graph networks.
Automated Ontology Generator System Based on Linked Data

In this work, an Automated Ontology Generator System (AOGS) is developed to autonomously create and populated ontologies based on the linked data paradigm. AOGS generates context-specific ontologies and populates these with information extracted from various linked data sources and pre-existing ontologies. AOGS allows reducing the time and effort required to create an ontology, ensuring consistency and accuracy in the ontology, and that domain experts to focus on higher-level tasks such as ontology refinement and evaluation. MEDAWEDE methodology is used to develop AOGS, which is then applied to create ontologies for COVID-19 and Energy. The quality of these ontologies is evaluated using precision, recall and f-measure metrics, which yield values between 0.9 and 0.7. The ontologies are also validated using Competence Questions, Consistency, and Quality Validation methods, all of which show good results.
Data Context-Aware Web Information Retrieval

When users search the Web it is difficult to return information that is as complete as possible. Also many short texts, such as tweets, are not self-contained and need to be explained since understanding them requires additional knowledge or related facts. Recent research focuses on the context of the user, not on complementing the search results, seeking to return more specific information for the search. Unlike previous works, this research focuses on the context of the retrieved data, attempting to enrich the returned information by incorporating complementary facts that give context to the information requested by the user.
Preparing the future workforce for 2030: the role of higher education institutions

The invasion of futuristic technologies has made it inevitable for the future workforce to confront this reality and be ready to work in the new world of work in 2030. Higher education institutions (HEIs) are obligated to assume a pivotal role in equipping students with the requisite competencies. COVID-19 has expedited the development process compelling HEIs to make a mega shift to prepare the future workforce. Primary data was collected from HEI students across 11 countries to analyse their confidence to work in the new world of work in 2030 and to understand the role of HEIs in influencing students’ confidence. Exploratory Factor Analysis (EFA) and Partial Least squares (PLS) based structural equation modeling (SEM) procedures were employed to estimate a structural model of awareness and readiness (cognitive) and confidence (affective) factors, and the combined effect of HEI, awareness, and readiness on overall student confidence in their knowledge, skills and abilities. The statistical results indicate that there are strong significant relations between the HEI-Awareness; HEI-Awareness-Confidence; HEI-Readiness; and HEI-Readiness-Confidence and these dependencies are not just by chance. The results of this research are significant for higher education policy developers and curriculum developers to incorporate future competencies in the program. Further, educators and researchers will benefit from the results to develop teaching strategies and content to equip the future workforce for 2030.et.
Blockchain Technology and Smart Contracts Revolutionizing Legal Processes and Dispute Resolution

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Improving Address Matching Using Siamese Transformer Networks

Matching addresses is a critical task for companies and post offices involved in the processing and delivery of packages. The ramifications of incorrectly delivering a package to the wrong recipient are numerous, ranging from harm to the company's reputation to economic and environmental costs. This research introduces a deep learning-based model designed to increase the efficiency of address matching for Portuguese addresses. The model comprises two parts: (i) a bi-encoder, which is fine-tuned to create meaningful embeddings of Portuguese postal addresses, utilized to retrieve the top 10 likely matches of the un-normalized target address from a normalized database, and (ii) a cross-encoder, which is fine-tuned to accurately re-rank the 10 addresses obtained by the bi-encoder. The model has been tested on a real-case scenario of Portuguese addresses and exhibits a high degree of accuracy, exceeding 95% at the door level. When utilized with GPU computations, the inference speed is about 4.5 times quicker than other traditional approaches such as BM25. An implementation of this system in a real-world scenario would substantially increase the effectiveness of the distribution process. Such an implementation is currently under investigation.
Secure &amp; Trusted Framework for Cloud Services Recommendation-A Systematic Review

A dependable technology that gives us access to the with a confidential and secure environment that can be adaptable to the needs of consumers. With the help of cloud data storage, it is now feasible to store a lot of data at a low cost and with real-time access. The only those with access rights to the information kept in the cloud may access it. Cryptography can be used to provide more security to the data. Cloud with lots of benefits consists of various vulnerabilities due to its nature because of which breach of data is possible, it can be handled with TMS (Trust Management System) i.e. the secure and trusted framework by using CSTA (Cloud Security Trusted Authority) which judges the user activity on the basis of its activity. Furthermore, it is presumed that the proposed architecture lacks faith in the cloud service provider. Cloud consumer feedback is an important source for assessing trust management in the cloud framework. The CSTA is applied on the cloud where data is uploaded by the consumer and hence the judgment will be relied on the feedback of the user such that it does not provide fake recommendations. Thus in the cloud environment, it will be providing us to maintain the correctness of trust from the feedback of the consumers.
Vision Transformers Applied to Indoor Room Classification

Real Estate Agents perform the tedious job of selecting and filtering pictures of houses manually on a daily basis, in order to choose the most suitable ones for their websites and provide a better description of the properties they are selling. However, this process consumes a lot of time, causing delays in the advertisement of homes and reception of proposals. In order to expedite and automate this task, Computer Vision solutions can be employed. Deep Learning, which is a subfield of Machine Learning, has been highly successful in solving image recognition problems, making it a promising solution for this particular context. Therefore, this paper proposes the application of Vision Transformers to indoor room classification. The study compares various image classification architectures, ranging from traditional Convolutional Neural Networks to the latest Vision Transformer architecture. Using a dataset based on well-known scene classification datasets, their performance is analyzed. The results demonstrate that Vision Transformers are one of the most effective architectures for indoor classification, with highly favorable outcomes in automating image recognition and selection in the Real Estate industry.
Leveraging TFR-BERT for ICD Diagnoses Ranking

This work describes applying a transformer-based ranking solution to the specific problem of ordering ICD diagnoses codes. Taking advantage of the TFR-BERT framework and adapting it to the biomedical context using pre-trained and publicly available language representation models, namely BioBERT, BlueBERT and ClinicalBERT (Bio + Discharge Summary BERT Model), we demonstrate the effectiveness of such a framework and the strengths of using pre-trained models adapted to the biomedical domain. We showcase this by using a benchmark dataset in the healthcare field—MIMIC-III—showing how it was possible to learn how to sequence the main or primary diagnoses and the order in which the secondary diagnoses are presented. A window-based approach and a summary approach (using only the sentences with diagnoses) were also tested in an attempt to circumvent the maximum sequence length limitation of BERT-based models. BioBERT demonstrated superior performance in all approaches, achieving the best results in the summary approach.
Sentence Fusion using Deep Learning

The human process of document summarization involves summarizing a document by sentence fusion. Sentence fusion combines two or more sentences to create an abstract sentence. Sentence fusion is useful to convert an extractive summary to an abstractive summary. The extractive summary contains a set of salient sentences selected from a single document or multiple related documents. Redundancy creates problems while creating an extractive summary because it contains sentences whose segments or phrases are redundant. Sentence fusion helps to remove redundancy by fusing sentences into a single abstract sentence. This moves an extractive summary to an abstractive summary. In this paper, we present an approach that uses a deep learning model for sentence fusion. which is trained over a large dataset. We have tested our approach through both manual evaluation and system evaluation. The result of our proposed approach shows that our model is good enough to fuse sentences effectively.
Approximating maximally localized Wannier functions with position scaling eigenfunctions

Position scaling eigenfunctions are generated by transforming compactly supported orthonormal scaling functions and utilized for faster alternatives to maximally localized Wannier functions (MLWFs). The position scaling eigenfunctions are first applied to numerical procedures solving Schr\"odinger and Maxwell's equations, and the solutions well agree with the preceding results. Subsequently, by projecting the position scaling eigenfunctions onto the space spanned by the Bloch functions, approximated MLWFs are obtained. They show good agreement with the preceding results using MLWFs. In addition, analytical explanations of the agreements and an estimate of the error associated with the approximation are provided.
Financial Development Convergence: Evidence from Top and Bottom Globalised Developing Economies

This paper investigates the pattern of the financial development convergence for the top (Europe and Central Asia) and the bottom (South Asia) globalized developing regions from 1984 to 2016. We employ the Philips-Sul club convergence approach to measure the financial development convergence’s speed. The results validate the convergence of financial development in all countries, including the top and bottom of globalized developing regions. Interestingly, the speed of financial development convergence is less in the bottom globalized developing region than in the top globalized developing region. However, these results vary across developing regions in the case of financial institutions and financial markets. Therefore, solid financial market governance can provide a productive and efficient financial system, particularly in the bottom globalized economies.