Reviewerly/exHarmony huggingface dataset:
Ontology-Semantic Alignment On Contrastive Video-Language Model for Multimodel Video Retrieval Task

Contrastive Learning-based models have shown impressive performance in text-image retrieval tasks. However, when applied in video retrieval, traditional contrastive learning strategies have faced challenges in achieving satisfactory results due to redundancy of video contents. We discern several potential reasons: (1)Current methodologies sometimes overlook the significant information imbalance between videos and query text, specifically neglecting the in-depth textual representation of the content within the videos. (2) Current video matching methodologies typically focus on cross-model alignment at general entity similarity level, without specific consideration for how entity pair preferences and similarity properties affect the task at hand. (3) Previous vectorized retrieval based on video content features have been somewhat flawed. They primarily focused on aligning overall features without having an video content tags feature for meaningful feature discrimination. Considering the shortcomings identified in the mentioned three aspects, we propose an ontology semantic labels augments retrieval model and introduce a method to integrate video ontology semantic labels into the contrastive learning framework. In particular, we have developed ontology semantic descriptions about entities encompassing both human figures and textual elements within the videos. Subsequently, we conducted training and testing on the CMIVQA dataset to assess the performance of our approach. The experimental results show that employing fine-grained ontology labels as sample pairs for contrastive learning leads to an increased level of precision in video retrieval tasks.
A Hybrid Resampling Technique with Adaptive Intervals Used in the Parallel/Distributed Particle Filters

Parallel/Distributed particle filters estimate the states of dynamic systems by using Bayesian interference and stochastic sampling techniques with multiple processing units (PUs). The sampling procedure and the resampling procedure alternatively execute to estimate the states in particle filters. There are two basic types of resampling techniques used in parallel/distributed particle filters. They are centralized resampling and decentralized resampling. The high communication between PUs in centralized resampling lowers the speedup factor in parallel computing but improves the estimation accuracy. The decentralized resampling can avoid the communication and improve the performance. Some types of hybrid resampling techniques mainly execute the decentralized resampling and only invoke the centralized resampling with constant intervals to achieve ideal performance without losing the estimation accuracy. However, the constant intervals cannot guarantee that the centralized resamplings are invoked timely. In this study, we proposed a hybrid resampling technique with adaptive intervals between centralized resamplings to overcome that issue. The experimental results indicate that the proposed hybrid resampling technique is able to improve the performance and the estimation accuracy.
Domain Adaptive Pedestrian Re-recognition for Oilfield Operations Security

In oil field operations, accidents often occur, with personnel error, improper behavior, and mechanical failures accounting for a significant proportion. To improve safety levels, pedestrian re-identification technology has been widely used. However, the complex nature of oil field operations leads to traditional technologies having high error and missing identification rates. Field adaptive pedestrian re-identification technology for oil field safety uses deep learning algorithms to self-adapt and learn, improving accuracy and stability to enable monitoring and management of personnel behavior for safety in oil field operations.
Soil Data Storage Framework based on Blockchain and Improved Merkle Mountain Range

Soil pollution control has become one of the environmental issues that governments in various regions are gradually paying attention to. However, under the Internet of Things (IoT) environment, the storage and traceability of soil environment data has become an important challenge for the future. In addition, the credibility of soil pollution data is also an important prerequisite for soil remediation. Third-party or on-machine regulatory units need real and credible soil data to support environmental governance and accountability. To tackle the problem of soil data tampering in IoT scenarios, this paper presents a blockchain-based data storage framework. The study is based on Ethereum smart contracts and Orbit-db soil data to achieve soil pollution data tamper-proofing. At the same time, we also solve the problem of on-chain of large amounts of real-time data or log data generated by IoT devices through the improved Merkle mountain (KMMR)computation data digest. Our experimental results show that KMMR not only improves the efficiency of data chain calculation but also reduces storage costs.
A Novel Classification Model for Automatic Multi-Label ICD Coding via BERT-LSTM

Clinical notes are text documents created by physicians at each patient visit to record details of diagnosis and treatment, and are labeled using medical codes. However, manually marking up these codes is time-consuming and error-prone. To address this problem, we propose a new multi-label classification method inspired by the encoder-decoder structure that utilizes the BERT-LSTM network structure to automatically assign ICD codes to clinical texts. The model is able to accurately predict the appropriate medical codes based on the content and contextual information of the clinical text, improving efficiency while reducing errors. By combining these two powerful neural network models, we are able to better handle the task of coding clinical notes. In comparative experiments, the application results of the model are better than some basic neural network architectures, achieving 85.7% of AUC, 61.2% of precesion@5 and 56.5% of Micro-F1. This result demonstrates the robustness of our proposed method and the effectiveness automatic ICD coding classification.
Efficient and Accurate Abdominal Multi-Organ Segmentation using Cross Pseudo-based Semi-Supervised Learning

Abdominal multi-organ segmentation is of great significance for preoperative treatment planning. At present, there are many public abdominal datasets and deep learning based segmentation methods have been proposed. However, the problem of polycentric and spatio-temporal inefficiency still remain unsolved. Additionally, expensive costs of labeling and lack of labeled data are also serious problems of this field. In this work, with a small amount of labeled CT images and large number of unlabeled data, we propose a novel Cross Pseudo based semi-supervision method, whose two branches can generate pseudo-labels to supervise each other. we add an organ shape constraint auxiliary branch and redesign the loss function to effectively improve the segmentation accuracy of the model. For quantitative evaluation on the FLARE2022 validation cases, this method achieves the DSC of 0.80, NSD of 0.75 within merely 20s for inference per image. It demonstrates the robustness and efficiency of our method.
Object Specialization to Partially Reduce Polymorphism of Attributes

In object-oriented programming languages, objects with polymorphic attributes can negatively impact performance and hinder static analysis. These attributes require dynamic dispatch, which is slower than static binding, and complicate the analysis process. We propose a novel algorithm for object specialization that addresses this issue by replacing polymorphic attributes with monomorphic ones, resulting in improved performance and simplified static analysis. Our algorithm is a new approach compared to existing function specialization algorithms. We provide a proof of the algorithm's soundness and correctness, and present an implementation of the algorithm as a software tool. Empirical evaluation shows that our approach achieves significant improvements in performance and simplifies the static analysis process. Our algorithm can be applied to a variety of object-oriented languages such as Java and Python.
Some General Heuristics in the Traveling Salesman Problem and the Problem of Reconstructing the DNA Chain Distance Matrix

With all their differences, the two problems under consideration, namely the traveling salesman problem and the problem of restoring the DNA chain distance matrix, have a lot in common. This generality primarily consists in the following. For real problems and for standard methods of solving them, such as gradient descent, these problems can be formally solved, but in fact they are described by systems of equations with several dozen variables, and sometimes hundreds. In this regard, to solve them, we use sequential algorithms (step-by-step) for filling matrices, sometimes also using backtracking for the variables already considered. We show that such heuristics in the situations we are considering give acceptable anytime algorithms.
Design and Architecture of Smart Agriculture System based on Big Data Analytics

The rise of smart agricultural systems marks the digital transformation of the agricultural sector, offering endless possibilities for improving agricultural productivity, sustainability and food supply chain management. The technical architecture of the intelligent agricultural system is the main technical support for realizing the functions of the whole system, and the architecture of the agricultural monitoring system for a long time in the past, mainly has three layers, namely the perception layer, the transmission layer and the application layer, to form the basic technical architecture of the system. This study aims at the current low level of agricultural intelligent management, lack of data analysis and inappropriate processing of collected data, and combines the current Internet of Things, big data and artificial intelligence and other cutting-edge technologies in the practical application of agricultural production and management, on the basis of which to design an intelligent agricultural system based on big data analysis. The system adds a data layer and a control layer compared with the previous agricultural system architecture, and the overall smart agricultural system architecture includes a perception layer, a transmission layer, a data layer, a control layer and an application layer. In this way, the intelligent agricultural system can monitor all kinds of parameters in the process of crop growth in real time, including temperature and humidity, soil composition, light intensity and other parameters affecting the growth of crops, through the data layer, the monitoring data to analyse the trend of its changes, and then through the control layer, the changes in the relevant parameters to make adjustments in order to ensure that the crops can be grown in the optimal environment. Thus, the purpose of improving crop yield and saving labour is achieved.
Vietnamese Voice2Text: A Web Application for Whisper Implementation in Vietnamese Automatic Speech Recognition Tasks: Vietnamese Voice2Text

The publication of the Whisper model by OpenAI inspired us with the idea of a web platform that provides voice-to-text conversion services for Vietnamese people. Using Whisper's powerful generalization capabilities, we have developed a web application with three main features: record-to-text, file-to-text, and subtitles generator for YouTube. We first fine-tuned Whisper with our target language dataset then deployed the model as a Rest API using the Python Flask framework with three paths for three different tasks. The web application has been developed using ReactJS, a popular JavaScript library for building user interfaces. Its architecture is grounded in component-based design principles, which means that the application is structured into reusable and modular components, enhancing code maintainability and scalability. The web application has been developed using ReactJS, a popular JavaScript library for building user interfaces. Its architecture is grounded in component-based design principles, which means that the application is structured into reusable and modular components, enhancing code maintainability and scalability. The record-to-text function will allow users to record audio on the web page, and then the audio will be processed and converted to text. As for the file-to-text function, the website will receive audio files uploaded by users and will return the transcript text of that audio file. And finally the subtitles generator for YouTube function, where users can enter the YouTube link as input, wait for the website to process and the website will display that video with the transcript attached to the video based on the timestamps of each transcript. This project can inspire and encourage the testing and application of new automatic speech recognition (ASR) models in specific applications.
Analysis of Graph Data Structure from the Perspective of Clustering

Deep graph clustering is a fundamental task that partitions graph nodes into distinct clusters based on their similarity features, without using human-annotated data. Graph representation learning methods are widely used for deep graph clustering, as they can learn node embeddings that capture the similarity features within each cluster. However, the existing methods have not clearly defined what kind of similarity they capture, and how it varies across different node categories. In this paper, we propose a novel approach for deep graph clustering that addresses these issues. We use a combination of qualitative and quantitative methods to analyze the attribute vectors and structure of graph data, and explore the sources of similarity captured by graph representation learning. We also investigate how the attribute and structure features of graph data affect the clustering performance, and identify the specific challenges faced by deep graph clustering tasks. Our approach provides insights and guidance for the future development of deep graph clustering.
Cultural Heritage Triple Information Extraction Based on Span Pointer Network

Knowledge in the field of cultural heritage has the characteristics of long data, large entity span, high semantic complexity and nesting among some entities, which increase the difficulty of knowledge extraction in the field of cultural heritage. In order to effectively solve the problem of knowledge extraction in the field of cultural heritage, and to solve the problem of error accumulation and entity redundancy in the pipeline method in the relationship extraction task, a relationship joint extraction model based on span pointer network is proposed to extract the triple information in the field of cultural heritage. In addition, in order to improve the extraction performance and generalization ability of the model, based on the model of span pointer network, ERNIE pre-training model which is more suitable for Chinese tasks is used as the coding layer and AdamW is used as the optimizer. The final experimental results show that this method has superior performance in solving the problem of knowledge extraction in the field of cultural heritage.
A Systematic Literature Review of Blockchain Technology: Applications Fields, Platforms, and Consensus Protocols

In the context of today's digital era, blockchain technology has established itself as one of the transformative innovations that pushes the boundaries of data management and security. Given this situation, the present work carries out a systematic literature review of this technology. It examines three essential aspects of the blockchain world. First of all, the various fields of application of this technology are analyzed, which go beyond the field of cryptocurrencies and extend to other industries such as the internet of things, supply chains, health, identity management, business, and much more. Secondly, the most used platforms for the development of blockchain applications are studied, such as Ethereum, Hyperledger Fabric, Solana among others; each platform has its particular characteristics, compatible programming languages and recommended application areas. Finally, an analysis of the consensus protocols is carried out, such as Proof of Work, Proof of Stake, Proof of Authority, RAFT, among others. This literature review provides a comprehensive overview of blockchain, shedding light on its versatility, challenges, and transformative potential in a variety of industries. It offers a solid foundation for those interested in exploring and taking advantage of the blockchain revolution in the 21st century.
Enhancing Maintenance Training with Mobile Augmented Reality: An AHP-Based Development Approach

The industrial landscape has witnessed a remarkable proliferation of automated systems in recent years, encompassing various technologies such as robotic arms, CNC machines, and AS/RS systems. These computerized systems, owing to their inherent flexibility, have swiftly adapted to the evolving contours of production processes. However, this paradigm shift in industrial operations necessitates a commensurate evolution in the workforce's skill set. This study's primary aim is to pioneer the development of a mobile augmented reality solution designed to elevate maintenance training. Utilizing the Analytic Hierarchy Process (AHP), our goal is to assess and prioritize the importance of course content, laying a solid groundwork for integrating mobile augmented reality into maintenance training. Through meticulous identification of user requirements and the reasonable prioritization of critical features throughout the development process, our innovative approach culminates in an augmented reality-based training application tailored to meet the specific needs of welding robot maintenance staff. This application offers an extensive repository of information on part components, preventive maintenance protocols, and troubleshooting insights. Our research substantially enhances maintenance training quality, bridging the chasm between theoretical knowledge and practical proficiency.
Research on wireless signal dataset and modulation pattern recognition technology

With the development of communication technology, the modulation methods of wireless signals show a diversified trend. Modulation pattern recognition is a very key technology in non-cooperative communication systems such as wireless signal spectrum resource regulation and modern military warfare. When performing wireless signal modulation pattern recognition, the type and quantity of the data set have an important impact on the recognition result, so it is also very important to select or construct a data set reasonably. This article mainly studies the wireless signal dataset and modulation pattern recognition technology. Firstly, provide an overview of wireless signal datasets and introduce the types and construction of wireless signal datasets. Subsequently, the principle of modulation pattern recognition is introduced and the research status of three types of modulation recognition methods, namely, likelihood ratio recognition method based on decision theory, modulation pattern recognition technique based on feature extraction and modulation recognition technique based on deep learning, is elaborated. And compare the performance of various modulation recognition technologies. Finally, a summary and outlook were made on future research directions.
Extrinsic calibration of 2D laser range finder and camera using photogrammetric test field and point-on-plane constraint

Extrinsic calibration of 2D laser range finder and camera using photogrammetric test field and point-on-plane constraint
On-Board Computing Platform Supporting Ag 4.0

CROSSCONTROL’S COMPUTING PLATFORM WILL BE ON DISPLAY IN HANOVER, ALONG WITH SEVERAL NEW PRODUCT LAUNCHES
Somewhat pairwise fuzzy e-irresolute continuous mappings

The concepts of somewhat pairwise fuzzy e-irresolute mapping and somewhat pairwise fuzzy irresolute e-open mapping is introduced. Also some properties and comparisions of those mappings are given.
Distance education: a methodological strategy at the higher level

The article analyzes Distance Education as part of a complex educational system, and therefore, as any system; its interrelated and interdependent components. This implies that a change in one component of the system will affect the others, thus, if a means of communication is changed or added, it will affect the entire instructional design, the nature of the interaction and the learning possibilities offered by the educational context. In this sense, educational networks should be, then, spaces for multiple conversations mediated by various instruments such as school teaching, the media, electronic networks, and school organizations. The result of this construction of varied, multiple information, of utilitarian and cultural purposes, will have to be a self-conscious citizen, possessor of professional skills and habits of coexistence, worker and capable of creative leisure, respectfully related to others and its environment.
Sentiment Analysis Using Machine Learning

In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms’ performance.
Host Detection and Classification using Support Vector Regression in Cloud Environment

Having the potential to provide global users with pay-per-use utility-oriented IT services across the Internet, cloud computing has become increasingly popular. These services are provided via the establishment of data centers (DCs) across the world. These data centers are growing increasingly with the growing demand for cloud, leading to massive energy consumption with energy requirement soaring by 63% and inefficient resource utilization. This paper contributes by utilizing a dynamic time series-based prediction support vector regression (SVR) model. This prediction model defines upper and lower limits, based on which the host is classified into four categories: overload, under pressure, normal, and underload. A series of migration strategies have been considered in the case of load imbalance. The proposed mechanism improves the load distribution and minimizes energy consumption and execution time by balancing the host in the data center. Also, it optimizes the execution cost and resource utilization. In the proposed framework, the energy consumption is 0.641kWh, and the execution time is 165.39sec. Experimental results show that the proposed approach outperforms other existing approaches.