Reviewerly/exHarmony huggingface dataset:
CNN based Ocean Eddy Detection Using Cloud Services

This study focuses on small-scale ocean eddy (<20km) detection in satellite remote images using Convolutional Neural Network (CNN) deployed on AWS cloud platforms, specifically SageMaker and EC2. Our goal is to streamline the workflow and make the services accessible in the climate change domain. In this work, we proposed a CNN-based Ocean Eddy detection model and it is deployed using Sage-Maker and EC2. Our proposed approach achieved more than 95% and 94% training and validation accuracy respectively by applying principal component analysis (PCA) on the dataset. We considered the usability, performance, and cost comparison while deploying the services. Our analysis shows that SageMaker and EC2 are highly capable of building CNN-based services, though there are some challenges in deploying services and limitations related to resources.
The EarthDaily Constellation – A Global Daily Scientific Quality Monitoring Mission

Climate change poses a significant threat to our planet, and it is crucial that we have the necessary tools and data to understand and address its impacts. Earth observing missions and systems play a critical role in this regard, providing valuable information on how the Earth’s landmass is evolving over time. While today’s governmental science missions provide a wealth of information, there remains a need for more frequent higher-resolution scientific quality monitoring to fully understand and address the changes taking place on our planet. EarthDaily Analytics is addressing this need with the development of the EarthDaily Constellation (EDC), a revolutionary earth observation system designed to provide daily global coverage of the Earth’s entire landmass with scientific-grade geometric and radiometric quality, spectral bands ranging from visible to long-wave thermal infrared, high signal-to-noise ratios, and a resolution capable of observing small-scale natural features (5 meters). EDC’s ten satellite constellation, launching in 2024, will collect and archive an unprecedented 100 TB of data per day for a long 10-year design life, providing policy makers and governments with the information they need to understand and address the impacts of climate change. The EarthDaily Constellation is equipped with 22 spectral bands that have been carefully modelled after today’s science missions to ensure both compatibility and continuity with historical archives which will support our long-term understanding of Earth’s evolution.
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification

We present a comprehensive analysis of quantitatively evaluating explainable artificial intelligence (XAI) techniques for remote sensing image classification. Our approach leverages state-of-the-art machine learning approaches to perform remote sensing image classification across multiple modalities. We investigate the results of the models qualitatively through XAI methods. Additionally, we compare the XAI methods quantitatively through various categories of desired properties. Through our analysis, we offer insights and recommendations for selecting the most appropriate XAI method(s) to gain a deeper understanding of the models’ decision-making processes. The code for this work is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Exploring Blockchain to Support Open Science Practices

Open science aims to foster transparent sharing of scientific processes including open access, incentivization, provenance, open source code and tools, metrics, and resource sharing. However, effective management of these processes remains a challenge. This paper explores the application of blockchain technology to address these key aspects of open science. Blockchain offers a decentralized and secure platform for information exchange and verification. By leveraging blockchain, open science can enhance transparency and reproducibility. In this paper, we present an implementation of blockchain for Earth science data synchronization across organizations, enabling tracking of data copying, citation, and download. The findings highlight the potential of blockchain in supporting open science objectives.
A CNN-Transformer Hybrid Feature Descriptor for Optical-SAR Image Registration

This paper presents a CNN-Transformer hybrid feature descriptor for the registration of optical and SAR images. The proposed feature descriptor is composed of a shallow feature extraction module based on CNN and a deep feature extraction module based on Transformer, which are tailored to extract features from different levels. Its effectiveness is demonstrated in experiments with comparison to state-of-the-art methods. Experimental results demonstrated its superior performance compare to the state-of-the-art methods, thus showcasing its efficacy and potential for practical applications.
3D-Mglnet: Moving Vehicle Detection in Satellite Videos with 3D Motion-Guided Lightweight Network

Object detectors based on convolutional neural networks have been widely-applied to detect moving vehicles in satellite videos. However, many detectors render superior detection accuracy at the expense of increased computational complexity and decreased inference speed. This prevents these detectors from being deployed into mobile devices. In this paper, an efficient 3D motion-guided lightweight network (3D-MGLNet) is proposed. Specifically, 3D-MGLNet constructs a motion-guided module based on 3D convolution to extract motion cues from spatial-temporal information. This module uses model compression strategies to detect moving vehicles in real-time while following the principle of "fewer channels, smaller convolution kernels," significantly reducing the number of parameters and computational complexity. Extensive experiments are conducted on the Jilin-1 and SkySat satellite video datasets. The results demonstrate that 3D-MGLNet gains strong performance by striking an excellent tradeoff between resource and accuracy, resulting in the fewest parameters (0.35M) and fastest speed (66.84 fps) compared to other popular models.
Speed Monitoring of Heavy Vehicles on Construction Plants by Fusing Camera Visual Image with UAV LiDAR Point Cloud

Driving heavy vehicles at high speeds through construction sections can pose significant dangers. Implementing a ground speed radar system during construction progress is challenging due to the high costs associated with installation and maintenance. This paper proposes an innovative and cost-effective method for monitoring vehicle speed on construction sites by combining 2D/3D data. This method involves collecting 3D point cloud data using UAV LiDAR and capturing 2D visual image data using IoT cameras placed around construction sections. The EPnP algorithm is employed to calculate the matching results of pixel coordinates and world coordinates using the 2D and 3D data. Subsequently, the YOLOv5 object detection algorithm is combined with the DeepSORT tracking algorithm to enable real-time detection and tracking of heavy vehicles. Lastly, vehicle speed monitoring is conducted by analyzing the 2D/3D coordinate matching and the results of vehicle detection tracking. Pertinent experiments conducted in real scenarios validate that the method achieves a maximum error of 7.24% and an average relative error of 4.16%, satisfying the requirements for real-time monitoring of heavy vehicle speeds on construction sites.
Relational Image Patch Matching for Remote Sensing

Feature descriptor-based methods have demonstrated remarkable performance in remote sensing image patch matching tasks and are usually optimized using contrastive loss and triplet loss. However, these optimization losses focus on calculating the distance between samples, ignoring the rich information of higher-order feature relationships between multiple image patches. The latter provides valuable information that can be used to improve task performance. Inspired by the superior performance of second-order relations in graph matching and clustering tasks, we aim to exploit the rich information available from high-order relations fully. This paper proposes a high-order relationship (HOR) learning method for remote sensing image patch matching. This method combines low-order feature relations between image patch pairs and high-order feature relations between multiple patches to enhance image matching performance. Extensive experimental results on a multimodel remote sensing image dataset, SEN 1-2, consisting of optical and SAR images, demonstrate that the proposed HOR learning method can improve the performance of remote sensing image patch matching.
Design of Knowledge Map Representation Learning Algorithm Based on Artificial Neural Network

In recent years, with the extensive application of artificial intelligence in various fields, the research on how to apply artificial intelligence to education has gradually become the focus of attention. Knowledge map is a giant semantic network composed of nodes and edges, which represents the semantic association between different nodes in the form of large-scale graph. Knowledge map, as a structured storage form of multi-domain data in the real world, provides semantic relevance between data and makes it possible for artificial intelligence to make better use of data. The research of representation learning based on knowledge map realizes the distributed representation of entities and relationships, significantly improves the computational efficiency, effectively alleviates the problem of data sparseness and can realize the fusion of heterogeneous information, which is of great significance for people to obtain effective information from massive data. In view of the above situation, taking WebQA data set as an example, this paper proposes a method to simplify the original text, introduce apriori knowledge related to answers from the knowledge map, and use neural network model to generate Chinese questions. The experimental results show that the method proposed in this paper can effectively reduce the difficulty of model training and improve the quality of model problems.
Construction and Optimization of Bilingual Translation Corpus Based on GLR Algorithm

When computers and other machines are used to translate English content, it often happens that a word is translated incorrectly or the context is not smooth due to the improper recognition of the word content and context, which is one of the shortcomings of traditional machine English translation. In order to ensure the accuracy of machine translation (MT), this paper puts forward a multi-outlet analysis table structure when applying GLR algorithm to shallow syntactic analysis, which reduces the complexity of the algorithm, collects and processes language signals, and extracts feature parameters to realize MT intelligent proofreading. The designed MT intelligent proofreading model is analyzed experimentally and the experimental data is recorded. The simulation results show that the proposed GLR algorithm has higher prediction accuracy and recall than the traditional translation proofreading model, which are 19.65% and 18.77% higher than the contrast BPNN. Therefore, it is feasible to apply this model to the construction and optimization of bilingual translation corpus. This algorithm can play a stable role in the construction and optimization of bilingual translation corpus.
Intelligent Internal Audit Platform Architecture Based on Machine Learning

With the development of science and technology and the improvement of people’s living standards, construction projects are developing towards large buildings, green buildings and intelligent buildings. However, with the increasing tasks of audit supervision and the increasing work pressure, the traditional computer audit methods have been difficult to meet the requirements of full coverage of audit supervision, and using machine learning to conduct government audit provides a new idea to solve the above problems. However, information technology has not been fully introduced into the internal audit work, and the internal audit informatization construction is characterized by localization and fragmentation, which leads to the mismatch between internal audit and enterprise informatization, and can not serve the development of enterprises well. The enterprise audit department should make every effort to build a new generation of Internal Audit Center, and put forward the intelligent internal audit data analysis process and internal audit application framework model on this basis, so as to transform the internal audit function from the traditional supervision and control function to the convenient and efficient digital audit and the intelligent self audit function of robot process automation. Combined with the internal audit theory and practice and program design method, this paper analyzes and puts forward the intelligent internal audit platform architecture based on machine learning from the application level, so as to make the internal audit work move forward in the direction of high quality, high efficiency and high accuracy, and realize the breakthrough and innovation of artificial intelligence in the field of internal audit. The application of intelligent internal audit platform based on machine learning can not only effectively solve the problems of slow speed and error prone when traditional audit technology processes massive social security data, but also help the audit team determine the audit focus, explore audit doubts, and improve the utilization of audit data resources.
Research on Tackle Recognition of Football Players Based on Deep Reinforcement Learning

For the purpose of addressing the issues of slow iteration speed and high false recognition rate of conventional football player tackle recognition methods, the paper puts forward a deep reinforcement learning-based method for recognizing the tackle of new football players. The image data of tackle is collected by inertial sensor, and the collected image data is denoised. According to the feature extraction results, the football player tackle action recognition model is established by deep neural network based on deep reinforcement learning. The output of the model is the tackle action recognition result. The results of experiments indicate that the proposed method has a higher iterative speed than conventional motion recognition methods, with a lower false recognition rate of tackle, up to no more than 3%. Therefore, it shows that the research method can be applied in practice.
Functional Noun Phrase Recognition in English Machine Translation Based on Machine Learning

With the development of science, the recognition of English translation noun phrases in machine learning plays an important role in machine translation. One of the bottlenecks in current English machine translation is the ambiguity resolution of noun phrases. This paper proposes a neural network machine translation functional phrase and its recognition method to eliminate noun phrase structures in English machine translation. The boundary of a noun phrase is determined based on the functional grammar of machine learning in clauses. Such noun phrases are referred to as functional noun phrases in this article. The research work mainly includes the analysis of incorrect translation of noun phrases in English machine translation, and the beneficial comparison between machine translation and human translation. The experiment of constructing a phrase corpus in this paper analyzes a small-scale phrase corpus with a tag size of about 580000 English words, which can construct approximately 20000 sentences and 10000 phrases, while a regular phrase corpus can only construct 10000 sentences.
Construction of Digital Multimedia Visualization Model Based on Artificial Intelligence Technology

With the continuous progress and development of digital multimedia technology, people’s data processing is no longer limited to text and image as before. Visual model is a very useful, intuitive and effective way to improve work efficiency. In order to improve people’s grasp of multimedia digital, visual models can be studied. This paper mainly used experimental method, investigation method and comparative method to study the digital multimedia visualization model. The results of experiments and survey data showed that more than 75% of people believed that the visual model needed to be improved in terms of its interactive function and perception.
Simulation of Blockchain Information Protection Prediction Model Based on Machine Learning

With the rapid development of blockchain, blockchain-based outsourcing computing has been widely used. Outsourcing computing allows users with limited resources to outsource complex computing to those with powerful resources by paying for it, so that they can easily obtain the computing results. However, users' privacy data may be leaked in the process of outsourcing computing, so the privacy, security and verifiability of computing results of users should be considered in the process of outsourcing computing. Blockchain is a technology for building distributed databases, which has the characteristics of decentralization. The data added to the blockchain is collectively maintained by the system participants, and is permanently recorded, so it can't be tampered with. The intelligent bidding scheme based on blockchain privacy protection proposed in this paper gives full play to the decentralized, tamper proof and other characteristics of the blockchain. At the same time, homomorphic encryption technology is used to ensure data privacy, and data statistics and analysis function is realized. In the bidding prediction stage, this paper introduces a fuzzy neural network model to simulate the human brain thinking mode, thus increasing the system’s "thinking" ability.
Lossless Image Encryption using Robust Chaos-based Dynamic DNA Coding, XORing and Complementing

In this paper, we present a lossless image encryption algorithm utilizing robust chaos based dynamic DNA coding and DNA operations (DNA XOR and DNA Complement). The entire process of encryption is controlled by the pseudo-random number sequences generated through a 1D robust chaos map that exhibits chaotic behaviour in a very large region of parameter space with no apparent periodic window and therefore possesses a fairly large key space. Due to peculiar feed-forward and feedback mechanisms, which modify the synthetic image (created to initiate the encryption process) at the encryption of each pixel, the proposed algorithm possesses extreme sensitivity to the plain image, cipher image and secret key. The performance analysis proves that the proposed algorithm exhibits excellent features (as expected from ideal image encryption algorithms) and is robust against various statistical and cryptanalytic attacks.
ClayBot: Increasing Human-Centricity in Conversational Recommender Systems

Conversational recommender systems are increasingly studied to provide more fine-tuned recommendations based on user preferences. However, most existing product recommendation approaches in online stores are designed to interact with people through questions that mainly focus on products or their attributes, and less on buyers’ core purchase needs. This work proposes ClayBot, a novel conversational recommendation agent, which aims to capture people’s intents and recommend products based on the jobs or actions that their buyers aim to do. Interactions with ClayBot are guided by an openly accessible knowledge graph, which connects a sample of computing products to the actions annotated in product reviews. A demonstration of ClayBot is presented as an Amazon Alexa Skill to showcase the feasibility of handling more human-centered interactions in the product recommendation and explanation process.
GLENDA: Querying RDF Archives with Full SPARQL

The dynamicity of semantic data has propelled the research on RDF Archiving, i.e., the task of storing and making the full history of large RDF datasets accessible. However, existing archiving techniques fail to scale when confronted with very large RDF datasets and support only simple SPARQL queries. In this demonstration, we therefore showcase GLENDA, a system that can run full SPARQL 1.1 compliant queries over large RDF archives. We achieve this through a multi-snapshot change-based storage architecture that we interface using the Comunica query engine. Thanks to this integration we demonstrate that fast SPARQL query processing over multiple versions of a knowledge graph is possible. Moreover, our demonstration provides different statistics about the history of RDF datasets that can be useful for tasks beyond querying and by providing insights about the evolution dynamics of the data.
Sparnatural: A Visual Knowledge Graph Exploration Tool

Knowledge Graphs are often relying on conceptual models with a high level of abstraction, making them hard to understand for nonexpert users, and are often hidden behind search forms, losing the flexibility to interact with the graph as a graph. In order to leverage knowledge graphs there is a need to propose end-user-oriented interactions demonstrating how they can allow to discover a knowledge domain. Sparnatural was designed and developed as a visual, configurable, easily deployable, open-source, client-side SPARQL query builder, allowing users to query any RDF knowledge graph. We describe here how Sparnatural works, how it can be configured, and we show how it was deployed in two demonstrators for the Archives Nationales de France and Bibliothèque Nationale de France. We show how Sparnatural has made it possible to give access to nonexpert users, with little effort, to these two large graphs. Workshops conducted with end-users showed they were enthusiastic about the possibility offered. They also revealed that this novel search paradigm is surprising and could benefit from user onboarding. The configuration of Sparnatural is key in providing efficient access to the graph and can be done through the specification of a search-oriented ontology.
A Geological Case Study on Semantically Triggered Processes

We present an approach to connect semantic descriptions of situations to program-based descriptions of processes. The main mechanism is a semantically formalised trigger that initiates a process. We demonstrate the viability of the approach by modelling scenarios and processes in petroleum geoscience.
Formalizing Stream Reasoning for a Decentralized Semantic Web

Decentralized storage of data is gaining increased attention as a means to preserve privacy and ownership over personal data. Simultaneously, the share of streaming data on the Web and other applications, e.g. Internet of Things, continues to grow. The large, uncoordinated amount of data streams within these applications requires methods that can coordinate them, especially when a central authority is lacking. We aim to perform said coordination via stream reasoning, using rules and facts to combine and derive information. Decentralized networks, however, present new problems for stream reasoning not yet (fully) addressed in the literature. This includes added expressivity for network heterogeneity, cross-storage referencing and schema variation, out-of-order arrival of data and variance in the representation of time. We aim to propose theoretical solutions that address challenges on temporal expressivity within the network, on out-of-order processing and on the alignment of temporal ontologies. Ultimately, this research aims to provide a solid formal basis for the processing of unbounded streaming data across different data vaults.