Reviewerly/exHarmony huggingface dataset:
Research on Apriori algorithm based on compression processing and hash table

Apriori improved algorithm based on compression processing and hash table is given to solve the problem that the traditional Apriori (association rules) algorithm must repeatedly scan the database when performing data mining frequent itemsets. It makes full use of the method of transaction compression, firstly compressing the data, and then compressing the obtained data into the hash table for the second time through hashing technology. In this process, you only need to scan the database once, and then even if the support degree changes, there is no need to perform a second scan. The experimental results show that the improved algorithm can not only mine the association rules of the dataset correctly, but also greatly improve the operation efficiency of the algorithm.
An improved Criminisi de-highlighting algorithm

In daily production and life, high-quality images are often pursued. But the reality is always the opposite, and highlighting is one of the reasons why the image quality is not so good. Therefore, this paper studies the effective removal of highlight phenomenon in a single image. First of all, significant detection algorithm based on YCrCb color space using the vision algorithm (LC) get significant figure of specular image, and then will meet the requirements of significant value as the initial seed points of region growing algorithm, and according to the rule of its growth will highlight areas detected, then uses the open operation and flood water filling algorithm will detect the highlight areas are optimized; Then, the optimized highlight region detection map is used as the mask image criminisi algorithm, and criminisi algorithm is improved from three aspects: priority calculation, matching criteria and updated confidence. Finally, based on the improved criminisi algorithm, criminisi can effectively remove the detected highlight area and obtain the original information of the image covered by the highlight. The experimental results show that the method proposed in this paper can obtain the details of the highlight region, remove the highlight well, and make the image clearer after the removal of the highlight.
UAV 3D path planning based on improved ant colony algorithm

Aiming at the problems that traditional ant colony algorithm is prone to fall into the local optimal solution and the convergence speed is slow in 3D path planning of UAVS, an improved ant colony algorithm is proposed for 3D path planning of UAVS. By improving the pheromone evaporation coefficient and combining with the evaluation function of A* algorithm, a three-dimensional environment model is constructed by using the grid method, and then experimental simulation is carried out. The results show that the convergence speed of the improved ant colony algorithm is faster and the optimal path is shorter than the traditional one.
A low-cost fall detection system using SVM algorithm based on G-Fall dataset collected by wearable device

Fall detection using wearable devices has the advantage of high accuracy and low cost. This paper discloses a large G-sensor dataset that relies on a self-developed wearable device that collects acceleration data from the waist, thigh, and wrist during daily activities and when falls occur. In this dataset, a total of 24 volunteers participated, including 15 types of daily actions and 11 types of fall actions. After data preprocessing, we experimented with four methods: threshold method, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN), and concluded that the waist was the best collection location and SVM algorithm was the optimal method. When using the support vector machine (SVM) for fall detection at the waist, the average accuracy of classifying daily actions and fall actions can reach 98.61%. And when using it to classify 26 types of actions, the average accuracy can reach 90.03%.
Redactable Distributed Ledgers: A Survey

Blockchain and distributed ledger technology started as a decentralized infrastructure to enable and manage digital currency like Bitcoin without relying on a central authority. One of the attractive features provided by blockchain technology is its append-only “immutability” feature, which means the stored data cannot be modified or manipulated by any means once it is validated in the blockchain ledger. Such immutability helps traceability, auditing, and non-repudiation, which builds decentralized trust among un-trusted parties. Despite that, immutability if misused could lead to the permanent existence of sensitive information and misinformation in the blockchain. Incidents like broadcasting illegal content have already taken their place in blockchain systems. Such incidents call for prompt solutions for mitigation. One emerging research theme, “redactable distributed ledgers” such as redactable blockchain provides approaches for modifying ledgers with certain controllability. This article aims to survey the current research landscape about redactable distributed ledgers. We will first describe the motivations behind redactable distributed ledgers. Compared to other relevant surveys, we comprehensively summarized and briefly explained the underlying technologies for supporting redactable distributed ledgers. We mainly focused on chameleon hash-based redactable blockchain structure and classifications, with detailed comparisons and illustrations. Further, we tackled new distributed ledger structures, including the new state-of-the-art block matrix structure. Furthermore, new applications that can be enabled by redactable distributed ledgers and future research directions are discussed in detail. This article emphasizes the motivation of utilizing the redactable distributed ledgers in several critical applications to mitigate misuse of immutability features threatening the original known design of distributed ledgers.
Use of Mathematical Concepts to Achieve High Levels of Security in Cryptography

Encryption, the process by which plaintext (readable information) is transformed into ciphertext (meaningless symbols), was essentially equivalent with cryptography until the advent of modern computing (decryption).To prevent unauthorized access, a sender of an encrypted (coded) communication only reveals the decryption (decoding) method to the intended receivers.Changing the letter or number on the outer disk with the letter right beneath on the inner disk is all that's needed to encode a message.The proposed work creates a novel cryptographic technique where the key is the number of multiples of mod n using Laplace transforms.The Laplace transform has recently found a new use in the world of cryptography, which we describe here.In this research, By encrypting the plaintext using the Laplace transform of an appropriate function and decrypting it with its inverse, we provide a revolutionary iterative method to cryptography.Encryption is frequently employed by organizations and even governments to protect confidential information online.The techniques of cryptography owe a great deal to the contributions of mathematics.
Distributed Computing for Efficient Data Processing and Storage

Distributed computing is now widely recognized as a key infrastructure component for effective data management.The exponential rise of big data produced by businesses and individuals has increased the need for scalable and affordable methods of managing and analyzing this data.This paper discusses the problems and potential solutions associated with big data processing and storage, as well as the history, definition, and architectures of distributed computing.We also give some instances of data collection methods frequently used in the field of distributed computing and talk about the various tools and technologies utilized in this area.The topic of distributed computing is rapidly developing, thus it's crucial to think about how these tools will affect society and ethics.Issues of privacy, security, and bias must be carefully considered for the appropriate use of distributed computing.Researchers and practitioners in this field have a duty to ensure the proper and ethical use of these technologies given the enormous potential of distributed computing to revolutionize the way we process and store data.
Finding optimal decision boundaries for human intervention in one-class machine-learning models for industrial inspection

Abstract Anomaly detection with machine learning in industrial inspection systems for manufactured products relies on labelled data. This raises the question of how the labelling by humans should be conducted. Moreover, such a system will most likely always be imperfect and potentially need a human fall-back mechanism for ambiguous cases. We consider the case where we want to optimise the cost of the combined inspection process done by humans together with a pre-trained algorithm. This gives improved combined performance and increases the knowledge of the performance of the pre-trained model. We focus on so-called one-class classification problems which produce a continuous outlier score. After establishing some initial setup mechanisms ranging from using prior knowledge to calibrated models, we then define some cost model for machine inspection with a possible second inspection of the sample done by a human. Further, we discuss in this cost model how to select two optimal boundaries of the outlier score, where in between these two boundaries human inspection takes place. Finally, we frame this established knowledge into an applicable algorithm and conduct some experiments for the validity of the model.
Visualization and Exploration of Complex Scientific Data

Researchers can acquire insights from complex data sets and better share their findings with others when they employ data visualization and exploration tools.We discuss existing techniques and tools for visualizing and exploring complex scientific data, identify the key concepts and principles of data visualization, and propose a new technique to address some of the limitations of existing approaches to data exploration and visualization.Our suggested method utilizes cloud computing and ML to deliver dynamic, interactive visualizations in real-time, regardless of data size or complexity.Our research provides a unique method for overcoming the limits of existing data visualization and exploration tools in the context of scientific inquiry, in addition to a detailed overview of existing methodologies and tools.We hope our results will stimulate greater investigation into this vital area, leading to improved methods and tools for visualizing and navigating intricate scientific data
Numerical Modeling of Hydrodynamic Performance on Porous Slope Type Floating Breakwater

A breakwater is a coastal building that aims to break up or withstand wave energy that enters the beach so that the characteristics of the incoming waves are by calculations and can reduce abrasion on the shoreline. Designing a floating breakwater is very complicated because it depends on many aspects. These fundamental aspects depend on each other, so if one of these aspects changes, the integrity of the floating breakwater structure will also change. One of these aspects is the magnitude of the transmission and reflection coefficients generated by the floating breakwater. This research will study the hydrodynamic performance of floating breakwater due to variations in slope and porosity in reducing and reflecting waves with computational fluid dynamics (CFD). The slope-porous floating breakwater dimension is based on previous experimental data, including a constant water depth of 0.75 m, a wave height of 0.05 - 0.125 m, and a wave period of 1.1 - 2 sec on regular waves. The results of the numerical model validation and experiments on all variations of the floating breakwater model are quite good, which is less than 10% for both wave transmission and reflection. Analysis of the influence of changes in the mooring line angle, the simulation is carried out at an angle of 30 deg to 90 deg and produces an average transmission coefficient of 0.79 and a reflection of 0.21. While the effect of changes in water level elevation (0.85 m, 0.75 m, and 0.65 m) gives a reasonably significant average transmission coefficient of 0.85 and a reflection of 0.13. The mooring line angle will be gentler at high tide, and the transmission and reflection coefficients will be higher. However, the mooring line will loosen at low tide, causing the structure to move more freely and eliminating the function of the floating breakwater itself so that the tidal phenomenon becomes a challenge for coastal experts in designing structures to produce effective and efficient hydrodynamic performance.
RUSSIAN UNIVERSITY SCIENCE: MAIN PROBLEMS AND WAYS TO SOLUTION THEM

Problems of university science in Russia are discussed and recommendations for their elimination are proposed. It is shown that state of university science &amp; research does not meet the requirements of the country’s innovative development. Support measures of the research activities of academics are not effective and stimulate imitation of the research activities. A qualitative leap in the potential of university science in the conditions of hard sanction pressure is impossible without changing state scientific and technical policy, which is associated, first of all, with the availability of long-term budget grant funding based on assessing research and its decentralization. Contribution of university science facing severe sanctions pressure does not meet the necessary condition for Russia’s survival – the achievement of technological sovereignty based on accelerated import substitution of its basic needs.
RESEARCH FOR ADAPTATION FACTORS OF THE HIGHER EDUCATION SPHERE TO THE NEEDS OF THE NATIONAL ECONOMY

The article is devoted to the issue of determining the adaptation factors of the higher education sphere to the needs of the national economy sectors. The relevance of the study is due to the necessity to find ways for increasing the productivity of results generated within the scope of higher education. In the course of the methodological substantiation of the solution of this scientific problem, the study of the results of the higher education sphere included in the research model was carried out, with the following enlarged groups identified: qualified personnel for the national economy, the results of research activities, the development of the territorial economy and the exchange of knowledge and the internationalization of higher education. The results obtained indicate that in order to increase the productivity of the higher education sector, it is necessary to develop programs for adapting the mechanism for managing the higher education sector while modifying the goals of scientific certification, modifying the goals in ensuring the connectivity of regional higher education systems and regional economic development programs as well as modifying the goals related to the projects on international mobility and attraction of foreign students and teachers.
SOSIALISASI PENGEMBANGAN PROTOTYPE COMPANY PROFILE BERBASIS WEBSITE UMKM PENGUSAHA GERABAH DAN KERAMIK PAGERJURANG MELIKAN BAYAT – WEDI KLATEN

The rapid development of Information Technology encourages Pagerjurang- Melikan MSME residents to always develop creations and have creativity in achieving maximum results in all fields. The need for easy, fast and timely information has become a way of life for humans, in the current 4.0 to 5.0 era. The rapid business competition forced the owners of the Pagerjurang - Melikan pottery craftsmen to find the right media to sell their products and innovate to develop more attractive and highly marketable types and forms of products. Purpose of Outreach Socialization Development of Website-Based Prototype Company Profile Umkm Pottery and Ceramic Entrepreneurs Pagerjurang Melikan Bayat – Wedi Klaten. Website is an internet application that can be accessed by almost all people today. The purpose of implementing PkM activities is to design and develop a website-based prototype company profile for Umkm Pottery and Ceramic Entrepreneurs Pagerjurang Melikan Bayat – Wedi Klaten. Because the sales made by MSMEs are still traditional, although there are some who have used the WA group and Facebook, but have never used a website application. The company profile prototype development method is waterfall, namely through several stages starting from planning and data collection, system design with Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), programming in PHPMySQL and XAMPP languages. Results of the Company Profile Website for UMKM Pottery Pager Jurang - Melikan Pay -Wedi Klaten, Outcome of implementing the Website which is expected to help provide solutions to problems encountered during the process carried out by UMKM manually.
Minerals System Approach to Sedimentary Rock-hosted Mineral Deposits: An Example from the Limerick Basin, Ireland

Summary The minerals system approach to mineral exploration mirrors in many ways the petroleum system model utilised successfully in the hydrocarbons industry for many decades. However, unlike the petroleum sector, which is concerned exclusively with the timely convergence of generation, transport, and trapping of liquid and gaseous hydrocarbons, mineral systems commonly involve generation of multiple fluids that must interact in order to precipitate (“trap”) metals. While there is a large diversity of types of mineral deposits ranging from those formed in magmatic systems to those in metamorphic environments, here we examine a basin-hosted sedimentary system in Ireland which has several similarities, but also key differences, with a classical petroleum system. Mineral exploration for sediment-hosted deposits increasingly on the intersection of basin modelling and mineral systems modelling to integrate the physical basin modelling geometries with the quantitative chemical descriptions of the subsurface, simulating the migration and chemical evolution of fluids from a metal source to the deposit sink that may include ingress of a second fluid containing reduced sulphur. Integration of geochemical and geophysical data is fundamental for this workflow which will also increasingly involve complex computer modelling.
Seismic first arrival picking with a U-SegNet network

Summary The traditional first break picking method has the disadvantages of low accuracy, low efficiency and low maturity of industrial application. In recent years, the first break picking methods based on deep learning also have some disadvantages, such as time-consuming and labor-intensive in label making process, too complex network structure leads to low efficiency of model training and application. For this reason, based on the U-Net and SegNet, this paper builds a hybrid network structure U-SegNet for first arrival picking: Based on SegNet, U-SegNet integrates the skip connection of U-Net to achieve the fusion of multi-scale features, and is more conducive to segmenting the background noise area and the valid signal area on the seismic traces; and its up-sampling prefer un-pooling used in SegNet rather than deconvolution used in U-Net, reducing the amount of parameters and accelerating the process of model training. The result of testing with shot data shows that the accuracy of first arrival picking using U-SegNet model is better than that of some software, and the efficiency is about 7 times that of manual picking. It is easy to industrialize and has good application prospects.
Improved Jacobian matrix estimation applied to snake robots

Two manipulator Jacobian matrix estimators for constrained planar snake robots are developed and tested, which enables the implementation of Jacobian-based obstacle-aided locomotion (OAL) control schemes. These schemes use obstacles in the robot’s vicinity to obtain propulsion. The devised estimators infer manipulator Jacobians for constrained planar snake robots in situations where the positions and number of surrounding obstacle constraints might change or are not precisely known. The first proposed estimator is an adaptation of contemporary research in soft robots and builds on convex optimization. The second estimator builds on the unscented Kalman filter. By simulations, we evaluate and compare the two devised algorithms in terms of their statistical performance, execution times, and robustness to measurement noise. We find that both algorithms lead to Jacobian matrix estimates that are similarly useful to predict end-effector movements. However, the unscented filter approach requires significantly lower computing resources and is not poised by convergence issues displayed by the convex optimization-based method. We foresee that the estimators may have use in other fields of research, such as soft robotics and visual servoing. The estimators may also be adapted for use in general non-planar snake robots.
ARTIFICIAL INTELLIGENCE IN SUSTAINABLE ENERGY INDUSTRY: STATUS QUO, CHALLENGES, AND OPPORTUNITIES

This study focuses on the future of AI in the energy sector, examining how AI can be used to improve efficiency and sustainability in the sector. The study aims to provide a realistic baseline of AI technology that can be used to compare efforts, ambitions, new applications, and challenges around the world. We covered three main topics: (i) how AI is being used in solar and hydrogen power generation; (ii) how AI is being used in supply and demand management control; and (iii) the latest advances in AI technology. In this research we explored how AI techniques outperform traditional models in controllability, energy efficiency optimization, cyber-attack prevention, IoT, big data handling, smart grid, robotics, predictive maintenance control, and computational efficiency. Our study found that AI is becoming an important tool for a new and data-intensive energy industry, which is providing a key magic tool to increase operational performance and efficiency in an increasingly cut-throat environment. KEYWORDS: Artificial Intelligence; Renewable Energy; Energy Demand; Decision Making; Big Data; Energy Digitization
Data sharing in agricultural supply chains: Using semantics to enable sustainable food systems

The agrifood system faces a great many economic, social and environmental challenges. One of the biggest practical challenges has been to achieve greater data sharing throughout the agrifood system and the supply chain, both to inform other stakeholders about a product and equally to incentivise greater environmental sustainability. In this paper, a data sharing architecture is described built on three principles (a) reuse of existing semantic standards; (b) integration with legacy systems; and (c) a distributed architecture where stakeholders control access to their own data. The system has been developed based on the requirements of commercial users and is designed to allow queries across a federated network of agrifood stakeholders. The Ploutos semantic model is built on an integration of existing ontologies. The Ploutos architecture is built on a discovery directory and interoperability enablers, which use graph query patterns to traverse the network and collect the requisite data to be shared. The system is exemplified in the context of a pilot involving commercial stakeholders in the processed fruit sector. The data sharing approach is highly extensible with considerable potential for capturing sustainability related data.