Reviewerly/exHarmony huggingface dataset:
Dynamically Changing Parameters Particle Swarm Optimization (DCPPSO) Based Trajectory Planning of 3-Links Articulated Robot

Time and Energy trajectory planning is an agile research topic among researchers in the field of robotics. This paper introduces a novel method for time-energy trajectory generation. Trajectory optimization is a vital problem in motion planning. The convexity and difficulties of the problem are increased by the practical constraints due to geometrical parameters, destination configurations, kinematic characteristics, and dynamic parameters of the robot. The proposed algorithm solved a convex optimal problem. The problem has two performance criteria, 13 constraints, and seven variables. A new variant of particle swarm optimization namely Dynamically Changing Parameters Particle Swarm Optimization (DCPPSO) is introduced. It prepared an optimal trajectory for a three RRR articulated robot. The results were contrasted with literature results.
The Innovative Application of Power Data Platform Construction Based on Blockchain Technology

With the application of the Internet in the power industry, the amount of power data is gradually increasing, but there is no interactivity between the data, and the amount of data in the island data is increasing. The power data can be sold as an asset by establishing a blockchain data platform. On the blockchain data platform, data can be encrypted through cryptographic technology. The data movements can be tracked through permissions. These measures can ensure data security. The power data can be used in multiple dimensions and multiple levels on this platform. The power data is verified to be true using technical verification on this platform.
Breast Cancer Detection Using Texture Features and KNN Algorithm

Breast Cancer is the most common form of cancer in women, majorly occurring in the age group of 40–70 years and the second most common cancer worldwide. There are several advances in image processing techniques and machine learning algorithms which aids the medical domain. The image processing works with image enhancement and object localization. Machine learning algorithms input image features to train the breast cancer detection model. It is important to extract the image features accurately to achieve promising results. This paper gives detailed insights of histopathological image features describing their technical and usability aspects. The study covers the whole spectrum of the histopathological images which include Haralick texture features and KNN Algorithm using Dimension Reduction Algorithm (LDA and PCA) for the detection of breast cancer.
Automatic model training under restrictive time constraints

Abstract We develop a hyperparameter optimisation algorithm, Automated Budget Constrained Training, which balances the quality of a model with the computational cost required to tune it. The relationship between hyperparameters, model quality and computational cost must be learnt and this learning is incorporated directly into the optimisation problem. At each training epoch, the algorithm decides whether to terminate or continue training, and, in the latter case, what values of hyperparameters to use. This decision weighs optimally potential improvements in the quality with the additional training time and the uncertainty about the learnt quantities. The performance of our algorithm is verified on a number of machine learning problems encompassing random forests and neural networks. Our approach is rooted in the theory of Markov decision processes with partial information and we develop a numerical method to compute the value function and an optimal strategy.
Stochastic Fractal Dynamic Search for the Optimization of CEC’2017 Benchmark Functions

This article develops an approach for dynamic parameter adaptation in the Stochastic Fractal Search (SFS) method, this by adding a fuzzy inference system for the dynamic adjustment of the diffusion parameter, thus generating the Stochastic Fractal Dynamic Search (SFDS) method. The SFDS implementation was carried out and tested with the optimization of CEC’ 2017 benchmark functions comparing its results with other optimization algorithms.
Understanding Short Text Through Lexical Semantic Analysis

Abstract Information of quick texts is crucial for many packages however there are many demanding situations. First, the fast texts do not constantly look at a written language’s syntax. As a end result, traditional natural language processing gear cannot be without problems applied, ranging from element-of-talk tagging to dependency parsing. second, brief texts usually do now not incorporate sufficient statistical signals to help a few of the state of play Thirdly, short texts are greater ambiguous land noisier, land are generated in a big quantity, which further will increase the problem of coping with them. We argue that semantic knowledge is important for better understanding of brief texts. on this paintings, we build a prototype gadget for short text expertise that takes advantage of semantic expertise furnished by using a famous know-how base and harvests it automatically Our methods for responsibilities which includes text segmentation, component-of-speech tagging, and idea labeling, inside the feel that during these kind of duties we awareness on semantic. We perform a complete evaluation of the overall performance on actual-existence facts. The outcomes display that semantic information is critical for the understanding of quick texts and our information-intensive technique.
Bitcoin Trading in Australia: Sneaking in Under the A$10,000 Reporting Threshold

Bitcoin trading on an Australian Cryptocurrency Exchange using an Australian bank means that transactions of A$10,000 or more are automatically reported to AUSTRAC. This analysis, of six years of Bitcoin trading on BTC Markets, finds that there is a propensity to keep trades just below the automatic reporting threshold. Unless these transactions arouse the suspicions of the exchange or financial institution then these trades go unreported. This may not be money laundering but there are traders who do not wish to draw attention to their Bitcoin transactions. The implication is that AUSTRACâ€™s AML/CTF regulation does have an impact on Bitcoin trading on an Australian exchange pointing to significant amounts, in this case A$23,750,000, going unreported over a six year period, as traders keep their transactions a fraction under A$10,000. A quick analysis of January 2021 indicates that this pattern continues with a significant amount of trading just below the reporting threshold.
Discussion on Application of the Internet of Things in Modern Agricultural Experimental Base

Modern information technologies, such as Internet of Things technology, image recognition technology and artificial intelligence technology, are used to conduct comprehensive intelligent perception and monitoring on the experimental bases distributed all over the country, and an intelligent experimental base management platform is built. All kinds of data acquisition devices are networked through intelligent network to realize automatic data acquisition, advanced computing, collaborative work, data mining and Intelligent and visual management of the experimental base. The construction of intelligent experimental base makes the management of the experimental base more scientific and standardized, avoids repeated labor, improves the efficiency of scientific research and expands the scope of scientific research.
A Literature Review on Technology-Based Problem-Posing Strategies in Academia

Problem-posing (PP) is an act by a learner to gain knowledge by generating new problems or questions in a given scenario. Studies have proved that PP is an effective student-centered method for learning. Moreover, it helps students to understand complex concepts and improve their learning outcomes. Many researchers have tried to develop technology-based learning systems using PP strategy in various domains and technology tools are playing a major role in education, in this digital era. Hence, the main objectives of this study are: a) To identify the strategies used to implement problem-posing based on technology and b) To identify the advantages of technology-based PP strategies over conventional problem-posing methods. To achieve these objectives we have conducted a literature review on 67 papers. The study has identified collaborative mobile learning, com-puter-based group activities, gaming activities, web-application-based activities and tablet-PC based activities as technology-based PP strategies. It has also identified that there are limited researches on PP in domains such as STEM, Nursing, English, and Biochemistry. We found that technology-based PP strategies have various advantages. They help students to acquire knowledge, make an impressive change in their learning outcomes, improve students' motivation and responsibility by engaging them in the learning process, improve collaborative skills and lower their cognitive load.
Optimal Inspection Path Planning of Loom Spindles Based on A* Algorithm and Ant Colony Algorithm

In order to improve the inspection algorithm for abnormal spindles in the textile industry, in this paper, the search neighborhood of the traditional A* algorithm is expanded to increase the diversity of searches, and a bi-directional search scheme is proposed to boost the search speed of the A* algorithm. After obtaining the shortest path between abnormal spindles, an improved ant colony algorithm with the elite strategy and optimized heuristic function is adopted, in which the maximum and minimum pheromone system is added to improve convergence speed and preventing local convergence. The probability of ant movement is ameliorated based on the actual production conditions, to make the algorithm more in line with production requirements.
OVERVIEW OF PRACTICAL IMPLEMENTATION OF MECHANISMS OF USER ACCESS TO UNIVERSITY OF THE UNIVERSITY

One of the hallmarks of the learning process is its manageability, which provides for the possibility of planning and designing the educational process. The article deals with practical issues related to the administration of the University Virtual Learning Environment in terms of organization and differentiation of access rights of different categories of users. The authors discuss the principles of administering a virtual learning environment in terms of distinguishing levels of access to individual elements of the system for different roles of different categories of users. In LMS Moodle, the list of hierarchical contexts is as follows: System (no parent context); Website – homepage (parent context – system); Course category (parent context – system); Course (parental context – course category or system); Course element (parent context – course); Block (parent context – system or course); User (parent context – system). In the Lviv Polytechnic University, only Super Administrator, Global Group Management, Methodist, and Library have global roles. Role assignments are performed to determine the role in context. The site (homepage) and course are examples of two different contexts. While the role assignment process is the same for different contexts, the path to the assignment page may be different. The article gives examples of how to organize a system of administration with a description of the processes of creating different types of roles and the logic of implementation of each role and the need for certain approaches. There are five types of roles in the article, namely: managerial, supervisory, training, informational, temporary, providing justification and practical advice on setting up each role for each type. The article describes a common responsibility matrix for each user role. The roles of management roles are different aspects of course administration at the level of course categories of different types of nesting. This type includes administrators. The role of the controlling role is, by name, to investigate the various aspects of educational and training activities within the VNS system. The objectives of the learning roles are to ensure and carry out the learning process itself. These roles include, first of all, the system role by default – the student, the main participant in the process of acquiring knowledge, passing checkpoints and other components of the educational process. The tasks of information roles are to create information messages for both students and teachers at different levels of the information system. Temporary role tasks are to involve third parties in the system. Of course, the guest is first and foremost, but in our case guest entry is forbidden, so the roles of Conference Participant and Olympiad Participant were created to provide access to individual system resources. The article describes a common responsibility matrix for each user role.
Research and design of blood cell detection device

Leukemia can be diagnosed early by blood cell examination technology, and its basic process needs to observe the blood smear containing the sample blood under the microscope, so the preparation and staining of blood smear is very important for the examination of leukemia. At present, there are some problems, such as high price, incomplete function, low efficiency and complex process. Based on this situation, this paper systematically studies the theory and application of blood cytology examination device. According to the different application requirements of blood smear preparation equipment, a high blood cell examination device is designed. The relationship between the push film parameters and the blood smear preparation effect is mainly studied. According to the conclusion, this paper design the structure, hardware, software, and finally the progressive the test results show that the device is stable in operation, high in automation, greatly improving the quality and efficiency of push sheet dyeing, high reliability, low cost, simple and convenient operation, achieving the expected design objectives.
Courier: Real-Time Optimal Batch Size Prediction for Latency SLOs in BigDL

Distributed machine learning has seen immense rise in popularity in recent years. Many companies and universities are utilizing computational clusters to train and run machine learning models. Unfortunately, operating such a cluster imposes large costs. It is therefore crucial to attain as high system utilization as possible. Moreover, those who offer computational clusters as a service, apart from keeping high utilization, also have to meet the required Service Level Agreements (SLAs) for the system response time. This becomes increasingly more complex in multitenant scenarios, where the time dedicated to each task has to be limited to achieve fairness. In this work, we analyze how different parameters of the machine learning job influence the response time as well as system utilization and propose Courier. Courier is a model that, based on the type of machine learning job, can select a batch size such that the response time adheres to the Service Level Objectives (SLOs) specified, while also rendering the highest possible accuracy. We gather the data by conducting real-world experiments on a BigDL cluster. Later on, we study the influence of the factors and build several predictive models which lead us to the proposed Courier model.
Coordination Approach to Mitigate Composition Attack

Data anonymization is one of the primary techniques, which is used in privacy preserving data publishing. Numerous methods have been proposed to anonymize data sets, that are used by different organizations to publish their data sets independently. However, in actuality, a data set is infrequently isolated, and data sets published by multiple organizations may obtain a record of the same person. An individually published data set may have a small privacy risk. However, the combination of two published data sets may breach individuals' privacy, who are common in both data sets by composition attack. A few researches have been carried out to prevent it without considering coordination among multiple organizations before data publishing. Though coordination is an excellent way to avoid such problems, organizations do not want to share their customer data sets with others. In this work, we introduce a coordination method where complete data sharing is not necessary. This will effectively overcome privacy concerns. It has been shown experimentally that the proposed technique anonymizes data set to satisfy the privacy requirement and preserves high data utility.
Peculiarities of Grammaticalization of the Italian Quantifiers of Indefinitely Big Quantity

The paper aims to identify peculiarities of the Italian quantifiers of indefinitely big quantity. Grammaticalization of quantitative lexemes is considered as a basic means to form quantifiers. The research objective is to develop a classification of the Italian quantifiers according to the degree of their grammaticalization. Scientific originality of the study lies in the fact that the Italian quantitative vocabulary is for the first time analysed through the lens of grammatical representation of the concept INDEFINITE QUANTITY. The research findings are as follows: the authors identify and describe three degrees of grammaticalization (minimum, average and maximum), propose a classification of the Italian quantifiers according to the degree of their grammaticalization.
Implementing an Innovative, Robust, and Encompassing Uncrewed System Data Enterprise

The use of uncrewed systems within NOAA as a research and operational tool is well-documented. The full benefits of uncrewed systems to achieving the NOAA mission have remained largely unrealized, primarily because of the programmatic nature of these deployments. Establishment of enterprise services to coalesce uncrewed systems activities is essential for NOAA to optimize the use and benefits of these systems. A robust and encompassing data enterprise is principal among these needed enterprise services, and is essential to the success of NOAA's uncrewed systems strategy. The NOAA Uncrewed Systems Strategic Implementation Plan presents a comprehensive multi-year plan of action that builds upon the framework established in the NOAA Uncrewed Systems Strategy. This manuscript describes the Uncrewed Systems Data Enterprise as outlined in the Implementation Plan. In Section II we describe the data enterprise milestones. The notional operational framework and planned case studies are described in Sections III and IV, respectively. The advantages of the data enterprise are summarized in Section V.
Individualized Cultivation Path of Mixed Teaching of Multimedia Technology Course Based on Data Analysis

With the promotion and implementation of “Internet + Education”, the organic combination of two teaching organization forms online teaching platform and traditional education can lead learners to shift from shallow learning to deep learning . Multimedia Technology is a compulsory course for digital media technology majors. This course implements teaching reform in terms of curriculum design, teaching organization and teaching evaluation. In addition, this course manages student learning in three aspects: warm-up, in-class supervision and post-class reflection, which generates a large amount of learning data. By analyzing and classifying students’ learning data and exploring the learning status, strengths and weaknesses of students, this course can provide a variety of teaching programs and individualized cultivation.
Create a Hybrid Algorithm by Combining Hill and Advanced Encryption Standard Algorithms to Enhance Efficiency of RGB Image Encryption

The greatest challenge of this century is the protection of stored and transmitted data over the network. This paper provides a new hybrid algorithm designed based on combination algorithms, in the proposed algorithm we combine the Hill and the Advanced Encryption Standard Algorithms, to increase the efficiency of color image encryption and increase the sensitivity of the key to protect the RGB image from Keyes attackers. The proposed algorithm has proven its efficiency in encryption of color images with high security and countering attacks. The strength and efficiency of combination the Hill Chipper and Advanced Encryption Standard Algorithm is tested by statical analysis for RGB images histogram and correlation of RGB images before and after encryption using hill cipher and proposed algorithm and also analysis of the secret key and key space to protect the RGB image from brute force attack. The result of combining Hill and Advanced Encryption Standard Algorithm achieve strength in encrypting images rather than encrypting images by using the single Hill algorithm.
Digital Operative Guides for Helping First Responders in Public Buildings During Emergency Interventions

Abstract Graphical tools for firefighters in emergency interventions have been proven to be very effective. Particularly, paper-based operational guides and digital guides with 360º images were already assessed during the drills developed, during a training program in a real, but obsolete and not in use, hotel. The obtained results were considered very interesting, being part of the study previously published by this research team. In the previous study, three different methods (two of them based on graphical contents) were compared in order to communicate the features of a building to firefighters, five minutes before starting a victim rescue during the drills of the mentioned live fire training program. These good results encouraged us to continue the development of the digital guides and this paper describes the first one created for a real building, placed in Zaragoza (Spain), which is in use nowadays. In this study, two versions of the digital guide are presented, one based on a PDF file and another one implemented with online 360º images.
Local Linear Model Tree with Optimized Structure

This paper investigates the local linear model tree (LOLIMOT) with optimized structure. The performance of the LOLIMOT model depends on how the neurons are constructed. In the typical LOLIMOT model, the number of neurons is initially set as one and starts to increase by repeatedly splitting an existing neuron into two equal ones until the required performance is achieved. Because the equal split of a neuron is not optimal, a large model size is often necessary for required performance, leading to high complexity and strong overfitting. In this paper, we propose a gradient-decent-search-based algorithm to optimally split an existing neuron into two new ones. Based on both numerical data and simulated engine data, through the evaluation of optimized structure, the effectiveness of proposed method has been verified.
A New Electronic Voting System Based on Elliptic Curve in a Cloud Computing Environment

The process of democratization is developing and promoting due to electronic voting. However, the increase of frauds and the increasing amount of attacks launched by hackers, gave birth to privacy and authentication problems. Cryptography offers multiple solutions to overcome the sensitive data protection issues in e-voting. In this paper, we study the application of elliptic curve cryptography, and use the homomorphic encryption properties to present a new electronic voting system. Our new scheme is based on the homomorphic cryptosystem EC-ELGAMAL [], and zero knowledge algorithm of Schnorr algorithm for identification and authentication.
INVESTIGATING ATTRIBUTE RISKS AND CONSTRUCTING LINKAGE ERROR MODELS FOR PROBABILISTICALLY-LINKED DATA

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