Reviewerly/exHarmony huggingface dataset:
Research on Identification Method for Interface Flange in Automatic Docking System of Fluid Loading and Unloading Arm for Bottom Loading

The automatic docking system of the loading and offloading arm of a tank car is the key link to realizing the unmanned operation of tank car loading and unloading. The spatial position detection of the flange port of a tank car can guide the automatic docking of the fluid loading and offloading arm and flange port of the tank car. In this paper, a flange position detection method based on image recognition was proposed. Firstly, the end state of the loading arm was analyzed to determine the expression mode of the loading arm’s spatial pose so as to form a unified expression with the flange position and docked pose on the tank car. Then, for the image processing of the flange port of the tank car, this paper binarized the edge of the flange end face based on the Otsu algorithm, used the Canny algorithm for edge detection, used the least squares method to fit the image edge coordinates into a spatial circle, calculated the center coordinates and normal vector of the flange end face, and used these parameters to guide the end of the loading arm to adjust the position and attitude so that it was consistent with the position and pose of the flange port to realize docking. Then, a circular object center detection and calibration experiment, a flange end face image experiment, and an automobile tank car flange port physical detection experiment were carried out. The test results show that the spatial coordinate accuracy of the flange port diameter and center detected by this method meets the requirements of the loading arm automatic docking system, providing a research idea for the design of an automatic docking system for the loading and unloading arm of a tank car.
Construction Project Claim Management under the Background of Wireless Communication and Artificial Intelligence

At this stage, construction projects involve more and more aspects, and the market environment they face is also increasingly complex. The application of wireless communication and artificial intelligence technology provides convenient conditions for the establishment of construction engineering claim management system. Based on this, this paper conducts research on the subject of construction engineering claim management under the background of wireless communication and artificial intelligence. This paper first briefly introduces the classification of construction engineering claims, designs the construction engineering claim management system, and analyzes the relevant functional requirements and functional modules of the system. In terms of grasping the current situation of relevant people’s cognition of construction engineering claims, the research adopts the method of questionnaire survey, then summarizes the existing problems according to the questionnaire data, and finally combines the predesigned functional modules to solve these problems. Systematic Recommendations. The survey data shows that in the case of 400 concurrent users, the transaction volume of the system reached 17.13 T/s, and the system transaction operations were all successful. These experiments demonstrate that the availability and adaptability of the preset functional modules are very high.
Toward real-time image annotation using marginalized coupled dictionary learning

In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming. In this paper, a novel coupled dictionary learning approach is proposed to learn a limited number of visual prototypes and their corresponding semantics simultaneously. This approach leads to a real-time image annotation procedure. Another contribution of this paper is that utilizes a marginalized loss function instead of the squared loss function that is inappropriate for image annotation with imbalanced labels. We have employed a marginalized loss function in our method to leverage a simple and effective method of prototype updating. Meanwhile, we have introduced $${\ell }_1$$ regularization on semantic prototypes to preserve the sparse and imbalanced nature of labels in learned semantic prototypes. Finally, comprehensive experimental results on various datasets demonstrate the efficiency of the proposed method for image annotation tasks in terms of accuracy and time. The reference implementation is publicly available at https://github.com/hamid-amiri/MCDL-Image-Annotation .
An Optimized Decision Method for Smart Teaching Effect Based on Cloud Computing and Deep Learning

In order to improve the effect of intelligent teaching and give full play to the role of intelligent technology in modern physical education, in this paper, cloud computing and deep learning methods are used to comprehensively evaluate the teaching effect of colleges and universities, and calculate the evaluation effect and accuracy. Cloud computing and deep learning algorithm combine the teaching evaluation scale, teaching content, and characteristics to formulate teaching plans for different students and realize targeted teaching evaluation. The results show that the teaching evaluation method proposed in this paper can improve students' learning interest by about 30%, enhance learning initiative by about 20%, and the matching rate between the actual teaching effect and the expected requirements is 98%. Therefore, cloud computing and deep learning model can improve the accuracy of teaching effect evaluation in colleges and universities, provide support for the formulation of teaching evaluation schemes, and promote the development of intelligent teaching in colleges and universities.
Going Beyond Brick and Mortar Self-Access Centers: Establishing a Satellite Activity Self-Access Program

Providing self-access learning opportunities in the form of brick and mortar self-access centers has long been part of universities. However, other self-access learning program options are available for contexts where limited resources may not support such structures. Accepting this, this paper describes the creation and operation of an alternative model, a satellite activity self-access program at a North-East Asian (Taiwanese) university. The paper also discusses the model’s generalizability to other contexts and offers suggestions for further study.
Knowledge Reuse of Multi-Agent Reinforcement Learning in Cooperative Tasks

With the development and appliance of multi-agent systems, multi-agent cooperation is becoming an important problem in artificial intelligence. Multi-agent reinforcement learning (MARL) is one of the most effective methods for solving multi-agent cooperative tasks. However, the huge sample complexity of traditional reinforcement learning methods results in two kinds of training waste in MARL for cooperative tasks: all homogeneous agents are trained independently and repetitively, and multi-agent systems need training from scratch when adding a new teammate. To tackle these two problems, we propose the knowledge reuse methods of MARL. On the one hand, this paper proposes sharing experience and policy within agents to mitigate training waste. On the other hand, this paper proposes reusing the policies learned by original teams to avoid knowledge waste when adding a new agent. Experimentally, the Pursuit task demonstrates how sharing experience and policy can accelerate the training speed and enhance the performance simultaneously. Additionally, transferring the learned policies from the N-agent enables the (N+1)-agent team to immediately perform cooperative tasks successfully, and only a minor training resource can allow the multi-agents to reach optimal performance identical to that from scratch.
Assessing the elements at risk in volcanic areas by combining deep convolutional neural network and multispectral satellite images

&amp;lt;p&amp;gt;Volcanic eruptions are spectacular but dangerous phenomena. Depending on their magnitude and location, they also have the potential for becoming major social and economic disasters. Some of the most important volcanic events include ash fallout, lava flows, and related phenomena, such as volcanic debris avalanches and tsunamis. The ongoing demographic congestion around volcanic structures, such as Mount Etna, increases the potential risks and costs that volcanic eruptions represent and leads to a growing demand for implementing effective risk mitigation measures. To fully evaluate the potential damage and losses that a volcanic eruption disaster may cause, the distribution and characterization of all the exposed elements must be considered. Over the past decades, advances in satellite remote sensing and geographic information system techniques have greatly assisted the collection of land cover data. However, assessment of the elements at risk is a lengthy and time-consuming process. In fact, usually data including all exposed elements and land uses are gathered from several Institutional web portals and very high-resolution satellite imagery, not freely available, manipulated by operators. Here, we propose a deep learning approach to automatically identify the elements at risk in high spatial resolution satellite images. In particular, a Convolutional Neural Network (CNN) model is adopted to classify land use and land cover in volcanic areas thus allowing to carefully assess the total exposure by using freely available satellite images. A retrospective analysis is conducted on Mount Etna highlighting changes in the exposure over the last decade.&amp;lt;/p&amp;gt;
Use of Game Learning As a Platform for the Concept of Peace EducationOptimization of the Board Game as a Platform for the Concept of Peace Education: A Survey Method Study

The implementation of peace education in the university environment has not yet found the right technical implementation, so the main goal in peace education has not been achieved, especially the application in the university environment. Therefore, this research aims to determine the general perception of board games as a media of peace education for students. This study uses a survey method collected from a representative sample of students from three different universities. The sampling technique in this study uses purposive sampling aimed at students who have courses in which peace education is integrated. The research locations this time are the Universitas Pendidikan Indonesia (UPI), Universitas Padjadjaran (UNPAD), and Institut Teknologi Bandung (ITB). This study indicates that students are very enthusiastic if this board game becomes a platform for delivering peace education in the student environment. This study concludes that students agree with the concept of delivering peace education through board game media.
Alat Pendeteksi Data Vaksinasi Sensor FPM10A dan NODEMCU ESP8266 Menggunakan Metode Sequential Searching

Making a finger vaccination data detection system using the IOT-based FPM10A sensor and Nodemcu esp8266 in order to facilitate the community process during a pandemic. Sequential Search Algorithm is a linear search algorithm, this algorithm performs searches faster because the search process is already in an ordered state. In this pandemic period, in every activity or traveling to a place, now you often have to show proof of vaccination and the community's capacity to use the internet is still not evenly distributed to show vaccination certification, of course this will make it easier, this can also avoid fake vaccination certifications. In this study, in addition to using a fingerprint recognition device, the Nodemcu esp8266 microcontroller and fpm10a sensor are also used as transmission media to the application, making it easier for the public and users of this tool to save costs and time.
Effectiveness analysing of modification of training data set for voice forgery detection system

Abstract Paper presents data of the experiments with the advanced model of voice authentication. The main idea of the research is to prove or deny the hypotese that adding generated fake data to the training set of the background model help to increase the probability of detecting fake data of the target announcer. Processing the results of a series of experiments did not confirm the hypothesis.
Indepth combinatorial analysis of admissible sets for abstract argumentation

Abstract In this paper we investigate, from a graph theoretical point of view, the notion of acceptability in Dung semantics for abstract argumentation frameworks. We advance the state of the art by introducing and analyzing combinatorial structures exploited for taming, in particular cases, the exponential blowout of acceptance algorithms. We conclude the paper by a series of observations allowing to deepen the intuition with respect to the practical use of Dung acceptance based semantics.
Design of New Word Retrieval Algorithm for Chinese-English Bilingual Parallel Corpus

Natural language processing is an important direction in the field of computer science and artificial intelligence. It can realize various theories and methods of effective communication between humans and computers using natural language. Machine learning is a branch of natural language processing research, which is based on a large-scale English-Chinese database. Due to the relatively poor alignment corpus of English and Chinese bilingual sentences containing unknown words, machine translation is unprofessional and unbalanced, which is the problem studied in this paper. The purpose of this paper is to design and implement a length-based system for sentence alignment between English and Chinese bilingual texts. The research content of this paper is mainly divided into the following parts. First, the evaluation function of bilingual sentence alignment is designed, and on this basis, the bilingual sentence alignment algorithm based on the length and the optimal sentence pair sequence search algorithm is designed. In this paper, China National Knowledge Infrastructure (CNKI) is selected as an English-Chinese bilingual candidate website and English-Chinese bilingual web pages are downloaded. After analyzing the downloaded pages, nontext content such as page tags is removed, and bilingual text information is stored so as to establish an English-Chinese bilingual corpus based on segment alignment and retain English-Chinese bilingual keywords in the web pages. Second, extract the dictionary from the software StarDict, analyze the original dictionary format, and turn it into a custom dictionary format, which is convenient and better to use the double-sentence sentence alignment system, which is conducive to expanding the number of dictionaries and increasing the professionalism of vocabulary. Finally, we extract the stems of English words from the established corpus to simplify the complexity of English word processing, reduce the noise caused by the conversion of word parts of speech, and improve the operation efficiency. A bilingual sentence alignment system based on length is implemented. Finally, the system parameters are adjusted for comparative experiments to test the system performance.
Performance Enhancement of Cloud Datacenters Through Replicated Database Server

Cloud computing has risen as a new computing paradigm providing computing, resources for networking, and storage as a service across the network. Data replication is a phenomenon which brings the available and reliable data (e.g., maybe the databases) nearer to the consumers (e.g., cloud applications) to overcome the bottleneck and is becoming a suitable solution. In this paper, the authors study the performance characteristics of a replicated database in cloud computing data centres which improves QoS by reducing communication delays. They formulate a theoretical queueing model of the replicated system by considering the arrival process as Poisson distribution for both types of client request, such as read and write applications. They solve the proposed model with the help of the recursive method, and the relevant performance matrices are derived. The evaluated results from both the mathematical model and extensive simulations help to study the unveil performance and guide the cloud providers for modelling future data replication solutions.
Capilarization Game Foster Childrens’s Cognitive and Language Abilities

The purpose of the study was to analyze effect of capillary games foster children’s cognitive and language abilities. The research was conducted using a quasi-experimental design with a quantitative approach. The experimental design used was a nonequivalent control group design. The research study was situated in an Indonesian early childhood education institution with a total of 30 children in the experimental group and 30 children in the control group. The data analysis technique in this study uses parametric statistics independent t test. The results showed that (1) there was an effect of capillary game foster children’s cognitiveve abilities, statistically the value of t = -4.382 with a significant level of p = 0.000 less than 5%; (2) there is an effect of the capillary game on childrens’s language abilities, statistically the value of t = -4.617 with a significant level of 0.000 less than 5%. Based on the results of the research and discussion, it can be concluded that cappilarry games foster childrens’s cognitive and language abilities in an Indonesian early childhood education institution.Through from cappilarry games makes the childrens was recognice many color so the cognitive abilities can be foster. Another ability, cappilarry games make the childrens fluent to be telling stories so the language abilities can be foster. The practical implication in this research is that capillary games can be the foster children’s cognitive and language abilities.
Optical dendrites for spatio-temporal computing with few-mode fibers [Invited]

Multimode fibers have been extensively used in short-range communication systems and optical imaging. More recently, they have been considered for optical computing by exploiting the complex spatial and spatio-temporal transformation at the fiber end. Mimicking the dendrites of real neurons, we consider here the spatial modes that propagate with different group velocities along the fiber as different dendritic branches. The multimode fiber plays the role of an optical dendritic unit and the signals obtained from the different dendritic branches are temporally mixed and used for spatio-temporal information processing. We numerically demonstrate the use of a few-mode, step-index fiber as a linear computing element in an ultra-fast spatio-temporal coincidence detector that operates at 40 Gb/s data encoding rate. We evaluate this detector as a linear classifier in header recognition and bit counting tasks.
An Analysis of Learning Outcomes Achieved through Pedagogical Competencies Acquisition Programme in the Republic of Croatia

Education has long been recognized as the key to the survivalnot only of the individualbut of society as a whole.A number of challenges emerged and one is required to master certain new knowledges and skills.The key role in training an individual to be able to readily tackle the challenges of the 21st century is one played by the education system, i.e. teachers.The importance and necessity of teacher training has been recognized in numerous European and Croatian documents and in recommendations based on various scientific research.The aims of this paper are to single out the key teachers' competencies and to analyse the learning outcomes of lifelong learning programmes that focus on acquisition of pedagogical competencies in the Republic of Croatia with both qualitative and quantitative research method.The conducted analysis showed that no programme with its compulsory part ensures the acquisition of the necessary key teachers' competencies but also seeks to provide them with the elective part of the programme.This means that acquisition of key pedagogical competencies is left to the volition of students who may or may not enrol in certain elective courses.The obtained results indicate there is a need to design and prescribe a single programme at the national level, one that would be certain to ensure that all the teachers acquire key pedagogical competencies through the completion of the programme.
Tighter Constraints of Multipartite Systems in terms of General Quantum Correlations

Monogamy and polygamy relations characterize the quantum correlation distributions among multipartite quantum systems. We investigate the monogamy and polygamy relations satisfied by measures of general quantum correlation. By using the Hamming weight, we derive new monogamy and polygamy inequalities satisfied by the β-th power and the α-th power of general quantum correlations, respectively. We show that these monogamy and polygamy relations are tighter than the existing ones, such as Liu [Int. J. Theor. Phys. 60, 1455–1470 (2021)]. Taking concurrence and the Tsallis-q entanglement of assistance as examples, we show the advantages of our results.
Otomatik Türkçe Kartvizit Tanıma için Bulut Tabanlı WEB Uygulama Tasarımı ve Performans Değerlendirmesi

In this study, digital-business card holder software was developed that digitally stores physical business cards prepared in Turkish in a cloud-based database. In the proposed software, the information on the physical business card is converted into text by optical character recognition method (OCR) using business card photos, and then the texts obtained with the help of developed algorithms are separated and grouped. Finally, the digitally obtained business card data is stored in the cloud-based database for later use. Considering the Turkish business cards, it is known that there are a wide variety of complex business cards unique to the country as well as the characters specific to the Turkish language. In this context, first of all, a method that correctly recognizes Turkish characters has been determined in the study. Later, name, mobile phone, e-mail address, company title, position and similar meaningful information were separated from the data read. In order to make these decompositions, special methods have been developed for each field and more accurate and meaningful data has been obtained with field-based algorithms. Thanks to the developed cloud-based platform-independent interface, it is possible to access data from more than one device with a single user over the internet. The study also offers a layered service architecture and database infrastructure that can be used by multiple accounts and multiple users connected to it simultaneously from a single platform. In addition, in the analyzes performed with the developed software, it was determined that 15 business cards with different features were read with an accuracy rate of over 80%.