Reviewerly/exHarmony huggingface dataset:
RETRACTED: Research on Robot Path Planning Based on Improved Ant Colony Algorithm under Computer Background

Abstract With the development of artificial intelligence technology, the intelligent requirement of robot path planning (hereinafter referred to as PP) is higher and higher. Therefore, people gradually study the optimization of PP algorithm more and more mature. At present, the traditional robot has been unable to meet the requirements of task efficiency, which requires the robot to have the function of autonomous and safe path finding. Therefore, we must optimize the traditional PP algorithm, which can find a time-consuming and safe path in many feasible paths. Therefore, PP is the basis of the exploration task. Traditional ant colony algorithm (hereinafter referred to as ACA) has many problems, such as slow convergence speed, low efficiency, easy local optimization and other defects, which have many restrictions on robot PP. Therefore, this paper proposes an Improved ACA (hereinafter referred to as IACA), which can optimize the path selection by improving pheromone. Firstly, this paper analyzes the TMR (hereinafter referred to as TMR) PP and the principle of IACA. Then, the PP of TMR is simulated and tested, which reduces the cycle times. Therefore, the IACA can effectively improve the search efficiency of the optimal path, which improves the overall performance of PP.
Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents.This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections.This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values.Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents.This R-HGN approach is validated against individual behaviour implementation and random behaviour selection.This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions.R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pretrained and unexpected environment conditions.
Introduction to the Monolithic Architecture

Nowadays, we use many methods to access online applications. For example, we can use a browser or client apps to access Facebook or Twitter. This is possible through their exposed Application Programming Interfaces (APIs). An API is a software boundary that allows two applications to communicate with each other using a specific protocol (HTTP, for example). In the Facebook context, when people use the mobile app while sending a message, the app uses the Facebook APIs to make the request. Next, the application uses its business logic and makes all the necessary databases queries and finalizes the request by returning HTTP responses.
Optimization and Simulation of Manuscript Management System Based on Fuzzy Genetic Neural Network

Manuscript management plays an important role in the whole periodical industry. Journals and magazine society receive different types of contribution documents from all over the world. Many submission files are mostly transmitted by e-mail, but there are some hidden disadvantages in e-mail. In view of this situation, this paper studies the establishment and optimization simulation of manuscript management system based on fuzzy genetic neural network (FGNN). On the basis of genetic neural network, combined with the advantages of FNN neural network algorithm, a FGNN structure is established to optimize the system, which is helpful to the learning and expression ability of the whole system. The results show that the FGNN can extract and express the structured data, and the submission management system runs faster. Then, UML modeling and PHP framework are used to realize the design and establishment of the system, and the simulation model of contribution system is constructed. The innovation of this study is to combine genetic neural network with FNN neural network, which can query, manage, and classify manuscripts quickly. It also improves the complex submission system and optimizes the submission process. The whole journal editing work has been effectively improved.
Report of the 1st International Workshop on Context-aware Autonomous and Smart Architectures (CASA@ECSA 2017)

The 2017 1st International Workshop on Context-aware, Autonomous and Smart Architectures (CASA 2017) was held in conjunction with the 11th European Conference on Software Architecture (ECSA 2017), on September, 12th, 2017 in Canterbury, United Kingdom. The goal of this one-day workshop was to bring together researchers and practitioners from academic environment and from the industry to share their solutions, ideas, visions, and doubts in the design of software architectures for context-aware, autonomous, and smart solutions. The workshop focused on architectural aspects offering a complementary vision of such solutions with respect to the available application- and user-oriented perspectives on context-awareness, autonomy, and smartness. Furthermore, the workshop aimed to enable discussions, partnerships, and collaborations among the software engineers interested in these solutions.
Perceptions and Practices of Scholarly Self-Publishing among Nigerian Librarians: Implications for Scholarly Communication

The study examined perceptions and practices of self-publishing among Nigerian librarians and its implications for scholarly communication. The study adopts sequential exploratory strategy of mixed methods research design. Interview and web-based questionnaire were used to collect data for the study. Twelve (12) participants were interviewed and 149 responses were collected using Web-questionnaire. It was found that scholarly self-publishing will increase the volume of literature but would have a negative effect on the standard of scholarly communication. It was found that scholarly self-publishing will increase poor quality in scholarly works and promote elements of bias. Findings show that issues of verifiability and reliability of scholarly self-published authors will be a concern in scholarly communication. It was shown that there is no statistically significant relationship between perceptions and practices of scholarly self-publishing. It was concluded that scholarly self-publishing will negatively affect the quality of scholarly communication but will promote open access initiatives.
Innovative Practice of Digital Audit Management Mode for Power Grid Enterprises

In recent years, power grid companies have actively carried out digital audit work, formed a good data analysis system, and carried out continuously auditing companies, which has a greatly achieved a digital audit. Work has got a brand new development opportunity. However, internal management puts forward more requirements on digital audit, so it is necessary to continuously strengthen the research on digital audit. In view of this, this article mainly analyzes the innovative practice of the digital audit management mode of power grid enterprises.
Prediction of Emotional Score of the multiple faces of a Photo Frame through Facial Emotion Recognition using the Deep Convolutional Neural Network

The facial movement of his or her is an important mark of understanding the emotion. The emotions are of different categories like angry, sad, neutral, happy, disgust, fear, surprise, etc. To classify the image into an appropriate class of emotion using the deep Convolutional neural network is a more scientific approach of classification. The classification is not only from the current observations but also from the past evidence, i.e. a training model is used to do this job. In this research article, we developed a model that derives the perception of a frame that consists of multiple faces by measuring each of its facial emotions. The developed model, therefore, claimed to be more efficient and robust against the variety of inputs.
Towards a Graphical DSL for Tracing Supply Chains on Blockchain

Nowadays, supply chain tracing notarization is among the most used non-financial blockchain applications. However, creating a blockchain based system for the management of a supply chain remains a complex task. In this paper, we propose a graphical domain specific language (DSL) and a tool allowing the supply chain domain expert to easily represent the supply chain he needs to trace. The graphical representation of the supply chain is then translated in automatic way in a set of solidity smart contracts implementing it. A small intervention of a programmer is required to customize and finalize such smart contracts. The obtained semi-automatic process of smart contract generation will boost the blockchain usage for supply chain traceability.
Data Science with Human in the Loop

The aim of this workshop is to stimulate research on human-computer interaction challenges in data science. We invite researchers and practitioners interested in understanding how to optimize the human-computer cooperation and how to minimize human effort along the data science pipeline in a wide range of data science tasks and real-life applications. One over-arching challenge is to raise the level of abstraction of human-computer interaction to more sophisticated interaction models that better reflect a human's conceptual model and understanding. This workshop will bring together the interdisciplinary researchers from academia, research labs and practice to share, exchange, learn, and develop preliminary results, new concepts, ideas, principles, and methodologies on understanding and improving human-computer interaction for cost-effective development of data science models and for knowledge discovery. We expect the workshop to help develop and grow a strong community of researchers who are interested in this topic, and yield future collaborations and scientific exchanges across the relevant areas of data mining, machine learning, data and knowledge management, human-machine interaction, and user interfaces.
Analysis of Feasibility Modeling of Multimode Physical Fitness Training for Long-Distance Runners

Because the traditional multimode feasibility modeling analysis method of physical fitness training for long-distance runners has the problems of long modeling time and low modeling accuracy, a new multimode feasibility modeling analysis method for physical fitness training of long-distance runners is proposed. The improved LLE (local linear embedding) method is used to reduce the dimensionality of the training data for the physical fitness of long-distance runners. According to the processing results, the information theory is used to analyze the information content of the physical fitness training features of the long-distance runners, the information entropy of each feature is calculated, and the long-distance runners are extracted. Athlete’s physical fitness enhancement training characteristics, combined with quantitative regression analysis method to carry out the information regression analysis of the long-distance runners’ physical fitness training multimode statistical sequence, construct the feasibility evaluation model of the long-distance runners’ physical fitness training multimode and complete the feasibility of the long-distance runners’ physical fitness training multimode feasibility study Mode analysis. The simulation experiment results show that the proposed method has higher accuracy and shorter modeling time for multimode feasibility modeling of physical fitness training for long-distance runners.
Strong and Weak Universalism in Bibliographic Services

Wilson stated that the work of providing bibliographic services is political in nature because catalogers have to adjudicate amongst the demands made for various forms of control by different communities. Although he did not address the work of the Documentalists directly, his claim is an implicit refutation of their universalist claims. Their efforts were to bring all the world’s knowledge together, organize it under a single principle, and make it available to an undifferentiated global community. The concept of “universality” is examined in the context of bibliographic services, looking at claims regarding the collection of knowledge, users, and access.
Genesis of the giant Kangjiawan lead-zinc ore deposit in Hunan Province: Evidences from fluid inclusion, H-O and S isotope

康家湾铅锌矿床位于湖南省水口山矿田，矿体主要产于二叠系当冲组下段泥灰岩、硅质岩与栖霞组灰岩的层间硅化破碎带中。根据矿物组合和穿插关系，可将该矿床的成矿作用过程划分为3个阶段：黄铁矿-石英阶段、闪锌矿-方铅矿（黄铁矿）-石英阶段和方解石-闪锌矿-方铅矿阶段。流体包裹体研究表明，康家湾铅锌矿床黄铁矿-石英阶段的流体主要为中-高温（243~343℃）、中-高盐度（18.4%~33.8% NaCleqv）的流体；闪锌矿-方铅矿（黄铁矿）-石英阶段的流体为中-高温（278~352℃）、中-低盐度（1.1%~20.7% NaCleqv）流体；晚期方解石-闪锌矿-方铅矿阶段的流体为低温（125~191℃）、低盐度（0.2%~6.7% NaCleqv）的流体。其中，闪锌矿-方铅矿（黄铁矿）-石英阶段的流体发生了沸腾作用。激光拉曼分析结果显示，该矿床成矿期的石英和闪锌矿中的液体包裹体气相成分主要为H<sub>2</sub>O。H-O、S同位素研究显示，康家湾铅锌矿床的成矿流体可能主要来源于岩浆水，并在运移过程中混合了大气水。结合矿床地质、流体包裹体和氢氧、硫同位素特征，流体混合导致温度、盐度降低和沸腾作用可能是导致康家湾铅锌矿床成矿物质发生大规模沉淀的重要因素。
Implementación de un objeto de aprendizaje: el caso de una docente de enseñanza secundaria

This research paper analyses the results of the implementation process of a Natural Science Learning Object (LO) by an in-service teacher with more than 20 years of experience in secondary education.It is justified considering that over the last decade a significant percentage of research on LO has focused on technical issues, while the educational implications and promises remain unproven, particularly in the secondary school classroom.The methodology carried out in this research is qualitative descriptive by means of a case study, through the following phases: contextualization phase; immersion phase; and results and analysis phase of the implementation of the LO.The data sources included classroom observations, audio and video recordings, semi-structured interviews, and area and classroom plans.The data were examined through in-depth analysis.The results allowed establishing that the LOs are linked to a concrete curricular proposal for teacher training in the educational institution, where the curriculum prescribed by the LO dialogues with the curriculum planned and developed by the teacher, in this two-way dialogue the proposed flow of learning undergoes modifications to support learning and teaching.
On-chip generation of telecommunications-compatible ultrafast time-bin entangled qubits

We demonstrate telecom compatible time-entangled two-photon pairs qubits using an on-chip unbalanced interferometer and spiral waveguide for state preparation/processing and generation, respectively. We show quantum interference and confirm Bell’s inequality violation.
Research on the Application of Computer Music Software in College Traditional Music Course

Abstract The traditional teaching mode of music course is still based on the theory teaching mode of teachers. It is difficult to give full play to the main position of students, which leads to the low learning enthusiasm of students and the difficulty to fully stimulate their interest in learning. Based on this, this paper first analyses the music knowledge and the application of music software, and then studies the commonly used computer music software in university traditional music teaching, and finally gives the advantages and specific utilization strategies of brain Music software in traditional university music course.
The Pros and Cons of Artificial Intelligence use in the Logistics Sector in Oman

This paper systematically discusses the impact of using AI in Oman's logistics sector and examines the opportunities and challenges of using AI in Oman's logistics sector. Besides, it suggests solutions for improving AI use in the logistics sector in Oman. The main objective of this paper is to investigate the impact of using AI in Oman's logistics sector. The specific objectives are to identify the opportunities for using AI in the logistics sector in Oman, identify the issues surrounding using AI in the logistics sector in Oman, and recommend possible solutions to improve the use of AI in the logistics sector in Oman. The study used both primary and secondary data based on mixed methods: quantitative and qualitative. The quantitative approach via a questionnaire tool to collect data from many respondents. The qualitative method has been observed using interviews and web articles to accumulate further information from people's opinions and thoughts. The outcomes indicated some advantages of AI: improving efficiency and reducing the challenges currently facing the logistics sector, diversifying the income sources, and enhancing Oman's economy. Findings revealed that the most significant AI challenges are unemployment, a long time and the high cost for implementation, lack of awareness of AI potentials, and requiring training with new skills. The findings came up with solutions related to educating students, spreading awareness, and training employees. The study has limitations such as insufficient information on the research subject, difficulties finding respondents who have enough experience to answer the questions efficiently, and time constraints.
Training Mode of Entrepreneurial Talents in Private Undergraduate Universities under the Background of Big Data

With the development and perfection of big data technology and artificial intelligence, as well as the popularization of higher education, local colleges and universities have changed to application-oriented and devoted themselves to cultivating high-quality skilled talents. Teachers are the main driving force to promote the development of universities and achieve the goal of educating people,However, while some universities are developing rapidly, they are faced with some problems, such as imperfect teachers, unreasonable curriculum system and backward teaching methods, which have become bottlenecks restricting the further development of universities. Big data technology can provide technical support for human resources reform, and also meet the needs of teaching reform in local universities. In this paper, combined with the problems faced by the cultivation of entrepreneurial talents in a local private university, we have a deep insight into the causes, put forward the cultivation mode of entrepreneurial talents in private universities under the background of big data, established a comprehensive view of human resources quality, and sought for a healthier and more scientific talent cultivation mode.
Innovative Teaching Mode of Economic Law in Colleges and Universities under the Background of Big Data

As a new industry and development model, big data has had a profound impact on all aspects of human life. Today, with the rapid development of information technology, traditional teaching models can no longer meet the needs of modern teaching, and the teaching reform of economic law courses is also an inevitable trend.This paper analyzes the existing problems in the traditional teaching mode of economic law, demonstrates the necessity of the reform of the teaching mode of economic law, and introduces the application value of big data technology in the teaching of economic law, and then puts forward the innovation path of the reform of the teaching mode of economic law.
RETRACTED ARTICLE: The application of twin network target tracking and support tensor machine in the evaluation of orienteering teaching

Siamese network target tracking task is one of the more difficult research topics in the field of computer vision. A large number of methods based on deep neural networks have been proposed, which are dedicated to solving some of the current problems in computer vision. And experts in various fields have expressed their opinions. More twin network target tracking algorithms have gradually appeared. The SiamFC target tracking algorithm is widely used, which is based on twin neural networks and has the advantage of fast tracking speed. Today’s era is an era covered by big data. There are many ways to classify tensor data. Among them, supporting tensor machine algorithm is a better method, and it is also a current hot research direction. This article mainly studies the relationship between supporting tensor machine analysis method and orientation sports teaching quality evaluation system, analyzes and explores related factors that affect and determines teaching quality, so that the evaluation can be more accurate and reliable, and gradually realize the improvement of leisure sports teaching quality. Through research, it is concluded that the teaching evaluation system contains a total of two indicators, namely 3 first-level indicators and 13 second-level indicators. A main content that can evaluate the quality of teaching is route design. At present, there are certain problems in the evaluation of the teaching practice of orienteering used by many schools. Among them are the obstacles caused by traditional teaching, and the professional ability of some teachers is not strong enough. In order to better solve these problems, this article proposes a more efficient school orienteering teaching practice strategy: improving the traditional teaching mode, and continuously improving the overall teaching ability of teachers.
On the Cultivation of Professional Identity of Preschool Education Majors

Professional identity is the basic quality that practitioners in every industry should possess, and it is more necessary for a higher vocational college to cultivate students' professional identity in teaching.For the training of preschool education majors, the curriculum design is short of some projects that convey the professional identity of preschool teachers to students, resulting in many graduates lacking of professional beliefs, so as to make the whole preschool education team scattered and chaotic, and make it difficult to promote the level of professionalism.Therefore, this article intends to be combined with the necessity of training professional identity and to explore the strategy of cultivating the professional identity of preschool education majors, hoping it is of some reference value for related work.
Dual Nature of Students’ Knowledge Formation in the Pandemic Period: Pedagogical and Psychological Aspects

The paper focuses on pedagogical and psychological facets of students’ knowledge formation impacted by the coronavirus pandemic situation worldwide. It was found out that the emergent transition to online education was mostly fruitfull for the development of new learning technologies, but at same time many students experienced frustration and uncertainty, studying independently; some lecturers were not ready to refuse from traditional frontal teaching methods. Theoretical analysis was applied to study classic and modern scientific works on the nature of the phenomenon of knowledge and its formation. Comparative analysis of the latest publications on this topic revealed that actual pedagogical approaches stated the importance of learners’ active participation in the interactions and interrelations within the system of the learning process and the relevance of the socio-cultural context of new knowledge presentation. Pedagogical observation and expert evaluation enabled authors’ exploring Generation Z students’ cognitive modes and psychological background, featured by practically oriented motivation, clip thinking, ability for multitasking, prevailing unvoluntary attention and weak memorization, preference for visual presentation of information. Theoretical synthesis and compilation implementation enabled working out practical recommendations for intensifying the process of knowledge formation of Generation Z students. The outcomes of the study were used to modernize an e-course “Methods of Teaching in Higher Education Institutions”, so that it met contemporary demands.