Reviewerly/exHarmony huggingface dataset:
Meteorlogical outliers detection based on artificial intelligence

Abstract To precisely evaluate the effect of artificial precipitation of Project Tianshui, anomaly data within the large dataset collected is supposed to be detected and dealt with reasonably, to enhance the analysis and prediction of the data. Using the data accumulated from 2008 to 2017 from Wushaoling Meteorological Station, and the data from May 2019 to October 2019 from the dedicated supervising network built for the project, taking temperature(0.1°C) and other meteorological data as example, anomaly dectection is conducted by the arithmetics Local Outlier Factor and Isolation Forest respectively. Consistency check is also conducted via the two arithmetics. The main outliers of the accumulated data for ten years are isolated outliers. LOF works pretty well, while Isolation Forest performs unsatisfying. The main outliers of the dedicated supervising network data are of a continuous outlier sequence. LOF can hardly detect the anomaly, while Isolation Forest precisely find out the outliers. Both arithmetics perform well on consistency check in space. The differences of LOF and Isolation Forest are compared. New ideas of using artificial intelligence arithmetics to detect meteorological outliers and pre-process the data are provided.
Quantum Deletion of Copies of Two Non-orthogonal Quantum States via Weak Measurement*

We propose a scenario to increase the probability of probabilistic quantum deletion and to enhance the fidelity of approximate quantum deletion for two non-orthogonal states via weak measurement. More interestingly, by pretreating the given non-orthogonal states, the probability of probabilistic quantum deletion and fidelity of approximate quantum deletion can reach 1. Since outcomes of the weak measurement that we required are probabilistic, we perform the subsequent deleting process only when the outcome of weak measurement is “yes”. Remarkably, we find that our scenario has better performance in quantum information process; for example, it costs less quantum resources and time.
Ant Lion Algorithm Optimizes Network Intrusion Detection for Extreme Learning Machines

In the era of the rapid development of the Internet, network security issues are receiving increasing attention. Aiming at the shortcomings of extreme learning machines in network intrusion detection, this paper uses Antlion optimization algorithm (ALO) to optimize parameters in extreme learning machine (ELM) to obtain a network intrusion inspection model (ALO-ELM) with higher detection accuracy.), And compare this algorithm model with the network intrusion detection model optimized using GA and PSO. The experimental results show that the network intrusion model established in this paper performs the best. It can improve the accuracy of network intrusion detection to a certain extent, and improve the detection efficiency and real-time performance.
Web Application Development for Expertise Search and Research Collaboration of Chiang Mai University’s Researchers Using Text Mining

Exploring university researchers with expertise related to various academic topics often takes a long time, and the use of different selection factors, such as the publication number or citations, causes inaccurate findings and tends to miss the targets that are set by the university or faculty's research authorities. This study aims to create a decision support application for exploring the expertise and research collaboration of researchers in Chiang Mai University by using the Spyder and Visual Studio Code from Anaconda to extract data from the Scopus database; create web application from the Python Flask Framework and HTML by working with MySQL database to store data according to Text Mining and Bootstrap. The web application development facilitates university executives or faculty's research department regarding the managerial decision in order to search for the expertise researchers according to the interested academic topics. The web results show the expertise of each researcher and the faculty's expertise; and the list of institutes that have collaborated with an individual researcher in Word Cloud format. Concerning the scoring criteria, different factors are used, such as the number of citations, the SJR values from the data sources published in the Scopus database, and the number of publications in each topic.
On quantum intrigue, or “what’s up with that adjective?”

There is no Abstract for an invited comment.
Ķīnieši apgūst latviešu valodu: problēmas un risinājumi

In the 21st century, especially after the 2010s, a new phenomenon in the field of Latvian teaching is observed in China: the Latvian Program was initiated in higher education institutions and more Chinese have started to learn Latvian. The causes for this situation are mainly as follows. Firstly, Latvian is one of the two remaining Baltic languages and it is of great value to academic researches. Secondly, as globalization goes deeper in the new century, the relations between China and the European Union (EU) also develop rapidly, thus boosting Chinese people’s interest in the EU’s member countries. Latvian is one of the official languages of the EU and it plays an important role in the China-Latvia and even the China-EU cooperation. Thirdly, since China proposed the 16+1 Format and The Belt and Road initiative, exchanges between China and Latvia have become more frequent. The motivation for learning Latvian is easy to understand: some learners are students, who are interested in the language and prepare to conduct studies in Latvian; some are common people, who are attracted by the environment in Latvia and plan to work or live in Latvia. Although the reasons to learn Latvian may be various, Chinese learners of Latvian are all influenced by the same mother tongue and culture; therefore, they have similar learning difficulties and problems. To explore possible learning obstacles, there were designed and distributed questionnaires to 47 Chinese learners of Latvian, who are either students in China’s universities or Chinese in Latvia. In order to have a thorough understanding, this paper also invited ten Latvian teachers to join in, who all have experience of teaching Latvian to Chinese learners. The results obtained from the questionnaires were analyzed and sorted into the categories of language skills, including listening, reading, writing and speaking. Latvian acquisition for Chinese learners is anew topic in teaching Latvian as a foreign language, and solutions to deal with the special learning difficulties and problems still need time to work out. This paper may provide some suggestions for future study. Firstly, most Chinese learners are visual learners, thus methods of visualization in learning process are very useful. Secondly, learning materials and practices should be tailored to the special difficulties of Chinese learners. Thirdly, to make the teaching process more systematic and targeted, teaching and research teams are suggested to include both Latvian and Chinese specialists in Latvian.
Recurrent Enhancement of Visual Comfort for Casual Stereoscopic Photography

Creating stereoscopic 3D media content has wide applications in virtual reality. In this paper, we are interested in a challenging application, casual stereoscopic photography, that allows ordinary users to create a stereoscopic photo using two images captured by a hand-held monocular camera. To handle the geometric constraints and disparity adjustment for casually captured left and right images, we present a coarse-to-fine framework. In the coarse stage, we propose a unified reinforcement learning-based method, in which the produced stereo image is iteratively adjusted and evaluated in the term of visual comfort. In addition, to further enhance the visual comfort of the stereoscopic image produced in the coarse stage, we introduce another independent recurrent network to fine-tune its disparity range. Lastly, we perform comprehensive experiments to evaluate our method and demonstrate the applicability of our model for real images.
The number of clusters in hybrid predictive models: does it really matter?

For quite a long time, research studies have attempted to combine various analytical tools to build predictive models. It is possible to combine tools of the same type (ensemble models, committees) or tools of different types (hybrid models). Hybrid models are used in such areas as customer relationship management (CRM), web usage mining, medical sciences, petroleum geology and anomaly detection in computer networks. Our hybrid model was created as a sequential combination of a cluster analysis and decision trees. In the first step of the procedure, objects were grouped into clusters using the k-means algorithm. The second step involved building a decision tree model with a new independent variable that indicated which cluster the objects belonged to. The analysis was based on 14 data sets collected from publicly accessible repositories. The performance of the models was assessed with the use of measures derived from the confusion matrix, including the accuracy, precision, recall, F-measure, and the lift in the first and second decile. We tried to find a relationship between the number of clusters and the quality of hybrid predictive models. According to our knowledge, similar studies have not been conducted yet. Our research demonstrates that in some cases building hybrid models can improve the performance of predictive models. It turned out that the models with the highest performance measures require building a relatively large number of clusters (from 9 to 15).
Information and Communication Technologies as a Phenomenon and Its Impact on Goals and Quality of Education

One of the most important factors for improving the quality of education and the successful achievement of modern educational results is the use of innovative forms and means of education in the educational process. The work notes that fundamentally new educational results, relevant to the needs of the modern education system, can be achieved only by new educational activities and support of new types of educational activities of subjects of education. Interaction of subjects of education in information and educational environment is a feature of contemporary training prosses.Being a social and cultural phenomenon, Internet-, Web-, Smart- technologies as a result of the development of information and communication technology, are becoming increasingly significant in modern information society changes, have a huge impact on the formation and development of the information and educational space. In this regard, the article deals with qualitative changes in education in the information society under the influence of modern information and communication technologies, media technologies, SMART technologies.The phenomenon of media culture (personal media culture of a subject of education) is relatively new for the analysis of cultural aspects of the information society in the information digital era and the range of discussion issues goes well beyond this paper. However, it should be emphasized that the influence of media culture leads to the formation of a particular type of mass consciousness in society - mass media consciousness. In this perspective, the issues of the formation of modern media culture, the awareness of which will help to preserve humanistic ideals in the age of digital technology, are outlined. All these circumstances encouraged the authors to express their opinion in this paper and discuss issues related to the expansion of educational opportunities and the didactic potential of information technologies and the degree of their impact on goals and quality of education, educational activities of subjects of education.
UWB 레이더를 이용한 비접촉 생체신호 검출에 관한 연구

This study relates to acquiring biological signal without attaching directly to the user using UWB(Ultra Wide Band) radar. The collected information is the respiratory rate, heart rate, and the degree of movement during sleep, and this information is used to measure the sleep state. A breathing measurement algorithm and a sleep state detection algorithm were developed to graph the measured data. Information about the sleep state will be used as a personalized diagnosis by connecting with the medical institution and contribute to the prevention of sleep related diseases. In addition, biological signal will be linked to various sensors in the era of the 4th industrial revolution, leading to smart healthcare, which will make human life more enriching.
Study on the Design of the Upper Space Parking Lot of Urban Multi-layer Tunnels

Abstract In recent years, with the continuous acceleration of the urbanization process, underground parking lots have developed rapidly, and they are continuously moving towards intelligence. Based on the multi-layer tunnel section of the Xiamen Haicang Subsea Tunnel, this paper conducts theoretical research on the liquid analysis of the surrounding area of the tunnel area, the graphic design of the parking lot, the architectural design, the function, and the fire prevention design. The research results are of great significance to the rational use of urban underground space, increase the utilization rate of underground parking lots and ease parking difficulties.
Carai: A Formative Evaluation Methodology For VR Simulations

Virtual reality (VR) has a long history of being used to simulate real-world tasks, often for training and educational purposes. Yet, developing a VR system that is usable and effective is not trivial and is still a challenging task. We present a methodology for formatively evaluating the usability of VR simulations by capturing a real-world task, automating data logging, running a rigorous user study, analyzing subtask user performances, and inspecting subpar subtasks for usability issues.
Keyword Search System Based On Klustree Method

KlusTree is a method for searching keywords into graph data. This method utilizes the use of language model to cluster keyword search results. The aim of this experiment is to build keyword search system using KlusTree method. The system accepts CSV files for data source and return keyword search results in a meaningful sentence form. Relational semantic mapping is used for converting CSV files into data with graph structure. TextRank and Depth First Search methods are used for modifying results in tree form to sentence form as cluster representation. The experiment shows that graph data produced by relational semantic mapping has directed graph structure. The quality of annotation on graph is affected by annotation in CSV files. The sentence form creates different perspective of keyword search result but it has a possibility to return ambiguous sentence.
An Adaptive Visibility Detection Method Based on a UAV-borne Real-time Panoramic Camera

Wang, M. and Zhou, S., 2020. An adaptive visibility detection method based on a UAV-borne real-time panoramic camera. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 282–288. Coconut Creek (Florida), ISSN 0749-0208.This paper presents an adaptive visibility detection method based on a UAV-borne real-time panoramic camera and designs a target board with strong absorption properties. One real-time panoramic camera and four target boards are carried by five UAVs. The horizontal visibility values in four vertical directions are measured with the brightness contrast method, and the visibility measurement errors are analysed for adaptation. The baseline length should be adjusted to the position where the minimum visibility error occurs to obtain high precision and refined atmospheric horizontal visibility results. This method uses UAV hovering in the near ground or half sky to measure the visibility, which can avoid the influence of background obstacles and uneven sky background, and even can be applied to the measurement of vertical visibility and slant visibility. Moreover, the inherent visual brightness ratio can be by improving the structure of the artificial target plate, the inherent visual brightness ratio can be increased by improving the structure of the artificial target board.
Mobile game applications Recommendation System with item-based Collaborative Filtering

Recommender Systems are software techniques that are being widely used in many applications to suggest products, services, and items to potential users. The main purpose of Recommender Systems is to provide meaningful recommendations about the items or products to a collection of users for their interested items. There are two popular approaches in recommendation: user-based and item-based collaborative filtering. The difference between them is that user-based takes users’ behaviors and item-based takes items’ rating values. The purpose of this paper is to present a recommender system that provides meaningful recommended mobile phone applications to mobile phone users which are relative to their needs or targets. This system emphasizes mainly on item-based collaborative filtering method that bases on rating values of the items because the computational complexity of user-based recommendation grows linearly with the number of users. By using this system, mobile phone applications users can obtain optimized suggestions without their waste of time and effort.
The Participatory Graphics of Cultural Heritage. New Methods of Analysis, Knowledge and Valorisation

The paper describes a project for the understanding of Cultural Heritage, through experimental procedures that can provide scientifically measurable results. The project, called HeGo (HeritageGo), provides for the creation of a model of social and participatory interaction applied to surveys in archaeological, architectural and urban contexts. The procedure is structured principally around the voluntary participation of various “players/users” (tourists, students, ordinary citizens, cultural associations, etc.). The key elements of the project are a specially designed App and a “Totem/Target” to be strategically placed near the site to be studied. They will allow metrically reliable processing, exploiting the potential of Structure From Motion (SFM) procedures. The direct participation of the players in the acquisition process, through recourse to the logic of gamification, is an innovative and easy-to-use procedure, which can stimulate the knowledge and evaluation of the Cultural Heritage of our country. The final objective is the constitution of a scientifically validated Database, obtained through the contribution provided, in the context of a “social game”, by the participation of users who are not necessarily - technically or scientifically - experts, transforming them into an active part of the process. The system opens up new perspectives for the knowledge and assessment of the Cultural Heritage, both in terms of involvement of players/users and in terms of cost savings with respect to the standard survey methods. In particular, the procedure of the HeGo project was checked in a small historic centre, limiting the area of interest to several monuments.
Events, processes, and the time of a killing

Abstract The paper proposes a novel solution to the problem of the time of a killing (ToK), which persistently besets theories of act‐individuation. The solution proposed claims to expose a crucial wrong‐headed assumption in the debate, according to which ToK is essentially a problem of locating some event that corresponds to the killing. The alternative proposal put forward here turns on recognizing a separate category of dynamic occurrents, viz. processes. The paper does not aim to mount a comprehensive defence of process ontology, relying instead on extant defences. The primary aim is rather to put process ontology to work in diagnosing the current state of play over ToK, and indeed in solving it.
Analysis of New Technologies in Information Security to Mitigate Data Risks in a Public Organization

Were analyzed references on alternative technologies or new proposals to help mitigate data risks. The problem is the lack of application of new technologies that secure information and reduce risks in organizations. The objective is to perform an analysis of new technologies in information security to minimize the risks of data in a public organization. It was used deductive method and exploratory research were used to analyze the information of the referenced articles. It turned out: Mixed Conceptual Model of Information Control, Double Security Architecture for Public Organization in Educational Area, Algorithm Control of Information Security. It was concluded that blockchain is considered as a fundamental option to provide control, robustness against failures and malicious attacks, reliability, availability, immutability and security.
A Method for Mining Temporal Association Rules in Single-Attributed Graph Sequence

This paper focuses on how to define and how to mine temporal association rules of an attributed graph sequence. Different from other temporal data, an attributed graph sequence contains edge-oriented structural data and vertex-oriented attribute data both of which vary with time. By reviewing the different forms of temporal association rules in the literature of related date types, we propose a definition of temporal association rule of an attributed graph sequence, which is a generalization of existing temporal association rules. In this definition, a two-tuple is used to describe the antecedent and consequent of a temporal association rule, which contains both structural information and attribute information. From this definition, a method for mining temporal association rules of an attributed graph sequence is given, and an Apriori algorithm-based mining algorithm is then designed. In the new mining method, two stages are given: the first one is for mining temporal structural association rules, while the second one is for mining temporal attribute association rules based on the result obtained in the first stage. This mining method exhibits good performance and high efficiency which was validated by the experiments carried on some datasets presented in this paper.
Application of vision aided strapdown integrated navigation in lane vehicles

The application of global navigation satellite system (GNSS) is extensive in lane applications with the development of science and technology. Vision aided strapdown integrated navigation is an effective aided-navigation method in the case of GNSS failure in lane vehicles, which plays an important role in realising high-precision navigation of lane navigation system. A vision-aided navigation system based on GNSS positioning is constructed using the electrical powered platform as the research object. The hardware platform of vision navigation system is presented and digital image processing is used to segment the collected lane image. The image pre-processing operation, including denoising filtering and greyscale processing, is carried out to complete the segmentation and get the effective navigation area. According to the effective area of navigation, a navigation datum line is extracted by the least squares linear fitting and Hough transform. According to the camera imaging model and the camera's internal and external parameters, the navigation datum line in the image coordinates is transformed into the world coordinates, and the heading angle is calculated. Kalman filter algorithm is used to fuse the navigation parameters of the vision navigation module and the GNSS positioning module, and the integrated navigation model is established.