Reviewerly/exHarmony huggingface dataset:
Risk and Privacy Evaluation for RDAP System

The Registration Data Access Protocol (RDAP) is a new protocol that will eventually replace the WHOIS to address security and privacy issues and other current conditions. Like WHOIS, RDAP obtains information about a domain name or an Internet Protocol (IP) registration. One of its features is “user differentiated access,” which enables the Public to view restricted info while Authenticated Users may access complete info. This feature needs complete registration of data in the Data Store, which may contain legally protected Registrant Personal Data. Previous researches focused on digital evidence linked to a domain and IP registrations and technical challenges to enhance request analysis mitigation. This research presents a cyber security and privacy risk analysis of the RDAP system based on ISO 27005, LINDDUN, and STRIDE models.
A special operation UAV in urban space

For special tasks such as disaster rescue, urban security and anti-terrorism investigation, due to the complex environment and task scene, UAV is often used to complete the tasks. Based on the requirement of special UAVs in these kinds of special mission environment, this paper designs a special UAV with three capabilities of flight, ground moving and wall climbing. The triad UAV adopts the flying wing aerodynamic layout. Through the comprehensive optimization design of material, layout and control, it has the characteristics of simple structure, light weight, convenient control and rapid deployment. It has strong adaptability to special mission environment with high risk, narrow operation space and need to be concealed and intruded, so it is very suitable for special missions.
Research on Business Administration Informatization under the background of Big data

The rapid development of computer technology in the country has made the country's network informationization also continue to develop, which is having an increasing impact on people's lifestyles. At the same time, network information processing technology is innovating and developing, and the application of big data technology in different industries has become more and more extensive, which has promoted the development of informationization in this industry. This article analyzes the related content of business management informationization under the background of big data.
Study on HS-RNN in Vibration Prediction of Mechanical Spindle

Mechanical spindle in the high-speed operation due to an uneven distribution of the rotor mass, causing the spindle to vibrate thus affecting its accuracy. Therefore, the model of mechanical spindle vibration prediction is of great significance. The mechanism of mechanical spindle vibration prediction is complex, and the amplitude of vibration varies nonlinearly with the speed. It is difficult to establish accurate model of mechanical spindle vibration prediction. In this paper, RNN (Recurrent Neural Network) recurrent neural network is used to establish the model of mechanical spindle vibration prediction. The vibration amplitude of the built-in balance block at different positions and spindle speeds is predicted. Harmony Search(HS)Through self-learning update, thereby improving the accuracy of the mechanical spindle vibration prediction model. The experimental results show that the HS- RNN -based mechanical spindle vibration prediction method proposed in this paper can automatically determine the network structure and accurately predict the vibration amplitude of the mechanical spindle.
Design of AC machine speed control system based on CFPWM

The traditional AC motor speed control system can not effectively control the abnormal current output value, and the output current value after control has no periodic change rule. Therefore, an AC motor speed control system based on CFPWM is proposed. After the description of the system hardware and the selection of m051 chip, the equivalent circuit diagram of CFPWM operation is drawn, and on this basis, the speed control model of AC motor is designed. On this basis, the introduction of PID algorithm, combined with the ideal running state of AC motor, control model closed-loop speed regulation, completes the design of system function. In addition, through comparative experiments, it is proved that the designed system can effectively control the current output value of AC motor, and the output current value can be controlled within the effective range after the effective control of current.
Image-based Visual Servoing of Unmanned Aerial Vehicles for Variable Angle Target

This paper presents a double virtual plane method for a quadrotor unmanned aerial ve- hicle(U AV) using a monocular camera and an inertial measurement unit sensor(IMU). Using the above method and an image-based visual servoing controller, the U A V can achieve the tracking of the target both on the horizontal plane and on the inclined plane. Utilizing the image moments, the image features are defined and and adopted by the IBVS controller. The inclination angle of the target plane can be obtained through the image distortion in the horizontal virtual image plane. The closed-loop system is proved globally asymptotic stable by means of Lyapunov analysis. And the simulation of the method is completed, which can be used to prove the correctness and stability of the theory.
ICCD low-light-level image denoising based on robust principal component analysis

In this paper, a method of removing ion-feedback noise based on RPCA and median filter is proposed, a removal mechanism based on iterative strategy of "detection-location-removal" is established to remove the noise step by step, and BM3D algorithm is used to remove the Gaussian noise. The experimental results show that the proposed method can effectively remove the noise, and protect the edges and details of ICCD LLL images as much as possible. In addition, we quantitatively evaluate the denoising performance of the method. Our method obtains better objective measurement values. It has better effectiveness and robustness for ICCD LLL image denoising.
The Teamwork Skills Formation of Elementary School Students after The Disaster Preparedness Training

To meet the Government's requirements, to increase preparedness in the face of disasters, every elementary school in Indonesia must conduct the disaster preparedness serial trainings. For these trainings are graded, everyone who plays a role in this program has to build every skill needed for these serial trainings. One of the skills is the teamwork skill. This study was taken using convenience sampling technique and used quantitative method by using the measurement of frequency for managing and interpreting the data. The respondents are students from X Elementary School in Timor Tengah Utara, 5th and 6th year students. There are 57 students as respondents. These questionnaires are valid and reliable (corrected item total correlations 0.46 and the Cronbach's Alpha 0.787. The results are the students’ teamwork skills are in the above average level. This result shows that the students were ready to continue to the next level of training, considering the training were conducted online and onsite.
A quantitative model for simply typed λ-calculus

Abstract We use a simplified version of the framework of resource monoids , introduced by Dal Lago and Hofmann, to interpret simply typed λ-calculus with constants zero and successor. We then use this model to prove a simple quantitative result about bounding the size of the normal form of λ-terms. While the bound itself is already known, this is to our knowledge the first semantic proof of this fact. Our use of resource monoids differs from the other instances found in the literature, in that it measures the size of λ-terms rather than time complexity.
Trends of Culture Education Research in Korean for Academic Purposes

This study set out to examine the research trends of culture education in Korean for academic purposes and to provide basic data and research directions to instructors of liberal arts courses based on the results. For these purposes, the investigator looked at research on culture education in Korean for academic purposes among dissertations for a master's or doctor's degree published in Korea, as well as in papers published in journals found in the Research Information Sharing Service, from which a total 128 dissertations and papers were selected after review. The selected research papers were examined for their research trends by the year, type, and research topic. The progress by the year and type show that there has been an ongoing stream of research regarding culture education in Korean for academic purposes since 2006, and that since 2018 the presentations of dissertations has increased. The analysis results by the research topic were examined according to “current state,” “curriculum,” “educational content,” “teaching plans,” “educational materials,” and “cultural factors”. This study found that there was more active research involving plans for cultural instructions than other topic areas across all the dissertations and papers published in journals. Futhermore, the proportion of research papers on literature was high. Based on these analysis results, this study proposed certain directions that instructors should take for culture education research that pertains to culture education in Korean for academic purposes.
Computing Component Alignment Normalization Degree in Digital Ink Chinese Character from International Students Based on Information Database

There are many irregularities of components in the handwritten Chinese character by junior international students. Among them, the problem of component alignment is more prominent. For digital ink Chinese characters (DICCs), this paper proposes a difference computing approach based on the component alignment information database of reference Chinese characters (RCCs). The component alignment database stores information related to various types of component alignments, and facilitates automatically extracting handwritten Chinese character components by reading the structural information in the database. Component alignment computing is based on the barycenter approach, which takes full account of the structure type and component layout rule of Chinese characters, supplemented by component boundary alignment computing, and the average value of each alignment data is normalized. Using the component alignment data of the RCC as a standard, the difference between the component alignment of the DICC and the RCC is computed to obtain the alignment difference. The experimental data includes three kinds of Chinese character structures, which are suitable for computing ten kinds of structures of Chinese characters. Experimental results show that the computed component alignment difference is consistent with human subjective visual perception. The quantitative value of the alignment difference can provide correction data, which is convenient for the visual correction of subsequent work and the teaching of component writing. It can also perform statistical analysis and rule discovery, and provide data support for the study of Chinese character acquisition.
The Design of Sentiment Analysis Application using Top-Down Development Approach

In today’s Big Data era, relational databases and NoSQL databases coexist. The relational database is known for its limitation in terms of scalability. On the other hand, sentiment analysis is a Big Data product that uses unstructured documents such as review results, social media content, survey responses, etc. For this reason, this study will focus on how to design and build a database that can handle thousands of documents containing opinions on social media. The database model built in this study focused on its conceptual design based on previous research frameworks. The proposed model will be implemented using one of the infamous document-oriented NoSQL database MongoDB then tested on a sentiment analysis application built under ShinyR framework. The results showed that when comparing the sentiment analysis process with a lesser amount of data, it only affects two operation speeds in the database: insert and get more operations. The speed for insert operations in the analysis process using 18,900 documents is at 16,800 ms and the speed for get more operations is at 3,000 ms.
On the Reform of College English Reading Teaching Based on the Network Multimedia

With the development of information technology in this modern society, more and more educational technology is used in the teaching course. In the traditional teaching of English reading, the teaching materials are relatively monotonous and the teaching contents always lag behind, which make it difficult to meet the teaching requirements of diversity of English reading. There are many advantages as well as disadvantages in the application of network multimedia in college English reading teaching, This paper aims to find the appropriate English reading teaching methods by comparing the traditional English reading teaching with the English reading teaching based on network multimedia.
Rainfall prediction climatological station of Banjarbaru using arima kalman filter

Abstract Time series analysis is a method built in a particular time sequence for prediction. One of the models in time series analysis used for prediction is the ARIMA model introduced by Box and Jenkins. As time goes by, the ARIMA model was developed by applying algorithms, one of which was the Kalman Filter algorithm. This study aims to estimate the parameters of the ARIMA model used as the Kalman Filter’s initial value to forecast rainfall using ARIMA and ARIMA Kalman Filter. Determination of the ARIMA model is done by dividing the data into training and testing. The results obtained from the three training data have the same model, namely ARIMA (0,0,0) × (0,1,1) 12 models but with different parameter values than those used as initial values for the Kalman Filter. The results obtained using the ARIMA model with Kalman Filter significantly affect the initial data of 90% training data model parameters with an RMSE value of 155,13. Then predictions are made, the results obtained by ARIMA Kalman Filter can follow the actual data, but from June to October, the prediction results cannot approach the actual data. According to events in the field, June to October is the dry season, where rainfall is deficient
Sentiment Analysis using SVM Machine Learning Techniques on Social Media

Sentiment analysis is a branch of Natural Language Processing (NLP) and machine learning in which text is classified into two categories: positive and negative. Because the use of the internet and social media is growing at a rapid pace, the products created by these two are receiving far more client input than in the past. Text generated by social media, blogs, posts, and product reviews, among other places, has become the best-suited examples for consumer sentiment, delivering the best-suited notion for that particular product. As a result, the hybrid feature selection proposed in this paper is a combination of particle swarm optimization (PSO) and cuckoo search. The hybrid feature selection technique surpasses the standard technique due to the subjective nature of social media reviews. Support Vector Machine (SVM) classifier was used to examine performance criteria such as f-measure, recall, precision, and accuracy on the Twitter dataset, and it was compared to a convolution neural network. The proposed work outperforms the existing work, according to the findings of this paper's experiments based on various factors.
Energy Storage Planning of Park Energy System Based On Multi-Dimensional Digital Twin Technology

Abstract The existing energy storage planning methods have the problem of imperfect equipment mathematical model, resulting in small installed capacity of renewable energy. An energy storage planning method of Park energy system based on multi-dimensional digital twin technology is designed. This article explains the basic connotation of multi-dimensional digital twin technology and park energy system, and obtains feedback information. Combined with the energy consumption of industrial users, the park’s electricity load is predicted. We used the multi-dimensional digital twin technology to construct the mathematical model of the equipment, extracted the energy conversion law, optimized the energy storage mode of the energy system, and achieved zero heat emission. Case analysis results: the average installed capacity of renewable energy after optimization is 2349.6 kW, 626.6 kW higher than that before optimization, indicating that the energy storage planning method integrating multi-dimensional digital twin technology has a broader application prospect.
Virtual reality exposure therapy

Virtual reality is a multimedia interactive experience in which users feel immersed within the computer generated, virtual environment. It is a powerful platform to use for therapy involving exposure to cues, particularly for persons with anxiety disorders. Virtual reality exposure (VRE) therapy has been found to be a successful tool for treating many phobias and posttraumatic stress disorder. Advantages of VRE therapy include the therapist having control over the scenarios, the ability to tailor the environments to the client's needs, and the convenience of being able to provide a range of virtual environments without leaving the therapist's office.
Design of Cloud-based Security Retrieval Scheme

The separation of permissions between file owners and file managers is one of the important issues in cloud computing. The paper first introduces the related concepts of cloud computing, from confidentiality, integrity and availability, and emphasizes the threat of cloud storage in secure retrieval. Then it introduces the current research status and development trend of solving this threat at home and abroad. At the same time, an inverted index scheme based on SM2 is proposed. The project is conceived and designed in terms of keyword encryption technology, indexing, trapdoors, and user inquiries. Finally, the program verification analysis is given. This basically solves the problems related to cloud storage security retrieval.
Offline evaluation of the recommender system initial user interview using estimation of the coverage of content characteristics

Recommender systems are used everywhere in everyday life and have been of interest to researchers for many years. In recent years, many new papers have been published in this area, but the problem of cold start remains one of the most significant problems of recommender systems which use historical user data. The simplest solution is to do an initial user interview, but existing solutions for selecting items for it do not use a recommender model. However, they work with it, and they can improve the efficiency by using it for generating interview. This paper proposes adaptive approaches for selecting items for an initial interview based on the model, predictability of users and usefulness of items for a model. We also show how to build an initial interview for a new user by historical information about interactions of other users with items and the corresponding ratings for those interactions only. This is important because the information about content attributes is often missing or unreliable, and the developer is often not aware of specific characteristics that initial interview need to cover. This article and the described research are part of a large project related to the study of the problem of cold start and other problems of recommender systems with a large number of objects and a small number of interactions. As part of this project, we are developing application with which we will continue our research and estimate initial interview with real users. In this paper, we describe the content coverage metrics and demonstrate that our method requires less number of initial user interview screens to cover it than most others. The proposed method requires only three screens to cover 92.9% of the coverage metrics, and only four to cover all, on our dataset which is very sparsed and has 99.99% of missing values.
Empirical Evaluation of a Cloud-Based Graph Database: the Case of Neptune

Since the announcement by Amazon of its own graph database service Neptune in November 2017, there have been many expectations on how to compare Neptune with other state-of-the-art enterprise graph databases. Neptune is defined as a high-performance graph database engine supporting popular graph models: RDF and Property Graph Model (PGM). This paper aims at giving an empirical evaluation of AWS Neptune on real-world RDF datasets. We use three different versions of Neptune (Preview, Neptune 1.0, and Neptune 1.0.1) to evaluate how fast and reliable the engine is with real-world SPARQL queries. Additionally, we compare some of the results with our previous benchmark with other enterprise RDF database graphs, even though one should be careful with such comparison since the hardware settings are not completely equivalent. The results of this evaluation give some preliminary insights about AWS Neptune in the RDF benchmark task. The results demonstrate that Neptune is the fastest in loading 2 Billion triples, performs better on analytical queries, and outperforms on updates queries. However, Neptune performs poorly on SELECT queries with the shortest response time (60 s).
Health monitoring system of large bridge engineering based on Wireless Sensor Network

In view of the traditional large-scale bridge engineering health monitoring system, which has the problems of low system communication stability and early warning performance, a large-scale bridge engineering health monitoring system based on wireless sensor network is designed. The wireless sensor network is used to complete the hardware design of the system through the design of terminal hardware structure, large-scale bridge engineering health monitoring circuits and communication drive modules. Based on the analysis of large-scale bridge engineering health monitoring data, the large-scale bridge engineering health monitoring data is preprocessed. By designing the software structure and function of the large-scale bridge engineering health monitoring system, the software design of the system is completed, and the large-scale bridge engineering health monitoring is realized. The specific software and hardware of the bridge monitoring system are used as the test platform to test the comprehensive performance of the software system. The system test results show that the communication stability and early warning performance of the large-scale bridge engineering health monitoring system based on wireless sensor networks are good.
The Trained Panel Method and Its Application in HCI Research

User interfaces utilising multiple modalities or even multisensory feedback are more common, creating the need for evaluation techniques that can consider various quality dimensions. This paper demonstrates how the trained panel method can support the design and evaluation of physical or complex technological artefacts by mapping out design spaces based on their descriptive attributes. It is an expert-based method, and the goal is to derive a comprehensive description of a sample of existing artefacts or prototypes. The method entails training as well as multiple feedback sessions to ensure consensus among panel participants. We describe the advantages and limitations of the method by presenting how it was applied to identify important attributes in the design or evaluation of smartwatches. Apart from the specific case described in detail, we also discuss how and in what context the trained panel method can provide value in HCI research and practice.