Reviewerly/exHarmony huggingface dataset:
A Study on the Organization of Office and Organization of The Korean Language Centers in Universities

The purpose of this study is to investigate and analyze the organizational status of Korean language centers in universities as one of the basic tasks for establishing the organizational infrastructure of Korean language centers in universities, and to present the principles and standards for designing educational support organizations in Korean language centers. In other words, the ‘organization of office’ and organization of Korean language education institutions in universities, the organizational form and name of the general organization of language education as a higher organization, and the organization and composition status of educational support organizations in Korean language centers were investigated on a case-by-case basis. The Korean language centers in the university were organized and operated in various ways according to the history and circumstances of the parent university, and it was analyzed that the form of organization and organization operation had a certain tendency depending on the size of the students and Korean language trainees. Accordingly, four organization-based preliminary considerations and four organizational design standards and principles for building an educational support organization for Korean language educational institutions were suggested.
A Suggestion on How to Teach Writing Using Keywords

In this paper, I have studied writing teaching method by using keywords for academic student learner especially with sentences and paragraphs. Previous studies related to writing mainly focused on curriculum, pedagogy, and teaching guidance, so that it was difficult to find researches related to writing instruction using keywords. In fact, the most difficult part for learners when writing is writing sentences, linking sentences, writing paragraphs, and composing paragraphs. However, matters related to this cannot be taught and learned with specific rules or principles etc.&#x0D; Therefore, in this paper, I propose to use keywords as a way to solve these difficulties in writing. To this end, first of all I examined the concept and the conditions of keyword. And I set the hierarchical relationship as ‘keyword as hypernym’ and ‘hyponymous keyword’ by applying lexical meaning, then I examined the matters related to the priority between keywords. Because the keywords appearing in the learners’ writings were not flat and simple, but had multifaceted and somewhat complex characteristics. Furthermore, I discussed how to use these keywords in terms of sentences and paragraphs.
3D path planning of UAV based on improved reinforcement learning

Aiming at the problem of slow convergence rate of reinforcement learning (RL), an improved RL algorithm based on A* algorithm and velocity obstacle (VO) is proposed. Firstly, a node path obtained by A* algorithm is used to initialize a part of Q table. Then, in the process of path planning by RL, the VO model is applied to output the minimum steering angle required by the obstacle-avoiding flight. The action which is greater than the angle will be taken to interact with the environment. Finally, in order to satisfy UAV performance constraints, B-spline curve approach is applied for path smoothing. The experiment results show that, compared with the standard Q-learning algorithm, the path length of improved RL algorithm is reduced by 26.37%, the success rate of algorithm planning path is increased by 30.76%, and the convergence round is advanced by 88.66%.
On Bayesian credibility mean for finite mixture distributions

Abstract Consider the problem of determining the Bayesian credibility mean $E(X_{n+1}|X_1,\cdots, X_n),$ whenever the random claims $X_1,\cdots, X_n,$ given parameter vector $\boldsymbol{\Psi},$ are sampled from the K-component mixture family of distributions, whose members are the union of different families of distributions. This article begins by deriving a recursive formula for such a Bayesian credibility mean. Moreover, under the assumption that using additional information $Z_{i,1},\cdots,Z_{i,m},$ one may probabilistically determine a random claim $X_i$ belongs to a given population (or a distribution), the above recursive formula simplifies to an exact Bayesian credibility mean whenever all components of the mixture distribution belong to the exponential families of distributions. For a situation where a 2-component mixture family of distributions is an appropriate choice for data modelling, using the logistic regression model, it shows that: how one may employ such additional information to derive the Bayesian credibility model, say Logistic Regression Credibility model, for a finite mixture of distributions. A comparison between the Logistic Regression Credibility (LRC) model and its competitor, the Regression Tree Credibility (RTC) model, has been given. More precisely, it shows that under the squared error loss function, it shows the LRC’s risk function dominates the RTC’s risk function at least in an interval which about $0.5.$ Several examples have been given to illustrate the practical application of our findings.
Research on semantic-based data integration

With the development of information technology, "information silo" problem is getting serious, which hinders the exchange of information between enterprises. Data integration is the key technology to solve this problem, but traditional data integration methods have problems such as weak scalability and differences in language representation. In this paper, a method is proposed to complete data integration by establishing ontology, data mapping and semantic inference, which effectively solves the differences in data structure, data source and language representation and has strong scalability. In order to verify the feasibility of the method, experiments are conducted in this study with data describing people, and the completion of ontology construction, data mapping and semantic reasoning illustrates that the experiments achieve the expected results. Finally, the ontology model of nucleic acid detection is constructed in this paper, and the integration of nucleic acid detection data is completed to verify the feasibility of engineering the method.
Time-frequency Analysis and Convolutional Neural Network Based Fuze Jamming Signal Recognition

Fuze jamming signal recognition plays a critical role in the battlefield environment. To improve the performance of fuze jamming signals detection, we propose a fuze jamming signal detector based on time-frequency analysis (TFA) and convolutional neural network (CNN), called TFA-CNN, in this paper. The detailed recognition process of the proposed TFA- CNN detector is provided, where the short-time Fourier trans- form (STFT) is first employed to convert the original jammed fuze signals into the time-frequency images and then the TFA- CNN detector is built to train the recognition model. Simulation results verify that the TFA-CNN detector outperforms the typical existing recognition detectors, such as LeNet, time-frequency images and convolutional neural network (TFI-CNN) and deep neural network (DNN), in the detection performance with a slightly higher time complexity. Specially, the average recognition accuracy of the proposed detector achieves 99.8% even at a low signal-to-interference-plus-noise ratio (SINR).
Research on video watermarking algorithm based on MPEG-4

According to the encoding characteristics of MPEG-4 video standard and the encoding principle of MPEG-4, this paper proposes a video watermarking algorithm based on MPEG-4, which is suitable for copyright protection. The basic idea of the algorithm is as follows: firstly, the watermark information is obtained from the middle and low frequency range of the DCT coefficients after the brightness component is quantified, and then the watermark information is embedded into the DCT coefficients of all the brightness components of the video sequence I frame. Finally, the simulation experiment and result analysis are given. Simulation results show that the algorithm has good invisibility.
Compile runtime system technology for airborne high-performance heterogeneous computing

Most tasks in airborne scenarios are composed of neural networks (NN) and non-NN tasks. On the one hand, Deep neural networks (DNNs) have become the mainstream method of AI task processing. DNNs are accompanied by a large number of complex tensor calculations, as well as network delays and data privacy risks caused by using the cloud to process data. The combination of dedicated AI hardware accelerators and edge computing has become a new solution. On the other hand, dedicated AI hardware accelerators cannot efficiently handle non-NN class tasks (such as signal processing, control flow, etc.), so onboard systems need to design new hardware, complete compiler stacks, and runtime systems to handle non-NN class tasks. In order to effectively deal with multiple tasks in the airborne environment, the airborne computing system needs to establish a set of unified compilation and runtime systems, which can optimize multiple tasks and be deployed on the heterogeneous computing platform to achieve "one compilation, multiple deployment." This paper analyzes the development of compiler and runtime systems at home and abroad, and compares the advantages and disadvantages of different technologies. Finally, this paper discusses the application scenarios of the compilation system and runtime in the airborne environment and the technical directions that need to be breakthroughs in the future, so as to provide guidance for the development of the airborne intelligent compilation and runtime system.
Research on multi-robot path planning based on deep reinforcement learning

Multi-robot path planning (MRPP) enables the robot teams complete the tasks quickly and efficiently without collision. Based on the domestic and foreign research, the mainstream centralized algorithms for path planning have many shortages, so IL-A3C algorithm, which is based on the Dec-POMDP model, is proposed to solve these shortages. Then, the performance of IL-A3C is computed in terms of average path planning length, average path planning time, average collision probability and average planning success rate in different dimensions, and the simulation result shows that IL-A3C performs very well under low obstacle density and can be easily extended to a team of 128 robots. After that, IL-A3C is also compared with the centralized algorithm CBS and A3C algorithm, and the comparison proves that IL-A3C has higher success rate, stronger scalability and stability than CBS and A3C. The conclusion is that IL-A3C can be easily scaled to a large-scale robot team.
Determination of full cycle condition by linear congruence method

As the most widely used pseudo-random number generation algorithm, the linear congruence algorithm has the characteristics of fast generation and can generate better random numbers in one-dimensional space. Random numbers are widely used in many fields, such as information security and communication, and it is important to study the generation of random sequences. However, the linear congruence method has some defects, for example, when the mode of the linear congruence method𝑚 takes 10, multiplier𝑎 takes 7, increment𝑐 takes 7 and the initial value𝑋<sub><i>0</i></sub> takes 7, the sequence obtained is: 7,6,9,0, 7,6,9,0, ..., which results in a sequence with a period of 4. In the linear congruence method𝑚 , the 𝑎 and 𝑐 are crucial to the size of the period of the whole sequence and will directly affect the security of the algorithm. In this paper, the conditions for the linear congruence method to reach the full cycle are given on the basis of previous work, and a rigorous mathematical proof is given.
Variational Bayesian adaptation of noise covariances in trajectory PHD filter

The trajectory probability hypothesis density (TPHD) filter is a promising multi-target tracking algorithm. However, in many practical applications, inaccurate measurement noise prior statistics can degrade the performance of TPHD filters. In this paper, we model the trajectory state in TPHD as the product of the Gaussian mixture with the inverse Wishart under the linear Gaussian model, and propose the IW-VB-TPHD filter to adaptively acquire the covariance of measurement noise. Compared with the IG-VB-TPHD filter, simulation results show that the IW-VB-TPHD filter has a better accuracy in target tracking.
Sensor resource management allocation strategy for multi-target tracking in distributed radar network

The complexity of combat environment makes it more and more difficult for traditional monostatic radar to track targets continuously. The radar network technology can effectively improve the overall tracking performance. Therefore, in the distributed radar network scenario, a joint sensor selection, bandwidth and dwell time resource optimal allocation algorithm is proposed for multi-target tracking. At first, the local state is estimated and corrected by the Covariance Intersection (CI) fusion strategy. Then, the algorithm aims to minimize the predicted Bayesian Cramer-Rao Lower Bound (BCRLB) of the worst target tracking mean square error. And a joint radar node selection, bandwidth and dwell time resource optimization model is established for multi-target tracking in distributed radar network. The radar node is selected with BCRLB as the metric criterion first. Then, the optimal model after sensor selection is carried out by using the cyclic minimum algorithm and the minimax algorithm. Simulation experiments show that compared with the uniform resource allocation and single-resource optimal allocation algorithms, the proposed algorithm can effectively improve the multi-target tracking accuracy under the limited resources.
Techno-crime prevention: the role of the private sector and its partnerships with the public sector

In the last quarter-century, the digital landscape of economic and personal interactions has increased exponentially - especially on an international scale - creating a myriad of new potential offenders, victims, and criminal opportunities. Furthermore, these non-physical spaces can often remain anonymous and lead to predatory encounters for many millions of Internet users who are unfamiliar in navigating the Internet and trust in public and private sectors to keep them safe. In this chapter, we examine the roles that both sectors have historically played in the continuous evolution of responsibility in preventing cybercrime. In particular, we examine the limitations of traditional law enforcement methods in the public sector while also exploring new and effective techniques being deployed to prevent crime and assist victims of cybercrime. Recent efforts by both public and private sectors have also yielded new partnerships and techniques from both international governments and multinational corporate actors in facilitating a digital space that limits criminal opportunities. Finally, we assess public and private partnerships and the future role of both sectors in preventing cybercrime.
The illicit stolen data market

Advancements in digital technology have significantly changed the way private personal data (e.g., identity documents, financial information) are used and accessed. In fact, the proliferation of the Internet has given rise to online financial portals that make engaging in commerce more efficient and convenient for all those involved. Despite its enhanced accessibility, the decentralization of private personal data into online spaces has engendered numerous opportunities for offenders to access, steal, and abuse such sensitive data. The growing nature of the threat is evident from recent high-profile data breaches and computer hacking attacks against both public and private organizations. Though estimates of monetary harm vary across different studies, most reports estimate the damage caused by data breaches to be in the millions of dollars. The accessibility of private personal data and its high economic value has led to the emergence of online stolen data markets, which are hosted on both the Open and Dark Web. Understanding the scope of these markets and the nature of their market participants are important as they could assist law enforcement devise effective intervention strategies targeted at disrupting the online illicit marketplace. The aim of this chapter is to provide a general overview of the online stolen data marketplace, including the scope of these markets, the types of products sold, and the nature of its market participants. The chapter will end with a discussion on the implications of the online stolen data marketplace, including policy recommendations designed to mitigate the damage caused by these online market actors.
Sale of private, confidential, and personal data

With the increased convenience from the advancement in information and technology, there is equally the danger and risks that are simultaneously exploited by individuals with malicious intent. One danger to most Internet users is the overwhelming amount of private, confidential, and sensitive data that are now in digital form that is highly accessible and easily removable. Given its high value, such data become targets of breachers and cyberattacks where the stolen data is subsequently traded on online marketplaces, either for direct financial gains or enablers to other types of crimes such as identity theft. All these developments point to the need for a more holistic and comprehensive understanding on the marketplaces, especially with predictors of successful transactions within these marketplaces. The aim of the chapter is to provide an overview on the short history of online stolen data marketplaces follow by more detailed discussions on the types of products normally available. The chapter then reviews the literature to understand the role of trust in the facilitation of transactions on these marketplaces. The chapter closes with suggestions on innovations in research methods and directions would allow us to catch up with the constantly changing creativity among criminals' techniques and mechanisms.
Hacking

This chapter develops the main three stages of unauthorized access to a victim's technological infrastructure by introducing the twelve typical profiles of cyber offenders and sharing the potential benefits of using those hacking stages and tools for anticipating and preventing cyber victimization. From an initial recognition and selection of the intrusion method, cybercriminals identify and select the software and technology to deploy their attack. Later, the intruders establish a channel to remain in the organization according to their criminal goals to finally take advantage of the accessed data according to their intrusion motivations. Illegal access motivations will include the main characteristics of cybercriminal profiles and potential contra measures. Experts consider hacking an increasing threat, but corporations and individuals can prevent such risk by using similar attack techniques but from the cybersecurity side. This approach is part of the challenges and opportunities of implementing ethical hacking procedures and software, which will be introduced at the end of the chapter. Identifying the modus operandi of criminals and their techniques from a criminology perspective is the message from these contents moving from a traditional crime investigation perspective to a multidisciplinary scope in the current digital scenarios.
A study on learners’ perceptions of American Studies class: A case study

This study, as a case study, aims to examine the learners’ perceptions of learning contents and team activities in American Studies class. For the purpose, a questionnaire survey was conducted on 68 students majoring in English language after the 15-week team-based classes of American Studies. IPA and text mining to find out the importance and performance level of class contents, and t-test and ANOVA to find out learners’ perceptions of TBL activities were used. The results of the study are as follows. First, the IPA showed the topics of ‘history’, and ‘business’ must be conentrated and develped more while the topics of ‘diversity’, ‘six values’, and ‘educational system’ should be maintained to remain at this level. Second, in the word cloud analysis, ‘freedom’ was the most frequently shown keyword for the United States, followed by ‘Obama’, ‘diversity’, ‘equality’, ‘race’, ‘individualism’, and ‘fast food’. Third, it was found that TBL activities were positively recognized for discussion skills, creativity, cooperation skills, and communication skills as well as the learning effects. The difference by grade was that the second grade was higher than the third grade in communication ability. Lastly, the direction of future American Studies course was discussed based on the results.
Correction

This article refers to:The Sacra Infermeria—a focus group evaluation of an augmented reality cultural heritage experience
A study of learner perception of an English speaking class using the Metaverse platform Gather.town

With recent developments in Metaverse technology, the topic of integrating Metaverse platforms into language classrooms has received increasing attention due to its enormous potential for language learning. This study aims to adopt one of the most popular Metaverse platforms, Gather.town, for EFL speaking, and to investigate learners' perceptions of their learning experiences. Specifically, the study investigated how college EFL learners perceived the use of this Metaverse platform for their English speaking. A total of 23 college EFL students participated in this study, engaging in various speaking activities offered in the Metaverse platform throughout the semester. To collect data, the survey and qualitative semi-structured interviews were conducted. The study results indicated that the participants perceived the interaction opportunities in the Metaverse platform positively overall. They thought this platform was useful for their learning, and they perceived that their engagement in speaking activities had enhanced and their confidence in speaking had improved.&#x0D; The affordance of using the Metaverse platform was its potential to create a more anxiety-free and comfortable learning environment for learners. Results suggest that Gather.town has potential to develop learners' speaking proficiency in the EFL classroom due to its learner-centered design advantages and ease of free communication among learners.