Reviewerly/exHarmony huggingface dataset:
A damage tree modeling method for shelter vehicle target

The shelter vehicle is a common target in the battlefield, it is of great significance to study the damage of trees. We present a target damage tree modeling method for shelter vehicles, including target function and structure analysis, damage level, target damage tree modeling and so on. Through this method, the function and structure of shelter vehicles can be analyzed, their damage level can be determined, and the damage tree model can be established.
Chinese finger language recognition based on deep learning

The study of hand gesture recognition has significant academic value and wide application prospects, but the accuracy of hand gesture recognition is affected by differences in environment, skin color, and hand shape. In this paper, we first preprocess the dataset, obtain the key points of the hand through the MediaPipe framework, and draw these key points on white pictures to form a new dataset, and then train the unpreprocessed dataset and the preprocessed dataset separately using the ResNet50 network structure. The results of the comparison experiments show that the accuracy obtained from the preprocessed dataset is 1.9% higher than that of the unpreprocessed dataset.
Portable High-Speed Optical Gaze Controller with Vision Chip

It is important to miniaturize robot systems while maintaining advantages such as high responsiveness and functionality for human-machine interactions and for achieving integration with other robotic systems such as drones. In this research, we focused on the miniaturization of a high-speed visual feedback system, and developed a “portable saccade mirror,” which is a system that can realize active target tracking using 1000 Hz image capturing, processing, and feedback actuation with only 3 ms latency in a handheld device. By using a three-dimensionally-stacked vision chip, the proposed system achieved high speed, low latency, low power consumption and compact size, and therefore, can be considered as a good example of a miniaturized high-speed visual feedback system. In this study, we evaluated the performance of the proposed system in comparison with the conventional optical gaze controller, and demonstrated some applications, such as tracking field scope and panorama target scanning.
Human face cartoon image generation based on CycleGAN

In recent years, the rapid development of deep learning has greatly promoted the progress of computer vision and image graphics. Many new image style transfer algorithms based on deep learning have made a huge leap in image generation quality and operation speed. Online animation is the expression of animation and comics, including animation books, films, television, audio-visual products, stage plays and the development of animation products based on modern information and communication technology. However, most of the cartoon faces are similar, and the facial features are single, but if a more detailed description is carried out, the production cost is high. In this paper, a cycle-consistent generative adversarial network is used to convert the real face image to the comic style face image. The network does not require paired matching data, so that the network can use a wide range of data and is less difficult to obtain. Through experiments, it is found that the improved CycleGAN model has a good effect on generating cartoon face images, which can save a lot of cost for cartoon makers, and the generated cartoon faces have the advantages of being more realistic and detailed.
Cyber crime fraud: Malaysian perspective

Industrial Revolution 4.0 and its technological diffusion progress is expected to grow exponentially in technical change and socioeconomic impact. This paper explains the effects of Industrial Revolution 4.0 on cybercrime fraud. Cybercrime is a criminal and unethical activity using internet facilities such as virus infections, identity theft, hacking, and others. It increases with the growing cyber space users despite various steps taken by the government to control and protect its cybercitizens. Thus, this paper provides an overview on cybercrime in Malaysia by discussing the common types that happen and several proactive actions that have been taken to battle cybercrime fraud. This paper also highlights cybercrime cases in Malaysia and discusses several influencing factors that can be considered for future study.
Accounting information systems and audit 4.0 during pandemics Covid-19

This article reveals how an accounting information system helps audit work during the COVID-19 pandemic and the possibility of developing an accounting information system to assist audit 4.0 post-pandemic. The method used is descriptive analysis through interviews with auditors at local KAP in East Java and making a literature review to provide an overview of accounting information systems for future audits. Data collection techniques are carried out by summarizing the results of interviews with informants and collecting secondary data through library research. This article explained that, in audit work during the pandemic, the accounting information system has not been used optimally due to the limited knowledge of the auditor regarding the development of accounting technology. Meanwhile, post-pandemic, there is the possibility of audit work that can be done to implement the 4.0 audit system through the concept of continuous auditing. In audit 4.0, audits are carried out by relying on big data, CPSs, IoT, IoS, cloud accounting, blockchain, GPS, sensors, and RFID to facilitate audit work, save time and allow more flexibility. Auditors’ unpreparedness and lack of technological expertise are obstacles for auditors in carrying out audit work during the pandemic.
IMPLEMENTASI PENDIDIKAN KEBENCANAAN DALAM MEMBENTUK DESA TANGGUH BENCANA BERBASIS PEMBERDAYAAN PELAJAR DAN MASYARAKAT DI DESA BESOLE KABUPATEN TULUNGAGUNG

Besole village is one of the villages in Tulungagung Regency which is near the center of the world's active plate collision and is directly adjacent to the Ocean. The influence of this location makes this village rich in minerals, namely as the number one largest marble producer in Indonesia. But behind that, this village has a big threat from natural disasters. This is evident over the years that this area has been hit by floods and landslides and has a threat of a tsunami. Therefore, an effort to empower the community is needed to form a disaster-resilient village through the implementation of disaster mitigation management education. This program is intended for students and the community due to their vulnerability to being affected by disasters. The purpose of this activity is to 1) determine the planning of disaster mitigation management education implementation programs 2) carry out several activities in disaster-based community empowerment efforts, 3) increase knowledge, skills and attitudes of disaster preparedness, 4) find out the advantages, disadvantages, supporters and obstacles of empowerment programs disaster-based society. This study uses a research-based Participatory Action Research (PAR) method which is divided into stages of planning, action, observation and reflection. The results of this study are 1) the planning stage is held to formulate various actions based on the results of preliminary research, focus group discussion and disaster mitigation guidance, 2) the action stage contains various activities, namely waste management on the coast, implementation of earthquake and tsunami learning simulation models at the elementary and junior high school levels, installation of evacuation route signs in several vital objects, use of biopore infiltration hole technology, counseling on organic and inorganic waste management through waste banks. , making a map of the disaster evacuation route for the coastal area of ​​Besole Village, 3) this activity contributes to the Besole village community in increasing knowledge, skills and attitudes of disaster preparedness, 4) the advantages and supporters of this activity, the most important of which is the support from various parties, both from the government and non-government, while the first factor that becomes a shortage and obstacle is the location factor which is difficult to access.
Remote Sensing Image Target Detection Based on Improved Faster R-CNN

Object detection is one of the core tasks of computer vision. With the development of artificial neural networks, object detection has been greatly improved and gradually applied to more fields. In urban traffic, object detection can be used for vehicle detection, autonomous driving, and judging traffic conditions. In the process of navigation, satellite remote sensing images can be used to detect urban vehicles to judge traffic conditions. Object detection can also be used to identify obstacles during car driving. Although accurate detection of larger objects in images has been achieved in many applications, accurate detection of smaller objects remains challenging. The main difficulties in the detection of small targets are low resolution, blurred images, and little information carried. As a result, its feature expression ability is weak, that is, in the process of special zone features, very few features can be extracted, which is not conducive to the detection of small targets. This paper proposes an improved Faster R-CNN algorithm for small target recognition in remote sensing images. In this paper, the feature pyramid structure is used to improve Faster R-CNN. Feature pyramid can perform feature extraction on images of each scale, and can generate multi-scale feature representation, and feature maps of all levels have strong semantic information, even including some high-resolution feature maps. Through the feature pyramid structure, the feature expression ability is enhanced, and the small target feature map resolution is increased at the same time. Secondly, the kmeans clustering transformation algorithm is used to optimize the size of the anchor box to improve the matching degree between the prior box and the ground-truth box. And a channel attention mechanism is introduced in feature fusion to highlight important features and reduce redundant features. Remote sensing images are generally obtained by space platforms, and most of the targets are small targets. The attention mechanism considers both the inter-channel relationship and the spatial position relationship, which enables the model to more accurately identify the target and lock the target position. The attention mechanism improves the accuracy and generalization of small object detection. Finally, we validate the model on RSOD-dataset. Through the experimental verification, the framework proposed in this paper can effectively improve the accuracy of small target detection in aerial remote sensing images, and effectively reduce the false alarm rate, which provides a basis for the research of target detection in aerial remote sensing images.
Digital Citizenship in Foreign Language Learning and Teaching

The sphere of digitality has transformed from something we merely use or consume to an actual manifestation of the very environment in which we live, learn and teach. This includes possibilities and potentials of digital technologies as well as restrictions and challenges of the digital in educational contexts. We have increasingly entered a sphere of worldwide civic activity through digital media. Digital citizenship departs from classic notions of citizenship “to focus on performative self-enactment of digital subjects” (Hink, et al., 2019, p. 20), thus building on digital acts and conceptualizing the resulting self-construction of people´s active role in society. In this chapter, we investigate how the concepts of digital citizenship and global citizenship can be united as “global digital citizenship” (Crockett & Churches, 2017). We aim to trace and review the current debate on global digital citizenship, identifying key terms and trends relevant for foreign language education. In this context, the role of educators needs to be addressed with a view to participating in and contributing to digital worlds. Recently, a discourse is emerging regarding the question of how to leverage the digital world to foster global citizens. Seeking to explore the theoretical foundations as well as chosen practical implications we turn to influential publications in the field. The impact of relevant frameworks as developed in different regions of the world (e.g., Ribble’s framework on Digital Citizenship in Schools from the US context, or the Digital Citizenship Education Handbook issued by the Council of Europe) will be scrutinized with a view to implications for foreign language education. This involves the question of how the contemporary debate moves from normative frameworks and governance, through activism and alternative models of digital participation “towards cultures of creativity” (McCosker et al., 2016) that help educate the citizens of the future in foreign language education, a task we feel has been neglected in the past.
Advanced Identification of Impulse Processes in Cognitive Maps

This chapter is devoted to the identification problem of a complex system using data obtained experimentally. Impulse processes in cognitive maps are very important and widespread class of such systems. Methods developed in this work take into account specific features of these systems. Model class that describes dynamic processes in cognitive maps is a state space discrete system of equations. So subspace identification approach is the most suitable method for solving the problem under consideration. Specific properties of cognitive maps are required to develop modified subspace method for this class of systems. Regularization procedure was introduced in standard subspace methods which allow finding an approximate solution consistent with errors in available data. Application of the developed methods is demonstrated in the example.
Effectiveness of College English Blended Teaching Based on Foreign Language Teaching Platforms in Universities

Effectiveness of college English blended teaching based on foreign language teaching platforms in universities is studied in this manuscript. From the perspective of teaching method, teachers should have a predetermined goal in teaching. In order to achieve the goal, teachers should first make his predetermined goal become the effort goal of students. Blended learning is the recent international international education field on the traditional education reform and the reflection on networked learning after the convergence of change. From the teacher's perspective, the student is the center of teaching and learning, and this paper gives the novel ideas of the solutions.
Research on Dynamic Scenario Predictive Description Algorithm of Automatic Driving

Intelligence is an inevitable trend of automobile technology and industry development, and its development pace is also accelerating. Autonomous driving has also become one of the research hotspots in the field of intelligent vehicles. Although there are many researches on autonomous driving, most of them are based on relatively simple scenarios. However, with the gradual expansion of the application scenario of autonomous driving, many scenarios present dynamic and complex characteristics, which makes the traditional path planning algorithm difficult to adapt. Therefore, the study of autonomous driving algorithm in dynamic and complex scenarios becomes the key to improve the adaptability of autonomous driving scenarios and promote the wide application of automatic driving. This paper proposes a dynamic obstacle tracking and prediction algorithm based on extended Kalman filter to reduce dynamic obstacle tracking. The tracking error caused by lag and its influence on trajectory planning. At the same time, a predictive description method of dynamic scenario is proposed. Based on approximate grid decomposition, hierarchical cost map updating method is adopted and the prediction cost layer of dynamic obstacles is integrated to complete the predictive description of dynamic scenario.
Newton’s laws and car-crash claims

Fraudsters routinely try to make money by pretending they have been injured in traffic accidents. But as Michael Hall explains, simple Newtonian physics can reveal which claims are genuine and which are bogus.
Sizzling research

Researchers have investigated the physics underlying a clever test used by cooks in some parts of Asia, who put moist bamboo chopsticks into their pans to judge the temperature by watching the bubbles that form and listening to the sizzling sound they emit.
Comparison between Time- and Observation-Based Gaussian Process Regression Models for Global Horizontal Irradiance Forecasting

With the development of predictive management strategies for power distribution grids, reliable information on the expected photovoltaic power generation, which can be derived from forecasts of global horizontal irradiance (GHI), is needed. In recent years, machine learning techniques for GHI forecasting have proved to be superior to classical approaches. This work addresses the topic of multi-horizon forecasting of GHI using Gaussian process regression (GPR) and proposes an in-depth study on some open questions: should time or past GHI observations be chosen as input? What are the appropriate kernels in each case? Should the model be multi-horizon or horizon-specific? A comparison between time-based GPR models and observation-based GPR models is first made, along with a discussion on the best kernel to be chosen; a comparison between horizon-specific GPR models and multi-horizon GPR models is then conducted. The forecasting results obtained are also compared to those of the scaled persistence model. Four performance criteria and five forecast horizons (10 min, 1 h, 3 h, 5 h, and 24 h) are considered to thoroughly assess the forecasting results. It is observed that, when seeking multi-horizon models, using a quasiperiodic kernel and time as input is favored, while the best horizon-specific model uses an automatic relevance determination rational quadratic kernel and past GHI observations as input. Ultimately, the choice depends on the complexity and computational constraints of the application at hand.
Quantum error precompensation for quantum noisy channels

Most previous efforts on quantum error correction focused on either extending classical error-correction schemes to the quantum regime by performing a perfect correction on a subset of errors or seeking a recovery operation to maximize the fidelity between an input state and its corresponding output state of a noisy channel. There are few results concerning quantum error precompensation. Here we design an error-precompensated input state for an arbitrary quantum noisy channel and a given target output state. By following a procedure, the required input state, if it exists, can be analytically obtained in single-partite systems. Furthermore, we also present semidefinite programs to numerically obtain the error-precompensated input states with maximal fidelities between the target state and the output state. The numerical results coincide with the analytical results.
The Transition from a Face-to-face Subject of a Formative Nature to Online Classes: Analysis of the Experiences of Teachers

Faced with the challenge initially posed by the confinement due to Covid-19, educational institutions had to move from an educational model with a predominance of face-to-face, to one online. The objective of this research was to investigate how teachers of a first-year medical subject in Mexico faced this transition and what emotions they experienced. A descriptive cross-sectional study was conducted, where 53 teachers answered online a questionnaire developed to obtain information on the use of technological resources and didactic strategies, their training needs, their opinion regarding online education and the emotions experienced. Descriptive, sex-comparative, and correlational analyses were performed. The majority of teachers reported using Zoom (77.4%) to teach and email (84.9%) to communicate with their students. While more than 90% expressed a positive attitude towards online education, 66% required more preparation and 70% felt it was better to give face-to-face classes. Between 30% and 50% experienced positive emotions, while nine to 30% reported negative emotions. No significant differences by sex were observed in the study variables. The information obtained has not only allowed teachers to obtain greater technological resources and accompaniment based on their specific needs, it has also highlighted the relevance of investigating the impact of these changes on the emotions of teachers, since the management they make of these will affect, in addition to their quality of life, their professional performance.
A study on factors influencing riot police turnover intention

Even in the face of COVID-19, the number of assemblies and demonstrations per year in which the police were mobilized was more than 10,000, which did not significantly decrease compared to the previous year. While the future assemblies and demonstrations are expected to increase further, the related duties were mainly dependent on conscripted police. If professional police officers with relatively high job competency replace conscripted police, the management of assemblies and demonstrations and security police duties will be performed more efficiently. However, the number of police officers replacing the conscripted police is only 30% of the conscripted police. If an existing or new professional police officers leave their job in a situation where the quota itself is absolutely insufficient, the damage will be passed on to the public.&#x0D; Against this background, this study, conducted to analyze the factors affecting the turnover intention of riot police, verified the effects of job stress, job satisfaction, organizational commitment, and burnout on turnover intention through the analysis of previous studies. As a result of analyzing using survey data of 1,007 riot police belonging to the Seoul Metropolitan Police Agency’s Mobile Police Headquarters in 2021, it was found that, among demographic and sociological variables, gender, working period, and age have different effects on turnover intention. It was confirmed that the overall level of job stress, organizational commitment, and burnout also had a statistically significant effect on turnover intention according to the level. Based on this, some policy suggestions for managing the turnover intention of riot police were presented.