Reviewerly/exHarmony huggingface dataset:
Changes in the Past Three Years in the Flying Robot Program in Department of Mechanical and Aerospace Engineering, Nagoya University

Department of Mechanical and Aerospace Engineering, Nagoya University (NU) has carried out a design and production class for autonomous glider models (flying robots) for undergraduate students since 2019. This paper reports the changes in the past three years in this program based on student questionnaire results. Not only NU but also Gifu University also started a similar class in 2020. Especially, this report focuses on the educational effectiveness of “Tokai Climax Series,” both universities’ final match, which is a collection of the top two to three aircraft from each university in Gifu Memorial Center on 29th of September 2021. This paper also reports some current problems and future subjects on this program from the viewpoints of an instructor (the first author) and a student (the second author).
Research on the Implementation of Project-based Learning in Junior High School Mathematics Teaching

With the advancement of education and the new requirements for personnel training in the era of core literacy, the reform of basic education is also advocating new teaching methods, and project-based learning is a new mode of teaching and learning that supports students to cultivate their literacy by solving real problems. In order to understand the current situation of implementing project-based learning of mathematics in junior high schools, this paper takes students from two junior high schools as the investigation objects, and adopts the research method of questionnaire to investigate from eight dimensions: cognitive attitude, project selection, planning, activity exploration, product production, achievement exchange, evaluation reflection and personal gain. Finally, this paper draws the following conclusions: at present, the overall situation of junior high school mathematics project-based learning is at a general level, and it is at a high level in two dimensions: cognition and personal gains, while it is relatively balanced in other dimensions, both of which are at a general level.
The changing perception of education and the concept of digital education during the Covid-19 pandemic

This article presents a study conducted with the purpose of identifying the views of university students regarding the concept of digital education and the changing perception of education during the COVID-19 pandemic period, as well as the positive and negative aspects of this new understanding of education.Discovering the positive and negative impacts of the new learning perception caused by the digital education process on the students, and contributing to the future process in the context of the results constitute the primary purpose of this student-focused study.The case study method, which is one of the qualitative research techniques, has been adopted while conducting this study.Data have been collected digitally through a structured interview form prepared by the researchers, and the purposeful sampling technique was used to choose the participant students In the light of the data collected, it has been concluded that the participants have been negatively affected by the digital education processes in general; and when comparing digital education to face-to-face education, it has been determined that the majority of them preferred faceto-face education to distance education in terms of different educational outcomes.With regard to students' negative views on digital education, the reasons such as not being in the university environment physically, not being able to access the university's digital education resources due to physical and/or economic factors, psychological (feeling of inadequacy) and demographic factors come to the fore.
Peculiarities of the application of the dual model in the conditions of a modern higher school

The aim of the research is to verify the effectiveness of the dual education in modern higher educational institutions (HEIs).Methods.The research involved the following methods: testing method (Self-Attitude Test) and methods of studying self-assessment through the ranking procedure; the peer-review method; Human Volitional Qualities Questionnaire; Student's t-test and the Kolmogorov-Smirnov test; Spearman's Rank correlation coefficient; Survey Response Rate calculation.The results.Students expressed the greatest satisfaction with such aspects as the relationship with the tutor (0.84), tutor's objectivity in work evaluation (0.91), safe working environment (0.92), individual workplace (0.89).There were 58% of respondents who favoured dual education compared to traditional education.A group of experts who studied academic performance noted the advantage of the flipped classroom-based model.The level of special competencies in the control group was 4.5 units, while it was 4.9 units in the experimental group.Conclusions.The results of the study showed the high efficiency of dual education based on the flipped classroom model.The research identified a more effective implementation model.Prospects.Further research can be aimed at finding effective models of using dual education under the current conditions.It is necessary to focus on comparing different educational models with dual education and identify the most effective educational model.
Application Of the Personalised Approach in Teaching Film Terminology: An Experience of Creation of The Educational Film Terms Dictionary

The article deals with one of the modern approaches in teaching film specialists in a film institution of higher education: personalized method of creating film dictionary.The hypothesis put forward in the process of the creation of dictionary entries presupposes that every student of every ability is able to take part in the educational dictionary compilation, whether it be the search of possible definitions of the word or its examples or transcription.Students who participate in the definition activity for the purpose of personalized educational approach may be divided into nine categories according to their language ability: the lower level comprises three beginner sub-types (shirk, kidult and amateur), the intermediate level includes three sub-types (performer, overachiever and genius) and the advanced level includes three sub-types (highbrow, realist and expert).The main difficulties in defining words are outlined, the method of creating a definition is described concerning the lexicographical work on the initial letter of the English alphabet.Film terminology vividly illustrates the interaction between two cultures, the Eastern and the Western, both of which carry signs of specific ideas, views and messages represented in specific terminological units.The theoretical foundations of the film terminology are based on the terminological studies, film text semiotics, discourse theory and the study of cultural interaction.The object of study is the terminological group of the English letter A, about 30 terminological units, taken as a free exemplary cluster for surveying basic lexicographical laws and specific tendencies.
Metaverse: Deconstruction and Differentiation of Identity Under the New Media Environment

The meta-universe is a new media scene based on VR and other technologies. Whether it will cause greater estrangement between people or even social disintegration or high-latitude healing in the future, the key lies in understanding the inner path of people's spiritual communication. From the perspective of the critical school, based on identity, this paper systematically analyzes and demonstrates the performance, causes, and risks of the deconstruction of identity by new media scenarios through a questionnaire survey on the correlation between virtual identity and real identity and adopts the interview method, to explore the social management of the metauniverse under the perspective of identity. By revealing the interaction between the virtual symbolic identity and the real identity, the paper seeks the value compliance of shaping a positive international image of the world with the help of the meta-universe. The research finds that: the hyperreal experience allows users to create and identify with a new self, the role deviation brought by the utopian world affects identity, the privacy leakage in the real world depends on the degree of identity, and the metauniverse challenges the national discourse system.
Synthesizing light field views based on superpixels in epipolar plane image domain

In the perspective of alleviating the inherent trade-off between the spatial and angular resolutions of light field (LF) images, much research has been carried out to increase the angular resolution of LFs by synthesizing intermediate views. Since the height of each EPI is equal to the angular resolution of LF, we tackle the view synthesis problem as doubling the height of each EPI in LF. To efficiently stretch the EPI while not consuming too much computing time, we propose to first segment the EPI into superpixels and then adaptively interpolate each superpixel separately. The test results on the synthetic and real-scene LF datasets show that our scheme can achieve average Peak signal-to-noise ratio (PSNR) / structural similarity index measure (SSIM) around 30.58dB / 0.9131 and 32.28dB / 0.9510, by taking computing time of 5.80 minute and 1.83 minute for HCI and EPFL dataset, respectively.
On The Impossibility of Providing Strong Anonymity in Blockchains via Linkable Ring Signatures

&lt;p&gt;Anonymity is a necessary property for a ring signature scheme and also its variant such as linkable ring signature and traceable ring signature schemes, which are especially useful in blockchains. Intuitively, those variants were designed for detecting or seeking the dishonest signatory, however, at the cost of reducing the anonymity of a traditional ring signature. As a result, while various constructions of strongly anonymous ring signatures were well-known, a linkable ring signature scheme with the same property was an open problem for a long time. In this work, we launched a so-called denying attack to show the gap between an arbitrary ring signature and linkable ring signature transparently, which further confirmed the widely believed impossibility in building a linkable ring signature with both strong anonymity and strong linkability. For a concrete instance, we also applied this attack to the scheme in IEEE TKDE, which to the best of our knowledge is the unique linkable ring signature both with strong anonymity and strong linkability so far. The concrete attack is easily launched in blockchain so that it shows the impossibility of providing strong anonymity via linkable ring signature for blockchain applications, since strong likability is indispensable.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Industrial Machine Learning Cloud Applications: Integrity and Confidentiality of Machine Sensitive Data

Many industries have adopted an industrial 4.0 environment to improve manufacturing processes and productivity through continuous monitoring of machine-sensitive trained dataset that is frequently shared between local and global cloud storage. It has enabled cloud collaborative editing with high security and quick user revocation for extracting features from a cloud-based trained dataset. Furthermore, it provides secure access to the accumulated data across multiple cloud terminals. Sometimes, the process may shut down unexpectedly due to interrupt of unknown entry anywhere, resulting in the entire manufacturing modality collapsing. Therefore, this paper proposes a Machine Learning Used Confidential (MLC) protocol for enhancing integrity and confidentiality service of machine sensitive data in industries cloud applications. It divides machine data into several segmented copies, each of which is encrypted before being stored in multiple cloud terminals. Additionally, if the actual machine data is changed in the field, the stored secured data is automatically updated. The local and global cloud servers keep track of all of these functions securely. Finally, the experimental results demonstrate that the proposed MLC protocol outperformed the others in terms of the performance and efficiency of the security provision.
A Deep Exposition of GAN and its applications

Generative Adversarial Networks (GANs) have revolutionized the field of machine learning and artificial intelligence by providing a powerful framework for generating realistic and high-quality synthetic data. GANs consist of two networks, a generator that produces synthetic data and a discriminator that distinguishes between the synthetic data and real data. The two networks are trained together in a game-theoretic setting, where the generator tries to produce synthetic data that is similar to the real data, while the discriminator tries to distinguish between the two. This paper provides a deep exposition of GAN and its applications, starting with the basics of GANs, their architecture, and how they work. We then discuss the training process of GAN, the challenges associated with it, and the techniques used to address these issues. We also describe the different variants of GANs, including conditional GAN, progressive GAN, and style-based GAN, and their applications. Next, we provide a comprehensive overview of the various domains where GANs have been successfully applied, such as image and video synthesis, text generation, and music composition. We discuss the potential future directions of GANs and their applications, including research areas that need further investigation. Finally, we highlight the challenges and limitations associated with GANs, such as mode collapse, vanishing gradients, and instability, and the ethical and legal issues associated with their applications. We conclude by summarizing the key points of the paper and highlighting the potential of GANs as a tool for generating realistic and high-quality synthetic data.
VOXReality: Immersive XR experiences combining language and vision AI models

In recent years, Artificial Intelligence (AI) technology has seen significant growth due to advancements in machine learning (ML) and data processing, as well as the availability of large amounts of data. The integration of AI with eXtended Reality (XR) technologies such as Virtual Reality (VR) and Augmented Reality (AR) can create innovative solutions and provide intuitive interactions and immersive experiences across various sectors, including education, entertainment and healthcare. The presented paper describes the innovative Voice-drive interaction in XR spaces (VOXReality)* initiative, funded by the European commission, that integrates language and vision-based AI with unidirectional or bidirectional exchanges to drive AR and VR, allowing for natural human interactions with XR systems and creating multi-modal XR experiences. It aligns Natural Language Processing (NLP) and Computer Vision (CV) parallel progress to design novel models and techniques that integrate language and visual understanding with XR, providing a holistic understanding of goals, environment, and context. VOXReality plans to validate its visionary approaches through three use cases such as a XR personal assistant, real-time verbal communication in virtual conferences, and immersive experience for the audience of theatrical plays.* Funded by European Union (Grant agreement ID: 101070521)
Attention-guided multi-scale illumination estimation for low-light image enhancement

Low-light image enhancement aims to solve the problems of low brightness and detail loss in images obtained under low illumination conditions. The design of decomposition and enhancement networks commonly used in this field mainly adopts simple convolutional neural network design, which lacks the guidance of relevant semantic information, resulting in certain noise and color distortion in enhanced images. To relieve the above problems, we designed a low-light image enhancement method based on attention mechanism and multi-scale illumination estimation. It can estimate illumination of low-light image at multiple global scales, and pay attention to the semantic information of spatial domain and channel domain through attention mechanism, so as to suppress the noise in the enhanced image. Then the detail reconstruction of the image after multi-scale illumination estimation fusion is carried out to reconstruct the detail and color of the image. Finally, we compared the current representative methods on the three evaluation criteria of PSNR, SSIM and LPIPS on commonly used public data sets. A large number of experimental results show that the proposed method has achieved better results in the subjective and objective comparison, and has excellent performance and potential.
Utilization of Data Mining Technology in University Students Management

In recent years, with the growth of computer technique, data mining (DM) has gradually become an important topic in people's research field. This paper introduces the current situation of university student management in China from two aspects of DM and management. Through the analysis of databases, we find that there are many problems. Finally, the performance of the system is tested. The test results show that the performance of the university management system based on DM technique can process data quickly and be compatible with the platform. This shows that the performance of the university management system meets the needs of users, and draws a conclusion that it is necessary to popularize relevant knowledge and skills in the school and strengthen the construction of campus network so as to provide convenient services for teachers to promote the growth and utilization of DM technique for college students in China, so as to promote the improvement of students' management level.
Research on Night Scene Image Enhancement Algorithm Based on Half-pixel Constraint

Due to the relatively concentrated distribution of gray value in night scene image, it is often necessary to improve the visual effect of the image by widening and lifting the gray value, so as to further clarify the details and facilitate the subsequent processing operation. To solve the problem that the traditional contrast enhancement algorithm often enlarges the noise when processing the image, which affects the visual quality of the enhanced image, a half pixel constrained image enhancement algorithm is proposed. The algorithm divides the night scene image into bright and dark regions according to the gray value corresponding to the half pixel of the image, and uses bi-directional histogram equalization to realize the half pixel constraint in the bright region, so as to suppress the signal-to-noise ratio, while the traditional double histogram equalization is still used in the dark region. The experimental results show that the algorithm can better preserve the details of the original night scene image, and has the ability to deal with the noise interference.
3D Face Recognition Algorithm Based on Deep Belief Network

Although the depth learning algorithm reduces the workload of face recognition to a certain extent, the local characteristics of 3D face images is ignored, resulting in low accuracy of 3D face recognition. Therefore, this paper proposed a new 3D face recognition method using LBP algorithm improve depth belief network. Firstly, LBP algorithm and depth belief network are analyzed, and then LBP texture feature vector of 3D face image is obtained, which is used as the input feature of depth belief network to capture the local information of 3D face image. Finally, this paper designed a 3D face image recognition process and realized 3D face recognition based on improved depth belief network. The proposed method is trained on FERET face image database, and the simulation results show that the proposed method has higher 3D face recognition rate and shorter recognition time, compared with the comparison method, which shows that the application effect of the improved depth learning algorithm in 3D face recognition is better.
Insulator Pollution Component Identification Method Based on Multi-scale 3D Convolution Network Mode

Pollution flashover and discharge caused by pollution deposition on the surface of insulators often occur in the field of power system. In the natural environment, insulators are affected by different weather and pollution sources, and the pollution deposition on the surface dissolved in water damages the insulation performance of insulators, and then leads to power failure of multiple transmission lines when pollution flashover accidents are serious, with serious consequences. Research and investigation have proved that the types of pollution components on the surface of insulators have great differences in their contribution to the pollution flashover phenomenon. In order to carry out regular cleaning of insulators, the identification and detection of pollution components on the surface of insulators is of great significance to predict insulator flashover voltage and prevent pollution flashover accidents. In this paper, based on hyperspectral image technology in the laboratory under ideal conditions to carry out the integrated pollution insulation uncleanness identification analysis experiment, established the insulator samples of hyperspectral image database, set the insulator impurity composition identification model based on deep learning, validate the convolutional neural network model for insulation slices of impurity composition to identify the feasibility and effectiveness.
A residual combined attention mechanism based on U-Net network for kidney tumor image segmentation

For the phenomenon that the kidney tumor target is small and difficult to segment, and the sample distribution is uneven, 3*3 residual convolution blocks are used to replace the 3*3 convolution of the original U-Net model. The problem of gradient disappearance is avoided while stabilizing the number of deepening layers to improve the effect of the model. The NAM (Normalization Attention Module) module is embedded in the upsampling process to enhance the network's ability to extract features. The proposed network achieves 0.917, 0.938, 0.921, 0.960 and 0.887 in terms of recall, Dice coefficient, IOU, F1 score and accuracy compared with other networks. It can be seen that the method proposed in this paper has improved the situation of small target misdetection and omission to a certain extent, and can effectively segment the kidney and tumour.
An Optimizing E-govenment Model for Data Sharing via Distributed Ledgers

With the development of terminal technology and the expansion of application areas, the value of e-government applications and service requirements continue to increase. To develop and apply e-government, data sharing efficiency is essential. Using the e-government data sharing as a research object, a blockchain-based method is developed for optimizing data transfer. Blockchain technology is used for storing and delivering distributed data. Rather than distributing data nodes randomly, our network scenario model employs a matrix lattice model based on a complete undirected graph. Dual optimization is then applied to solve the problem. In order to find the optimal solution, a mathematical model and an artificial neural network are developed. To verify the performance and packet loss rate of the government data transmission method, a case study is conducted. Data Sharing is optimized to increase the lifetime of static aggregation nodes by at least 30% and lifetime of static aggregation nodes by at least 20%. The network lifetime can be extended by optimizing static aggregation nodes. Furthermore,it effectively improves the robustness.