Reviewerly/exHarmony huggingface dataset:
Construction of Educational Resource Metadata Management Platform Based on Service-Oriented Architecture

The existing cloud sharing system of educational resources is in the form of centralized management, which has the problems of small number of concurrent users and low score of resource retrieval results and cannot meet the current needs of implicit educational resource sharing. Therefore, a cloud sharing system of college implicit educational resources based on service architecture is proposed. At present, the sharing of educational resources mainly includes network teaching, distance education, and other methods. These schemes alleviate the sharing problem of educational data resources to a certain extent. However, the sharing and rational integrated utilization of educational application software are still under exploration, and there is no mature solution at present. At present, there is a lack of unified planning for the construction of the educational resource database of the management cadre college, and there are problems such as repeated construction, complex quantity, and nonstandard form, which directly leads to the failure of effective dissemination of resource information in the network distance education system, which is an urgent problem to be solved in the distance education system of the management cadre college. This paper first understands some domestic and foreign standardization and development of educational resources. Then, it introduces the ontology theory and the basic theory of metadata. At present, the construction of educational resource database in colleges and universities in China lacks unified planning, and there are problems such as repeated construction, complex quantity, and nonstandard form, which makes the resource information in the network distance education system unable to be shared and disseminated effectively. In order to improve the utilization of online educational resources and share the data of resource databases with different structures, a metadata management platform model of educational resources is designed. As a new architecture idea, service-oriented architecture can package existing assets and reuse existing assets. It can also minimize the impact of demand changes. This paper puts forward the process of metadata management under the service architecture and independently develops a metadata management platform suitable for the development and use of service mode. It is combined with automatic testing and continuous integration to effectively improve the data quality and development efficiency of service development.
Detection of Fake Educational Sites Using Fuzzy String Match

The analysis of scientific paper and Internet sources devoted to the problem of information counteraction to threats of phishing sites of organizations is carried out. The need for the use of protective equipment on the side of organizations is shown. The relevance of the research topic is confirmed by the presence in the RU zone registry of domain names similar in spelling to well-known educational resources. A method for detecting fake websites based on checking the visual similarity of domain names is proposed. The developed method is based on the use of the Levenshtein metric used in the problem of fuzzy string comparison. The proposed method was tested on the dataset of domain names from the RU zone registry for a certain period of time. When implementing the fake site detection method, the Java programming language and the JGraphT library were used. The test results show an improvement in the quality of the search for domain names similar in spelling to the specified domain name of a web resource. The proposed method can be used in the implementation of one of the modules of a software tool for countering the threats of fake educational sites.
Creating information and records for development work: Working through the lens of information and records continuum models

Information studies characterize the beginning of information as creation, a point of origin. Adapting a continuum approach, this study focuses on the create dimension of the Information and Records Continuum models developed by Frank Upward and his colleagues (Upward, 2000). The context of this study is humanitarian aid and how the interactions of actors within the not-for-profit sector create information used to develop campaigns, design strategies for fundraising, and support beneficiaries, the recipients of aid. Semi-structured interviews and participatory observation were conducted in an international non-governmental organization working with vulnerable communities in Bangladesh. Applying the two continuum models provides insights into the development of aid workers' complex information ecosystem. Using the continuum models in analyzing the data for information and records creation suggest the potential use for these models to provide a theoretical framework in further information behavior studies.
Parallelism Network with Partial-aware and Cross-correlated Transformer for Vehicle Re-identification

Vehicle re-identification (ReID) aims to identify a specific vehicle in the dataset captured by non-overlapping cameras, which plays a great significant role in the development of intelligent transportation systems. Even though CNN-based model achieves impressive performance for the ReID task, its Gaussian distribution of effective receptive fields has limitations in capturing the long-term dependence between features. Moreover, it is crucial to capture fine-grained features and the relationship between features as much as possible from vehicle images.
Towards harnessing feature embedding for robust learning with noisy labels

The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy labels. However, we observe that the model will make mistakes during label prediction, resulting in unsatisfactory performance. By contrast, the produced features in the early stage of learning show better robustness. Inspired by this observation, in this paper, we propose a novel feature embedding-based method for deep learning with label noise, termed Lab El Noise Dilution (LEND). To be specific, we first compute a similarity matrix based on current embedded features to capture the local structure of training data. Then, the noisy supervision signals carried by mislabeled data are overwhelmed by nearby correctly labeled ones (i.e., label noise dilution), of which the effectiveness is guaranteed by the inherent robustness of feature embedding. Finally, the training data with diluted labels are further used to train a robust classifier. Empirically, we conduct extensive experiments on both synthetic and real-world noisy datasets by comparing our LEND with several representative robust learning approaches. The results verify the effectiveness of our LEND.
A Note on Quotient Reflective Subcategories of O-REL

In this paper, we examine the category of ordered-RELspaces. We show that it is a normalized and geometric topological category and give the characterization of local <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mover accent="false"> <mrow> <mi>T</mi> </mrow> <mrow> <mo>¯</mo> </mrow> </mover> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> , local <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msubsup> <mrow> <mi>T</mi> </mrow> <mrow> <mn>0</mn> </mrow> <mrow> <mo>′</mo> </mrow> </msubsup> </math> , and local <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> ordered-RELspaces. Furthermore, we characterize explicitly several notions of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> ’s and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> objects in O-REL and study their mutual relationship. Finally, it is shown that the category of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> ’s (resp. <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> ) ordered-RELspaces are quotient reflective subcategories of O-REL.
Hand Gesture Recognition System Using the Dynamic Vision Sensor

With the rapid development of computer vision and artificial intelligence, human-computer interaction has become an inevitable part of people’s lives. Gestures can bring more natural, comfortable, and effective communication between people and machines. However, in some complex scenarios, such as rooms with looming lighting, the robustness and universality of hand gesture recognition based on traditional cameras are insufficient, and the supporting algorithms tend to underperform in real-time, especially for embedded devices. This article explores methods of implementing gesture recognition based on Dynamic Vision Sensor (DVS). We obtained frame data by event-based accumulation and time-based accumulation. Then we apply preprocessing techniques such as sub-time window and overlapping frame to achieve higher accuracy on hand gesture recognition with the DVS. In this paper, we built a DVS-based gesture recognition system with the advantages of efficient data preprocessing, low memory cost, low latency, and competitive recognition ability in interaction scenarios. The recognition accuracy reaches 94.6%.
What Will It Take to Get to Acceptable Privacy-Accuracy Combinations?

What will it take to achieve acceptable privacy-accuracy combinations?I discuss this question in five parts.In Section 1, I review the technicalities of the privacy-accuracy trade-off problem.In Section 2, I introduce the two articles in this symposium that study real-world privacy-accuracy combinations.In Sections 3 and 4, I ask, respectively: What is acceptable accuracy?What is acceptable privacy?I consider these questions through the lens of the two articles.I conclude in Section 5, connecting the technical questions that practitioners study (from Sections 1 and 2) with the normative questions that only society as a whole can and should answer (from Sections 3 and 4).I argue that while great technical progress has been made, much more work is needed before we can talk of getting even close to an acceptable privacy-accuracy combination.I touch on some currently investigated ideas for moving forward, and end with a call to open and broaden the conversation, bringing the rest of society in.
A Proposal of Fuzzy Rule Partition and Its Application to Controller Design Based on Affine <scp>T‐S</scp> Fuzzy Model

When designing a controller based on a fuzzy model composed of fuzzy rules, in most cases controllers are designed only based on the consequent parts of the fuzzy rules, equally meaning the information of the antecedent parts is merely ignored. This paper first partitions all of the rules into two groups based on the information of the antecedent parts, in which one contains certain rules around the origin, and the other is a group of rules away from the origin. While being able to make the former arbitrarily small, each rule of the latter leads to a favorable quadratic inequality, which will be useful in controller design by the help of the S‐procedure. On the other hand, while the affine T‐S fuzzy model is much more effective for describing the dynamics of the system concerned in comparison with the regular T‐S fuzzy model, it encounters some difficulties when designing a controller based on the model because of the affine terms within. This paper makes clear that the difficulties can be overcame by using the quadratic inequalities based on the fuzzy rule partition. In addition, the effectiveness of the approach is demonstrated by applying it to the CSTR for an exothermic irreversible reaction. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks

Measuring joint kinematics is a key requirement for a plethora of biomechanical research and applications. While x-ray based systems avoid the soft-tissue artefacts arising in skin-based measurement systems, extracting the object's pose (translation and rotation) from the x-ray images is a time-consuming and expensive task. Based on about 106'000 annotated images of knee implants, collected over the last decade with our moving fluoroscope during activities of daily living, we trained a deep-learning model to automatically estimate the 6D poses for the femoral and tibial implant components. By pretraining a single stage of our architecture using renderings of the implant geometries, our approach offers personalised predictions of the implant poses, even for unseen subjects. Our approach predicted the pose of both implant components better than about 0.75 mm (in-plane translation), 25 mm (out-of-plane translation), and 2° (all Euler-angle rotations) over 50% of the test samples. When evaluating over 90% of test samples, which included heavy occlusions and low contrast images, translation performance was better than 1.5 mm (in-plane) and 30 mm (out-of-plane), while rotations were predicted better than 3-4°. Importantly, this approach now allows for pose estimation in a fully automated manner.
Visual Fatigue Phenomenon in Visual Communication Design Integrating Artificial Intelligence

In the process of cognition, people focus on self-feeling and realize the cognition of the world by judging the information conveyed by vision. Design changes lives, and our lives are always surrounded by design. This research mainly discusses the phenomenon of visual fatigue in visual communication design integrated with artificial intelligence. The article first analyzes the characteristics of optical illusion art. Taking typical works as an example, it expounds the application of optical illusion in modern design and the influence of optical illusion on design works. At the same time, it combines the basic principles of optical illusions to find out the elements and types of optical illusions, as well as various optical illusions that exist in life. This research uses artificial intelligence technology to efficiently create visual design, providing a basic space for higher quality design output. The proportion of subjects who could find the location of the inserted content was 66.7%. This study helps to better emphasize the visual design effect and link visual fatigue. The article starts from three aspects: web page, mobile phone, and traditional media. It analyzes the phenomenon of visual fatigue in modern visual communication design. While giving solutions, it also puts forward the dialectical concept of the balance between visual comfort and design.
Convergence of Traditional and Online Property Crime Victimization in a City with Little Offline Crime

This paper analyzes the correlates of traditional (burglary, vehicle theft, theft from vehicles, and pickpocketing) and online property crime (data theft and online fraud), with particular attention given to the relation between them and the risk of multiple victimizations. Data are gathered from a large and representative victimization survey conducted in a Swiss city (N = 7,885). The findings suggest that both traditional and online property victimization are related positively to the participants’ lifestyle routines, physical and online protection measures, and educational level. Men, young persons, those employed actively, and university graduates are overrepresented among the multiple victims of online-offline property victimization.
A Threshold Segmentation Algorithm for Sculpture Images Based on Sparse Decomposition

Aiming at the problem of low efficiency and insufficient accuracy of threshold solution in multithreshold sculpture image segmentation, this paper proposes a threshold segmentation algorithm for sculpture images based on sparse decomposition. In this paper, sparse decomposition is introduced to optimize the model to reduce the impact of local noise on segmentation accuracy, and an energy functional based on pixel coconstraint is built to make up for the defect that pixels cannot retain local details. At the same time, the weighted sum of elite solution sets is used to determine Neighborhood centers increase communication between groups. Experiments show that compared with other algorithms, the above method has significant advantages in convergence efficiency and accuracy.
Multiframe Correction Blind Deconvolution for Solar Image Restoration

Abstract A series of short-exposure images are often used for rich, small-scale structure, high-quality, and high-resolution astronomical observations. Postprocessing of the closed-loop adaptive optics (AO) image using ground-based astronomical telescopes plays an important role in astronomical observations due to it further improving image quality after AO processing. These images show several main characteristics: random spatial variation blur kernel, unclear model after AO correction, unclear physical characteristics of observation objects, etc. Our goal is to propose a multiframe correction blind deconvolution (MFCBD) algorithm to restore AO closed-loop solar images. MFCBD introduces a denoiser and corrector to help estimate the intermediate latent image and proposes using an L q norm of the kernel as the sparse constraint to acquire a compact blur kernel. MFCBD also uses the half-quadratic splitting strategy to optimize the objective function, which makes the algorithm not only simple to solve, but also easy to adapt to different fidelity terms and prior terms. In tests on three data sets observed from the photosphere and chromosphere of the Sun, MFCBD not only restored clearer and more detailed images, but also converged smoothly and monotonically in terms of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) after a few iterations. Taking the speckle-reconstructed image as a reference, the clear image restored by our method performs best both in PSNR and SSIM compared with the state-of-the-art traditional methods OBD and BATUD.
Deep objectness hashing using large weakly tagged photos

CNN-based hashing methods have greatly boosted the performance of image retrieval, under the strong supervision of large amounts of manually annotated labels. In recent years, a large number of social media images with user tags have been generated on the Internet. These images can be regarded as weakly labeled training data, which can provide rich samples for training hash network, and greatly reduce the cost of obtaining training data. However, there are noise and visual irrelevant tags in user tags, and different tags may describe different objects in the image. In the previous CNN-based hashing method, a training image usually corresponds to a manual label and generates a hash code. These methods are difficult to use the image described by user tags with noise. For solving the above problem, we propose a CNN-based objectness hash learning method using user tags as a guide for training. First of all, the user tags are roughly filtered to remove noise tags that are not related to the visual content of images. Secondly, we quantify user tags into a unified semantic space and extract the highest-frequency words of the semantic space from similar objectness areas as their labels. Then, these objectness areas with their labels are grouped into a series of triple units as training data. So that the generated hash code can inherit the semantic similarity of the objectness areas well, that is, the Hamming distance between hash codes generated by similar objectness areas is closer, the reverse is the farther. Experimental results on NUS-WIDE and Flickr datasets show that our method can effectively extract object-level semantic information from weak user tags, and improve the accuracy of image retrieval.
Formation of Digital Competencies for Scientific and Educational Activities of Students

The article deals with the problem of formation of digital competencies of higher education students, some of them determining the effectiveness of the organization of digital learning. The emphasis is placed on the project form of learning as one of the leading educational technologies for training modern specialists who meet the requirements of the digital economy. Particular attention is paid to the formation of interdisciplinary abilities and skills to conduct project activities as one of the basic competencies of a modern graduate.
Enhanced Privacy for Voice-Controlled Digital Assistants

Voice-Controlled Digital Assistants (VCDAs) are increasingly used in both private and professional environments. While the convenience of use and the broad area of applications increase user adoption, the use of VCDAs involves a trade-off between user experience and privacy. In our research, we focus on developing solutions to better protect users&#x2019; privacy by helping them to control the data shared with VCDAs.
Identification of Relevant Scientific Articles using a Modified Naive Bayes Classifier

The rapid growth in the number of scientific publications increases researchers’ need for systems that provide quick access to the most relevant articles on their subjects of interest. Existing solutions do not have enough flexibility to adapt to the needs of each user of the system. This study presents a scientific articles recommendation mechanism which takes into account the user’s individual preferences. The precision of the recommendations is 70%.
Understanding Entertainment Trends during COVID-19 in Saudi Arabia

Studying people’s opinions is a growing research field. This area of research is known as sentiment analysis. The COVID-19 pandemic changed everything around the world and reduced social contact among people. Citizens and residents of Saudi Arabia experienced high stress during the pandemic, seeking entertainment via games and publishing their activities on social media platforms such as Twitter. In this paper, we focus on applying the Mazajak sentiment analyzer on tweets containing game keywords in Arabic collected using Twitter API during the lockdown period to decide whether users preferred playing individually or in groups. This can help designers and developers, as well as the Saudi General Entertainment Authority (GEA), focus on creating the most interesting games for individuals and improving them to meet users’ expectations. Our approach has three main stages: tweet collection, tweet preparation, and finally, the application of sentiment analysis to get the desired goal based on people’s behavior toward the games. The result of this paper confirms that people, in general, preferred playing in groups, instead of alone, during this period.