Reviewerly/exHarmony huggingface dataset:
High-Level Semantic Feature Detector: Pedestrian Detection Based On Improved Mask R-CNN Algorithm

Pedestrian detection is a crucial topic that must be solved for a variety of reasons, including its usefulness in the domains of advanced mechanics, car safety, and surveillance. A significant portion of the progress over the last several years has been driven by the ease with which people may test their hypotheses on publicly available datasets and offer forth workable answers. In the current day, as deep learning approaches, it is common practise to use sliding-window classifiers in bespoke or anchor-based expectations for object recognition. To keep up with the rapid pace of development, this paper presents a new perspective in which object recognition is conceived of as a semantic object detection task at an undeniable level, and it also introduces refined evaluation metrics that demonstrate how commonly used per-window measures are ineffective and can fail to predict performance on full images. The proposed hybrid model combines the strengths of MobileNet and ResNet50's skip connection with Faster RCNN to analyse the whole image and pull out the relevant characteristics for detection. However, the suggested approach departs from the standard, low-level provisioning and instead focuses on a higher-level detection. Therefore, this research simplifies pedestrian identification using convolutions, reducing it to a focus and scale expectation job. As a result, the suggested method is straightforward, offers competitive accuracy and remarkable speed, and inspires the development of a novel, appealing pedestrian detector.
Generic Construction of Forward-Secure Revocable Identity-Based Signature and Lattice-Based Instantiations

Forward-secure revocation is a powerful cryptographic technique to alleviate key exposure attacks on identity-based cryptosystems. In recent years, quantum computers have made some breakthroughs, so in the foreseeable future, existing cryptographic systems will be subject to quantum attacks. However, known forward-secure revocable identity-based signature (FS-RIBS) schemes were designed over bilinear pairing groups and may suffer from quantum computing attacks. To address this issue, this paper proposes a generic method to construct FS-RIBS schemes, taking (hierarchical) IBS schemes as a basic component. By instantiating it with some post-quantum (hierarchical) IBS schemes, e.g., lattice-based (hierarchical) IBS, we immediately obtain six FS-RIBS schemes under the hardness of the small integer solution problem, which is secure against quantum computing attacks.
Keterampilan Literasi Siswa Ditinjau dari Pengetahuan Metakognisi dalam Menyelesaikan Soal Asesmen Kompetensi Minimum di Sekolah Dasar

The background that drives this research is that the literacy skills of students at SDN 01 Wonokromo are good but there are still some that are lacking, especially in solving the existing Minimum Competency Assessment questions. The purpose of this study was to analyze students' literacy skills in solving Minimum Competency Assessment questions at SDN 01 Wonokromo. This type of research uses a qualitative description approach using the methods of observation, interviews, questionnaires, and documentation. The results showed that student's literacy skills in solving AKM questions were well known from observations to the distribution of questionnaires carried out in class V. Knowledge of AKM and student literacy was carried out well and went smoothly with an average score of 74%, supporting facilities and infrastructure the activities owned are very good with a score of 81%. The involvement of teachers, principals, and education personnel is good with an average score of 73%. All school communities participate in helping students in the AKM process and overcoming various obstacles that students experience with an average score of 63%. Based on the results of the research, the suggestions concluded that students' literacy skills in solving Minimum Competency Assessment questions can be improved again so that students will understand more about the questions and can get more satisfactory grades.
Credit Card Fraud Detection

CONCLUSIONConcluding our R Data Science project, we learnt how to develop a credit card fraud detection model using machine learning.We used a variety of ML algorithms to implement this model and also plotted the respective performance curves for the models.We also learnt how data can be analyzed and visualized to discern fraudulent transactions from other types of data.Hope you enjoyed this credit card fraud detection project of machine learning using R.
Modern libraries of Ukraine as an inclusive space: promotion of reading and popularisation of books

The purpose of the article is to present the libraries of Ukraine as an inclusive space for the promotion of reading and popularisation of books in contemporary conditions. The research methodology is based on the general scientific principles that cognition is systematic, and comprehensive. In order to achieve this goal, the following special methods are used: comparison, analysis, and generalization. Scientific novelty of the article focuses on the issue of an inclusive space of libraries, which is successfully solved by the librarianship of Ukraine. Legislative initiatives to ensure access of visually impaired people to adapted information resources and promotion of reading are presented. The best experience of libraries in Ukraine under contemporary conditions for servicing readers with disabilities is revealed. Conclusions. The article argues that currently scientific studies in Ukrainian librarianship as a science of the regularities of library forming, developing, and functioning should update the theoretical basis of the promotion of reading as an inclusive space under contemporary conditions, that is, in this aspect, library science falls behind the achievements of library practice. It is established that with Ukraine gaining independence, a new stage began of promoting reading, books, and popularisation of the library as an inclusive space for development, education, and socialisation at the state level. It is asserted that Ukraine's orientation towards the development of the civilized world encourages the introduction of internationally proven practices to improve library and information services for people with disabilities and visually impaired users, ie the introduction of inclusion in a library sphere is one of the trends of state cultural policy. It is determined that information and communication services for people with disabilities are focused on such vectors as social, informational, psychological, and leisure ones in order to fully meet their informational, educational, and cultural needs.&#x0D; Keywords: libraries of Ukraine, inclusive space, promotion of reading, popularisation of books, people with disabilities, visually impaired users.
Challenges and advantages of distance learning in art institutions of tertiary education

The purpose of the work is to highlight the peculiarities of the organisation of the distance learning process, to justify the current need and importance of distance learning, and to structure its weaknesses and strengths. The research methodology consists in the application of theoretical and empirical methods, namely the analysis and synthesis of scientific works of the authors on the study issue; the method of visualisation of research findings, review-analytical, description, comparison methods enable revealing the importance and role of distance education. The scientific novelty of the article lies in the expansion of theoretical knowledge and practical skills of distance learning in art institutions of tertiary education, focusing on features and development prospects of distant learning. The comprehensive comparative analysis of distance education in art institutions of tertiary education has been carried out. It has been established that the considered form of distance learning is regulated by documents with access to public information. The main tasks that can be solved using the distant format have been defined. Conclusions. This study proved the effectiveness of the use of distance education in the field of education on the example of the National Academy of Culture and Arts Management. The advantages and disadvantages of distance education have been considered. The organisation of distance (online) education in art institutions of tertiary education has been outlined. Attention is focused on the main factors that must be taken into account to achieve a high professional level in the educational process. The list of the legislation of Ukraine which regulates and ensures the organisation of the distance educational process has been described.&#x0D; Keywords: distance learning, disadvantages of distance learning, quality of education, technologies of distance learning.
Supplemental Material: Apatite records metamorphic and hydrothermal fluid evolution at the large Shuangqishan orogenic gold deposit, SE China

&lt;p&gt;Text: Analytical methods and analytical data. Table S1: LA-ICP-MS zircon U-Th-Pb isotope data of ore-hosting metamorphic rocks of the Shuangqishan gold deposit. Table S2: LA-ICP-MS U-Th-Pb isotope data for zircon grains from the diorite and granite porphyry dikes in the Shuangqishan gold deposit. Table S3: LA-ICP-MS apatite (Ap1 and Ap2) U-Th-Pb data of metamorphic and auriferous quartz veins from the Shuangqishan gold deposit. Table S4: Trace-element concentrations in multiple-generation apatite from the Shuangqishan gold deposit, analyzed by LA-ICP-MS. Table S5: In-situ Sr isotopic compositions of multiple-generation apatite from the Shuangqishan gold deposit, analyzed by LA-MC-ICP-MS. Table S6: Electron microprobe geochemical data for hydrothermal xenotime from the Shuangqishan gold deposit. Table S7: SHRIMP U-Th-Pb isotope data for hydrothermal xenotime from the sub-stage IIb gold ores in the Shuangqishan gold deposit.&lt;/p&gt;
Contributions to Dynamic Analysis of Differential Evolution Algorithms

Abstract The Differential Evolution (DE) algorithm is one of the most successful evolutionary computation techniques. However, its structure is not trivially translatable in terms of mathematical transformations that describe its population dynamics. In this work, analytical expressions are developed for the probability of enhancement of individuals after each application of a mutation operator followed by a crossover operation, assuming a population distributed radially around the optimum for the sphere objective function, considering the DE/rand/1/bin and the DE/rand/1/exp algorithm versions. These expressions are validated by numerical experiments. Considering quadratic functions given by f(x)=xTDTDx and populations distributed according to the linear transformation D-1 of a radially distributed population, it is also shown that the expressions still hold in the cases when f(x) is separable (D is diagonal) and when D is any nonsingular matrix and the crossover rate is Cr=1.0. The expressions are employed for the analysis of DE population dynamics. The analysis is extended to more complex situations, reaching rather precise predictions of the effect of problem dimension and of the choice of algorithm parameters.
KEMAMPUAN PENGGUNAAN KATA YANG DAPAT DIPISAHKAN OLEH MAHASISWA TINGKAT III STBA-PIA 亚院华文系三年级学生使用汉语离合词的能力考查

This research aims to describe the ability of STBA-PIA Medan third-year undergraduates in using the separable words in Chinese language. Quantitative descriptive research is conducted on 40 students whom are both the population and sample through saturated sampling technique. The instrument created to collect the required data for analysis is a test with 25 questions. Content validity is utilised, and the reliability coefficient of each question in the instrument with KR-20 formula is 0.734, meaning the instrument has highly reliable. The result shows that the students' average ability in using the separable words in Chinese language is 65.3, indicating that they are not competent.
Vehicular Environment Identification Based on Channel State Information and Deep Learning

This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connected vehicle in order to identify the environment type in which the vehicle is driving, without any need to implement specific sensors such as cameras or radars. We consider environment identification as a classification problem, and propose a new convolutional neural network (CNN) architecture to deal with it. The estimated CSI is used as the input feature to train the model. To perform the identification process, the model is targeted for implementation in an autonomous vehicle connected to a vehicular network (VN). The proposed model is extensively evaluated, showing that it can reliably recognize the surrounding environment with high accuracy (96.48%). Our model is compared to related approaches and state-of-the-art classification architectures. The experiments show that our proposed model yields favorable performance compared to all other considered methods.
High-Accuracy Relative Pose Measurement of Noncooperative Objects Based on Double-Constrained Intersurface Mutual Projections

Relative pose measurement for noncooperative objects is an important part of 3D shape recognition and motion tracking. The methods based on scanning point clouds have better environmental adaptability and stability than image-based methods. However, the discrete points obtained from a continuous surface are sparse, which leads to point-to-point dislocations in the overlapping area and seriously reduces the accuracy. Therefore, this paper proposed a relative-pose-measurement algorithm based on double-constrained intersurface mutual projections. First, the initial corresponding set was constructed using mutual projections between the areas with similar feature descriptors, and then the final corresponding set was determined through the rigid-transformation-consistency constraint to improve the accuracy of the matchings and achieve a high-accuracy relative pose measurement. In the Stanford dataset, the rotation error and translation error were reduced by 19.3% and 13.4%, respectively. Furthermore, based on the proposed evaluation method, which separated the error of the pose-measurement algorithm from that of the instrument, the experiments were carried out with a self-made swept-frequency interferometer. The rotation error was reduced by 39.8%, and the surface deviation was reduced by 4.9%, which further proved the advancement of the method.
DATA ANALYSIS FOR UNDERSTANDING THE IMPACT OF COVID–19 VACCINATIONS ON THE SOCIETY

In the context of COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discuss about data analysis can help in several aspects of COVID-19 vaccination on the society. We present a system in which machine learning technology is used to guaranty data integrity and vaccination, manufacturing and Supply. Defined to monitor and track the proper vaccine distribution conditions against the safe handling rules defined by vaccine producers enabling the awareness. In this paper author analyzing vaccines dataset to forecast required vaccines compare to manufacturing or available vaccines and by using this forecasting manufacturers may increase and decrease their manufacturing quantity. This forecasting can impact society by taking decision on manufacturing vaccines and if in society more cases occurred then forecasting will be high and by seeing forecasting manufacturers may increase production.
Strategi promosi dalam meningkatan layanan di UPT Perpustakaan Proklamator Bung Karno Blitar pada masa pandemi

Background. Promotion is the most important part of an organization to inform a product that is produced. The challenge is that special libraries need to promote services and collections so that users can also take advantage of during the Pandemic. The purpose of this research is to find out the promotion, strategy, and innovation used by UPT Bung Karno Library during the pandemic in improving its services.Methodology. This study uses qualitative methods. This study obtained data through interviews, observation, and documentation from informants who were taken by purposive sampling, then analysed them using descriptive analysis techniques.Results and discussion. The study can be seen that the UPT Bung Karno Proclaimer Library conducts promotions using social media, brochures, exhibitions, and socialization. In addition, the strategy used by Bung Karno's proclaimer UPT Library in promoting the library to the public focused more on Bung Karno's ideas and thoughts. How to improve services at the UPT Bung Karno Proclaimer Library by creating the "SILAKAN" program and creating the "ISU Karno" application which is used to access collections at the UPT Bung Karno Proclaimer Library.Conclusions. The Bung Karno Proclamator Library UPT has been working on promotions, strategies and innovations to improve its services during the pandemic, and carried out agitation so that it is hoped that more people will visit the library.
Efficient multiparty quantum secret sharing scheme using single qudit

The aim of quantum secret sharing, as one of most promising components of quantum cryptograph, is one-to-multiparty secret communication based on the principles of quantum mechanics. In this paper, an efficient multiparty quantum secret sharing protocol in a high-dimensional quantum system using a single qudit is proposed. Each participant's shadow is encoded on a single qudit via a measuring basis encryption method, which avoids the waste of qudits caused by basis reconciliation. Security analysis indicates that the proposed protocol is immune to general attacks, such as the measure-resend attack, entangle-and-measure attack and Trojan horse attack. Compared to former protocols, the proposed protocol only needs to perform the single-qudit measurement operation, and can share the predetermined dits instead of random bits or dits.
Face Mask Detection by Using Deep Learning with the Generative Adversarial Network-Based Feature

COVID-19 is one of the diseases that causes a lot of trouble in social communication. To prevent the spread of the Covid-19, people must wear a mask during conversation, shopping, or studying. Therefore, it is necessary to develop an application that helps to verify whether people wear a face mask or not. Many approaches have been proposed to detect a facemask based on using machine learning-based methods, such as support vector machines, and convolutional neural networks. However, the performance of existing systems still has limitations under difficult deployment environments where the camera's quality is not good enough for detection. Therefore, in this research, we study the benefits of generative adversarial networks to produce a stable feature for robust deep learning model-based facemask detection. First, the generative adversarial network is used to learn and discover stable features from the input dataset. Second, the Yolo is then employed to learn the stable feature to effectively detect and recognize facemasks under various testing conditions. With comprehensive experimental results, we found that our proposed method achieved a detection rate of 84.45% by using the MaskedFace-Net dataset.
Contextualising Horizon: Connecting community to envision Australasia’s future educational technologies and practices

Over its 18-year history, the Horizon Report, originating in the United States, has become an important tool for administrators, academics and practitioners in envisioning future technologies likely to impact higher education and in determining institutional strategy. ASCILITE’s Contextualising Horizon project aims to complement the Horizon Report and provide an Australasian regional lens to trends in educational technologies and practices. Beginning in 2021, Contextualising Horizon is a project aimed to connect members of the ASCILITE community and affiliates to engage in environmental scanning and evidencing the current state of play in higher education and the educational technologies and practices likely to be of importance in the 12-18 months following the process. This paper highlights the process of Contextualising Horizon, key findings, and plans for future iterations of the project.
BHGAttN: A Feature-Enhanced Hierarchical Graph Attention Network for Sentiment Analysis

Recently, with the rise of deep learning, text classification techniques have developed rapidly. However, the existing work usually takes the entire text as the modeling object and pays less attention to the hierarchical structure within the text, ignoring the internal connection between the upper and lower sentences. To address these issues, this paper proposes a Bert-based hierarchical graph attention network model (BHGAttN) based on a large-scale pretrained model and graph attention network to model the hierarchical relationship of texts. During modeling, the semantic features are enhanced by the output of the intermediate layer of BERT, and the multilevel hierarchical graph network corresponding to each layer of BERT is constructed by using the dependencies between the whole sentence and the subsentence. This model pays attention to the layer-by-layer semantic information and the hierarchical relationship within the text. The experimental results show that the BHGAttN model exhibits significant competitive advantages compared with the current state-of-the-art baseline models.
AN FPT.AI-BASED TEXT-TO-SPEECH APPLICATION'S END-TO-END CONVERSION SPEED ANALYSIS

In this paper, an FPT.AI-based text-to-speech (TTS) application is developed that converts Vietnamese text into spoken words. the applying is developed supported Django for Python ANd within the style of AN interactive website that is connected to an FPT.AI server through its application programming interface (API). the applying supports conversion of text to seven totally different Vietnamese speeches. Four out of seven voices are often wont to convert up to five hundred characters in an exceedingly single group action whereas the others support that of four hundred characters. supported the results obtained, the primary conversion time takes up to ten s to convert 400-character text into speech whereas the following times, given same text, it takes beneath one.8 s for the conversion. this is often applicable to any or all voices.
Time-aware Embeddings of Clinical Data using a Knowledge Graph

Meaningful representations of clinical data using embedding vectors is a pivotal step to invoke any machine learning (ML) algorithm for data inference. In this article, we propose a time-aware embedding approach of electronic health records onto a biomedical knowledge graph for creating machine readable patient representations. This approach not only captures the temporal dynamics of patient clinical trajectories, but also enriches it with additional biological information from the knowledge graph. To gauge the predictivity of this approach, we propose an ML pipeline called TANDEM (Temporal and Non-temporal Dynamics Embedded Model) and apply it on the early detection of Parkinson's disease. TANDEM results in a classification AUC score of 0.85 on unseen test dataset. These predictions are further explained by providing a biological insight using the knowledge graph. Taken together, we show that temporal embeddings of clinical data could be a meaningful predictive representation for downstream ML pipelines in clinical decision-making.