Reviewerly/exHarmony huggingface dataset:
The Design of Mathematics Teaching Optimization Scheme Based on Data-Driven Decision-Making Technology

In order to improve the effect of classroom teaching and realize the improvement of mathematics teaching quality, this article combines data-driven decision-making technology to design a mathematics teaching plan system and applies data-driven decision-making technology to the design of mathematics teaching plan by improving the big data algorithm. In addition, this paper designs a mathematics teaching plan design system based on data-driven decision-making technology. The system learning module displays the knowledge points in a chapter-sequential navigation mode and stores data or information in the nodes, which are connected to form a network structure through chains. Finally, this paper verifies the designed system with experiments. From the results of the experimental research data, it can be seen that the mathematical teaching plan design system based on the data-driven decision-making technology constructed in this article has good practical effects. On this basis, the system constructed in this article can be verified through further practice.
Implementation of VBM3D and Some Variants

VBM3D is an extension to video of the well-known image denoising algorithm BM3D, which takes advantage of the sparse representation of stacks of similar patches in a transform domain.The extension is rather straightforward: the similar 2D patches are taken from a spatio-temporal neighborhood which includes neighboring frames.In spite of its simplicity, the algorithm offers a good trade-off between denoising performance and computational complexity.In this work we revisit this method, providing an open-source C++ implementation reproducing the results.A detailed description is given and the choice of parameters is thoroughly discussed.Furthermore, we study and compare several extensions of the original algorithm: (1) a multiscale implementation, (2) the use of 3D patches, (3) the use of optical flow to guide the patch search.These extensions yield results which are competitive with the most recent state of the art.
Analysis of MOOC on Programming for IT Specialist Training

The article provides a quantitative analysis of online programming courses on such platforms as Alison, Udemy, Edx, Coursera, Udacity, Codecademy as of July 2021. The authors analyzed the number of courses that can be found either in thematic sections or by automatic keyword search. It was found that students can find 6,979 online programming courses on the analyzed platforms with the help of thematic sections. The Udemy platform provides the largest number of programming courses. Popular programming languages which have the largest number of online courses turned out to be Python, JavaScript and Java.
A Single-Stage, Bottom-up Approach for Occluded VIS using Spatio-temporal Embeddings

The task of Video Instance Segmentation (VIS) involves segmenting, tracking and classifying all object instances present in a given video clip. Occluded VIS is a more challenging extension of this task which involves longer video sequences where objects undergo significant occlusions over time. Most existing approaches to VIS involve multiple networks which separately handle segmenting, tracking and classifying object instances, and potentially a set of heuristics to combine the individual network outputs. By contrast, we employ just one, single-stage network without any heuristics or post-processing for the end-to-end task. Our approach is called ’STEm-Seg’, which is a bottom-up method for Segmenting object instances in videos using Spatio-Temporal Embeddings. We achieve 3rd place in the Occluded VIS challenge with an mAP score of 21.6% on the test set.
The Importance of Multimedia Technology in pe Learning

Technology has taken a role in the education sector as a supporter of the learning process because the use of technology is very important for sharing information and communication.Through a variety of applications that can be used for education, the quality of the Internet network, the participation of teachers, and competence of students themselves they can see one of the advantages of educational technology that is learning media without face to face.Physical education is a subject that is not currently possible with face to face directly, this is where multimedia plays an important role.This study tries to provide an overview of the importance and use of multimedia technology in physical education.Search for articles using Google Scholar with the keywords Education Technology, published since 2000-2020.Conclusion multimedia technology can increase student motivation to conduct learning activities independently because the main modern supporting equipment is widely available at all times
Quantitative Distribution of Verbal Structures with Reference to the Authorship Factor in Legal Stylistics

Abstract The paper aims at describing the findings and conclusions formulated in the analysis of the authorship factor in legal discourse. It is hypothesised that verbal structures show systemically varied distribution across legal discourse and the relevant distinctions run through the authorship categories. When it comes to the aim of the research it draws on the tradition of sociolinguistic methodology targeting issues related to language variation which follows the basic assumptions of functional grammar. From the point of view of the material covered by the analysis it contributes to the research on legal discourse and specifically on its specialised domain referred to as corporate, company or business discourse. It provides additional empirical data pointing to the non-homogeneity of the legal style and formal distinctions originating from rich contextual background. The study is conducted on the material of a custom-designed corpus of English legal texts, classified as secondary genres. Methodologically, the study makes use of the tenets of supervised search of digitalised corpora and automatic data extraction based on discrete units, subsequent identification of recurring longer contiguous and/or non-contiguous sequences, if any, built around the axis of specific verbal structures and finally qualitative comparative analysis (characterisation) of the material. The discussion presents sample data and focuses on the most salient categories, both quantitatively and qualitatively. The inductive approach confirms the formal divergencies in the communicative situation covered by the analysis. The findings encapsulate patterns and tendencies in the quantitative distribution of verbal structures depending on the authorship category. It may be concluded that authorship is a factor delineating distinctions as regards (i) the repertoire of grammatical instruments exploited (verbal structures), which contributes to the specific stylistic profile of given authors. This shows that the thesis posed is verified positively and the study shows further, more detailed distinctions running through groups of subcategories distinguished within the authorship categories specified upon the start of the research.
Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and Animals

The increasingly close proximity between people and animals is of great concern for public health, given the risk of exposure to infectious diseases transmitted through animals, which are carriers of more than 60 zoonotic agents. These diseases, which are included in the list of Neglected Tropical Diseases, cause losses in countries with tropical and subtropical climates, and in regions with temperate climates. Indeed, they affect more than a billion people around the world, a large proportion of which are infected by one or more parasitic helminths, causing annual losses of billions of dollars. Several studies are being conducted in search for differentiated, more sensitive diagnostics with fewer errors. These studies, which involve the automated examination of intestinal parasites, still face challenges that must be overcome in order to ensure the proper identification of parasites. This includes a protocol that allows for elimination of most of the debris in samples, satisfactory staining of parasite structures, and a robust image database. Our objective here is therefore to offer a critical description of the techniques currently in use for the automated diagnosis of intestinal parasites in fecal samples, as well as advances in these techniques.
EXPERT SYSTEM FOR DIAGNOSIS OF MALARIA AND TYPHOID

An expert system is a computer program designed to solve problems in a domain that has human expertise. The knowledge built into the system is usually obtained from experts in the field. Based on this knowledge, an expert system can replicate the thinking process of the human experts and make logical deductions accordingly. Malaria and Typhoid are major health challenge in our society today (Nigeria), its symptoms can lead to other illness which include prolonged fever, fatigue, headaches, nausea, abdominal pain and constipation or diarrhea. People in endemic areas are at risk of contracting both infections concurrently. According to the world malaria report 2011, there were about 216 million cases of malaria and typhoid and estimated 655,000 deaths in 2010. (WHO report, 2011). The main challenging issue confronting the healthcare is lack of quality of service at minimal cost implying from diagnosing to predicting patients correctly. This issue can sometimes lead to an unfortunate clinical decision that can result in devastating consequences that are unacceptable. Although many studies were carried out by different researchers in the medical domain using various data techniques. In this research work, an efficient expert system that diagnoses patients with malaria and typhoid was developed. A secondary data was collected from university of Maiduguri teaching hospital for the period of four years which ranges from 2017 to 2020. The work explored the potential benefits of proposing a new model for prediction and diagnosis of malaria and typhoid using symptoms. The model adopted the Naive bayes and was implemented using the python. The system diagnoses a patient in real time (within 30 minutes) without necessarily visiting the laboratory for a test. Three algorithms were used these are, Support vector machine, Artificial neural network and Naïve bayes. From our finding, it is observed that Naïve bayes and support vector machine give the best result which is 100% in terms of accuracy of diagnosis. Keywords: Diagnosis, Prediction, Expert System, Typhoid, Malaria
Recent Advances in Dialogue Machine Translation

Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including data limitation, discourse properties and personality traits. In this article, we give the first comprehensive review of dialogue MT, including well-defined problems (e.g., 4 perspectives), collected resources (e.g., 5 language pairs and 4 sub-domains), representative approaches (e.g., architecture, discourse phenomena and personality) and useful applications (e.g., hotel-booking chat system). After systematical investigation, we also build a state-of-the-art dialogue NMT system by leveraging a breadth of established approaches such as novel architectures, popular pre-training and advanced techniques. Encouragingly, we push the state-of-the-art performance up to 62.7 BLEU points on a commonly-used benchmark by using mBART pre-training. We hope that this survey paper could significantly promote the research in dialogue MT.
AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization

Code summarization aims to generate brief natural language descriptions for source code. As source code is highly structured and follows strict programming language grammars, its Abstract Syntax Tree (AST) is often leveraged to inform the encoder about the structural information. However, ASTs are usually much longer than the source code. Current approaches ignore the size limit and simply feed the whole linearized AST into the encoder. To address this problem, we propose AST-Transformer to efficiently encode tree-structured ASTs. Experiments show that AST-Transformer outperforms the state-of-arts by a substantial margin while being able to reduce 90 ~ 95% of the computational complexity in the encoding process.
Do Youths with Neuromotor Disorder and Their Therapists Prefer a Mixed or Virtual Reality Head-Mounted Display?

To evaluate the usability of 2 head-mounted displays in youths undergoing neurorehabilitation; a mixed reality head-mounted display and a virtual reality head-mounted display.Observational cross-sectional study.Thirteen youths (age range 7.8-16.5 years) with neuromotor disorder.Youths wore a mixed reality or a virtual reality head-mounted display while being verbally guided through a scene with virtual objects. Differences between the 2 systems, regarding usability, user experience, and acceptability, were evaluated using standardized questions for the youths and their therapists. System preferences and symptoms of cybersickness were noted.Both head-mounted displays were easy to mount and adjust to the children's heads, but the mixed reality system was unstable in 40% of the youths. Participants stated that they could move naturally with both devices. Object appearance scored higher with the virtual reality system, while therapists rated youths' movement execution and needed additional support in favour of the mixed reality system. Most youths preferred the virtual reality device, mainly due to the more distinct appearance of objects and the objects' richer colours. Therapists' preferences were balanced. Two children reported minimal signs of cybersickness.Youths and therapists accepted both systems well, with advantages regarding usability, user experience, and preference for the virtual reality, and acceptability for the mixed reality head-mounted display.
Adolescentes com diabetes mellitus tipo 1 e o seu processo de construção da autonomia para o autocuidado

Adolescents with type 1 diabetes mellitus and
Agricultural Machinery Virtual Assembly System Using Dynamic Gesture Recognitive Interaction Based on a CNN and LSTM Network

The agricultural machinery experiment is restricted by the crop production season. Missing the crop growth cycle will extend the machine development period. The use of virtual reality technology to complete preassembly and preliminary experiments can reduce the loss caused by this problem. To improve the intelligence and stability of virtual assembly, this paper proposed a more stable dynamic gesture cognition framework: the TCP/IP protocol constituted the network communication terminal, the leap motion-based vision system constituted the gesture data collection terminal, and the CNN-LSTM network constituted the dynamic gesture recognition classification terminal. The dynamic gesture recognition framework and the harvester virtual assembly platform formed a virtual assembly system to achieve gesture interaction. Through experimental analysis, the improved CNN-LSTM network had a small volume and could quickly establish a stable and accurate gesture recognition model with an average accuracy of 98.0% (±0.894). The assembly efficiency of the virtual assembly system with the framework was improved by approximately 15%. The results showed that the accuracy and stability of this model met the requirements, the corresponding assembly parts were robust in the virtual simulation environment of the whole machine, and the harvesting behaviour in the virtual reality scene was close to the real scene. The virtual assembly system under this framework provided technical support for unmanned farms and virtual experiments on agricultural machinery.
Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data

Convolutional neural networks (CNNs)-as a type of deep learning-have been specifically designed for highly heterogeneous data, such as natural images. Neuroimaging data, however, is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. To harness spatial homogeneity of neuroimaging data, we suggest here a new CNN architecture that combines the idea of hierarchical abstraction in CNNs with a prior on the spatial homogeneity of neuroimaging data. Whereas early layers are trained globally using standard convolutional layers, we introduce patch individual filters (PIF) for higher, more abstract layers. By learning filters in individual latent space patches without sharing weights, PIF layers can learn abstract features faster and specific to regions. We thoroughly evaluated PIF layers for three different tasks and data sets, namely sex classification on UK Biobank data, Alzheimer's disease detection on ADNI data and multiple sclerosis detection on private hospital data, and compared it with two baseline models, a standard CNN and a patch-based CNN. We obtained two main results: First, CNNs using PIF layers converge consistently faster, measured in run time in seconds and number of iterations than both baseline models. Second, both the standard CNN and the PIF model outperformed the patch-based CNN in terms of balanced accuracy and receiver operating characteristic area under the curve (ROC AUC) with a maximal balanced accuracy (ROC AUC) of 94.21% (99.10%) for the sex classification task (PIF model), and 81.24% and 80.48% (88.89% and 87.35%) respectively for the Alzheimer's disease and multiple sclerosis detection tasks (standard CNN model). In conclusion, we demonstrated that CNNs using PIF layers result in faster convergence while obtaining the same predictive performance as a standard CNN. To the best of our knowledge, this is the first study that introduces a prior in form of an inductive bias to harness spatial homogeneity of neuroimaging data.
Summary of fourth annual <scp>MCBK</scp> public meeting: Mobilizing computable biomedical knowledge—metadata and trust

Abstract The exponential growth of biomedical knowledge in computable formats challenges organizations to consider mobilizing artifacts in findable, accessible, interoperable, reusable, and trustable (FAIR+T) ways 1 . There is a growing need to apply biomedical knowledge artifacts to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations lack the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are insufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Fourth Annual MCBK public meeting, which was held virtually July 20 to July 21, 2021 and convened over 100 participants spanning diverse domains to frame and address important dimensions for mobilizing CBK.
Face Mask Detection: A Real-Time Android Application Based on Deep Learning Modeling

The accelerated spread of the COVID-19 (coronavirus) disease has put stress on healthcare systems. Some safety measures are provided, such as keeping social distance and wearing a mask, which can help curb transmission and save lives. This paper aims to detect whether a person is wearing a mask or not with video surveillance to enforce health and safety regulations in real-time. We propose a solution for face mask detection using two deep learning models, the MobileNetV2 and the Modified Convolutional Neural Network (MCNN). The trained models are converted to TensorFlow Lite to deploy an Android Application. Our models can achieve up to 99% accuracy. In this paper, an analysis of the number of individuals not wearing masks is provided by capturing the face and storing it on a mobile-backend-as-a-service. Our application can be adopted to increase health measures in real-time and control the spread of COVID-19.
A Novel Hierarchical Key Assignment Scheme for Data Access Control in IoT

Hierarchical key assignment scheme is an efficient cryptographic method for hierarchical access control, in which the encryption keys of lower classes can be derived by the higher classes. Such a property is an effective way to ensure the access control security of Internet of Things data markets. However, many researchers on this field cannot avoid potential single point of failure in key distribution, and some key assignment schemes are insecure against collusive attack or sibling attack or collaborative attack. In this paper, we propose a hierarchical key assignment scheme based on multilinear map to solve the multigroup access control in Internet of Things data markets. Compared with previous hierarchical key assignment schemes, our scheme can avoid potential single point of failure in key distribution. Also the central authority of our scheme (corresponding to the data owner in IoT data markets) does not need to assign the corresponding encryption keys to each user directly, and users in each class can obtain the encryption key via only a one-round key agreement protocol. We then show that our scheme satisfies the security of key indistinguishability under decisional multilinear Diffie-Hellman assumption. Finally, comparisons show the efficiency of our scheme and indicates that our proposed scheme can not only resist the potential attacks, but also guarantee the forward and backward security.
A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks

With the rapid development of Internet of Things technology, a large amount of user information needs to be uploaded to the cloud server for computing and storage. Side-channel attacks steal the private information of other virtual machines by coresident virtual machines to bring huge security threats to edge computing. Virtual machine migration technology is currently the main way to defend against side-channel attacks. VM migration can effectively prevent attackers from realizing coresident virtual machines, thereby ensuring data security and privacy protection of edge computing based on the Internet of Things. This paper considers the relevance between application services and proposes a VM migration strategy based on service correlation. This strategy defines service relevance factors to quantify the degree of service relevance, build VM migration groups through service relevance factors, and effectively reduce communication overhead between servers during migration, design and implement the VM memory migration based on the post-copy method, effectively reduce the occurrence of page fault interruption, and improve the efficiency of VM migration.
Cornell Library FOLIO Case Study

Cornell University is a private university with a public mission. With a student body of about 25,000, Cornell is the federal land-grant institution of New York State, a private endowed university, a member of the Ivy League/Ancient Eight, and a partner of the State University of New York. Cornell Library supports the university’s mission with 20 different physical and digital libraries, a collection of 8.5 million volumes and 1.7 million e-books, and about 400 staff.&#x0D; After 20 years using the vended application Voyager by Ex Libris as the Library’s integrated library system (ILS), on July 1, 2021 Cornell Library completed our migration to the open-source platform, FOLIO.