Reviewerly/exHarmony huggingface dataset:
Data Sharing System Based on Blockchain Technology

The data sharing system based on blockchain technology is a decentralized data exchange platform in which users can share their data with any other users. Blockchain technology makes it possible to store and track all information in a secure manner. It also helps ensure that no one has access to this information. Users of the system can share their data with others by uploading it to the Web site or through an application and then store it securely on a server connected to the Internet. All these data are encrypted, so only authorized personnel can access them. Users can select the type of data to be shared and the conditions of the data to be shared. For example, if you want your friends to have access only to certain information about you, you can do this by setting up a private key for this purpose. In addition, you can set a password that you need before others can access your data. The main advantage of such systems is that they are very secure because they use cryptography as one of their security mechanisms.
Enhancing 2D Hand Pose Detection and Tracking in Surgical Videos by Attention Mechanism

Hand pose estimation is a fundamental task for many human-robot interaction-related applications. In this work, we proposed novel hand pose estimation models, which leverage two types of attention mechanisms: self-attention and channel attention. By incorporating a simple yet efficient Squeeze-and-excitation (SE) block into Res152-CondPose, our best method, SERes152-CondPoseSE, successfully models interdependencies between channels. It outperforms the baseline, Res152-CondPose, by an absolute 9.56% in mean Average Precision and 17.78% in Multiple Object Tracking Accuracy.
Research on employment psychological guidance of college students under the background of new media

With the rapid progress of our society, people have paid more attention to the development of university education.Career guidance is one of the important tasks in college education, especially in the current situation of college students' employment pressure, whether the employment guidance work can be carried out well will have a direct impact on the employment situation of college students.Under the development of information technology, a series of technical means led by new media have emerged one after another, which has greatly improved the efficiency of information dissemination and has a very good auxiliary role in the development of university employment guidance, and the psychological guidance of college students' employment has become inevitable.
Research on Key Performance Index Prediction of Distributed Database Based on Machine Learning Algorithm

In the era of mobile Internet, data generated by all walks of life has become an important factor of production, which contains great value. However, in the face of the growing data scale and the continuous analysis requests, the database query under the single machine architecture has been unable to use the limited hardware resources to meet the data analysis needs of low latency and high throughout. The research on the prediction of key performance indicators of distributed database based on machine learning algorithm is the research of distributed database system using machine learning technology. KPIs are metrics used to evaluate the efficiency and effectiveness of data processing in an organization or any other entity. It can be used as an indicator to determine organizational performance.
WAYS OF ORGANIZING AND DEVELOPING RESEARCH AND INNOVATION CENTERS IN UNIVERSITIES

The article proposes innovative ways of creating research laboratories (centers) for the development of scientific and innovative research in higher education institutions. The study also explains the basic criteria and conditions for selecting relevant research and innovation topics, the sources of funding to create research laboratories, the mechanisms of gradual employment of qualified scientific personnel in the laboratory and the use of a credit-module system.
CHALLENGES OF THE NEW AGE: ENSURING THE QUALITY OF EDUCATION IN THE “UNIVERSITY 3.0” MODEL

The article analyzes institutional models of ‘a modern university’, tasks, functions and features of higher education institutions in the context of the “University 3.0” concept, which envisages integration between commercialization of educational achievements, science, innovation and research in higher education institutions, as well as modernization and development of higher education system in our republic, as well as internationalization of the academic education network. The stages of change for transition to a university - entrepreneurship (3.0) depending on transformational processes observed in the current higher education system, have been justified. Opening of venture funds and banks ensuring secured benefits and introduction of a mechanism for providing special incentives to residents who deal with commercializing existing innovative developments and accommodating residents with innovative research and production centers (Technoparks) established at universities, has been recommended on the basis of advanced universities.
Improved Initialization Method for Simple and Fast K-medoids Clustering

Initial solution selection is an important process in determining an optimal solution for the efficient classification of clustering methods in machine learning. This study proposes an improved initialization method for a simple and fast Kmedoids clustering algorithm, considering not only the sum of the relative distance rates for each data point, but also the distance between the previously selected medoids (representative objects). Selection of the balanced initial medoids causes the dissimilarity of patterns in the same cluster to be small and the dissimilarity of patterns in different clusters to be large. The performance of the proposed method is verified and validated based on two valid indices of clustering solutions by employing seven machine learning repository datasets of prominent applications with large improvements. The Mann–Whitney test is employed to assess the statistical significance of the performance differences between the method proposed by Park and Jun (2009) and the initialization method for clustering proposed here. The proposed initial selection method is effective, highly reliable, and computationally practical even for large problems.
Enhancing Answer Selection via Ad-Hoc Knowledge Extraction from Unstructured Web Texts

Answer selection aims to identify the most relevant answers to a given question from a set of candidates. It is the fundamental component of intelligent question answering system. To improve performance, it gradually becomes an effective strategy to integrate external structured knowledge bases (KBs) into the answer selection model. Due to expensive cost of construction and maintenance of such KBs, these models are suffering from domain barriers and information incompleteness. In this paper, we propose a two-stage extraction–comprehension answer selection model, which can extract ad-hoc knowledge from unstructured web texts to enhance the performance of answer selection. For the extraction, two types of snippets are extracted from unstructured web pages and utilized as the source of ad-hoc knowledge. For the comprehension, a selective attention mechanism is employed to extract and integrate ad-hoc knowledge from multiple text snippets obtained in the first stage, which can enrich the representation of question–answer pairs and more accurately identify the correct answers. By incorporating ad-hoc knowledge extracted from both types of snippets, the proposed model achieves state-of-the-art results on two public available benchmark datasets. In particular, on WikiQA, in terms of the two evaluation metrics (mean average precision and mean reciprocal rank), it achieves 9.9[Formula: see text] and 8.4[Formula: see text] higher than the previous non-pretraining-based models, and 3.4[Formula: see text] and 3.2[Formula: see text] higher than the pretraining-based models.
Data-driven Object-centric Framework for Peg-in-hole Learning

In this study, a data-driven object-centric learning framework was developed for dealing with various shapes of pegs and holes in the peg-in-hole task. This framework enables efficient data sampling in simulation environments comprising pegs and holes of different shapes. A small regression model with minimal computational costs can be trained for inserting the peg into the hole. The model takes the contact force generated by the peg and the hole as input and infers the relative posture of the peg and the hole, moving the peg to minimize the relative pose. Peg-in-hole experiments were conducted using three shapes of pegs and holes, and the well-known spiral search strategy was compared with the proposed data-driven peg-in-hole strategy learning model. The proposed strategy outperformed the spiral search strategy in all three shapes of peg-in-hole tasks. Finally, the inference model was applied to a real-world environment, and a success rate of 73.3% was achieved in the one-degree-of-freedom peg-in-hole experiment.
Fuzzy Hidden Markov Model Using Aggregation Operators

The Fuzzy Markov Model (FMM) is a fascinating domain for dealing with ambiguity in real-world scenarios. In type 2 fuzzy set (T2F), it has an uncertainty footprint, and the region circumscribed by the lower and upper interval membership functions is uncertain. In fuzzy sets, Triangular norms (t-norms) are a valuable tool for understanding the conjunction in fuzzy logic and, as a result, determining where fuzzy sets intersect. Norms and conforms in Triangular operations that generalize logical conjunction and disjunction. They also provide a natural explanation for the conjunction and disjunction in mathematical fuzzy logic semantics. Fuzzy Frank Triangular Norms have been used to verify this t-norm as there are many of them the aggregation qualities of Trapezoidal Interval Type-2 numbers (TpIT2FNs) because triangular norm meets the compatibility with Frank norms. Frank triangular norms provide more flexibility and robustness this requires more justification in the information fusion process than other triangular norms. Previous works on not concentrate on Frank’s norms. Other Aggregation works on norms that are not flexible to get the solution. Because of that, the Frank norms are used for the hidden Markov model. We've also used them in the Viterbi method with TpIT2FNs for Fuzzy Hidden Markov Model in the staff selection process.
Needs Analysis of the Importance and Implementation Requirements of the Fourth Basic Plan for Lifelong Education Promotion in Korea

This study analyzes the differences in importance and implementation level for the detailed tasks of the fourth(2018-2022) basic plan for lifelong education promotion. To this end, a needs analysis has been conducted targeting 19 persons involved in lifelong education for 44 detailed tasks within the basic plan. For the analysis, the t-test, Borich need assessment model, and Hershkowitz's criticality function were used. As a result of the analysis, 13 policy tasks had high levels of both importance and implementation (e.g. support for career-interrupted women, expansion of support for literacy education to all citizens who wish). Eight policy tasks were recognized as highly important, but the actual implementation is low (e.g., support for career planning consulting during the transition period of life, securing investment in lifelong education at the national level), while 13 tasks rated low at the important level, but the level of implementation is high (e.g. the elderly customized learning support, expansion of learning support for multi-cultural families) with 10 tasks with low levels of importance and implementation (e.g. expansion of paid study leave system, development and utilization of learning ability diagnosis tools for adults). Based on this, we identified policies that should be given more attention in terms of implementation in the 5th Basic Plan, and discussed tasks that should be considered when establishing the future 6th Basic Plan. We also provided implications for the future.
The Effect of Language Games on Students' Proficiency in Spoken English: A Focus on Junior Secondary Schools in Ikere Local Government Area of Ekiti State

The study examined the effect of language games on students' spoken English proficiency, focusing on junior secondary schools in Ikere Local Government Area of Ekiti State. The report provides a systematic review of published relevant studies on the use of language games in improving students' spoken English competency from 2010 to 2019. The primary goal of this systematic review is to provide researchers and reading educationists with a synthesis of the evidence from previous studies on the impact of language games on students' spoken English proficiency. This will allow for more development and implementation research to be conducted in this area. This study aims to address two questions: how effective are language games in enhancing students' speaking abilities, and what are students' experiences like while playing the game? The findings of this investigation shed light on the value and positive effect that language games have on improving students' speaking abilities. The study recommends, amongst others, that there is a need for the government and all other relevant agencies to incorporate language games into the language class. Keywords: Language Games, Speaking Skills, Junior Secondary School, Learner's Experience, Systematic Review, Ekiti State
Research on Nondestructive Testing Technology of Grounding Network

A state of grounding mesh conductor nondestructive testing system and method, injection of a certain frequency and signal emission system to ground net.through must have the strength of the current sine wave signal, the signal receiving system of remote control of unmanned aerial vehicle (uav) to carry signal collecting device, detection in ground surface fast cruise for data collection, complete branch conductor of grounding systems located in the space area nearby the surface excitation magnetic field detection. The Beidou BDS/GPS dual-mode satellite positioning and navigation system, magnetic field detection unit and data wireless transmission system carried by the remote-controlled UAV wirelessly transmit space coordinate data information and synchronous magnetic field data information to the terminal host computer system composed of wireless receiving unit, computer and software. By receiving, storing, summarizing and analyzing the data in real time, the magnetic field distribution on the ground surface of the ground grid can be obtained quickly, the state of the ground grid can be transmitted and the safety performance of the ground grid can be evaluated. The technique studied in this paper is characterized by simple operation, high efficiency and accurate diagnosis.
A fully digitally integrated workflow for brain MRI Point Cloud generation and augmented reality 3D model visualization

This study aims to refine an automated workflow for neuroimages that generates three-dimensional (3D) point clouds in the Polygon File Format (.ply) to be deployed into augmented reality (AR) head mount displays (HMD). Our current work involves enhancing and refining core features, along with improving the point-cloud application to optimize the brain MRI intake. Our brain image segmentation algorithm and web-based point cloud generator show promise for clinical workflows, where high-quality point-cloud AR models can be generated from patient MRIs.
Multi-structure segmentation on cardiac MRI using multilayer perceptron mixer network

In this work, we propose MLP-Vnet, a token-based U-shaped multilayer linear perceptron-mixer (MLP-Mixer) network, incorporating a convolutional neural network for multi-structure segmentation on cardiac magnetic resonance imaging (MRI). The proposed MLP-Vnet is composed of an encoder and decoder. Taking an MRI scan as input, the semantic features are extracted by the encoder with one early convolutional block followed by four consecutive MLP-Mixer blocks. Then, the extracted features are passed to the decoder with mirrored architecture of the encoder to form a N-classes segmentation map. We evaluated our proposed network on the Automated Cardiac Diagnosis Challenge (ACDC) dataset. The performance of the network was assessed in terms of the volume- and surface-based similarities between the predicted contours and the manually delineated ground-truth contours, and computational efficiency. The volume-based similarities were measured by the Dice score coefficient (DSC), sensitivity, and precision. The surface-based similarities were measured by Hausdorff distance (HD), mean surface distance (MSD), and residual mean square distance (RMSD). The performance of the MLP-Vnet was compared with four state-of-the-art networks. The proposed network demonstrated statistically superior DSC and superior sensitivity or precision on all the three structures to the competing networks (p-value &lt; 0.05): average DSC of 0.904, sensitivity of 0.908 and precision of 0.902 among all structures. The best surfaceased similarities were also demonstrated by the MLP-Vnet: average HD = 3.266 mm, MSD = 0.684 mm, and RMSD = 1.487 mm. Compared to the competing networks, the MLP-Vnet showed the shortest training time (7.32 hours) inference time per patient (3.12 seconds). The proposed MLP-Vnet is capable of using reasonable number of trainable parameters to solve the segmentation task on the cardiac MRI scans more quickly and accurately than the state-ofthe- art networks. This novel network could be a promising tool for accurate and efficient cardiac MRI segmentation to assist cardiac diagnosis and treatment decision making.
Sociocultural Causes of Ambiguity in Arab Academic Writings

Although ambiguity in written, oral, and visual communication is inevitably present across all human societies and cultures, variation among these societies and cultures occurs in the sociocultural causes of its existence. This study helps formulate a conceptual framework that enables the enrichment of knowledge about this variation. It takes a first step by highlighting Arab-specific reasons behind ambiguity in academic writings in humanities and social sciences. The investigation entails thematically analysing the thoughts of 905 Arabs in academia. The findings point to a ‘doing-the-minimum’ mentality, whereby one may act hastily and impatiently and do just enough for one’s manuscript to be published in any journal, thereby rushing into publication while skimping on quality and diminishing attention to manuscript clarity. Another finding is the ambition for rewards that Arab institutions assign to publication, whereby one may boost their publication records to reap these rewards, resulting in high quantity while sacrificing quality (e.g., clarity). Another reason discovered is the conceptualisation of writing as a formulaic and ‘fill-in-the-blanks’ task (templates to be completed and, thus, manuscripts to be constructed), instilling a focus on technicality instead of cognitive depth and clarity. An additional reason found is the passive application of foreign theories and conceptual frameworks without subjecting them to critical reflection, reapplying foreign surveys and mimicking survey-based articles, thereby making their articles culturally shallow, suffer from cultural irrelevance, and thus, ambiguity. This is along with the integration of poetry (wherein ambiguity is culturally viewed as desirable and showing poets to be sophisticated) into Arabs’ daily social and educational lives and mindsets, encouraging the acceptability of ambiguity as a possible linguistic quality in scholarly writing as well. The social context lacks direct, explicit, and free articulation, encouraging one to resort to roundabout ways of composing their manuscripts, thus, making the manuscripts fall into ambiguity.
Evaluation of the dispensation of antidepressants in primary health care in the city of Santiago – RS

Introduction: The Coronavirus disease-19 (COVID-19) pandemic has generated significant impacts both economically and socially, as well as on the physical and mental health of the general population, causing an increase in depression, anxiety and stress. Depression is considered a multifactorial disorder that involves biological, psychological and social aspects and has become a relevant and growing public health problem. It is noteworthy that the pharmacological treatment for this pathology includes the use of antidepressant drugs, many of which are available in the Unified Health System (SUS). In this sense, it is worth mentioning that, in Brazil, there are still few studies evaluating the impact of the pandemic on the consumption profile of antidepressant drugs in the population. Objective: The objective of this work was to analyze the impact of the COVID-19 pandemic on the distribution of antidepressants in primary care in the municipality of Santiago, in Rio Grande do Sul. Methodology: To carry out this research, a survey of antidepressant drugs dispensed at the Basic Pharmacy of SUS in the city was carried out. Based on information contained in the Integrated Health Services Management System (SIGSS), used in the distribution management and stock control of the Basic Pharmacy, data referring to the three-year period, from March 2019 to March 2019, were analyzed. 2022. Results and Discussion: It was possible to observe an increase of 38.10% in 2020 and 115.47% in 2021 in the dispensation of antidepressant drugs, in relation to the year 2019. In addition, it was verified that the female gender presented higher rate of consumption of antidepressants, corresponding to the percentage of 77.59% in relation to males. The drug fluoxetine 20 mg was the most used drug, totaling 50.89% of dispensations during the three years, where predominantly users are aged between 46 and 60 years. Amitriptyline 25mg corresponded to 30.28% and Imipramine 25mg represented 9.33% of dispensations, both from the tricyclic antidepressant class. It is noteworthy that users of this class of medication are mostly elderly people over 61 years old. Furthermore, it was possible to observe an increase of 134.68% in the dispensations of selective serotonin reuptake inhibitors (SSRIs), 78.15% in tricyclic antidepressants and 1002.5% in atypical antidepressants during the pandemic period compared to the year 2019 , pre-pandemic period. Conclusion: It can be concluded that the pandemic had an impact on the consumption of antidepressants by the Santiaguense population served by the Basic Pharmacy. Still, the results found in this work may help in the planning of strategies that can collaborate with issues related to the mental health of the population and the consumption of antidepressants, seeking the rational use of this class of medication.
A PPM-based UNet for Tumour and Kidney Segmentation in CTScans

Kidney cancer is one of the leading causes of cancer death worldwide. The high rates of mortality and occurrences of cancer show the importance of research and the development of means for the early diagnosis. The correct segmentation of kidney becomes an essential step to help with the case’s analysis and degree of severity. Computational methods have been used for this purpose, emerging as alternatives to manual segmentation and as mechanisms to reduce fatigue during diagnosis. Fully convolutional neural networks have presented great prominence in automatic segmentation in medical images. This work aims to build a novel network based on the U-Net architecture, using the concept of Pyramid Pooling Module (PPM) blocks that we call PPM-UNet. We propose this model to force learning features in multiple resolutions during their encoding phase and emphasise differences between similar regions. The hypothesis is that in cases where the texture of the images is very similar, the PPM block will be able to represent information with greater precision. In the experiments, we verified that the PPM-UNet obtained promising results of 0.9466 of Dice for the kidney and 0.9383 of Dice for tumour regions when the method was applied to the Kits19 database.