Reviewerly/exHarmony huggingface dataset:
Medical BigBERTa: An Optimized Transformer for Long Medical Document

&lt;p&gt;Specific-domain language models (LMs) are ubiquitous in natural language processing. Effective biomedical text mining requires in-domain models of biomedical and clinical documents, which involve particular challenges. Most existing biomedical LMs rely on self-attention mechanisms, which do not scale to clinical-document length because of space complexity and quadratic execution times. In addition, in-domain LMs ignore the importance of a dedicated tokenizer and of learning optimal hyperparameter values for downstream tasks. In this paper, we introduce a model that addresses these three main issues. Inspired by the BigBird architecture model, we extend the embedding size through an attention mechanism that scales linearly with the sequence length to enable the processing of longer sequences, by a factor of up to eight. Our model was trained on biomedical and raw clinical corpora, and the same data were used to build a domain-specific sentence-piece tokenizer. In our experiments comparing our tokenizer with those in existing models, it was found to have the lowest fertility rate. To optimize the model performance, additional data-oriented hyperparameter fine-tuning was performed to enhance the accuracy and reproducibility of the model. Using the BLURB benchmarks, we evaluated our model with respect to named-entity recognition, relation extraction, sentence similarity, and question-answering tasks. Our model demonstrated competitive results, outperforming other strong self-attention baseline models for several datasets.&lt;/p&gt;
Drop Noise For CTR Prediction

Click-through rate (CTR) prediction is task to estimate the possibility of user clicking on a recommended item. The ground truth labels of this task are the click history of users. As there are many reasons why noise data or low quality data may be generated, the training data has a certain amount of noise data or low quality data. In this work, We propose a simple but effective method to find the noise data. Our method could improve the offline AUC from 0.60 to 0.75 on our realworld dataset.
PENGEMBANGAN BOARDGAME UTASI TERINTEGRASI APLIKASI GOOGLE SLIDES DALAM PEMBELAJARAN IPS PADA SISWA KELAS V DI MI HUSNUL KHATIMAH KOTA SEMARANG

This study aims to develop a board game integrated with google slides application in social studies learning. The type of research used is Research and Development (RD) with the research subjects being teachers and students of MI Husnul Khatimah Semarang City. The data collection instruments used questionnaires, interviews, tests, and documentation. The boardgame development process in this study used the Borg and Gall model (Potential and problems, Data collection, Product design, Product validation, Product improvement, Product trial, Product revision, Product revision, Product revision). The product is developed according to the initial conditions and then tested for feasibility and revised until it is suitable for implementation in learning. The results of the feasibility test / validation of media experts get an average percentage of 93%, social studies material experts as much as 85%. Based on the results of each validator shows that the boardgame integrated google slides application in social studies learning is feasible to use. The effectiveness of the boardgame integrated google slides application in social studies learning is shown from the results of the calculation of the t test on the pretest and posttest scores of students. Paired Samples Statistics results show the t value of 0.000 0.05, then Ha is accepted and Ho is rejected. There is a significant difference between pretest and posttest data, so it can be concluded that the development of board games integrated with google slides application in social studies learning is effective for improving the learning outcomes of fifth grade students of MI Husnul Khatimah Semarang City.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS VIDEO WEBHOST PADA MATERI SISTEM PENCERNAAN MANUSIA SISWA KELAS V MIS NURUL HUDA KLANGON

The purpose of this research and development is to determine the need for developing webhost on the human digestive system material, to describe the form of webhost on the human digestive system material and effectiveness of student learning outcomes cognitively in the human digestive system material at MIS Nurul Huda Klangon. This type of research is a Research and Development (R D) research using the ADDIE model with Analysis, Design, Development, Implementation, and Evaluation. Data collection techniques using questionnaires, and tests. The limited-scale test was carried out in class VA with 15 students and the broad-scale test was carried out in class V with 31 students. The data analysis technique used normality test, homogeneity test, Anova test, t test and gain test. The results of the study indicate that: (1) The need for webhost is needed for students to improve student learning outcomes. (2) The development of webhost based on expert validation, media validation and teachers. For material experts with a percentage of 93.75% the category is very feasible, for media experts it is 89.23% very feasible category media development webhost are very effective in influencing students' cognitive learning outcomes which are indicated by increasing students' cognitive learning outcomes with the normality test showing that student learning outcomes are normally distributed with evidence that for pretest 0.001 0.050 and 0.151 at the post test. The homogeneity test showed that = 0.248 0.005, which means that the learning outcomes have a homogeneous variance. The ANOVA table shows that there is a significant difference in student learning outcomes (F = 12.105, p 0.05), which explains that the data are homogeneous. For the results of the average difference (t test) shows that that is = 0.00 0.005 so it is rejected and accepted, which explains that there are differences in students' cognitive learning outcomes. Gain test shows that the limit is 0.3 g 0.7, with a difference between the pre test and post test of 13.23. Gain value of 0.422 is included in the medium criteria.
Investigating Transfer Learning Approaches for Mining Opinions in the Electoral Domain

The use of social media data to mine opinions during elections has emerged as an alternative to traditional election polls. However, relying on social media data in electoral scenarios comes with a number of challenges, such as tackling sentences with domain specific terms, texts full of hate speech, noisy, informal vocabulary, sarcasm and irony. Also, in Twitter, for instance, loss of context may occur due to the imposed limit of characters to the posts. Furthermore, prediction tasks that use machine learning require labeled datasets and it is not trivial to reliably annotate them during the short period of campaigns. Motivated by these issues, we investigate how to boost and speed-up the performance of opinion mining tasks during elections. We start by proposing a transfer learning approach that leverages curated datasets from other domains. To avoid negative transfer, i.e. introducing a knowledge from the other domains that could end up by disturbing the task, we propose to use similarity metrics (Jaccard, Cosine and Euclidean distance based on word embeddings) to point out whether or not the dataset should be used. Our preliminary results show that taking into account the (dis)similarity between different domains, it is possible to achieve results closer to the ones that would be achieved with classifiers trained with annotated datasets of the electoral domain.
A Risk Assessment Optimization Model Based on TOPSIS Algorithm

With the development of information technology and data processing technology, mining data to reasonably monitor and avoid production risks in real time can provide an important guarantee for enterprise production. Combining the current mainstream research - mathematical modeling analysis and machine learning analysis, this paper puts forward the production risk monitoring and evaluation method based on the detector data of a production enterprise. First, the Median Absolute Deviation Algorithm is used to detect the abnormal value of the real-time data recorded by the sensor. On this basis, the autocorrelation coefficient and Spearman correlation coefficient are used to calculate, and the characteristics of 'Contingency', 'Persistence' and 'Linkage' are extracted as distinguishing the abnormal risk, and 28 groups of detectors are obtained; Finally, 'Deviation Degree', 'Change Rate', 'Abnormal Linkage' and 'Abnormal Persistence' are extracted as evaluation indicators, and a gray correlation degree evaluation model is established to evaluate the risk degree at each time, so as to calculate the risk level at each time. The experimental results show that there are high probability production risks in about 8:42PM and 10:50PM, which need to be paid more attention to. This paper provides a new idea for production monitoring in actual enterprise operation.
Realization of Fuzzy PID Controller on FPGA for Source Measurement Unit

The classic Source Measurement Unit's (SMU: Source Measurement Unit) shortcomings include a sluggish transient response and a significant overshoot. This paper proposed an optimization method that made use of fuzzy PID controller on FPGA for SMU. With the aid of the software Matlab, we first analyzed the SMU's circuit diagram and obtained the system's transfer function. Next, we constructed the system simulation model adjusted by a fuzzy PID controller in Simulink. The initial P, I, and D parameters were obtained by the PID tuner, and they were adjusted by the fuzzy controller until they can achieve the optimal performance. Finally, we designed a Verilog program with the initial parameters to implement the fuzzy PID controller, in which the parameters were modified dynamically according to error and its increment to make sure they were optimal. We tested the controller using the HDL simulation tool Modelsim to ensure the simulation results matched those from Simulink. The results of simulation showed that the proposed method outperformed the conventional approach. In detail, our fuzzy PID controller can achieve shorter rising time, shorter setting time, and smaller overshoot than the NI SourceAdapt technology does.
The main ways to solve the problems of document management in higher education (the Ukrainian case)

The purpose of the research is to analyze the problem of document management in the sphere of higher education and to determine potential ways to solve them using the example of the Ukrainian experience. The content analysis of modern pedagogical literature showed the problems of the documentary support. On the basis of the system method, document management is considered as a dynamic system that changes according to modern conditions. Based on the prognostic method, possible prospects for the development of the latest documentation systems are highlighted. In results it is shown, that the modern documentary system completely deviates from the old principles of the formation of the administrative apparatus in several directions. Framework recommendations have been formed for determining the development vectors of document and communication support, and they can be extended to similar systems of formation, support, and storage of documents of the management (administrative) staff. The conclusions established that the current system of organizing documentary support in the educational sphere of Ukraine actually contains archaisms. It is proposed to integrate Blockchain-technologies into the implemented educational SEMDs, which will potentially ensure inter-level communication channels of the participants in the document creation process of higher education of Ukraine.
A Qualitative Study on the Genre Recognition of Digital Narrative Audience: Focused on HN2 Hypernarrative

The purpose of this study is to find out how the positive/negative perception of the hypernarrative genre is formed for audiences who are new to hypernarrative, and what characteristics of hyper-narrative have the greatest impact on the audience. For this purpose, the reviews of hyper-narratives written by 91 college students majoring in literary creation were analyzed based on the qualitative method. As a result of the analysis, 91 research participants mainly formed a positive perception of the hypernarrative that they had first encountered. They recognized hypernarrative as a genre that is interesting, impressive, and brings a sense of concentration and immersion. On the other hand, research participants understood 'audience centrality' as the basic characteristic of hypernarrative and 'expansion of narrative through connection' as the core characteristic. Through this, it can be confirmed that the research participants understood the change and expansion of the narrative created by hyperlinks as the genre essence of hypernarrative.
A Case Study on the Operation of 'Excellent Liberal Arts Lecture Certification System'

The purpose of this study is to examine the implementation and improvement plans of the excellent liberal arts lecture certification system as a way to improve the lecture quality of liberal arts education. This is to enhance instructors' willingness to develop subjects by combining quantitative values (30%) of lecture evaluation results and qualitative evaluation of professors and students (70%) and discovering and sharing subjects that show outstanding improvement efforts. To this end, through a preliminary implementation plan in 2019, each contest was held in 2020 and 2021, and each of nine liberal arts subjects applied were reviewed and four subjects were selected. The subjects included 3 advanced liberal arts subjects, 1 personality subject in 2020, and 4 advanced liberal arts subjects in 2021. The subjects were certified for the next two years, and the instructors received rewards and certificates, and presented them as excellent cases at the instructor workshop to share teaching strategies and know-how. Through this study, it can be confirmed that it is important not only to select and discover excellent liberal arts lectures, but also to encourage and spread instructors' efforts to improve lectures.
Identifying Learning Barriers and Their Impact on the Resilient Educational Climate

Resilience is a key point in the current educational policies.Throughout this study, we aimed to take a step forward in defining and describing a resilient educational climate, starting from the learning barriers that need to be overcomed.We qualitatively and quantitatively analysed the learning barriers identified by pre-university teachers through a questionnaire-based survey.From the primary lexical analysis of the data, we found that students' and their families' lack of interest in school is the main threat to school learning from the perspective of pre-university teachers.This highlights the discrepancy between the educational offer and the needs of the beneficiaries of educational services.However, we coded responses from N=110 participants -pre-university teachers and found that the most common type of learning barrier mentioned by them was digital distractions.This may raise questions about teachers' openness to the digitisation of the educational process, since they perceive digital devices as a barrier to learning.A one-way ANOVA was conducted to compare the effect of learning barriers on the resilient educational climate.There were no statistically significant differences in the means of the resilient educational climate between categories of learning barriers.
Video-based in-vehicle action recognition for continuous health monitoring

Driver action recognition is essential in vehicle safety and smart car systems targeting vehicles as a diagnostic space. In the context of video-based action recognition, attention-based architectures have surpassed conventional methods in deep learning. Former frameworks have produced excellent results on the public Drive&Act dataset. However, present frameworks do not consider the temporal ordering of frames in the video and the spatial layout of the relevant interacting objects yielding poor performance in certain actions that include semantic reversals. This includes so-called conjugate actions that originate when performed backward in time. An example would be moving the hand rightward vs. leftward. We propose a feature engineering approach to model the motion of human pose. We use key points relevant to the action to incorporate the sequential order. We implement video swin architecture on the Drive&Act dataset. Then, we utilize the histogram of oriented displacements on human joint locations and their displacements and train a support vector machine to classify actions in conjugate pairs. Performance increases in two conjugate actions namely fastening/ unfastening seat belt and taking off/ putting on sunglasses. Integrating our module with existing deep learning models increases the overall accuracy by 3% to 72%. Furthermore, our approach can be extended to other action classes.
Machines Perceive Emotions: Identifying Affective States from Human Gait Using On-Body Smart Devices

Emotions are a crucial part of our daily lives, and they are defined as an organism’s complex reaction to significant objects or events, which include subjective and physiological components. Human emotion recognition has a variety of commercial applications, including intelligent automobile systems, affect-sensitive systems for customer service and contact centres, and the entertainment sector. In this work, we present a novel deep neural network of the Convolutional Neural Network - Bidirectional Gated Recurrent Unit (CNN-RNN) that can classify six basic emotions with an accuracy of above 95%. The deep model was trained on human gait data captured with body-mounted inertial sensors. We also proposed a reduction in the input space by utilizing 1D magnitudes of 3D accelerations and 3D angular velocities (maga^, magω^), which not only minimizes the computational complexity but also yields better classification accuracies. We compared the performance of the proposed model with existing methodologies and observed that the model outperforms the state-of-the-art.
Research on Practical English Writing Teaching of Colleges and Universities in the Internet Era

In the Internet era, colleges and universities carry out practical English writing teaching and research to meet the needs of students' future development.In the process of practical English teaching, due to the limitation of immobilized assessment standards, relatively single teaching model and inconsistent basic situation of students, the teaching effect is not good, and the traditional college teaching model can no longer meet the requirements of the development of The Times.Therefore, practical English writing teaching in colleges and universities should create a new assessment standard, through different assessment indicators to achieve the purpose of serving students to improve their ability, make full use of the Internet environment to create a network environment to meet the needs of learning at any time.At the student level, it can improve students' learning autonomy, help students seize the fragmented time, provide students with a realistic site, form a practical English writing evaluation mechanism, guide students to correctly understand themselves, make full use of online teaching, multi-pronged approach, research more practical English teaching methods, and contribute to students' future development.
Experimental verification of the Gaussian quantum correlation measure in continuous variable systems

The presence of quantum correlations in composite quantum systems is one of the main features of quantum mechanics. However, by now, all known quantifications of this correlation for continuous-variable systems are very difficult to compute. Therefore, it makes sense to find simpler and computable quantifications of Gaussian quantum correlations. Recently, a computable Gaussian quantum correlation M is proposed, which can be obtained based on the covariance matrix. Here, we experimentally demonstrate the Gaussian quantum correlation 𝑀 based on the two-mode entangled state carrying orbital angular momentum (OAM) generated from a four-wave mixing process in a hot Cesium atom vapor cell and investigate the evolution of such correlation in the lossy channel. Our results show that quantum correlation 𝑀 is robust in the lossy channel.
Unraveling Soft Squeezing Transformations in Time-Variant Elastic Fields

Quantum squeezing, an intriguing phenomenon that amplifies the uncertainty of one variable while diminishing that of its conjugate, may be studied as a time-dependent process, with exact solutions frequently derived from frameworks grounded in adiabatic invariants. Remarkably, we reveal that exact solutions can be ascertained in the presence of time-variant elastic forces, eschewing dependence on invariants or frozen eigenstates formalism. Delving into these solutions as an inverse problem unveils their direct connection to the design of elastic fields, responsible for inducing squeezing transformations onto canonical variables. Of particular note, the dynamic transformations under investigation belong to a class of gentle quantum operations, distinguished by their delicate manipulation of particles, thereby circumventing the abrupt energy surges commonplace in conventional control protocols.
OS-Net: A novel oriented ship detector based on RetinaNet

Ship inspection plays an important role in maritime traffic management and the maintenance of national maritime security. However, the different bow orientations and dense arrangement of vessels make horizontal frame detection inappropriate for ship detection. Therefore, in this paper, a rotating frame detection network based on RetinaNet [1], named OS-Net, is proposed. In order to cope with the situation of obscured vessels such as clouds, the data enhancement strategy represented by Cutout [2] is used in this paper to improve the network generalization and learn to cope with the situation of obscured pictures. In order to fully extract the ship features to improve the detection accuracy, this paper adopts the ConvNext [3] network as the backbone network and explores the improvement effect on the detection accuracy by deepening the ConvNext depth. The well-known HRSC2016 dataset is used in the paper, and through a large number of ablation experiments, we can find that the detection accuracy of OS-Net is more excellent.
Blockchain Security Risk Monitoring of Power Supply Chain Based on Fuzzy Neural Network

In recent years, with the continuous development and progress of information technology, the information system has had a huge impact on people's daily work and life, and has become an irreplaceable factor in our work and life. With the increasing number of applications of information technology, the information industry revolution is sweeping the world. Almost all industries, including the power industry, have begun to pay attention to the construction and innovation of information systems, and building a "modern supply chain" has become China's new economic development direction. The main purpose of this paper is to conduct research on the security risk monitoring of power supply chain (PSC) blockchain based on fuzzy neural network (FNN). This paper takes the theory of supply chain and the application of information system in an electric power enterprise as the starting point, and analyzes and studies the application of information system in the supply chain of an electric power enterprise. Using the concepts and theories of information system and SCM, the application of the information system of a power enterprise in its supply chain is analyzed and studied in detail. Experiments show that in the matching process, smart contracts play an important role according to different situations. Once the energy matching is successful, both parties will complete the energy transaction according to the content of the smart contract, and the entire EBN-IESCOM can be solved using blockchain technologies such as consensus algorithms and smart contracts..