Reviewerly/exHarmony huggingface dataset:
Utilization of Macromedia Flash in Mathematics Learning

This research is to find out whether there is an increase in mathematics learning outcomes in flat shape material after being taught using learning media using macromedia flash and how to find out the effectiveness of learning using learning media using macromedia flash. This research was carried out at the Palopo Model State MTs for the 2021/2022 Academic Year. This research uses The One Group Pretest-Posttes Design. The experimental units in this study were determined randomly from all class VII. The data collection technique in this study was to use tests, namely pretest and posttest, student activity observation sheets and student response questionnaires. The results of students' mathematics learning before being taught using learning media by utilizing macromedia flash on flat shape materials have an average value of 50.50. The results of students' mathematics learning after being taught using learning media by utilizing macromedia flash on flat shape material have an average value of 72.65. Student activity when learning media was applied using macromedia flash on flat shape material was active, namely 2.68. Student response when learning media was applied using macromedia flash on flat shape material was in the good category, namely 3.1. There is an increase in mathematics learning outcomes in students' flat shape material after being taught using learning media using macromedia flash with an average acquisition of 72.65. The results of inferential statistical analysis using the t-test concluded that learning media using macromedia flash on student data structure material is said to be effective because it meets the criteria for learning effectiveness. The novelty in this study can be seen in the average achievement of each good aspect of learning media using Macromedia Flash which is in the active category, as well as the responses of students who are taught with learning media using Macromedia Flash which are in the good category.
A novel comprehensive interpolation to produce spatiotemporally continuous daily evapotranspiration

<b><sc>Abstract.</sc></b> Evapotranspiration (ET) is a significant component of the global hydrologic cycle, playing a crucial role in transferring water and the Earth‘s energy from the surface to the atmosphere through latent heat. Due to its importance in the hydrologic cycle, the estimation and mapping of ET using satellite remote sensing data have been the subject of numerous research in agriculture and applied hydrology. However, a single satellite ET retrieval still has a couple of limitations, including a trade-off between spatial and temporal resolutions and missing information due to cloud contamination (e.g., cloud cover and its shadows) and defective satellite sensors (e.g., the Landsat 7 scan line corrector-off). As a solution to the current issues of a single satellite ET product, the present study developed a season and land cover specific interpolation (SLSI) technique using decision tree-based machine learning models and various remote sensing products. Using the SLSI, this study produced spatiotemporally continuous daily ET data (ET<sub>SLSI</sub>) on a statewide scale using the existing Landsat-derived ET product (ET<sub>LS</sub>) that are discontinuous in time and space. Based on a comparative analysis between three decision tree-based models, including Classification and Regression Trees (CART), random forest (RF), and eXtreme Gradient Boosting (XGB), the SLSI adopted RF for predicting ET over the missing areas due to its robust performance with high accuracy. The developed SLSI was tested in Illinois. The resulting ET<sub>SLSI</sub> was compared with the two eddy covariance flux site measurements. The results validated the reliable performance of the SLSI, showing good agreement with observations and keeping the accuracy of the source data (i.e., ET<sub>LS</sub>). In addition, the results of the experimental evaluation, where the non-cloud contaminated ET<sub>LS</sub> scene was masked with artificially created clouds with four different cloud cover rates (20, 40, 60, 80%) and then reconstructed by the SLSI, proved that the SLSI could successfully reproduce the missing spatial information for all cloud rates regardless of cloud shape and cloud cover rates.
MENINGKATKAN KEMAMPUAN BERBICARA BAHASA INGGRIS SISWA MELALUI PENGAJARAN BAHASA KOMUNIKATIF UNTUK TINGKAT PEMULA DI DESA INGGRIS SINGOSARI MALANG

Speaking is one of the most difficult talent to master in everyday life and be the second language skills in Indonesia which everyone requires it to communicate with others to express facts, thinking, and message between two or more people. The students’ of Desa Inggris Singosari Malang namely Be a Trainer program who are still in the beginner level get some difficulties in Speaking skill. As a result, the researcher uses Communicative Language Teaching approach which aim to repair all the problem and improve the students’ speaking skill. The type of researcher is Classroom Action Research (CAR). During the implementation of Communicative Language Teaching approach, it shown that the students’ motivation was increased and shown significant progress during the research. This research was carried out in two cycles. The researcher also suggested that the future researcher conduct experimental research to find the differences between the communicative language teaching approach with the other approach in teaching speaking, and more. The researcher also suggested that the future researcher conduct classroom action research to know whether the implementation of communicative language teaching in teaching speaking for the students in the different levels has the same benefit.
IDENTIFIKASI SAMPAH LAUT MAKRO DI PANTAI LE GRANDEUR KOTA BALIKPAPAN KALIMANTAN TIMUR

There is a lot of waste that is difficult to decompose and accumulates in nature, especially at the coastal and ocean, resulting in environment disturbance. This study was aimed to identify macro-size marine debris collected from Le Grandeur beach, Balikpapan City, East Kalimantan. A 100 x 20 m transect line was set at the beach. Each transect equipped with 5x5 m subtransect where the macro-debris was sampled. All debris in subtransect were collected and were cleaned prior sorted and grouped based on their categories namely plastic, glass, metal, rubber, wood and other types. Samples from each type were counted and measured in weight. Based on the results of the study, samples of macro marine debris collected at the study site were plastic, glass, metal, rubber, wood and others with a total debris of 435 items and total weight of 2,263.772 g/m2. Total density of waste is 86.6 particles/m2 with the percentage of each waste being 45% plastic, 0% glass, 2% metal, 2% rubber, 2% wood and 49% others.
Rank intraclass correlation for clustered data

Clustered data are common in biomedical research. Observations in the same cluster are often more similar to each other than to observations from other clusters. The intraclass correlation coefficient (ICC), first introduced by R. A. Fisher, is frequently used to measure this degree of similarity. However, the ICC is sensitive to extreme values and skewed distributions, and depends on the scale of the data. It is also not applicable to ordered categorical data. We define the rank ICC as a natural extension of Fisher's ICC to the rank scale, and describe its corresponding population parameter. The rank ICC is simply interpreted as the rank correlation between a random pair of observations from the same cluster. We also extend the definition when the underlying distribution has more than two hierarchies. We describe estimation and inference procedures, show the asymptotic properties of our estimator, conduct simulations to evaluate its performance, and illustrate our method in three real data examples with skewed data, count data, and three‐level ordered categorical data.
Ontology-driven development of dialogue systems

A new technique and its software implementation are presented to create a deeply semantically structured ontology using plain natural language text as input, without regular structure or any previous tagging and markup. The new approach is primarily aimed at highly inflectional languages, and is implemented for Ukrainian. The automatically created ontologies (in OWL) could be easily converted to other graph databases formats, such as Neo4j, and were successfully evaluated as valid ontologies using Protégé, RDFlib and Neo4j environments. An integrated approach is proposed for the development of natural language dialogue systems driven by the ontologyrelated graph database using the Cypher language for the formal queries. The original phrases are subject to a special method of semantic analysis, which determines the type of formal query to the database. The essence of the analysis is that the text of the user’s phrase goes through a series of checks. Based on their results, a set of basic templates for the formal requests are determined, as well as additional constructions that are attached to the basic template. Some of the checks may also return the notion of substitution to certain specified positions of the formal query. Formal queries can return both contexts and lists of ontology concepts. In addition to concepts, queries can also return information about specific semantic predicates that connect them, which simplifies the synthesis of natural language responses. The synthesis of answers is based on special templates, the choice of which is directly related to the corresponding template of the formal query.
Sievenet: An Efficient Model Utilizing H.265 Codec Structure for Video Object Detection

In the field of video content analysis, object detection is a crucial task. The High Efficient Video Coding (H.265, HEVC) standard’s coding structures are strongly correlated with the video content, creating an opportunity to utilize these structures for video object detection in a computationally efficient way. To address this, we present a video object detection method that partitions frames into macroblocks based on the H.265 structure. Blocks with spatially high-frequency content go through a dynamic-layer approach that subjects them to deeper analysis with more layers, while blocks with spatially low-frequency content undergo fewer layers to enable a lower computational load. Results on ImageNet-Vid Dataset indicate that our approach has the potential to save significant computational resources while maintaining accurate object detection performance.
POTÊNCIAS-LIMITES NA REABILITAÇÃO DE PESSOAS COM SEQUELA PÓS-COVID-19 NO QUOTIDIANO DA ATENÇÃO PRIMÁRIA À SAÚDE

Primary Health Care played an essential role in the management of care in the fight against COVID-19 in Brazil, mainly for the rehabilitation of people with persistent symptoms of the disease. Thus, the objective was to understand the strengths and limits in the rehabilitation of people with post-COVID-19 sequelae in the daily lives of Primary Health Care professionals, based on an interpretative study of a qualitative nature, based on Comprehensive and Daily Sociology, involving 11 health professionals. The sources of evidence were remote individual interviews, carried out in March 2022, guided by a semi-structured script, from which three categories and subcategories emerged: The pandemic everyday life in Primary Health Care: know-how, know-how and know-how; Powers in the rehabilitation of people with post-COVID-19 sequelae in Primary Health Care: longitudinality: bond that rehabilitates; multidisciplinary and community support; work instruments that favor professional engagement and organic solidarity and; Limits in the rehabilitation of people with post-COVID-19 sequelae in Primary Health Care: lack of a care flow; structural difficulties, human resources and support for rehabilitation in Primary Health Care. The pandemic context caused significant changes in the daily life of Primary Health Care, allowing professionals to develop new skills such as the rehabilitation of people with post-COVID-19 sequelae, highlighting the existing powers such as longitudinality and multidisciplinarity. The limits are expressed in work management, transiting between the lack of physical structure, deficits in financial support, generating overload for professionals, as well as the limiting factors due to the disease itself.
Retracted: Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images

[This retracts the article DOI: 10.1155/2022/3394475.].
Retracted: Cognitive Attitudes of International Mainstream Media to China during the Contaminated Water and Human Health Under Big Data

[This retracts the article DOI: 10.1155/2022/9033781.].
Retracted: Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

[This retracts the article DOI: 10.1155/2021/3056428.].
Retracted: Machine Vision and Intelligent Algorithm Based on Neural Network

[This retracts the article DOI: 10.1155/2022/6154453.].
Retracted: Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches

[This retracts the article DOI: 10.1155/2022/6333573.].
Retracted: Evaluation of Multimedia Courseware-Assisted Teaching Effect of Medical Images Based on the Deep Learning Algorithm

[This retracts the article DOI: 10.1155/2022/5991087.].
Retracted: An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model

[This retracts the article DOI: 10.1155/2022/1626458.].
Retracted: UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm

[This retracts the article DOI: 10.1155/2022/4782850.].
Retracted: Wireless Sensor Technology-Based Physical Education Teaching Evaluation

[This retracts the article DOI: 10.1155/2022/3544457.].
Retracted: Analysis of Therapeutic Effect of Elderly Patients with Severe Heart Failure Based on LSTM Neural Model

[This retracts the article DOI: 10.1155/2022/7250791.].
Retracted: Machine Learning Based Comparative Analysis for Breast Cancer Prediction

[This retracts the article DOI: 10.1155/2022/4365855.].
TENTH GRADERS’ PERCEPTIONS ON THE USE OF SOCIAL MEDIA IN LEARNING ENGLISH AT SMK N 1 PGRI MEJOBO KUDUS IN THE ACADEMIC YEAR OF 2021/2022

This study aims to determine the perception of X APH students at SMK N 1 PGRI Mejobo Kudus on the use of social media in learning English. In this study, researchers used descriptive qualitative research with questionnaires and interviews. The participants in this study were 18 students of class X APH SMK N 1 PGRI Mejobo Kudus. The results show that WhatsApp, YouTube, Instagram, and TikTok are applications that are widely used by students when learning English. The results also show that social media has a positive perception for students. This is because social media applications help them improve their English language skills, so students think that social media applications are important and useful in learning English.
The Benefits of Interactive Media Websites Through Google Sites on Learning Outcomes of Elementary School Students

The application of technology in education will improve teachers’ ability to convey material to students, make it easier for students to understand the material presented, and make time more effective and efficient. Advances in computers are also closely related to the world of education. Most schools also include computers as a compulsory subject. Technology's role in education is growing rapidly, and the need for technology is indispensable in the current era of globalization. By utilizing recent technological developments, the aims of this research are (1) to develop website-based learning media; (2) to analyze the differences in learning outcomes between students who use websites and conventional media. Researchers use a Google Sites application to support the development of web-based learning media. Teaching staff can use Google Sites to carry out various learning activities. The stages of this research method are experimental research with a posttest only control group design. The subjects of this study were students of class IV A and B at SDN 2 Karangsari—intake of research class through random sampling technique. Activities in data analysis include data processing and statistical analysis. The conclusion of this study is that the learning model through website learning media can generally motivate students to improve learning outcomes. Students gave a positive response to the implementation of the website learning model.
CaT: Cyclic-Accumulation Transformer for Lane Detection

Lane detection is a special task in autonomous driving. Its most prominent inherent feature is to learn the imagination of severely occluded objects. Traditional CNN-based networks learning the imagination tend to perform poorly. In this work, we propose a novel architecture, called Cycle_accumulation-Transformer (CaT), which is the first structure to handle the lane detection by fusing CNN and Transformer. In particular, Cycle_accumulation structure and Transformer structure complement each other, and they adopt the four-direction cyclic accumulation process of “up to down”, “down to up”, “left to right” and “right to left” in the convolutional mode and the self-attention mechanism of “QKV” to fuse global information respectively. Our method is based on pixel-level semantic segmentation with high detection accuracy while meeting real-time requirements. Moreover, our proposed method achieves state-of-the-art results on the Tusimple and also achieves competitive results on the CULane.
Methodology for automatic extraction of red flags in public procurement

Procurement fraud brings severe economic and social damage around the World. Losses in revenue reach the magnitude of trillions of dollars. Control bodies around the World expend great efforts in an attempt to reduce such waste. The audit work involves the analysis of procurement by specialists. The high number of processes analyzed and the time required for fraud analysis come in an inefficient and low fraud detection rate. Several computational models have been proposed in recent years to automate the detection and prediction of procurement fraud. However, most of these models depend on human intervention to extract red flags that the machine should consider in detecting fraud. We propose the use of BERT with NLP techniques for the automatic extraction of red flags used in detecting fraud in procurement. Experimental results show that pre-trained contextualized language models are competitive with other methods.
Gradient Sparsification For Masked Fine-Tuning of Transformers

Fine-tuning pretrained self-supervised language models is widely adopted for transfer learning to downstream tasks. Fine-tuning can be achieved by freezing gradients of the pretrained network and only updating gradients of a newly added classification layer, or by performing gradient updates on all parameters. Gradual unfreezing makes a trade-off between the two by gradually unfreezing gradients of whole layers during training. This has been an effective strategy to trade-off between storage and training speed with generalization performance. However, it is not clear whether gradually unfreezing layers throughout training is optimal, compared to sparse variants of gradual unfreezing which may improve fine-tuning performance. In this paper, we propose to stochastically mask gradients to regularize pretrained language models for improving overall fine-tuned performance. We introduce GradDrop and variants thereof, a class of gradient sparsification methods that mask gradients during the backward pass, acting as gradient noise. GradDrop is sparse and stochastic unlike gradual freezing. Extensive experiments on the multilingual XGLUE benchmark with XLMR-Large show that GradDrop is competitive against methods that use additional translated data for intermediate pretraining and outperforms standard fine-tuning and gradual unfreezing. A post-analysis shows how GradDrop improves performance with languages it was not trained on, such as under-resourced languages.
Local and Global Context Modeling with Relation Matching Task for Dialog Act Recognition

In dialog act recognition (DAR) of an utterance in a conversation, the prior studies have focused either on the global context using the whole utterances in the dialog, or the local context using the neighbouring utterance flow in the dialog. However, their methods attempt to deal with all types of dialogs indiscriminately. In this study, we propose a model to extract the local context information by an inter-utterance relation matching task (RMT), and a DAR framework to incorporate the local context information into a hierarchical network to fulfil both local and global context modeling. Extensive evaluations were conducted on a Mandarin dialog corpus and two benchmark English corpora. It is found that the different dialog types possess different window lengths for RMT, which is related to the length of subtopics in a given type of dialog. According to ablation experiments, the global information contributed more to the DAR in the hierarchical framework, while the contribution ratio of the local to the global context information was larger than 0.1. The results demonstrated that the proposed RMT and DAR framework significantly improved the DAR performance.
Mass-Based Short Term Selection of Classifiers in Data Streams

Dynamic classifier selection (DCS) regards well-known machine learning techniques in the batch setting that leverage ensemble performance. Most of the methods use similarity-based methods as a proxy, culminating in high computation costs and becoming unfeasible in many streaming scenarios. In this paper, we propose a DCS method able to cope with the high-speed streaming setting, which is based on the performance of base learners in the most recent instances. The impact of our method is evaluated with different ensembles for data streams. We also propose modifications to an Online Boosting method, which has its performance improved with DCS. Our method increases the accuracy and kappa statistic of state-of-the-art ensembles with low overhead of time processing and memory.
Information System Of Student Point Violations (Case Study Of Sma Muhammadiyah 7 Serbelawan)

Efforts to strengthen school discipline can be done in various ways. For example, at the middle school level, points for violations committed by students are calculated based on the rules set by the school. The number of fault points counted varies from warning to calling the parent at the highest level, and notifying the maximum number of fault points to the parent. SMA Muhammadiyah 7 serbelawan has issued a code of conduct. The school will apply penalty points for all infractions committed by students in an attempt to enforce disciplinary action. To improve teacher performance in dealing with problem students, the authors designed a system for calculating student violation points using the oriented objects technology method. By using the object oriented technology method, it will build a system systematically and regularly in order to achieve a goal based on the abstraction of existing objects and the presence of this system will certainly greatly help teacher performance, especially counseling guidance teachers and counseling guidance teachers, problems in recapitulating all student violations that occur in Muhammadiyah 7 Serbelawan High School. With a system that has been computerized and integrated with databases, it will be able to facilitate the process of finding student data that violates and conducting counseling easily and documents are stored neatly in the database.