Reviewerly/exHarmony huggingface dataset:
An evolving ensembles method based on genetic programming and whale optimization algorithm for imbalanced datasets

Genetic Programming (GP) algorithm has been successfully applied to classification tasks for many years, with a long history and remarkable effects. It is worth noting that GP tends to evolve biased classifiers when it is used for class imbalanced datasets. Biased classifiers are often unreliable and will seriously affect the classification performance. In addition, when GP is used to train the model to adapt to the high-dimensional imbalanced datasets, it is easy to be affected by the majority class and ignore the existence of the minority class. Therefore, classification tasks based on GP in high-dimensional and imbalanced datasets need to be further studied. In this paper, we propose a GP approach for imbalance classification tasks with the aim of improving the classification performance and minimizing the time consumption, named HGPWOA. A multi-objectivization fitness function is adopted to solve the problem, supplemented by Whale Optimization Algorithm (WOA) for feature selection, and then feature augmentation strategy is adopted to improve the classification efficiency. The method was tested on five imbalanced datasets. The experimental results show that this method is superior to the traditional classification algorithms, which provides a new way to study the class imbalance problems.
Deep dynamic feature alignment-based few-shot learning for figure skating action localization

Temporal action localization is a challenging and important task in computer vision. Although great progress has been made in recent years in this area with the help of deep learning, few jobs focus on applications on competitive sports, such as figure skating. To better analyze the players’ actions in figure skating, action localization is needed. Hence we collect 82 videos from worldwide figure skating contests, including championship and grand prix, etc. These videos are annotated by experts for action start timestamps, action end timestamps, and action names. We further analyze these videos by our proposed Deep Dynamic Feature Alignment (DDFA) method, which employs deep convolutional features and learns the action pattern between the target action and reference action. Experiments on models’ performances and results based on these datasets are presented, showing the proposed methods can predict proper action candidates for raw figure skating videos.
An effective graph attention network for aspect-based sentiment analysis

Aspect Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity of a review towards a given aspect terms. One main challenge of this task is to correlate relevant opinion words with aspect terms, especially for the aspect terms consisting multi words. To this end, attention mechanism plays an important role in previous models due to its capability of modeling the relationship between the context and aspect words, and solving the defect of parallelization which can't be realized in conventional neural network like recurrent neural network. However, the attention mechanism may introduce irrelevant noise and reduce models’ performance. To alleviate the above problem, graph neural network is proposed which utilize the syntactic information for ABSA task. Following this line, we propose a semantic enhancement graph attention network model named SEN-GAT, which innovatively introduce the aggregation and aspect-aware attention mechanism. The experimental results on standard datasets show that our proposed model can substantially improve ABSA tasks performance.
Research on the indicator system for civil aircraft project evaluation based on logical framework approach

This article is based on the methodology logical framework approach (LFA), developed by the USAID organization, suitable as well to guide large-scale equipment manufacturing project evaluations. The main application of the indicator system is to determine the feasibility in evaluation of a new civil aircraft project, from the view of enterprise‘s “project portfolio” management. In the mean time, LFA could also help enterprise management level to monitor the projects in process, as review techniques, to help make decisions whether to resume or end a project in time.
Improved cascaded partial decoder for boundary aware salient object detection

Compared with low level features, high level features, have greater spatial resolution and less performance contribution, but are more computatively expensive. At the same time, most of the previous researches focused on the regional accuracy, and the boundary mass was less studied. In this paper, we improve the CPD framework to use a new hybrid loss for boundary aware salient object detection. Method of this paper integrating the features of the deeper layer to obtain relatively accurate salient maps. And using hybrid loss, the improved CPD framework can effectively conduct significance detection to obtain clear boundaries. Experiments on six benchmark data sets show that the proposed method not only runs faster than the existing model, but also performs well in accuracy.
Signal classification based on self-attention mechanism in unlicensed bands

With the rapid development of the Internet era, the demand for data traffic has exploded, and this dramatic growth has brought higher requirements for spectrum resources. Unlicensed long term evolution (LTE-U) technology is considered one of promising solutions to address the problem. However, the unlicensed band is rich resource, which is mainly occupied by wireless fidelity (WiFi). The biggest challenge for the LTE-U technology is how to achieve friendly coexistentence with WiFi. Many Researchers in academia and industry have proposed various solutions to deal with this problem. In this paper, we propose a classification method based on self-attention mechanism algorithm in order to distinguish unlicensed LTE and WiFi to further realize the friendly coexistence of the two signals. The proposed algorithm can enhance the feature extraction capability of the model by fusing the features extracted by the convolutional neural network through the self-attentive mechanism. Simulations show that our proposed algorithm has higher accuracy and stability compared to other algorithms at the same signal-to-noise ratio. When the signal-to-noise ratio is higher than 0 dB, the recognition accuracy can reach above 90%. Especially when SNR id -5 dB, the accuracy of self-attention network is 12% higher than CNN’s.
Contrasting YOLOv7, SSD, and DETR on Insulator Identification under Small-Sample Learning

Background: Daily inspections of insulators are necessary because they are indispensable components for power transmission lines. Using deep learning to monitor insulators is a newly developed method. However, most deep learning-based detection methods rely on a large training sample set, which consumes computing resources and increases the workload of sample labeling. The selection of learning models to monitor insulators becomes problematic. Objective: Through comparative analysis, a model suitable for small-sample insulator learning is found to provide a reference for the research and application of insulator detection. objective: We intend to find a model suitable for small-sample learning of insulators, which can provide a reference for the research and application of insulator detection. Methods: This paper compares some of the latest deep learning models, YOLOv7, SSD, and DETR, for insulator detection based on small-sample learning. The small sample here means that the number of samples and their proportion to the total sample are relatively small. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in the natural background are selected to test the performance of these models. method: This paper compares some latest deep learning models which are the YOLOv7, the SSD, and the DETR, for insulator detection based on small-sample learning. Few public insulator datasets are available on the internet. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in natural background, are selected to test the performance of these models. Results: Tests on two public insulator image sets, InsulatorDataSet and TLP, show that the recognition rates of YOLOv7, DETR, and SSD are arranged from high to low. The DETR and the YOLOv7 have stable performance, while the SSD lacks stable performance on the learning time and recognition rate. Conclusion: The in-domain and cross-domain scenario tests show that YOLOv7 is more suitable for insulator detection under small-sample conditions among the three models. other: None
Measurement of truck carriage size based on lidar

In order to solve the problem of measuring the size of the truck carriage and positioning the vehicle, a lidar-based non-contact dimensional measurement device for truck carriage is designed. The measuring device is composed of a vertical two-dimensional laser radar, a four-degree-of-freedom manipulator, a sliding rail and an industrial computer. The point cloud information of the length and width of the carriage is obtained by the laser radar. The linear features are extracted by the probabilistic Hough transform algorithm, and then the outline segments of the carriage are extracted according to the prior knowledge, so as to measure the length, width and height of the carriage and position the vehicle. The measurement result of the carriage model shows that the device is characterized by small measurement error, high measurement efficiency and simple structure and so on.
Global-local transformer for unsupervised person re-identification

Unsupervised person re-identification (Re-ID) focuses on studying the issues of person Re-ID in the open world where there are no available annotated labels for pedestrian images. The original transformer only learns global features. It ignores local features and fails to consider high-level local information on images. Therefore, we introduce a transformer-based multi-branch unsupervised person Re-ID framework, named GLTReID, with learning more robust features of pedestrian images. Specifically, we split the original transformer into one global branch and two local branches, with learning global and local features in parallel. The global branch learns global features of pedestrian images, and two local branches learn local features of different parts of pedestrian images. Numerous experiments are conducted on several person Re-ID datasets, including CUHK03, Market-1501 and MSMT17. Numerous experiments denote that our method is better than most unsupervised learning methods and unsupervised domain adaptation methods in person Re-ID.
Multi-grained Chinese spelling error correction for electricity nameplate text recognition

Although the optical character recognition (OCR) is a mature technology in theory and application, some obstacles still exist in studying scene text recognition (STR). The STR that the electricity nameplate text recognition belongs to always is limited by various factors such as the crappy quality of image, the instability of environments, and the differentiation of fonts. To correctly identify the information on the electricity nameplate, we propose a novel architecture which consists of two pipeline networks, an improved text recognition network based on Transformer OCR and an enhanced spelling error correction network based on Soft-Masked BERT. The former ensures that the glyph knowledge is left, and the latter is to preserve the expressions of diversity. To validate the effectiveness of our method, we evaluated it on a self-annotated electricity nameplate corpus and reported the across-the-board performance gains compared to competing prior models. We further discuss the ablation results for dissecting the gains obtained above.
A swift and efficient method for face mask recognition based on off-the-shelf models

Since the outbreak of COVID-19, the widespread use of face masks has prompted significant research efforts in face mask recognition. Based on the previous work, it is crucial to develop a face mask recognition system swiftly using offthe-shelf techniques, while it is equally important to ensure that the system's performance and efficiency meet the requirements of real-life scenarios. In this paper, we propose a two-fold face mask recognition method, by first detecting faces in an image using YuNet and then recognizing whether the faces are wearing a mask or not using MobileNet. After validation on a self-built dataset, compared to similar related works, our method can effectively improve the face detection rate, even in situations of occlusion and blurring, and the detection and recognition efficiency has been significantly improved too.
Chatbot PTIPD Customer Care Center Service using Dialogfow

Chatbot research is a unique innovation in the development of Artificial Intelli- gence and has promising prospects in the field of Education. One form of information service available at the university is the Customer Care Center (C3) PTIPD UIN Suska Riau, which is responsible for handling problems submitted by students. However, with so many questions or problems submitted to the PTIPD Customer Care Center, it is difficult for the PTIPD Cus- tomer Care Center to respond to student questions submitted, the service becomes ineffective and the response to the answers to the problems submitted becomes late. To overcome this problem, chatbot development was carried out for PTIPD UIN Suska Riau Customer Care Center Services using Dialogflow to improve services and overcome existing problems. Di- alogflow as conversation development platform that uses natural language processing (NLP) to understand and interpret user intent in conversations. Through User Acceptance Test (UAT) testing, the chatbot managed to achieve an acceptance rate of 84% overall. This shows that users, in this case, students respond positively to the use of chatbots in PTIPD Customer Care Center services. In addition, Usability Testing was also conducted to evaluate the level of usability of the chatbot. Based on this test, the chatbot achieved a score of 76, which indicates a good level of usability in interaction with users. The test results illustrate that the chatbot at the Customer Care Center PTIPD UIN Suska Riau has provided a positive user experience.
Unveiling the power of unpaired multi-modal data for RGBT tracking

RGBT tracking receives increasing interests due to its flexible application in all-day and all-weather environments. However, the training of deep RGBT trackers usually relies on large-scale aligned RGBT pairs, which usually require high human labor and time cost. Considering the strong commonality and specificity of multi-modal data, we propose a novel two-stage learning framework to capture modality-shared and modality-specific features using large-scale unpaired RGBT data, and thus achieve state-of-the-art performance in RGBT tracking. In specific, in the first stage, we aim to learn the modality-shared representations and thus design a generic transformer network which only requires mixed modal data for training. In the second stage, we aim to learn the modality specific representations and achieve adaptive back-propagation using unpaired data. To achieve these goals, we design a modality transformer network, in which two modality encoders are used to capture modality-specific features and a modality-adaptive attention module is designed to enforce the interchange of information between different modalities in a separate-gather way. Since the two training stages do not rely on paired or aligned multi-modal data, the power of unpaired multi-modal data is unveiled in the training of deep RGBT tracker. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method against state-ofthe-art RGBT trackers.
3-Dimensional Quartic Bézier Curve Approximation Model by Using Neutrosophic Approach

In a 3-dimensional data collection process, there exists noise data that cannot be included to visualize the process. Therefore, it is difficult to deal with since fuzzy set and intuitionistic fuzzy set theories did not consider the indeterminacy problem. However, using a neutrosophic approach with three memberships: truth, false, and indeterminacy membership function, the error data will be treated as uncertain data by using the indeterminacy degree. Thus, this study will visualize the 3-dimensional quartic Bézier curve model by using neutrosophic set theory. To construct the model, the neutrosophic quartic control point must first be introduced to approximate the neutrosophic quartic Bézier curve. Next, the Bernstein basis function as the methodology of this study will be blended with the neutrosophic fundamental notion. At the end of this paper, a numerical example of the 3-dimensional neutrosophic quartic Bézier curve will be visualized by using the approximation method as the finding of this study. Finally, this study provides significant contributions to making it easier for data collectors to visualize all data, which means that no data will be eliminated since the uncertainty data will also be used.
CURRENT STATUS OF THE BENCHMARK DATABASE BEMEDA

Abstract. Open science is an important attribute for developing new approaches. Especially, the data component plays a significant role. The FAIR principle provides a good orientation towards open data. One part of FAIR is findability. Thus, domain specific dataset search platforms were developed: the Earth Observation Database and our Benchmark Metadata Database (BeMeDa). In addition to the search itself, the datasets found by this platforms can be compared with each other with regard to their interoperability. We compare these two platforms and present an update of our platform BeMeDa. This update includes additional location information about the datasets and a new frontend design with improved usability. We rely on user feedback for further improvements and enhancements.
Use of E-Comic media through canva to increase the Maharah Kitabah

Every language skill must have its disadvantages and advantages, as well as writing skills (Maharah Kitabah). Based on the results of observations, there are difficulties in writing skills, namely different backgrounds When attending elementary school, lack of practice and uninteresting material taught by educators to students from the description above shows that there is a need for interesting media to increase enthusiasm for learning in students in writing. One media that can increase students' enthusiasm in writing skills is e-comic. E-comic is a digital comic that is used to convey information about the field of knowledge excitingly and entertainingly, besides the use of e-comics are more practical and can be opened anywhere and anytime as long as they have a file or access link; e-comic is also an attraction for students so that it can help in learning Arabic, especially in maharah kitabah. The research was conducted at SMP Daarut Tauhiid Bandung in class VII, which aimed to determine whether e-comic media was influential in improving maharah kitabah. This study used a quantitative approach with a quasi-experimental design in the form of a Non-Equivalent Control Group Design, which was divided into two research groups: a control group and an experimental group. Data is taken using tests in the form of pretest and post-test. The results showed that e-comic media using Canva was influential in increasing Maharah kitabah with an increase of 23.5; besides that, it was strengthened by a signification value of 0.00, meaning there is a difference in student learning outcomes. This research is expected to be able to use e-comic as one of the supporting media for learning Arabic, especially in Maharah Kitabah Keywords: E-comic, Canva, Maharah kitabah
Bologna Experience: Successes and Doubts

The expected formal withdrawal of Russian higher education from the Bologna process raises the question of assessing the experience gained in this area over two decades. The question of the possibility and expediency of using this experience in the further development of existing education is of high relevance. The article uses data from the monitoring of the Bologna process in education management bodies, in higher and secondary educational institutions, as well as among their administrations and teaching staff. Specific data were obtained by a joint study of the Center for the Sociology of Science and Education of the Federal Research Sociological Center of the Russian Academy of Sciences and the Center for Social Forecasting LLC (N=3677, October 2021) For three subsamples of graduates: bachelor's degree, master's degree, specialty - the following indicators were analyzed: employment in the received specialty, expected and actual wages, readiness for additional and continuing education. An assessment and forecast of the applicability in the conditions of modern Russian realities of such "Bologna" practices as the creation of National research universities, the Unified State Examination, GIFO, educational lending, intra-country educational migration and inter-university exchanges, overcoming inequalities and the availability of quality education.
Developing a late fusion of multi facial components for facial recognition with a voting method and global weights

With the development of deep learning, many solutions have achieved outstanding performance in solving facial recognition problems. Nevertheless, many challenges still stand, such as occluded face or illumination. This paper proposes a late fusion of many weighted weak classifiers to form a strong classifier for facial recognition. We train convolutional neural network models as weak classifiers on specific facial components. We build a strong classifier by lately fusing those weak classifiers with corresponding weights calculated locally or globally. A voting method is applied to determine the identity of the face. We experimented on five databases: ORL, CyberSoft, Georgia Tech, Essex Grimace and Essex Faces96. Performances of our method in those databases varied between 99% and 100%. Our proposed method can be used efficiently when a facial image only contains a few facial components. Also, our proposed global weights worked well on many facial databases.
Experience of nurses in caring for patients with dementia

The research reveals the positive and negative experiences of nurses in working with persons with dementia, allows to analyze the emerging problems and difficulties and to properly solve them in practical work, thereby improving the quality of healthcare services.&#x0D; The purpose of the study is to analyze the experience of nurses in caring for patients with dementia.&#x0D; Research methods. The applied qualitative research method of descriptive phenomenology reveals and explains the researched phenomenon as it is understood and experienced personally by each individual. The study interviewed 6 nurses working in the X nursing home.&#x0D; Research results and conclusions. The study revealed that dementia requires individual care and nursing. When working with patients with dementia, nurses have positive and negative experiences, and the involvement of relatives in patient care has a positive effect on both the physical and emotional state of the patient. Dementia requires individualized care and nursing as well as support and fulfillment of basic needs.