Reviewerly/exHarmony huggingface dataset:
Video-based complex human event recognition with a probabilistic transformer

Complex human events are high-level human activities that are composed of a set of interacting primitive human actions over time. Complex human event recognition is important for many applications, including security surveillance, healthcare, sports and games. Complex human event recognition requires recognizing not only the constituent primitive actions but also, more importantly, their long range spatiotemporal interactions. To meet this requirement, we propose to exploit the self-attention mechanism in the Transformer to model and capture the long-range interactions among primitive actions. We further extend the conventional Transformer to a probabilistic Transformer in order to quantify the event recognition confidence and to detect anomaly events. Specifically, given a sequence of human 3D skeletons, the proposed model first performs primitive action localization and recognition. The recognized primitive human actions and their features are then fed into the probabilistic Transformer for complex human event recognition. By using a probabilistic attention score, the probabilistic Transformer can not only recognize complex events but also quantify its prediction uncertainty. Using the prediction uncertainty, we further propose to detect anomaly events in an unsupervised manner. We evaluate the proposed probabilistic Transformer on FineDiving dataset and Olympics Sports dataset for both complex event recognition and abnormal event detection. The dataset consists of complex events composed of primitive diving actions. The experimental results demonstrate the effectiveness and superiority of our method against baseline methods.
Unsupervised registration for liver CT-MR images based on the multiscale integrated spatial-weight module and dual similarity guidance

Multimodal registration is a key task in medical image analysis. Due to the large differences of multimodal images in intensity scale and texture pattern, it is a great challenge to design distinctive similarity metrics to guide deep learning-based multimodal image registration. Besides, since the limitation of the small receptive field, existing deep learning-based methods are mainly suitable for small deformation, but helpless for large deformation. To address the above issues, we present an unsupervised multimodal image registration method based on the multiscale integrated spatial-weight module and dual similarity guidance. In this method, a U-shape network with our multiscale integrated spatial-weight module is embedded into a multi-resolution image registration architecture to achieve end-to-end large deformation registration, where the spatial-weight module can effectively highlight the regions with large deformation and aggregate discriminative features, and the multi-resolution architecture further helps to solve the optimization problem of the network in a coarse-to-fine pattern. Furthermore, we introduce a special loss function based on dual similarity, which represents both global gray-scale similarity and local feature similarity, to optimize the unsupervised multimodal registration network. We verified the effectiveness of the proposed method on liver CT-MR images. Experimental results indicate that the proposed method achieves the optimal DSC value and TRE value of 92.70 ± 1.75(%) and 6.52 ± 2.94(mm), compared with other state-of-the-art registration algorithms. The proposed method can accurately estimate the large deformation field by aggregating multiscale features, and achieve higher registration accuracy and fast registration speed. Comparative experiments also demonstrate the effectiveness and generalization ability of the algorithm.
A study of the quasi-probability distributions of the Tavis–Cummings model under different quantum channels

We study the dynamics of the spin and cavity field of the Tavis–Cummings model using quasi-probability distribution functions and the second-order coherence function, respectively. The effects of (non)-Markovian noise are considered. The relationship between the evolution of the cavity photon number and spin excitation under different quantum channels is observed. The equal-time second-order coherence function is used to study the sub-Poissonian behavior of light and is compared with the two-time second-order coherence function in order to highlight the (anti)-bunching properties of the cavity radiation.
Construction of Power Knowledge Graph based on Entity Relation Extraction

In order to integrate the fragmented text data in the power domain and solve the problems of disordered and weak correlation of transmission protocols, an improved BERT model was proposed by combining deep learning and knowledge graph for entity relationship extraction in the power domain. This method uses the BERT model based on a full word mask to generate sentence vectors, word vectors with contextual semantics, and then takes the average value of word vectors to get entity vectors. The sentence vectors and entity vectors are combined by the attention machine. Finally, the combined new vectors are put into a fully layer for sequential labeling and finding the optimal tag to implement the entity extracted object. The experimental results show that the precision, recall value, and F1 score of this method are 90.12%, 85.25%, and 87.56 % respectively when entity extraction is performed on the corpus data set of transmission procedures.
Research on the Practical Application Analysis of EDA in Mathematical Electronic Technology

After a comprehensive investigation of the current level of social science in China, it can be found that the digital technology in China has generally maintained a high level of development. Under this premise, EDA technology has gradually walked into the public, and has been widely used in various fields of society. From the analysis of current practice, EDA technology industry is the most common industry used in digital electronic technology industry. EDA technology in the practice of digital electronic technology has greatly improved the technical level of digital electronic experiment. Therefore, the practice of digital electronic technology needs to pay attention to the use of EDA technology. In this paper, the practical application of EDA technology in mathematical electronic test is studied, and the application performance of this technology is further explored.
INCREASING STUDENTS’ READING COMPREHENSION THROUGH THINK-PAIR-SHARE TECHNIQUE

This study aims to find out whether or not the use of think-pair-share technique can increase reading comprehension of the eleventh grade students of SMA Negeri 1 Tinombo. This research used a pre-experimental research design. The research population was the eleventh grade students of SMA Negeri 1 Tinombo which consisted of 147 students. The sample was selected by using a purposive sampling technique. The number of the sample was 27 of class XI MIA 1. The data were collected through pre-test and post-test about reading. The result of this research showed the mean score of the pre-test of the students was 58.44 and the post-test was 85.11. Therefore, the t-counted value (12.59) is higher than the t-table value (1.795). It proves the hypothesis is accepted which means the Think-Pair-Share Technique can increase students’ Reading comprehension.
PERCEPTIONS OF EFL LEARNERS ON THE APPLICATION OF READING ALOUD TECHNIQUE IN LEARNING PRONUNCIATION

The objective of this research was to find out whether or not the use of Sequence Pictures Technique can improve the ninth grade students’ skill in writing procedure text. The researcher applied a quasi-experimental research design. The population of this research was the ninth grade students of SMP Negeri 14 Palu which consisted of 119 students. The sample of this research was 30 students of class IX Ahmad Yani as the experimental class. Meanwhile, 29 students of class IX Cut Nyak Dien categorized as the control class. The sample was selected by using cluster random sampling technique. The technique of data collection was test. The test included pre-test and post-test, with the mean score of 56.11 for experimental class, and 71.67 for control class on pre-test. While on post-test, the results were 73.33 for experimental class and 77.22 for control class. By applying 0.05 level of significance and 28 degree of freedom (df), as can be seen that the value of t-counted was (3.58) and t-table was (1.701). The t-counted value was higher than t-table value, which meant that the research hypothesis was accepted. This study suggests that the use of Sequence Pictures technique can improve students’ writing skill in procedure text.
Directionality of paper reviewing and publishing of a scientist: A Granger causality inference

It has been evidenced that peer review activities are positively correlated to scientists' bibliometric performance (e.g., Ortega, 2017Ortega, , 2019)).However, how the number of paper 'reviewing' interacts with a scientist's 'publishing' has not been addressed in previous studies.This paper attempts to employ the Granger causality inference to explore the directionality between a scientist's publication performance and his/her review activities.Our dataset comprises scientists' reviewed articles derived from Publons in the Web of Knowledge database, and their publications retrieved from PubMed.We find that scientists who reviewed less or published less tend to have Granger causality between reviewing and publishing activities.In addition, compared with early-career researchers, reviewing advances publishing for senior scientists.
INVESTIGATION OF STUDENTS’ PERCEPTION ON THE IMPLEMENTATION OF DISCOVERY LEARNING METHOD IN READING COMPREHENSION

The objectives of this research are to investigate and analyze the students’ perception on the implementation of Discovery Learning Method in reading comprehension and the challenges that the students face on the implementation of Discovery Learning Method in reading comprehension. In addition, this research was conducted through qualitative approach. The data of this research were taken from 30 students at SMK Negeri 1 Terpadu Sindue. In collecting the data of this research, the researcher used questionnaire and interview to the students. Questionnaire was used as the main instrument of this research and interview was used to support the results of the questionnaire. Questionnaire was in the form of statement and it included four degrees of answer option. Then, after analyzing the data obtained, it was concluded that Discovery Learning Method is effective to be implemented. It was perceived by the students that the method gives them insights of learning materials, allows them to explore the reading material, makes them more active in class, improves their analytical thinking, and gives them an opportunity to discover information collected earlier with friends. However, the students faced some challenges during the implementation of this method. The challenges that the students face on the implementation of Discovery Learning Method in reading comprehension are: the difficulty in understanding reading text which is too long, unclear instruction from the teacher, lack of vocabulary, and the learning situation (current teaching and learning process during the pandemic).
Segmented Entanglement Establishment With All-Optical Switching in Quantum Networks

There are two conventional methods to establish an entanglement connection in a Quantum Data Networks (QDN). One is to create single-hop entanglement links first and then connect them with quantum swapping, and the other is forwarding one of the entangled photons from one end to the other via all-optical switching at intermediate nodes to directly establish an entanglement connection. The two methods both have pros and cons. Respectively, the former method has a higher success probability of constructing entanglement link, but it would consume more quantum resources. The latter method, however, has a lower success probability to deliver a photon across multiple quantum links with fewer quantum resources. Accordingly, we are expecting to establish significantly more entanglement connections with limited quantum resources by first creating entanglement segments, each spanning multiple quantum link, using all-optical switching, and then connecting them with quantum swapping. In this paper, we design SEE, a Segmented Entanglement Establishment approach that seamlessly integrates quantum swapping and all-optical switching to maximize quantum network throughput. SEE first creates entanglement segments over one or multiple quantum links with all-optical switching, and then connect them with quantum swapping. Accordingly, SEE can theoretically outperform conventional entanglement link-based approaches. Large scale simulations show that SEE can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100.00\%$</tex-math> </inline-formula> larger throughput compared with the state-of-the-art entanglement link-based approaches, e.g., Redundant Entanglement Provisioning and Selection (REPS).
Image Enhancement and Brightness Equalization Algorithms in Low Illumination Environment Based on Multiple Frame Sequences

Images captured in low illumination environments can affect people’s judgment of image information. To raise the visual effect of low illumination images and preserve the integrity of image information, this study proposes two algorithms. One is an image enhancement algorithm for low illumination, which combines the WASOBI image denoising method for deep denoising of images and adaptive gamma correction for brightness adjustment of images. The other is an image brightness equalization algorithm for uneven lighting. This algorithm divides the image according to the illumination area, and uses guided filtering and adaptive gamma correction to equalize the brightness of the image. The laboratory outcomes demonstrate that the proposed image enhancement algorithm can effectively reduce the noise level of the image and adjust the brightness of the image, and the algorithm has a certain degree of stability. The subjective evaluation score of the image processed using the brightness equalization algorithm is 4.7, indicating that the image has good visual effects. The objective evaluation results prove the capability of the equilibrium algorithm, and comparing to other algorithms, the algorithm has the shortest operation time, only 0.9452 seconds. Therefore, the enhancement algorithm and brightness equalization algorithm proposed in the article have certain application value.
KEBERTERIMAAN GURU SEKOLAH DASAR TERHADAP MEDIA PEMBELAJARAN BERBASIS WEB PADA MATERI PERKEMBANGAN TEKNOLOGI KELAS 3 SEKOLAH DASAR

This study aims to determine whether elementary school teachers are amenable to incorporating online learning materials into the class 3 technology development curriculum. In web-based learning resources, information and communication technology is utilized to support teaching and learning. This study will identify the factors that affect teachers' acceptability of using online learning resources. Additionally, the advantages of media use for educators and students will be examined. The use of online learning resources in elementary schools for class III is anticipated to present challenges and opportunities, which this study will help to clarify.
Deep Learning Techniques for Medical Image Segmentation and Object Recognition

Segmentation of a target object in the form of closed curves has many potential applications in medical imaging because it provides quantitative information related to its size and shape. Over the last few decades, many innovative methods of performing segmentation have been proposed, and these segmentation techniques are based on the basic recipes using thresholding and edge-based detection. Segmentation and classification in medical imaging are in fact experiencing a paradigm shift due to a marked and rapid advance in deep learning (DL) techniques. DL methods have nonlinear representability to extract and utilize global spatial features and local spatial features simultaneously, showing amazing overall performance in medical image segmentation. DL methods mostly lack transparency due to the black-box output, so clinicians cannot trace the output back to present the causal relationship of the output diagnosis. Therefore, in order to safely utilize DL algorithms in the medical field, it is desirable to design the models to transparently explain the reason for making the output diagnosis rather than a black-box. For explainable DL, a systematic study is needed to rigorously analyze which input characteristics affect the output of the network. Despite the lack of rigorous analysis in DL, recent rapid advances indicate that DL algorithms will improve their performance as training data and experience accumulate over time.
Towards a Model-Driven Development of Environmental-Aware Web Augmenters Based on Open Data

In order to achieve the SDGs (Sustainable Development Goals), society must be able to consume open data to be aware of environmental situations. However, the existence of many related heterogeneous open data sets hampers citizens to use them to rise environmental awareness. In order to overcome this scenario, in this paper, we introduce a model-driven approach to automatically generate Web Augmenters that (i) homogeneously access environmental open data, and (ii) visualize this data while users are surfing the Web. In order to show the feasibility of our approach a case study is defined, based on generating a Web Augmenter that uses open data and Wikipedia related to “Mar Menor” coastal lagoon; a highly anthropized ecosystem located in South-East Spain.
Face Mask Detection using MobilenetV2 of Convolution Neural Network

In light of the fact that the COVID-19 pandemic is spreading swiftly over the earth, it is crucial to create new technologies to study and resist the disease. Face masks and gloves are required for protection against the coronavirus, and scientists and doctors have advised everyone to wear the mask for the whole day. Thus, various procedures are accessible to different people wearing face masks. Masks are advised as a basic barrier to stop respirational beads from receiving into the air and against other people once someone is found to be using cover hacks. Additionally, this is known as source governance. This arti- cle is based on the current understanding of respiratory beads’ function in the escalation of COVID-19 infection. In this problem, the face mask procedure was built using MobileNetV2. Compared to the current system, Mobile Net V2 can be used to identify face masks among individuals with greater accuracy. The input data file contains 500 images taken from the Kaggle face mask Detec- tion Dataset. A scene with a mix of people donning masks and without mask. The output is a segmented picture of the same. Later, this process is improved by using a webcam to capture real-time video. The video is then segmented into the frame and resized as required, and the result is a video-segmented image. The model was then run to determine whether or not individuals were wearing masks after performing the pre-processing function. An accuracy of 80 was used to acquire the results.
Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehicle counter system has been integrated between Raspberry Pi 4 and a mobile application on a smartphone where the smartphone functions to display information such as day, date, month, year and together with the number of cars and motorcycles. We tested this automatic vehicle counting system on steam services (car and motorcycle washing) for 3 days where 10 vehicles were collected every day. The test results show that the system is capable of detecting cars and motorcyles with an average accuracy rate of 46.6%.
Multi-angle 3D measurement of small workpieces using EDMS algorithm

Abstract Binocular vision can get the three-dimensional information of the objects according to two-dimensional images. However, when the background texture information of the workpiece to be measured is weak, or the depth information cannot be recognized due to the change of viewing angle, it will lead to poor three-dimensional measurement accuracy. To address this problem, the paper proposes a multi-view workpiece 3D measurement method based on binocular vision. First, an experimental bench with a Chessboard is designed. The corner point reconstruction is realized by extracting the corner point of the calibration plate. The checkerboard plane is fitted by the least squares method to obtain the checkerboard plane mathematical model. Then, the vertices of the workpiece are extracted at the subpixel level, and a minimum distance sparse vertex stereo matching algorithm (EDMS) based on Euclidean distance metric is proposed to achieve accurate and fast corner matching. Finally, the three-dimensional dimensions of the workpiece are calculated. Through experiments on multiple angles of the two workpieces, the results show that the average absolute error measured by the method at different angles is 0.33 mm, the total relative error is 0.90%, and the variance is less than that 0.01 mm 2 , realizing the more accurate measurement of multi-view three-dimensional dimensions of small workpieces. This paper provides a new binocular vision handheld mobile 3D measurement equipment method.
5th Workshop on Explainable User Models and Personalised Systems (ExUM)

Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest music or movies to personal assistants who proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the EU General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face intelligent systems. Unfortunately, current research tends to go in the opposite direction since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of model explainability. The workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in recent methodologies for building user models or developing personalized and adaptive systems.