Reviewerly/exHarmony huggingface dataset:
IDENTIFICATION OF EPIDEMIC PARAMETERS DURING FUZZY OPTIMIZATION

Target. Obtain an analytical form of an approximate solution for a system of firstorder nonlinear differential equations that describes the dynamics of the epidemic in the SI model of two cohorts: healthy – infected. Methodology. The solution is sought in the form of a logistic curve using one of three forms: normal distribution Ф, lognormal distribution L and risk function Risk. Results. During fuzzy optimization, the parameters of logistic curves are identified both during the solution of a system of differential equations with fixed parameters, and when constructing a predictive model based on the presented short section of historical data. Epidemic statistics are best interpreted by a possibilistic process, in the cross section of which there is a fuzzy number of a general form. Conclusion. The solution for the SI model is well approximated by logistic curves of type Ф. In a more complex case, both curves of type Ф and profile curves of Risk, determined on the basis of four parameters, are involved in the approximation.
Survey of Deep Fake Creation Technologies and Deep Fake Detection using LSTM

Artificial intelligence known as Deep Fake is one of many techniques that have been successfully developed in recent years for altering faces in images and videos. It can produce convincingly faked images, audio, and video. Deep Fake can create problem, especially when there is a media component involved. Even if it is helpful, when it is used maliciously, such as for disseminating fake news or cyber bullying, it can pose a threat to society. It is necessary to develop a complete fake detection method to handle such issues. Too far, numerous methods have been developed to distinguish between authentic and fraudulent videos.&#x0D; The objective of this work is to give a summary of different approaches for Deep Fake creation and to provide an overview of LSTM algorithms for deep fake video detection.
PENGARUH FAKTOR LINGKUNGAN TERHADAP JUMLAH BAKTERI UDARA KAMAR MANDI

The bathroom is a damp area that has a variety of microorganisms. It is feared that these microorganisms can cause health problems. This study aims to determine the effect of environmental factors, in this case temperature, humidity, and light intensity on the number of air bacteria in the bathroom. Sampling of bathroom air bacteria in the UNIROW environment was randomly selected as many as 4 bathrooms, then the number of air bacteria was counted using the TPC (Total Plate Count) method at the UNIROW Biology Laboratory. Measurement of environmental factors in the form of light intensity, temperature, and humidity of the bathroom. The results of this study indicated that the highest air temperature at the 4 bathroom locations used as sampling locations was at KMKA, namely 300C with the lowest number of airborne bacteria, namely 83 colonies. While the lowest temperature was at KMMI (28.90C) with the highest number of airborne bacterial colonies (483 colonies). This shows that the higher the temperature, the lower the number of bacteria. The relative humidity in the four bathrooms ranges from 82% to 82.5%, but has a different number of airborne bacterial colonies. The higher the light intensity value, the lower the number of bacterial colonies counted. The results of the correlation analysis also showed that there was a significant correlation between light intensity and the number of bacterial colonies with a correlation coefficient of -0.99. Based on this, environmental factors have an influence on the number of bacteria in the bathroom
A Full-Level Based Network to Detect Every Aircraft in Airport Scene

The rapid development of civil aviation has led to increasingly crowded airports. The complex airport environment and large number of aircraft make airport object detection a difficult task. Due to the large extension of the fuselage and wings, close-range aircraft objects have scattered features, making it difficult for prediction boxes to accurately locate them. Objects at far distances are too small in scale, and their features are easily lost during network feature extraction and fusion processes, resulting in frequent missed detections. Lower-level signals in the network contain more object positioning information and small object detection information,but existing networks usually do not fully utilize low-level signals. Therefore, this paper focuses more on utilizing lower-level signals and designs an object detection network for airport scenarios with several advantages as follows: (1) We designed a fusion module to incorporate low-level network signals into the neck, reducing the network’s missed detection rate for small objects and improving its ability to locate objects. (2) We add a detection head on the low-level network for detecting small and medium objects in airports, improving the network’s versatility in detection scale. (3) The anchor-free improvement of the network greatly not only reduces the computational complexity during training, but also improves the network’s full-scale accuracy. We validate our experimental method on the AGVS (Airport Ground Video Surveillance) airport scene detection dataset.
Weakly Supervised Image Matting via Patch Clustering

Image matting aims to extract the accurate foreground opacity mask for a given image. State-of-the-art approaches are usually based on encoder-decoder neural networks and require a large dataset with ground-truth alpha matte to facilitate the training process. However, the alpha matte annotation process is extremely time-consuming and labor-intensive. To lift such a burden, we propose a novel deep learning-based weakly supervised image matting method. It can simultaneously utilize data with and without ground-truth alpha mattes to boost the matting performance. The key idea is to exploit the patch-wise similarity of the alpha mattes without explicitly relying on ground-truth alpha mattes. To this end, we design a novel patch clustering module to cluster patches with similar alpha mattes and subsequently propose a new loss function to supervise the matting network by utilizing the clustering prior. Experimental results show that our proposed method can effectively cluster image patches by their corresponding alpha patches’ similarity and improve the matting performance. To our knowledge, our method is the first to tackle the weakly supervised image matting problem with only trimaps as the annotation.
Local-Fusion Diffusion Model for Enhancing Few-Shot Image Generation

In recent research, few-shot generation models have attracted increasing interest in computer vision. They aim at generating more data of a given domain, with only a few available training examples. Although many methods have been introduced to handle few-shot generation tasks, most of them are usually unstable during the training process and can only generate cookie-cutter images. To alleviate these issues, we propose a novel few-shot generation method based on the classifier-free conditional diffusion model. Specifically, we train an autoencoder on seen categories and then use patch discriminator adversarial training to achieve better reconstruction quality. Subsequently, for the k-shot task, we extract k image features and calculate the conditional information to guide the training generation of the diffusion model. To avoid the singularness of conditional information caused by the prototype model, we use the latest Feature Fusion module (LFM) to learn various features. We conduct extensive experiments on three well-known datasets and the experimental results clearly demonstrate the effectiveness of our proposed method for few-shot image generation.
Referring Expression Comprehension Based on Cross Modal Feature Fusion and Iterative Reasoning

The task of Referring Expression Comprehension is a multimodal task, which involves two different fields: Computer Vision and Natural Language Processing. Specifically, the task is to locate image region that correspond to the description provided in the given a image and a natural language expression. This paper aims to address the problem that the current task can not effectively fuse visual and textual features in the multimodal alignment stage and can not effectively utilize visual and textual formation in the prediction stage. Two improvement measures are proposed: multimodal feature fusion and iterative reasoning based on multimodal attention mechanism. In the multimodal feature fusion stage, three feature fusion modules are used to fuse visual and textual features from different perspectives to obtain rich visual and textual information; in the iterative reasoning stage, visual and textual features are accessed several times to gradually optimize the target prediction region. In order to verify the performance of the proposed method in this paper, a large number of experiments were conducted on three public datasets.
A Novel Global–Local Feature Extraction Method Based on Deep Learning for Football Movement Training

Recently, deep neural network-based methods have been used to solve some intractable hot problems. In addition, image features are extracted by deep learning algorithms, which enables researchers to make great breakthroughs in image retrieval and detection area. The new feature extraction algorithm using deep neural networks can simultaneously extract the low- and high-level semantic information of the image. We propose a novel global—local feature extraction method for football movement training in this article. A global—local feature extraction model is constructed based on the convolutional network structure. The football images in different regions are extracted and different convolution features are obtained. Finally, the similarity of global features is measured to obtain the retrieval results. Experiments on the constructed football image dataset demonstrate that the new method can effectively improve the training accuracy.
NEW FUZZY PARAMETER PROBABILITY DISTRIBUTION

The aim of the study is to obtain a new probabilistic distribution of the random moment T of complete transition of patients from cohort S (susceptible to infection) to cohort R (recovered and cured). Methodology. The distribution is constructed under the assumption that in the SIR Kermack – McKendrick model, the nonlinear system of firstorder differential equations has an approximate solution in the form of logistic functions based on the profile of the normal probability distribution F. The obtained distribution AN(t) has an analytical form and is threeparameter, and all these parameters of the distribution can be represented in a fuzzy form (fuzzified). Thus, T is a fuzzy random variable (FRV) and AN(t) takes the form of a fuzzy function of general form. Results. The form of the distribution function AN(t) and the density distribution an(t) with fuzzy parameters of transition frequencies β and γ from cohort S to cohort I (infected) and from cohort I to cohort R, respectively, are obtained. Conclusion. Similar distributions, given on the time axis t, can be obtained for all other cohorts.
A New Approach for Evaluating Movie Summarization

An important need in many situations involving video collections (archive video search/reuse, personal video organization/search, movies, tv shows, etc.) is to summarize the video in order to reduce the size and concentrate the amount of high value information in the video track. The Movie Summarization (MSUM) track in the annual TRECVID (TREC Video Retrieval Evaluation) benchmark was proposed to evaluate automatic systems working on the movie summarization domain. This track used a licensed movie dataset from Kinolorberedu, in which the goal is to summarize the storylines and roles of specific characters during a full movie. The goal of this track is twofold: Efficiently capture important facts about certain persons during their role in the movie storyline, and assess how well video summarization and textual summarization compare in this domain.
Design of Virtual Map Building Using Unity 3D with MDLC Method

Lampung University does routine activities every new academic year for students, especially for new students college in the Faculty of Engineering, Introduction to Campus Life for New Students (PKKMB), one of the main agendas of PKKMB is introducing every building and public facilities in Civil Engineering, also every building and the room in it. The current state of the COVID-19 pandemic makes the University of Lampung conduct learning activities by network (online).The area and number of buildings' public facilities will be an issue for new students and visitors since it takes longer to find information, explore, and understand the layout of the building environment. To address these issues, there is a need for technology that fulfills the requirements of information, efficiency, and new methods in introducing a building to visitors. These problems are expected to be resolved by developing a virtual 3D map application for Building A, the Dean of the Faculty of Engineering, and Building B the architectural engineering. The Multimedia Development Life Cycle (MDLC) method was used to develop this application. The application testing in this research uses two types of testing namely Alpha and Beta testing with a black box scheme. Alpha testing results show that the app is compatible with Android OS 8.1 to 11.1 and smartphones with 5.45 to 6.4-inch displays. It requires at least 4GB to 8GB RAM According to the Beta Testing the application resulted in an outstanding 90.2% satisfaction rate. It can be concluded that the virtual 3D map application for Dean's Office Building A and Architecture Engineering Building B of the Faculty of Engineering at Lampung University functions effectively and is rated as "Very Good" based on the established criteria and index.
Application of Fake News Detection in Stock Market Analyzer and Predictor Using Sentiment Analysis

In the field of quantitative investment, it has always been a problem to obtain more accurate forecasts of stock price. Analyzing investor sentiment from online emotional text to find out the stock price trend has become a hot issue. However the large amount of fake information used for misguiding investor sentiment on the network may influence the performance of the stock price forecasting model. So we try to use fake news detection to remove these information, which may increase the accuracy of the model. And we successfully improve the performance of the stock price forecasting model with the help of fake news detection, and the improvements of accuracy are from 0.29% to 4.37%.
G3RHW: A Unified Model for the Generation and Removal of Real Rain and Haze Weather

Supervised deep learning is widely used in the field of rain and haze weather removal currently and has achieved excellent results. However, it is challenging to learn the potential relationship between rain and haze image input and clean image output in learning based network training because it is extremely difficult to obtain the matched Noising-Clean image data pairs in the real world for supervised learning model train. To overcome this obstacle, in this paper, we propose a high-order rain and haze degradation process to generate a large number of more realistic and complex rain haze image training pairs, and build an end-to-end unified model G3RHW for rain haze generation and removal based on generative adversarial networks. Extensive experiments demonstrate that the proposed method achieves more superior image detail recovery and the removal of the effect of rain and haze results on both synthetic and realistic dataset than state-of-the-art methods fine-tuned for adverse weather removal.
Comparative Study of ARIMAX and BP Neural Network Models in the Prediction of Spodoptera Frugiperda Occurrence

This study compares the performance of the ARIMAX (Autoregressive Integrated Moving Average model-X) and BP (Backpropagation) neural network models in predicting the quantity of Spodoptera Frugiperda in grassland. The Spodoptera Frugiperda is a crop pest that causes significant losses in agricultural production. Accurately predicting its quantity is crucial for implementing appropriate prevention and control measures. We collected quantity data of Spodoptera frugiperda from 2021 to 2022 in grassland and extracted several exogenous variables that might influence its quantity, such as temperature, humidity, and precipitation. Firstly, we employed the ARIMAX model for prediction, which combines autoregressive, moving average, and integrated methods, while also incorporating the adjustment of the quantity using exogenous variables. Next, we utilized the BP neural network model, which employs the backpropagation algorithm to learn and train the network, in order to capture the nonlinear relationships within the data. By comparing the performance of the two models in quantity prediction, we found that the ARIMAX model demonstrates outstanding performance in short-term forecasting. It accurately captures the trends and periodic variations in quantity. The research findings can provide a scientific basis for pest control in agricultural production and serve as a reference for further improvement of prediction models.
Free-XOR in Card-Based Garbled Circuits

This paper shows a free-XOR technique in card-based garbled circuits. Card-based cryptographic protocols were proposed as a secure multiparty computation using physical cards instead of computers. They can be used when users cannot trust software on computers. Shinagawa and Nuida proposed card-based garbled circuits that compute any Boolean functions using a single shuffle. Their protocol uses $$24g+2n$$ cards, where g is the number of gates and n is the number of inputs. Tozawa et al. reduced the number of cards to $$8g+2n$$ . This paper introduces the free-XOR technique for standard garbled circuits to card-based garbled circuits. It is unnecessary to prepare a garbled table for XOR gates. The number of cards is reduced to $$8g_1+2g_2+2n$$ , where $$g_1$$ is the number of gates other than XOR and $$g_2$$ is the number of XOR gates whose output is used as a final output. The card-based garbled circuits proposed by Shinagawa and Nuida have one restriction the final outputs cannot be used for inputs to the other gates. This paper eliminates the restriction with two different techniques. The second technique uses the idea in free-XOR.
Evaluating STEAM-competence of prospective natural science teachers

Introduction. The article studies the problem of developing STEAM competence in undergraduates pursuing their first degree in natural science education. The authors justify the necessity of introducing the discipline called ‘STEAM-approach in natural science education’ into the curriculum. The purpose of the article is to assess the readiness of future teachers of natural science subjects to implement STEAM education. Materials and Methods. The research methodology follows integrative, complex and contextual principles and the essential ideas of STEAM education aimed at the formation of STEAM-competencies in future natural science teachers. The authors summarized existing approaches to identifying key competencies, developed the structure of STEAM competence and designed the syllabus for ‘STEAM approach in natural science education’ discipline. The academic discipline ‘STEAM approach in natural science education’ was integrated in the curriculum of ‘Biology and Geography’ undergraduate programme and tested in the educational process. Final evaluating procedures which assessed the level of developing the components of STEAM competence included the comparative analysis of data obtained by means of questionnaires of teachers and students and the comparative assessment of some components of the STEAM competence in the control and experimental groups. Experimental work was conducted in secondary education settings in the Republic of Belarus and in the Faculty of Natural Sciences at Belarusian State Pedagogical University named after Maxim Tank. The study involved 33 natural science teachers and 70 undergraduates doing their first degrees in the Faculty of Natural Sciences at BSPU named after Maxim Tank. The methods of theoretical analysis, comparison, questionnaire, testing, and statistical data processing were used in the study. Results. In the course of the research, the need for the formation of STEAM competence in future teachers of natural science subjects is substantiated; a system of their preparation for the implementation of STEAM education is developed and justified. The results of experimental work confirm that the effectiveness of training future natural science teachers to implement STEAM approach is determined by academic disciplines, which involve students in STEAM education. Conclusions. The results obtained prove the effectiveness of developing STEAM competence in future natural science teachers by means of targeted method training provided at the first stage of higher education. Teaching ‘STEAM approach in natural science education’ discipline as one of the elements of future teachers’ method preparation for implementing STEAM education contributes to the improvement of their STEAM competence.
The Mediating Role of Open Innovation in the Relationship Between Organizational Justice and Organizational Pride in Egypt’s Healthcare Sector

Until now, studies in Egypt have not focused on investigating the mediating role of open innovation in the relationship between organizational justice and organizational pride in any sector. This study employed social exchange theory to examine the role of open innovation in the joint relationship between organizational justice and organizational pride. Using Smart PLS and PROCESS macro, the analysis of data obtained from 275 employees in the Egyptian healthcare sector revealed a positive impact of organizational justice on organizational pride. Furthermore, there was a positive impact of organizational justice on the implementation of open innovation. The study also demonstrated the positive mediating role of open innovation in the relationship between organizational justice and organizational pride.
Parameter-free restoration of piecewise smooth images

We propose a novel strategy for the automatic estimation of the two regularization parameters arising in the image decomposition variational model employed for the restoration task when the underlying corrupting noise is known to be additive white Gaussian.In the model of interest, the target image is decomposed in its piecewise constant and smooth components, with a total variation term penalizing the former and a Tikhonov term acting on the latter.The proposed criterion, which relies on the whiteness property of the noise, extends the residual whiteness principle, originally introduced in the case of a single regularization parameter.The structure of the considered decomposition model allows for an efficient estimation of the pair of unknown parameters, that can be automatically adjusted along the iterations with the alternating direction method of multipliers employed for the numerical solution.The proposed multi-parameter residual whiteness principle is tested on different images with different levels of corruption.The performed tests highlight that the whiteness criterion is particularly effective and robust when moving from a single-parameter to a multi-parameter scenario.