Reviewerly/exHarmony huggingface dataset:
Multi-directional rain streak removal based on infimal convolution of oscillation TGV

Image deraining is essential in both image processing and computer vision. However it is still challenging to remove rain streaks, due to the fact that they are complex and usually have multiple directions. In this paper, we propose a novel infimal convolution type functional based model to remove multi-directional rain streaks. Specifically, we employ total generalized variation (TGV) to represent the rain-free background and m-fold infimal convolution of oscillation TGV (ICTGVmosci) to capture the rain streaks with m different directions. The existence of solutions to the proposed model is proved. We also design an algorithm using the classical first-order Primal–Dual method for solving the proposed model. Numerical experiments on both synthetic and real images demonstrate that the proposed model outperforms the state-of-the-art methods quantitatively and qualitatively.
Data Mining Menggunakan Algoritma K-Means untuk Klasifikasi Penyakit Infeksi Saluran Pernafasan Akut

Acute Respiratory Infection (ISPA) is. Diseases that can attack one or more parts of the respiratory tract, from the nose (upper tract) to the alveoli (lower tract) including adnexal tissues such as sinuses, middle ear cavity, and pleura. And ISPA is also a type of infectious disease, especially to people who have abnormalities in the immune system, the elderly, and children whose immune systems are not yet fully formed. ISPA is a contagious disease in the world caused by the main cause of morbidity and mortality. And ISPA can reach four million people die every year and 98% of them are caused by lower respiratory tract infections. In Indonesia even ranks first cause of death in children and adults. ISPA in Indonesia ranks first cause of death in children and adults. ISPA also occupies the list of 10 most diseases. To get precise and fast accuracy in classifying the symptoms of ISPA. In this study, there were 250 patient data sourced from the Rahmatan Lil Alamin Clinic. Furthermore, the data is processed using RapidMiner software. Produced 3 clusters, namely cluster C1 (Ordinary ISPA) with 141 members, cluster C2 (Medium ISPA) with 17 members, C3 (severe ISPA) cluster with 7 members. . The test results for cluster 1 get 62 data sets, cluster 2 get 60 data sets and cluster 3 get 43 data sets. because the number of data sets from cluster 3 is smaller, the cluster can be called optimal.
An Analysis Model of Potential Topics in English Essays Based on Semantic Space

&lt;p&gt;With the reform of English examination in China in recent years, the automatic evaluation of English essays as subjective questions has always been the focus and difficulty of research. The existing automatic essay evaluation system (AEE) has obtained good feedback on the vocabulary, syntactic and other features of English essays, but there is still a problem of low accuracy in the analysis of potential topic in English essay. In order to solve this problem, this paper takes relational triples as the carrier to analyze the potential topics of English essays. By constructing the hierarchical topic trees hybrid semantic spaces to carry out topic clustering, distributed representation of topic relational triples and topic set extension in English essays. Then, based on the improved on-topic analysis algorithm in this paper, the paper analyzes the topic of English essay in multiple dimensions to obtain more abundant potential on-topic semantic information. The experiment results show that the proposed model can reduce the noise caused by non-topic words effectively, and improve the fine-grained topic semantic space in English essays, and the proposed model has better performance than the current methods of on-topic analysis in English esssays.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
SUNIT: multimodal unsupervised image-to-image translation with shared encoder

Image-to-image translation that refers to the synthesis task of transferring images from the source domain to the target domain has gained significant progress in recent times. Multimodal image-to-image translation aims to generate images with multiple styles of target domains. However, the existing multimodal image-to-image translation network architectures are incapable of accurately transferring the style of a specified image. Moreover, they require an additional deep encoder network to extract the image style code, which increases the network parameters. To address this problem, we propose Sunit, a multimodal unsupervised image-to-image translation with a shared encoder. Sunit shares an encoder network between the discriminator and style encoder. This method reduces the number of network parameters and uses the information from the discriminator to extract the style. Furthermore, we design a training strategy in which the style encoder solely uses the style reconstruction loss and does not follow the generator to train. In this manner, the target of the style encoder becomes clearer. Finally, extensive experimental validations are carried out on the AFHQ and Celeb-HQ datasets. The results demonstrate that our approach outperforms the state-of-the-art methods in the task of reference-guided image translation and transfers the style of the specified image more accurately.
Data characteristics aware prediction model for power consumption of data center servers

Summary Due to the rapid increase in the number and scale of data centers, the information and communication technology (ICT) equipment in data centers consumes an enormous amount of power. A power prediction model is therefore essential for decision‐making optimization and power management of ICT equipment. However, it is difficult to predict the power consumption of data centers accurately due to the complex power patterns and nonlinear interdependencies among components. Existing methods either rely on standard formulas, or simply treat it as time series, both leading to poor power prediction accuracy. To overcome those limitations, in this article, we present a systematic power prediction framework called characteristic aware attention‐augmented deep learning‐based prediction method. In particular, we first analyze the different power consumption series to illustrate their different temporal characteristics. Second, we perform different data processing for the corresponding characteristics of power series samples. Third, we propose an accurate and efficient neural network model to predict future power consumption with the pretreated data. The experimental results show that the proposed model is able to achieve superior prediction accuracy.
PERFECT CODES OVER HURWITZ INTEGERS INDUCED BY CIRCULANT GRAPHS

In this paper, a new family of t−error correcting perfect codes over Hurwitz integers is presented. To obtain these perfect codes, the perfect t−dominating sets over the circulant graphs are used. The codewords of such perfect codes are generated by the elements of a subgroup of the considered group.
Students’ Perceptions of Learning Platform: The Advantages and Disadvantages of Quipper School

Quipper School is an online learning platform which consists of two portals, namely Learn for students and Link for teachers This research aimed to investigates the students‟ perception on the advantages and disadvantages of Quipper School platform in learning English. This research was conducted at SMAN 3 Mandau, and the sample was 52 students of the eleventh grade. This research used quantitative research, and the instrument is questionnaire. The questionnaire consists of 20 questions, divided into 10 questions of the advantages and 10 questions of the disadvantages. The results of this study were processed quantitatively using the Likert scale formula. The finding revealed the index advantages of 1st statement to the 10th statement were about between 60%-75%. The result indicates that students completely agree with the 10 questions of the advantages of Quipper School‟s. Equally important, the researcher also found that the students were dominant disagree with the 9 questions and were agree of 1 question of disadvantages of Quipper School‟s with the index 52%-61%. In conclusion, the students of SMAN 3 Mandau dominant disagree with the disadvantages of Quipper School and they assumed that Quipper School is an online learning platform that suitable for teachers and students in the learning process, especially in learning English.
Dynamic Monitoring of Football Training Based on Optimization of Computer Intelligent Algorithm

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied.
Video Face Detection Technology and Its Application in Health Information Management System

Computer face detection, as an early step and prerequisite for applications such as face recognition and face analysis, has attracted people's attention for a long time. With the popularization of computer applications, the improvement of performance, and the gradual maturity of research in the field of image processing and pattern recognition, face-related applications have become more and more a reality, so the research on face detection and positioning is also receiving more and more attention. Face detection and positioning are an important part of face analysis technology. Its goal is to search for the location of facial features (such as eyes, nose, mouth, and ears) in images or image sequences. It can be widely used in the fields of face tracking, face recognition, gesture recognition, facial expression recognition, head image compression and reconstruction, facial animation, etc. Based on the health information management system, this study mainly discusses the application of face recognition technology in video systems. Compared with other biological characteristics, such as fingerprints and eye masks, human faces are easier to obtain. In research and exploration, stable and effective face detection and face recognition algorithms have been proposed, which can achieve good recognition results even in real-time video surveillance. Aiming at the automatic face recognition technology in video surveillance, this study introduces in detail the video face detection technology in the health information management system of video image collection, image preprocessing, face detection, and face recognition. The prototype system of hygiene management is recognized.
DiFLUsion: A new spatiotemporal early warning system for HPAI

S130reflecting the number of individuals shedding the virus ranged from 9.3E + 10 copies/day/1,0 0 0 people to 8.2E + 8 copies/day/1,0 0 0 people.Since the end of May, WWS tested negative to SARS-CoV-2 detection.Conclusion: According to the epidemiological features of the first wave of SARS-CoV-2 epidemic in Lombardy, the highest amount of SARS-CoV-2 RNA was detected in WWS collected in the areas most affected by .This optimized workflow of WWS surveillance can help assessing the real number of individuals -both symptomatic and asymptomaticable to spread the virus and appraising the effect of preventive measures.
Application of the Artificial Intelligence Algorithm in the Automatic Segmentation of Mandarin Dialect Accent

In recent years, as the research objects of phonetics have expanded to accent and colloquial natural speech, the construction of the dialect accent Mandarin voice database has become another important research direction in the field of computer technology. Among them, voice segmentation is a time-consuming and laborious link in the construction of the voice database. The application of artificial intelligence technology helps to improve the construction efficiency of the Mandarin dialect voice database. Based on this, this article mainly researches the application of the artificial intelligence algorithm in the automatic segmentation of dialect accent Mandarin. This paper constructs a voice corpus of dialect accents and Mandarin Chinese and specifically describes the construction process of the voice corpus. This paper uses artificial intelligence algorithms, combined with the HMM (hidden Markov model), and Viterbi algorithm to propose a new method of automatic speech segmentation. This paper studies the automatic speech segmentation model, extracts the general parameters of the training data in the Mandarin corpus, and conducts HMM training. This paper conducts tests based on the voice of the test set to verify the accuracy of the method proposed in this paper. The experimental results show that, in the speech data of 60 people, the error range of each sentence time period is less than 5 ms accounting for 79.16%, less than 10 ms accounting for 82.96%, less than 20 ms accounting for 83.14%, and less than 50 ms accounting for 86.92%. It can be seen that the algorithm proposed in this paper can meet practical applications in automatic speech segmentation.
Giant Panda Video Image Sequence and Application in 3D Reconstruction

Giant panda 3D reconstruction technology plays an important role in the research of giant panda protection. Through the analysis of giant panda video image sequence (GPVS), we prove that it has the long-range–dependent characteristics. This article proposes an algorithm to accurately reconstruct the giant panda 3D model by using the long-range–dependent characteristics of GPVS. First, the algorithm uses a skinned multi-animal linear model (SMAL) to obtain the initial 3D model of giant panda, and the 3D model of the single-frame giant panda image is reconstructed by controlling shape parameters and attitude parameters; then, we use the coherence information contained in the long-range–dependent characteristics between video sequence images to construct a smooth energy function to correct the error of the 3D model. Through this error, we can judge whether the 3D reconstruction result of the giant panda is consistent with the real structural characteristics of the giant panda. The algorithm solves the problem of low 3D reconstruction accuracy and the problem that 3D reconstruction is easily affected by occlusion or interference. Finally, we realize the accurate reconstruction of the giant panda 3D model.
Linear fusion approach to convolutional neural networks for facial emotion recognition

Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model ) and non-pre-trained model convolutional neural networks (CNNs) for the task of image classification. The VGG19 network and convolutional network derive two channels of expression related characteristics from the facial grayscale images. The linear fusion algorithm calculates the class by taking an average of each classification decision on training samples of both channels. Final recognition is calculated using convolution neural network architecture followed by a softmax classifier. Seven basic facial emotions (BEs): happiness, surprise, anger, sadness, fear, disgust, and neutral facial expressions can be recognized by the proposed algorithm, The average accuracies for standard data set’s “CK+,” and “JAFFE,” 98.3 % and 92.4%, respectively. Using a deep network with one channel, the proposed algorithm can achieve well comparable performance.
Mobile recommender system based on smart city graph

&lt;span&gt;Mobile recommender systems have changed the way people find items, purposes of intrigue, administrations, or even new companions. The innovation behind mobile recommender systems has developed to give client inclinations and social impacts. This paper introduces a first way to build a mobile recommendation system based on smart city graphs that appear topic features, user profiles, and impacts acquired from social connections. It exploits graph centrality measures to expand customized recommendations from the semantic information represented in the graph. The graph shows and chooses graph algorithms for computing chart centrality that is the center of the mobile recommender system are exhibited. Semantic ideas, for example, semantic transcendence and likeness measures, are adjusted to the graph model. Usage challenges confronted to settle execution issues are additionally examined.&lt;/span&gt;
Distance Education and Human Resource Development: An Empirical Review

Distance education has been justified by many researchers to augment the development of human resources. This paper aimed to find out the extent to which distance education contributes to human resource development around the globe. The uniqueness of this study stem from its focus on bringing together on one platform differing empirical findings on distance education and human resource development. The study sought to synthesize the various studies focusing on the degree to which distance education influence human resource development. The rationale for this approach was to make it easier for decision makers and practitioners interested in such information to have unfettered access to varieties of opinion to inform strategic decision making. The paper explored empirical studies that have examined the relationship between distance education and human resource development. In all, 94 articles were consulted, however, 63 were analyzed qualitatively, using thematic analysis. These 63 articles were considered eligible based on their relevance to the study and for the fact that they were written in English Language. The review found the contribution of distance education to human resource development in areas of improved educational and professional capabilities of individuals, increased employability, income, standards of living and reduced marginalization. The review however, did not find any national policy framework geared towards the funding of distance education. The implication is that government and nations which have established distance education as an alternative mode of providing higher education should as a matter of importance focus on the establishment and implementation of national policies addressing issues of funding, technological innovation and the efficient running of distance education.
Resource Allocation in Cloud

Abstract: A cloud environment is the popular shareable computing environments where large number of clients/users are connected to the common cloud computing environment to access the resources and the services. The presented work is focused on the concept of effective resource allocation, de-allocation and reallocation in a cloud environment. To present the concept, we have taken a cloud environment with multiple clouds along with multiple virtual machines. All the machines are homogenous. These all clouds are assigned by a specific priority. Now as the user request arrive, it performs the request to the priority cloud under its requirements in terms of memory &amp; processor capabilities. When the client stops the task then the service allocated to the client is released &amp; same can be reallocated to another client in the waiting. We can also some migration work so that if a cloud is under utilize then its services are migrated to nearest cloud having sufficient utilization. Hence the work provides efficient allocation, de-allocation and reallocation of cloud services with minimum need for migration of service from one cloud to another. Keywords: Resource Allocation, Resource Scheduling, Resource Migration
Sistem Informasi Sistem Informasi Penyewaan Bus Pariwisata Berbasis Web Pada PT Asli Prima Inti Karya

The existence of tourism facilities and infrastructure are elements to facilitate the process of tourism activities. With the facilities and infrastructure, it is hoped that tourism activities can run smoothly. Among the facilities in tourism activities are transportation/tour buses, travel operators, travel agents, restaurants, hotels/inns, tourist attractions, and tourist attractions. PT Asli Prima Inti Karya is a company engaged in tourism bus rental services, located in Pandeglang Regency, precisely in Labuan, Banten province. PT Asli Prima Inti Karya organizes several tour package and travel agency activities. The conventional bus rental system did not initially cause problems in terms of service. Along with the increasing demand for rental, it takes a complete service so that customers can be served well. The author analyzes the problem and makes a bus rental application design as a solution to the problems in serving customers who will rent buses. The application design uses UML, and uses the Sublime Text 3 programming language, PHP, XAMPP Version 3.2.4, and the database used is MySQL. The results of the application test show that the existence of this information system can make it easier for tenants to carry out the bus rental process.
Generative Adversarial Network Based on Multi-feature Fusion Strategy for Motion Image Deblurring

&lt;p&gt;Deblurring of motion images is a part of the field of image restoration. The deblurring of motion images is not only difficult to estimate the motion parameters, but also contains complex factors such as noise, which makes the deblurring algorithm more difficult. Image deblurring can be divided into two categories: one is the non-blind image deblurring with known fuzzy kernel, and the other is the blind image deblurring with unknown fuzzy kernel. The traditional motion image deblurring networks ignore the non-uniformity of motion blurred images and cannot effectively recover the high frequency details and remove artifacts. In this paper, we propose a new generative adversarial network based on multi-feature fusion strategy for motion image deblurring. An adaptive residual module composed of deformation convolution module and channel attention module is constructed in the generative network. Where, the deformation convolution module learns the shape variables of motion blurred image features, and can dynamically adjust the shape and size of the convolution kernel according to the deformation information of the image, thus improving the ability of the network to adapt to image deformation. The channel attention module adjusts the extracted deformation features to obtain more high-frequency features and enhance the texture details of the restored image. Experimental results on public available GOPRO dataset show that the proposed algorithm improves the peak signal-to-noise ratio (PSNR) and is able to reconstruct high quality images with rich texture details compared to other motion image deblurring methods.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;