Reviewerly/exHarmony huggingface dataset:
The Design, Use and Impact of Cloud Computing During the Covid-19 Crises

The incidence and severity of cybercrime have increased recently, and worries about personal data protection are widespread. Cybercriminals are taking advantage of the COVID-19 pandemic's crucial reliance on cloud computing emerging virtual environments by organizations and individuals. One of the biggest issues is the security of data that is stored and transmitted over the cloud. It is crucial to protect the privacy of data stored by Cloud Service Providers since in the Cloud, private and confidential information is kept on external servers and hidden from Cloud Service Users. Concerns regarding the safety of personal data are pervasive, and cybercrime has lately grown in frequency and severity. The various researchers presented their work of vulnerabilities of the data security over the cloud environment and provides encryption data at rest with symmetric key cryptography where the same secret key is used for encryption and decryption. This research takes a different strategy than that which has previously been suggested by other researches. An application-level security is proposed instead of data at rest encryption to safely store data at rest. In the public cloud computing environment, our developed system guarantees the privacy and confidentiality of data while it is at rest. In order to guarantee data security against hostile infiltration, the system also ensured that data must be safeguarded during, and after a transaction while it is being stored on the cloud storage. It can be inferred from the result that our system, when compared to other symmetric algorithms with less consistent behavior and scatter of value from mean, took less time to perform cryptographic operations.
The Prediction of Cryptocurrency Prices Using Neural Architectures and Sentiment Analysis

Cryptocurrency is the most secure, traceable, and reliable intangible currency because it uses blockchain technology. It eliminates the middle layer of financial institutes in the traditional economic system. Because of high returns in cryptocurrencies, investors and other firms invest a lot of money. But the prices of the cryptocurrencies are uncertain. Prices of cryptocurrencies are influenced by many factors like sentiments, trading volume, and similar. Researchers are doing plenty of work to predict the accurate prices of various cryptocurrencies. However, many of these methods cannot be used in real-time. Several deep learning models such as Neural networks (NN), Long short-term memory (LSTM), and Gated recurrent unit (GRU) have been utilized by researchers for predicting the price of cryptocurrencies and yet, are unable to achieve significant results. This work combines LSTM and GRU with sentiment analysis to precisely estimate bitcoin values. We have used Root means square error (RMSE) to evaluate the model performance with and without sentiments. Empirically, we have compared the results with the other state-of-the-art models and found better results using the proposed hybrid model incorporated with sentiments.
RDED: Recommendation of Diet and Exercise for Diabetes Patients using Restricted Boltzmann Machine

As per World Health Organization, noncommunicable diseases such as untimely birth, heart attacks, diabetes, and cancers are on the upswing. Diet intake that is insufficient or improper is known to cause a wide range of well-being illnesses. Due to the complexity of food components and a large number of dietary sources, it is difficult to select diets that must match one’s nutrition demands in real-time. Because of irrelevant material on proper food, patients are dependent on medicine rather than having precautionary steps in food consumption. Appropriate diet is especially crucial for persons living with chronic conditions and nutritionist food is essential for optimal health. An effective way to prevent disease is to eat a healthy nutritious diet. This study introduces the food and physical activity recommender system, which is capable of providing users with individualized and healthy nutrition recommendations based on their tastes as well as pathological medical data. Prescriptions characterize the ideal patient’s nutrition likes. In this paper, we show how Restricted-Boltzmann Machines, a type of two-layer undirected graphical model, can be utilized to describe ratings of food products. For this simple model, we provide effective learning and inference strategies that would be successfully applied to a food data set with over 100 million user-food ratings. When the predictions of the RBM model are created using different learning rates and several iterations, we attain an error rate of considerably below 0.30 percent in 50 epochs using 100 hidden nodes which fulfills our requirements. Hence, we want patients to use nutritious food rather than taking medicine to avoid an expensive trip to a physician.
Extracting the most important Discrete Cosine Transform (DCT) Coefficients for Image Compression using Deep Learning

Image compression is all about reducing storage costs and making the transmission of huge image files feasible. This paper targets lossy image compression by estimating the most important Discrete Cosine Transform (DCT) coefficients through employing deep learning. DCT basically results in the transformation of an image to the frequency domain from the spatial domain. The first few coefficients, in the frequency domain of a transformed image, have great importance. They are the most informative, while the others are of the least importance. The abilities of Multi-Layer Perceptron (MLP) and Convolutional neural network (CNN) were exploited in order to find a reasonable estimate of important DCT coefficients. The target was to get a deep neural network (DNN) for the compression of digital images that has a reduced number of DCT coefficients that is; higher compression rate and better image quality upon reconstruction and improved generalization ability. To shorten the encoding-decoding time and to fasten the training of our deep neural networks, RELUs and Tangent Sigmoid were used. Experiments performed on a large set of grayscale images shows that only 15 out of 64 total available DCT coefficients result in more than 70% image quality and have a good compression ratio of 4:1. Moreover, the quality of images upon a subjective and objective evaluation of unseen data proves that our proposed MLP achieved better generalization as compared to CNN.
Pengaruh Penggunaan Aplikasi Multi APP.V.1.0 Berbasis Online Terhadap Peningkatan Kompetensi Profesional Guru Pendidikan Agama Islam di SMA Negeri 1 Gebang

The problem of this research is that the teaching staff and students do not have special expertise in implementing multi App.V.1.0 applications. So that learning and teaching activities have not been effective and efficient due to the application of the model in using wireless internet devices so that not all teachers can operate it. Device limitations to access multiple App.V.1.0 applications are also a major problem. The purpose of the study was to determine the use of multi-app applications. V.1.0 based online in improving the competence of professional teachers at SMA Negeri 1 Gebang. The type of research used is quantitative research. The population was 28 students and took a total sampling (100%) of 28 students. Methods of collecting data using questionnaires, observations, interviews and documentation instruments. The data analysis technique used simple linear regression analysis. Results The conclusion in this study is that there is an effect between the use of Multi App.V.1.0 Applications. Online based on increasing professional competence of SMA Negeri 1 Gebang teachers by knowing the percentage of determination of the value of r xy is as follows: 0.471 x 100% = 47.1%. Thus it can be understood that the use of Multi App.V.1.0 Applications. Online-based has contributed 47.1% in one direction to the improvement of teacher professional competence at SMA Negeri 1 Gebang. The teacher's role in the activity of using Multi App.V.1.0 Applications. Online-Based is the teacher as a facilitator and organizer, which regulates how students learn and provides direction for the material in the use of Multi App.V.1.0 Applications. Online-based, which is followed by students, is easy to understand and interpret in terms of learning outcomes.
An Improved Semantic Query Expansion Approach Using Incremental User Tag Profile for Efficient Information Retrieval

The World Wide Web (WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. The Keyword matching mechanism faces word mismatch problems while retrieving relevant information. Furthermore, the inherent ambiguity of short keyword queries demands enhanced methods for Web retrieval. Ontological-based query expansion is one of the primary methods for Web retrieval, and it handles the vocabulary mismatch problem by computing semantics from the ontology knowledgebase. However, the retrieval of information relevant to user interests is a major challenge. In this paper, we seek to improve retrieval performance by leveraging user preferences and ontology semantics in the process of query expansion. The expansion words are added to the user query using WordNet lexicon and domain ontology. Additionally, the search intent of the user is also added as expansion words by exploiting a tag-based user profile. When it comes to obtaining relevant documents, the proposed framework outperforms the keyword-based approach by achieving a 76% F1-score. This noticeable improvement accurately reflects the importance of including user intents in the process of semantic query expansion.
Current Stance on Requirement Prioritization Techniques: Challenges, Opportunities, and Future Directions

Requirement Engineering is an important aspect of Software Engineering, while Requirement prioritization is a crucial phase of Requirement Engineering, specifically in agile development. In today's world, software systems are nothing but the backbone for business organizations. However, for business operations, requirements are changing rapidly, and it seems to be impossible to accomplish the requirements at once. It may be possible with the help of requirement Prioritization as it decreases the risk for failure. It helps implement the most required and prioritized feature given by stakeholders. The entire purpose of this research is to investigate and rate the complete process of Requirement Engineering, specifically the current requirement prioritization techniques, by conducting an extensive literature review. Furthermore, to identify the opportunities, gaps, and challenges for future
The Effectiveness Of Using Word Cards As A Media On Learning Vocabulary For The Tenth Graders Of Ma Nw Sikur

Word cards are exactly the right media for students to enrich students’ vocabulary mastery. To prove this argumentation this study was focus on using word cards on students’ achievement in learning vocabulary in classroom. This study used an experimental design and to collect the data, the present researcher used multiple choices instrument (vocabulary test) to get students’ score in learning vocabulary. In analyzing data, the present researcher used t-test to find out the effectiveness of the word card in teaching vocabulary. The result of analysis data that the t-test was 0.74 and t-table was 0.44 with level of significance (95%) from the total of sample was twenty students. It means t-test was higher than t-table (0.74 0.44). Thus, mean alternative hypothesis (Ha) was accepted, and the null hypothesis (Ho) was rejected. This founding means that there was a significantly effective of using word cards on students’ achievement in learning vocabulary for the tenth grade of MA NW Sikur in the school year 2021-2022.
SISTEM REKOMENDASI TEMPAT WISATA DI BALI MENGGUNAKAN IMDB WEIGTED RATING DAN CHATBOT

The Bali government is trying to re-optimize Bali's tourism potential after the Covid-19 pandemic. To improve the Balinese economy which had declined during the Covid-19 pandemic where foreign tourist visits decreased by 99.93%. In 2022, the tourism industry in Bali finally recovered with an increase in foreign tourist visits by 12.23%. The Bali government needs to optimize the number of foreign tourist visits to re-optimize the tourism sector. One way that can be done is to improve digital-based information services to introduce Bali to tourists. Therefore, to assist the Bali provincial government in optimizing the tourism sector, a recommendation system for tourist attractions in Bali called "MELALI" was created by utilizing the IMDb Weighted Rating algorithm and equipped with a chatbot as a customer service. Based on the results of system testing using Root Mean Square Error (RMSE), the result is 0.0126 (close to zero), this shows that the model made has a small error rate, besides that the chatbot feature has also been able to identify keywords and provide output in accordance with the requested keywords. Overall the system is running well.
Development of physical education class awareness measurement scale for secondary school students after the era of COVID-19 and group classification through latent profile analysis

This study developed physical education class awareness measurement scale(PECAS) for secondary school students after the era of COVID-19, and latent profile analysis(LPA) was applied to explore how many profiles the responding secondary school students were classified. WINSTEPS 5.6.3 program was used for the analysis the PECAS, and the Mplus 8.2 program was used for the LPA. 600 secondary school students(300 middle school students, 300 high school students) were surveyed. The results were as follows. First, based on literature review and consultation of content and measurement experts, the 25 items and 4-point rating scale was generated. Second, by applying the Rasch rating scale model, the validation of response category was verified, and the differential item functioning were verified by gender and grade. The PECAS was finally developed for secondary school students, consisting of the 19 items and 4-point rating scale. Third, the PECAS was confirmed that no problems in measuring secondary school students awareness of physical education classes utilizing the person and item map in Rasch model. Fourth, after the perception of physical education classes in secondary schools was classified into three potential classes, and according to the characteristics of each latent class, they were named ‘negative physical education class’, ‘neutral physical education class’, and ‘positive physical education class.’
Aplikasi Realitas Tertambah untuk 12 Gerakan Pengaturan Lalu Lintas

In order to guarantee the safety, security, order, and smoothness of traffic, it is necessary to carry out traffic management which includes the activities of planning, regulating, supervising, and controlling traffic. However, learning about traffic science, including the science of traffic control movements, is still very minimal among the public. Meanwhile, knowledge of traffic control movements is important for the public to understand in order to create a safe and orderly atmosphere when driving on the road, and to reduce the potential for traffic accidents. So a research was made that could develop an application for recognizing 12 traffic control movements by applying augmented reality technology to the public in an easy-to-understand way. The application developed uses augmented reality technology with the image target method. Application development is carried out using the Multimedia Life Cycle (MDLC) development method. This research succeeded in producing an application that is able to introduce 12 traffic control movements by applying augmented reality technology to the public in an easy-to-understand way.
Application of Augmented Reality and 3D Animation in School Promotion Media

SMK Negeri Pertanian Pembangunan Kalasey, located in Kalasey Satu, Kec. Mandolang, Kab. Minahasa, North Sulawesi Province, currently still uses printed media as a school promotion media. However, there are limitations of printed media in describing the school environment and facilities in detail due to visualization that is limited to text and images, so media that can provide information in a more interactive and detailed manner is needed. Through the advancement of multimedia technology, such as augmented reality and 3D animation, these two technologies can be utilized to complete detailed information that has not been included in school promotional media. The purpose of this research is to produce augmented reality and 3D animation applications that will be used as school promotional media to the public. This research uses the Multimedia Development Life Cycle (MDLC) method which consists of 6 stages and marker based tracking method to detect marker. Based on the results and testing conducted, the conclusion is that augmented reality and 3D animation can be used as a school promotional media in introducing school facilities and infrastructure and to provide a real picture of the school environment that is easy to understand by prospective new students.&#x0D; Key words— 3D Animation; Augmented Reality; Marker Based Tracking; School Promotion Media
A Bibliometric Analysis of Research on Knowledge Management in Tax Administration

Knowledge Management (KM) is crucial in the public sector, especially for tax administration. The aim of this study is to review the literature of KM in tax administration and outlining a future research agenda. This study uses bibliometric approach with data collected from the international academic database such as Proquest, Science Direct, Scopus, and Google Scholar. Finally, 83 articles from a variety of academic journals specializing in the field of Knowledge Management and Tax Administration used in this study. The result found that Iran and UK lead the world regarding the number of research publications in the area KM in tax administration. Considering the publication period, 2019 and 2020 are the most productive years regarding publication in this field. The analysis of the co-occurrence network of the literature resulted four clusters represent issue such as: (i) emerging information and communication technology (ICT) support KM implementation, (ii) critical success factor (CSF) and investigation the relation of KM process toward organization’s performance, (iii) knowledge management framework and its relation toward tax knowledge, and (iv) knowledge sharing in KM implementation. This study is the first study that explore KM literature within tax administration by using bibliometric
A Literature Review of Knowledge Management Role in Employee Performance

This review aimed to discuss current literatures of Knowledge Management (KM) role in employee performance. This paper reviewed articles about the effect of KM on performance from several dif- ferent countries. The result of the study shows that all article reviewed have the same same conclusion that there is a positive relationship between KM and employee performance. The important findings suggest that KM is a major indirect factor of organizational performance. Also, KM occupies an impor- tant role in forming an effective platform for learning organizations. However, how knowledge man- agement influences competitive advantage such as knowledge, behavior, attitude, and its influence on performance had not attract the attention of researchers. This study suggests future studies to include intervening variables such as competitive advantage, knowledge, behavior, and attitude, to gain a deeper understanding of knowledge management’s role in improving employee performance..
Analysis of Disorder Structure in Surface Dyslexia Whose LARC Errors Appear in Real Words and Lexicalization Errors Appear in Nonwords

単純ヘルペス脳炎によって表層失読を呈した語義失語例を報告する。本例の障害機序を検討するため, 漢字刺激を用いた実在語と非語の音読課題, 文字判断課題, 語彙判断課題を実施した。その結果, 実在語の音読成績には頻度および一貫性効果を認め, LARC エラーが多かった。非語は正答数が多いものの, 一部の刺激にて語彙化錯読が認められた。誤反応であっても過半数では 1 文字目は正しく音読した。文字判断や語彙性判断課題では, 非文字や非語を日本語にあると反応する誤りが多かった。二重経路モデルから本例の障害機序を分析すると, 実在語では意味システムの障害により LARC エラーが出現したと考える。 非語では文字認知や語彙判断の障害により語彙ルートが誤って活性し, 1 文字目の音から喚起された語を表出することで語彙化錯読が出現したと考える。
Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data

Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training. In this paper, we propose a frustratingly simple but effective step-wise learning framework to gradually enhance the capability of the model in detecting all categories of long-tailed datasets. Specifically, we build smooth-tail data where the long-tailed distribution of categories decays smoothly to correct the bias towards head classes. We pre-train a model on the whole long-tailed data to preserve discriminability between all categories. We then fine-tune the class-agnostic modules of the pre-trained model on the head class dominant replay data to get a head class expert model with improved decision boundaries from all categories. Finally, we train a unified model on the tail class dominant replay data while transferring knowledge from the head class expert model to ensure accurate detection of all categories. Extensive experiments on long-tailed datasets LVIS v0.5 and LVIS v1.0 demonstrate the superior performance of our method, where we can improve the AP with ResNet-50 backbone from 27.0% to 30.3% AP, and especially for the rare categories from 15.5% to 24.9% AP. Our best model using ResNet-101 backbone can achieve 30.7% AP, which suppresses all existing detectors using the same backbone. Our source code is available at https://github.com/dongnana777/Long-tailed-object-detection.
StyleLipSync: Style-based Personalized Lip-sync Video Generation

In this paper, we present StyleLipSync, a style-based personalized lip-sync video generative model that can generate identity-agnostic lip-synchronizing video from arbitrary audio. To generate a video of arbitrary identities, we leverage expressive lip prior from the semantically rich latent space of a pre-trained StyleGAN, where we can also design a video consistency with a linear transformation. In contrast to the previous lip-sync methods, we introduce pose-aware masking that dynamically locates the mask to improve the naturalness over frames by utilizing a 3D parametric mesh predictor frame by frame. Moreover, we propose a few-shot lip-sync adaptation method for an arbitrary person by introducing a sync regularizer that preserves lip-sync generalization while enhancing the person-specific visual information. Extensive experiments demonstrate that our model can generate accurate lip-sync videos even with the zero-shot setting and enhance characteristics of an unseen face using a few seconds of target video through the proposed adaptation method.
Rapid Network Adaptation:Learning to Adapt Neural Networks Using Test-Time Feedback

We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the shift, we create a closed-loop system and make use of test-time feedback signal to adapt a network on the fly. We show that this loop can be effectively implemented using a learning-based function, which realizes an amortized optimizer for the network. This leads to an adaptation method, named Rapid Network Adaptation (RNA), that is notably more flexible and orders of magnitude faster than the baselines. Through a broad set of experiments using various adaptation signals and target tasks, we study the generality, efficiency, and flexibility of this method. We perform the evaluations using various datasets (Taskonomy, Replica, ScanNet, Hypersim, COCO, ImageNet), tasks (depth, optical flow, semantic segmentation, classification), and distribution shifts (Cross-datasets, 2D and 3D Common Corruptions) with promising results.