Reviewerly/exHarmony huggingface dataset:
Model Kerjasama Antar Perpustakaan Umum Dalam Negeri

&#x0D; &#x0D; In order to produce energy in the pursuit of achievement, public libraries that are open to all citizens must collaborate and network. It enables broad access to collection, better user and technical services, increased resource sharing activities, decreased duplication, and the creation of an effective service. In order to achieve the establishment of collaboration and networking in the setting of public libraries, a foundation should take a number of factors into account.&#x0D; Keywords: Public Library, Library Cooperation, Library Networking
Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

With the development of online travel services, it has great application prospects to timely mine users’ evaluation emotions for travel services and use them as indicators to guide the improvement of online travel service quality. In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the sentiment classification based on the Capsule Network and sentiment lexicon (SCCL). SCCL model aims at the lack of consideration of local features and emotional semantic features of the text in the language model. And make following improvements to the shortcomings. Capsule Network (CapNet) is introduced to extract local features while retaining good context features. On the other hand, the sentiment lexicon is introduced to extract the emotional sequence of the text to provide richer emotional semantic features for the model. To enhance the universality of the sentiment lexicon, the improved Semantic Orientation Pointwise Mutual Information (SO-PMI) algorithm based on Term Frequency-Inverse Document Frequency (TF-IDF) is used to expand the lexicon, so that the lexicon can also perform well in the field of online travel reviews.
Improved Whale Optimization Algorithm Based on Halton Sequence

Since standard whale optimization algorithms doesn’t have global and conflict-free search capabilities, an improved WOA based on the Halton Sequence (HGS-WOA) is proposed. The Halton sequence is introduced in the population initialization stage to obtain a more uniformly distributed search population; meanwhile, an adaptive weighting determinant is included in the whale position update strategy to improve the search algorithm as a whole by dynamically altering the effects of ideal and undesirable conditions; the variable cosine control coefficient is used to prevent the algorithm from adapting to local optimal solutions and to avoid low accuracy of the search of the algorithm in the future. With the help of simulation experiments on six optimization problems of complex high-dimensional functions, it is shown that HGS-WOA has excellent computational results.
Bank Locker Security System using Machine Learning with Face and Liveliness Detection

One of the main problems that financial systems are currently facing is ensuring the security of transactions. Banks from all over the world spend a significant amount of money using biometric authentication of consumers because it is convenient and widely used. Particularly in offline settings where digital selfies and ID document facial photos are matched. In reality, nowadays, more extensive programs like automatic immigration control also use selfie-ID comparisons. Limiting the discrepancies between comparative facial photos given their various origins is the procedure's greatest challenge. We suggest a unique architecture based on deep features derived by two well-referenced convolutional neural networks for the cross-domain matching problem (CNN). The results from the data collection, known as Face Bank, show that the proposed face-to-face comparison problem is strong and should be included into actual banking security systems with more than 93% accuracy. Keywords: Face Bank, Convolutional Neural Networks (CNN), automatic immigration control, digital selfies, face to face comparison problem.
Human Resource Management Practices and Job Performance in UAE: The Mediation Role of Employee Motivation

This study assessed the mediating role of employee motivation on the relationship between Human Resource Management (HRM) practices and job performance in the United Arab Emirates (UAE) banking sector. To achieve this aim, data was garnered using questionnaires and quantitative analytical methods were applied to test the hypotheses. The researcher used statistical tools such as descriptive statistics, bootstrapping technique, path analysis and Standardized regression coefficient to analyze data for the research study. A sample of 450 employees out of 13,960 people were utilized in the survey from three banks in UAE (HSBC Bank, Abu Dhabi commercial bank, and Emirates Islamic Bank). A survey descriptive research design, stratified and simple random sampling strategies were as well used to gather information for the rational study. The results revealed that employee motivation partially mediates the effect of HRM practices towards job performance of the staff in these banks. Hence, it is recommended that the HR manager should not only focus on HRM practices but also on certain factors that motivate the staff of the bank, such as bonuses, monetary and non-monetary incentives, increase salaries according to performance, attempting to understand and operationalize the intrinsic motivation parameters of employees so that they can be efficient and effective at work places.. In addition, implementing modern training programs become vital in the banking industry to ensure that banks’ employees use their skills in favor of better banking services for the clients and to allow development to blossom.
Modeling SO2 degassing of the 1990-1992 unrest in Mount Merapi using telemetric monitoring data

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Situmorang Roni Marudut, Humaida Hanik, Harijoko Agung; Modeling SO2 degassing of the 1990-1992 unrest in Mount Merapi using telemetric monitoring data. AIP Conference Proceedings 30 January 2023; 2613 (1): 020002. https://doi.org/10.1063/5.0119398 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Automatic face classification for marking attendance

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Suvathi Devaraj, S. Senthil Kumar; Automatic face classification for marking attendance. AIP Conference Proceedings 30 January 2023; 2523 (1): 020152. https://doi.org/10.1063/5.0112899 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A study on virtual reality equipment enhancing the gaming and sports experience amongst the users

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Sanket Joshi, Raghav Sandhane; A study on virtual reality equipment enhancing the gaming and sports experience amongst the users. AIP Conference Proceedings 30 January 2023; 2523 (1): 020115. https://doi.org/10.1063/5.0110612 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Regulating and adopting crypto currency, is it feasible? A survey-based study

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Jatin Shewale, Pradnya Purandare; Regulating and adopting crypto currency, is it feasible? A survey-based study. AIP Conference Proceedings 30 January 2023; 2523 (1): 020127. https://doi.org/10.1063/5.0110646 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Classifying the sentiment polarity of Amazon mobile phone reviews and their ratings

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation T. K. Venkataraman, Anil Jadhav; Classifying the sentiment polarity of Amazon mobile phone reviews and their ratings. AIP Conference Proceedings 30 January 2023; 2523 (1): 020123. https://doi.org/10.1063/5.0110605 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
High-speed operation, an FPGA-based convolutional neural network by pipelined accelerator

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation K. Umapathy, E. Sivanantham, P. Epsiba, B. Gopi, S. Mohan Kumar; High-speed operation, an FPGA-based convolutional neural network by pipelined accelerator. AIP Conference Proceedings 30 January 2023; 2523 (1): 020059. https://doi.org/10.1063/5.0110693 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Elegant detection of human voice

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation B. Sarala, I. Sowmy, M. Suganthi, R. Hemalatha; Elegant detection of human voice. AIP Conference Proceedings 30 January 2023; 2523 (1): 020023. https://doi.org/10.1063/5.0110587 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
DEVELOPMENT OF THE DIGITAL STORY BOOK ’LALU DIA LALA JINIS’ AS A LEARNING MEDIA FOR 5TH GRADE ELEMENTARY SCHOOL STUDENTS

The Sumbawa folklore book is currently a story book whose existence is not well identified. This can be proven by the fact that there are not many Sumbawa folklore books that can be used in learning at school or stored in the national library. In this regard, this study aims to design a digital storybook model, determine readability and determine the feasibility of digital Sumbawa folk stories that were tested on fifth grade students at Gusus 2 Elementary School, Sumbawa Besar. The method used in this study is Research and Development using the ADDIE model. The results of each stage of the research are detailed as follows; 1) at the analysis stage from the observations it was found that the use of folklore books was limited to limited printed textbooks and had not yet used storybooks in digital form, 2) at the design stage an update of the storybook model was produced from the printed version to the digital version as well as improving the display quality such as improvements visual, 3) at the development stage changes the shape of the cover image, adding color to the character of the character which was previously black and white, adjusting the vocabulary and story content which is then assessed by media experts and material experts with a feasibility level of 92.5% and 82.5% , 4) in the implementation stage which involved respondents from teachers and students produced a feasibility level of 91% and 98%, 5) the final stage, namely the evaluation stage by reviewing all the results obtained so that it can be concluded that the use of Sumbawa folk digital story books is very feasible for used as a learning medium in 5th grade elementary school Group 2 Sumbawa Besar.
Penentuan Kelayakan Masyarakat Miskin Penerima Bantuan Menggunakan Metode Naïve Bayes (Studi Kasus: Kabupaten Penajam Paser Utara)

Contents Poverty is the inability to meet the necessities of life, such as food, clothing, and shelter. The poor have an average monthly per capita expenditure below the poverty line. The case of poverty in Indonesia is still unresolved; the Government continues to try to give the best to the entire community so that the problem of poverty can at least continue to decrease. One form of government concern for the poor is the assistance program provided to the poor. This study will classify based on data from the North Penajam Paser (PPU) community obtained from the results of the National Socio-Economic Survey (Susenas) to know how the Naïve Bayes method is in determining the eligibility of the poor recipients of assistance. Based on the research that has been carried out, a system for determining the poor recipients of assistance is produced, where the test results get the highest accuracy in the third scenario, namely 60% or 328 training data and 40% or 218 test data, where the accuracy obtained is 77.98%.
USV Target Interception Control With Reinforcement Learning and Motion Prediction Method

In this paper, an unmanned surface vehicle (USV) target interception problem is studied with reinforcement learning (RL)-based method. In the proposed new structure, the proximal policy optimization (PPO) and proportional derivative (PD) are combined. First, the PD controller is used to predict the interception position. Then, the PPO algorithm is trained to control the USV, so that it can move quickly to the predicted position. By comparing with the traditional PPO algorithm, the simulation results verify that the proposed algorithm spends less time solving the problem of the USV interception of a moving target.
Brati: Bidirectional Recurrent Attention for Time Series Imputation

Missing data is a relevant issue in time-series tasks, difficulting any analysis. There are several solutions to deal with this problem, being imputation the most popular solution. Imputation is the process of estimating the missing values as accurately as possible. This paper proposes BRATI, a Deep Learning method for imputation in multivariate time series that combines Attention mechanisms and Recurrent layers to process temporal dependencies and feature correlations in long and short terms. The model consists of a weighted combination of two imputation blocks: in each block the recurrent layers process the time series in one direction, aided by the attention layers to solve long-term temporal dependencies. We have evaluated the proposed architecture on three real-world datasets using three different scenarios: imputing values that are missing in a random manner; imputing values that are missing in a sequence of fixed length; and imputing missing values in sequences of random length. We chose the missing sequence problem in addition to the more common random one, because after analyzing the selected datasets, this was the most prevalent occurrence. The experimental results showcase the capabilities of BRATI, outperforming the current state-of-the-art methods to impute time-series on the evaluated scenarios.
Preserving the autocovariance of texture tilings using importance sampling

Abstract By‐example aperiodic tilings are popular texture synthesis techniques that allow a fast, on‐the‐fly generation of unbounded and non‐periodic textures with an appearance matching an arbitrary input sample called the “exemplar”. But by relying on uniform random sampling, these algorithms fail to preserve the autocovariance function, resulting in correlations that do not match the ones in the exemplar. The output can then be perceived as excessively random. In this work, we present a new method which can well preserve the autocovariance function of the exemplar. It consists in fetching contents with an importance sampler taking the explicit autocovariance function as the probability density function (pdf) of the sampler. Our method can be controlled for increasing or decreasing the randomness aspect of the texture. Besides significantly improving synthesis quality for classes of textures characterized by pronounced autocovariance functions, we moreover propose a real‐time tiling and blending scheme that permits the generation of high‐quality textures faster than former algorithms with minimal downsides by reducing the number of texture fetches.
An Exploration of Cross-Patch Collaborations via Patch Linkage in OpenStack

Contemporary development projects benefit from code review as it improves the quality of a project. Large ecosystems of inter-dependent projects like OpenStack generate a large number of reviews, which poses new challenges for collaboration (improving patches, fixing defects). Review tools allow developers to link between patches, to indicate patch dependency, competing solutions, or provide broader context. We hypothesize that such patch linkage may also simulate cross-collaboration. With a case study of OpenStack, we take a first step to explore collaborations that occur after a patch linkage was posted between two patches (i.e., cross-patch collaboration). Our empirical results show that although patch linkage that requests collaboration is relatively less prevalent, the probability of collaboration is relatively higher. Interestingly, the results also show that collaborative contributions via patch linkage are non-trivial, i.e, contributions can affect the review outcome (such as voting) or even improve the patch (i.e., revising). This work opens up future directions to understand barriers and opportunities related to this new kind of collaboration, that assists with code review and development tasks in large ecosystems.
Commit-Based Class-Level Defect Prediction for Python Projects

Defect prediction approaches have been greatly contributing to software quality assurance activities such as code review or unit testing. Just-in-time defect prediction approaches are developed to predict whether a commit is a defect-inducing commit or not. Prior research has shown that commit-level prediction is not enough in terms of effort, and a defective commit may contain both defective and non-defective files. As the defect prediction community is promoting fine-grained granularity prediction approaches, we propose our novel class-level prediction, which is finer-grained than the file-level prediction, based on the files of the commits in this research. We designed our model for Python projects and tested it with ten open-source Python projects. We performed our experiment with two settings: setting with product metrics only and setting with product metrics plus commit information. Our investigation was conducted with three different classifiers and two validation strategies. We found that our model developed by random forest classifier performs the best, and commit information contributes significantly to the product metrics in 10-fold cross-validation. We also created a commit-based file-level prediction for the Python files which do not have the classes. The file-level model also showed a similar condition as the class-level model. However, the results showed a massive deviation in time-series validation for both levels and the challenge of predicting Python classes and files in a realistic scenario.
ANALYSIS OF EXTRAVERT TYPES IN THE BOOKISH LIFE OF NINA HILL: A Novel By Abbi Waxman

Abstract&#x0D; This thesis analyzes Extravert personality types in the novel ‘The Bookish Life Of Nina Hill’. The goals of this research is to find out what type of extravert personality that can be seen in the novel ‘The Bookish Life Of Nina Hill’ which is related to the personality approach using Carl Jung's personality theory. This research focuses on Nina Hill's personality. The reason is because Nina Hill's personality is a nerd girl who wants to change her life to become more open to the world. The method that is used for this thesis is a qualitative method by collecting data through library books and online data. The results show that extravert personality types found in the novel are divided into four types namely Thinking, Feeling, Sensing, and Intuiting. The first is Extravert Thinking. It is a personality that She thinks logically so that She is understanding and explaining things with logical rules. Second is Extravert Feeling. It is a personality that shows She responds to something emotionally. She is rejecting opinions that do not suit her emotionally. Third is Extravert Sensing. It is a personality who seeks pleasure by enjoying her activities in her weekly activities. Furthermore, Extravert Intuiting. It is a personality that She has creativity and takes advantage of the opportunity by making new ideas and taking advantage of opportunities to increase her knowledge.
Uncertainty Propagation in Node Classification

Quantifying predictive uncertainty of neural networks has recently attracted increasing attention. In this work, we focus on measuring uncertainty of graph neural networks (GNNs) for the task of node classification. Most existing GNNs model message passing among nodes. The messages are often deterministic. Questions naturally arise: Does there exist uncertainty in the messages? How could we propagate such uncertainty over a graph together with messages? To address these issues, we propose a Bayesian uncertainty propagation (BUP) method, which embeds GNNs in a Bayesian modeling framework, and models predictive uncertainty of node classification with Bayesian confidence of predictive probability and uncertainty of messages. Our method proposes a novel uncertainty propagation mechanism inspired by Gaussian models. Moreover, we present an uncertainty oriented loss for node classification that allows the GNNs to clearly integrate predictive uncertainty in learning procedure. Consequently, the training examples with large predictive uncertainty will be penalized. We demonstrate the BUP with respect to prediction reliability and out-of-distribution (OOD) predictions. The learned uncertainty is also analyzed in depth. The relations between uncertainty and graph topology, as well as predictive uncertainty in the OOD cases are investigated with extensive experiments. The empirical results with popular benchmark datasets demonstrate the superior performance of the proposed method.