Reviewerly/exHarmony huggingface dataset:
Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization

Sparse orthonormal transform is based on orthogonal sparse coding, which is relatively fast and suitable in image compression such as analytic transforms with better performance. However, because of the constraints on its dictionary, it has performance limitations. This paper proposes an extension of a sparse orthonormal transform based on unions of orthonormal dictionaries for image compression. Unlike unions of orthonormal bases (UONB), which implement an overcomplete dictionary with several orthonormal dictionaries, the proposed method allocates patches to an orthonormal dictionary based on their directions. The dictionaries are constructed into a discrete cosine transform and an orthonormal matrix. To determine a trade-off parameter between the reconstruction error and sparsity, which hinders efficient implementation, the proposed method adapts Bayesian optimization. The framework exhibits an improved performance with fast implementation to determine the optimal parameter. It is verified that the proposed method performs similar to an overcomplete dictionary with a faster speed via experiments.
Perbandingan Model Proses Algoritma Alpha dan Alpha++ Pada Aplikasi E-commerce

Utilization of information technology is currently growing rapidly in helping activities especially in storing an event log. The activity which is behavior of the user can be analyzed using process mining. The process mining purpose to extract information from event logs on business processes that working. Discovery technique is used in this research. The purpose of this study is to compare two algorithms applied by creating an e-commerce application that is aware of the processes. E-commerce applications require event logs to read the behavior of visitor activities against the application. This research method starts from understanding the business processes that working, then designing a website by creating the application used. Furthermore, data collection through applications that are promoted through social media. The application will be recorded user activity and formed an event log. The event log that formed then discovered using alpha and alpha++ algorithms by utilizing the ProM Lite 1.2 tools. The evaluation results show that the alpha algorithm has shortcomings, namely length one loop, length two loop and non-free choice. And the alpha++ algorithm fixed this deficiency.&#x0D;
Aplikasi Game Cerita Rakyat Kodok Dan Harta Karun Berbasis Android

Abstrak - Indonesia kaya akan cerita rakyat yang mempunyai nilai kearifan lokal yang dapat digunakan untuk pembelajaran karakter. Terdapat fenomena kurangnya waktu orang tua untuk menceritakan cerita rakyat kepada anak. Penelitian ini bertujuan merancang dan membangun aplikasi game dengan tema cerita rakyat. Cerita rakyat yang digunakan dalam penelitian ini adalah kodok dan harta karun. Cerita tersebut mempunyai nilai selalu berbuat baik, tidak serakah, saling menyayangi dan kerja keras. Basis platform untuk aplikasi game adalah Android. Target pengguna dari aplikasi game ini adalah anak usia 5 sampai 10 tahun. Jenis game yang digunakan dalam penelitian ini adalah puzzle dan slide scrolling. Hasil penelitian ini menunjukkan aplikasi game telah berhasil dibangun. Hasil pengujian terhadap 30 orang menunjukkan sangat menarik untuk dimainkan.Kata kunci: Game, Cerita Rakyat, Android. Abstract - Indonesia is rich in folklore, which contains indigenous wisdom that may be used to develop character. There is a situation where parents are unable to recount traditional stories due to a lack of time. The purpose of this study is to design and develop a folklore-themed gaming application. The folklore that was consulted for this study is about frogs and treasure. The story teaches the importance of always doing what is right, of not being greedy, of loving one another and of working hard. The story of frogs and treasure teaches us the value of always doing good, not being greedy, loving each other and working hard. Android is the primary platform on which these gaming applications run. Children from 5 to 10 years old are the intended audience for this gaming application. Puzzles and slide-scrolling games were used in this study. The study's findings show that the gaming application was effectively developed. Thirty people were tested, and the findings indicated that it was a lot of fun to play.Keywords: Game, Folklore, Android
An Improved Meerkat Clan Algorithm for Solving 0-1 Knapsack Problem

Meerkat Clan Algorithm (MCA) is a nature-based metaheuristic algorithm which imitates the intelligent behavior of the meerkat animal. This paper presents an improvement on the MCA based on a chaotic map and crossover strategy (MCA-CC). These two strategies increase the diversification and intensification of the proposed algorithm and boost the searching ability to find more quality solutions. The 0-1 knapsack problem was solved by the basic MCA and the improved version of this algorithm (MCA-CC). The performance of these algorithms was tested on low and high dimensional problems. The experimental results demonstrate that the proposed algorithm had overcome the basic algorithm in terms of solution quality, speed and gained optimality with low dimensional problems. Furthermore, in high dimensional problems, it has competitive results in comparison with the other algorithms.
Forecasting of The Crime Rate Using Automatic Clustering and Fuzzy Logic Relationship Method In North Sumatra

Currently the crime rate is very alarming and reported in various mass and electronic media. The high crime rate in the Province of Nort Sumatra is very unsettling for the community. The purpose of this research is to get the result of forecasting the crime ratel in the 2021 - 2024 using Automatic Clustering And Fuzzy Logic Relationship (ACFLR) method. The advantage of this method is that the method has a high level of accuracy because the Mean Absolute Percentage Error (MAPE) value is relative small and the results of forecasting analysis obtained in 2021 there are 31522 cases, in 2022 are 31533 cases, in 2023 are 31574 cases and the last one in 2024 was 31602 cases. In addition, the prediction error rate MAPE obtained is 0,35 %&#x0D; &#x0D; Keywords: Crime rate, Automatic Clustering And Fuzzy Logic Relationship.&#x0D;
Bank Management System Using Salesforce

Abstract: Salesforce is a very hot cloud computing technology in the IT industry, which is available on the cloud, with no need to install any software as well as no hardware required. Salesforce is one on-demand CRM, which runs on the platform, as well as CRM, which is a model used to manage organization interactions like phone calls, Emails, Meetings, and Social media with customers and also prospects penetrating to Sales, Marketing, and Support. In this paper, we are discussing Introduction to Cloud Computing, Service models in Cloud Computing, Types of Cloud Computing Architecture of Cloud Computing. This paper aims to show mainly the importance of Salesforce.com which is a software giant that manages to give the buyer an easyto-use as well as extremely effective CRM solution. Keywords: Cloud Computing; CRM;
Objective Type Question Generation using Natural Language Processing

Automatic Question Generation (AQG) is a research trend that enables teachers to create assessments with greater efficiency in right set of questions from the study material.Today's educational institutions require a powerful tool to correctly assess learner's mastery of concepts learned through study materials.Objective type questions are an excellent method of assessing a learner's topic understanding in learning process, based on Information and Communication Technology (ICT) and Intelligent Tutoring Systems (ITS).Creating a set of questions for assessment can take a significant amount of time for teachers, and obtaining questions from external sources such as assessment books or question banks may not be relevant to the content covered by students during their studies.This proposed system involves to generate the familiar objective type questions like True or False, 'Wh', Fill up with double blank space, match the following type question have generated using Natural Language Processing(NLP) techniquesfrom the given study material.Different rules are created to generate T/F and 'Wh' type questions.Dependence parser method has involved in 'Wh' questions.Proposed system is tested with Grade V Computer Science text book as an input.Experimental result shows that the proposed system is quite promising to generate the amount of objective type assessment questions.
An Effective Algorithm for Finding Shortest Paths in Tubular Spaces

We propose a novel algorithm to determine the Euclidean shortest path (ESP) from a given point (source) to another point (destination) inside a tubular space. The method is based on the observation data of a virtual particle (VP) assumed to move along this path. In the first step, the geometric properties of the shortest path inside the considered space are presented and proven. Utilizing these properties, the desired ESP can be segmented into three partitions depending on the visibility of the VP. Our algorithm will check which partition the VP belongs to and calculate the correct direction of its movement, and thus the shortest path will be traced. The proposed method is then compared to Dijkstra’s algorithm, considering different types of tubular spaces. In all cases, the solution provided by the proposed algorithm is smoother, shorter, and has a higher accuracy with a faster calculation speed than that obtained by Dijkstra’s method.
Cost analysis of on-premise versus cloud-based implementation of moodle in Kufa University during the pandemic

&lt;span&gt;Many organizations often use physical hardware resources, such as storage devices and firewall, to store their essential applications and data. Recently, the Coronavirus pandemic represented a significant challenge for the University of Kufa, which utilize an on-premise data center for e-learning since 2009. Whether the learning management system (LMS) is installed on an on-premise data center or the cloud, it is crucial for any university, to decide which implementation is more suitable because of the differences between the two options, especially in terms of cost. This study uses the total cost of ownership (TCO) model to highlight the cost aspect when using on-premise datacenter versus cloud-based implementation for e-learning and to determine which option is cost effective. The results may help other universities, inside or outside Iraq, deciding which implementation is more suitable (financially) for the organization. The final results show that the cloud-based solution costs approximately 20% less than the currently used on-premise option. Despite all drawbacks of on-premise datacenter such as unstable electricity, bad Internet service, and costing more than cloud hosting, it maybe still more convenient in the case of the University of Kufa due to the sensitive data stored in the data center.&lt;/span&gt;
KERNEL DAN SIFATNYA PADA TRANSLASI NAIK DAN TURUN RING FUZZY INTUITIONISTIK

Intuitionistic fuzzy ring is a generalized of ring combined with intuitionistic fuzzy sets. It also has impact on ideal so it become intuitionistic fuzzy ideal. The existence of the generalization process, needs to be studied about the properties of kernel at ring homomorphism in which we know that the kernel of a ring homomorphism is ideal. In addition, the study of fuzzy sets there are operators that are subject to membership and non membership functions. One of the known operators is up translates. This research examines the structure of the kernel at homomorphism rings in fuzzy intuitionistic rings. After that the structure of the kernel is also examined if the intuitionistic fuzzy ring is subjected to an up translates operator.
An empirical evaluation of phrase-based statistical machine translation for Indonesia slang-word translator

&lt;span&gt;The use of slang (non-standard language), especially in social media, is increasing. It causes reducing the level of understanding when communicating because not everyone understands slang (non-standard language). The purpose of this work is to develop a slang-word translator. The other objective is to find the minimum number of sentences and &lt;/span&gt;&lt;span&gt;BiLingual Evaluation Understudy&lt;/span&gt;&lt;span&gt; (BLEU) score used as a benchmark to determine that the translation is understandable. The approach used in this project is a Phrase-based statistical machine translation (PBSMT) approach, suitable for low resource language, with a dataset of 100,000 sentences taken from the comments column of several online political news portals. The comments are then manually translated to produce a parallel corpus of non-standard language-standard language. The sample sentences are taken from the dataset then distributed using questionnaires to obtain the human understanding level regarding the translation result. The result of the implementation is a BLEU score of 64 and the minimum number of sentences to have an understandable machine translation is 500. The conclusion drawn from the distributed questionnaires is that humans can understand the sentences produced by the translation machine.&lt;/span&gt;
Fast image watermarking based on signum of cosine matrix

&lt;span&gt;In the field of image watermarking, the singular value decomposition has good imperceptibility and robustness, but it has high complexity. It divides a host image into matrices of U, S, and V. Singular matrix S has been widely used for embedding and extracting watermark, while orthogonal matrices of U and V are used in decomposition and reconstruction. The proposed signum of cosine matrix method is carried out to eliminate the generation of the three matrices at each block and replace it with a signum of cosine matrix. The proposed signum of cosine matrix is performed faster on the decomposition and reconstruction. The image is transformed into a coefficient matrix C using the signum matrix. The C matrix values are closer to the S value of singular value decomposition which can preserve high quality of the watermarked image. The experimental results show that our method is able to produce similar imperceptibility and robustness level of the watermarked image with less computational time.&lt;/span&gt;
Enhanced CP-ABE with RSA for Secure and Revocable Data Transmission of Big Data in Cloud

Cloud computing is a diversified domain and is a huge phenomenon that is a widely preferred technology in the current times.The users are unable to utilize cloud computing to the maximum, due to challenges in the security issues.To store sensitive or useful data in an insecure region is not an option for the user.Various techniques have been developed to provide security for the big data in the cloud.During voluminous data transmissions, it will not maintain the end-to-end security and the prevailing approaches do not solve the key complexities and not provides security for the keys.The Attribute-Based Encryption (ABE) can deliver a mechanism of fine-grained control by providing one-to-many encryption.But, the revocable process of accessing the privilege of encrypted data of users from cloud servers is challenging.Further, the complex process of ABE takes up huge computational time hence it becomes a burden for system users.To solve such issues, the proposed Enhanced Cipher-text Policy-based Attribute Encryption (CP-ABE) protect, broadcast and control the access of information, especially over the cloud server.In the proposed Enhanced CP-ABE method, proxy is added to securely transmit the key to the users.The Rivest Shamir Adleman (RSA) Algorithm provides security to outsourced big data in the cloud that enables public key encryption secure data when it is being sent over an insecure network such as the internet.The proposed Enhanced CP-ABE with RSA method effectively identifies the users who decrypt the ciphertexts during decryption.The experimental results show that the proposed Enhanced CP-ABE with RSA Scheme method acquires lesser encryption and decryption times of 1638 ms and 1102 ms respectively for a 256-bit key length.Whereas, the existing methods acquire encryption and decryption times of 1721ms and 1134 ms respectively.
Eye Tracking Usability Testing Using User-Centered Design Analysis Method

The interaction between humans and computers has entered a new phase, where modern humans can no longer be separated from computers in their daily lives.This interaction raises a new topic of discussion, namely Human-Computer Interaction (HCI).HCI is the intersection of cultural, social, cognitive, and aesthetic with computing and information technology.It covers a large number of problems, theories, technologies, designs, tools, environments, and human experiences in knowledge work, leisure and leisure activities, teaching and learning, and the anthology of everyday life.This study aimed to develop Eye Tracking Usability Testing System Development Using User-Centered Design Analysis Method.This study used a Tobii Eye Tracking sensor which was extracted and used as input to the GOM method.The results of this study were applications that could analyze the quality of interaction from users to website applications with a reading accuracy rate of 88%, especially in service applications, so that they could be used to make application improvements to improve services.
A gamma self‐correction method via chord distribution coding in fringe projection profilometry

This paper presents a gamma self-correction method via chord distribution coding to eliminate the system's gamma effect. For this purpose, the prior gamma-Gaussian characteristic value, which is extracted by chord distribution Gaussian fitting of the preset gamma's wrapped phase, is used to map the projector's gamma. Experimental results demonstrate that the proposed method has competitive accuracy and feasibility for fringe acquisition and gamma calculation. Thus, this method can be used for online gamma self-correction in continuous three-dimensional measurement.
ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

Abstract Glossary of Terms extraction from textual requirements is an important step in ontology engineering methodologies. Although initially it was intended to be performed manually, last years have shown that some degree of automatization is possible. Based on these promising approaches, we introduce a novel, human interpretable, rule-based method named R eq T agger , which can extract candidates for ontology entities (classes or instances) and relations (data or object properties) from textual requirements automatically. We compare R eq T agger to existing automatic methods on an evaluation benchmark consisting of over 550 requirements and tagged with over 1700 entities and relations expected to be extracted. We discuss the quality of R eq T agger and provide details showing why it outperforms other methods. We also publish both the evaluation dataset and the implementation of R eq T agger .
Skill Reward for Safe Deep Reinforcement Learning

Reinforcement learning technology enables an agent to interact with the environment and learn from experience to maximize the cumulative reward of specific tasks, and get a powerful agent to solve decision optimization problems. This process is highly similar to our human learning process, that is, learning from the interaction with the environment. As we know, the behavior of an agent based on deep reinforcement learning is often unpredictable, and the agent will produce some weird and unsafe behavior sometimes. To make the behavior and the decision process of the agent explainable and controllable, this paper proposed the skill reward method that the agent can be constrained to learn some controllable and safe behaviors. When an agent finishes specific skills in the process of interaction with the environment, we can design the rewards obtained by the agent during the exploration process based on prior knowledge to make the learning process converge quickly. The skill reward can be embedded into the existing reinforcement learning algorithms. In this work, we embed the skill reward into the asynchronous advantage actor-critic (A3C) algorithm, and test the method in an Atari 2600 environment (Breakout-v4). The experiments demonstrate the effectiveness of the skill reward embedding method.
Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

&lt;span lang="EN-US"&gt;The Indonesian tafseer and translation of Holy Quran is an important source of information and knowledge for Indonesian muslims, since not many Indonesian muslims understand Arabic language in the Quran. However, the tafseer is full of the commentaries and explanation of each surah (chapter) and/or ayah (verse), which form a large document and not so easy to be accessed. Thus, the challenge is how to refer to both tafseer and translation in faster and accurate ways as one needs to always refer to them back and forth. Hence, this study proposes several text mining approaches, i.e. most frequent words, K-means clustering, and association rules, to analyze an Indonesian tafseer and translation of Quran and provide insights of hidden knowledge and relationships based on statistical information derived from it. These insights could be useful for muslims in general and for people that doing research in related areas. This study shows interesting results from combined analysis of the approaches used which can help people accessing information in tafseer more efficiently. As well, interesting relationships have been drawn from terms in the tafseer which could provide further and deeper knowledge on messages in the Quran.&lt;/span&gt;
Evaluation of Google Image Translate in Rendering Arabic Signage into English

When people travel to another country for work or leisure, they regularly need a medium to help them understand the written messages in other languages. Google Translate offers a new service: translating the content of images (texts) instantly and freely into 100 languages powered by the Neural Machine Translation approach (NMT). In this vein, the current research paper attempts to evaluate the accuracy of Google Image Translate service in rendering the texts printed on Arabic signage: banners and road and shop signs from Arabic into English. Besides, it aims to identify the capacity of Google Translate in rendering Arabic signage into English effectively without the help of human translators. The paper adopts the Linguistic Error Analysis Framework of Costa et al. (2015) in analyzing the output of Google image service in terms of orthography, grammar, lexis, and semantics. The paper shows that Google Translate made the following errors while rendering the content of images into English: mistranslation, omission, additions, wrong choice, misordering, subject-verb disagreement, and semantic errors. In conclusion, the Google Image Translate service helps the users configure the gist of the image. However, a human translator is still needed since MT may not provide an adequate and effective translation as humans do.