Reviewerly/exHarmony huggingface dataset:
Penerapan Algoritma Apriori Untuk Rekomendasi Produk Bagi Pelanggan Toko Online Berbasis Website

Today, almost all activities in various sectors of life have been transformed into digital systems to support various related activities. Consideration of the efficiency of dissemination of information outreach and data management that can be recorded properly is the main reason for switching to a digital system. In the economic sector, global business developments are rife with the use of digital devices in marketing various products to increase marketability. Based on this, this research was conducted to apply the Apriori algorithm association data mining to provide product recommendations for online shop customers. The CodeIgniter framework and the waterfall method are used to build a website-based information system. Sampling data was obtained from transaction data at the Rizvenastore store which was used for the database using MySql. The application of the a priori algorithm can form association rules as a reference in-store product promotions and decision support in providing product recommendations to customers based on predetermined minimum support and confidence values. From testing the system using the black box method, the results obtained for all activities on all actors can run as expected.
Simple Contrapositive Assumption-Based Argumentation with Partially-Ordered Preferences

We show that assumption-based argumentation frameworks, based on contrapositive logics and partially-ordered preference functions, provide a solid platform for argumentation-based reasoning. Two useful properties of the preference functions are identified (selectivity and max-lower-boundedness), and extended forms of attacks relations are supported (exists-attacks and forall-attacks), which assure several desirable properties and a variety of reasoning modes.
Revisiting Inferential Benchmarks for Knowledge Graph Completion

Knowledge Graph (KG) completion is the problem of extending an incomplete KG with missing facts. A key feature of Machine Learning approaches for KG completion is their ability to learn inference patterns, so that the predicted facts are the results of applying these patterns to the KG. Standard completion benchmarks, however, are not well-suited for evaluating models' abilities to learn patterns, because the training and test sets of these benchmarks are a random split of a given KG and hence do not capture the causality of inference patterns. We propose a novel approach for designing KG completion benchmarks based on the following principles: there is a set of logical rules so that the missing facts are the results of the rules' application; the training set includes both premises matching rule antecedents and the corresponding conclusions; the test set consists of the results of applying the rules to the training set; the negative examples are designed to discourage the models from learning rules not entailed by the rule set. We use our methodology to generate several benchmarks and evaluate a wide range of existing KG completion systems. Our results provide novel insights on the ability of existing models to induce inference patterns from incomplete KGs.
Active Disjunctive Constraint Acquisition

Constraint acquisition (CA) is a method for learning users' concepts by representing them as a conjunction of constraints. While this approach works well for many combinatorial problems over finite domains, some applications require the acquisition of disjunctive constraints, possibly coming from logical implications or negations. In this paper, we propose the first CA algorithm tailored to the automatic inference of disjunctive constraints, named DCA. A key ingredient there, is to build upon the computation of maximal satisfiable subsets. We demonstrate experimentally that DCA is faster and more effective than traditional CA with added disjunctive constraints, even for ultra-metric constraints with up to 5 variables. We also apply DCA to precondition acquisition in software verification, where it outperforms the previous CA-based approach PreCA, being 2.5 times faster. Specifically, in our evaluation DCA infers more preconditions in just 5 minutes than PreCA does in an hour, without requiring prior knowledge about disjunction size. Our results demonstrate the potential of DCA for improving the efficiency and scalability of constraint acquisition in the disjunctive case, enabling a wide range of novel applications.
Diagnosis for Post Concept Drift Decision Trees Repair

Decision trees are commonly used in machine learning since they are accurate and robust classifiers. After a decision tree is built, the data can change over time, causing the classification performance to decrease. This data distribution change is a known challenge in machine learning, referred to as concept drift. Once a concept drift has been detected, usually by experiencing a decrease in the model's performance, it can be handled by training a new model. However, this method does not explain the drift harming the performance but only handles the drift's effects. The main contribution of this paper presents a novel two-step approach called APPETITE, which applies diagnosis techniques to identify the feature that has drifted and then adjusts the model accordingly. For the diagnosis step, we present two algorithms. We experimented on 73 known datasets from the literature and semi-synthesized drifts in their features. Both algorithms are better at handling concept drift than training a new model based on the samples after the drift. Combining the two algorithms can provide an explanation of the drift and is a competitive model against a new model trained on the entire data from before and after the drift.
Factors Affecting the Working Motivation of Teachers and Staff: A Case Study at a Public University in Vietnam

Lecturers and staff are important resources in universities. Their work performance is closely related to work motivation. The motivation of lecturers and staff in the university is affected by many different factors. In this study, we focused on assessing the influence of 5 factors: income, co-worker relationship, development opportunities, working conditions and nature of work. The study was carried out on a sample of 163 subjects who are lecturers and staff of a public university in Vietnam. Methods of exploratory factor analysis EFA, linear regression analysis were used to test the research hypotheses. Research results showed that all 5 factors considered have a positive impact on the work motivation of lecturers and staff. In which, the income factor has the strongest influence. The results of this study provide data as a basis for building motivational strategies for lecturers and staff at universities in Vietnam.
The Effect of the Modeling the Way Method in Improving Students' Speech Skills at SMA Swasta Nurul Iman Tanjung Morawa

This study aims to determine the effect of the modeling the way method in improving the speech skills of Nurul Iman Tanjung Morawa High School students in the 2021-2022 Academic Year. This study uses a quantitative approach in an effort to test the hypotheses that have been prepared. The location of the research being conducted was at Nurul Iman Tanjung Morawa High School in the 2021-2022 Academic Year. The reason for taking this research location is because the population is sufficient, this location is easily accessible by public transportation and not far from the researcher's residence so that it can save time, effort and money. Based on the review carried out, it was obtained data that all class XI students of Private High School Nurul Iman Tanjung Morawa for the 2021-2022 Academic Year totaled 98 people. Based on the results of the research and discussion it can be concluded that in general the speech skills of class XI SMA Nurul Iman Tanjung Morawa before the modeling the way method was applied to composing speeches in Indonesian were categorized as low. The average score of the students' posttest showed a result of 87.96 which was above the KKM score with the percentage of completeness criteria composing student speeches that had completed learning by 91% and those that had not been completed by 9%, so learning by applying the modeling the way method was declared successful because the number students who score above the KKM above 45%.
Perancangan UI/UX Website E-learning Amikom Solo

E-Learning is an approach to developing knowledge, skills and competencies through internet-based means. E-Learning allows materials to be sent to students via the internet. The use of web-based E-Learning is currently widely applied for schools and universities, but for STMIK Amikom Solo still does not have an E-Learning system. The method used in this research is the literature study method which aims to develop the use of E-Learning websites from various journals that have been analyzed and processed by researchers into an Amikom Solo E-Learning website. From the website built, it can be concluded that it can facilitate students and lecturers in learning activities.
Interaction Modalities for Augmented Reality Applications in Manufacturing

Augmented Reality (AR) applications are beginning to see rapid adaptation in the manufacturing sector. AR systems in the manufacturing industry can assist operators in simple manufacturing tasks, such as virtual instructions, as well as complex remote assistance and maintenance tasks. There are many ways to interact with AR content, commonly known as interaction modalities. These modalities are classified into three categories: touch, touchless, and wearable, or sometimes a combination of more than one modality known as multimodal interfaces. However, in the manufacturing industry, there is no one-size-fits-all solution regarding the interaction with AR content. Current research explores various input interaction modalities in AR and assesses the advantages &amp; disadvantages associated with them in the manufacturing context. We adopt both touch and touchless modalities in our case study of assembling a planetary gear and present the merits and demerits of each modality assessing the extent of natural user interaction with AR content. Interaction with a wearable AR system is not part of this investigation.
Sentiment Analysis Naive Bayes Method on SatuSehat Application

The SatuSehat application is an application that provides health services to users. This application is a development of the PeduliLindungi application which is used to handle vaccination history in the new normal era. Therefore, it is important to classify user reviews into positive and negative sentiments using the Naïve Bayes method. The use of this method because it can produce a model that is quite accurate and effective. The results of data collection in this study were 25,000 of which 18,359 were negative and 6,641 were positive. The results of the Naïve Bayes accuracy test are 97% with negative sentiment results, namely precision has a value of 98%, recall has a value of 98% and f1-score has a value of 98%, while positive sentiment results, namely precision has a value of 94%, recall has a value of 94 % and f1-score has a value of 94%. This study aims to classify user reviews of the SatuSehat application into positive and negative sentiments and assess the performance of the Naïve Bayes method regarding public opinion on the use of the SatuSehat application based on reviews from the Google Playstore application.
MIX-TPI: a flexible prediction framework for TCR–pMHC interactions based on multimodal representations

Abstract Motivation The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs. Results To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR–pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR–pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset. Availability and implementation The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.
“English-Tren” Program in Developing Santris’ Speaking Skills for Facing Global Challenges

Today, international communication plays a vital role for facing the global challenges and English as Lingua Franca is quite helpful to serve the purpose of communication with people from different countries. In pesantren world, English is very essential to teach and “English-Tren” can be a way of asssisting santri to develop the santris’ speaking skill. “English-Tren” is English for Pesantren, a branch of English for Specific Purposes (ESP), designed to meet the target of Pesantren in improving Santris’ English skills especially speaking. This study aims to explain how is English-Tren implemented in developing santris’ English Speaking Skill. This study is a case study with the setting in Pondok Pesantren Darussalam Dukuhwaluh Purwokerto promoting the santris’ improvement in speaking skill through the course of “English-Tren”. The data collection techniques included interview to three English teachers and five santris supported by observation and documentation. The result shows that English-Tren is implemented through three ways namely Morning English biweekly, English taught as a subject in madrasah diniyah classes, and English taught and practiced in English program. Hence, English-Tren is influential in developing santris’ English speaking skill.
Head of educational programs in a modern university: position, responsibility, functionality

Currently, the system of higher professional education in Russia is undergoing a major transformation.Fundamental shifts are taking place in the understanding of the mission, goals, objectives and functionality of universities as a whole, as well as individual units and positions.This article attempts to form an idea of the head of educational programs as a fairly new element of the university system, to determine what kind of position it is, what functionality it implies, and how it correlates with the position of the head of the department.
NEOLOGISMS OF MODERN MEDIA SPACE

The article has been devoted to the investigation of neologisms in the Internet media space as a new phenomenon of the post-industrial society.The occurrence of specific phenomena and their correlation with media reality has been observed.The notion of "media space" as a means transmitting information of various content has been studied.The ties between the emergence of neologisms and the so-called "information explosion" has been pointed out.Two contradictory tendencies -the tendency of language development and the tendency of its preservation with the factor of novelty were stressed.It has been emphasized that modern processes of globalization, digitization, and informatization inspire the need for new words.The birth of neologisms in the English language at the modern stage is determined by a consequence of the technological and economic development of the society, and the emergence of new realities that require nomination, and by intra-linguistic factors, the desire for economy, emotional expression, and stylistic differentiation.It has been observed that semantic neologisms are primarily formed in the course of internal semantic derivation by a new seme in a word.Conversational neo-phraseologisms stand close to substandard language formations, characterized by deviation from the norm of literary language and depend on the communicative situation they are used.Such stylistic characteristics of neologisms as metaphorization and metonymization have been studied.The role of abbreviated neologisms as the way to update the vocabulary of the field of information and computer technologies and some of their grammatical peculiarities has been stressed.It has been pointed out that the emergence of neologisms is determined by the pragmatic needs of communication.The functions of neologisms in the modern media space have been distinguished.
Secure Sensor Data Collection and Blockchain Storage System for IoT Networks

Our research presents a Secure Sensor Data Collection and Blockchain Storage System (SSDCBSS) for IoT networks. This system ensures the security and integrity of IoT-generated data by employing advanced encryption during data collection and transmission. Blockchain technology is utilized to create a decentralized, tamper- resistant ledger for data storage, eliminating the need for a central authority. Consensus mechanisms validate data entries, reducing the risk of fraudulent information. The SSDCBSS offers a robust and scalable solution to the security challenges faced by IoT networks, fostering trust and reliability in the IoT ecosystem for diverse applications. mood. This process is repeated for the other five emotions. Key Words: blockchain, IoT, storage, Encryption
Pose-free visual servoing from 3D measurements

In this paper, a novel approach to visual servo control robotic systems is proposed. It is focused on developing a solution using 3D point features without recovering the rigid object’s pose. Pose-free motion is achieved using motion parameterization techniques based on dual numbers and dual vectors. Considering an imposed velocity field over the motion of the 3D point features ensemble, this work proposes a close-form solution to a visual servoing problem. The solution provides stable motion control while preserving the image features in the field of view. However, when some point features leave the field of view, their contribution to the control law is dropped without losing stability. The proposed solution is easy to tune and implement. Various scenarios are used in simulations and real experiments to show how the proposed solution overcomes classic servoing problems.
Image Denoising using CNN in Deep Learning

Since noise distribution cannot be predicted, removing mixed noise from a picture is difficult. Additive white Gaussian and impulse noise are the most common mixed noises in noisy images. Many methods can eliminate picture noise. When removing noise from a photograph, examine its texture, edge, smoothness, and other factors. Deep learning convolutional neural networks denoise a photo in this research. This work has eliminated Gaussian, salt & pepper, impulse, and periodic noise. three metrics PSNR, SSIM, and MSE to evaluate denoised images.
Comparative Evaluation of Machine Learning Algorithms for Detecting Credit Card Fraud

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. The main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. This research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%.
Machine Learning based Private Documents Vault

With the development of PC frameworks, the amount of sensitive information to be stored as well as the number of threats to this information increases at an unprecedented rate, making information secrecy progressively critical to PC users. At present, for the gadgets generally associated with the Web, the utilization of cloud information capacity services has become practical and normal, permitting fast admittance to such information from any place and at any time. Such easy access undoubtedly carries with it a concern about the secrecy of the information which is conveyed to outsiders. Records may likewise be leaked by the concerned members. A direct arrangement for the client to encode all reports prior to submitting them is essential. This technique, in any case, makes it difficult to proficiently look for archives as they are completely encrypted. This research proposes a confidential record vault with server-side encryption, which also enables clients to easily convey encryption and other security arrangements by offering robust, focused service of encryption keys; moreover, this work creates and implements a model framework. As a result of confirmation, examination, convenience & security, it is demonstrated that the system can fulfil the requirements for information security assurance of sensitive e-records in the open organization environment. Streamlit, a well-known open-source structure is used for implementing AI and perception applications in Python. The initial evaluation shows that this system presents satisfactory results when compared with directly uploading records to a cloud storage service.
Providing Access Isolation to Information in the Database

Most modern database management systems (DBMS) provide the ability to restrict user access to database objects. The problems of restricting access to individual records are arising increasingly in connection with the growing need for flexibility in restricting access in modern databases, including when using them to store information classified as state or commercial secrets. Currently, these tasks are solved individually, and there are no commonly formalized approaches to designing such access restrictions. The study aims to reduce the design time for database schemes when it is necessary to restrict access to individual records of the database tables and improve the quality of the designed schemes by using the proposed algorithms. Questions about the design of the protected databases with the use of access restrictions to separate records are considered. Both restriction of illegal access to records, and granting false camouflage information instead of required is considered. The proposed software package will consist of 2 main parts: a database and a client application. To differentiate access, the study proposed the use of a record-protecting lock.
Business Model Research on Industrial Augmented Reality: A Systematic Literature Review on the Current State and Future Research Areas

This systematic literature review aims to present the current state of business model research on industrial augmented reality (IAR) and suggest future research areas. Our analysis of 60 previous literature reviews on IAR shows that the business model perspective is missing from these reviews. To address this gap, we conducted a systematic literature review using eight scientific databases in late 2022. Although we aimed for a comprehensive literature search, a total of only 48 augmented reality (AR)-related business model publications were found, of which only six focused on IAR. To present the current state of research, these six publications were analyzed in terms of research purpose and results, the IAR application(s) studied, and the research methodology used. Based on this analysis, we propose future research areas, considering the suggestions for future research and the problems and challenges mentioned in the analyzed publications.