Reviewerly/exHarmony huggingface dataset:
Efficient Computation of General Modules for ALC Ontologies

We present a method for extracting general modules for ontologies formulated in the description logic ALC. A module for an ontology is an ideally substantially smaller ontology that preserves all entailments for a user-specified set of terms. As such, it has applications such as ontology reuse and ontology analysis. Different from classical modules, general modules may use axioms not explicitly present in the input ontology, which allows for additional conciseness. So far, general modules have only been investigated for lightweight description logics. We present the first work that considers the more expressive description logic ALC. In particular, our contribution is a new method based on uniform interpolation supported by some new theoretical results. Our evaluation indicates that our general modules are often smaller than classical modules and uniform interpolants computed by the state-of-the-art, and compared with uniform interpolants, can be computed in significantly shorter time. Moreover, our method can be used for, and in fact, improves the computation of uniform interpolants and classical modules.
A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

Geometry problem solving (GPS) is a high-level mathematical reasoning requiring the capacities of multi-modal fusion and geometric knowledge application. Recently, neural solvers have shown great potential in GPS but still be short in diagram presentation and modal fusion. In this work, we convert diagrams into basic textual clauses to describe diagram features effectively, and propose a new neural solver called PGPSNet to fuse multi-modal information efficiently. Combining structural and semantic pre-training, data augmentation and self-limited decoding, PGPSNet is endowed with rich knowledge of geometry theorems and geometric representation, and therefore promotes geometric understanding and reasoning. In addition, to facilitate the research of GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K, labeled with both fine-grained diagram annotation and interpretable solution program. Experiments on PGPS9K and an existing dataset Geometry3K validate the superiority of our method over the state-of-the-art neural solvers. Our code, dataset and appendix material are available at \url{https://github.com/mingliangzhang2018/PGPS}.
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization

Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as memory usage, or latency on top of the performance requirement. In this work, we propose constrained TPE (c-TPE), an extension of the widely-used versatile Bayesian optimization method, tree-structured Parzen estimator (TPE), to handle these constraints. Our proposed extension goes beyond a simple combination of an existing acquisition function and the original TPE, and instead includes modifications that address issues that cause poor performance. We thoroughly analyze these modifications both empirically and theoretically, providing insights into how they effectively overcome these challenges. In the experiments, we demonstrate that c-TPE exhibits the best average rank performance among existing methods with statistical significance on 81 expensive HPO with inequality constraints. Due to the lack of baselines, we only discuss the applicability of our method to hard-constrained optimization in Appendix D. See https://arxiv.org/abs/2211.14411 for the latest version with Appendix.
Teaching What You Should Teach: A Data-Based Distillation Method

In real teaching scenarios, an excellent teacher always teaches what he (or she) is good at but the student is not. This gives the student the best assistance in making up for his (or her) weaknesses and becoming a good one overall. Enlightened by this, we introduce the "Teaching what you Should Teach" strategy into a knowledge distillation framework, and propose a data-based distillation method named "TST" that searches for desirable augmented samples to assist in distilling more efficiently and rationally. To be specific, we design a neural network-based data augmentation module with priori bias to find out what meets the teacher's strengths but the student's weaknesses, by learning magnitudes and probabilities to generate suitable data samples. By training the data augmentation module and the generalized distillation paradigm alternately, a student model is learned with excellent generalization ability. To verify the effectiveness of our method, we conducted extensive comparative experiments on object recognition, detection, and segmentation tasks. The results on the CIFAR-100, ImageNet-1k, MS-COCO, and Cityscapes datasets demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs. Furthermore, we conduct visualization studies to explore what magnitudes and probabilities are needed for the distillation process.
Optimal Decision Trees For Interpretable Clustering with Constraints

Constrained clustering is a semi-supervised task that employs a limited amount of labelled data, formulated as constraints, to incorporate domain-specific knowledge and to significantly improve clustering accuracy. Previous work has considered exact optimization formulations that can guarantee optimal clustering while satisfying all constraints, however these approaches lack interpretability. Recently, decision trees have been used to produce inherently interpretable clustering solutions, however existing approaches do not support clustering constraints and do not provide strong theoretical guarantees on solution quality. In this work, we present a novel SAT-based framework for interpretable clustering that supports clustering constraints and that also provides strong theoretical guarantees on solution quality. We also present new insight into the trade-off between interpretability and satisfaction of such user-provided constraints. Our framework is the first approach for interpretable and constrained clustering. Experiments with a range of real-world and synthetic datasets demonstrate that our approach can produce high-quality and interpretable constrained clustering solutions.
A Heuristic-Primed Decision-Making Model under the Assumption of Bounded Resources

Existing decision-making models are generally based on the classical normative paradigm, which seldom considers the limited cognitive and environmental resources of humans when making decisions in real environments. We draw on the theory of Artificial General Intelligence technology, Non-Axiomatic Reasoning System and heuristic-primed decision-making processes of psychology to propose a heuristic-primed decision-making model under the assumption of bounded resources. Firstly, we propose a spreading activation belief network based on experience-grounded semantics, which takes concepts as nodes and semantic relevance as edges, and changes the weights of logical reasoning links according to the evidence presented using multiple inferences rules so as to achieve the self-organization of its network topology. Next, multiple dynamically changing and conflicting goals are represented by a Non-Axiomatic Logic statement with two-dimensional desire-values. Finally, a controlled concurrent heuristic algorithm that can dynamically allocate limited computational resources and perform multiple inferences is proposed.
A NEW HYBRIDIZATION FOR IMPROVING THE CONVERGENCE OF THE MOMA-PLUS METHOD

We propose in this article a hybridization of the algorithm of the MOMA-Plus method and that of the Differential Evolution method. This hybridization consists of defining a simplex around an efficient solution generated by MOMA-plus and applying the Differential Evolution algorithm to find a better solution than that obtained by MOMA-plus. The results interpreted through a performance study of the solutions obtained on multiobjective optimization test problems show that this hybridization improves the convergence of the basic MOMA-plus algorithm. Moreover, a better complexity than that of basic MOMA-plus is obtained.
A Blind Image Steganography Algorithm Based on Knight Tour Algorithm and QR Codes

Internet proliferation and technological progress have made multimedia information quickly accessible, but they have also posed a threat to privacy and security. Researchers have been interested in digital images due to their capacity to store large amounts of data due to the possibility of protecting sensitive information through digital steganography. Despite their visual imperceptibility, robustness, and ability to embed information, existing image steganography techniques face several challenges. To overcome these challenges, a novel image steganography approach based on a blind model strategy has been proposed for hiding covert messages. The model consists of two stages: embedding and extracting. In the embedding stage, a suitable cover image is selected using the FAST feature point detector. A text message is then converted to a QR code and embedded in the feature points' neighbors using knight tour steps in a chess game. The result is a stego image that appears identical to the original cover image but contains the secret message in its feature points' neighbors. The extracting stage involves finding the feature points and extracting the QR code to obtain the original text message. Since the feature points were not altered during the embedding process, the proposed model is known as a blind model. This approach eliminates the need for the original cover image during the extracting stage. The proposed model was evaluated using several metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results demonstrate that the proposed algorithm can effectively embed and extract secret messages with high accuracy while maintaining the visual quality of the cover image with 100% of (SSIM), and 73.48 as an average of (PSNR).
Virtual Reality for Future Education: Systematic Literature Review

The development of information and communication technology is very rapid at this time contributed greatly to the development process of various kinds of learning media based on the application of technology as one of the innovations in education, Virtual Reality (VR) technology itself is a way in a way perform the appearance of learning pictures in the form of media three-dimensional or what is usually better known as 3D. Where the purpose of research is to explain Virtual Reality for Future Education. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. Virtual Reality (VR) has the potential to enhance education by enabling greater learning potential than ever before, in addition to luring us into new realms. Learning can reach students' emotional core in novel and fascinating ways thanks to virtual reality. This study also states that learning subjects that can benefit the most from implementing Virtual Reality as learning media are science, social studies, and history. However, the potential is much greater.
Implementation of Naive Bayes Classification Algorithm in Determining Appropriate Help Targets of Unlimited Houses (RTLH) in Bojonegoro District

Rumah merupakan salah satu kebutuhan primer bagi setiap individu, dan termasuk kedalam aset terpenting yang harus dimiliki. Kelayakan rumah yang layak huni dan tidak layak huni harus dipertimbangkan. Rumah yang tidak memenuhi kecukupan minimum dari segi ruang dan luas ruangan dianggap sebagai Rumah tidak layak huni (RTLH). Untuk mengatasi terjadinya peningkatan RTLH maka pemerintah menanggulanginya dengan memberikan bantuan kepada masyarakat yang layak menerima dengan tepat sasaran. Penelitian ini bertujuan untuk menerapkan metode Naïve Bayes dalam menentukan bantuan tepat sasaran menggunakan dua kelas penelitian, yakni layak menerima bantuan RTLH dan tidak layak menerima bantuan RTLH. Dari analisis klasifikasi menggunakan confusion matrix didapatkan hasil akurasi sebesar 63%, recall 100% dan presisi 25% untuk 400 data training dan 100 data testing dari total 500 data dengan 10 atribut pengujian.The house is one of the primary needs for every individual, and is included in the most important asset that must be owned. The livability of the livable and uninhabitable houses should be considered. A house that does not meet the minimum adequacy in terms of space and room area is considered an uninhabitable house (RTLH). To overcome the increase in RTLH, the government overcomes it by providing assistance to people who deserve to receive it on target. This study aims to apply the Naïve Bayes method in determining targeted assistance using two research classes, namely eligible to receive RTLH assistance and not eligible to receive RTLH assistance. From the classification analysis using the confusion matrix, the results obtained are 63% accuracy, 100% recall and 25% precision for 400 training data and 100 testing data from a total of 500 data with 10 test attributes.
Information Center Chatbot in Higher Education Using Dialogflow

Fakultas Teknologi Industri dan Informatika UHAMKA, memiliki website resmi yang mengacu pada sistem informasi universitas yang berfungsi untuk pelayanan yang efektif dan menggunakan teknologi terkini. Faktanya, masih memiliki beberapa permasalahan yang terjadi yaitu dosen dan mahasiswa masih sering bertanya via japri ke pihak sekretariat, akademik maupun pimpinan prodi dan fakultas jika memerlukan informasi, serta masih sering melewatkan informasi yang ada di broadcast Grup WhatsApp. Oleh karena itu, tujuan dari penelitian ini untuk merencanakan dan menerapkan teknologi chatbot yang memberikan mahasiswa akses informasi yang cepat dan mudah tentang akademik Fakultas Teknologi Industri dan Informatika UHAMKA secara mudah dan cepat. Chatbot ini diimplementasikan menggunakan tools Dialogflow dengan metode Prototype dan Telegram API. Setelah melakukan uji coba blackbox, Bot Telegram yang dibuat telah terbukti mampu memberikan respons yang memuaskan terhadap pertanyaan yang diajukan, sehingga dapat memberikan bantuan kepada user dalam memperoleh informasi akademik yang dibutuhkan. Selain itu, pengguna tidak perlu menunggu terlalu lama untuk menerima balasan dari chatbot, sehingga membuat chatbot yang digunakan dapat beroperasi dengan cepat dan tepat.The UHAMKA Faculty of Industrial and Informatics Technology has an official website that refers to a university information system that functions for effective service and uses the latest technology. In fact, there are still several problems that occur, namely lecturers and students still often ask via email to the secretariat, academics or study program and faculty leaders if they need information, and they still often miss information that is WhatsApp Group. Therefore, the purpose of this research is to plan and implement chatbot technology that gives students quick and easy access to information about the academics of the Faculty of Industrial Technology and Informatics UHAMKA easily and quickly. This chatbot is implemented using Dialogflow tools with the Prototype method and the Telegram API. After conducting a blackbox trial, the Telegram Bot that was created has been proven to be able to provide a satisfactory response to the questions asked, so that it can provide assistance to users in obtaining the required academic information. In addition, users don't have to wait too long to receive a reply from the chatbot, so that the chatbot used can operate quickly and precisely.
Study of foreground extraction image segmentation techniques

The field of computer vision has advanced significantly in the current era of technology.Digital image processing has undergone significant advancements.Image Generation, Image Enhancement, and Image Restoration are the three categories into which digital image processing techniques are commonly divided.Segmentation Procedure is one of several stages in image processing that divides an image into its individual components or objects [1] .Image segmentation is a technique that divides a digital image into a number of smaller groups or regions known as image segments, thereby lowering the complexity of the image and facilitating easier image processing and analysis [3] .There are numerous image segmentation techniques that are based on the two fundamental concepts of similarity/region and discontinuity/boundary.Techniques for image segmentation are frequently utilized in biomedicine, document processing, object recognition, automated industrial production, computed tomography (CT) images, and many other fields.Here, the goal is to compare and learn about fundamental image pre-processing and accessible segmentation approaches.ML-based algorithms are among the most effective foreground extraction methods currently available for masking images.However, it is discovered that ML approaches are unreliable.According to edges and appearance models, graph-based methods like Graphcut/Grabcut can successfully recover the foreground [6] .Both the foreground and the background are modelled using the Gaussian Mixture Model (GMM).However, with these graph-based techniques, manual creativity is necessary.The main issues with current techniques for foreground extraction in image segmentation are accuracy and performance.
Fever Classification Using the Neighbor Weighted K-Nearest Neighbor Method

Demam merupakan gejala atau reaksi tubuh terhadap suatu infeksi atau penyakit. Demam dapat disebabkan karena adanya infeksi virus, bakteri, dan parasit. Serta demam akibat gigitan nyamuk. Beberapa penyakit penyebab demam yang perlu diwaspadai antara lain Demam Berdarah Dengue (DBD), Demam Tifoid, dan Malaria dikarenakan gejala klinis dari ketiga penyakit tersebut sangat mirip dan sulit untuk dibedakan. Akibat dari gejala yang mirip, seringkali menyebabkan kesulitan dalam mendapatkan diagnosis awal sehingga kurang tepat dalam penanganan. Oleh karena itu, pada penelitian ini dibangun sebuah sistem yang dapat mengklasifikasikan demam menggunakan metode Neighbor Weighted K-Nearest Neighbor. Data yang digunakan berjumlah 300 data dengan komposisi rasio data latih dan data uji sebesar 70%:30% sehingga data latih yang digunakan berjumlah 210 data dan data uji berjumlah 90 data. Penelitian ini dilakukan dengan mengamati variasi nilai ketetanggaan (K) dan nilai exp (E) terhadap akurasi sistem klasifikasi demam. Hasil pelatihan menunjukkan bahwa nilai K dan E yang bervariasi tidak mempunyai pengaruh terhadap akurasi tersebut. Hasil pengujian yang dilakukan mendapatkan akurasi sebesar 100% pada setiap variasi nilai K dan E.Fever is a symptom of the body's reaction to an infection or disease. Fever can be caused by viral, bacterial, or parasitic infections. as well as fever due to mosquito bites. Several diseases that cause fever that need to be watched out for include dengue hemorrhagic fever (DHF), typhoid fever, and malaria because the clinical symptoms of these three diseases are very similar and difficult to distinguish. As a result of similar symptoms, it often causes difficulties in getting an early diagnosis, so treatment is not appropriate. Therefore, in this study, a system was developed that could classify fever using the neighbor weighted K-nearest neighbor method. The data used totaled 300, with a composition ratio of 70% training data to 30% test data, for a total of 210 training data and 90 test data. This research was conducted by observing the variation in the value of neighborliness (K) and the value of exp (E) on the accuracy of the fever classification system. The results of the training show that the varying K and E values have no effect on accuracy. The results of the tests carried out obtained an accuracy of 100% for each variation in the values of K and E.
Pentingnya Manajemen Perkantoran dan Informasi dalam Sebuah Organisasi

Any organization, including companies, a leader is required to be able to develop a company and in developing the company a leader can apply effective leadership management by applying leadership that puts family and togetherness at the forefront, with such leadership employees will feel comfortable doing their jobs, the company will increase and be more developed if employees feel happy in doing the work given by the company. This study contains the following problem formulations: (1) What is the purpose of office administration? (2) What are the Scope of Office Management? (3) What is Management Information System? (4) What is the Importance of Management Information Systems in Organizations. Thus the purpose of the problem of this research is (1) Want to Know What is the Purpose of Office Administration? (2) Do you want to know what is the Scope of Office Management? (3) Want to Know What is a Management Information System? (4) Want to Know What is the Importance of Management Information Systems in Organizations. This study uses a qualitative method, namely the author analyzes data based on information obtained from literacy in books, journals and other scientific works. From the results obtained. The purpose of office administration in general; Implementing and developing communication skills, Implementing and developing information technology capabilities, implementing and developing the ability to plan, implement, organize and evaluate tasks that are their responsibility, implement and develop skills in managing letters/documents according to standard operations and procedures to support the main tasks of the institution, implementing and developing services for relationships so that each party benefits, implementing and developing the ability to manage financial administration
The Relationship between Communication in the Family and the Development of Speech in Children Aged 5-6 Years in RW 03 Patenggangan

This research is motivated by the low language skills of children. This is evidenced by the number of children who cannot provide answers to complex and clear questions, children are not able to communicate in simple and easy-to-understand sentences, suspected of lack of communication in the family. This study aims to: 1) see the picture of communication in the family. 2) Look at the picture of the language development of children aged 5-6 years. 3) Seeing the relationship of family communication with language development in children aged 5-6 years. The method used in this research is a correlational type quantitative approach. The population in this study are mothers who have children aged 5-6 years in RW 03 Air Tawar Barat Village as many as 56 people. The sample used by the researcher is cluster random sampling. The data collection technique used is in the form of a questionnaire. The data analysis technique uses the percentage formula and product moment. The results showed: 1) The description of family communication in RW 03 was considered good. 2) The description of the language development of children aged 5-6 years in RW 03 is not good. 3) There is a relationship of communication in the family with the language development of children aged 5-6 years in RW 03. Keyword: Communication, Speech development