Reviewerly/exHarmony huggingface dataset:
A Method for Breast Mass Segmentation using Image Augmentation with SAM and Receptive Field Expansion

With the development of deep learning methods and their wide application in medical image segmentation tasks, mammography, used for early breast cancer screening, can further assist clinicians in diagnosis to a certain extent. Due to the loss of fine-grained features in the downsampling process and the very few available mammograms, the main methods for extracting masses from the whole mammography are relatively ineffective. In this paper, we propose a novel mass segmentation model for mammograms based on two U-Net network models, and integrate the Receptive Field Block (RFB) module in-between to enhance the deep features captured by the model. We augmented the input image using Segment Anything Model (SAM) and evaluated the proposed architecture on two public datasets, namely the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INBreast, as well as on a private dataset. The results show that the proposed model for segmenting masses in ROI regions can achieve high Dice scores of 92.18% and 89.85%, and Intersection over Union (IoU) scores of 85.47% and 80.80% on both INBreast and the private dataset. In addition, our model for segmenting masses on whole mammographs in CBIS-DDSM dataset can achieve Dice scores of 58.10% and 41.96% IoU scores. Besides, our model performance improved to Dice score 61.78% and IoU score 43.00% on whole mammographs in private dataset using SAM-augmented input images.
Single Image Reflection Removal with Diffusion Model

The removal of undesirable reflections from workpiece surface images captured under industrial conditions is a challenging task in image enhancement for the industry. While existing reflection removal methods have shown promising results in natural situations, they often struggle with image reflections encountered in industrial settings. This is mainly due to the evaluation of these methods on synthetic or unrepresentative datasets. To address this issue and advance research in this area, we introduce a new dataset called the Workpiece Surface Image Reflection Dataset (WSIRD). This dataset comprises 1000 image pairs captured under various imaging conditions in industrial environments. With WISRD, we aim to facilitate the development of new techniques for reflection removal specifically tailored to workpiece surface images. In line with this, we propose a novel diffusion-based model called ReflectDiffusion. This model leverages a reflection image as a constraint and employs a guided denoising process during the inference stage to remove reflections from workpiece surface images. Through comprehensive experiments, we demonstrate that our proposed method effectively eliminates reflections in workpiece surface images.
PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation

Large language models(LLMS) have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes PatternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation.
Penggunaan Media Gambar Langkah Kaki Terhadap Hasil Belajar Pencak Silat Seni Tepak Tilu Jalan Muka Satu

Abstract&#x0D; Learning media has a big enough role in efforts to improve learning outcomes. Pencak silat is one of the sports that is studied in schools as part of efforts to preserve national culture, however, learning pencak silat is often learning material that is considered old-fashioned and boring and movements that are quite difficult for students because the movements must be memorized in pencak silat. silat is quite diverse, especially in the category of ibing martial arts, so that it has an impact on learning outcomes. The purpose of this study was to determine the implementation of media images of footsteps on the learning outcomes of the pencak silat art of tap tilu one way street in high school students. This study uses the experimental method, the research design used in this study is the Posttest Only Control Design. The research sample was class X high school students with a total of 30 people. The research instrument uses the standard assessment of the national jury referee in the ibing category. Based on the research results obtained (1) the learning outcomes using the normality test show that the value (L count) in the post-test of the two &#x0D; classes is smaller than the table value with degrees of freedom (α = 0.05). This means that the variances of the two samples are the same or homogeneous. (2) Then the calculation of the t-test in table 4.5 obtained a significance value at the time of the posttest, a significance value of 2.36 ˃ 1.753 was obtained, then H0 was rejected, meaning that there was a significant difference in the effect of learning outcomes between the experimental group and the control group, it can be explained that the implementation of media images of footsteps has a significant influence on the learning outcomes of pencak silat, the art of tepak tilu, one way face for students in senior high schools..&#x0D; Keywords: images, footsteps, learning outcomes, martial arts, tepak tilu.
Cyberbullying Against Female : The Case of Indonesian Celebrities

Cyberbullying has been noted as one of the fundamental problems in the world that needs thoughtful action. It is treated this way due to the range and the risk it may create. There have been abundant studies on cyberbullying that focus on the cases undergone by celebrities. However, there still needs some further research concerned on how cyberbullying cases take place in Indonesian contexts linguistically. This study aims to provide insight on the cyberbullying cases experienced by Indonesian female celebrities based on linguistic point of view. The data were collected from the comments left by Instagram users on Indonesian female celebrities’ Instagram accounts. The data were subsequently classified into cyberbullying forms. Speech acts classifications theory by Searle (1969) and Yule (1996) are used in this study to identify the intention of the comment left by the netizens as well as to analyse the findings. It is shown through the result of this study that cyberbullying toward Indonesian female celebrities attacks physical appearance dominantly through the form of flaming and harassment cyberbullying. Speech acts found from the netizen’s utterances are representatives, expressives, and directives using indirect form of language uses. This implies that in term of gender-based cyberbullying, Indonesia netizens commonly attack the sexuality, behaviour, and physical appearance including face, clothing, and body shape.
THE EFFECT STORYTELLING TO 2ND SEMESTER STUDENTS OF UNIVERSITY MUHAMMADIYAH TANGERANG

ABSTRACT Storytelling is an activity or activity to tell a story organize and attractively. This research is aimed to discover the differences between students who given storytelling and the students who given conventional technique in improving students’ speaking skill. The research conducted to 2nd semester student from universitas Muhammadiyah Tangerang with the total sample is 56 students. The methodology of the research is quantitative approach with quasi experimental method. The researcher did 2 tests, consist of pre-test and post-test. This research was conducted of two classes, control class and experiment class. The result of the research shows that students who taught by using storytelling have higher score than the students who taught by using conventional technique. The students can explore their ideas well and more interested in learning speaking by using storytelling. As suggestion, the researcher hopes that the teachers must be able to choose appropriate technique and media in teaching English.
Bimbingan Penulisan Artikel Bagi Mahasiswa UIN Ar-Raniry dan Universitas Teuku Umar pada Harian Serambi Indonesia

The importance of guidance on writing scientific articles for students which is carried out based on field research and analysis, in order to improve their competence as campus people. The purpose of this service is to understand and have the ability to write scientific papers for students of UIN Ar-Raniry and University of Teuku Umar (UTU). This guidance is carried out on the basis of students' lack of ability to produce scientific articles that can inspire the public. The advantages that can be obtained from these contributions are considered to arouse talent, interest, and enthusiasm as well as the enthusiasm of academic people to work. After conducting guidance on student productivity as an effort to develop intelligence to produce written works, also at least students are increasingly motivated to work to realize the ethics and personality of insightful undergraduates on campus. In this condition, it was seen that the guidance students (participants) were very enthusiastic about carrying out the assignments given by the resource persons. The assignments and exercises given complete the scientific articles of each participant. Enriching scientific knowledge in the form of articles for journals and books as a standard for a university graduate.
Klasifikasi Penyakit Gagal Jantung Menggunakan Algoritma K-Nearest Neighbor

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.
Densest Multipartite Subgraph Search in Heterogeneous Information Networks

Cohesive multipartite subgraphs (CMS) in heterogeneous information networks (HINs) uncover closely connected vertex groups of multiple types, enhancing real applications like community search and anomaly detection. However, existing works for HINs pay less attention to searching CMS. In this paper, we leverage well-established concepts of meta-path and densest subgraph to propose a novel CMS model called the densest P -partite subgraph. Given a multipartite subgraph of an HIN induced by i =| P | types of vertices defined in a query meta-path P (i.e., a P -partite subgraph), we devise a novel density function which is the number of the instances of P over the geometric mean of the sizes of i different types of vertex sets in the subgraph. A P -partite subgraph with the highest density serves as the optimum result. To find the densest P -partite subgraph in an HIN with n vertices, we first design an exact algorithm with a runtime cost equivalent to solving Θ(|M|) instances of the min-cut problem where |M|= O (( n/i ) i ). Then, we attempt a more efficient approximation algorithm that achieves a ratio of 1/ i but still incurs the cost of solving Θ(|M|) instances of our proposed peeling problem. Both approaches struggle with scalability due to Θ(|M|). To overcome this bottleneck, we improve the exact algorithm with novel pruning rules that non-trivially reduce the number of min-cut problem instances to solve to O (|M|). Empirically, 70-90% instances are pruned, making the improved exact algorithm significantly faster than the approximation algorithm. Extensive experiments on real datasets demonstrate the effectiveness of the proposed model and the efficiency of our algorithms.
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads

Large models such as GPT-3 and ChatGPT have transformed deep learning (DL), powering applications that have captured the public's imagination. Such models must be trained on multiple GPUs due to their size and computational load, driving the development of a bevy of "model parallelism" techniques and tools. Navigating such parallelism choices, however, is a new burden for DL users such as data scientists, domain scientists, etc., who may lack the necessary systems knowhow. The need for model selection , which leads to many models to train due to hyper-parameter tuning or layer-wise finetuning, compounds the situation with two more burdens: resource apportioning and scheduling. In this work, we unify these three burdens by formalizing them as a joint problem that we call SPASE: Select a Parallelism, Allocate resources, and Schedule. We propose a new information system architecture to tackle the SPASE problem holistically, exploiting the performance opportunities presented by joint optimization. We devise an extensible template for existing parallelism schemes and combine it with an automated empirical profiler for runtime estimation. We then formulate SPASE as an MILP. We find that direct use of an MILP-solver is significantly more effective than several baseline heuristics. We optimize the system runtime further with an introspective scheduling approach. We implement all these techniques into a new data system we call Saturn. Experiments with benchmark DL workloads show that Saturn achieves 39-49% lower model selection runtimes than current DL practice.
PetHIS: A New Histopathological Image Dataset for Detecting Breast Cancer in Domestic Canines

Cancer is a severe disease that demands early discovery to increase the chances of survival and the probability of a cure. Its discovery is usually made through biopsy, a slow process subject to fatigue and psychological effects. Building intelligent computational visual tools to accelerate the diagnostic process is crucial in this context. The problem is that tools based on machine learning or deep learning, such as convolutional neural networks, depend on datasets to train the algorithm to create a proper model. Thus, this work proposes a dataset using histopathological images and augmentation of data from examinations of pets by the Laboratory of Veterinary Pathology. Tumor samples were obtained at the Veterinary Hospital School of the State University of Maranhão. Then, the dataset is used in a case study using VGG16 along with double transfer learning to demonstrate the applicability of the dataset. Results show that double transfer learning improves VGG16 efficiency, increasing metrics by around 5% using k-fold with k=5.
Analisis hambatan pembelajaran dalam jaringan (daring) pada mata pelajaran ips peserta didik kelas v di sekolah dasar

&lt;p class="Abstract"&gt;&lt;em&gt;This study aims to: (1) describe the barriers to the online learning process in social studies subjects experienced by teachers and students of grade-V SD Negeri 1 Krasak Boyolali, (2) describe solutions in overcoming the barriers of teachers and students in the online learning process at grade-V social studies subjects at SD Negeri 1 Krasak Boyolali. This study is a qualitative descriptive study. Research subjects include V-grade teachers, 10 V-grade students, principals. Data collection is conducted using passive participating observation techniques, document analysis and in-depth interviews. The validity test technique used is triangulation of methods and data sources. Data analysis techniques using interactive analysis&lt;/em&gt;&lt;em&gt; techniques. &lt;/em&gt;&lt;em&gt;The results of the research that have been carried out are that the obstacles that occur in the learning process are in the planning of teachers not making lesson plans because of the difficulty in preparing media and methods so that they use offline lesson plans. Barriers in the implementation of the application used, internet connection, implementation of media, implementation of methods, delivery of materials, facilities, assignments. Barriers to attitude assessment because teachers cannot meet directly with students. It can be concluded that in online learning, there are obstacles in the planning, implementation, and assessment.. So there are solutions to overcome obstacles by establishing communication between teachers regarding technology in online learning, the role of parents in supporting online learning activities, the time gap in task collection, the same assessment&lt;/em&gt;&lt;em&gt; system.&lt;/em&gt;&lt;/p&gt;&lt;p class="Abstract"&gt; &lt;/p&gt;
Book Catalog Application In Man 2 Bengkulu City Using The Levenstein Distance Algorithm

MAN 2 Bengkulu City is one of the public madrasah aliyah schools in Bengkulu City. The school already has a library which is used as a medium for library members or users to search for books, read books, and borrow books. So far, the library at MAN 2 Bengkulu City is still conventional and has not used related applications in terms of searching book catalogs. Members/librarians come to the library and look directly at each shelf to pick up the desired book. In addition, the data processing of the book catalog has not utilized the office application package, all book data is processed using double polio paper that has been prepared. The book catalog application at MAN 2 Bengkulu City is an application that can be used to facilitate the library in managing book data, members, borrowing, and returning books and can print the output needed in archiving library data. The book catalog application at MAN 2 Bengkulu City has applied the Levenstein Distance Algorithm which is used to facilitate the search for books when the book borrowing process is carried out. The book catalog application at MAN 2 Bengkulu City was made using the Visual Basic programming language.
The Application of Internet of Things in Healthcare Systems with a Focus on Advantages and Challenges: A Scoping Review

Background: The internet of things (IoT) has become a heterogeneous and exceedingly disseminated structure that can react to the everyday needs of individuals and distinctive organizations. The present study aimed to investigate the application of the IoT in healthcare systems, with a focus on its strengths and challenges. Methods: Electronic databases, including PubMed, Scopus, SID, and Magiran, were searched for this review study. In addition, a review of the gray literature was undertaken to identify relevant studies in English on the current subject. The titles, abstracts, and full texts were independently screened based on PRISMA guidelines for the scoping literature. General information containing the author(s) name, year of publication, the aim of the study, and specific information, including technologies used in IoT, the medical field, the application of IoT in health, limitations, and strengths, were extracted and analyzed. Results: Forty-four papers were qualified and contained in the process to 2023. Different studies have mentioned the opportunities for different forms of IoT applications, such as remote consultation, health monitoring and care, medication management, quick diagnosis, and effective treatment. In addition, studies have addressed the challenges in different forms, such as remembering everyday events, fitness, remote rehabilitation, management of chronic diseases, E-Visit, and better management of daily activities. Conclusion: The challenges and the benefits were classified into nine and ten main groups, respectively. Each of these challenges and benefits, which include smaller and more current elements, can help health policymakers apply the IoT and use its benefits.
Firms and Technology Adoption: The Role of Political Institutions and Market Size

The study presents a political economy model and analyses how firms behave towards technology up-gradation given the different dynamics of political and market institutions. The model presented here depicts that political power is controlled by the elite, who formulate trade policy to consolidate power. While the middle-class access the production technology and the labour class provides labour inelastically. The model shows that the technology adoption decision of a firm essentially depends upon the political institutions and the market size of the country. Firms in a country with strong democratic institutions adopt new technology more rapidly. While in a weak democracy, firms successfully persuade the elite policymaker to impose higher trade restrictions and obtain higher protection from technologically advanced foreign firms. Moreover, the model also shows that firms operating in a large market adopt technology more rapidly since a large market has a high price elasticity of demand and supports a large number of larger firms. Furthermore, firms adopt technologies more swiftly when the productivity gains from the adoption are relatively large.
Modeling of Optimal Fully Connected Deep Neural Network based Sentiment Analysis on Social Networking Data

Abstract In recent times, the user reviews and opinions postedon social-media like Twitter, Facebook, and Google+, and so on have received considerable attention in the domain of sentiment analysis (SA), offering valued feedback to public as well as private organizations. The examination of reviews roles an essential role for enhancing the product and service qualities. Although numerous SA methods are available in the literature, it is still needed to boost the outcome of the SA for understanding the customer feedbacks and thereby enhances the product quality. The presented article proposesan optimum fully connected deep neural network (OFCDNN) based SA, called OFCDNN-SA technique on social networking data. The OFCDNN-SA technique encompasses distinct procedureslike feature extraction, classification, preprocessing, and parameter optimization. Additionally, Glove technique is applied for the transformation of input data into feature vectors. Moreover, salp swarm optimization (SSO) based hyperparameter optimization technique is derived for maximally selecting the hyperparameters utilized in the DL method. Finally, the FCDNN methodis employed for classification purposes. To investigate the supreme achievement of the OFCDNN-SA method, a wide-ranging simulation assessment is performed and the experimental outputs highlighted the betterment over current methods by means of diverse measures.