Reviewerly/exHarmony huggingface dataset:
Blockchain and Classification of Mammograms and Histopathology Images in Breast Cancer Lesions

This work provides an overview of the research that has been done on using Computer-Aided Detection (CAD) for the diagnosis of breast cancer. The focus is on mammographic and histopathology images and the different techniques used for image pre-processing, segmentation, and classification. The accuracy of the different algorithms was evaluated on different datasets, including MIAS, IRMA, DDSM, and CBIS-DDSM, and the results showed that deep learning models such as Convolutional Neural Networks (CNNs) and Random Forest, along with Multi-Layer Perception and Nave Bayes, were effective in detecting and classifying breast cancer. The results of these studies show the potential of CAD in making the diagnosis of breast cancer easier and more accurate. In the segmentation stage, the U-Net model has been modified to perform better in terms of accuracy. The DDSM database has been found to have a higher accuracy percentage compared to the MIAS and CBIS-DDSM databases. This indicates that the modified U-Net model has performed well on the DDSM database in terms of accurately segmenting the images. The use of Computer Aided Diagnosis (CAD) has improved the accuracy of breast cancer diagnosis in mammography and histopathology images. The four-step process of preprocessing, segmentation, feature extraction, and classification has proven to be effective in detecting malignant and benign tumors. Different algorithms like Naive Bayes, Multilayer Perception, Random Forest, and Convolutional Neural Network (CNN) have been used with varying accuracy levels. The best results were obtained by using the modified U-Net model for segmentation and the Random Forest algorithm for classification, as they showed higher accuracy compared to other methods and databases. In this study it has been analyzed that the use of Computer-Aided Diagnosis (CAD) in mammography and histopathology images has shown promising results in the diagnosis of breast cancer. Different algorithms and techniques have been used to improve the accuracy of the classification of malignant and benign tumors. The MIAS database has shown the best results when using a Convolutional Neural Network approach. However, histopathology images have some limitations in comparison to radiologist images, but the use of CAD is still an important tool in cancer diagnosis.
The Advance of Multi-Round Dialogue System with Deep Learning

Multi-turn dialog systems have seen significant advances in recent years, driven by various approaches. The Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method have all shown promising results in advancing the state-of-the-art in this field. However, challenges remain in developing models that can handle complex and diverse user inputs and generate responses that are not only informative but also engaging and natural. Further research is needed to address these challenges and advance state of art in multi-turn dialogue systems. This paper reviews four critical methods for improving the quality of multi-turn dialogue systems: Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method. The Multi-layer Semantic Method utilizes multi-layer neural networks to model dialogue context and generate responses with improved coherence and relevance. The reinforcement Learning Method employs a reward-based approach to optimize response generation by training models to maximize long-term dialogue success. The knowledge Graph Method incorporates external knowledge sources, such as knowledge graphs, to enrich the dialogue context and improve response quality. The Medicine Knowledge Graph Method focuses on integrating medical knowledge into dialogue systems for healthcare applications. Each of these methods has demonstrated promising results in enhancing the quality of multi-turn dialogue systems.
A Critical Review on CT Image denoising in Wavelet domain

Medical images are commonly used today by medical practitioners for the purposes of diagnosis. For the purposes of diagnosis, diagnostic images are widely used today by medical professionals. In general, MRI works on soft tissues, and CT works on hard tissues. Due to device and hardware limitations, mathematical calculations, transition mechanisms in computers, there are chances of creating noise in medical images.In yhis paper, a critical review on CT image denoising has been performed in wavelet domain. In the transform domain, the process of removing noise from an image starts with the image or data being divided up into a representation in scale space. It has methods for setting thresholds, rules for shrinking, and a way to clean up noise based on wavelets, among other things.
Studies Advanced in Weakly Supervised Fine-grained Image Classification based on Deep Learning

Recently, the concept of fine-grained classification has arouse much attention., which has aroused heated disscussion of academia and industry. The extraction of picture characteristics in early efforts on fine-grained image classification relied on dense annotations, but acquiring these annotations was time-consuming and labor-intensive. Lately, weakly supervised fine-grained image classification has gradually emerged, which can mainly be separated between techniques using the attention mechanism and techniques using various neural networks. In this paper, focusing on the above two types of frameworks, we first introduce representative algorithms, including their innovation, basic processes, advantages and disadvantages. We then quantitatively compare the results of different algorithms on mainstream datasets, where the attention based methods can achieve excellent accuracy. We finally discuss the existing challenges and future development of the fine-grained classification task, which we believe can provide some new insight for this task.
Comparison of Decision Support Systems with DEMATEL-SAW and DEMATEL-TOPSIS in the Process of Journal Acceptance: Case study in The Postgraduate E-Journal of State University of Malang

Concerns about scientific publications have been proliferating in Indonesia. Consequently, the number of published journals in e-journal has been rapidly increasing over the past few years. The growing trend of e-journal hence requires a decision support system in its application. The DSS will help the reviewers in determining the eligibility of an article in the journal’s verification process. Several DSS methods such as DEMATEL, SAW, and TOPSIS, among others, are proposed to provide an effective means in the process. This research aims to present a solid comparison of two combined methods, DEMATEL-SAW and DEMATEL-TOPSIS, as they overcome each method’s shortcomings, in determining the eligibility of an article. The eligibility criteria have been determined as a guide. The calculation results show that the DEMATEL-SAW has a relatively higher degree of accuracy compared to that of DEMATEL-TOPSIS when fewer criteria variables are included, whereas the DEMATEL-TOPSIS method has a higher degree of consistency when being utilized on a variable with more criteria
Factors Affecting Students in the Kampung Bahasa Program at Public Vocational High School 1 of Boyolangu During Covid Pandemic

The Kampung Bahasa Program at vocational high schools are developed to get students used to English speaking at the school. Unfortunately, the number of students interested in joining the program decreased significantly during the pandemic. This research tried to identify main factors influencing students interest in the Kampung Bahasa Program at Public Vocational High School 1 of Boyolangu during covid pandemic. This was descriptive research using a qualitative approach. The data in this research were collected using some techniques including interviews, observation, and documentation. The data analyzed by interactive technique. The result of the research showed that internal factors affecting students in the Kampung Bahasa Program were the physiological factor which caused most students to be afraid of being infected with covid and the psychological factor that explained students’ need for English communication, on the one hand, and, on the other hand, their shyness in English communication. The external factors included familial support, socio-economic backgrounds of students’ family which allowed them to pay the tuition, the role of the English teacher of the tenth grade, and the fact that many fellow students joined the program. In addition, students’ interest in the program was also influenced by the socio-demographic factors.
Generating Mosaic Image Using Color Feature Extraction for Content Based Image Retrieval

This research aims to create an image mosaic from a collection of images by utilizing color feature extraction with CBIR (Content Based Image Retrieval) technique. Some of the things to be considered in CBIR are the selection of the color model, how to represent color features, and metrics for calculating the distance between color features. The system consists of two main parts, preprocessing and image mosaic generating. In the preprocessing stage, image quantization is performed on images in the dataset, then the results are saved to a file that will be used in the image mosaic generating stage. We use two types of color quantization, 27 colors and 64 colors. In the image mosaic generating stage, the input image is divided into many small images (called image tiles), the size of which is adjusted to the size of the dataset. To obtain candidate images, the LCH (Local Color Histogram) values of each image tile are calculated using a certain scheme. Those schemes are 4-block separated, 4-block overlapped, and 5-block. MOSS or DOSS algorithm is used to determine the most similar image for each image tile. Based on the experiments that have been carried out, the 4-block overlapped scheme and 64 color quantization provide the most similar image mosaic. &#x0D; Penelitian ini berusaha untuk membentuk image mosaic dari kumpulan gambar dengan memanfaatkan ekstraksi fitur warna dengan teknik CBIR (Content Based Image Retrieval). Beberapa hal yang menjadi perhatian dalam CBIR adalah pemilihan model warna, bagaimana merepresentasikan fitur warna, dan metric untuk menghitung jarak antara fitur warna. Sistem terdiri dari dua bagian utama, yaitu tahap preprocessing dan tahap generate image mosaic. Pada tahap preprocessing dilakukan image quantization pada gambar dalam dataset, kemudian hasilnya disimpan ke dalam file yang nantinya digunakan dalam tahap generate image mosaic. Kami menggunakan dua macam color quantization, yaitu 27 warna dan 64 warna. Pada tahap generate image mosaic, input gambar dipotong-potong menjadi banyak gambar kecil (disebut image tile), yang ukurannya disesuaikan dengan ukuran dataset. Untuk mendapatkan kandidat gambar, diambillah nilai LCH (Local Color Histogram) dari setiap image tile dengan menggunakan skema tertentu. Skema pembagian tersebut adalah 4 block separated, 4 block overlapped, dan 5 block. Kemudian ditentukan satu gambar yang paling mirip untuk setiap image tile, dengan menggunakan algoritma MOSS atau DOSS. Dari percobaan yang telah dilakukan, skema 4 block overlapped dan 64 color quantization menghasilkan image mosaic yang paling mirip.
Learning-Free Grasping of Unknown Objects Using Hidden Superquadrics

Robotic grasping is an essential and fundamental task and has been studied extensively over the past several decades.Traditional work analyzes physical models of the objects and computes force-closure grasps.Such methods require preknowledge of the complete 3D model of an object, which can be hard to obtain.Recently with significant progress in machine learning, data-driven methods have dominated the area.Although impressive improvements have been achieved, those methods require a vast amount of training data and suffer from limited generalizability.In this paper, we propose a novel two-stage approach to predicting and synthesizing grasping poses directly from the point cloud of an object without database knowledge or learning.Firstly, multiple superquadrics are recovered at different positions within the object, representing the local geometric features of the object surface.Subsequently, our algorithm exploits the tri-symmetry feature of superquadrics and synthesizes a list of antipodal grasps from each recovered superquadric.An evaluation model is designed to assess and quantify the quality of each grasp candidate.The grasp candidate with the highest score is then selected as the final grasping pose.We conduct experiments on isolated and packed scenes to corroborate the effectiveness of our method.The results indicate that our method demonstrates competitive performance compared with the state-of-the-art without the need for either a full model or prior training.
Understanding Readers, Texts and Reading Practices

This research attempts to understand the apparently simple but complex relationship between readers and texts and various reading practices in terms of meaning making. What prompted this research was the idea that knowledge, experiences, and expectations of readers as well as what texts embody, consciously or unconsciously, interact with each other and a new text consequently comes into being. This alludes to the power of both the readers and texts. With this in the background, this article attempts to investigate the relationship between readers and the text. The study is conducted through close reading and articulates that there are different kinds of readers like surface readers, informed readers, educated readers, ideal readers, the optimal readers, implied readers, intended readers, effective readers, proficient readers, less-able readers and critical readers. The impact of this difference in readership can result in differences of interpretation, understanding and meaning making of the texts. Hence, it is recommended that the interaction between the readers and texts needs to be taken seriously and no reading practice be given preference over another.
Foreign language learning strategies of three Indonesian multilinguals: A narrative inquiry study

Foreign language learners usually learn a foreign language using their first language. There has been limited report on study discussing the ability of foreign language learners to learn a foreign language using their learned foreign language. This study employed an autobiographical narrative inquiry based on the experience of three multilinguals. This study aimed to find out their strategies in learning foreign languages and the factors affecting in learning foreign languages. This study found that there were two Indonesian multilinguals that learn Japanese using their limited ability in English. The other multilingual learn English at school and learned Dutch at a language course. The results proved that there are factors such as learner characteristics, linguistic factors, learning processes, age and acquisition, instructional variables, context, purpose, motivation, attention, attitude, study habit, self-concept, and student aptitude affecting learners in learning foreign languages. This study gives little contribution to our understanding on the strategies that might been applied by a foreign language learner to learn a new foreign language.&#x0D; &#x0D;
E-Modul Menggunakan Aplikasi EPUB Pada Jaringan Tumbuhan Untuk Siswa Kelas XI Madrasah Aliyah

This study aimed to create Electronic Publication (E-Pub) Modules for Class XI students at MAN 1 Padang Panjang on the subject of Plant Tissue Structure and Function. This study used the 4-D development model, which stands for define, design, develop, and disseminate, in accordance with the research and development (R&amp;D) methodology. The product was evaluated using e-module validation test sheets. Three professionals, including a biology lecturer from IAIN Batusangkar and two biology instructors from MAN 1 Padang Panjang, verified the e-module utilizing validation tools. The analysis included both quantitative and qualitative data analysis, considering ideas, critiques, and comments from the experts, and to assess the development of the e-module, quantitative data analysis from the validators' survey responses was used. The study's conclusions show that the Electronic Publication (E-Pub) Based E-Modules have a validity score of 80% and are aligned with the measured components. E-Modules that are based on Electronic Publication (E-Pub) was feasible to use to students.
Analisis Sentimen Pencitraan Perguruan Tinggi di Yogyakarta Menggunakan Metode Naїve Bayes Classifier

This research utilizes data from Twitter to analyze sentiment in Yogyakarta's universities using the Naïve Bayes Classifier method. The Naive Bayes Classifier method is one of the text classification methods based on the probability of keywords in comparing training and testing documents. The data used consists of tweets in Indonesian language with keywords from the top 10 universities in Yogyakarta based on webometrics, as well as four other relevant keywords about Yogyakarta that are frequently searched through Google. From the conducted research, there are 1710 data collected from Twitter, which are used for classification and categorized into 3 labels: positive, negative, and neutral. The data is divided into 70% for training and 30% for testing randomly. The result of sentiment analysis classification from the test data shows that 82.1% of the data is categorized as neutral, 14.8% as positive, and 3.1% as negative, with an accuracy value of 73%.
Hierarchical Decompositions and Termination Analysis for Generalized Planning

This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of generalized plans has been a longstanding goal in AI, it remains challenging due to fundamental gaps in methods for analyzing the scope and utility of a given generalized plan. This paper addresses these gaps by developing a new conceptual framework along with proof techniques and algorithmic processes for assessing termination and goal-reachability related properties of generalized plans. We build upon classic results from graph theory to decompose generalized plans into smaller components that are then used to derive hierarchical termination arguments. These methods can be used to determine the utility of a given generalized plan, as well as to guide the synthesis and learning processes for generalized plans. We present theoretical as well as empirical results illustrating the scope of this new approach. Our analysis shows that this approach significantly extends the class of generalized plans that can be assessed automatically, thereby reducing barriers in the synthesis and learning of reliable generalized plans.
THE IMPLEMENTATION OF ACCREDITATION SCORING CALCULATIONS IN ALL WEB-BASED ACCREDITATION AGENCIES

Accreditation is an assessment activity to determine the eligibility of a department and higher education. Accreditation aims to determine the eligibility of departments and universities based on criteria that refer to the national higher education standards and guarantee the quality of departments and universities externally in both academic and non-academic fields to protect the interests of students and the community. All accreditation agencies have different accreditation instruments. The scoring calculation can be done automatically through the application of the respective accreditation agency and can only be seen by the assessment team, while departments and universities cannot see it. so far, universities and departments can only calculate accreditation scores manually using the excell application based on accreditation assessment instruments and matrices. Therefore, the research team wants to create a web-based application that can calculate accreditation scores independently to calculate accreditation assessment scores quickly and accurately.
Commentary: Machine learning in clinical decision-making

GENERAL COMMENTARY article Front. Digit. Health, 31 July 2023Sec. Personalized Medicine Volume 5 - 2023 | https://doi.org/10.3389/fdgth.2023.1214111
Junior High School Teachers’ Perspectives on Integrating Computers in Lessons

The Global and Innovation Gateway for All (GIGA) School Program, an initiative by the Japanese government to provide each student in compulsory education with a computing device, was implemented in March 2021. This case study followed two junior high school English teachers for their first year of using the devices. The aim was to discover, through a series of semi-structured interviews, how the devices were integrated into the classroom as well as the participants’ beliefs regarding the pedagogical benefits of the technology. Through trial and error, both teachers found applications that suited their pedagogical style, and made extensive use of the technology over the course of the year; one participant utilized the devices to enhance a teacher-centered approach and the other to facilitate a student-centered one. With device programs becoming prevalent around the world, such case studies can contribute to the ongoing collective learning experience. 文部科学省 が全国の小中学校の生徒一人一人にパソコンを支給する「GIGAスクール構想」が2021年3月に施行された。本研究は、1人1台端末の導入1年目である中学校英語教師2名 に対して追跡調査を行った。その目的は、半構造化インタビューを通じて、コンピュータがどのように教育現場に組み入れられているのか、また、コンピュータがもたらす教育上の利点に関して参加者の見解を明らかにすることにある。2名 の教師は試行錯誤の結果、それぞれの教育スタイルに合ったアプリケーションを見つけ、1年の間にテクノロジーを幅広く活用していたことが明らかとなった。1名は教師主体 のアプローチの強化、もう1名は生徒主体のアプローチ の促進のために端末を活用した。1人1台端末が世界中で普及する中、本研究が継続的な集合学習に貢献することが期待される。
Moving object detection using super-pixel cell in vibe

In moving object detection research, the result of traditional mixed Gaussian model algorithm are often splited, and effect the results of segmentation and tracking.In our research, the image is detected by the super pixels , and then we use the super pixels as the unit for the following detection, which can overcome the splited objects and get the better effect then the pixel is used as the unit for detection.
Self-Supervised Representation Learning for Knee Injury Diagnosis From Magnetic Resonance Data

In medical image analysis, the cost of acquiring high-quality data and annotation by experts is a barrier in many medical applications. Most of the techniques used are based on a supervised learning framework and require a large amount of annotated data to achieve satisfactory performance. As an alternative, in this paper, we propose a self-supervised learning (SSL) approach for learning the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful, context-invariant spatial representations. The downstream task in our paper is a class-imbalanced multi-label classification. Different experiments show that the features learned by the pretext model provide competitive performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy towards imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in imbalanced multi-label classification tasks on MR scans. The code for evaluation of the proposed work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sadimanna/skid</uri> .