Reviewerly/exHarmony huggingface dataset:
Combating Medical Label Noise via Robust Semi-supervised Contrastive Learning

Deep learning-based AI diagnostic models rely heavily on high-quality exhaustive-annotated data for algorithm training but suffer from noisy label information. To enhance the model’s robustness and prevent noisy label memorization, this paper proposes a robust Semi-supervised Contrastive Learning paradigm called SSCL, which can efficiently merge semi-supervised learning and contrastive learning for combating medical label noise. Specifically, the proposed SSCL framework consists of three well-designed components: the Mixup Feature Embedding (MFE) module, the Semi-supervised Learning (SSL) module, and the Similarity Contrastive Learning (SCL) module. By taking the hybrid augmented images as inputs, the MFE module with momentum update mechanism is designed to mine abstract distributed feature representations. Meanwhile, a flexible pseudo-labeling promotion strategy is introduced into the SSL module, which can refine the supervised information of the noisy data with pseudo-labels based on initial categorical predictions. Benefitting from the measure of similarity between classification distributions, the SCL module can effectively capture more reliable confident pairs, further reducing the effects of label noise on contrastive learning. Furthermore, a noise-robust loss function is also leveraged to ensure the samples with correct labels dominate the learning process. Extensive experiments on multiple benchmark datasets demonstrate the superiority of SSCL over state-of-the-art baselines. The code and pre-trained models are publicly available at https://github.com/Binz-Chen/MICCAI2023_SSCL .
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation

Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. Unsupervised Domain Adaptation (UDA) techniques have been proposed to adapt models trained in the source domain to the target domain. However, those methods require a large number of images from the target domain for model training. In this paper, we propose a novel method for Few-Shot Unsupervised Domain Adaptation (FSUDA), where only a limited number of unlabeled target domain samples are available for training. To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain. We then developed a Sensitivity-guided Spectral Adversarial MixUp (SAMix) method to generate target-style images to effectively suppresses the model sensitivity, which leads to improved model generalizability in the target domain. We demonstrated the proposed method and rigorously evaluated its performance on multiple tasks using several public datasets. The source code is available at https://github.com/RPIDIAL/SAMix .
StainDiff: Transfer Stain Styles of Histology Images with Denoising Diffusion Probabilistic Models and Self-ensemble

The commonly presented histology stain variation may moderately obstruct the diagnosis of human experts, but can considerably downgrade the reliability of deep learning models in various diagnostic tasks. Many stain style transfer methods have been proposed to eliminate the variance of stain styles across different medical institutions or even different batches. However, existing solutions are confined to Generative Adversarial Networks (GANs), AutoEncoders (AEs), or their variants, and often fell into the shortcomings of mode collapses or posterior mismatching issues. In this paper, we make the first attempt at a Diffusion Probabilistic Model to cope with the indispensable stain style transfer in histology image context, called StainDiff. Specifically, our diffusion framework enables learning from unpaired images by proposing a novel cycle-consistent constraint, whereas existing diffusion models are restricted to image generation or fully supervised pixel-to-pixel translation. Moreover, given the stochastic nature of StainDiff that multiple transferred results can be generated from one input histology image, we further boost and stabilize the performance by the proposal of a novel self-ensemble scheme. Our model can avoid the challenging issues in mainstream networks, such as the mode collapses in GANs or alignment between posterior distributions in AEs. In conclusion, StainDiff suffices to increase the stain style transfer quality, where the training is straightforward and the model is simplified for real-world clinical deployment.
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-Empowered Learning

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect labels) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL .
Visual Grounding of Whole Radiology Reports for 3D CT Images

Building a large-scale training dataset is an essential problem in the development of medical image recognition systems. Visual grounding techniques, which automatically associate objects in images with corresponding descriptions, can facilitate labeling of large number of images. However, visual grounding of radiology reports for CT images remains challenging, because so many kinds of anomalies are detectable via CT imaging, and resulting report descriptions are long and complex. In this paper, we present the first visual grounding framework designed for CT image and report pairs covering various body parts and diverse anomaly types. Our framework combines two components of 1) anatomical segmentation of images, and 2) report structuring. The anatomical segmentation provides multiple organ masks of given CT images, and helps the grounding model recognize detailed anatomies. The report structuring helps to accurately extract information regarding the presence, location, and type of each anomaly described in corresponding reports. Given the two additional image/report features, the grounding model can achieve better localization. In the verification process, we constructed a large-scale dataset with region-description correspondence annotations for 10,410 studies of 7,321 unique patients. We evaluated our framework using grounding accuracy, the percentage of correctly localized anomalies, as a metric and demonstrated that the combination of the anatomical segmentation and the report structuring improves the performance with a large margin over the baseline model (66.0% vs 77.8%). Comparison with the prior techniques also showed higher performance of our method.
Gamified Violin Playing in Virtual Reality Based Metaverse Environment

Violin is one of the most significant musical instruments of both Turkish and western music. It is a stringed instrument played with a bow. Due to its fretless nature, it is relatively difficult to learn and play the instrument, compared with other musical instruments. In this study, a violin has been modeled to be played in virtual reality and metaverse environment is for a better and more entertaining violin education. Even if the student does not actually have a violin in hand, by means of virtual reality glasses, the modeled violin can be seen in the virtual reality environment. The camera of the Oculus Quest 2 Virtual Reality Glasses and haptic gloves are used to sense the both student's finger pressing and bow movements. By means of this study, the student does not need to own a real violin. Considering that a good violin has a high cost, the student will not need to spend high cost to buy the instrument. Besides, the biggest problem of the violin and other stringed musical instruments is the tuning of the instrument. Without a good tuning, the strings will not sound harmoniously and the student will not be able to play correctly because of not hearing the correct sound. Tuning ability needs some experience. But there is no tuning problem in the violin modeled in this study since it does not have real strings which means that even a beginner level student will not have a tuning problem. Also, in this study, while playing various exercises and songs, the musical notes needed to be pressed on the violin are highlighted to ensure that the student confirms whether to play correctly. The study is an alternative material of violin education in a more entertaining way.
Happy 10th Anniversary, re3data!

&lt;em&gt;In this post, the authors celebrate the 10th anniversary of re3data, sharing insights about the history, the service itself, and how it has developed over the last decade.&lt;/em&gt;
Avaliação de projetos de instrução virtual de unidades de ensino de matemática

The objective of the research was to develop an instrument for assessing the instructional design quality of virtual classrooms created by undergraduate students majoring in Mathematics Education for Secondary Education, with the aim of enhancing mathematics education at that level. The instrument was validated using a quantitative methodology, which included expert evaluations and internal consistency based on 237 assessments of 21 virtual classrooms. The instrument is based on criteria improvements from Spain's INTEF, evaluating didactic description, content quality, adaptability, interactivity, motivation, format and design, navigation, and accessibility of audiovisual content, with adaptations made for improvement. High validity and reliability were achieved. The instrument allows for the evaluation of didactic units in the Dominican mathematics curriculum at the secondary level. This research promotes effective instructional design in virtual classrooms, thereby preventing confusion and frustration among students in poorly designed online courses.
The Degree of Practicing of Basic Stage Teachers of E-Learning Skills in Jerash Governorate as Related to Some Variables

This study aimed at identifying at the degree of practicing of basic stage teachers of e-learning skills in Jerash Governorate from their point of view, the study followed the descriptive methodology. To collect data, a questionnaire was developed, consisting of (30) items, the validity and reliability of the instrument have been verified through applying on a sample of (185) teachers chosen by using stratified random sample method. The results of the study showed that the degree of practicing of basic stage teachers of e-learning skills in Jerash Governorate from their point of view as a whole was medium, The results indicated that there were no statistically significant differences at level (α ≤ 0.05) between the average responses of the estimates of the individuals of the study sample in the degree of practicing of basic stage teachers of e-learning skills to variables of sex, and years of experience in the total score, and for all fields. In light of the results, the study recommended a set of recommendations, the most important of which are: Integrating the skills of the e-learning, especially e-learning management skills in the into in-service training programs for teachers.
The Psychological stress and its Relationship to Corona virus Pandemic among Players of the First-Class Volleyball Clubs the in the State of Kuwait

This study aims at identifying the psychological stress and it relation with Corona virus among the players of the first-class Volleyball of clubs in the State of Kuwait. The researchers have used the descriptive methodology. The study sample consisted of (150) players who were selected deliberately from the population of the study. The researchers have developed a questionnaire to measure the psychological stress and its relation to the outbreak of Corona among volleyball players of first- class clubs in the State of Kuwait. The results showed that there was a great amount of psychological stress among the players, especially from the side of administration. Moreover, they show that Corona pandemic had a substantial role in increasing psychological stress and decreasing the players’ performance level according to their skills and experience. The researchers have insisted on the necessity of psychological care to the players through making courses dealing with the psychological side as well as making workshops where discussions and views can be exchanged to confront the problems that the volleyball players face.
SURVEI TINGKAT KEDISIPLINAN SISWA PADA PEMBELAJARAN PENDIDIKAN JASMANI OLAHRAGA DAN KESEHATAN DI SMP N 2 SEDAYU

study used a descriptive method. The subject of this study was a PJOK teacher at SMP Negeri 2 Sedayu. Data collection was carried out using observation, interview, and documentation methods. If there is no discipline, it will be an obstacle for teachers in carrying out learning activities in theory and practice in PJOK learning. The purpose of this study was to determine the picture of student discipline at SMP Negeri 2 Sedayu. From the results of student discipline research in PJOK learning at SMP Negeri 2 Sedayu, it is quite disciplined in applying the discipline that exists in PJOK learning both in theory and practice. This study concluded that developing a disciplinary module needs to be applied to teachers of SMP Negeri 2 Sedayu.
Reference-based genome compression using the longest matched substrings with parallelization consideration

A large number of researchers have devoted to accelerating the speed of genome sequencing and reducing the cost of genome sequencing for decades, and they have made great strides in both areas, making it easier for researchers to study and analyze genome data. However, how to efficiently store and transmit the vast amount of genome data generated by high-throughput sequencing technologies has become a challenge for data compression researchers. Therefore, the research of genome data compression algorithms to facilitate the efficient representation of genome data has gradually attracted the attention of these researchers. Meanwhile, considering that the current computing devices have multiple cores, how to make full use of the advantages of the computing devices and improve the efficiency of parallel processing is also an important direction for designing genome compression algorithms.We proposed an algorithm (LMSRGC) based on reference genome sequences, which uses the suffix array (SA) and the longest common prefix (LCP) array to find the longest matched substrings (LMS) for the compression of genome data in FASTA format. The proposed algorithm utilizes the characteristics of SA and the LCP array to select all appropriate LMSs between the genome sequence to be compressed and the reference genome sequence and then utilizes LMSs to compress the target genome sequence. To speed up the operation of the algorithm, we use GPUs to parallelize the construction of SA, while using multiple threads to parallelize the creation of the LCP array and the filtering of LMSs.Experiment results demonstrate that our algorithm is competitive with the current state-of-the-art algorithms in compression ratio and compression time.
PEMANFAATAN DUA METODE CLUSTERING DAN ASSOCIATION RULE TERHADAP PRESTASI BELAJAR BERDASARKAN NILAI MATA PELAJARAN SISWA

Data mining is a series of processes to extract new information from a pile of data. Student learning achievements are the results obtained by students after undergoing the learning process. There are quite a lot of data on student achievement in SMK Taman Siswa Binjai. But the student data has not been utilized to the maximum, making it difficult for the School to monitor the progress of students in the school. Therefore, it is necessary to create a system to find out the implementation of Data Mining based on the K-Means Clustering Method and to know the centroid distance between 1 group and other groups and to know the implementation of Data Mining based on Apriori Algorithm and to know the Support and Confidence of student learning achievement towards eye scores study, discipline, and majors. With this system can provide benefits to the school to be able to provide knowledge about student achievement while attending teaching and learning activities and to students to be able to know their learning achievements are good what needs to be improved again and can improve it again. By implementing k-means and a priori data mining of student achievement data in 2016 - 2018, there were 604 data, and from 100 data produced 3 clusters, where 1 48 data clusters, 2 24 data clusters, 3 28 data clusters, and with the algorithm a priori produce 16 rules that are formed and get the best rule, if someone has a good enough course value (70.00 - 76.99) and has enough discipline, then most likely will be in the Department of Motorcycle Engineering with a supporting value of 9% and 88% certainty value.
Implementasi Logika Fuzzy Mamdani pada Penentuan Beasiswa Peningkatan Prestasi Akademik

Scholarships are financial assistance provided to individuals intended to be used for the sake of continuing education taken. Scholarships can be given by government agencies, companies via foundations. The awarding of scholarships can be categorized as free-giving only with work ties after the completion of education. In mathematics and computer science, algorithms are sequences or steps for counting or for solving problems written by a sequence. Related, programming programming sequence or steps to solve computer programming problems. Scholarship for increasing academic achievement or abbreviated as PPA is a scholarship program from the Ministry of Research and Higher Education (Kemenrisetdikti) aimed at D3, D4, or S1 students who support education for at least 2 semesters and is specifically intended for students with pretending students whose parents can help to learn about education. In general, fuzzy systems are very suitable for penalties based on systems that address problematic problems using a mathematical model. For example, the input values and system parameters are less accurate or unclear, it is difficult to change the mathematical model. The resulting system is a unit consisting of components or elements that together provide a source of information, material, or energy to achieve a goal. This term is often used to describe a set of entities that are used, in which a mathematical model can be made.
Error mitigation of quantum teleportation on IBM quantum computers

Quantum computers can perform computational tasks beyond the capability of classical computers, such as simulating quantum systems in materials science and chemistry. Quantum teleportation is the transfer of quantum information across distances, relying on entangled states generated by quantum computing. It is becoming a more secure way of sending information, but there is noise in the results. We sought to mitigate the error of quantum teleportation which was simulated on IBM cloud quantum computers. We hypothesized that the noise on all IBM quantum computers could be mitigated with noise-mitigation matrices. We created a quantum teleportation circuit which ran with shots of 500, 1000, 5000, and 8192 for four different qubit states. We studied general error trends in each machine and created two types of noise-mitigation matrices: universal and machine-specific. We then compared the mitigated results of both types of matrices. We found that there was noise for every IBM quantum computer during the trials. The universal noise-mitigation matrix for quantum teleportation for the three tested machines decreased the error for most trials and varied between 1%-5% for most cases after mitigation. The machine-specific noise-mitigation matrix mitigated the error of most machines to just 1-2%, which was a dramatic decrease from unmitigated results (varied between 1%-16% across three machines). The error rates for the machine-specific matrices have less variability than the universal mitigation matrix. We concluded that a universal mitigation matrix could be found for the three machines, but the machine-specific noise mitigation matrices were able to achieve more accurate results.
MENGHITUNG PENCARIAN RUTE TERDEKAT PADA OBJEK WISATA MENGGUNAKAN ALGORITMA A-STAR ( STUDI KASUS : KECAMATAN NYALINDUNG

The search for the shortest path becomes a tourist attraction that is in great demand by tourists who have never visited a tourist attraction in Nyalindung District. At this time information about tourism has spread on the internet but no one has made a special article about the shortest path to travel in Nyalindung District.Therefore, the authors make a special case study of the search for the closest route to attractions in the district of Nyalindung. The goal is to find an alternative route with the shortest search path from the initial location to the final location. This study uses qualitative, data collection based on Google Maps and direct monitoring. To solve the problem of finding the shortest path using the A-Star Algorithm. The A-Star algorithm is used to find the shortest path of two paths (alternative routes). The shortest path search results use the A-Star Algorithm to make it easier for tourists and find find tourist locations by saving search time.Keywords: A-Star Algorithm, Google Maps, Shortest Path, Travel, heuristik
PENGELOMPOKAN PENDISTRIBUSIAN PRODUK OLAHAN AYAM MENGGUNAKAN ALGORITMA K-MEANS

&#x0D; Increasing population growth in Indonesia has triggered a large number of food product demand, so the higher the level of innovation of the company to produce processed food. PT Charoen Pokphand Indonesia Food Division is one of the companies engaged in processed food made from chicken meat. However, companies currently have not been able to process distribution data and rely solely on information from sales data to find out the number of product sales with which brands are sold the most each year. In fact, it is not enough to increase income and to be able to compete with other similar companies. From the existing data stack, it must be processed to find out new information from these data. Data processing methods like this are referred to as data mining. the results of cluster 3, again - it is seen that consumer characteristics in cluster 3 are also dominated by consumers who are more interested in Okey's processed chicken products, which is as much as 5615.67 Pack / Box. Whereas for enthusiasts in processed chicken products with the Fiesta brand as many as 4614.67 Pack / Box. and enthusiasts of processed chicken products under the Champ brand 1792.67 Pack / Box. So, from all clusters, it can be concluded that cluster 3 consists of cities with the lowest sales level.&#x0D;