Reviewerly/exHarmony huggingface dataset:
Semi-supervised Domain Adaptive Medical Image Segmentation Through Consistency Regularized Disentangled Contrastive Learning

Although unsupervised domain adaptation (UDA) is a promising direction to alleviate domain shift, they fall short of their supervised counterparts. In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially. Specifically, we propose a two-stage training process. First, an encoder is pre-trained in a self-learning paradigm using a novel domain-content disentangled contrastive learning (CL) along with a pixel-level feature consistency constraint. The proposed CL enforces the encoder to learn discriminative content-specific but domain-invariant semantics on a global scale from the source and target images, whereas consistency regularization enforces the mining of local pixel-level information by maintaining spatial sensitivity. This pre-trained encoder, along with a decoder, is further fine-tuned for the downstream task, (i.e. pixel-level segmentation) using a semi-supervised setting. Furthermore, we experimentally validate that our proposed method can easily be extended for UDA settings, adding to the superiority of the proposed strategy. Upon evaluation on two domain adaptive image segmentation tasks, our proposed method outperforms the SoTA methods, both in SSDA and UDA settings. Code is available at GitHub .
Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform

Objects with complex structures pose significant challenges to existing instance segmentation methods that rely on boundary or affinity maps, which are vulnerable to small errors around contacting pixels that cause noticeable connectivity change. While the distance transform (DT) makes instance interiors and boundaries more distinguishable, it tends to overlook the intra-object connectivity for instances with varying width and result in over-segmentation. To address these challenges, we propose a skeleton-aware distance transform (SDT) that combines the merits of object skeleton in preserving connectivity and DT in modeling geometric arrangement to represent instances with arbitrary structures. Comprehensive experiments on histopathology image segmentation demonstrate that SDT achieves state-of-the-art performance.
M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision

Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different imaging modalities. This shortcoming, amplified by the limited availability of expert annotated data, has been hampering the deployment of such methods at a larger scale across modalities. To address these issues, we propose M-GenSeg, a new semi-supervised generative training strategy for cross-modality tumor segmentation on unpaired bi-modal datasets. With the addition of known healthy images, an unsupervised objective encourages the model to disentangling tumors from the background, which parallels the segmentation task. Then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality. We evaluated the performance on a brain tumor segmentation dataset composed of four different contrast sequences from the public BraTS 2020 challenge data. We report consistent improvement in Dice scores over state-of-the-art domain-adaptive baselines on the unannotated target modality. Unlike the prior art, M-GenSeg also introduces the ability to train with a partially annotated source modality.
Detection-Free Pipeline for Cervical Cancer Screening of Whole Slide Images

Cervical cancer is a significant health burden worldwide, and computer-aided diagnosis (CAD) pipelines have the potential to improve diagnosis efficiency and treatment outcomes. However, traditional CAD pipelines have limitations due to the requirement of a detection model trained on a large annotated dataset, which can be expensive and time-consuming. They also have a clear performance limit and low data utilization efficiency. To address these issues, we introduce a two-stage detection-free pipeline, incorporating pooling transformer and MoCo pretraining strategies, that optimizes data utilization for whole slide images (WSIs) while relying solely on sample-level diagnosis labels for training. The experimental results demonstrate the effectiveness of our approach, with performance scaling up as the amount of data increases. Overall, our novel pipeline has the potential to fully utilize massive data in WSI classification and can significantly improve cancer diagnosis and treatment. By reducing the reliance on expensive data labeling and detection models, our approach could enable more widespread and cost-effective implementation of CAD pipelines in clinical settings. Our code and model is available at https://github.com/thebestannie/Detection-free-MICCAI2023 .
Implementation of The Parents and Teachers’ Initiatives in Teaching Grade 1 Learners in Phonetic Blending Amidst Covid-19 Pandemic

This research determined the implementation of the parents’ and teachers’ initiatives in teaching Grade 1 learners phonetic blending in the public elementary school in Cebu during the School Year 2020-2021 as the basis for a home reading action plan. It utilized the adapted descriptive-comparative research design using survey questionnaires and supported interviews to gather data. The respondents are 84 learners with phonetic blending difficulty, 29 Grade 1 teachers, and 231 parents. Collected data were statistically treated using frequency, percentage, weighted mean, and rank. Results showed that parent-respondents are mostly 29-35 years old, female, high school graduates, dominated by mothers, available materials at home are books and grandmothers as other learning adults at home. Teacher-respondents are all females, under 29-40 years old, with masteral units, teacher III and taught for more than ten years. Parents perceived the level of learners’ phonetic blending ability as skilled and less skilled for teachers.Moreover, the level of implementation of the respondent groups’ initiatives in the development of the learners’ phonetic blending abilities is perceived as implemented for parents and highly implemented for teachers. Results show that the parents’ and teachers’ perceptions are significant. Lastly, the extent of the problems encountered by the respondents is found to be high for parents and teachers. Hence, it is concluded that both respondent groups differ in implementing initiatives in phonetic blending during the pandemic. Adoption of the home reading action plan is recommended.
Response and adaptations made by the integrated child development services stakeholders towards the digitalisation of its record keeping systems of the Anganwadi centres in urban Gujarat, India

Initiated on 12th February 2018, Anganwadi centres (AWC) of Gujarat adapted the integrated child development services common application software (ICDS-CAS), for real time monitoring and record keeping, for Anganwadi workers (AWWs), Anganwadi supervisors (AWS) and the child development project officers (CDPO).This application was abruptly discontinued in September 2020 and replaced by ‘Poshan tracker’ ,a mobile based application, launched by the ministry of women and child development, government of India on 1st March 2021. Documentation of challenges and benefits due to shift to software-based record keeping system by frontline workers of ICDS in urban Gujarat. Informal meetings were held with stakeholders like the CDPO, AWS and AWWs to discuss the challenges faced due to digitalisation of record maintenance. We planned to conduct two focused group discussions (FGD) for the same purpose. FGDs were conducted at two different Anganwadi, within city corporation area. Audio recordings of both FGDs were repeatedly played and analysed to prepare qualitative report. ‘ICDS-CAS’ didn’t store older records so the AWWs had to refer to their older registers. The software was English based but AWWs preferred Gujarati(local) language. ‘Poshan tracker had no ‘auto-update’ and ‘delete’ option, to rectify inaccurate entries. One could easily view all the information in one glance. Software database had compiled data from anganwadi, removing the need for supervisors to separately contact each AWWs for an anganwadi data. It was concluded that elder AWWs preferred old register entry system, while the younger AWWs preferred the software data entry system.
IMPLEMENTASI DATA MINING DALAM PREDIKSI TARGET PRODUKSI PADA PROSES KERJA MESIN MOLDING MENGGUNAKAN ALGORITMA LINEAR REGRESSION (STUDI KASUS : PT. AIM KARAWANG)

Production is a process activity from input to produce an output. In the process of working good production will produce good quality products and in large quantities, this is due to a lack of products that are wasted due to production defects (NG products). In the world of industrial production will run simultaneously with a target. A target is a measure of the number of achievements in a certain period of time. The company will make continuous improvements for the smooth running of production and the achievement of production targets. This is because the more products produced, the turnover obtained will also be higher. The purpose of this research is to optimize production targets in the molding machine work process at PT. AIM Karawang. Where the products produced are o rings and rubber products by processing the material in the form of rubber. The method used in this study is the data mining linear regression algorithm. The reason for using this algorithm is because it is the most suitable for the existing conditions, so the daily data report some time ago is used as a reference for determining future target achievement.
PENERAPAN FACE RECOGNITION DENGAN ALGORITMA VIOLA JONES DALAM SISTEM PRESENSI KEHADIRAN SISWA DAN GURU PADA SEKOLAH IDN BOARDING SCHOOL JONGGOL

The Viola-Jones algorithm is one algorithm that is often used to detect faces. The human face recognition system is an important technology that functions as a security system, control system, and others. This is in accordance with the research objectives, namely to determine the application of the Viola-Jones algorithm in detecting the faces of students and teachers at SMK IDN Boarding School. This research aims to develop a student and teacher attendance system at IDN Boarding School Jonggol using face recognition technology with the Viola-Jones algorithm. The developed attendance system aims to increase the effectiveness and efficiency in managing the attendance of students and teachers. Then identify attendance by comparing face images registered in the system database. The application used by the face recognition program is PyCharm Community 2023 Version 1.4 with the macOS operating system. The test used various expressions totaling 200 images of 20 people. The percentage of successful face recognition process is 95%. Some factors that affect the success of recognition are lighting, face distance and face position.
PENERAPAN ALGORITMA K-MEANS UNTUK ANALISIS NILAI TAHFIDZ SANTRI BERDASARKAN NILAI FASHOHAH DAN TAJWID (STUDI KASUS : PESANTREN MODERN AT-TAQWA BOGOR)

Modern Islamic boarding schools, like other Islamic boarding schools, are part of Islamic education in Indonesia. The purpose of this research is to develop a system that uses the K-Means Clustering method to analyze the tahfidz values ​​of students based on fashohah and tajwid values. This system utilizes the RapidMiner Application as a testing tool. This research has several important contributions. First, by applying the K-Means Clustering method, the system can group students based on similar tahfidz abilities. This can help educators (Tendik) Modern Islamic Boarding School At-Taqwa Gunungputri Bogor in monitoring and evaluating the progress of students in memorizing the Qur'an. With this research, the Leaders of the At-Taqwa Modern Islamic Boarding School hope that the New Santri Acceptance Committee (PSB) and especially the Educators of the At-Taqwa Modern Islamic Boarding School can be useful and assist them in determining the value of Tahfidz and can improve the quality of the Tahfidz Program at this Islamic Boarding School
SISTEM REKOMENDASI FILM MENGGUNAKAN METODE CONTENT-BASED FILTERING STUDI KASUS MATERI DATA MINING DI SMK IDN BOARDING SCHOOL

Movie recommendation systems have become an interesting research topic, as they can assist users in finding movies that match their preferences. The Content-Based Filtering method is one of the methods commonly used in film recommendation systems, which focuses on film content to make recommendations. This study aims to develop a film recommendation system using the Content-Based Filtering method. This method is used to make film recommendations based on the similarity of the features of the films that have been watched by the user. This research uses case studies at SMK IDN Boarding School, where this film recommendation system was built as a means of learning data mining materials for students. Movie data is taken from the film API and processed using an algorithm to extract features from film content. Then, the system recommends films based on the similarity of features with the films that are liked by the user. The test results show that the developed film recommendation system has good performance in recommending films based on user preferences. Users can provide feedback to improve the accuracy of recommendations and the system can continue to be improved using more sophisticated methods.
IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN UNTUK MENGETAHUI POLA PEMBELIAN KONSUMEN PADA KOPERASI IDN BOARDING SCHOOL

School cooperatives are established to fulfill common interests and meet the needs of students. The transaction data of a cooperative is increasing day by day, but sometimes in a cooperative the data is left without any further utilization, as happened in the IDN Boarding School Jonggol cooperative. Utilization of the collected data can produce new information that can be used as a reference for cooperatives in determining a business strategy. Therefore, research was conducted on sales transaction data in the IDN Boarding School cooperative as an object to be analyzed using data mining techniques with an a priori algorithm, resulting in a pattern of consumer purchases that occur in the Jonggol IDN Boarding School cooperative. The transaction data used is transaction data at the IDN Boarding School cooperative. This study succeeded in implementing data mining techniques using the a priori algorithm in an application that will be used to process available transaction data, resulting in consumer purchasing patterns that tend to occur from a combination of available items. The association pattern that is formed with a minimum support of 40% and a minimum confidence value of 70% produces 9 association rules. The strong rule obtained is that if you buy a white uniform, you will buy a brown uniform with a support value of 50% and a confidence value of 83.33% items.
TATA KELOLA REKAM MEDIS BERBASIS ELEKTRONIK DALAM MENUNJANG PELAPORAN OPERASI DENGAN MENGGUNAKAN METODE AGILE

For registration and reporting of operating patients at the Hermina Arcamanik Bandung hospital, manual recording is still done using paper forms, so stored data can be damaged or lost. The purpose of this study was to determine the governance of electronic-based medical records in supporting operational reporting using agile methods. The system developed for registration of surgical patients is based on a web, intending to make it easier for officers to record operating patients, minimize work time, and store data in the cloud service so that operating patient data is not damaged or lost. Development of a system for registering and reporting surgical patients at the Hermina Arcamanik Bandung hospital using the Agile method is very suitable for short-term projects because this method will be very adaptable if changes occur in a project. The system that has been made is tested using the black-box testing method. Researchers hope that the system built will be able to solve problems that often occur with the damage or loss of operational reporting documents
Virtual Learning and Memory Dissonance

This study aimed to find out declarative memory recalls during online classes. Using a qualitative approach, the instruments used were simple and difficult word-sets. The data were collected through Zoom and WhatsApp platforms and later analyzed using interactive analysis. The results revealed two dissonances encountered: Informational Loss and Built-Up Dissonance. Initially, in the simple-word set, the majority of the students encountered Informational Loss 74 times (after 30 seconds) increasing to 97 times (after 30 minutes). In difficult word sets, it occurred 144 times, increasing to 154 times within identical spans. Regarding Built-Up Dissonance, it occurred 19 times (after 30 seconds), decreasing to 13 times (after 30 minutes) in simple word sets, and 27 times (after 30 seconds), increasing to 30 times (after 30 minutes) in difficult word sets. Surprisingly, it was found that in the recall test, linguistic buffering was generally employed instead of visual buffering. This finding helps teachers understand that virtual learning prompts need to be accompanied by activities involving procedural memory to minimize dissonances.
A Perspective on NFTs in the Arts-and-Music Industry<sup>*</sup>

Abstract Significant interest in non-fungible tokens (NFTs) as a means of changing the music industry has motivated this investigation. First, a comprehensive literature review (of strengths, weaknesses, opportunities and threats [SWOT]) provides a summary of the benefits and costs associated with the deployment of blockchain- and NFT-based transactions in the music industry. Considerable effort has been devoted to identifying the economic, legal and regulatory benefits and drawbacks of applying the new technology. NFTs may be the final realisation of the digital universe, bringing exclusivity and revenues to the creator economy and the chance to revalue creative work. The technology's drawbacks may exceed its advantages. Navigation within the crypto regulatory landscape is still deemed as exploring uncharted territory. It is further complicated by the fact that it is largely uncertain which legal framework may apply due to the lack of jurisdiction-identifying criteria. Second, quantitative research is conducted as an online survey directed towards two research questions: 1) What is the extent of familiarity and knowledge related to NFTs, as well as perspectives on NFTs as potential disruptors? 2) What is the extent of music creators’ perception of NFT-related opportunities and NFT integration into the music industry? Twenty EU countries were selected for the survey. Respondents answered twenty questions focused largely on demographics, their awareness of NFTs and their perceived potential for disrupting the music industry. It is too early to say whether NFTs will be utilised successfully, especially by independent musicians. The arguments in favour of NFTs are compelling, but there are issues regarding the levels of awareness and competence required for implementation.
Satellite poetics and provocations

This paper outlines a speculative artistic research project, Orbital Reveries, which develops an alternative representational practice for engaging with satellite sensing. The project appropriates terrestrial satellite imagery and parses it through various algorithmic processes to generate visual-poetic ‘textscapes’. The goal of this exercise is to resituate satellite sensing within other modes of representational knowledge-making, exploring the value of creative speculation and experimental emergence in mapping ecological crises.
Improving Phishing Email Detection Using the Hybrid Machine Learning Approach

Phishing emails pose a severe risk to online users, necessitating effective identification methods to safeguard digital communication. Detection techniques are continuously researched to address the evolution of phishing strategies. Machine learning (ML) is a powerful tool for automated phishing email detection, but existing techniques like support vector machines and Naive Bayes have proven slow or ineffective in handling spam filtering. This study attempts to provide a phishing email detector and reliable classifier using a hybrid machine classifier with term frequency-inverse document frequency (TF-IDF) and an effective feature extraction technique (FET) on a real-world dataset from Kaggle. Exploratory data analysis is conducted to enhance understanding of the dataset and identify any conspicuous errors and outliers to facilitate the detection process. The FET converts the data text into a numerical representation that can be used for ML algorithms. The model’s performance is evaluated using accuracy, precision, recall, F1 score, receiver operating characteristic (ROC) curve and area under the ROC curve metrics. The research findings indicate that the hybrid model utilising TF-IDF achieved superior performance, with an accuracy of 87.5%. The paper offers valuable knowledge on using ML to identify phishing emails and highlights the importance of combining various models.
Deep residual neural networks for increasing the resolution of CCTV images

Images from closed-circuit television (CCTV) cameras are often stored with poor resolution due to technical and data storage limitations, reducing the utility of these images. In this study, we propose that informed design and training of an enhanced deep residual network (EDSR) can increase the resolution of CCTV images. This work is the first published application of the EDSR methodology for increasing CCTV image resolution. We utilized a dataset of 986 CCTV images that trained and tested an EDSR for generating high-resolution images from a low-resolution dataset. Implementing an EDSR model, we demonstrate that with 60 layers, performing image super-resolution is extremely effective, producing a peak signal-to-noise ratio value of 76.83 decibels. Furthermore, this technique may be used more generally as a technique for file compression enabling the storage of images at smaller file sizes at low-resolution and the ability to reproduce the same images in high-resolution form.
Systemic arterial hypertension: characterization of pathology prevalence in a health unit in Sao Luis/MA/Brazil

More than 70% of annual deaths in the world are occasional due to chronic non-communicable diseases, with emphasis on pathologies resulting from systemic arterial hypertension, which affect the cardiovascular system. Males constitute the population with the highest rates of morbidity and generally do not have the habit of seeking health care for preventive and self-care measures. The objective of this study was to analyze the epidemiological profile of men diagnosed with systemic arterial hypertension in Sao Luis - MA, from 2018 to 2022. This is a descriptive, retrospective epidemiological study, with a quantitative approach to the incidence of systemic arterial hypertension, with analysis documentation of 147 men diagnosed with SAH in the described period. The variables used were age group, obesity, smoking, sedentary lifestyle, medication use, skin color and marital status. The results obtained show the highest number of cases of SAH according to the profile of patients aged between 30 and 69 years, obese, non-smokers, sedentary, using medication and living with a partner. The study made it possible to discover new ways of addressing the health demands imposed by the destructive processes and male needs, highlighting the need for early diagnosis and treatment and the implementation of the principles of integrity and equality of the SUS. This research characterized specific groups and identified their health needs to facilitate interventions that provide effective results.