Reviewerly/exHarmony huggingface dataset:
Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning

Segmentation studies in medical image analysis are always associated with a particular task scenario. However, building datasets to train models to segment multiple types of organs and pathologies is challenging. For example, a dataset annotated for the pancreas and pancreatic tumors will result in a model that cannot segment other organs, like the liver and spleen, visible in the same abdominal computed tomography image. The lack of a well-annotated dataset is one limitation resulting in a lack of universal segmentation models. Federated learning (FL) is ideally suited for addressing this issue in the real-world context. In this work, we show that each medical center can use training data for distinct tasks to collaboratively build more generalizable segmentation models for multiple segmentation tasks without the requirement to centralize datasets in one place. The main challenge of this research is the heterogeneity of training data from various institutions and segmentation tasks. In this paper, we propose a multi-task segmentation framework using FL to learn segmentation models using several independent datasets with different annotations of organs or tumors. We include experiments on four publicly available single-task datasets, including MSD liver (w/ tumor), MSD spleen, MSD pancreas (w/ tumor), and KITS19. Experimental results on an external validation set to highlight the advantages of employing FL in multi-task organ and tumor segmentation.
Exploring teachers' strategies in teaching writing for young learners

Teaching writing for young learners (YL) needs unique strategies from each teacher. The purpose of this study is to help teachers explore strategies for teaching writing for young learners. The survey was used with a descriptive-qualitative method which was carried out by giving an open-ended questionnaire to five English teachers and tutors from a different institution. Then, the most appropriate strategy could not be generalized because there are different conditions of students' and teachers' qualifications. However, teachers must pay attention to three aspects of teaching such as understanding the characteristics of students, developing fun ways of learning, and peer feedback strategies. Teachers must be able to evaluate whether or not the strategy they provide can produce good results. From this research, with the exploring three different teachers' strategies in teaching writing, most of the students have succeeded in the minimum passing grade in each class. Furthermore, this paper pursued teachers to be able to design teaching writing for young learners in the right ways.
Cervical cell extraction network based on optimized yolo

Early screening for cervical cancer is a common form of cancer prevention. In the microscopic images of cervical cells, the number of abnormal cells is small, and some abnormal cells are heavily stacked. How to solve the segmentation of highly overlapping cells and realize the identification of single cells from overlapping cells is still a heavy task. Therefore, this paper proposes an object detection algorithm of Cell_yolo to effectively and accurately segment overlapping cells. Cell_yolo adopts a simplified network structure and improves the maximum pooling operation, so that the information of the image is preserved to the greatest extent during the model pooling process. Aiming at the characteristics of many overlapping cells in cervical cell images, a non-maximum suppression method of center distance is proposed to prevent the overlapping cell detection frame from being deleted by mistake. At the same time, the loss function is improved and the focus loss function is added to alleviate the imbalance of positive and negative samples in the training process. Experiments are conducted on a private dataset (BJTUCELL). Experiments have verified that the Cell_yolo model has the advantages of low computational complexity and high detection accuracy, and it is superior to common network models such as YOLOv4 and Faster_RCNN.
Comparison of polygon and voxel-based visualization of miroscopic serial sections

Nowadays, the examination of digitized samples with a 2 dimensional visualization software is a common solution in digital pathology. In addition to these solutions, a new and emerging display technology is 3 dimensional visualization. Using 3 dimensional visualization technology, doctors and researchers may be able to examine areas of tissue samples that 2 dimensional imaging programs do not allow. Examples include the internal structure of a tissue sample, the interconnection of individual tissue portions, and even volumetric lesions. Basically, 3 dimensional visualization currently has 2 major branches, all of which are covered in this paper. The first is the polygon-based visualization, the second is the voxel-based visualization. The goal of visualization is basically the same in both methods, to be able to enable 3 dimensional visualization using 2 dimensional data, but their implementation and operation are different.
Penerapan Algoritma Adaboost Untuk Peningkatan Kinerja Klasifikasi Data Mining Pada Imbalance Dataset Diabetes

According to the World Health Organization (WHO), it has been recorded that up to now more than 150 million people have diabetes, whether they are elderly people, adults, teenagers, men or women. Early knowledge of diabetes can be seen based on data from patients who already have diabetes. The patient's disease data has previously been stored and arranged in a data warehouse or what is commonly referred to as a dataset. Therefore, it is necessary to process the data contained in the dataset. But the use of data mining techniques themselves must be assisted by using the techniques contained in the data mining, namely classification techniques. K-Nearest Neighbor (K-NN) is one of the methods used in the classification technique. In the results of the classification of the level of confidence obtained in the process, it is seen based on the amount of accuracy. However, there are important issues that need special attention. In the dataset used for the classification process, the data collected contains unbalanced class results (balance). The unbalanced data classification process becomes an important problem, this is because it can cause a decrease in performance. Adaboost is a technique in data mining that can be used to increase the level of accuracy in classification methods. The results showed that the adaboost algorithm can help improve classification performance. This can be seen from the increasing level of accuracy obtained from the process carried out before and after using the adaboost algorithm. The results obtained from the research show that the adaboost algorithm can be used properly to help the performance of the K-Nearest Neighbor algorithm for the classification process on diabetes datasets. It can be seen from 5 tests with values of K = 7, 13, 19, 25 and 31 there is an increase in the accuracy results obtained after using the adaboost algorithm.
BACS: A comprehensive tool for deep learning-based anomaly detection in edge-fog-cloud systems

The Internet of Things (IoT) technology has shown to offer promising benefits in various domains, including smart cities, smart factories, and smart manufacturing. However, the large number of heterogeneous devices deployed in such IoT systems increases the attack surface and hence poses additional security challenges. In this context, many of the relevant threat detection tasks can be formulated as deep learning (DL)-based detection of anomalies in the data acquired by the IoT system. There is hence a significant need for comprehensive anomaly detection (AD) tools in IoT applications. Indeed, as typical applications usually lack labeled data, multiple models need to be tried and compared one against another in order to get solid results. Moreover, the AD offerings should be adapted to the computational and storage capabilities of the underlying infras-tructure for the given application. In this paper, we present BACS (Behavioral Analysis and Cognitive Security), a comprehensive DL-based tool for anomaly detection in IoT systems designed to be adaptable to edge-to-fog-to-cloud (E2F2C) environments. We provide an open source implementation of BACS, describe the internal architecture, explain how it is used by the developers, and illustrate its performance on real-world data provided by the Centro Ricerche Fiat (CRF) in the context of smart factory and smart logistics applications.
Restoration of Laser Interference Image Based on Large Scale Deep Learning

Although the deep learning method has achieved some results in the challenging task of repairing the missing areas of images over the years, there is no report on the images disturbed by the laser entering the field of view. We propose a restoration model that can restore the laser interference image, the corresponding countermeasure network deep learning model and a new model training method, and no additional manual marking work is required in the data training set. After a large number of experiments, the loss function can rapidly converge under this method, accurately give the reasonable reconstruction results of the image disturbed by laser, and significantly improve the scores of many common image quality evaluation methods: high-quality repair results are obtained on the laser interference composite dataset of face (CelebA), Stanford automobile, aircraft, buildings (Facade) and satellite images. It has the characteristics of fast training speed, strong robustness, modular model design and wide application.
Manajemen Pengendalian Logistik Berbasis Sistem Informasi Gudang

The purpose of this study is to determine the process of handling logistics control management based on warehouse information systems in Building H, State University of Semarang. This qualitative descriptive study used 10 informants consisting of 5 related informants, 2 involved informants, and 3 principal informants. Data collection techniques in this study used observation, interviews, and documentation techniques. Data analysis used data reduction, data presentation, and drawing conclusions. The results showed that logistics control was controlled by an online-based Asset and Warehouse Information System (SiAgung) and an offline-based SIMAK BMN. The problem with the system used is that the online system is still under development, so sometimes the control of the demand for goods is wrong, while the offline system continues to be updated continuously from the center. In addition to the system, unorganized warehouse arrangements, unavailability of reports on the use of goods per unit, and the absence of SOPs (Standard Operating Procedures) for logistics arrangements in warehouses are also obstacles to the logistics control management process.
Using SVM and PSO-NN Models to Predict Breast Cancer

Breast cancer has become one of the most common cancers among modern women. With the increase of breast cancer patients, the diagnosis data of relevant images, pathology and test reports have also increased dramatically. However, with the increasing workload of doctors, they are also faced with the problems of higher misdiagnosis rate. The imaging and pathological features of breast masses are widely used in the diagnosis and risk prediction of breast cancer. Artificial intelligence can predict the outcomes of breast cancer through the features that are computed from digitized images, which has a good clinical prospect of application. In this paper, Support Vector Machine (SVM) and Particle Swarm Optimization-Neural Network (PSO-NN) were used to establish two different breast cancer prediction models combined with the same Wisconsin Diagnostic Breast Cancer (WDBC) dataset (569 samples, including 357 benign tumors and 212 malignant tumors). The established SVM and PSO-NN models achieved a sensitivity of 95.28% and 95.75%, a specificity of 98.58% and 99.44%, an accuracy of 97.36% and 98.07%, and an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.9592 and 0.9873 for breast cancer prediction, respectively. The experimental results show that SVM and PSO-NN prediction models both have high classification accuracy, which can provide new tools for the diagnosis of breast cancer.
LL-VAS: Adaptation Method for Low-Latency 360-degree Video Streaming over Mobile Networks

With the ability to provide an “immersive experience”, 360-degree video-based applications are becoming more and more popular nowadays. In this paper, we propose LL-VAS, a novel adaptation method for low-latency 360-degree video streaming over mobile networks. By applying tile-based streaming, the proposed method allows 360-degree video streaming over resource-constrained mobile networks. In addition, by actively monitoring network throughput at the tile level, the proposed method can detect reductions in network throughput, and adapt video content in a timely manner to avoid re-buffering. Trace-driven experiments show that the proposed method can significantly decrease the number of re-buffering and re-buffering time under strong network throughput fluctuations and small buffer size when compared to reference methods.
IMPROVING STUDENTS’ ABILITY IN MASTERING BASIC ENGLISH GRAMMAR THROUGH BOARD GAMES

Grammar is central to the teaching and learning of languages. It is also one of the most difficult aspects of language to teach. Teachers teach grammar by explaining the forms and rules and then drilling students on them. This results in bored and disaffected for students. Actually teacher can teach grammar through game. There is a game that can support teachers to teach grammar which called board game. This research aimed to improve students’ mastery in Basic English grammar though board games and to figure out students’ perceptions on the use of board game in teaching Basic English grammar. This research was classroom action research. The research was done in two cycles. The sample of this study were the second semester students of English Department at STKIP MuhammadiyahMuaraBungo. They were chosen through total sampling. The data were gathered by using grammar test and Interview. The data were analyzed with qualitative and quantitate analysis. The result shown that the use of board games could improve students’ mastery in Basic English Grammar. It could be seen from the students’ scores. Moreover, students gave positive responses toward the use of board games in teaching basic grammar.
Modulation recognition of terahertz signals by deep learning

The availability of wireless communication at the terahertz frequency band has emerged as a key research field for the next generation of networks, as 5G spectrum resources become increasingly limited due to the rise in the number of wireless connections. However, the burden on base-band signal processing is increased because of the erratic fluctuations in signal waveforms caused by elements like severe weather and urban multipath scattering. Therefore, a method is needed to reduce this burden and improve processing efficiency. This work investigates the recognition of signal modulation schemes by employing deep learning under various SNR situations. It explores two networks of CNN and LSTM, and establishes both indoor and outdoor environments to accomplish effective signal processing in severe weather.
TransMarker: A Pure Vision Transformer for Facial Landmark Detection

Recent years, Convolution Neural Networks (CNNs) have achieved impressive results in facial landmark detection task. Especially, the u-shaped architecture, also known as U-net, has become the de-facto standard and achieved tremendous success. However, due to the locality property of convolution operation, it has a limitation in modeling global and long-range semantic information interaction, which is essential in localization tasks. In this work, we propose a Unet-like pure transformer method TransMarker, in which we give a new perspective to tackle facial landmark detection task in a sequence-to-sequence manner. We first split the input image into non-overlapping patches, which are seen as tokens in NLP tasks. Then, we feed the image patches into a symmetric u-shaped Encoder-Decoder architecture for local-global semantic feature learning. In addition, we introduce a Dense Skip-Connection schema to leverage the multi-level information within different resolutions. Note that, unlike conventional U-net architecture, we design the network with pure Transformer blocks, without any conventional operations. Extensive experiments demonstrate the state-of-the-art performance of our method on several standard datasets, i.e., WFLW, COFW and 300W, which remarkably outperform previous convolutional-based methods.
Multi-State Recognition Method of Substation Switchgear Based on Image Enhancement and Deep Learning

s. Since the current substation robot inspection process exists in the high-voltage switchgear status recognition is highly susceptible to the influence of complex environments such as low image contrast, light clutter interference, and blurred reading status details, this paper proposes an image enhancement and deep learning based substation switchgear state recognition method. A multi-scale Retinex-based image enhancement is proposed to enhance the adaptability of outdoor switchgear images to light changes; improve the YOLOx target detection network to introduces a lightweight ECA attention mechanism without dimensionality reduction based on the original YOLOx model’s backbone network CSPDarknet, allowing the model to learn classification features while also focusing on learning spatial features. The experimental results show that the improved network can accurately identify the boundary information of anomalies, and the quality of its prediction results will not be reduced for noise-containing data, and the network shows strong generalization, robustness, accuracy and rapidity, providing certain conditions for realizing substation equipment condition monitoring.
Eliciting Researchers’ Behaviour as the Foundation of Research Data Management Service Development

Background.Research data management (RDM) has become an important activity in universities, for researchers to fulfil funding agencies' and journal publication requirements, and to promote open science practices.Academic libraries have been identified as the locations to base RDM services.However, to develop effective RDM services, an understanding of RDM from the researchers' perspectives is needed, including how researchers manage their research data.Objectives.This study aims to discover researchers' behaviours and practices in RDM, and propose how the library can incorporate RDM into the research services offered.Methods.This case study, carried out at a research university in Malaysia, involved both quantitative and qualitative data gathering, focusing on three
Analisis Kesulitan Siswa pada Pembahasan Materi Pengelolaan Data Kelas VI SDN Kalideres 06 Pagi

This study aims to analyze student’s difficulties in discussing the material for data management for class VI. Regarding Mathematics learning related to understanding the material, discussion, student difficulties and the teacher's efforts to overcome the student's difficulties. The research method used is a qualitative research method with data collection in the form of interviews, documentation, and observations. The results of this study reveal how the difficulties in data management are (mean, median, and mode). In grade VI students, it has been seen how many students already understand and which students have difficulty understanding the learning material. Students' difficulties can be influenced by lack of attention in learning (concentration), lack of student response when participating in teaching and learning activities (reaction) and the slow thinking power of students in understanding the material.
Intelligent Dashboards to Monitor the Occurrences in Smart Cities – A Portuguese Case Study

This article concerns the needed response by the Professional Fire Brigade Regiment (FBR) in the city of Lisbon. To solve and answer the question "How to improve FBR intervention requests when an emergency is detected?" the project aims to create a functional prototype containing interactive dashboards allowing the analysis of indicators that improve decision capacity. As results attest, 58% of false alarms are cancelled even after the emergency and rescue means have been activated to the location. About 97% of the suspended requests are not cancelled before the means are sent. The number of records of occurrences tends to increase over the 8 years of study. Sunday is the weekday with the highest number of associated records, with 23.33%, specifically at 9 am and 8 pm. Autumn is the season with more occurrences, with 26.51%. More than 50% of the occurrences are in the administrative services closing time and more than 50% of the registrations send only one vehicle to the place. These indicators aim to understand if these variables are probabilistically associated with requests for interventions to be able to anticipate these scenarios and help in decision-making whenever necessary.
Augmented Reality as Early Childhood Learning Media: Environment Theme

Augmented Reality is a result of technological advances that can be used to improve learning outcomes. The purpose of this study is to provide an overview of the development of learning media based on Augmented Reality on the theme of my environment with the discussion of saving in the bank and buying and selling in the market which is applied to early childhood learning. The method used is Research and Development with the following stages: (1) preliminary investigation, (2) design, (3) realization/construction, (4) test, evaluation, and revision. Expert judgment is used in the development of this product, followed by trials with early childhood students to get various responses as input for improvement. The results of the assessment carried out by experts are included in the category of good scores and are suitable for use. The response from the students was positive, they preferred the material to be presented in 3D accompanied by an audio explanation.
Efficient convex region-based segmentation for noising and inhomogeneous patterns

Image segmentation, under the broader area of computer vision and digital image processing, facilitates comprehending a digital image by partitioning it into small and meaningful segments, regions or objects. Researchers have come up with numerous solutions, which have produced promising results. However, these results are compromised when applied to global segmentation, where all the objects/features are needed to be segmented for inhomogeneous and severe noisy images. The reasons are many including (ⅰ) the use of region-based active contour method, especially when its noise distribution is unknown; (ⅱ) lack of robustness to handle images corrupted by some multiplicative and additive noise; (ⅲ) inability to handle convex images at local minima. To fill this gap in the literature and come up with a more effective solution, this paper proposes a global segmentation model that implements a local image difference technique combined with average convolution local factor based on spatial distances and intensity differences in the local region. The proposed model has been tested and evaluated on outdoor, synthetic, and medical images, where it has outperformed the latest baselines. The model is capable of tackling efficiently noise/outliers and coping with intensity inhomogeneity issues.