Reviewerly/exHarmony huggingface dataset:
Fed-CSA: Channel Spatial Attention and Adaptive Weights Aggregation-Based Federated Learning for Breast Tumor Segmentation on MRI

Breast tumor segmentation is an important task in medical image analysis, especially in the diagnosis and treatment of breast cancer. Magnetic resonance imaging (MRI) is a widely used imaging modality for breast tumor segmentation due to its high spatial and temporal resolution. However, there are several challenges associated with breast tumor segmentation on MRI data. One of the main challenges is the heterogeneity of MRI data collected from different imaging centers, which is usually caused by inconsistencies in the imaging protocols, quality, and resolution. Additionally, there are privacy concerns associated with the use of patient data, which limit the sharing of data across institutions. To address these challenges, we propose a novel federated learning framework called Federated Channel Spatial attention adaptive weight Aggregation (Fed-CSA) for breast tumor segmentation on MRI data. The proposed framework uses a CSA module to handle the heterogeneity of MRI data collected from different centers, which can pay more attention to the target regions and ignore non-related regions. Furthermore, the adaptive weight aggregation (AWA) method is proposed to improve the efficiency of model weight aggregation in federated learning across multiple clients while preserving patients’ privacy. The proposed Fed-CSA framework is trained and tested on a large-scale dataset of 1460 patients from different centers, and the results demonstrate its effectiveness in terms of efficiency and feasibility, outperforming other baseline algorithms.
Siamese Adaptive Template Update Network for Visual Tracking

Siamese-based trackers have achieved strong performance in single-target tracking. Effective feature response maps are fundamental to improving tracker performance when dealing with challenging scenes. However, most Siamese-based trackers have constant template features when tracking. This approach greatly limits the effectiveness of the tracker in complex scenes. To solve this issue, we proposed a novel tracking framework, termed as SiamATU, which adaptively performs update of template features. This update method uses a multi-stage training strategy during the training process so that the template update is gradually optimized. In addition, we designed a feature enhancement module to enhance the discriminative and robustness of the features, which focuses on the rich correlation between the template image and the search image, and then makes the model more focused on the tracking object to achieve more precise tracking. Through extensive experiments on GOT-10K, UAV123, OTB100, and other datasets, SiamATU has a leading performance, which runs at 26.23FPS, exceeding the real-time level of 25FPS.
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models

Training an ensemble of diverse sub-models has been empirically demonstrated as an effective strategy for improving the adversarial robustness of deep neural networks. However, current ensemble training methods for image recognition typically encode image labels using one-hot vectors, which overlook dependency relationships between the labels. In this paper, we propose a novel adversarial ensemble training approach that jointly learns the label dependencies and member models. Our approach adaptively exploits the learned label dependencies to promote diversity among the member models. We evaluate our approach on widely used datasets including MNIST, FashionMNIST, and CIFAR-10, and show that it achieves superior robustness against black-box attacks compared to state-of-the-art methods. Our code is available at https://github.com/ZJLAB-AMMI/LSD .
Blockchain-Based Access Control Mechanism for IoT Medical Data

With the proliferation of medical Internet of Things (IoT) devices that collects sensitive healthcare data, access control is more crucial than ever to ensure medical IoT data from unauthorized use. The security and privacy of medical records must be addressed rigorously. The security protocols and distributed system architecture are designed for effectively implementing the access control mechanisms to prevent privacy leakage and insecure data sharing. Blockchain is a promising candidate for strengthening the security and privacy needs of IoT medical systems. This paper proposes a blockchain-based access control mechanism for IoT medical data. Medical IoT data can be encrypted and stored off-chain in a distributed file storage system. Data access transactions only record the address of the data ciphertext and the encryption-key ciphertext into the blocks, which can significantly improve the scalability of the blockchain and strengthen privacy protection of medical data. The immutable nature of blockchain ensures the integrity and authenticity of medical data. Our BAC-IOMT scheme embeds the access policies into smart contracts to execute data access control mechanism. Finally, this scheme is proven to be secure and its performance efficiency, data volume and delay are analyzed in detail, the experimental results show that it is efficient and feasible.
Challenges Encountered by Teachers in the Implementation of Senior High School Curriculum

The study examined and identified the challenges encountered by the teachers in the implementation of the Senior High School Curriculum and the relationship with the performance rating of teachers. The descriptive method was used with the questionnaire as the main tool in the gathering of data. The Chi-square, Pearson Product Moment Correlation, and T-test as the statistical tools used in the analysis. The findings revealed that teachers are tasked to handle subjects not within their field of specialization; Unavailability of textbooks, teachers' manuals, guides, and modules for students resulting in them spending their resources on the reproduction of these instructional materials; Students’ misuse of technology; Lack of physical facilities; Services rendered during Saturdays and holidays are not compensated with monetary benefits as these challenges were considered high extent. It was further revealed the extent of challenges encountered by senior high school teachers significantly affects their performance. Hence, Senior High School (SHS) teachers should pursue their graduate and post-graduate studies to reskill and upskill their professional and academic competencies with the recent trends in global education, in a way that will enhance their competence. In a similar direction, they are encouraged to be resourceful and innovative in the use of locally available instructional materials that will have a significant impact on the delivery of instruction. Establishing a strong partnership with the officials in government and private sectors, other community leaders, and folks in a manner that will motivate the political officialdom to lend support to schools and the stakeholders in addressing encountered issues towards quality delivery of education. Hence, there is no significant difference between the degree of challenges encountered by the respondents in the implementation of the Senior High School Curriculum when grouped and compared according to selected variables such as age, sex, civil status, and highest educational attainment.
Analysis of Student Needs for PBL-Based Mobile Learning Media

The purpose of this research is to analyze the needs of students for learning media android based on high school economics subjects. The offered mobile learning media is a learning application that is integrated with a problem-based learning model, which aims to improve critical thinking skills. The research subjects were 30 grade 11 high school students in Wonogiri district. This study uses a qualitative descriptive method. The research instrument used is interview guidelines and questionnaires about student needs. The results of this study are in the form of data obtained from observations, interviews with one of the economics teachers, and also a questionnaire filled out by the student as research subject. The data analysis technique used refers to the Miles and Huberman model, namely (1) data reduction (data collection), (2) data presentation, and (3) concluding. From the results, this research concluded that it takes an innovative mobile-based learning media that can be accessed on smartphones.
ANALISIS KEGIATAN PRESERVASI BAHAN PUSTAKA DI PERPUSTAKAAN UNIVERSITAS TRISAKTI

One of the criteria for evaluating the effectiveness of library management is the preservation of library materials. Based on these problems, it is interesting to do research on Trisakti University library efforts in preserving library materials. A qualitative descriptive approach is used in conducting research on efforts to preserve library materials at the Trisaksi University Library. The findings show that the Trisakti University Library has implemented environmental control and kept the environment clean in the library. As for curative measures, the library only manually rebinds collections that have been damaged. The Trisakti University Library does not have a written policy that regulates preservation activities so that the implementation becomes unstructured. Thus, efforts to preserve library materials at the Trisakti University Library are considered to have not run optimally.
PERAMALAN INDEKS JAKARTA ISLAMIC INDEX (JII) DENGAN PENDEKATAN REGRESI PARAMETRIK LINIER SEDERHANA DAN REGRESI NONPARAMETRIK KERNEL DILENGKAPI GUI R-SHINY

Investment in Islamic stocks in Indonesia has increased from 2019 to 2021. One of the references for investors in monitoring Islamic stock price movements is the Jakarta Islamic Index_(JII). The_purpose_of_this_research_is_to model the index (JII) using nonparametric kernel regression. The kernel_functions_used_in nonparametric regression are Gaussian, Uniform, Triangle, and Epanechnikov._The research data-is-divided-into-In-Sample-data-for the period January-2010-to-December 2020 and-Out-Sample-data.for the_period_January_2021_to_December_2021. The_best_model_is selected based_on_the smallest MSE-value-obtained by the Triangle kernel regression with an optimum bandwidth (h) of 48, 2. The R2 value is 0.897. Based on the criteria for the R2 value, it-can-be-stated that_the_best model_is_a strong model_with a proportion of_the influence-of-the-previous index-on-the.current index value of-89.7%, and-there-maining_10.3%_is_influenced_by_other_factors.-The best model forecasting ability can be seen from the MAPE data out sample value of 3.04%, which is less than 10%, meaning that the performance of the kernel model in predicting the JII index is very good. This research uses R software which is equipped with R-Shiny GUI to help with data processing.
Analisis Sentimen Pada Perusahaan Penyedia Jasa Logistik J&amp;T Menggunakan Algoritma Multinomial Naive Bayes dan Support Vector Machine

Online shopping is a way to a faster and easier process of buying things or needs for people these days. Logistic services are essential in the process of buying things online, for they will be the one who ship the package to the buyer. PT. Global Jet Express or J&amp;T is one of many logistics service provider company that are available in Indonesia. J&amp;T has a Twitter account which is used for communicating with their customers. Opinions that were posted by J&amp;T consumers on Twitter could be used as a data to do sentiment analysis which the purpose is to extract information that are told by people in Twitter about J&amp;T. Data crawling was done for 15.000 tweets that were posted during the period of 4th to 10th of July 2022, duplicated tweets and those who has the exact same contents were removed resulting the data reduced to 2500 tweets. Tweets will be divided into two class; positive class and negative class Some classification methods are commonly used in text classification, such as Random Forest, Decision Tree, Naïve Bayes Classifier, Support Vector Machine etc. Data in this research will be classified using Multinomial Naïve Bayes and Support Vector Machine to compare their accuracy, the reason for the comparison is these methods have significant difference in their concept complexity. Multinomial Naïve Bayes classify data by finding the greatest conditional probability value, whilst Support Vector Machine classify data by finding the best hyperplane to divide into two class. Multinomial Naïve Bayes has the accuracy of 72,80% and Support Vector Machine has the accuracy of 82,40%. Based on their accuracy, Support Vector Machine has the best performance in classifying public opinions about J&amp;T on Twitter.
K-NEAREST NEIGHBOR DENGAN ADAPTIVE BOOSTING DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE UNTUK KLASIFIKASI DATA TIDAK SEIMBANG

Breast cancer is non-skin cancer that is caused by several factors, including glandular ducts, cells, and breast support tissue, except for the skin of the breast. Breast cancer if not treated immediately will be fatal for the sufferer, so early detection of breast cancer is important for the patient's safety. The success of breast cancer detection depends on the right diagnosis. Measurement of the accuracy of a breast cancer diagnosis can be assisted by statistical methods, namely classification. K-Nearest Neighbor is a classification algorithm based on the nearest neighbor that is easy to implement. In the classification process, there are several problems including when faced with imbalanced data. Imbalanced data can cause classification algorithms to tend to focus on the majority class. Data imbalance can be overcome by using Synthetic Minority Oversampling Technique (SMOTE). Ensemble methods can be applied to improve the performance of imbalanced data classification, one of which is Adaptive Boosting. This study applies K-Nearest Neighbor combined with Adaptive Boosting and SMOTE for handling imbalanced data classification. The results of this study are, SMOTE can handle the problem of imbalanced data and the application of K-Nearest Neighbor with Adaptive Boosting can produce an accuracy of 80%, a sensitivity of 83,33%, a specificity of 66,67%, and a G-Mean value of 74,54%. So it can be concluded that K-Nearest Neighbor combined with Adaptive Boosting and SMOTE can be applied for handling imbalanced data classification.
MEDIA PEMBELAJARAN CD INTERAKTIF UNTUK SISWA SEKOLAH DASAR

Learning media can make it easier for students to understand the material presented. With the development of the media era, it is possible to take advantage of increasingly modern technology, but schools still rarely use learning media in their learning. This research and development aims to produce a product in the form of an CD interactive media and to test the feasibility of an interactive CD media product on the sub-themes I and my dreams with the Microsoft PowerPoint for the fourth grade elementary school students. This research was conducted at SDN Kacangan I Lamongan with a class IV research sample. This research is a development research that uses the modified Borg and Gall development research model. The results of this study are learning media in the form of Interactive CDs and based on the results of product trials by material experts and media experts, the product has a decent criteria with a percentage of 90%. Based on the product trial on a limited scale, the product received the eligible criteria with a percentage of 79,4%. Based on these results, the Interactive CD learning media on the sub-themes I and my ideals is suitable for use in learning.&#x0D; Keyword: Learning media, CD interactive, sub-themes I and my ideals
The Role of Technology in Modern Management and corporate Workplace: An Analytical Study

The rapid advancement of technology has brought about significant transformations in the realm of modern management and the corporate workplace. This comprehensive study aims to delve into the intricate role of technology in shaping various facets of management practices and organizational dynamics. By conducting an extensive analysis of existing literature, this study seeks to examine how technology impacts communication, collaboration, decision-making, productivity, and overall organizational effectiveness. The findings of this study shed light on the transformative influence of technology on communication channels. It has revolutionized the way individuals interact by enabling real-time exchanges, fostering global connectivity, and facilitating seamless information sharing across geographical boundaries. Consequently, these advancements have resulted in enhanced collaboration and improved work efficiency within organizations.
ANALYSIS OF FLIGHT CHARACTERISTICS OF PAPER AIRPLANES

The trajectory of a paper airplane is dependent on its lift and drag performance and aerodynamic stability. Historically, darts were the perfect choice for distance competitions because of their excellent stability, but John Collins has changed this tradition with his incredible design, Susanne, by making a new distance record in 2012. In this paper, differences between this world record design and other typical paper airplanes are represented using typical models of a dart and glider since the wingspan of Susanne fits in between them. Solid models of these three airplanes are created with Solid Works and analyzed with ANSYS Fluent at different attack and sideslip angles. Finally, a comparison is made considering lift, drag, and aerodynamic stability. From the simulated results, it is observed that Susanne performed moderately in terms of lift and drag performance and yaw stability; however, it possesses the maximum amount of roll stability.
EduAction: A College Student Action Dataset for Classroom Attention Estimation

With the development of the action recognition technique, it is possible to automatically analyze the college students’ attention status through the recognition of their behavior in the classroom, which is of great significance to the evaluation of teaching quality in class. In the consideration that the college student behavior dataset is scarce, we set up a novel college students’ action dataset in the classroom for attention estimation in this paper, named as EduAction dataset. The EduAction dataset consists of 7 types of actions and 718 action clips, collected in the real college classroom environment. Furthermore, an improved two-stream ConvNet is conducted on this dataset with the 5-fold cross-validation for the performance evaluation. The results of our benchmark model achieve an overall accuracy of 83.01%. This spontaneous dataset will be a great aid for the teaching quality analysis in the learning environment.
Use the Detection Transformer as a Data Augmenter

Detection Transformer (DETR) is a Transformer architecture based object detection model. In this paper, we demonstrate that it can also be used as a data augmenter. We term our approach as DETR assisted CutMix, or DeMix for short. DeMix builds on CutMix, a simple yet highly effective data augmentation technique that has gained popularity in recent years. CutMix improves model performance by cutting and pasting a patch from one image onto another, yielding a new image. The corresponding label for this new example is specified as the weighted average of the original labels, where the weight is proportional to the area of the patch. CutMix selects a random patch to be cut. In contrast, DeMix elaborately selects a semantically rich patch, located by a pre-trained DETR. The label of the new image is specified in the same way as in CutMix. Experimental results on benchmark datasets for image classification demonstrate that DeMix significantly outperforms prior art data augmentation methods including CutMix. Oue code is available at https://github.com/ZJLAB-AMMI/DeMix .
AN INNOVATIVE WAVE ENERGY CONVERTER FOR MARINE DATA BUOY APPLICATIONS

A current research focus is on how to give steady and dependable power to offshore equipment with unique power needs, such as marine data platforms and buoys that are located far from the coast. Most of these gadgets currently make use of photovoltaic power generation, high- capacity batteries, and routinely replace electrical supply systems. Wave energy is being examined as a solution due to the significant reliance of photovoltaic power generation on solar radiation and the expensive cost associated with frequent battery replacements. Wave energy converters (WECs) are machinery that transform wave energy into electrical energy. Their main benefit is a steady supply of electricity. In this study, a brand-new integrated wave energy converter for marine data buoys is proposed and examined. It has great dependability and low maintenance costs. A magnetic lead screw (MLS)-based direct-drive energy conversion unit serves as its central component. To maximise its power production, the device can passively track the buoy' s pitching status as it is being affected by waves. Voltage, current, and power for marine data buoys were measured using specific data values that were depending on the turbulence. In order to improve the system's accuracy and decrease power dissipations, artificial neural networks (ANN) are used. Using the necessary power for the data buoy and the remaining energy stored in the electric battery to power another marine system INDEX TERMS: - Oscillating-body buoy, magnetic lead screw, and wave energy conversion.
Upaya Meningkatkan Minat Belajar Siswa pada Mata Pelajaran IPS dengan Menggunakan Pendekatan Paikem di Kelas IV SDN 097522 Siantar

This study addresses the issue of low student interest in math learning and aims to enhance their engagement through the implementation of the Participatory Learning and Knowledge Exchange Model (PALKEM) approach. The research focuses on a sample of 15 fifth-grade students, consisting of 7 boys and 8 girls, with the objective of investigating the impact of the PALKEM approach on student learning. The findings reveal a significant increase in student interest during the instructional cycles, with all 15 students (100%) demonstrating heightened engagement. These outcomes suggest that the utilization of the PALKEM approach effectively enhances students' interest in math learning. The results provide promising implications for improving students' motivation and involvement in the subject matter, specifically within the context of the LPS (math) curriculum. Therefore, the PALKEM approach emerges as a potential solution for addressing the issue of low interest in math learning and promoting a more engaging and effective learning environment.