Reviewerly/exHarmony huggingface dataset:
SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction

3D image reconstruction from a limited number of 2D images has been a long-standing challenge in computer vision and image analysis. While deep learning-based approaches have achieved impressive performance in this area, existing deep networks often fail to effectively utilize the shape structures of objects presented in images. As a result, the topology of reconstructed objects may not be well preserved, leading to the presence of artifacts such as discontinuities, holes, or mismatched connections between different parts. In this paper, we propose a shape-aware network based on diffusion models for 3D image reconstruction, named SADIR, to address these issues. In contrast to previous methods that primarily rely on spatial correlations of image intensities for 3D reconstruction, our model leverages shape priors learned from the training data to guide the reconstruction process. To achieve this, we develop a joint learning network that simultaneously learns a mean shape under deformation models. Each reconstructed image is then considered as a deformed variant of the mean shape. We validate our model, SADIR, on both brain and cardiac magnetic resonance images (MRIs). Experimental results show that our method outperforms the baselines with lower reconstruction error and better preservation of the shape structure of objects within the images.
Scholar’s vocabulary-knowledge favored listening-reading tenets: Concurrences from Madura

The study objective was to seek the favored tenets betwixt mobile-assisted-language-learning (MALL) listening-reading appliances by video-conferencing-technology (VCT) and direct-listening-reading appliances to Madurese-scholar’s second-language (L2) vocabulary-knowledge (VK) listening-reading competencies. The analysis of variance (ANOVA), which produced concurrences, contained 41 scholars in two distinct emplacements amidst a Madurese-scholars preponderance and the different amounts in groups. Several significant conclusions emerged from the current ANOVA analysis. First, the comprehensive implication betwixt MALL listening-reading-appliance and direct-listening-reading-appliance to scholar’s L2-VK listening-reading competencies was not substantiated because it was more than level significant (0.440&amp;gt;0.05). Second, we convened cognizance by direct perception, inside and outside deliberation, and documented it. Third, the cross-examination eventualities of perception-documentation were coded, and research eventualities established the Madurese-scholar higher education framework to pervade case study evidence vacuity that aims to scrutinize: i) the Madurese-scholar teaching-learning peculiarities performance of MALL-VCT-Zoom and Smart-TV multimedia edification; ii) Madurese-scholar interminable concurrences in MALL-VCT-Zoom and Smart-TV; and iii) impediments obtain by MALL-VCT-Zoom and Smart-TV during participation in edification. Irrevocably, concurrence betwixt MALL-VCT-Zoom and smart-TV to scholars’ L2-VK listening-reading competencies were interrelated impacts as Madurese-scholars can learn conveniently with their necessity and concurrently utilize smart-TV or Zoom despite their impediments.
Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation

Electronic health records (EHRs) have been increasingly adopted in clinical practices across the United States, providing a primary source of data for clinical research, particularly observational cohort studies. EHRs are a high-yield, low-maintenance source of longitudinal real-world data for large patient populations and provide a wealth of information and clinical contexts that are useful for clinical research and translation into practice. Despite these strengths, it is important to recognize the multiple limitations and challenges related to the use of EHR data in clinical research. Missing data are a major source of error and biases and can affect the representativeness of the cohort of interest, as well as the accuracy of the outcomes and exposures. Here, we aim to provide a critical understanding of the types of data available in EHRs and describe the impact of data heterogeneity, quality, and generalizability, which should be evaluated prior to and during the analysis of EHR data. We also identify challenges pertaining to data quality, including errors and biases, and examine potential sources of such biases and errors. Finally, we discuss approaches to mitigate and remediate these limitations. A proactive approach to addressing these issues can help ensure the integrity and quality of EHR data and the appropriateness of their use in clinical studies.
PEMBANGUNAN INFRASTRUKTUR FIBER TO THE HOME OLEH PT IKHLAS CIPTA TEKNOLOGI DI KAWASAN DESA JATIRANGGON

In the current era of globalization, the world of technology and information, especially the computer world, is developing very rapidly. Fiber to the Home (FTTH) is one example of the results of the development of the Internet. Fiber to the Home (FTTH) is a fiber-optic-based network communication device that provides various services such as the Internet, telephone network as well as live television broadcasts to private users. Today, many people need the Internet for many activities that require an Internet connection. When we build a FTTH (Fiber to the Home) network, we must build a quality network that is not only connected to the Internet, especially now that many ISPs are building FTTH (Fiber to the Home) networks requiring innovation. In this research, we tested the quality of the Internet, by testing the Quality of Services, which took into account download speed, upload speed, latency, delay and packet loss. The results of the test show that the download speed is 66.22Mbps, upload is 73.69Mbps, ping latency is around 5-8ms, and jitter is 7.84
Analysis of Social Media Data Using Contextual Embedding Leverage Models with Convolutional Neural Networks

This research proposes a model approach that uses leverage contextual embedding with Convolutional Neural Networks (CNN) in the context of sentiment analysis. The use of contextual embedding leverage aims to get a more contextual representation of words by using BERT. The results obtained from this study are a combination of batch size and epoch parameters with parameter values including batch values of 36, epoch values of 30 get an accuracy of 0.84, batch size values of 64, epoch values of 60 get an accuracy of 0.89, batch size values of 128, epoch values of 80 get an accuracy of 0.92 and batch size value of 256, epoch value of 100 obtains an accuracy of 0.94. so that the combination of CNN parameters and the use of contextual embedding leverage affects accuracy. This research is expected to provide an understanding of semantics in the context of sentiment analysis.
PERAN ORANG TUA TERHADAP PRESTASI BELAJAR PESERTA DIDIK DI SMP NEGERI 2 MEGO

This study aims to find out how the role of parents has on the learning achievement of students at SMP Negeri 2 Mego, how is the learning achievement of students at SMP Negeri 2 Mego. The method used in this research is a qualitative research. The data sources obtained by the author were obtained from observations, interviews and documentation. The results of this study indicate that parents who always pay attention to their children, especially attention in their learning activities at home, make children more active and more enthusiastic in learning. the same one. The role of parents in their children's learning motivation in the learning process, because parents are the basis for educating children in the family. Learning achievement of students can be known through a series of tests, both text and non-tech. To get the achievement of student learning achievement. So the role of parents can provide motivation for students at home including attention to the learning process, giving advice, giving gifts and punishments that educate, and providing learning facilities to students.
JPEG Image Enhancement with Pre-Processing of Color Reduction and Smoothing

JPEG is the international standard for still image encoding and is the most widely used compression algorithm because of its simple encoding process and low computational complexity. Recently, many methods have been developed to improve the quality of JPEG images by using deep learning. However, these methods require the use of high-performance devices since they need to perform neural network computation for decoding images. In this paper, we propose a method to generate high-quality images using deep learning without changing the decoding algorithm. The key idea is to reduce and smooth colors and gradient regions in the original images before JPEG compression. The reduction and smoothing can suppress red block noise and pseudo-contour in the compressed images. Furthermore, high-performance devices are unnecessary for decoding. The proposed method consists of two components: a color transformation network using deep learning and a pseudo-contour suppression model using signal processing. The experimental results showed that the proposed method outperforms standard JPEG in quality measurements correlated with human perception.
Iterative Feature Selection with Redundancy Accounting for the Neural Network Solution of Inverse Problems of Magnetotelluric Sounding

In this paper, we consider a neural network solution of the inverse problem (IP) of magnetotelluric sounding (MTS) which consists in constructing the electrical conductivity distribution in the Earth's interior from the values of the electromagnetic field components measured on its surface. It has a high input dimension (thousands of features), so it is necessary to reduce the input data dimension to achieve a more accurate and stable solution while reducing computational complexity. Neighboring measurement points and neighboring frequencies carry similar information dictating the need to use a selection method that considers this feature. The present work is devoted to the study of a method based on the iterative selection of features with the highest correlation with respect to the target variable and the exclusion of features with high cross-correlation. This method was compared with the traditional selection method, the cross-correlation filter.
Automated GUI Testing for Enhancing User Experience (UX): A Survey of the State of the Art

Recent developments in the area of Artificial Intelligence (AI) have broadened opportunities for users to interact with various technologies. In this context, many enterprises have been shifting their priorities to offering distinctive user experiences (UX) from purely concentrating on effective production and distribution of products/applications. They believe that creating a good user experience is the key to develop efficient and persuasive products/applications. The survey in this paper considers the user experience of mobile applications. Many times mobile application users encounter bugs which may affect the functionality of applications and it is not a good user experience. This survey is based on recognizing and analyzing existing research work that outlines various approaches used for mobile GUI testing tasks to enhance user experience. It is carried out by collecting similar research publications from reputed journals and conferences from 2018 till date. To present an organized literature survey, Research Questions (RQs) are framed. Research questions provide a clear focus and direction for the review paper. They help to define the scope of review and may ensure that the review paper addresses relevant and significant aspects of the topic. To present a detailed analysis, the results of Research Questions are collated. In the end, conclusions and future work are presented.
A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a viable answer to the shortage of healthcare professionals. However, the medical field's ability to utilize this technology may be constrained by rules governing the sharing of data and privacy issues. Furthermore, endangering human life is what happens when a medical machine learning system is tricked or hacked. As a result, robust protections against cyberattacks are essential in the medical sector. This research uses two technologies, namely federated learning and blockchain, to solve these problems. The ultimate goal is to construct a trusted federated learning system on the blockchain that can predict people who are at risk for developing diabetes. The study's findings were deemed satisfactory as it achieved a multilayer perceptron accuracy of 97.11% and an average federated learning accuracy of 93.95%.
TINDAK TUTUR IMPERATIF DALAM SLOGAN DAN POSTER TEMA LINGKUNGAN HIDUP DI SEKOLAH SMA ADIWIYATA

This study has two objectives. (1) Describe the imperative speech acts contained in the slogans and posters at Adiwiyata high school environment, (2) Describe the real form of speech acts imperative in slogans and posters on environmental activities in high school activities Adiwiyata. Data used in the form of text language slogans on banners and posters in the school environment Adiwiyata High School, both located in the room (indoor) and outdoor (outdoor) and interviews to teachers and students. This is a qualitative descriptive study. Data were analyzed using observation, documentation, and interviews. The results of this study were (1) Follow-speech imperatives contained in slogans and posters at the high school environment is imperative sentences Adiwiyata usual, the imperative sentence request, solicitation imperative sentence, errand imperative sentences and imperative sentences restrictions. (2) Analysis of the study of pragmatics related to writing slogan / poster can be concluded that the writing slogan / poster requires creativity and an understanding of the content of the slogan and poster itself, students should be familiar with the characteristics and the type of slogan and poster itself, so that the choice of words persuasive and attractive and easy to understand.
IMPLEMENTASI SIMPLE ADDITIVE WEIGHTING DALAM PENENTUAN BIBIT JAGUNG VARIETAS UNGGUL DI WILAYAH LAHAN LAHAN KERING

Corn is a staple food source that is rich in carbohydrates. As a source of carbohydrates, some people consume corn as daily food. Determination of corn seeds is an aspect that needs to be considered by farmers before planting because inaccurate selection of seeds can cause unsatisfactory yields and cause farmers to suffer losses. Determination of corn seeds carried out by farmers in Nansean Village and East Nansean Village is still using a manual method by looking at the size of the large fruit and choosing from seeds that have a fast harvest time but will produce unsatisfactory yields which causes farmers to feel crop failure. So it is necessary to build a decision support system that can provide answers to problems using the Simple Additive Weighting (SAW) method. The capability of the system built can provide solutions in selecting superior corn seeds for farmers and the results are in the form of rankings. The system development model used in developing this system is the waterfall model. Based on the research results, the chosen alternative in this study was alternative A01, namely NASA 29 Hybrid Corn with a total value of 0.88.
MULTIMEDIA SUMMARIZATION USING SPACY

The main objective of this paper is to propose a summarizing system based on natural language processing to summarize the data that can be in either video, audio or textual format. Video summarization is to generate a short summary of the content of a video by selecting and presenting the most informative or interesting materials for potential users. With the arrival of high-speed Internet and low-cost storage, the amount of data has been generated at a rocket pace, most of it is in the form of visual or video data. This makes them an important information dispensing tool. Searching and analyzing such a large number of videos is an extremely tedious task Unlike images, where information may be gathered from a single frame, videos need the user to watch the full content, regardless of duration, which presents a challenge to information extraction. Similarly, we save time by extracting crucial information from audio recordings and text. The system focuses on the development of a module using Natural Language Processing with python to summarize the YouTube videos, audio files and textual data. KEYWORDS - Multimedia, Natural Language Processing, Spacy, Summarization, Tokenization
Efficient Neural Representation of Volumetric Data using Coordinate‐Based Networks.

In this paper, we propose an efficient approach for the compression and representation of volumetric data utilizing coordinate-based networks and multi-resolution hash encoding. Efficient compression of volumetric data is crucial for various applications, such as medical imaging and scientific simulations. Our approach enables effective compression by learning a mapping between spatial coordinates and intensity values. We compare different encoding schemes and demonstrate the superiority of multi-resolution hash encoding in terms of compression quality and training efficiency. Furthermore, we leverage optimization-based meta-learning, specifically using the Reptile algorithm, to learn weight initialization for neural representations tailored to volumetric data, enabling faster convergence during optimization. Additionally, we compare our approach with state-of-the-art methods to showcase improved image quality and compression ratios. These findings highlight the potential of coordinate-based networks and multi-resolution hash encoding for an efficient and accurate representation of volumetric data, paving the way for advancements in large-scale data visualization and other applications.
The use of mixed reality in construction

This article describes Mixed Reality and its differences from Virtual and Augmented Reality. The article also deals with the concrete use of Mixed Reality in the construction industry, which is checking the conformity of the real building and the designed model. As part of the article, this method of use was actually used on the construction site. The work describes the procedure for using Mixed Reality to check the match between the real building and the model. Obstacles and problem areas that were discovered during use and will be subject to further research are also described in the article.
A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications

Miscommunications between air traffic controllers (ATCOs) and pilots in air traffic control (ATC) may lead to catastrophic aviation accidents. Thanks to advances in speech and language processing, automatic speech recognition (ASR) is an appealing approach to prevent misunderstandings. To allow ATCOs and pilots sufficient time to respond instantly and effectively, the ASR systems for ATC must have both superior recognition performance and low transcription latency. However, most existing ASR works for ATC are primarily concerned with recognition performance while paying little attention to recognition speed, which motivates the research in this article. To address this issue, this article introduces knowledge distillation into the ASR for Mandarin ATC communications to enhance the generalization performance of the light model. Specifically, we propose a simple yet effective lightweight strategy, named Target-Swap Knowledge Distillation (TSKD), which swaps the logit output of the teacher and student models for the target class. It can mitigate the potential overconfidence of the teacher model regarding the target class and enable the student model to concentrate on the distillation of knowledge from non-target classes. Extensive experiments are conducted to demonstrate the effectiveness of the proposed TSKD in homogeneous and heterogeneous architectures. The experimental results reveal that the generated lightweight ASR model achieves a balance between recognition accuracy and transcription latency.
Review on Donation System

The Donation System is a software project designed to facilitate and streamline the process of charitable donations. In an increasingly digital world, this system serves as a bridge between donors and beneficiaries, ensuring a transparent and efficient mechanism for supporting those in need. The system enables donors to make contributions online, view various charitable organizations, and track the impact of their donations. On the beneficiary side, the system helps organizations manage and distribute donations effectively. Through this project, we aim to promote a culture of giving and enhance the overall charitable ecosystem, making a positive difference in the lives of those who require assistance
Resilient Risk-Based Adaptive Authentication and Authorization (RAD-AA) Framework

In recent cyber attacks, credential theft has emerged as one of the primary vectors of gaining entry into the system. Once attacker(s) have a foothold in the system, they use various techniques including token manipulation to elevate the privileges and access protected resources. This makes authentication and token-based authorization a critical component for a secure and resilient cyber system. In this paper, we discuss the design considerations for such a secure and resilient authentication and authorization framework capable of self-adapting based on the risk scores and trust profiles. We compare this design with the existing standards such as OAuth 2.0, OIDC, and SAML 2.0. We then study popular threat models such as STRIDE and PASTA and summarize the resilience of the proposed architecture against common and relevant threat vectors. We call this framework Resilient Risk-based Adaptive Authentication and Authorization (RAD-AA). The proposed framework excessively increases the cost for an adversary to launch and sustain any cyber attack and provides much-needed strength to critical infrastructure. We also discuss the machine learning (ML) approach for the adaptive engine to accurately classify transactions and arrive at risk scores.
Attributed network embedding based on self-attention mechanism for recommendation method

Abstract Network embedding is a technique used to learn a low-dimensional vector representation for each node in a network. This method has been proven effective in network mining tasks, especially in the area of recommendation systems. The real-world scenarios often contain rich attribute information that can be leveraged to enhance the performance of representation learning methods. Therefore, this article proposes an attribute network embedding recommendation method based on self-attention mechanism (AESR) that caters to the recommendation needs of users with little or no explicit feedback data. The proposed AESR method first models the attribute combination representation of items and then uses a self-attention mechanism to compactly embed the combination representation. By representing users as different anchor vectors, the method can efficiently learn their preferences and reconstruct them with few learning samples. This achieves accurate and fast recommendations and avoids data sparsity problems. Experimental results show that AESR can provide personalized recommendations even for users with little explicit feedback information. Moreover, the attribute extraction of documents can effectively improve recommendation accuracy on different datasets. Overall, the proposed AESR method provides a promising approach to recommendation systems that can leverage attribute information for better performance.