Reviewerly/exHarmony huggingface dataset:
Noise-Robust Diffusion Based Semantic Segmentation

Many semantic segmentation methods that are either fast or having high accuracy based on Jaccard index(IoU) have been proposed in the literature. Therefore, diffusion models, which have shown very successful results recently, have also been used in semantic segmentation studies. Although diffusion based segmentation methods work faster than the current state- of-the-art methods, diffusion models did not provide sufficient performance in terms of accuracy. However, it is important observation that noisy and ordinary images can be used together since the diffusion models have a structure that is robust for learning. In this study, the NRSeg architecture in which diffusion models can use both noisy and ordinary images together was created. The new model performance was measured in terms of IoU and these results were compared with the performances of the state-of-the-art methods in the literature.
Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation

Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such of such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to 66× while preserving the information required to perform segmentation with a dice coefficient of 0.84 as compared to 0.88 achieved using decompressed images while reducing the overall compute by 11%.
Application of Deep Learning in the Problem of Image Restoration

In recent years, deep learning has become a popular approach for image restoration tasks such as de-noising, super-resolution, and inpainting. In this paper, the current state of the art in applying deep learning techniques to solve these problems was reviewed. In this paper various network architectures and loss functions used in the field are covered, and their strengths and limitations are analyzed. Also, an overview of the evaluation metrics used to assess the performance of these algorithms is provided. Furthermore, several real-world applications were discussed and how deep learning methods have been successfully used to improve the quality of degraded images was demonstrated. Overall, this paper provides a comprehensive survey of the application of deep learning in the field of image restoration and highlights the potential for future research and development in this area.
The correlation between digital competence and steam readiness of biology pre-service teacher

Digital competence is one of the things that pre-service teachers need to have in order to survive in global competition. The goal of this reasearch is to know the correlation between digital competence of biology pre-service teachers and their readiness to apply STEAM-oriented learning. This research is a bivariate correlational quantitative research. The sampling technique used in this study is the quota sampling technique with number of sample are 90 students. The instrument used in this study was a questionnaire to measure students' digital competency skills and their readiness for STEAM. The data analysis technique in this study is inferential statistical analysis in the form of a bivariate correlation test (Spearman Rank correlation). The Correlation Coefficient value shows a coefficient of 0.622, meaning there is a strong relationship between digital competence and readiness for STEAM. The digital competence of pre-service teachers is considered necessary in STEAM-oriented learning because technology and digital tools play an essential role in improving the quality of learning and enabling more interactive, innovative, and fun learning experiences.
Enhancing neural collaborative filtering using hybrid feature selection for recommendation

The past decade has seen substantial growth in online transactions. Accordingly, many professionals and researchers utilize deep learning models to design and develop recommender systems to suit the needs of online personal services. These systems can model the interactions between users and items. However, existing approaches focus on either modeling global or local item correlation and rarely consider both cases, thus failing to represent user-item correlation very well. Therefore, this article proposes a deep collaborative recommendation system based on a convolutional neural network with an outer product matrix and a hybrid feature selection module to capture local and global higher-order interaction between users and items. Moreover, we incorporated the weights of generalized matrix factorization to optimize the overall network performance and prevent overfitting. Finally, we conducted extensive experiments on two real-world datasets with different sparsity to confirm that our proposed approach outperforms the baseline methods we have used in the experiment.
THE EFFECT OF CHORAL READING STRATEGY ON TEACHING STUDENTS READING COMPREHENSION

This study aims to determine the reading comprehension of SMP BEREA Tondano eighth-grade students who use the choral reading approach, as well as the reading comprehension of SMP BEREA Tondano second-grade students who do not use the choral reading technique. Those who are taught using a choral reading technique have superior reading comprehension than those who are taught using a non-choral reading strategy. The research method is experimental, with one experimental and control class using the pre-test and post-test procedure. The population for this study is 20 students from the second class of SMP BEREA Tondano in the year 2020. The findings of this study suggest that students who employ the choral reading approach get higher reading comprehension scores than those who do not.&#x0D; Keywords : Choral reading, experiment, reading, comprehension, EFL
Scalable Differentially Private Model Publishing via Private Iterative Sample Selection

Model publishing and deployment are essential for artificial intelligence applications. A major challenge in model publishing is efficiently distributing the models in a scalable way without violating the privacy of sensitive data. With the wide adoption of machine learning techniques, the privacy concern has also drawn much attraction. Differential privacy has become an important notion for privacy protection and is popular in private learning. However, it may bring much accuracy loss to fulfill data privacy. In addition, the private models are also hard to train in terms of convergence, which makes the existing approaches not scalable for private model publishing. This paper proposes a model publishing framework that provides a novel way to train privacy-preserving machine learning models with fast convergence and a lower privacy budget. By incorporating the concept of iterative machine teaching and the techniques in differential privacy, we have explored a way to privately select more suitable examples in the training process for achieving good accuracy with fewer iterations. Our analysis shows the privacy and convergence performance of the proposed method, and extensive experiments have been performed on real-world datasets to demonstrate its effectiveness.
Sistem Pakar Diagnosa Tingkat Depresi Pada Lansia Di Balai Sosial Tresna Werdha Menggunakan Metode Certainty Factor

Depressive disorder in the elderly is a clinical and public health issue that has yet to be addressed.&#x0D; The elderly who live at home with their families have better physical, psychological, and environmental health than those who live in orphanages. Mental health issues, particularly depression, frequently go undiagnosed in the elderly. This is because older people who are depressed often have physical symptoms like insomnia, loss of appetite, stomach problems, and headaches. When older people are depressed, their bodies tend to hurt more. It is hard to tell whether someone is complaining about their body or mind, so depression is often found too late. To anticipate these issues, an expert system was created that can take the place of an expert in solving the problem. A clinical psychologist is an expert in the field of elderly depression. This study classified elderly depression into three levels: mild depression, moderate depression, and major depression. This study was carried out with the help of a case study from the Tresna Werdha Pakem Social Center in Sleman, DI Yogyakarta.The Certainty Factor method was used to make an expert system for determining how depressed an older person is. This is because it is a sure thing based on evidence or an expert's opinion. The goal of developing an expert system for diagnosing depression in the elderly is for expert and non-expert officers to diagnose the level of depression in the elderly. Based on 10 elderly data that the system and experts have tested by applying the Certainty Factor method, the accuracy was 98%.
IMPROVING STUDENTS’ DICTATION ABILITY USING ANIMATED SHORT MOVIE THE FANCY GENTLEMEN AS A MEDIA OF LEARNING

The purpose of this research to find out the effectiveness of using the movie as a media of learning to improve students listening skills especially in Dictation. The researcher uses Pre – Experimental Research and the sample of the study is the 9th grade students in SMP Anugrah Tondano and the total number of the sample are 20 students. The instrument of this study is an answer sheet consist of 20 numbers that students have to fill with word. The data were calculated by the result of students’ Pretest are 41 and Posttest are 77. The students gained scores from Pretest to Posttest are 36. The result of this research showed that using a movie as a media of learning to improve students listening skills especially in dictation is effective.
Contrastive Learning with Video Transformer for Driver Distraction Detection through Multiview and Multimodal Video

Distracted drivers are more likely to get involved in a fatal accident. Thus, detecting actions that may led to distraction should be prioritized to reduce road accidents. However, there are many actions that cause a driver to pivot his attention away from the road. Previous works on detecting distracted drivers are done through a defined set of actions that are considered as distraction. This type of dataset is known as ‘closed set’ since there are still many distraction actions that were not considered by the model. Being different from previous datasets and approaches, in this work, we utilize constructive learning to detect distractions through multiview and multimodal video. The dataset used is the Driver Anomaly Detection dataset. The model is tasked to identify normal and anomalous driving condition in an ‘open set’ manner, where there are unseen anomalous driving condition in the test set. We use Video Transformer as the backbone of the model and validate that the performance is better than convolutional-based backbone. Two views (front and top) of driving clips on two modalities (IR and depth) are used to train individual model. The results of different views and modalities are subsequently fused together. Our method achieves 0.9892 AUC and 97.02% accuracy with Swin-Tiny when considering both views and modalities.
Pengembangan Aplikasi Layanan Informasi Pers Menggunakan Agile (STUDI KASUS : PERS JEMPOL PNP PADANG)

Websites have a very important role because they provide various information. Websites are useful to make it easier for someone to find information, ranging from obsolete to the latest information. Websites provide information that is up to date and currently being discussed. Therefore, we need a system to make it easier to provide information to readers. The system created is designed by using Agile where this methods is used for software development in the sort term. In addition, there is a method for sorting the most viewed data by using the MFEP ( Multifactoring Evaluation Process ) method. This research can help information providers in providing information to readers quickly, well and in a more structured manner.
Applying virtual stories to enhance University students learn-to-learn competence for practicum preparation: an empirical exploratory study

Though considerable literature conducted in the context of Western societies has concluded that the use of virtual reality (VR) technology can facilitate students’ learning, the applicability of this technology to advancing students’ practicum learning in the Chinese context remains uncertain. This presentation describes the design and implementation of a learning project based on the pedagogical foundation of learning-to-learn and the application of VR technology. The extent to which the project intervention can enhance students learn-to-learn competence (i.e. creativity, professional identity and teamwork skills) was assessed through a longitudinal survey study. ANOVA analysis was conducted based on the data collected at three time points (pre-intervention, post intervention and follow-up stages) on a group of 107 University students. Results indicate significant positive changes among student participants in their self-perceived creativity, teamwork skills and the accomplishment of course intended learning outcome. Issues and implications for University education in the digital and the COVID-19 pandemic era are discussed.
Cryptographic Money Value Expectation Utilizing ARIMA Model

Abstract: In the beyond couple of years, the cryptocurrency market has developed at a rate that has never been seen. Cryptocurrency works like standard cash, however virtual installments for labor and products are made without the assistance of a national bank or government. Digital money utilizes cryptography to ensure that all exchanges are legitimate and remarkable, however this business is simply beginning, and serious worries have been made about its utilization. To get a full image of individuals' opinion on digital money, it is vital to take a gander at how they feel about it. Along these lines, this study utilizes an assortment of tweets about cryptographic money, which are frequently used to foresee the cost of digital currency available, to do both temperament examination and feeling acknowledgment. A deep learning outfit model called LSTM-GRU is prescribed to deal with the accuracy of the assessment. It integrates two reasons for recurrent neural network: long short-term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are collected with the end goal that the GRU gains from the features that LSTM finds. Using term repeat in reverse report repeat, word2vec, and bag-of words (BoW) incorporates, a couple of ML and profound learning methods and a suggested group model are considered. Furthermore, TextBlob and Text2Emotion are looked at to see how they can be used to analyze sentiments. Similarly, more individuals feel blissful when they use cryptographic money. Dread and shock are the following most normal sentiments. The outcomes show that when BoW qualities are utilized, ML models perform better compared to when they don't. The accuracy of the recommended LSTM-GRU gathering for breaking down state of mind and anticipating feelings is 0.99 and 0.92, separately. This is superior to both ML and cutting edge models.
CONTEMPORARY PROPERTY (RIGHTS) CHALLENGES : DIGITAL ASSETS, ANIMALS AND HUMAN BODY PARTS

Contemporary general social development reflects its challenges in inducting into three fundamental categories: digital, green and health. Each of the three categories above has its civil (private) law issues, which primarily concern the concept of property itself. The paper focuses on key stakeholders from three mentioned categories: digital assets, animals and human body parts. Technology has had a significant impact on human life, and as a result, a person, during his/her lifetime, accumulates a huge number of digital assets. The most important questions concerning digital assets are: can they be treated as corporeal things (or incorporeal entities equalized with corporeal things), and what are the users’ legal rights over these assets? To a certain extent, the mentioned question is transferred to animals as well, through various animal ethical and biocentric considerations. In a situation where animals also greatly influence human life, the question arises whether the conception of thing(s) in the context of animals has become inadequate. Can we still treat animals as property, or are new concepts needed to understand animals’ legal status? Are new concepts also necessary for understanding the (civil) law status of human body parts? Increasing biomedical technological development has led to different ways of preserving human life and health. However, such preservation carries with it a priori various legal and bioethical questions that need to be answered in order to distinguish whether and under what conditions parts of the human body can be the objects of property rights. In observing the mentioned civil law and in certain situations, (bio)ethical and legal philosophical problems and questions, the authors approach analytically, comparatively and casuistically.
Optimasi Parameter Support Vector Machine Dengan Algoritma Genetika Untuk Analisis Sentimen Pada Media Sosial Instagram

Social media is an online media that users use to interact with each other by expressing themselves by giving comments, and one example is Instagram. All the collected comments will form a public opinion. This opinion can be used with sentiment analysis to become information. The method commonly used to carry out sentiment analysis is classification using machine learning. One of the machine learning that is often used is the Support Vector Machine (SVM). However, on non-linear problems such as sentiment analysis, SVM requires the kernel to map vectors into high-dimensional spaces to solve non-linear problems. The problem faced in using the kernel is to choose the optimal parameters for the classification model to produce a good classification model. This study proposes a new approach to obtain optimal parameters for SVM using Genetic Algorithm (GA). This study designed an SVM-GA classification model from the data collection, processing, classification, and evaluation stages. The results showed that the best accuracy produced with parameters optimized with the genetic algorithm was 81.6%, or an increase of 2.4% from the SVM sentiment analysis method without GA optimization.
The Digital Age of Higher Art Education (European Experience): Professional Competence, Development, Innovation

The present article's objective is to investigate the alterations that govern the circumstances for accomplishing educational goals in the context of the digital era. The literature review revealed major patterns in the European educational landscape that provided the framework for empirical confirmation. The sample was formed according to the stratometric approach. Respondents are art university students and educators from five European Union nations. In the study, a mixed research design was employed. The limitations and perspectives of instructors' and students' innovative educational engagement are underlined. The personality predictors of new digital environment adoption were established.
MiroAR: Ubiquitous AR Teleconferencing Through The Mirror

Video call systems rely on being able to capture and transmit a self view, while at the same time rendering the view of the other party. Due to the lack of inwards facing cameras in XR devices (AR Glasses, HMD etc.) this is not a straightforward process. As a solution, recent XR teleconferencing platforms are trying to create a more "immersive" experience by replacing the self view with avatars, placing 3D models in space, creating shared spaces and other engaging features; approaches that are quite demanding and even then do not create a "traditional" teleconferencing experience. In this work, we are using an XR device (AR Glasses, or smartphone) to create a seamless and natural video calling experience. By using the AR Glasses to record an existing self-view from a reflective surface, like a mirror, the user is able to easily conduct a video call with a party, even if they are using a different setup. To demonstrate this concept, we present the MiroAR application. We conclude this paper by discussing the roadmap, shortcomings and possible extensions of our work.
Subjective Test Environments: A Multifaceted Examination of Their Impact on Test Results

Quality of Experience (QoE) in video streaming scenarios is significantly affected by the viewing environment and display device. Understanding and measuring the impact of these settings on QoE can help develop viewing environment-aware metrics and improve the efficiency of video streaming services. In this ongoing work, we conducted a subjective study in both laboratory and home settings using the same content and design to measure QoE in Degradation Category Rating (DCR). We first analyzed subject inconsistency and confidence intervals of the Mean Opinion Scores (MOS) between the two settings. We then used statistical models such as ANOVA and t-test to analyze the differences in subjective tests on video quality between the two viewing environments. Additionally, we employed the Eliminated-By-Aspects (EBA) model to quantify the influence of different settings on the measured QoE. We conclude with several research questions that could be further explored to better understand the impact of the viewing environment on QoE.
College English Listening Learning Platform Driven by Deep Learning Algorithm Model

The deep learning algorithm model can help to achieve an online English listening learning platform, which shows its unique convenience, excellent performance and good application prospect. Based on the deep learning algorithm, the intelligent English listening learning platform is implemented by the improved GoogLeNet model. The training accuracy of the platform is 97.2% and the test accuracy is 96.5%. Through quantitative analysis and evaluation of behavioral data, we can accurately grasp students' listening level, listening habits, listening interests and preferences. On the other hand, the system also greatly help students choose their own learning materials according to their own level and characteristics, and arrange their own learning progress and difficulty. This makes it easier for students to take a proactive attitude towards learning.