Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Twitter Using Machine Learning Algorithms

Twitter sentiment analysis has numerous applications in social media monitoring, brand management, customer service, political analysis, and more. By analysing the sentiment of tweets, businesses can gain insights into customer behaviour, improve customer engagement, and enhance brand reputation. In the following paper, based on many factors including id, location, target, and text, we suggest using machine learning as a method to examine the sentiment of a number of tweets. We use different algorithms such as Random Forest, Naive Bayes, logistic regression and K-nearest neighbours, Gradient Boosting Classifier, Support Vector Machine(SVM), Decision Tree Classifier, Bagging Classifier, Ada Boosting classifier, gradient boosting classifier to train and test our model on a dataset of 1000 tweets with positive and negative sentiments. A comparison of the classifiers' performance is made using measures for accuracy, precision, recall, and Fl-score. We find that logistic regression and SVM algorithms achieve the highest of the accuracy which is 58% and Fl-score of 0.62 for logistic regression and 0.57 for SVM. We come to the conclusion that our machine learning strategy can offer a trustworthy and effective tool for sentiment analysis of tweets.
Enhancing Privacy in Graph Algorithms: Data-Oblivious Approaches to DFS and Dijkstra's Algorithm

Data obliviousness is characterized by a consistent sequence of operations irrespective of input data and data-independent memory accesses, making it a suitable solution for users utilizing outsourced storage data services who aim to conceal their data access patterns. In ACM SIGSAC 2013, data-oblivious algorithms were introduced for breadth-first search, single-source single-destination (SSSD) algorithms, maximum flow, and minimum spanning tree. In this study, we present novel data-oblivious algorithms designed for depth-first search and single-source shortest path (Dijkstra’s algorithm). Our proposed data-oblivious algorithms demonstrate efficiency comparable to non-data-oblivious counterparts, particularly for graphs containing fewer than 1000 nodes. This research contributes to advancing data privacy in outsourced storage services by providing effective data-oblivious solutions for common graph algorithms.
Automated machine learning and neural architecture optimization

Automated machine learning (AutoML) and neural architecture optimization (NAO) represent pivotal components in the landscape of machine learning and artificial intelligence. This paper extensively explores these domains, aiming to delineate their significance, methodologies, cutting-edge techniques, challenges, and emerging trends. AutoML streamlines and democratizes machine learning by automating intricate procedures, such as algorithm selection and hyperparameter tuning. Conversely, NAO automates the design of neural network architectures, a critical aspect for optimizing deep learning model performance. Both domains have made substantial advancements, significantly impacting research, industry practices, and societal applications. Through a series of experiments, classifier accuracy, NAO model selection based on hidden unit count, and learning curve analysis were investigated. The results underscored the efficacy of machine learning models, the substantial impact of architectural choices on test accuracy, and the significance of selecting an optimal number of training epochs for model convergence. These findings offer valuable insights into the potential and limitations of AutoML and NAO, emphasizing the transformative potential of automation and optimization within the machine learning field. Additionally, this study highlights the imperative for further research to explore synergies between AutoML and NAO, aiming to bridge the gap between model selection, architecture design, and hyperparameter tuning. Such endeavors hold promise in opening new frontiers in automated machine learning methodologies.
The Influence of Teacher Professionalism Development of Youth Capacity Improvement in Deli Serdang

&#x0D; &#x0D; &#x0D; Teachers have a strategic role in education. Other educational resources become less significant if not accompanied by quality teachers. Teacher plays a key role to advanced education in the withdrawal of an educational unit. Teachers are the spearhead in the effort to improve service quality and educational outcomes. The purpose of this study is to analyze the availability of Senior High School teacher to improve youth capacity. This research was conducted at three of State Senior High School in Deli Serdang regency sample of 53 teachers. Variable research is the development of teacher professionalism as an independent variable and the improvement of youth capacity as a variable bound. Data collection using questionnaires, documentation, interviews and observation. Data were analyzed descriptively and using simple linear regression analysis. Teacher professionalism has a significant effect on youth capacity in Deli Serdang Regency.&#x0D; &#x0D; &#x0D;
Increasing Design Competency of Muhammadiyah Vocational High School Teachers

The number of Vocational High School (VHS) graduates in the Yogyakarta Special Region who are unemployed is approximately 20% of the total 28,000 graduates annually. The absorption of VHS graduates in the workforce can be improved by providing them with competencies. One of the competencies that can be given is competence in technical drawing design. A teacher has an essential role in providing expertise to vocational students. There needs to be education for teachers of Muhammadiyah VHS in the Yogyakarta Special Region about technical drawing design. This community service program aims to increase awareness and understanding of Muhammadiyah vocational school teachers in the Yogyakarta Special Region about technical drawing design using the SolidWorks application. The output of this community service program is published in the form of journal articles of the National Scientific Forum, publications in the Mass Media, and documentation in the form of activity videos. The methods used in the service program were observation, training related to technical drawing design using Solidworks applications, monitoring, and evaluation. The dedication to improving Muhammadiyah VHS teachers' competence in the design field has been carried out successfully. 89% of trainees passed the certification and obtained the Certified SOLIDWORKS Associate license.
Development of Integrated Journal Management Information System to Achieve a Good-Governance UNNES Journals

Journals have been showing a significant support for the teachers’ and lecturers’ promotions and acting as a potential source of income generating for universities. Recently, Universitas Negeri Semarang (UNNES) has managed 166 journals in two categories, i.e., National Journals UNNES (NJU) and Scientific Journals UNNES (SJU). This large number of journals highly requires a good governance system, particularly through the development of an integrated and democratic information system and applying the principle of mutual cooperation. Therefore, the information could be provided quickly, accurately and in a real-time basis. This study aims to develop an integrated journal governance information system to achieve a good-governance journals. In addition, this study was also aimed to support the performance improvement of the university journals. This study used a Research &amp; Development Research (R&amp;D) design using a purposive sampling technique. A team of experts from the Center of Journal Development and Publication Universitas Negeri Semarang and all representatives of journal managers at Universitas Negeri Semarang have been involved in all stages of the study, i.e., the discussion process for identification of needs, design validation, system development trials, the process of assessing the development of information systems. The journal performance system (as referred to SiKeNal in this study) as a newly-developed system was able to provide 9 main journal services, forensic audits, periodic monitoring and evaluation, as well as statistical information on the journal management and financial performances. An integration with existing systems, both financial and the university performance appraisal systems, was necessary to support the university income for the better future.
Emergency Care Patient Prediction using Electronic Health Records (EHR) Data: An End-to-End Machine Learning Pipeline

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. This paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. This pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than $75 \%$ is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency.
Image Data Hiding in Neural Compressed Latent Representations

We propose an end-to-end learned image data hiding framework that embeds and extracts secrets in the latent representations of a generic neural compressor. By leveraging a perceptual loss function in conjunction with our proposed message encoder and decoder, our approach simultaneously achieves high image quality and high bit accuracy. Compared to existing techniques, our framework offers superior image secrecy and competitive watermarking robustness in the compressed domain while accelerating the embedding speed by over 50 times. These results demonstrate the potential of combining data hiding techniques and neural compression and offer new insights into developing neural compression techniques and their applications.
Semi-supervised Network for Thyroid Nodule Segmentation via Joint Consistency Learning and Co-Training

Thyroid nodule is a common clinical disease, although most nodules are benign, the incidence rate of thyroid cancer has risen rapidly in recent years. Even though many methods have achieved automated thyroid nodule segmentation based on deep learning, these methods are based on supervised learning and require a large amount of labeled data for training. However, the labeling work must be carried out by professional doctors, which results in a small number of datasets and difficulty in labeling. To address this problem, this paper proposes a semi-supervised thyroid nodule segmentation model via joint consistency learning and co-training. This model includes two branches: consistency learning and co-training. In the consistency learning branch, based on consistent regularization, the teacher model guides the student model to optimize. In order to make the teacher model more stable, we design a co-training framework to further optimize the teacher model. In co-training branch, the teacher model and TransUNet extract different representations of the same sample and teach each other to prevent consistent but incorrect predictions between the teacher model and the student model. This semi-supervised model can learns useful feature representations from unlabeled data, and effectively trains the model with a small amount of labeled data, reducing the dependence on labeled data during the model training process.
Multi-Scale Receptive Field Attention Free Transformer Feature Extractor

Image feature point extraction is critical in several computer vision tasks like object detection, image stitching, visual re-localization, 3D reconstruction, and simultaneous localization and mapping (SLAM). In recent years, learning-based feature extractor approaches have become increasingly prevalent in computer vision and can achieve even better matching performance. Nevertheless, how to label and describe the discriminative feature is problematic. In this paper, we proposed a weakly-supervised learning approach that integrates a transformer and U-net-like convolution networks to better consider global and local contexts. Moreover, we concatenate multiple-size dilation convolutions to achieve a wider receptive field. Experiments show that the proposed approach has a performance and is robust to viewpoint change and illumination variation.
A Near Lossless Learned Image Coding Network Quantization Approach for Cross-Platform Inference

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios.
Reseach in Collaborative Space Using Complex Experimental Model

Theories, methodologies, practices, and software for problem solving by model definition, analysis and simulation, and communication were essentially developed in engineering during the past decades. This paper first gives an analysis of the above main advances which are tied to paradigm shifts and essential for PhD and other engineering related research programs. Following this, scenario of a proposed new style of research is introduced. This research is relied upon methods and software tools offered by high end engineering modeling platform. Another main contribution in this paper is full research in a purposefully configured autonomous and reactive research centered experimental model (RCEM) which is continuously developed during the life cycle of research. RCEM collects and organizes model and simulation representations which accommodate results of the research. Research process is also controlled by contexts from industrial and other application environments. Essential representations in RCEM are behaviors to achieve active knowledge and intelligent computing intensive solution. New concentration area is model representation for medical problem related solutions in this paper. Virtual Research Laboratory (VRL) was founded to realize globally participated collaborative space organized high level PhD research on the cloud utilizing world level engineering modeling platform. Communication with other collaborative spaces and outside world sources and controls is also a main concern. Implementation and initial application of the results in this paper is done at the VRL.
A Feedback-inspired Super-network Shrinking Framework for Flexible Neural Architecture Search

Neural architecture search (NAS) has been proved to be effective in automatically designing the architectures of deep neural networks (DNNs). Currently, most of cell-based NAS methods are subjected to the prior rules of human experts on neural architectures (e.g., the topology-sharing strategy), but it is not in line with the sustainable development of automated machine learning (Auto-ML). In this paper, a feedback-inspired super-network shrinking (FSS) method is developed under the framework of differentiable architecture search (DARTS) such that neural networks with flexible architectures are derived without strong reliance on human experts. In FSS, the information entropy is utilized to measure the sparsity of the super-network and an adaptive shrinking method is developed to balance the cross-entropy and the sparsity entropy. The experiments on CIFAR10/100 demonstrate that the proposed ESF derives a set of neural architectures with competitive performance-computation trade-offs in one single search procedure, which is superior to the state-of-the-art NAS methods.
A Dual Branch Segmentation Network for Forward-looking Sonar Image

Accurate segmentation of marine objects holds crucial significance and value in underwater resource exploration and marine rescue. However, in complex and noisy underwater environments, existing CNN-based algorithms face limitations such as insufficient global feature extraction capabilities and incomplete segmentation of certain targets in the sonar image. To overcome these inevitable problems, this article innovatively introduces a segmentation network based on transformer blocks. The algorithm encompasses a dual-branch structure that involves local and global feature extraction. The main components of the algorithm include four parts: Image preprocessing, Local feature extraction branch, Global feature extraction branch and Segmentation head. Finally, we conducted experiments on the public dataset, and the mIoU value of the proposed method surpasses other CNN-based algorithms. The results illustrate the effectiveness and feasibility of the segmentation network.
Quantum State Generation Via Deep Reinforcement Learning

The quantum state generation problem is a major research goal of quantum control and quantum variational algorithms, which use iterative optimization methods to evolve the initial state to the target state. The Twin Delayed Deep Deterministic Policy (TD3) algorithm in reinforcement learning achieves high learning efficiency and better stability in continuous control tasks. Here, using the TD3, we propose a new quantum state preparation method that does not require case-by-case optimization, to find a suitable evolution path to obtain the desired state. Specifically, we input the initial state into the trained actor-network, which can output the parameters of the unitary gates step by step, thus gradually evolving the initial state to the fixed quantum state. According to the reversibility of the unitary transformation, we can obtain a sequence of unitary gates to evolve the fixed state to the desired state. To verify the effectiveness of the algorithm, we perform simulations for one-qubit, two-qubit, and four-qubit cases, and the results show that the trained actor-network can provide appropriate unitary transformations to obtain the fixed state.
Research on Deep Learning-based Group Recognition

The task of group recognition is a process of automatically recognizing and analyzing individuals or groups from a large population using computer vision and machine learning techniques. Currently, the main methods for group recognition using deep networks can be divided into four categories: methods based on convolutional neural networks (CNN), methods based on recurrent neural networks (RNN), combined CNN-RNN-based methods, as well as pose analysis methods. This article specifically analyzes these four categories of methods: CNN-based, RNN-based, and pose analysis, and summarizes the characteristics and functions of iterative deep learning models, graph convolutional network models, long short-term memory models, pose models, and skeleton models. In addition, a detailed introduction to commonly used datasets in group recognition is provided, and the main evaluation metrics are analyzed. Finally, important research directions in group recognition are discussed, followed by a conclusion of the entire article.
PERANCANGAN UI/UX APLIKASI PEMBELAJARAN BAHASA JEPANG MENGGUNAKAN METODE GOAL DIRECTED DESIGN

Japanese language learning has been available at SMAN 2 Purwakarta since 2005. Students of SMAN 2 Purwakarta have difficulty learning Japanese due to the lack of variety in class. So researchers took the initiative to design a Japanese learning application. User Interface (UI) is a design before the creation of applications that focuses on visuals and product elements such as colors, layouts, buttons and typography. User Experience (UX) is the experience of a user when using an application. UI and UX have an important role in the problems that exist at SMAN 2 Purwakarta because they can add new innovations to Japanese language learning. Goal Directed Design is the method used in this study because it focuses on goals and user experience. The test method used is the System Usability Scale to find the average value of the final results for the application design made.
Local-Adaptive Transformer for Multivariate Time Series Anomaly Detection and Diagnosis

Time series data are pervasive in varied real-world applications, and accurately identifying anomalies in time series is of great importance. Many current methods are insufficient to model long-term dependence, whereas some anomalies can be only identified through long temporal contextual information. This may finally lead to disastrous outcomes due to false negatives of these anomalies. Prior arts employ Transformers (i.e., a neural network architecture that has powerful capability in modeling long-term dependence and global association) to alleviate this problem; however, Transformers are insensitive in sensing local context, which may neglect subtle anomalies. Therefore, in this paper, we propose a local-adaptive Transformer based on cross-correlation for time series anomaly detection, which unifies both global and local information to capture comprehensive time series patterns. Specifically, we devise a cross-correlation mechanism by employing causal convolution to adaptively capture local pattern variation, offering diverse local information into the long-term temporal learning process. Furthermore, a novel optimization objective is utilized to jointly optimize reconstruction of the entire time series and matrix derived from cross-correlation mechanism, which prevents the cross-correlation from becoming trivial in the training phase. The generated cross-correlation matrix reveals underlying interactions between dimensions of multivariate time series, which provides valuable insights into anomaly diagnosis. Extensive experiments on six real-world datasets demonstrate that our model outperforms state-of-the-art competing methods and achieves 6.8%-27.5% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> score improvement. Our method also has good anomaly interpretability and is effective for anomaly diagnosis.