Reviewerly/exHarmony huggingface dataset:
Noisy tensor-ring approximation for computing gradients of a variational quantum eigensolver for combinatorial optimization

Variational quantum algorithms, especially quantum approximate optimization and the variational quantum eigensolver (VQE), have demonstrated their potential to provide computational advantage in the realm of combinatorial optimization. However, these algorithms suffer from classically intractable gradients limiting the scalability. This work addresses the scalability challenge for the VQE by proposing a classical gradient computation method which utilizes the parameter-shift rule but computes the expected values from the circuits using a tensor-ring approximation. The parametrized gates from the circuit transform the tensor ring by contracting the matrix along the free edges of the tensor ring. While the single-qubit gates do not alter the ring structure, the state transformations from the two-qubit rotations are evaluated by truncating the singular values, thereby preserving the structure of the tensor ring and reducing the computational complexity. This variation of the matrix-product-state approximation grows linearly in number of qubits and the number of two-qubit gates as opposed to the exponential growth in the classical simulations, allowing for a faster evaluation of the gradients on classical simulators.
Denoising Diffusion Models on Model-Based Latent Space

With the recent advancements in the field of diffusion generative models, it has been shown that defining the generative process in the latent space of a powerful pretrained autoencoder can offer substantial advantages. This approach, by abstracting away imperceptible image details and introducing substantial spatial compression, renders the learning of the generative process more manageable while significantly reducing computational and memory demands. In this work, we propose to replace autoencoder coding with a model-based coding scheme based on traditional lossy image compression techniques; this choice not only further diminishes computational expenses but also allows us to probe the boundaries of latent-space image generation. Our objectives culminate in the proposal of a valuable approximation for training continuous diffusion models within a discrete space, accompanied by enhancements to the generative model for categorical values. Beyond the good results obtained for the problem at hand, we believe that the proposed work holds promise for enhancing the adaptability of generative diffusion models across diverse data types beyond the realm of imagery.
<scp>Multiloss</scp> strategy for breast cancer subtype classification using digital breast tomosynthesis

Abstract Breast cancer is the most common cancer for women, and it also is the leading cause of cancer‐related deaths. As a highly heterogeneous disease, it is crucial to identify the molecular subtypes of breast cancer before individualized therapy. Therefore, we proposed a multiloss network framework to classify the breast cancer subtype based on digital breast tomosynthesis (DBT) images. We employed the multiloss strategy to learn the low‐level features more efficiently and effectively, which would provide a better basis for extracting the high‐level features. Additionally, we also proposed a decomposed attention block (DA), which not only captured the interdependencies between all channels but also the precise positional information at the x‐ and y‐dimensions. The multiloss strategy enables the network to learn useful feature representations of breast cancer subtypes, and the DA block further improves the classification performance by capturing the information between channels and positions.
Chatbot Deteksi Awal Gangguan Kecemasan Menggunakan Dialogflow

Nowadays, anxiety disorders are experienced by many individuals, making a significant impact on one's quality of life. Some people are unaware of the symptoms of anxiety disorders, making anxiety disorders trivial. This situation can cause serious physical and emotional discomfort, in some cases, leading to more severe impacts if not treated appropriately. One of the first steps in overcoming anxiety disorders is early detection. The earlier the disorder is detected, the better the chances of providing effective treatment and reducing its impact. The development of artificial intelligence technology has opened up new opportunities to address this problem. This research proposes an innovation in the form of a chatbot. The purpose of this study is to determine the feasibility and acceptability of a chatbot to identify and provide information related to symptoms of anxiety disorders. The research methodology includes Data Collection, conversation formation, model formation, implementation using Dialogflow, testing and results. The results of UAT testing on respondents consisting of students and psychologists obtained results of 84% and 74%, respectively.
ASSESSMENT OF THE EFFICIENCY OF THE ONLINE ENROLLMENT SYSTEM OF BATO INSTITUTE OF SCIENCE AND TECHNOLOGY

The Bato Institute of Science and Technology Online Enrollment System deals with student details, grade reports, enrollment, and billing. It keeps track of all the information on a student from the first day of their course until completion. Which complete every semester each year, and all of these will be available for future reference. Almost all the work used is computerized, so accuracy was maintained. Maintaining the backup is easy. The system has two access modes: administrator and user (staff and student). A standardized questionnaire was developed and administered to system users, covering areas such as ease of use, speed, and overall satisfaction level to assess the system's efficiency. Based on the results of the gathered and treated data, the researcher was able to conclude. It shows that the "Efficiency of the Online Enrollment System" is effective because of "very good” result during the survey. It is recommendable that the current system continuously improves from time to time to avoid problems in the future
Public cybersecurity awareness good practices on government-led websites

Globally, governments should prioritise and lead cybersecurity awareness as they have a duty to inform their citizens about responsible online behavior. This study aims to search for government-led cybersecurity awareness practices followed by the four world-leading countries (the United States of America (USA), the United Kingdom (UK), Saudi Arabia and Estonia) as evident on their websites. This qualitative study employs content analysis by performing a review of the four world-leading countries’ websites, with a specific focus on support materials and other relevant activities as part of cybersecurity awareness. Analysis of the websites found that cybersecurity awareness practices had a wide reach. Firstly, a host of multimedia content (videos, readings, infographics, memes, games, and quizzes) was used in addition to social media platforms. Secondly, the content was categorized for a range of users, with parents and children being favored. Lastly, for countries where the first language was not English, websites were in the official language in addition to English. The study contributes to the literature on cybersecurity awareness. The good practices employed by these countries could serve as a benchmark globally for other countries seeking to formulate or update their websites. Furthermore, showcasing the four world-leading countries serves to assist countries which have a lower Global Cybersecurity Index organizational measure can improve their cybersecurity awareness websites.
Design and Exploration of Online and Offline Hybrid Teaching Systems Based on CiteSpace’s Visual Knowledge Graph Combined with MOOCs – A Case Study of Surgical Nursing

Knowledge map is a modern concept that combines theories and methods of applied mathematics, graphics, information visualization technology, information science and other disciplines with metrology citation analysis, co-occurrence analysis and other methods. It uses the visual map to display the core structure, development history, frontier fields and overall knowledge structure of the discipline to achieve multidisciplinary integration. Our school’s “Surgical Nursing” course is a provincial-level blended online and offline first-class course. With the help of modern information technology and the carrier of the Super Star MOOC platform, we make full use of all kinds of high-quality course teaching resources and explore the mixed online and offline teaching systems. In order to improve the nursing students’ scientific research literacy and ability, cultivate the concept of lifelong education and learning for students, an attempt is made here, by taking the section “Upper urinary tract stones” in “Surgical Nursing” as an example, to illustrate the design of an online and offline hybrid teaching systems based on CiteSpace’s visual knowledge graph combined with MOOCs.
The Survey on the Development of Language Training Industry from the Perspective of New Media

The study abroad education industry has witnessed significant transformations with the advent of new media technologies. Online platforms, interactive resources, and multimedia content have revolutionized language training methods and opened up new opportunities for students. Understanding the impact of new media on language training in the study abroad context is essential for language training institutions to adapt their strategies and meet students evolving needs. This paper aims to explore the role of new media in language training for study abroad and its impact on the study abroad education industry. The research is conducted through a questionnaire analysis, which gathers data on students current level of study, demand for English language learning, willingness to spend on language training, perception of the epidemics impact on study abroad, preferences for language training institutions and courses, reasons for not choosing online training, preferences for online learning formats, future adoption of online language training, and more. The research findings provide insights into the challenges, preferences, and expectations of students in the study abroad language training industry in the new media era.
Analysing the Structure of Proper Names Used as Vocatives in English under the Systemic Functional Linguistics Model

This paper analyses the structures of English proper names which are used as vocatives under the SFL (systemic functional linguistics) model. The procedure of analysis is as follows: first, viewing previous literature offers a chance to refer to plenty of examples of vocatives while providing a theoretical basis for this research. Second, most examples used in this paper are proposed by an English native speaker and the author together. This native speaker is qualified to cover the most common uses of vocatives considering his experience of saying vocatives to others and hearing them from others. Third, the method of attesting to some relatively uncommon usage is that the author comes up with a potential structure first and then searches for real-world examples that can support that assumption in trustworthy sources such as specific corpora and public speech. Therefore, although this may be a preliminary analysis, leaving issues that require further discussion, hopefully, this work will prove a step forward in this under-researched area.
Deep Learning for Detection and Classification of Nuclear Protein in Breast Cancer Tissue

Breast cancer is a medical condition in which the cells of the mammary gland grow uncontrollably, creating malignant tumors, capable of dividing, and invading surrounding areas. Currently, the incidence of breast cancer is increasing and getting younger. Medical methods such as Ultrasound, Computed Tomography (CT), X-rays, and Magnetic Resonance Imaging (MRI) are the first options to diagnose breast cancer as well as other diseases in the mammary gland. However, these imaging methods are not enough to accurately determine the status of a breast tumor. To be able to accurately identify breast cancer and assess the extent of the disease, immunohistochemistry will be used to characterize the cells in the tissue sample, including breast cancer cells. In this work, we propose a method to detect and classify nuclear proteins in breast cancer cells on histological images using deep learning techniques. We take advantage of deep learning networks and the transfer learning approach to train network models on the SHIDCB-Ki-67 dataset of Shiraz University of Medical Sciences in Shiraz, Iran. DeepLabv3-MobileNet-V2, DeepLabv3-Xception, and DeepLabv3-DenseNet-121 are used to detect and classify nuclear proteins of breast cancer cells on histological images. Experimental results show that the proposed method with DeepLabv3-DenseNet-121 achieves higher accuracy (98.6%) than DeepLabv3-Xception and DeepLabv3-MobileNet-V2.
Bangla Social Media Cyberbullying Detection Using Deep Learning

The growth of social media over the past decade has been nothing short of phenomenal. An exponential increase has been seen on platforms like Facebook, Twitter, Instagram, LinkedIn, and YouTube, in their user base, accumulating billions of active users worldwide. Technology advancements, the ubiquitous use of smartphones, and the innate human desire for connection don't always contribute constructively; they might additionally end up in spreading violence in the form of bullying of others which is known as cyberbullying. As a result of cyberbullying, the victims will frequently experience anxiety, depression, and other mental diseases which can even result in suicide. Therefore the extensive need of detecting and controlling cyberbullying motivated us to automate this process. In this paper, we have introduced an approach to detect cyberbullying from social media data by using deep learning models. We have used Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network in the proposed hybrid approach along with word embedding technique called fastText. We achieved an accuracy of 91.63% with the proposed model by using a publicly available dataset containing 16,073 samples which outperformed all the state of the art models.
The Internal Structure of Copular Sentences with desu and da in Japanese

Japanese is a language known for its rich honorific morphology. Different from many Indo-European languages, Japanese language has its honorifics built into the grammar system. Japanese honorifics have many different forms and their usages are not always clearly predictable, which makes it quite challenging for second language learners to grasp. This paper introduces a less-known honorific pattern that clearly shows how Japanese honorifics behave grammatically. The copula verb be in Japanese has two forms: a non-honorific form da and an honorific form desu. Da is used under conditions where the speaker indicates an informal or casual talk, while desu is used when the speaker intends to present good manners to the listener. From a formal linguistic perspective, a sentence with the plain copula form and one with the honorific copula form have a different underlying structure represented in the grammar of native Japanese speakers. The paper explains such difference between copular sentences with desu and da by presenting a syntactic structure analysis. It proposes a different internal structure for a copular sentence with desu and da, and further argues that the sentence with desu has more structure in the left periphery and presents a different verb position in the internal structure in comparison to the sentence with da. The analysis motivates a syntactical way to present and explain the differences in grammatical structures and social contexts of copula use when teaching honorifics in Japanese second language classrooms. It suggests that modern Japanese second language education start teaching honorifics with copular sentences where the distinction between honorific and non-honorific use is very clear and simple.
A Novel Ensemble K-Nearest Neighbours Classifier with Attribute Bagging

Classification of supervised learning in machine learning is a challenging task. K-Nearest Neighbours (kNN) is one of the simple classifiers that is commonly used in many real-life pattern recognition applications. The kNN is a lazy learner as it does not build any decision line from historical training data. In this paper, we have proposed a novel ensemble based K-Nearest Neighbours classifier with attribute bagging technique. The proposed method is designed to reduce the time complexity of the traditional kNN classifier, which also ameliorates the classification accuracy. It employs the attribute bagging technique and selects a sorted sub-set of instances from the original training data to apply kNN classifier. We have applied binary search technique to effectively reduce the search space to find the nearest neighbours. The proposed approach reduces the time consumption of kNN classifier from linear to logarithmic making the algorithm feasible for Big Data. We have tested the performance of the proposed algorithm with traditional machine learning classifiers e.g. kNN, Random Forest, AdaBoost on 10 benchmark datasets. The datasets are taken from UCI Machine Learning Repository. The results show that the proposed method outperformed the basic kNN and AdaBoost classifiers, and is compatible with Random Forest classifier.
Application of Swin Transformer Model to Retrieve and Classify Endoscopic Images

The machine learning community is very interested in image classification and retrieval, especially in the area of computer vision and with an emphasis on medical image retrieval. Numerous machine learning approaches have been used for image retrieval problems and have made as a result of the ongoing developments in techniques like Convolutional Neral Networks (CNN) and Vision Transformers with quite good performances. The Swin Transformer model is used to create a specialized medical image retrieval system in this paper that is well suited to gastric endoscopic pictures. The suggested technique takes advantage of the Swin Transformer model's classification process to create feature vectors by combining fragmented image segments collected from local windows, making it easier to calculate similarity on the Kvasir dataset that we have added some additional images. Empirical results show that the Swin Transformer model retrieves endoscopic images with a remarkable classification accuracy of 90.5% and an 85% mean average precision at top 20 (mAP@20).
FaceMask Detection Using Transfer Learning

Wearing face masks in public places has reduced the global spread of COVID-19 and other bronchi/lung diseases. This paper proposes an approach for the face detection of people from the photo with or without a facemask. This task is implemented based on the features around their eyes, ears, nose, and forehead by using the original masked and unmasked images to form a baseline for face mask detection. In this work, we have used the Caffe-MobileNetV2 model for feature extraction and image classification. First, the convolutional architecture for the fast feature embedding Caffe model is used as a face detector, and then the MobileNetV2 is used for facemask identification. Experimental results revealed that the proposed approach performed well, with an accuracy of 98.54%. The work is expected to be deployed in practical cases.
Analysis and comprehensive assessment of the development and application of the neural network dialogue system ChatGPT

Objective . Today, significant and in many ways sensational results are being achieved in the field of artificial intelligence systems, and the ChatGPT bot, which is based on the GPT-3 neural network, is called a real revolution in the world of technology. The aim of the study is to analyze and evaluate the application features, advantages and limitations, as well as development factors and reasons for the extraordinary popularity of the neural network dialogue system ChatGPT. Method . A review of domestic and foreign sources, systematization of data, analysis of the architecture and mechanism of action of the neural network was carried out. Result. Functions, opportunities, scopes and risks of using ChatGPT are summarized and evaluated. Conclusions . The main function of ChatGPT – text generation based on given input data – allows to effectively solve a wide range of tasks that have not been automated before, and the quality of the solution is comparable to human work. However, it is important to avoid the risks associated with the possibility of abuse and receiving incorrect and malicious responses from artificial intelligence. This requires control measures, the development and introduction of standards and norms.
Using Deep Learning to Build a Chatbot Supporting the Promotion of Speciality Dishes in Mekong Delta

Based on the images of dishes in the Mekong Delta along with questions about the dishes such as: What is the name of this dish? Where is it famous? What are the main ingredients? How is it made? An application chatbot will be built to promote the speciality dishes of the Mekong Delta. This report outlines a method for training a Visual Question Answering (VQA) model for classification tasks using Transformer-based models, such as ViT for image data, BERT/PhoBERT for text data, or ViLT for simultaneous processing of image and text data. After that, a Visual Encoder-Decoder model for the task of generating sentences will be built using the VQA model as a Visual Encoder and a GPT-2 as a Decoder. The experimental dataset, which includes 7,694 photos of dishes from the Mekong Delta, is a subset of the datasets 30VNFoods and VinaFood21. The accuracy metric was used to evaluate the VQA models, and the results were relatively good. For Model 1: ViT and BERT, the accuracy scores for English and Vietnamese are 94% and 95%, respectively, while the accuracy score for Model 2: ViLT is over 92% on English-only. According to the ROUGE evaluation method, Model 3's answer sentence generation model, on English only, which used ViLT along with GPT-2, yielded results of 49.92, 39.26, and 47.53 for the ROUGE-1, ROUGE-2, and ROUGE-L, respectively. Finally, the trained models were applied to build a chatbot.
Monitoring Attendance and Checking School Uniforms Using YOLOv8

The performance of students is affected by various factors, including attendance tracking and school uniform checking. This study develops a system for automatically tracking student attendance and checking school uniforms using the pre-trained YOLOv8-based models. The system consists of six models: YOLOv8Students for detecting humans, YOLOv8Face and ArcFace for identifying students, YOLOv8Shirts and YOLOv8Pants for detecting and predicting types of shirts and pants, respectively, and YOLOv8Card for detecting student ID cards. Our research addresses practical concerns in educational institutions. The experimental results show that the models perform fairly well in optimal lighting conditions.
Transliterating Nom Script into Vietnamese National Script Using Multilingual Neural Machine Translation

Traditional methods for Nom transliteration have been relying on statistical machine translation. While there have been initial attempts to apply neural machine translation (NMT) to Nom transliteration, these approaches have not effectively utilized the shared characteristics of multilingualism. To overcome this, we propose a new method that employs a highly shared parameter multilingual NMT model, using a unified parameter set the encoder and decoder. This approach utilizes a single encoder-decoder pair for all translation directions. Experimental evaluation of our proposed method demonstrates a significant improvement of +6.3 BLEU score compared to the NMT baseline. We also conduct preliminary experiments on incorporating BERT-NMT into multilingual machine translation to enhance the performance of low-resource translation tasks in general.