Reviewerly/exHarmony huggingface dataset:
Neural Image Caption Generation with Visual Attention : Enabling Image Accessibility for the Visually Impaired

The internet is saturated with images that convey messages and emotions more effectively than words alone in today's digital age. Individuals with visual impairments, who are unable to perceive and comprehend these images, face significant obstacles in this visual-centric online environment. As there are millions of visually impaired people around the globe, it is essential to close this accessibility gap and enable them to interact with online visual content. We propose a novel model for neural image caption generation with visual attention to address this pressing issue. Our model uses a combination of CNNs and RNNs to convert the content of images into aural descriptions, making them accessible to the visually impaired. The primary objective of our project is to generate captions that accurately and effectively describe the visual elements of an image. The model proposed operates in two phases. First, a text-to-speech API is utilized to convert the image's content into a textual description. The extracted textual description is then converted to audio, allowing visually impaired individuals to perceive visual information through sound. Through exhaustive experimentation and evaluation, we intend to achieve a high level of precision and descriptivism in our system for image captioning. We will evaluate the performance of the model by undertaking comprehensive qualitative and quantitative assessments, comparing its generated captions to ground truth captions annotated by humans. By enabling visually impaired individuals to access and comprehend online images, our research promotes digital inclusion and equality. It has the potential to improve the online experience for millions of visually impaired people, enabling them to interact with visual content and enriching their lives through meaningful image-based interactions.
Formation and identification of interrelations of causes and consequences of atmospheric air pollution in Almaty by methods of system analysis and artificial intelligence

The problem of air pollution in Almaty is one of the topical issues today. In order to determine the opinion of experts and citizens of the city, a survey of the population of Almaty was conducted to assess the causes of air pollution in the city and the consequences that these pollutants entail. An expert assessment was carried out based on the data obtained from the survey for the causes and consequences of air pollution. Artificial intelligence algorithms with machine learning elements have been applied to build a model that determines the relationship between data on the causes of pollution and the consequences of atmospheric air pollution, and an analysis is carried out based on the results of the study. An algorithm for implementing the construction of the model is constructed. The results and metric estimates of the developed model are presented. The results of the study show that stationary, mobile sources and household and industrial waste have the greatest impact on public health and the environment. This work is aimed at developing a unified ecosystem for collecting and processing air pollution monitoring data in industrial cities of Kazakhstan using modern monitoring systems, mathematical modeling and information and communication technologies. Key words: atmospheric air pollution, system analysis, machine learning, expert assessment.
Unveiling Anime Preferences: A Data-driven Analysis using MyAnimeList API

This research paper presents a data-driven analysis of anime preferences using the MyAnimeList (MAL) API. The objective of this study is to uncover valuable insights into the preferences of anime viewers by analyzing data obtained through MAL API. The paper begins with a comprehensive background that includes a detailed literature survey to understand anime recommendation systems. The methodology employed in this study involves utilizing the MAL API to collect and analyze data related to anime. The API provides access to a wide range of anime related information including ratings, ranks, genres etc. The collected data is then preprocessed to ensure its quality and suitability for analysis, involving data cleaning and transformation. Various data analysis techniques are applied to the preprocessed data. These techniques help identify patterns and trends in different fields such as genres, studios, media type etc. The findings from the data analysis provide valuable insights into the preferences of anime viewers and help to identify features that may be crucial while building the model. Then using cosine similarity model, content-based anime recommendation system is built after feature engineering. The results of this research highlight the importance of leveraging data-driven approaches in understanding and catering to the diverse preferences of anime viewers. By EDA uncovering patterns and trends in anime preferences, anime recommendation systems can be enhanced to provide more relevant recommendations to viewers. The implications of this research extend to the anime industry, where a deeper understanding of viewer preferences can inform content production and distribution strategies. In conclusion, this research paper presents a data-driven analysis and content-based recommendation system for anime using the MAL API and data science. The methodology employed involves data collection, preprocessing, Exploratory Data Analysis(EDA), Feature engineering and Model building. The findings contribute to enhancing anime recommendation systems and understanding the diverse preferences of anime viewers.
ViT VO - A Visual Odometry technique Using CNN-Transformer Hybrid Architecture

Localization is one of the main tasks involved in the operation of autonomous agents (e.g., vehicle, robot etc.). It allows them to be able to track their paths and properly detect and avoid obstacles. Visual Odometry (VO) is one of the techniques used for agent localization. VO involves estimating the motion of an agent using the images taken by cameras attached to it. Conventional VO algorithms require specific workarounds for challenges posed by the working environment and the captured sensor data. On the other hand, Deep Learning approaches have shown tremendous efficiency and accuracy in tasks that require high degree of adaptability and scalability. In this work, a novel deep learning model is proposed to perform VO tasks for space robotic applications. The model consists of an optical flow estimation module which abstracts away scene-specific details from the input video sequence and produces an intermediate representation. The CNN module which follows next learn relative poses from the optical flow estimates. The final module is a state-of-the-art Vision Transformer, which learn absolute pose from the relative pose learnt by the CNN module. The model is trained on the KITTI dataset and has obtained a promising accuracy of approximately 2%. It has outperformed the baseline model, MagicVO, in a few sequences in the dataset.
PCM with Linguistic Contradiction Degree Representations in Decision making on Academic Stress causing Factors

Plithogenic Cognitive Map (PCM) is the generalized form of Cognitive maps that has recently ebbed into the field of decision-making. The first developed PCM model comprises of factors, connection matrix with numeric contradiction degree between the factors. In this research work a PCM model with linguistic contradiction degree representations between the core and sub factors is developed to make the decision-making more comprehensive. The model formulated in this research work is illustrated with the factors causing academic stress to the students of digital educational system. Personal, Social, Economic and Institutional are considered as the core factors and the contradiction degree in linguistic sense is considered with respect to each of these core factors and ten sub factors. The obtained results on comparing with conventional models are highly promising and this model will certainly set new benchmarks of a comprehensive decision-making model.
Usability Evaluation of a Mobile App for Prostate Cancer Patients

전립선암은 전 세계적으로 남성에게 발생하는 암 중에서 두 번째로 흔한 암이며, 다섯 번째 주요사망원인이다. 하지만 전립선암은 높은 발생률에 비해 비교적 생존율도 높으므로 이에 따른 통합적인 전립선암 환자의 관리가 지속적으로 필요하다. 따라서 코로나 19 범유행과 관련하여 환자가 의료기관이나 교육 장소 등과 같은 물리적 공간의 이동을 하지 않은 상태에서 시간과 장소의 구애 없이 어디서나 건강을 관리할 수 있는 추가적인 대처전략을 찾는 것이 필요하다. 최근 스마트폰의 보급과 소비자의 요구에 따라 최신 정보 통신 기술(Information and communication technology, ICT)이 접목된 모바일 기반의 헬스케어 어플리케이션의 활용이 증가하는 추세로 다양한 질병의 관리 및 의료수단의 일환으로 그 효과가 입증되고 있다. 이에, 본 연구에서는 암 환자 대상 헬스케어 앱의 현황을 분석하고 사용성 항목 기준에 관한 문헌적 선행연구를 토대로 앱 사용성 평가항목을 도출하였으며 이를 활용하여 사용자 12명을 대상으로 국내 유일의 전립선암 환자 대상 헬스케어 앱인 ‘전립선암 by 세컨드 닥터’의 사용성 평가를 실시하였다. 최종적으로 앱 태스크 분석, 구조화된 설문지와 심층인터뷰 결과를 바탕으로 전립선암 헬스케어 앱의 개선 방향을 도출하였다. Prostate cancer is the second most common cancer among men worldwide and the fifth leading cause of death. However, prostate cancer has a relatively high survival rate compared to its high incidence rate, so integrated management of prostate cancer patients is continuously required. In relation to the COVID-19 pandemic, it is necessary to find an additional coping strategy that allows patients to access at any time without restriction of time without moving to a physical space such as a medical institution or educational place. Recently, the using of mobile-based healthcare applications incorporating the latest information and communication technology (ICT) is increasing in accordance with the spread of smartphones and consumer demand, and as part of the management of various diseases and medical means Its effectiveness has been proven. In this regard, this study the current status of healthcare apps for cancer patients was analyzed and app usability evaluation items were derived based on previous literature studies on usability item standards. Using this, we conducted a usability evaluation of ‘Prostate Cancer by Second Doctor’, the only healthcare app for prostate cancer patients in Korea, targeting 12 users. Finally, the improvement direction of the prostate cancer healthcare app was derived based on the app task analysis, structured questionnaire, and in-depth interview results.
Learning technology for detection and grading of cancer tissue using tumour ultrasound images1

BACKGROUND: Early diagnosis of breast cancer is crucial to perform effective therapy. Many medical imaging modalities including MRI, CT, and ultrasound are used to diagnose cancer. OBJECTIVE: This study aims to investigate feasibility of applying transfer learning techniques to train convoluted neural networks (CNNs) to automatically diagnose breast cancer via ultrasound images. METHODS: Transfer learning techniques helped CNNs recognise breast cancer in ultrasound images. Each model’s training and validation accuracies were assessed using the ultrasound image dataset. Ultrasound images educated and tested the models. RESULTS: MobileNet had the greatest accuracy during training and DenseNet121 during validation. Transfer learning algorithms can detect breast cancer in ultrasound images. CONCLUSIONS: Based on the results, transfer learning models may be useful for automated breast cancer diagnosis in ultrasound images. However, only a trained medical professional should diagnose cancer, and computational approaches should only be used to help make quick decisions.
Data Mining for Non-Redundant Big Data Using dynamic KMEAN

There is an increasing demand for techniques that can process and collect valuable information from huge data in the Big Data era.Duplicates can seriously influence data processing and data mining, so the major challenge is finding as many duplicate records as possible.Data deduplication (or Redundancy Removal) removes redundant data and stores only one copy, promoting single instance storage.The main idea suggests using K-Means clustering for big data deduplication.K-Means Clustering, a localized optimization approach, is vulnerable to the starting point chosen from the cluster's center.The K-Means Clustering technique will produce more errors and bad cluster outcomes if the center of a defective cluster is used as the starting point.The suggested deduplication solution is based on the numeric conversion of the dataset and pre-processing them to extract useful information utilized by Dynamic K-Mean clustering (DKMEAN) to categorize replicated chunks.The proposed system greatly improves dataset quality and ultimately reduces resource consumption.It outperformed Traditional K-Means (TKMEAN) in terms of the number of detected redundant chunks, accuracy, the number of iterations, and efficiency.
<scp>CLADAG</scp> 2021 special issue: Selected papers on classification and data analysis

Statistical Analysis and Data Mining: The ASA Data Science JournalVolume 16, Issue 4 p. 317-318 EDITORIAL CLADAG 2021 special issue: Selected papers on classification and data analysis Chiara Bocci, Chiara Bocci orcid.org/0000-0001-8189-4445 Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Florence, ItalySearch for more papers by this authorAnna Gottard, Anna Gottard orcid.org/0000-0002-8246-4962 Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Florence, ItalySearch for more papers by this authorThomas Brendan Murphy, Thomas Brendan Murphy orcid.org/0000-0002-5668-7046 School of Mathematics and Statistics & Insight Research Centre, University College Dublin, Dublin, IrelandSearch for more papers by this authorGiovanni C. Porzio, Giovanni C. Porzio orcid.org/0000-0003-1208-6991 Department of Economics and Law, University of Cassino and Southern Lazio, Cassino, ItalySearch for more papers by this author Chiara Bocci, Chiara Bocci orcid.org/0000-0001-8189-4445 Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Florence, ItalySearch for more papers by this authorAnna Gottard, Anna Gottard orcid.org/0000-0002-8246-4962 Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Florence, ItalySearch for more papers by this authorThomas Brendan Murphy, Thomas Brendan Murphy orcid.org/0000-0002-5668-7046 School of Mathematics and Statistics & Insight Research Centre, University College Dublin, Dublin, IrelandSearch for more papers by this authorGiovanni C. Porzio, Giovanni C. Porzio orcid.org/0000-0003-1208-6991 Department of Economics and Law, University of Cassino and Southern Lazio, Cassino, ItalySearch for more papers by this author First published: 04 July 2023 https://doi.org/10.1002/sam.11633Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume16, Issue4CLADAG 2021 special issue: Selected papers on classification and data analysisAugust 2023Pages 317-318 RelatedInformation
On-Again, Off-Again Lake Cahuilla Likely Enhanced Earthquakes in Southern California

The disappearance of the ephemeral lake has made earthquakes along the San Andreas Fault even more unpredictable.
SNR: Symbolic network-based rectifiable learning framework for symbolic regression

Symbolic regression (SR) can be utilized to unveil the underlying mathematical expressions that describe a given set of observed data. At present, SR can be categorized into two methods: learning-from-scratch and learning-with-experience. Compared to learning-from-scratch, learning-with-experience yields results that are comparable to those of several benchmarks and incurs significantly lower time costs for obtaining expressions. However, the learning-with-experience model performs poorly in terms of unseen data distributions and lacks a rectification tool, apart from constant optimization, which exhibits limited performance. In this study, we propose a Symbolic Network-based Rectifiable Learning Framework (SNR) that possesses the ability to correct errors. SNR adopts Symbolic Network (SymNet) to represent an expression, and the encoding of SymNet is designed to provide supervised information, with numerous self-generated expressions, to train a policy net (PolicyNet). The training of PolicyNet can offer prior knowledge to guide effective searches. Subsequently, the incorrectly predicted expressions are revised via a rectification mechanism. This rectification mechanism endows SNR with broader applicability. Experimental results demonstrate that our proposed method achieves the highest averaged coefficient of determination on self-generated datasets when compared with other state-of-the-art methods and yields more accurate results in public datasets.
An Exploratory Study on the Use of Services for Women in Vulnerable Social Groups during Pregnancy, Childbirth and Early Child-rearing Period

본 연구에서는 2019년부터 2021년까지 3년간 진행된 사회복지공동모금회의 <취약계층 임신, 출산 지원을 통한 초기양육환경 조성사업>에 참여한 취약계층 여성 1,488명을 유형별로 분류하여, 빈도분석, 교차분석, 네트워크 분석 등을 통해 긴급 지원, 교육 및 정보, 의료, 산후 관리, 심리정서지원, 물품, 주거 등의 서비스 이용에 어떤 차이를 보이는지를 비교 분석하였다. 분석 결과, 결혼 여부, 연령, 내국인 여부, 외국인 등록 여부, 결혼이주여성 여부, 장애인 여부 등 취약계층 여성의 유형에 따른 차이가 있는 것으로 나타났다. 서비스의 중복 이용 양상도 취약계층 여성 유형에 따라 유의미한 차이를 보였다. 본 연구의 결과는 취약계층 여성 내부 유형에 따라 서비스 욕구에 많은 차이가 있으며, 이에 따른 서비스 체계 구축의 필요성을 시사한다. This study investigated how the utilization of services for childbirth and early childcare varies among different types of vulnerable pregnant women and new mothers. For this purpose, the researchers analyzed data from 1,488 women who participated in “the Project to create an early childcare environment through pregnancy and childbirth support for the vulnerable”, which was implemented by Community Chest of Korea for three years from 2019 to 2021, by comparing the differences in the use of services such as emergency support, education and information, medical care, postpartum care, emotional and material supports, and housing. The results showed that there were differences in service uses among different types of vulnerable groups, which included marital status, age, disability status and nationality and visa status (e.g. Korean citizens, marriage migrant women and registered foreigners). Moreover, the pattern of overlapping usage of services also showed significant differences among various types of vulnerable women. Based on this results, it is suggested to build on service delivery system reflecting needs of different groups within vulnerable women.
A Study on the Development of Input-Output Efficiency in China’s Digital Cultural Industry

디지털 문화산업의 급속한 발전은 중국 경제 성장에 새로운 동력을 불어넣었을 뿐만 아니라 사회 문화생활에도 지대한 영향을 미쳤다. 중국 디지털 문화산업의 발전 추세를 분석하고 잠재적 문제를 식별하고 정책 건의를 하는 것은 중국 디지털 문화산업의 지속 가능한 발전을 촉진하는 데 중요한 의미가 있다. 2015~2020년 중국 28개 성의 디지털 문화산업 관련 데이터를 바탕으로 DEA-BCC 모델과 Malmquist 지수를 활용하여 동적인 관점과 정적인 관점의 두 가지 측면에서 중국 디지털 문화산업의 투입산출 효율성을 분석하였으며, 연구 결과는 다음과 같다. (1) 정적인 관점에서 볼 때 중국의 디지털 문화산업 효율은 여전히 전체적으로 개선할 여지가 많고 ‘동부> 중부> 서부> 동북’의 지역적 차이의 특성을 보인다. (2) 동적인 관점에서 보면 중국 디지털 문화산업의 총요소 생산성은 전반적으로 상승 추세를 보이며 ‘동부> 중부> 동북> 서부’의 지역적 차이의 특성을 보인다. 또한, 본 연구는 중국 각 성의 디지털 문화산업의 총요소 생산성을 이중구동형, 효율 구동형, 효율 제약형, 기술제약형, 이중제약형의 5가지 유형으로 구분하였으며, 그중 효율 구동형 성(省)의 비중이 가장 크다는 것을 발견하였다. 본 연구는 중국 각 지역의 문화산업이 서로 조화롭게 발전하고, 디지털 문화산업의 기술 효율성 향상과 기술 발전의 시너지 효과를 파악하여 디지털 문화산업을 전개하고 투입산출 효율성을 향상시켜야 한다고 생각한다. The rapid development of the digital cultural industry not only brought new power to China"s economic growth but also had a great influence on social and cultural life. Analyzing the development trend of China"s digital cultural industry, identifying potential problems and suggesting policies is important for promoting the sustainable development in China"s digital cultural industry. Based on the data related to digital cultural industries in 28 provinces in China from 2015-2020, the DEA-BCC model and Malmquist index are used to analyze the input and output efficiency of China"s digital cultural industry from dynamics and static. The results of the research show that (1) From a static perspective, the overall efficiency of China"s digital cultural industry still has a lot of room for improvement, presenting the characteristics of regional differences in the "Eastern> Central> West> Northeast". (2) From a dynamic perspective, the entire productivity of the Chinese digital cultural industry has generally risen, showing the characteristics of regional differences in the "Eastern> Central> Northeast> West". In addition, this study divides the full element productivity of the digital cultural industries in China into dual -drive shapes, with efficiency -driven type, efficiency constraints, technical constraints, and dual -made approximate types. Studies believe that the digital cultural industries in various regions of China should develop jointly, grasp the improvement of technological efficiency of the digital cultural industry and the synergistic role of technological progress, thereby improving the efficiency of the production of the digital cultural industry.
Harnessing the Potential of social media for Effective Library Services and Outreach

The use of social media has become an essential part of our daily lives, and libraries have recognized its importance in connecting with their users.Social media platforms such as Facebook, Twitter, Instagram, and LinkedIn offer libraries an effective means of engaging with their users and reaching a wider audience.Through social media platforms, libraries can disseminate information about their services, resources, events, and activities.Additionally, libraries can use social media to promote reading and literacy, provide online reference services, and offer virtual programming and activities.Moreover, social media allows libraries to communicate with their users in real-time and respond to their feedback and inquiries promptly.Social media has become a vital tool for libraries to stay relevant and competitive in the digital age.With the increasing popularity of online research and e-books, libraries need to embrace social media to remain a valuable resource for their users.
Deep Learning-Based Cursive Text Detection and Recognition in Natural Scene Images

Cursive text detection and recognition in natural scene images are complex tasks due to the variability and intricacy of handwriting styles. This research focuses on developing a deep learning-based approach to address these challenges. The proposed solution leverages advancements in deep learning techniques to improve the accuracy and robustness of cursive text detection and recognition. The research involves collecting and annotating a diverse dataset of natural scene images containing cursive text. A deep learning model for cursive text detection is trained on the annotated dataset, and techniques for text line segmentation are investigated to enhance accuracy. Furthermore, a deep learning-based recognition system is designed and implemented to transcribe cursive text into machine-readable text. The proposed approach is evaluated and compared with existing methods using appropriate metrics and benchmark datasets. The research aims to provide insights into the challenges and opportunities of cursive text analysis in real-world scenarios and contribute to advancements in document digitization, handwriting analysis, and information retrieval.