Reviewerly/exHarmony huggingface dataset:
Defining the study population

After defining the research question, a study must identify the study population to assess. Study populations can include a whole target population (i.e., census); however, most studies include sampling, in which the sample represents a subset of the target population. When deciding to sample, an important consideration is the sample frame, which is a group that participants can be selected from; it is important to select an appropriate sample frame so that the research question can be answered and be representative of the entire population. The two common methods for sampling include probabilistic and non-probabilistic sampling with subsets for each type. Clinical studies may have unique sampling methods based on the type of study. As part of defining a study population, investigators should evaluate the sample size required based on the population characteristics and event rates to ensure that while the population is most likely to benefit, it is also an appropriately achievable size.
Learning sentiment analysis with word embeddings

Feature representation methods have delivered impressive results in computer vision, natural language processing, speech synthesis, and neuroscience. Moreover, Natural Language Processing (NLP) has many feature representations that help understand contextual and non-contextual information for various existing tasks. Sentiment analysis is a well-established task in many business applications like movie reviews can cause or damage a movie's revenue, and product reviews can encourage the quality of a product or damage the product sales. With the recent developments of efficient feature representations, sentiment analysis has achieved better accuracies. Neural word embeddings have delivered impressive results in sentiment analysis. The quality of the word embedding determines the performance of a supervised model. However, choosing the right set of word embeddings for a given dataset is a primary challenging task for enhancing the results. This chapter showcases an overview of word embeddings on the sentiment-analysis task. It is done in two steps: (i) Explanation of the mixture of classification experts (MoCE) model for the sentiment-classification task, (ii) then compare and improve the classification accuracy by different combinations of word embedding as the first level of features and pass it to the cascade model inspired by gcForest for extracting various features. Each expert learns specific positive or negative examples corresponding to its category in the first step. In the second step, the resulting features on the sentiment-analysis task achieve competitive performance in accuracy, precision, and recall.
Visual analytics

An estimated 40% of all cancer cures have been attributed to radiotherapy treatments, an outcome made possible by treatment guidelines optimized for tumor type, size, and shape. Unfortunately, such tumor-based guidelines often result in a wide variation in treatment responses, despite the patients having similar clinical profiles. Recent research suggests that this wide variation in responses could be the result of molecular heterogeneity across patients, a well-known characteristic of most cancers. This chapter discusses how advanced methods from visual analytics could enable (1) the quantitative modeling of molecular heterogeneity to identify patient subgroups with distinct molecular profiles and outcomes, and (2) the visual modeling of patient subgroups to enable clinicians to infer mechanisms that precipitate adverse outcomes in each subgroup, with the goal of designing precision radiotherapy treatments.
Enhanced Hybrid Optimal and Intelligent Technique for Data Storage in Cloud

Electronic health records are rapidly becoming more prevalent in healthcare organizations. They are being used to store and share patient data. Due to the increasing cost of healthcare and the limited resources available to healthcare organizations, many are now adopting cloud-based electronic health records (EHRs). The concept of PAYG e-health cloud models enables healthcare organizations to reduce their costs while still keeping their data secure. Due to the nature of the systems involved, there are various risks associated with using these technologies. Despite its increasing popularity, not much is being done to address these issues. Privacy preservation is an important aspect of cloud computing to ensure that the sensitive information of users is not exposed. However, it can be challenging to implement in the cloud environment. The data collected for the privacy preservation process is then analyzed and processed through an AI model. The resulting data is then filtered using Whale Optimization algorithm and Spider Monkey Optimization. The collected data is then stored in the cloud through a management system. The goal is to provide better performance and greater privacy when compared to the previous method.
An Improved JPEG Image Blocking Artifact Detector

Sharing digital images on social media has become very common these days.People must check for authenticity to share images on social media websites.The shared image may be forged intentionally or unintentionally and can defame someone's reputation, leading to severe events, such as public riots.Thus, authentication of digital images which are posted on social media websites is of paramount importance.Our social media should be intelligent enough to check on these forged images such that no false information spreads around society.Many image forgery detection algorithms have been used by big social groups based on JPEG compression artifacts, but these may not work well in the presence of anti-forensics.JPEG compression is the most widely used standard in social media these days.Two important artifacts, quantization and blocking artifacts, are being exploited by various experts for forensic analysis.JPEG antiforensic techniques clear away these artifacts to fool forensic detectors.This work presents a novel technique derived from the inter-block interdependence of DCT coefficients for ferreting out JPEG-blocking artifacts in the presence of anti-forensics.In the case of JPEG images, the cropping operation shifts the blocking artifacts within the block, changing the inter-block interdependence.We propose to take advantage of this change to HIGHLIGHTS• Authentication of forged digital images posted online is very important.• An improved JPEG image blocking artifact detector is proposed.• The proposed artifact detector has outperformed the existing methods.• Authenticity of digital images posted online can be verified using proposed method.
Overcoming Incomplete Answer Problems In Chat GPT And Wolfram Alpha With Peter Chew Method For Solution Of Triangle

This article discusses the limitations of Chat GPT and Wolfram Alpha specifically in terms of their restricted knowledge and their inability to answer some Solution of Triangle questions. Chat GPT's knowledge is confined to data up until 2021. However, these limitations can be addressed by incorporating future knowledge, Peter Chew Method for Solution of Triangle into Education 4.0 Calculator. By utilizing Peter Chew Method into the Education 4.0 calculator, significant advancements can be achieved. This innovative tool allows us for finding the value of third side when given 2 sides and a non included angle. The integration of Education 4.0 provides a promising solution to the existing limitations in Chat GPT and Wolfram Alpha.By programming the Education 4.0 calculator with Peter Chew Method, these systems can enhance their capabilities and offer simple solution, more comprehensive and up-to-date answers to all triangle problem. This approach demonstrates the potential of leveraging advanced mathematical concepts to overcome the limitations posed by restricted knowledge in AI systems like Chat GPT and Wolfram Alpha.By incorporating Peter Chew Method and Rule for Solution of triangle and leveraging advanced mathematical tools, AI systems can expand their capabilities and offer complete answer , simple solution, more accurate and comprehensive responses in the domain of Solution of Triangle. This can be effective in let student interest in using technology while learning mathematics especially when analogous covid- 19 issues arise in the future.
Research on Copyright Protection of We Media Based on Blockchain

We media has gained rapid development on the fast train of digital era, and everyone has a microphone. However, due to the low threshold, large flow and fast realization, we media is also very prone to problems such as infringement, etc. Blockchain, as a kind of distributed ledger, can provide technical support and application in the problem of copyright protection of we media with its characteristics of decentralization, non-tamperability, trustworthiness and privacy protection, etc. This paper discusses and researches the problems existing in the process of we media copyright protection, and provides reasonable ideas and measures for we media copyright protection based on blockchain technology from three perspectives of confirming, using and defending rights, in order to better use technical means to combat infringement in we media field, safeguard the legitimate rights and interests of we media, and build a positive and clear we media communication environment.
The Use of Blockchain in Vietnam’s Global Fishery Supply Chain Management

The issue of traceability has emerged as a fundamental tenet of sound logistics and supply chain management. In order to assess this issue, our study investigates the use of blockchain-based traceability frameworks to manage fisheries supply chains in Vietnam. From a methodological perspective, we assessed the contemporary empirical literature to research to achieve four key objectives. First, to provide a comprehensive analysis of the advantages and potential applications of blockchain from a Vietnamese fisheries perspective. Second, to present an application framework that consists of four stages, with the aim of improving the transparency and security of transaction information by adopting Internet of Things (IoT) and blockchain in each of the connections throughout the fishery supply chain. Third, to illustrate the five transformational phases in which blockchain technology can be applied to small and medium enterprises (SMEs) operating in the fishery industry. Finally, to establish a set of policy implications based around the infrastructural, regulatory gaps and human capital that exist in the fishery industry in Vietnam and affect blockchain. From our analysis, we find substantial cause for optimism about the role of blockchain can play as an engine of growth in Vietnam’s fishery export industry.
ROF-Models in Random Walk Spaces

Let us recall the classic problem in image restoration. Given a noisy/corrupted image $$f:\Omega \rightarrow {\mathbb R}$$ on, for example, a rectangle $$\Omega $$ in $${\mathbb R}^2$$ , the aim is to remove the noise or corruption in order to obtain the desired “clean” image $$u : \Omega \rightarrow {\mathbb R}$$
YinYang, a Fast and Robust Adaptive Document Image Binarization for Optical Character Recognition

Optical Character Recognition (OCR) from document photos taken by cell phones is a challenging task. Most OCR methods require prior binarization of the image, which can be difficult to achieve when documents are captured with various mobile devices in unknown lighting conditions. For example, shadows cast by the camera or the camera holder on a hard copy can jeopardize the binarization process and hinder the next OCR step. In the case of highly uneven illumination, binarization methods using global thresholding simply fail, and state-of-the-art adaptive algorithms often deliver unsatisfactory results. In this paper, we present a new binarization algorithm using two complementary local adaptive passes and taking advantage of the color components to improve results over current image binarization methods. The proposed approach gave remarkable results at the DocEng'22 competition on the binarization of photographed documents.
OntG-Bart

Automating the process of clinical text summarization could save clinicians' reading time and reduce their fatigue, acknowledging the necessity of human professionals in the loop. This paper addresses clinical text summarization, aiming to incorporate ontology concept relationships via a Graph Neural Network (GNN) into the summarization process. Specifically, we propose a model, extending Bart's encoder-decoder framework with GNN encoder and multi-head attentional layers for decoder, producing ontology-aware summaries. This GNN interacts with the textual encoder, influencing their mutual representations. The model's effectiveness is validated on two real-world radiology datasets. We also present an ablation study to elucidate the impact of varied graph configurations and an error analysis aimed at pinpointing potential areas for future improvements.
LYLAA

Chart Data Extraction (CDE) is a complex task in document analysis that involves extracting data from charts to facilitate accessibility for various applications, such as document mining, medical diagnosis, and accessibility for the visually impaired. CDE is challenging due to the intricate structure and specific semantics of charts, which include elements such as title, axis, legend, and plot elements. The existing solutions for CDE have not yet satisfactorily addressed these issues. In this paper, we focus on two critical subtasks in CDE, Legend Analysis and Axis Analysis, and present a lightweight YOLO-based method for detection and domain-specific heuristic algorithms (Axis Matching and Legend Matching), for matching. We evaluate the efficacy of our proposed method, LYLAA, on a real-world dataset, the ICPR2022 UB PMC dataset, and observe promising results compared to the competing teams in the ICPR2022 CHART-Infographics competition. Our findings showcase the potential of our proposed method in the CDE process.
Tabular Corner Detection in Historical Irish Records

The process of extracting relevant data from historical handwritten documents can be time-consuming and challenging. In Ireland, from 1864 to 1922, government records regarding births, deaths, and marriages were documented by local registrars using printed tabular structures. Leveraging this systematic approach, we employ a neural network capable of segmenting scanned versions of these record documents. We sought to isolate the corner points with the goal of extracting the vital tabular elements and transforming them into consistently structured standalone images. By achieving uniformity in the segmented images, we enable more accurate row and column segmentation, enhancing our ability to isolate and classify individual cell contents effectively. This process must accommodate varying image qualities, different tabular orientations and sizes resulting from diverse scanning procedures, as well as faded and damaged ink lines that naturally occur over time.
Read-Write-Learn

Handwriting recognition relies on supervised data for training. Annotations typically include both the written text and the author's identity to facilitate the recognition of a particular style. A large annotation set is required for robust recognition, which is not always available in historical texts and low-annotation languages. To mitigate this challenge, we propose the Read-Write-Learn framework. In this setting, we augment the training process of handwriting recognition with a language model and a handwriting generator. Specifically, in the first reading step, we employ a language model to identify text that is likely detected correctly by the recognition model. Then, in the writing step, we generate more training data in the same writing style. Finally, in the learning step, we use the newly generated data in the same writing style to finetune the recognition model. Our Read-Write-Learn framework allows the recognition model to incrementally converge on the new style. Our experiments on historical handwritten documents demonstrate the benefits of the approach, and we present several examples to showcase improved recognition.
Algorithm Parallelism for Improved Extractive Summarization

While much work on abstractive summarization has been conducted in recent years, including state-of-the-art summarizations from GPT-4, extractive summarization's lossless nature continues to provide advantages, preserving the style and often key phrases of the original text as meant by the author. Libraries for extractive summarization abound, with a wide range of efficacy. Some do not perform much better or perform even worse than random sampling of sentences extracted from the original text. This study breathes new life to using classical algorithms by proposing parallelism through an implementation of a second order meta-algorithm in the form of the Tessellation and Recombination with Expert Decisioner (T&R) pattern, taking advantage of the abundance of already-existing algorithms and dissociating their individual performance from the implementer's biases. Resulting summaries obtained using T&R are better than any of the component algorithms.
Addressing the gap between current language models and key-term-based clustering

This paper presents MOD-kt, a modular framework designed to bridge the gap between modern language models and key-term-based document clustering. One of the main challenges of using neural language models for key-term-based clustering is the mismatch between the interpretability of the underlying document representation (i.e. document embeddings) and the more intuitive semantic elements that allow the user to guide the clustering process (i.e. key-terms). Our framework acts as a communication layer between word and document models, enabling key-term-based clustering in the context of document and word models with a flexible and adaptable architecture. We report a comparison of the performance of multiple neural language models on clustering, considering a selected range of relevance metrics. Additionally, a qualitative user study was conducted to illustrate the framework's potential for intuitive user-guided quality clustering of document collections.
Multi-Task CTC for Joint Handwriting Recognition and Character Bounding Box Prediction

Deep learning based models continue to push the frontier of OCR and Handwritten Text Recognition (HTR) both in terms of increasing accuracy and minimizing the burden of data annotation. For example, the CTC loss [3] allows for line-level training without the explicit spatial positions of the characters and words. Other recent works have further reduced the annotation effort by training systems on entire paragraphs or pages of text without line-level annotations. While these techniques have greatly increased accuracy and significantly reduced the data collection burden, useful outputs beyond just the Unicode character prediction have been lost. Because these systems have fewer explicit segmentation steps, they do not produce character-level or word-level bounding boxes. As a result, downstream applications such as highlighting, redaction tools, and basic document correction, are not provided and must be obtained with other methods.
Genetic Generative Information Retrieval

Documents come in all shapes and sizes and are created by many different means, including now-a-days, generative language models. We demonstrate that a simple genetic algorithm can improve generative information retrieval by using a document's text as a genetic representation, a relevance model as a fitness function, and a large language model as a genetic operator that introduces diversity through random changes to the text to produce new documents. By "mutating" highly-relevant documents and "crossing over" content between documents, we produce new documents of greater relevance to a user's information need --- validated in terms of estimated relevance scores from various models and via a preliminary human evaluation. We also identify challenges that demand further study.
Quality, Space and Time Competition on Binarizing Photographed Document Images

Document image binarization is a fundamental step in many document processes. No binarization algorithm performs well on all types of document images, as the different kinds of digitalization devices and the physical noises present in the document and acquired in the digitalization process alter their performance. Besides that, the processing time is also an important factor that may restrict its applicability. This competition on binarizing photographed documents assessed the quality, time, space, and performance of five new algorithms and sixty-four "classical" and alternative algorithms. The evaluation dataset is composed of laser and deskjet printed documents, photographed using six widely-used mobile devices with the strobe flash on and off, under two different angles and places of capture.
Synchronous Recognition of Music Images Using Coupled N-Gram Models

Handwritten music recognition researches the use of technologies to automatically transcribe handwritten music pieces that are only found in image format, and make them available to the general public. Many historical music pieces are composed by a music part and a lyrics part. Handwritten music recognition has focused mainly on transcribing the music elements in historical images, but there exist many pieces where both music and lyrics are present and of relevance. The recognition of both music and lyrics is generally carried out as separate tasks. Both parts are synchronized in many historical documents at line level and loosely at word level. These two elements are strongly related having each one affecting the other. Discovering this relation may be very relevant to improve recognition results in both parts and to further steps like music analysis, composition analysis, etc. This paper introduces a preliminary system that transcribes synchronously and simultaneously both the music and lyrics elements of handwritten historical music images. The results obtained over a historical manuscript dataset show that this system obtains an improvement of up to 15.4% at symbol rate on stave recognition and up to an approximately average 7.6% improvement when both the music and lyrics part are jointly considered.
WEATHERGOV+

Tables, ubiquitous in data-oriented documents like scientific papers and financial statements, organize and convey relational information. Automatic table recognition from document images, which involves detection within the page, structural segmentation into rows, columns, and cells, and information extraction from cells, has been a popular research topic in document image analysis (DIA). With recent advances in natural language generation (NLG) based on deep neural networks, data-to-text generation, in particular for table summarization, offers interesting solutions to time-intensive data analysis. In this paper, we aim to bridge the gap between efforts in DIA and NLG regarding tabular data: we propose WEATHERGOV+, a dataset building upon the WEATHERGOV dataset, the standard for tabular data summarization techniques, that allows for the training and testing of end-to-end methods working from input document images to generate text summaries as output. WEATHERGOV+ contains images of tables created from the tabular data of WEATHERGOV using visual variations that cover various levels of difficulty, along with the corresponding human-generated table summaries of WEATHERGOV. We also propose an end-to-end pipeline that compares state-of-the-art table recognition methods for summarization purposes. We analyse the results of the proposed pipeline by evaluating WEATHERGOV+ at each stage of the pipeline to identify the effects of error propagation and the weaknesses of the current methods, such as OCR errors. With this research (dataset and code available here1), we hope to encourage new research for the processing and management of inter- and intra-document collections.