Reviewerly/exHarmony huggingface dataset:
Toward Community-Based Natural Language Processing (CBNLP): Cocreating With Communities

Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural considerations. While traditional health care NLP techniques typically include clinical subject matter experts to extract health information or aid in interpretation, few NLP tools involve community stakeholders with lived experiences. In this perspective paper, we draw upon the field of community-based participatory research, which gathers input from community members for development of public health interventions, to identify and examine ways to equitably involve communities in developing health care NLP tools. To realize the potential of community-based NLP (CBNLP), research and development teams must thoughtfully consider mechanisms and resources needed to effectively collaborate with community members for maximal societal and ethical impact of NLP-based tools.
Residue number systems with six modules and efficient circuits based on power-of-two diagonal modulus

The paper suggests an algorithm for finding Residue Number Systems (RNS) with six modules (6-tuples) with the Sum of Quotients SQ=2k for some positive integer k. It is shown that there are exactly thirteen such 6-tuples (m1,…,m6) with m1+m2≤10000 and m3+m4≤m5+m6≤10000 and investigated the three smallest among them, namely (5,2399,7,11,23,1691), (47,293,25,193,41,257), and (23,1433,13,29,681,821), having k=34, 36, and 40, respectively. The hypothesis is that such RNS allow efficient hardware implementations of non-modular operations — division, sign detection, comparison of numbers, and reverse conversion from the point of hardware resource usage and balancedness. The special case SQ=2k allows a significant simplification of these operations and increased efficiency of hardware implementations. Hardware modeling of circuits based on the above 6-tuples that implement magnitude comparison and reverse RNS to binary conversion (reverse conversion) is presented. The suggested magnitude comparison and reverse conversion devices, which operate with the six modules, are built using the methodology and values of the diagonal function, carry-save, and Kogge-Stone adders. The results of FPGA-based hardware modeling and the theoretical parameters of devices calculated using the unit-gate model are compared with state-of-the-art approaches. The unit-gate model showed that the use of the proposed circuits allows us to reduce the area by 17.82%–50.81% and the delay by 1.56%–97.76% for the implementation of the magnitude comparison, and to reduce the delay by 2.80%–95.03% for the implementation of reverse conversion. The FPGA synthesis also showed that the new design has reduced the area and delay by 46.96%–86.80% and 7.15%–47.65%, respectively, for the implementation of magnitude comparison and reduced the delay by 13.63%–81.53% for the implementation of reverse conversion. It is shown that a proposed technique to measure the RNS balance can adequately reflect differences in the RNS performance.
Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets From Large Pre-Trained Models

For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These large pre-trained models or “Jacks of All Trades” (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this paper conducts an initial exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, the Set of Sets is formulated to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively “Masters of All Trades”.
A Qualitative Study on Image Quality Enhancement, Object Detection Methods to Assist Visually Impaired Users

Object Detection and Tracking with Recognition (ODTR) is a predominant field that finds significant applications in surveillance and assisting Visually Impaired (VI) people. Though ODTR is possible with the implication of Computer Vision (CV) models, the achievement of accuracy levels seemed to be meagre due to lack of clarity of images being captured. Hence a hybrid of Image Processing methods could improve the efficiency of the prediction. The significant objective of this research work is to study and explore the various Digital Image Processing (DIP) methods to refine the image and Computer Vision methods to perform ODTR that could possibly improve the performance of prediction at the end of the detection. The various image processing methods like Filtering, Segmentation and Feature Extraction were studied with the technical methods from MATLAB interface and Object detection and Tracking evaluation methods with Confusion Matrix were analyzed and presented with expected outcomes and discussions. The evaluation methods were presented with expected models for supporting the proposed framework models.
An Improved Crime Scene Detection System Based on Convolutional Neural Networks and Video Surveillance

Since criminals may engage in a wide variety of crime scenes in public spaces, the time immediately before and after these events must be monitored. As a rule, there will be cameras set up for monitoring purposes. Videos or still photographs captured by these cameras would reveal any criminal behavior to authorities after the fact. Consequently, many people many look for signs of fraud. However, there are currently no safeguards to prevent the theft of high-value items. Pre-incident warning alarm systems based on ML and DL may now be customized to track crime behavior. Human behavior analysis and identifying out-of-the-ordinary events would provide the basis for predicting the occurrence of crime scenes The necessity for a highly accurate, precise, low-false-positive, and low-false-negative prediction system has led to the development of several ML and DL-based techniques; however, a hybrid or upgraded ML- or DL-based system may better answer this demand. A better criminal activity detection system based on an enhanced convolutional neural network (E-CNN) is presented in this proposed study. It was stated that the experiment was successful. The SPSS software is used to examine the data. The findings revealed that crime activity detections had an average accuracy of 97.050%, precision of 96.743 %, the false-positive rate of 2.957%, and false-negative rate of 2.927%. The convolutional neural network (CNN) method was also compared to this outcome. The findings of this study may be used to better detect and prevent potentially dangerous situations by enhancing the security warning system for potential criminal activities.
TripleMIE: Multi-modal and Multi Architecture Information Extraction

The continuous development of deep learning technology makes it widely used in various fields. In the medical scene, electronic voucher recognition is a very challenging task. Compared with traditional manual entry, the application of OCR and NLP technology can effectively improve work efficiency and reduce the training cost of business personnel. Using OCR and NLP technology to digitize and structure the information on these paper materials has gradually become a hot spot in the current industry. Evaluation task 4 (OCR identification of electronic medical paper documents (ePaper)) of CHIP2022 [15, 16, 25] requires extracte 87 fields from the four types of medical voucher materials, including discharge summary, outpatient invoice, drug purchase invoice, and inpatient invoice. This task is very challenging because of the various types of materials, noise-contained data, and many categories of target fields. To achieve the above goals, we propose a knowledge-based multi-modal and multi-architecture medical voucher information extraction method, namely TripleMIE, which includes I2SM: Image to sequence model, L-SPN: Large scale PLM-based span prediction net, MMIE: multi-modal information extraction model, etc. At the same time, a knowledge-based model integration module named KME is proposed to effectively integrate prior knowledge such as competition rules and material types with the model results. With the help of the above modules, we have achieved excellent results on the online official test data, which verifies the performance of the proposed method.(https://tianchi.aliyun.com/dataset/131815#4)
Domain Robust Pipeline for Medical Causal Entity and Relation Extraction Task

Medical entity and relation extraction is an essential task for medical knowledge graph, which can provide explanatory answers for medical search engine. Recently, PL-Marker, a deep learning based pipeline, has been proposed, which follows a similar NER &ER paradigm. In this method, medical entities are first identified by a NER model, and then they are combined by pairs to feed into a ER model to learn the causal relation among the medical entities. In this way, the pipeline cannot handle the complex entity relationships contained by CMedCausal due to its own defects, such as exposure bias and lack of relevance between entities and relationships. In this paper, we propose a novel pipeline: Domain Robust Pipeline (DRP) which tackles these challenges by introducing noisy entities to solve the exposure bias, adding KL loss to learn from samples with noisy labels, applying multitask learning to escape semantic traps and re-targeting the relationships to increase the robustness of the pipeline.
Distributed and Adversarial Resistant Workflow Execution on the Algorand Blockchain

We provide a practical translation from the Dynamic Condition Response (DCR) process modelling language to the Transaction Execution Approval Language (TEAL) used by the Algorand blockchain. Compared to earlier implementations of business process notations on blockchains, particularly Ethereum, the present implementation is four orders of magnitude cheaper. This translation has the following immediate ramifications: (1) It allows decentralised execution of DCR-specified business processes in the absence of expensive intermediaries (lawyers, brokers) or counterparty risk. (2) It provides a possibly helpful high-level language for implementing business processes on Algorand. (3) It demonstrates that despite the strict limitations on Algorand smart contracts, they are powerful enough to encode models of a modern process notation.
An Automatic Construction Method of Diagnosis and Treatment Decision Tree Based on UIE and Logical Rules

In traditional information extraction, entity relation triples are mainly extracted from text, and there is no further logical relationship between triples. Therefore, an information extraction model based on triple extraction and decision tree generation is proposed. The model first extracts the triples in the medical text through the UIE method, and then forms the triples into a binary tree according to the condition node and the decision node. The condition node represents the condition judgment that needs to be made, and the decision node represents the diagnosis and treatment decision that needs to be made. This decision can not only mine the core entities and relationships in the text, but also realize the connection of entity relationship information to form a complete decision process. The correct rate of decision tree construction has achieved good results, which proves that the model can effectively generate decision trees.
Website Phishing Detection of Machine Learning Approach using SMOTE method

One of the most serious cyber-attacks is phishing, and experts are working to discover a cure. Evaluate how the accuracy of predictions varies in an unbalanced dataset in this study. The dataset is balanced using the Synthetic Minority Over-Sampling Technique (SMOTE). The SMOTE-sample approach is suggested for identifying genuine websites from phishing sites. The suggested model outperforms popular binary-classification techniques like CatBoost, Random Forest, and XGBoost, according to experimental results using data from the Phishtank website's dataset for the identification of phishing websites. People who lack the domain knowledge for machine learning's modelling and parameter tuning approaches might benefit from the method of creating phishing website detection models. Phishing attempts and early detection of suspicious features can both be helpful. According to the findings, CatBoost has a detection accuracy of up to 97%, making it a far superior classifier than Random Forest and XGBoost. Finally, the results of the experiment showed that five-fold cross-validation outperformed the best accuracy of 97.7%.
An Efficient and Speedy approach for Hierarchical Clustering Using Complete Linkage method

In recent days to deal with the major problem of increasing data size, Clustering is a highly useful tool that not only helps in shrinking the dataset by grouping them into clusters but also finds hidden information from the unlabeled data. The Complete Linkage algorithm is a highly preferred distance-based Hierarchical Clustering algorithm that provides compact clusters but suffers from the disadvantage of high convergence time. This algorithm needs the entire dataset in advance to take a clustering decision and hence is unsuitable for “on the fly” data clustering. This paper presents a two-staged partially incremental Complete Linkage Clustering algorithm that partially clusters data alongside the collection. The proposed method without compromising the space complexity reduces a lot of redundant distance computations thereby reducing the runtime of the algorithm to a much lower value. Although the clustering result may slightly deviate from the original Complete Linkage algorithm, the characteristics of the Complete Linkage Clusters are always met in all scenarios under any given threshold. The advantage of this algorithm over the existing methods has been verified experimentally.
Towards Smartphone Based Complete Blood Cell Estimation from Blood Smear Images

Blood Cell Counting plays a significant role in diagnosis and determining proper treatment because deviations from the standard value might point to severe diseases. Techniques for counting blood cells include both manual and automatic methods, each of which has inherent limitations. Hence, we propose a smartphone-based blood cell counting system which includes a deep learning algorithm for detecting blood cells and an mobile application to show the result. The smartphone Application will be developed using Flutter Framework and will be equipped with YOLOv5 algorithm as backend that will automatically determine the number of red blood cells, white blood cells and platelets present on a blood slide smear.
Medical Decision Tree Extraction: A Prompt Based Dual Contrastive Learning Method

The extraction of decision-making knowledge in the form of decision trees from unstructured textual knowledge sources is a novel research area within the field of information extraction. In this paper, we present an approach to extract medical decision trees from medical texts (aka. Text2DT) in the 8th China Health Information Processing Conference (CHIP 2022) Open Shared Task $$^{1}$$ . Text2DT task involves the construction of tree nodes using relation triples, which extends upon the foundation of the named entity recognition and relation extraction tasks. Compared to the fixed event schema typically defined in the event extraction task, the tree structure allows a more flexible and variable approach to representing information. To achieve this novel task, we propose a prompt based dual contrastive learning method. The experimental results demonstrate that the decision tree constructed by our model can achieve an accuracy of 55% (65% using the relaxed metric). $$^{1}$$ (http://www.cips-chip.org.cn/2022/eval3)
Optimal Stacked Sparse Auto-encoder based Online Analytic Processing (OLAP) Hypercube Text Analysis

Online Analytical Processing (OLAP) is a crucial area of interest to several organizations in the previous decade. But these approaches are established as extremely useful to analyze and mine structured data, it can face problems in controlling text data. Usually, OLAP queries were ineffective for evaluating text data which are commonly compared with some contextual data but which suppose that examined for retrieving accurate outcomes. Usually, the OLAP applies to numeric data and there has been comparatively limited research in the domain of OLAP to text data. In this view, this article develops an Optimal Stacked Sparse Autoencoder based OLAP Hypercube Text Analysis (OSSAE-OLAP) technique. The presented OSSAE-OLAP technique focuses on the classification of appropriate data for text analysis. In the presented OSSAE-OLAP technique, the SSAE model is used for classification process which contains multiple stacks of sparse autoencoders. To enhance the performance of the SAE method, the pigeon inspired optimization (PIO) approach is utilized for the hyperparameter tuning process. For exhibiting the improved outcomes of the presented OSSAE-OLAP technique, a widespread experimental analysis is made and the outcomes revealed the supremacy of the OSSAE-OLAP technique.
Sanctioning hate speech on the Internet: In search of the best approach

The borderless nature of the Internet, different national approaches to the understanding of what constitutes hate speech, as well as the danger of restrictions on the freedom of speech, make it difficult to develop appropriate mechanisms against this phenomenon. The limitations of international law in providing a universal definition of hate speech, due to the different national approaches to freedom of expression, have thwarted attempts to produce an effective international treaty in order to deal with this issue. Imposing obligations on Internet portals to establish self-regulatory mechanisms for removing hate speech content has raised concerns of non-competent censorship and potential limitations of the freedom of expression. This paper focuses on the challenges encountered in the struggle against hate speech online and possible mechanisms for combating this phenomenon.
A Web-based Dicom Image and Plane Viewer

Preclinical research, clinical diagnosis, and treatment can all benefit from the information that a medical image can offer. Due to the increased usage of digital medical imaging, numerous researchers are actively creating medical image processing algorithms and systems to provide the clinical community with improved results, such as accurate clinical parameters or processed images from the original images. We describe a Web-based DICOM reader in this work that was created solely using web technology, specifically Python, Streamlit, and Docker. When it comes to programming languages, Python has established itself as a competitor to MATLAB and Julia, two prominent scientific programming languages. In this work, we examine the potential of Python and Docker as co-implementers of the DICOM viewer. A demonstration field is available to new users, for learning purposes. Three different types of image planes namely axial, coronal, and sagittal are also available, which helps doctors readily detect cancers. Additionally, we can adjust the image plane's threshold levels to suit our needs. We also provide a JSON version of the website for use in forthcoming research initiatives.
A Novel Pipeline for Compressing Image Buffers in Remote Education Video Conferencing using Harris Corner Detection and Pixel Map Array

The COVID-19 pandemic has compelled educational institutions worldwide to shift to remote online education. Addressing the growing trend, an Oxford University Press report titled ‘Addressing the Deepening Digital Divide’ states that poor digital access is the most significant barrier to digital learning according to 68 percent of academicians. Students in many remote parts of India frequently have access to limited bandwidth internet, which is insufficient for the modern standards of network-hogging online video conference software solutions. This paper provides an algorithmic compression of image buffers to aid low-cost remote online video education. This compression can be done by translating the teacher's blackboard images to pixel arrays projected on a canvas on the student's dashboard while the instructor constantly communicates via real-time voice. The image is first converted to grayscale and dilated with a square kernel. Using Harris Corner Detector, probable board corners are identified and compared to a geometrical center of the points and the corners recovered by cornerSubPix. An adaptive threshold is employed, distinguishing the board's contents from the backdrop on the cropped picture based on the recovered points. The pixel-mapped array is then transmitted to the students through the webRTC real-time protocol, which includes support for two-way audio, allowing the teacher to deliver lectures. Using Canvas API on the application front-end, the array is projected onto the student's device as a dot matrix display. This paper has achieved an effective rate in the video transmission format, aiding online remote education on low-bandwidth network devices.
An End-to-End secured Blockchain framework for Internet of Things based smart Healthcare

Supply chain challenges can be overcome by using the latest technology, Blockchain, which is exploding in popularity. The decentralised, distributed nature of the blockchain enables enormous networks of independent, decentralised individuals and organisations to grow and operate with ease. Immutable, transparent, and kept in a distributed ledger using consensus methods, the data in the blockchain can't be altered. There has been a huge increase in the amount of data generated by the Internet of Things, which connects millions of computers and sensors. As part of this study, researchers suggest a secure authentication and data access between Blockchain and IoT end-users. In the wireless transmission research field, scalability of network data transfer is a major topic. The integrity, data quality, and originality of the data on the transmission line must all be maintained. To provide a secure route of communication, this paper investigates the use of Blockchain terminology for file sharing. A clustering infrastructure is used to forecast the internal correlation of bounded nodes and peers. Secure data flow between producers, suppliers, and customers becomes critical in this context in order to maintain responsiveness to market fluctuations. That is why this study is focused on introducing a privacy-preserving approach to data transmission. There are two techniques recommended for privacy preservation: Data Sanitization and Data Restoration. The original data is cleaned before transmission via blockchain technology, and the data is transformed into sanitised data. SCM data exchange with block chain technology will be tested by comparing it with traditional methods in terms of security.