Reviewerly/exHarmony huggingface dataset:
A COMPARISION OF CONVENTIONAL TUBE METHOD AND COLUMN AGGLUTINATION TECHNOLOGY IN AN IMMUNOHEMATOLOGY LABORATORY

Background: Rapid technological advancements in pretransfusion procedures in an immuno-hematological laboratory are a result of the increased focus on the quality and safety of blood products. Numerous automated pretransfusion testing platforms have been developed as a result of the inherent drawbacks of the gold standard conventional tube method. The invention of Column Agglutination Technology was the biggest advancement among these. Aim: 1. To compare the results of blood grouping and crossmatching by Conventional Tube Method and Column Agglutination Technology. 2. To statistically evaluate the results obtained between Column Agglutination Technology and Conventional Tube Technique in our immunohematology laboratory. Materials and Methods: The study was performed in blood bank of a tertiary hospital in Mangalore during the month of January,2022 in which 500 samples were subjected to blood grouping and cross matching. The data obtained was extensively studied. Sensitivity, specicity and statistical analysis were done by Fisher's exact test. Results: In comparative evaluation of CAT and CTT of 500 samples, there was concordance rate of 99.6% in blood grouping and concordance of 99.8% in crossmatching. In both the scenarios, the discordance was evaluated and found CATis superior to CTT. The most common blood group of both studies was 0 positive. Statistical analysis using Fisher's test proved statistically signicant difference between grouping and cross-matching via both tests with a p value of 0.000001 and 0.01 respectively. The sensitivity and specicity of both tests showed CAT to be superior with 100% sensitivity and 100% specicity while in CTT, the sensitivity was 100% with 66.6% specicity. Conclusion: The study establishes that CAT is more sensitive, specic, rapid, reliable procedure and is a better substitute of CTT in pretransfusion testing.
Temporal Relation Extraction from Clinical Texts Using Knowledge Graphs

An integral task for many natural language processing applications is the extraction of the narrative process described in a document. For understanding such processes we need to recognize the mentioned events and their temporal component. With this information, we can understand the sequence of events i.e. construct a timeline. The main task dealing with the temporal component of events is temporal relation extraction. The goal of temporal relation extraction is to determine how the times of two events are related to one another. For example, such relation would tell us whether one event happened before or after another one. In this paper, we propose a novel architecture for a temporal relation extraction model combining text information with information captured in the form of a temporal event graph. We present our initial results on the domain of clinical documents. Using a temporal event graph with only correct relations, the model achieves F1 score of 83.6% which is higher than any of our state-of-the-art baseline models. This shows the promise of our proposed approach.
A secure, blockchain-based data storage scheme for cloud environments

Cloud storage has become a widespread trend with its efficient and convenient features. However, this also brings new security risks, such as identity forgery, data theft, privacy disclosure and other security problems. Based on blockchain, elliptic curve cryptography and other technologies, this paper proposes a blockchain-based cloud environment data storage scheme, which can provide users with decentralized identity authentication and data integrity verification functions, and the transaction information of data storage is stored on the chain to realize the safe storage of data.
Multi-style art image generation from sketch

Although generative models have made great progress in art image generation, few of them pay attention to sketch-based art image generation. Generating art images from sketch is a challenging problem suffering from two main issues: (1) how to constrain the generation of art images with sparse sketch features and (2) how to fully utilize the style information of a reference image without being influenced by their content features. To tackle these, we propose a GAN-based method for art image generation given the sketch and reference style. Specifically, a style feature enhancement module and a sketch-adaptive normalization module are constructed to enable the disentanglement of the content information from the reference style image. Experiments and comparisons demonstrate the superiority of our model over current generative models in sketch-based art image generation.
Design and experiment of path tracking control system for hedge trimming robot

To realize the autonomous walking of the hedge trimming robot, a hedge trimming robot path tracking control system based on vision and an improved pure pursuit model is designed, and a path tracking algorithm based on genetic algorithm to dynamically optimize the foresight distance of the improved pure pursuit model is proposed. This paper first establishes the dynamics model of the robot and the improved pure pursuit model, extracts the operation navigation line, and obtains the navigation parameters by using the vision navigation system; based on the navigation parameters and the tracking error, uses the genetic algorithm to dynamically optimize the forward-looking distance in the pure tracking algorithm, and proposes a nonlinear steering control algorithm considering the heading error. The simulation test results show that when the robot driving speed is set to 2 m/s and the initial error value is 0.2 m, the proposed algorithm can converge quickly within a short time and the maximum absolute error value is much smaller than the initial value of 0.2 m, and the mean value of path tracking error is 0.022 m. The outdoor test results show that when the hedge trimming robot driving speed is 0.5 m/s, the path tracking algorithm with the improved pure pursuit model can achieve the maximum absolute error of 0.022 m. The outdoor test results show that when the hedge trimming robot travels at a speed of 0.5 m/s, the maximum absolute value of error is 0.081 m and the mean value of tracking error is 0.036 m. This paper provides technical support for the automatic navigation of the hedge-trimming robot.
Sign language recognition using MediaPipe

Sign language recognition is a popular and important problem in computer vision. This paper uses MediaPipe as a method of feature extraction to extract coordinates of the joint points of the human body. The sign language dataset used in this paper is 64 Argentine sign languages (LSA64). Then the coordinate data is input to Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. After testing two models in 640 videos, the result has shown that the accuracy of the trained LSTM and GRU model reaches 94.0625% and 94.5312%, respectively. It is able to recognize 64 Argentinian sign language words or phrases. As a convenient method of sign language recognition, it is feasible in reality.
The transfer method on the prompt for the summarization task

Prompt tuning is a parameter-efficient method that even surpasses traditional fine-tuning methods in few-shot scenarios. Nowadays, pre-trained language models are getting larger and larger, with more and more parameters, which makes the traditional fine-tuning method impractical to implement and consumes a lot of computing resources. Therefore, prompt-based methods have a broad application prospect. In the experiments, it is found that prefix tuning, a prompt-based method, has the problem of non-convergence or is quite slow to converge when the training samples are small. This paper proposes a cross-task parameter transfer method, which transfers the trained parameters from prompt tuning tasks to prefix tuning to improve the training speed and alleviate the problem of non-convergence or slow convergence in prefix tuning tasks.
Lifecare Management system using Machine Learning Techniques

A life care management system is a web-based application that generally manages the client details, hires the agent, and provides the portal so that he can add clients and study the policy details. This project aims to create a portal that allows personal information to be updated securely using a website to get updates about the agent and client and then follow up with the client to pay the premium. There are many possibilities of fraud customers or some customers who may not be eligible for that policy, where a machine learning algorithm will help to find whether the policy should be approved or not. So that the agent doesn’t go on to any complications of adding the customer and find any difficulty. To help the CLIA officer(admin) and agent, this work helps to record the data and handle the data Smoothly.
A proposal for a Blockchain based applicationto trace Healthcare Supply Chain across Sri Lanka

Healthcare Supply Chain (HSC) traceability involves a wide range of stakeholders, including raw material suppliers, manufacturers, distributors, regulatory authorities, pharmacies, hospitals, and patients. For a HSC system that is unalterable, trustworthy, accountable, and transparent, blockchain-based drug tracking offers a promising solution. In this piece, we examine the challenges associated in tracking products within the HSC and then go on to discuss how blockchain technology may be used to effectively combat counterfeit pharmaceuticals by providing a provenance, track, and trace solution. To fulfill the fundamental requirements for drug tracing, such as confidentiality, honesty, openness, safety, authentication, and scalability, we introduce two different blockchain-based decentralized architectures: Hyperledger Fabric and Besu. We highlight many open research issues about blockchain technology's potential for use in medication tracking and provide answers to those questions. Research and Development motivation toward the Health Informatics Sector now has a useful guide for creating and implementing a comprehensive blockchain-based solution for the healthcare industry in Sri Lanka. Through this paper we discuss how blockchain can improve the traceability in the healthcare supply chain sector.
Assessing the Potential Energy Savings of a Fluidified Infrastructure

This article evaluates the benefits of the distribution of a fluid workload across heterogeneous devices focusing on the power consumption of the computing continuum and proposes a suitable methodology to transform current desktop applications into proof-of-concept fluid software.
Designing POCAB Using Powerpoint as a Medium for Teaching Adjectives to the 7th Grade Students

This paper aims to describe the result of the project of creating the POCAB (PowerPoint Vocabulary) game as a medium for teaching English adjective vocabulary. The main objective of the research was to create vocabulary exercises to assist the students in learning descriptive adjective vocabulary. The research method was development research involving four phases of the ADDIE (Analyze, Design, Develop, Implement, and Evaluation). The hyperlink features and slide animation from the Microsoft PowerPoint application were combined to create an attractive game in this research. This product was implemented for eleven students of 7th-grade students at SMP Sentosa Pemangkat, West Kalimantan. The research findings show that the POCAB game is applicable as a medium to teach vocabulary materials related to descriptive adjective vocabulary. It is proven by the high percentage of recommendations from both the teacher and students. This means the product is categorized as "very good." From the research result, it can be concluded that the POCAB game is applicable as a medium to teach descriptive adjective vocabulary and is categorized as an excellent product.Keywords: development research, POCAB game, media, descriptive adjective vocabulary
Monocular Depth Estimation Using Res-UNet with an Attention Model

Depth maps are single image metrics that carry the information of a scene in three-dimensional axes. Accurate depth maps can recreate the 3D structure of a scene, which helps in understanding the full geometry of the objects within the scene. Depth maps can be generated from a single image or multiple images. Single-image depth mapping is also known as monocular depth mapping. Depth maps are ill-posed problems that are complex and require extensive calibration. Therefore, recent methods use deep learning to develop depth maps. We propose a new method in monocular depth estimation to develop a high-quality depth map. Our approach is based on a convolutional neural network in which we used Res-UNet with a spatial attention model to develop depth maps. The addition of an attention mechanism increases the capability of feature extraction and enhances the boundaries features. It does not add any extra parameters to the network. With our proposed model, we demonstrate that a simple CNN model aided with an attention mechanism can create high-quality depth maps with a small iteration and training time. Our model performs very well compared to the existing state-of-the-art methods on the benchmark NYU-depth v2 dataset. Our model is flexible and can be applied to any depth mapping or multi-segmentation tasks.
Editorial for special section ‘ICPR 2020’

Kernel logistic regression is a widely used method in machine learning applications. However, the common kernels are not flexible or representative enough due to their simplistic parametric formulation. Non-parametric learning on kernels can enlarge the representation capability of the kernel, but the low-rank property is required to suppress the model complexity. To improve the flexibility of kernels, we perform low-rank decomposition on an adjust matrix, which can explicitly control the model complexity without using complicated rank penalty terms. In this paper, we also extend our method to learn an indefinite kernel by a different decomposition. Experimental results demonstrate that the proposed learning method for non-parametric kernels outperforms other representative algorithms.
Sentiments Analysis of Bank Customers

In this paper, we propose a solution based on convolutional neural networks (CNNs) to analyze the emotions and identify facial expressions of human beings in banking systems. Modern computer vision relies heavily on these tasks, which also have many other applications like scan vehicle number plate, scan FastTrack etc. This paper aims to develop an Artificial Intelligence model in banking system which can help us to recognize human face expression like happy, sad, angry, neutral, fear, surprise, disgust etc. with the help of deep learning model, Keraâ€™s Sequential Model. Our proposed model shows with accuracy of 63.13%.
Research on Meteorological Public Opinion Combining Domain Keywords and LinearSVC

In recent years, there has been a trend of massive growth in online microblogs and news commentary information, and getting public opinion information from it has become a hot research issue in the field of data mining. Aiming at the problems of difficult identification of domain features and difficulty of multi-category classification in the meteorological domain data of specific network microblogs, this paper proposes a meteorological public opinion classification method combining domain keywords and LinearSVC. This method is based on Sina Weibo meteorological field data and analyzes Weibo evaluation data, so as to judge the public opinion information in meteorological information data in case of abnormal weather conditions. First, we obtain the training meteorological domain review data. Then, we use the data preprocessing method to obtain domain keywords in the review information. Finally, the LinearSVC algorithm is used to classify the data, and the classifier is continuously optimized until the result reaches the set effect. The comparison results show that the algorithm proposed in this paper can well identify public opinion information in meteorological field and has achieved good results compared with the traditional classification methods.
MERGING THE REAL AND THE VIRTUAL: IMPROVING THE INTELLECTUAL STRUCTURE OF THE SMART CITY

The improvement of the concept “Smart City” and its influence on the formation of real space through the introduction of information technology in the urban environment, especially in the era of large-scale crisis caused by pandemic is described. The urgency of the influence of the virtualization of urban space on the social life of the person, through research of strategies aimed at more effective meeting the needs of the population was substantiated. The necessity of introduction of information systems is shown, which contributes to the creation of safe, energy efficient and, at the same time, identical environment of cities. Advantages of human interaction with smart city system by means of personal devices, different software and user interfaces connected to information resources of the city are described. A number of foreign and domestic examples of newest cities with high level of digital technologies, which are the basis for creation and transformation of architectural and spatial integrity of the city are considered. The need for the process of virtualization of social interactions based on the search of appropriate principles for the organization of architectural space, taking into account the impact of network communications on various aspects of community life, increase of competitiveness of cities, with focus on human innovation development.
One-shot colored reflectance direction field imaging system for optical inspection

Detecting microscale defects on the surface of an object is often difficult with conventional cameras. Microscale defects are known to greatly affect the bidirectional reflectance distribution function (BRDF) of light rays reflected from the surface. Therefore, an imaging system for capturing the reflectance direction field by color mapping using a multicolor filter placed in front of an imaging lens is proposed, which can have a simple structure. From the color variations of light rays passing through several different color regions of the multicolor filter, this imaging system can detect the extent of broadening of the BRDF. The effectiveness of the imaging system for optical inspection is experimentally validated by testing it on a plastic surface that has a shallow scratch with a depth of a few micrometers.
An Ensemble Of Machine And Deep Learning Models For Real Time Credit Card Scam Recognition

As the E-commerce sector is getting large, the use of electronic money and is getting wider and wider. Credit cards are the most useful and easy tools for payment. It is easy to use and reduces the efforts made by humans. But with advantages some disadvantages also come hand in hand. Many frauds take place while making the transactions and due to this many people lose millions of money. Hence, there need to be a detection system so that people can make the transactions without the fear of frauds. In today’s time there are many technologies which can help in making such a system. Some technologies are “Neural Network, Artificial Intelligence, Bayesian Network, Data mining, Artificial Immune System, K-nearest neighbor algorithm, Decision Tree, Fuzzy Logic Based System, Support Vector Machine, Machine learning, Genetic Programming etc”. This paper will include many surveys which will be conducted in which people will use different techniques to make a strong system. The work will also be aiming at making a strong detection system using libraries like numpy, sklearn and other py libraries. The problem is solved by using a classifier which can differentiate between fraud and legit transactions based on the class and time. The dataset contains 31 columns among which 28 columns are named as v1, v2, v3…. Due to security purposes, 2 columns are time and amount [1]. The total amount of transactions were 283.806 with only 492 fraud cases and rest legit transactions. In today’s time there are credit cards in the market for kids who are under 18 as well. Therefore it is important for a system to be developed for safety. Fraudsters can use the money for many illegal practices as well. This paper will use Random Forest Classifier and Decision tree to test the dataset [2]. The dataset is of some card holders from Europe.
A Peculiar Image Encryption Technique For Mobile Application

The upgradation in the field of mobile applications is predominantly increasing. Nowadays mobile applications are used in various platforms on one-handled devices in addition, attackers can use similar technology to anonymize their malicious behaviors and hide their identification of behaviors. Thus, security is important. In this project, we are focusing on the precautionary encryption and decryption algorithms like PNSR metric and Elliptic curve Digital signature algorithm which help us to provide secured transmission of a personal image between the mobile stations. Based on these algorithms a defense application will be developed. There are 4 different levels of technology that will be applied in this project which help to improve security transmission. The first level is selecting a secret image. The secret image will support file types like jpg,png. In the second level of security, we encode the image that we get from thefirst level using an encryption algorithm. Here the image quality is measured by using PSNR metric, the third level is finding the LSB, along with 3m (Mean,Mean,Mode) of the image to hide the message inside the cover image. Then the obtained stegnographic image is compressed using GZIP is the final security level. An Elliptic curve, a Digital signature algorithm is used to enhancea security process. Therefore, this method is suggested to send a secret message through applications of special importance across the mobile application.
Effects of Academic Workload on the Burnout of Students during Online Learning

As a preventative measure against the spread of the coronavirus disease, most educational systems worldwide have shifted to remote learning (COVID-19).However, the quick shift to online learning has produced some difficulties that have yet to be overcome.This paper aims to describe the effects of academic workload on student burnout during online classes.Students in the Philippines face difficulties with remote learning after the COVID-19 crisis.The researchers used data from a sample of 204 respondents obtained from a digital survey containing their responses.The research aims to analyze and give an interpretation of the survey results with the use of Pearson correlation analysis.The content analysis revealed the following categories of effects of academic workload on student burnout during online classes: work is hurried and rushed, workloads are challenging to manage during online classes, feeling discouraged, irritated, stressed, and annoyed, feeling burnout, and feeling fatigued.Furthermore, regression analysis proved that students' academic burnout is significantly influenced by physical workload, reduced sense of performance, effort, and frustration.Thus, education and counseling support are needed to reduce the student's burnout during online learning.