Reviewerly/exHarmony huggingface dataset:
Detection and Classification of Acute Lymphocytic Leukemia

Acute Leukemia is a life - threatening disease common both in children and adults that can lead to death if left un treated. Acute Lymphoblastic Leukemia (ALL) spreads out in children’s bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer –aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its sub types and reactive bone marrow (normal) in stained bone marrow images. Classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results reveal that the proposed method achieved more than 96% accuracy having four different classes. The obtained results exhibit that the proposed approach could be used as a tool to diagnose. Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.
A Hybrid CRNN Model for Multi-Class Violence Detection in Text and Video

Gender-based violence is a critical issue that not only poses a threat to physical safety but also has significant impacts on mental health. Shockingly, up to 1 billion children aged 2-17 years are estimated to have experienced gender-based violence globally, making it a pressing concern for the machine learning and deep learning communities to address. To end this, a novel approach has been proposed in the form of a Convolutional Neural Network and bi-directional LSTM (CRNN) to classify three types of violence present in both text and video data, thereby making the internet a safer space for individuals. The proposed approach utilises two datasets consisting of 400 and 600 samples each for videos and text, respectively, to improve the precision and accuracy of the model. The use of a Convolutional Recurrent Neural Network framework combined with LSTM layers has resulted in an accuracy of 97% on text and 96% on videos, surpassing the performance of existing RNN models. Additionally, the inclusion of dropout and regularizer layers has helped the model avoid overfitting and generalise better on unseen data. Overall, the CRNN-based approach presents a promising solution to the problem of gender-based violence detection, with the potential to significantly improve the safety of individuals online. By leveraging the power of machine learning and deep learning, we can contribute towards creating a safer and more equitable world for all.
Object Identification Using Deep Learning

Human visual system is very fast that he can easily detect and identify the objects using this visual system it can easily identify multiple objects and detect the obstacle. This time there is a very large amount of data, a faster CPU and good algorithm that can easily train computers to detect and classify multiple objects with high accuracy. It uses an algorithm to detect and identify the objects that is “You Only Look Once” (YOLO) algorithm. This project is based on a deep learning approach to solve the problem of object detection and recognition. This network is trained on the most challenging dataset known as (PASCAL VOC), in which object detection is conducted annually manner. The resulting system is very fast and accurate. Object recognition is a collection of related tasks for identifying objects in digital photograph. R-CNN is a family of techniques for addressing object localization and recognition tasks. Propose a new object detection method which improves the existing model at every stage of object detection and identification, use COCO dataset model in this lot of categories of images are stored.
Post-training with Data Augmentation for Improving T5-Based Question Generator

Post-training is known to be effective for boosting the performance of a pre-trained language model. However, in the task of question generation, question generators post-trained with a well-designed training objective show poor performance without sufficient training examples. To handle this problem, this paper proposes a novel post-training for question generation which adopts a data augmentation technique to increase the number of training examples as well as post-training objectives. As post-training objectives, this paper introduces a new training objective, wh-words deletion, in addition to the well-known question infilling. Moreover, this paper employs back-translation techniques to increase the number of instances for post-training. To prove the effectiveness of the proposed method, this paper applies the post-training strategies to T5, a large-scale pre-trained language model, on SQuAD-QG. The experimental results demonstrate that the proposed post-training is helpful for enhancing the performance of answer-aware question generation.
Blockchain Technology Drives the Transformation and Upgrading of Audit Mode

In recent years, blockchain, as a distributed shared database generated based on cryptography technology, is silently changing our world. Due to the valuable characteristics of blockchain, such as openness, transparency, non-counterfeiting, traceability and decentralization, the information obtained based on blockchain data analysis is of great value. Under the influence of blockchain, the audit field is also undergoing changes. Centering on the research topic that blockchain technology drives the transformation and upgrading of audit mode, this paper discusses how blockchain technology drives the transformation and upgrading of audit mode from the three aspects of technological innovation, audit process and risk control.
Artificial Intelligence Recognition of Human Body Actions for Bionic Applications (New Combination of Indicator Parameters)

Modern sensing technologies are a significant resource of information whose rapid development can contribute to changing health care and increase quality and performance in learning patterns. Human activity recognition applications are marked by a particular connection to healthcare sensors. Their development depends on the efficiency of exploiting the data received from these sensors. Successful activity recognition can be extremely beneficial in healthcare applications such as autonomous and intelligent activity monitoring systems for the elderly and disabled. The following aspects must be carefully considered when designing a recognition system: pattern classification, sensing environment, feature extraction and arrangement, classifier prototype and learning strategy, training and test sample selection, and performance evaluation. This project represents the first phase of our research that mainly focuses on recognizing the different movements of the extremities of the body, with the aim of contributing to research concerned with the control of prosthetics using electromyography signals. To enhance future research and findings related to EMG signals, we decided to direct our research as a first stage to determining physical activity using data from two sensors. We have clearly proven this approach in a specific context by taking advantage of the data recorded by the accelerometer and gyroscope. These two sensors were connected to a person's body to receive physiological signals that define human activity. Acceleration signals for seven sorts of physical activities (climbing and descending stairs, walking, running, standing, sitting, and laying) were recorded for four users. Several classifiers were tested with an original set of qualitative parameters and statistical properties. The results obtained are promising. We are able to create features that characterize the activities and the evolution of the learning process; this enables us to perform accurate classification of user activities with an overall accuracy rate of 96%.
Research and implementation of natural language processing algorithm in fiber home arts design

Smart home as a major issue in the field of modern home, with the continuous innovation of Internet technology, related research work has entered a new height, the residents of the community to furniture crafts intelligent requirements are increasingly high. Nowadays, the research and design of intelligent home arts and crafts is not only reflected in the technical level, but also involves the creativity of humanization and comfort. In the innovation and development of modern social economy and technology, the field of architecture and home gradually develops from the traditional button control to the current intelligent technology. People begin to expect to use language to achieve home equipment control, so scientific researchers began to pay attention to the integrated development of natural language processing and smart home in practice. On the basis of understanding the concept of natural language processing algorithm, according to the design situation of fiber home crafts in recent years, this paper deeply discusses how to apply and realize natural language processing method in smart home system. The final experimental results prove that the integration of natural language processing and intelligent home furnishing can promote the innovation and development of home furnishing in China.
First Steps of the Russian Copyright Law (a Decision of the Ruling Senate on the Course of Lectures by D.I. Meyer)

This essay sets out the unusual details of court proceedings and copyright issues regarding the course of lectures by famous Russian lawyer Dmitrii Meyer and analyses the precedent laid down by the Senate when resolving the case.
IMPLEMENTASI PEMBELAJARAN DALAM JARINGAN PADA MATA PELAJARAN MATEMATIKA PADA PESERTA DIDIK DI SEKOLAH MENENGAH ATAS NEGERI 9 PALEMBANG

This study aims to determine the process of online learning when it is implemented in SMA Negeri 9 Palembang and the impact of this implementation. The research method used in this study is a descriptive qualitative research method using data collection techniques, namely interview techniques. Researchers used interview techniques by collecting information obtained from informants at the school. From the data obtained by the researchers concluded that learning in the network, especially in mathematics is considered less effective. This is due to the lack of focus on students when the teacher gives explanations through the network because in fact mathematics lessons must be explained directly to students.
QMX-BdSL49: An Efficient Recognition Approach for Bengali Sign Language with Quantize Modified Xception

Sign language is developed to bridge the communication gap between individuals with and without hearing impairment or speech difficulties.Individuals with hearing and speech impairment typically rely on hand signs as a means of expressing themselves.However, people, in general, may not have sufficient knowledge of sign language, thus a sign language recognition system on an embedded device is most needed.Literature related to such systems on embedded devices is scarce as these recognition tasks are very complex and computationally expensive.The limited resources of embedded devices cannot execute complex algorithms like Convolutional Neural Network (CNN) properly.Therefore, in this paper, we propose a novel deep learning architecture based on default Xception architecture, named Quantized Modified Xception (QMX) to reduce the model's size and enhance the computational speed without compromising model accuracy.Moreover, the proposed QMX model is highly optimized due to the weight compression of model quantization.As a result, the footprint of the proposed QMX model is 11 times smaller than the Modified Xception (MX) model.To train the model, BDSL 49 dataset is utilized which includes approximately 14,700 images divided into 49 classes.The proposed QMX model achieves an overall F1 accuracy of 98%.In addition, a comprehensive analysis among QMX, Modified Xception Tiny (MXT), MX, and the default Xception model is provided in this research.Finally, the model has been implemented on Raspberry Pi 4 and a detailed evaluation of its performance has been conducted, including a comparison with existing state-of-the-art approaches in this domain.The results demonstrate that the proposed QMX model outperforms the prior work in terms of performance.
A Multi-Factor Certificateless Authenticated Key Agreement Protocol on ECC

Key negotiation can establish a shared key between two or even multiple parties in a public networkenvironment, ensuring communication confidentiality and integrity. Certificateless public key cryptography(CL-PKC) aims to achieve succinct public key management without using certificates, while avoiding thekey escrow property in identity-based cryptography. As an important part of CL-PKC, certificateless authentication key agreement (CLAKA) has also received widespread attention. Most CLAKA protocols are constructed from bilinear mappings on elliptic curves which need costly operations. To improve theperformance, some pairing-free CLAKA protocols have been proposed. In this paper, we propose a multifactor authentication CLAKA protocol that can achieve local authentication factors joint unlocking. Theprotocol does not require bilinear pairing computation and has been proven to be secure under the mBRmodel.
Review of Modern Technologies of Computer Vision

Today, the use of artificial intelligence technologies is becoming more and more popular. Scientific and technological progress contributes to increasing the power of hardware, as well as obtaining effective methods for implementing methods such as machine learning, neural networks, and deep learning. This created the possibility of creating effective methods for recognizing images and video data, which is what computer vision is. At the time of 2022, a huge number of methods, technologies, and techniques for using computer vision were received, in this paper a study was conducted on the use of computer vision in 2022. Results were obtained on the decrease in the popularity of computer vision in the scientific community, its introduction into industry, medicine, zoology and human social life, the most popular method of computer vision is the ResNet neural network model.
A code-based hybrid signcryption scheme

Abstract A key encapsulation mechanism ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="sans-serif">KEM</m:mi> </m:math> {\mathsf{KEM}} ) that takes as input an arbitrary string, i.e., a tag, is known as tag- <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="sans-serif">KEM</m:mi> </m:math> {\mathsf{KEM}} , while a scheme that combines signature and encryption is called signcryption. In this article, we present a code-based signcryption tag- <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="sans-serif">KEM</m:mi> </m:math> {\mathsf{KEM}} scheme. We utilize a code-based signature and an <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="sans-serif">IND</m:mi> <m:mstyle> <m:mspace width="0.1em" /> <m:mtext>-</m:mtext> <m:mspace width="0.1em" /> </m:mstyle> <m:mi mathvariant="sans-serif">CCA2</m:mi> </m:math> {\mathsf{IND}}\hspace{0.1em}\text{-}\hspace{0.1em}{\mathsf{CCA2}} (adaptive chosen ciphertext attack) secure version of McEliece’s encryption scheme. The proposed scheme uses an equivalent subcode as a public code for the receiver, making the NP-completeness of the subcode equivalence problem be one of our main security assumptions. We then base the signcryption tag- <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="sans-serif">KEM</m:mi> </m:math> {\mathsf{KEM}} to design a code-based hybrid signcryption scheme. A hybrid scheme deploys asymmetric- as well as symmetric-key encryption. We give security analyses of both our schemes in the standard model and prove that they are secure against <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="sans-serif">IND</m:mi> <m:mstyle> <m:mspace width="0.1em" /> <m:mtext>-</m:mtext> <m:mspace width="0.1em" /> </m:mstyle> <m:mi mathvariant="sans-serif">CCA2</m:mi> </m:math> {\mathsf{IND}}\hspace{0.1em}\text{-}\hspace{0.1em}{\mathsf{CCA2}} (indistinguishability under adaptive chosen ciphertext attack) and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="sans-serif">SUF</m:mi> <m:mstyle> <m:mspace width="0.1em" /> <m:mtext>-</m:mtext> <m:mspace width="0.1em" /> </m:mstyle> <m:mi mathvariant="sans-serif">CMA</m:mi> </m:math> {\mathsf{SUF}}\hspace{0.1em}\text{-}\hspace{0.1em}{\mathsf{CMA}} (strong existential unforgeability under chosen message attack).
Improved Techniques for Training Tabular GANs Using Cramer’s V Statistics

Considering the growing global demand for machine learning training data, synthetic data generation is a reasonable way to address the versatile challenges in data acquisition.Conditional Tabular Generative Adversarial Network (CTGAN), an extension of the widely used Generative Adversarial Network (GAN), is considered one of the most promising techniques in the field of tabular data generation.Despite numerous successes of CTGAN, a lack of preserving categorical dependencies within the data has been identified.In prior work, the Cramer's V (CV) as a natural metric for representing the correlation of categorical dependencies was proposed for hyperparameter tuning of CTGAN models.In this paper, we explore two novel strategies to directly integrate CV statistics of data batches within CTGAN training.The first approach is a generator loss term that penalizes differences between the CV statistics of the original and generated data.The second innovation is the extraction of the CV matrix as an additional feature for the critic.By applying our proposed methods to three benchmark datasets, we improve the averaged accuracy of supervised learning models trained on synthesized data by 11 % compared to the legacy CTGAN.We also outline the impact of CV statistics on preserving dependencies between categorical data columns in terms of integrity and contingency similarity, discuss existing challenges, and identify potential improvements.
Impact of Distributed Ledger Technology on Supply Chain Sustainability

This chapter describes the challenges and vulnerabilities of current supply chain management and presents the benefits of implementing blockchain and DLT technology in the supply chain processes. For a more precise understanding, the chapter presents a summary of the characteristics of blockchain technology and an overview of the energy consumption of different blockchain protocols to demystify the negative criticism of blockchain technologies in terms of energy consumption. The purpose of analyzing the characteristics and energy requirements is to present the potential of DLTs to positively impact supply chains and make the blockchain industry more sustainable. After describing the sector's challenges and the technology's characteristics, the chapter presents case studies in which DLT has been successfully integrated into the supply chain of various products and goods. The chapter concludes by highlighting the necessary conditions to promote the adoption of DLT technology globally.
IMPLEMENTASI STRATEGI PEMBELAJARAN MENYIMAK MODEL DICTOGLOSS PADA GURU PESERTA PENDIDIKAN PROFESI GURU (PPG) PRA-JABATAN FKIP UNIVERSITAS SYIAH KUALA

This study aims to (1) implement the dictogloss listening learning strategy and (2) record the advantages and disadvantages of the strategy based on the implementation results, and (3) produce recommendations for the feasibility of using this strategy to improve listening skills in learning Indonesian and English at the school level. medium. The sources of study data are Indonesian language subject teachers and English subject teachers who are members of the Pre-Occupational Teacher Professional Education (PPG) Batch I of 2022, FKIP Universitas Syiah Kuala. Research uses qualitative principles. Data was collected using documentation, observation, and interview techniques. Data analysis of the results of the study was carried out using the principles of qualitative data analysis. The results of the study show that the learning strategy for listening to the dictogloss model: (1) is in accordance with the demands of creative, innovative, and collaborative Indonesian and English learning as expected in the 2013 Curriculum, (2) is effective and efficient in its implementation, (3) can involve all students at the same time to be active in learning, (4) facilitate individual learning and group work, (5) activate schemata and cognitive aspects of students when reconstructing texts, (6) easy to design and implement by teachers, (7) precise and effective for a tool to measure students' listening skills, and (8) teachers can apply this dictogloss model listening learning strategy at all grade levels by making adjustments to reading texts based on Basic Competency demands
Visualization of Alumni Performance Using Dashboard to Support Higher Education Decision Making

Universities in Indonesia create fierce competition. Alumni have an essential role for the educational institution, not only as a tool for measuring the performance quality and ability of education implementation but also as the standard or feedback to the perfection of the education system. One effective data presentation model is the dashboard system seen from the complexity, interactivity, and informative data presented. Therefore, this study aims to build a dashboard system at Universitas Muhammadiyah Yogyakarta (UMY). This study utilized the dashboard life cycle method in its implementation. This method had six stages: project initiation, design, construction, testing, launching and monitoring. This method allowed the study to show data on the number of judicium students, the number of graduates, the average GPA of the alumni, the average study period of alumni, and the number of alumni per graduation level for the 2012/2013 to 2017/2018 academic year, according to the accreditation forms and needs analysis. The dashboard system can provide information about alumni at UMY from 2012 to 2017. The dashboard system is ready to be applied as a decision-making tool, particularly as a strategy for improving the quality and empowering strategy for alumni of the university.
Departmental Web Portal

Abstract: The departmental portal is that the raising content of internet sites for colourful associations is well known. Here and now, a well-planned digital plan plays a suggestive role in an institution’s marketing strategies especially considering the youthful generations ’ growing dependence on technology. The Departmental portal using MERN stack and Sanity.io is a webbased application designed to manage the administrative tasks of a college. It is built using MongoDB, Express.js, React.js, and Node.js. The system provides a platform for students, teachers, and administrators to manage academic and non-academic tasks. During this paper, we talk about the models submitted by our team such as student registration, Notes management, faculty management, Event management, and Project management. The MERN stack sanity.io provides a powerful and scalable platform for building this system, allowing for efficient data management and real-time updates. With its user-friendly interface and extensive functionality, the departmental portal using MERN stack and sanity.io is a comprehensive solution for colleges looking to streamline their administrative processes.