Reviewerly/exHarmony huggingface dataset:
A Study on Optimization of Sparse and Dense Linear System Solver Over GF(2) on GPUs

There are various crypt-analytic techniques where solving a large dense or sparse system of linear equations over finite field becomes a challenge due to high computation. For instance, the problem like NFS for factorization of large integers, symmetric ciphers for crypt-analytic problem, discrete log problem, and algebraic attacks involves solving large sparse or dense linear systems over finite field. Here, we consider GF(2) finite field. Gaussian elimination is the popular and relevant method for solving large dense systems while Block Lanczos and Block Wiedemann algorithms are well known for solving large sparse systems. However, the time complexity of such popular method makes it reluctant and hence, the concept of parallelism is made compulsory for such methods. In addition, the availability of high end parallel processors and accelerators such as general-purpose graphics processing units (GPGPUs) solves computationally intensive problems in reasonable time. The accelerators with thousand of cores available today explore the bandwidth of memory and take advantage of multi-level parallelism on multi-node and multi-GPU units. Here, we consider Nvidia GPUs like Keplar, Pascal, and Volta along CUDA and MPI. Also, CUDA-aware MPI leverages GPU-Direct RDMA and P2P for inter- and intranode communication.
ANALISIS KESALAHAN PENGGUNAAN TANDA BACA DALAM PEMBELAJARAN BAHASA INGGRIS SISWA

This research is about the error analysis of using punctuation mark by EFL students.  The objectives of this research are to know the most error of using punctuation marks by the EFL students, the error punctuation trend made by the students, and the affecting factors of error in using punctuation mark in writing task. This research will picture the information about the factors affecting the students error in their writing paper and present about the description about the data from the error in students writing paper. The method of this research is qualitative and the data are taken from the students writing task by documentation lecturer and interview at two universities. They are IAIN Metro and Muhammadiyah University of Metro which consist of three participants in each institution. The reseacher concludes that the EFL students hardly understand in using punctuation mark. The reseacher found the error approximately more than 70% in using capitalization. By the interview, the reseacher found some factors such as lack of balance in the curriculum, knowledge, concentration and carelessness, practice. Key w ords: Aspect of writing, Error Punctuation, Punctuation.
Interval Type-2 Fuzzy Framework for Healthcare Monitoring and Prediction

The popularity of interval type-2 fuzzy logic systems (IT2 FLSs) in the last decade cannot be overemphasized as they have shown superior and more accurate presentation in many applications. In this paper, we investigate healthcare monitoring and prediction using an interval type-2 fuzzy logic system (IT2FLS) based on Mamdani fuzzy inference. Also, the study investigates healthcare monitoring and prediction problems using conventional type-1 fuzzy logic system (T1FLS) for comparison purposes. The empirical comparison was carried out on the developed work using cardiovascular disease patients’ health datasets. The study observed that interval type-2 fuzzy logic could cope with more information and could handle more uncertainties in health data compared to it counterpart. The Root Mean Squared Errors (RMSEs) evaluation results of 0.018 and 0.0006 were observed for type-1 fuzzy logic and interval type-2 fuzzy logic respectively in cardiac shock level prediction experiment, which showed the superiority of IT2FLS paradigm over T1FLS.
Finding Entities and Related Facts in Newspaper

Information production is increasing very fast. Most of this information is in free text format, and to extract meaningful knowledge is a difficult task. Many techniques can help with the problem of processing a large amount of data and its relations. One of these tasks is Relation Extraction (RE). RE is a Natural Language Processing (NLP) task and can be defined as the extraction of relations among two or more entities. Besides, semantic relation extraction, sentiment analysis, opinion mining, question answering are areas that may apply RE to ease their processing. In our work, we propose to use RE to find entities and related facts in newspaper articles. To carry out this task, we segment the text into sentences. Withing each sentence, we tokenized the terms and extracted their dependencies by using the spaCy tool. Moreover, we applied the Named Entities Recognition (NER) to extract some of the entities-classes. And finally, we use an inductive logic programming-based model to model some logic relations we find within sentences. To train our model, we defined a proportion for training and tests from the newspaper corpus to evaluate our solution by comparing the annotated relations against that a human has done in the same dataset. The results show a competitive model for Relation Extraction in Portuguese.
Implementation of Image Enhancement and Image Segmentation In Disease Diagnosis (Lung Cancer)

Recently, image processing structures have been routinely used in a few clinical areas for improving prior different validation and therapy thresholds, particularly in different mischief tumours, such as cell breakdown in the lungs, where the time factor is critical to identify the affliction in the patient as quickly as possible. Cell breakdown in the lungs has been attracting the thought of clinical and sciatic organizations in the latest years because of its high inescapability lined up with the inconvenient treatment. Estimations from 2008 show that cell breakdown in the lungs, all through world, is the one that attacks the best number of people. Early disclosure of cell breakdown in the lungs is critical for productive treatment. There are very few methods open to recognize harmful cells. Here procedure for division, for instance, watershed is used to recognize the infection cell and to find better approach out of them.
Selection of criteria for a decision support system for an art university

Automation of decision support is proposed by including special criteria with the overall configuration of an automated decision support system for creative universities. A prototype of an automated decision support system for creative universities has been developed, which will allow assessing the achievements particularly talented students and identifying the needs in the learning process in order to help organize the educational process in accordance with identified capabilities. Use of a decision support system based on the Bayesian classifier is suggested to assess and evaluate factors contributing to the progress in teaching students particular techniques, and in perspective to assess the possible resources that will be required to make changes to the learning plan. The existing approaches to the assessment of students academic performance are analyzed. The list of specific performance indicators, which are important to be taken into account when assessing the achievements of students of creative specialties, is given. The system should contribute to the formation of the learning plan, taking into account the capabilities of both a group art workshop as a whole, and special needs of an individual to develop, if necessary an individual approach.
Detection of Bot Accounts on Social Media Considering Its Imbalanced Nature

Social media has completely transformed the way people communicate. However, every revolution brings with it some negative impacts. Due to its popularity amongst tons of global users, these platforms have a huge volume of data. The ease of access with minimal verification of new users on social media has led to the creation of the bot accounts used to collect private data, spread false and harmful content, and also poses many security threats. A lot of concerns have been raised with the increment in the quantity of bot accounts on different social media platforms. Also there is a high imbalance between bot and non-bot accounts where the imbalance is a result of 'normal behavior' of bot users. The research aims at identifying the artificial bots accounts on Twitter using various machine learning algorithms and content-based classification based on features provided on the platform and recent tweets of users respectively.
A Gist Information Guided Neural Network for Abstractive Summarization

Abstractive summarization aims to condense the given documents and generate fluent summaries with important information. It is challenging for selecting the salient information and maintaining the semantic consistency between documents and summaries. To tackle these problems, we propose a novel framework - Gist Information Guided Neural Network (GIGN), which is inspired by the process that people usually summarize a document around the gist information. First, we incorporate multi-head attention mechanism with the self-adjust query to extract the global gist of the input document, which is equivalent to a question vector questions the model “What is the document gist?”. Through the interaction of the query and the input representations, the gist contains all salient semantics. Second, we propose the remaining gist guided module to dynamically guide the generation process, which can effectively reduce the redundancy by attending to different contents of gist. Finally, we introduce the gist consistency loss to improve the consistency between inputs and outputs. We conduct experiments on the benchmark dataset - CNN/Daily Mail to validate the effectiveness of our methods. The results indicate that our GIGN significantly outperforms all baseline models and achieves the state-of-the-art.
Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering

Stream is endlessly inbound sequence of information, streamed information is unbounded and every information are often examined one time. Streamed information are often noisy and therefore the variety of clusters within the information and their applied mathematics properties will change over time, wherever random access to the information isn’t possible and storing all the arriving information is impractical. When applying data set processing techniques and specifically stream clustering Algorithms to real time information streams, limitation in execution time and memory have to be oblige to be thought-about carefully. The projected hymenopteran colony stream clustering Algorithmic is a clustering Algorithm which forms cluster according to density variation, in which clusters are separated by high density features from low density feature region with mounted movement of hymenopteran. Result shows that it created denser cluster than antecedently projected Algorithmic program. And with mounted movement of ants conjointly it decreases the loss of data points. And conjointly the changed radius formula of cluster is projected so as to increase performance of model to create it a lot of dynamic with continuous flow of information. And also we changed probability formula for pick up and drop to reduce oulier. Results from hymenopteran experiments conjointly showed that sorting is disbursed in 2 phases, a primary clustering episode followed by a spacing part. In this paper, we have also compared proposed Algorithm with particle swarm optimization and genetic optimization using DBSCAN and k -means clustering.
Optimization of Human Resource Management in Small and Medium-Sized Enterprises Based on Artificial Intelligence

In recent years, due to the continuous development of artificial intelligence related technologies, it has begun to involve various fields of resource management. For human resource management, in the context of the development of artificial intelligence, it is necessary to clarify the impact of human resources in all aspects of the artificial intelligence environment, including the selection of talents. Only by understanding these issues can we better grasp the direction and method of human resource management research. Based on this, this article carried out a research on the optimization of human resource management for small and medium-sized enterprises based on artificial intelligence, aiming to explore the contribution of small and medium-sized enterprises in the artificial intelligence environment to the improvement of corporate human resource efficiency and the improvement of employee innovation capabilities. This article analyzes the application of artificial intelligence in human resource management for the experimental group, and analyzes and compares traditional human resource management methods from the perspective of human resource structure. Research shows that in the current human resource management mode, there are management misunderstandings and intelligent technology in the use of human resources, which effectively promotes the progress of human resource management.
Learning Controlled Semantic Embedding for Cross-Modal Retrieval

Cross-modal retrieval has caught appealing attentions as it supports querying across different modalities. However, most existing methods have emphasized on directly mapping heterogeneous features into the common subspace, which inevitably results in highly entangled representations, thereby preventing them from bridging the modality gap. This paper presents a novel deep framework called Controlled Semantic Embedding (CSE), which is the first attempt to learn disentangled representations with controlled semantic structure for cross-modal retrieval. Specifically, we design two generative networks based on variational autoencoder, which incorporate semantic discriminators for effective prediction of structured semantics. Meanwhile, a self-supervised semantic network is seamlessly integrated into the generative networks to supervise the semantic embedding process, which is further coupled with a quantizer for controlling the quantizability of semantic representations. Extensive experiments show the superiority of CSE over other state-of-the-art methods in cross-modal retrieval.
Effectiveness Evaluation of Business English Practice Teaching Based on Decision Tree Algorithm

Focusing on the problems in the current business English practice teaching, such as the lack of connection between talents and actual needs, vague training objectives, insufficient cooperation between schools and enterprises, and irregular teaching system, this paper designs a time teaching effectiveness evaluation model of business English in Higher Vocational Colleges Based on the improved ID3 decision tree algorithm, and optimizes the attribute selection and information gain of the data set based on the ID3 decision tree algorithm. The simulation results of station gate show that the improved algorithm greatly reduces the operation cost of meta algorithm. Then, it evaluates the colleges and universities with the help of the improved model, and designs the improvement strategies based on the evaluation results of examples, including the practical teaching objective system, the practical teaching content system, the practical teaching management and evaluation system, etc.
Rejection Sampling Revisit: How to Choose Parameters in Lattice-Based Signature

Rejection sampling technology is a core tool in the design of lattice-based signatures with ‘Fiat–Shamir with Aborts’ structure, and it is related to signing efficiency and signature, size as well as security. In the rejection sampling theorem proposed by Lyubashevsky, the masking vector of rejection sampling is chosen from discrete Gaussian distribution. However, in practical designs, the masking vector is more likely to be chosen from bounded uniform distribution due to better efficiency and simpler implementation. Besides, as one of the third-round candidate signatures in the NIST postquantum cryptography standardization process, the 3rd round version of CRYSTALS-Dilithium has proposed a new method to decrease the rejection probability in order to achieve better efficiency and smaller signature size by decreasing the number of nonzero coefficients of the challenge polynomial according to the security levels. However, it is seen that small entropies in this new method may lead to higher risk of forgery attack compared with former schemes proposed in its 2nd version. Thus, in this paper, we first analyze the complexity of forgery attack for small entropies and then introduce a new method to decrease the rejection probability without loss of security including the security against forgery attack. This method is achieved by introducing a new rejection sampling theorem with tighter bound by utilizing Rényi divergence where masking vector follows uniform distribution. By observing large gaps between the security claim and actual security bound in CRYSTALS-Dilithium, we propose two series of adapted parameters for CRYSTALS-Dilithium. The first set can improve the efficiency of the signing process in CRYSTALS-Dilithium by factors of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>61.7</mn> <mo>%</mo> <mtext> and </mtext> <mn>41.7</mn> <mo>%</mo> </math> , according to the security levels, and ensure the security against known attacks, including forgery attack. And, the second set can reduce the signature size by a factor of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>14.09</mn> <mo>%</mo> </math> with small improvements in efficiency at the same security level.
Clinical trials, real-world evidence, and digital medicine

The development of new medicines through the clinical trials process offers countless opportunities for the application of approaches such as those offered by artificial intelligence and machine learning. Broadly speaking, the application of these techniques is usually aimed at doing one of the following: Increasing the speed and therefore reducing the time to develop a medicine and get it to patients who need it. Becoming more accurate at picking the winners—those potential new medicines that will go on to succeed, rather than produce negative clinical trial results. Generating productivity or efficiency gains to offset the large costs of running clinical trial programs. These are all difficult problems to solve. However, the good news is that the clinical trials process tends to generate large volumes of data, some of which are of good quality—because it is required to be according to regulatory standards. These data are vital for using AI and machine learning techniques to achieve gains in one of the three opportunity areas outlined above. In addition, new data streams are maturing all the time—such as patient data from sensors and wearable devices, genomic testing, and real-world evidence.
An IoT Model-Based Traffic Lights Controlling System

Several problems of pandemic situations arise in various countries and also around the world in recent years. In recent days, the situation of traffic was going very great in number due to these pandemics. Most of these pandemics are occurring due to the viral spread with the contacts or nearby people. As a result, most of the people are interested to purchase their own vehicles that may be either two-wheeler vehicles or any four-wheeler vehicles. As a result, the traffic problems in the cities are growing at a faster level, and also, the pollution levels are getting increased. Hence, in the current article, an attempt had been made to analyse the traffic on the roads to control the congestions on the city roads at regular intervals of time. The current model was developed on the Python platform, for understanding the performance of the current model, four different cases of input images were selected and tested with the current developed model, and the results were discussed in the results section.
Low-light image enhancement based on deep convolutional neural networks

A reference-free low-illumination image enhancement method based on deep convolutional neural networks is proposed to address the problem that low-illumination image enhancement algorithms do not take into account noise suppression while achieving detail enhancement. First, the illumination and reflection components are extracted from the input lowillumination image based on Retinex theory, and optimised separately, and then the optimised illumination and reflection components are multiplied to obtain the enhanced image. loss to update the network parameters. The experimental results show that our algorithm can effectively enhance the contrast and brightness of low-illumination images compared to existing mainstream algorithms, while maintaining the naturalness of the images.
Application Practice of Computer Network Technology in Education Industry

Abstract With the advancement and development of science and technology in our country, computer networks have spread across the country at a fierce rate. It brings opportunities and challenges to all walks of life, and its advantages have made our country pay more and more attention to the application of computer network technology. This paper mainly elaborates the application principles of computer network technology in educational practice, and this paper also proposes some measures to strengthen its application in the education field.
Blockchain and Scholarly Publishing Industry

Blockchain has broad application in academic publishing and addresses necessary benefits in cost containment, improved workflow, and business management. In particular, blockchain facilitates greater control over copyrighted content and royalty administration, citations, and billing and collection. Blockchain is a user-friendly technology that can improve profitability and cash flow while reducing administrative errors. Blockchain technology has become ubiquitous within various market segments but slow to be adopted within academic publishing environments, but with the pressure on revenue growth and cost containment, blockchain represents a new tool in the arsenal of workflow products to create more accurate reporting. In particular, royalty accounting has been an area of varying reliability and uncertainly, relying on many data sources and data aggregation generally confusing to authors, researchers, and writers. Blockchain takes the guesswork out of this process by documenting digital content access and usage through artificial intelligence engines and machine learning tools.