Reviewerly/exHarmony huggingface dataset:
Agricultural text named entity recognition based on the BiLSTM-CRF model

Named entity recognition (NER) is an essential part of many natural language processing applications and serves as the basis of natural language processing. NER tasks aim to identify the entity boundaries and types of specific meanings in texts, which include person names, place names, organizations, and dates. Due to the complexity of the composition of agricultural entities and limited quantity of agricultural corpora, often with low quality, agricultural text named entity recognition (ANER) is a more difficult and challenging problem compared to general NER tasks. Existing models often have poor performance in ANER tasks. To solve the existing problems of ANER, we constructed a named entity dataset of agricultural context through manual annotation using collected massive agricultural texts. The dataset contains 8873 entities composed of six entity categories: crop type, disease type, pest type, pesticide type, fertilizer type, and crop part type. We propose an agricultural text named entity recognition model based on BiLSTM-CRF. Experimental results show that the F1 score of the model on the test set achieved 91.20%, and our model also achieved the highest precision of 93.23% and the highest recall of 89.39% compared to other existing models.
ProGAN-based convolutional neural network for improving gender classification

To resolve the issue of limited data during the deep learning model training, various data augmentation approaches that increase the amount of data based on existing data are proposed. In this paper, the possibility of using fake face images generated by Progressive Growing GAN (ProGAN) was examined, a type of generative adversarial network, based on the existing limited data set to improve the final accuracy of Convolutional Neural Network (CNN) for image classification. This paper used ProGAN to generate 100 fake faces and added two CNNs to construct two classifiers that determine the gender of the face images. The first gender classifier only used 100 real faces, namely 50 male and 50 female faces, that were randomly selected from CelebFaces Attributes Dataset (CelebA) for training. And for the second gender classifier, this study added 100 fake faces, namely 50 male and 50 female faces, based on the original 100 real faces. It can be observed that the accuracy and fitting degree of the two CNN classifiers through 50 epochs to see the influence of adding fake images. The experimental results showed that adding 100 generated fake images to the original real face images improved the final accuracy of the classifier and reduced the problem of overfitting. Thus, it can be concluded that using images generated by ProGAN is a valid method for improving the performance of the CNN classifier when data is limited.
Performance evaluation of power technology R&amp;D project based on analytic network process

Electric power is an important guarantee for the overall development of the national economy. The state invests more than 100 million yuan in research and development of key scientific and technological projects in the electric power field every year to ensure the continuous innovation and development of the electric power industry. The benefit evaluation of power science and technology projects is very important for power grid enterprises, and its scientificity and accuracy have a direct impact on the correctness of science and technology investment decisions of power grid enterprises. There is still a lack of scientific evaluation methods for power science and technology projects, and the evaluation results cannot be fully utilized. This is because all factors have influence on the results. According to the characteristics of R&D project performance evaluation, this paper selects the analytic hierarchy process, which can realize that the weight setting of each level will ultimately directly or indirectly affect the results. The impact of each factor in each level on the results can be clearly quantified, and subjective factors such as expert scoring in project evaluation can be organically integrated. Effectively solve the problems of multiple objectives, multiple tasks, multiple criteria, multiple levels and multiple elements in the evaluation of power science and technology projects. Explore the whole implementation process of the project, select and design a more comprehensive and specific indicator system, and use the network analytic hierarchy process to objectively evaluate the performance of the project.
MAMMOGRAM IMAGE TO DETECT BREAST CANCER USING K-MEANS CLUSTERING ALGORITHM

Breast cancer is the uncontrolled proliferation of a group of cells in the breast and is the second leading cause of death for women in the world.The disease can be cured if detected in the early stages.A lot of research has been done to correctly detect the tumor, but a 100% accurate method has not been found.Research on breast cancer detection using digital image processing is not new, but many new approaches are being considered in this field to accurately predict the tumor area.The current approach consists of detecting the tumor area visually and also finding out in which area the tumor is most concentrated.CC and MLO dualview mammographic screening images are widely used in the diagnostic process.This project presents a method for detecting a tumor area and classifying a normal and oncological patient.Preprocessing operations are performed on the input mammographic image and unwanted parts are removed from the image.Tumor regions are segmented from the image using a morphological operation and are highlighted on the original mammographic image.If the image on the mammogram is normal, it means that the patient is healthy.This work mainly focuses on finding the best algorithms for detecting tumors present in the breast.A number of algorithms were used in the proposed work, but the most suitable for cancer detection is the combination of K-Means clustering algorithm.K-Means classification accuracy is 95% accurate output will be predicted.
Embezzlement

In this chapter, we describe the extent of the embezzlement problem in the United States, and globally, focusing on how the new generation of embezzlers are now taking advantage of opportunities provided by our increased reliance on e-commerce and web-based financial transactions. We include a description of the various forms of financial frauds currently in play, focusing on the role of technology in crime commission. We also provide an assessment of the likely causes of embezzlement, and then link this assessment to a review of the effectiveness of current embezzlement prevention and control strategies.
Indonesian Perspective Analysis: With a Little Help from Our Friends: Private Fundraising and Public Schools in Philadelphia (Good &amp; Nelson, 2021)

One of the problems that always hit education in various parts of the world is the budget. The limited education budget will affect the quality of education services provided to the community. Therefore, increasing public awareness to participate in education funding is important. Not only for better quality but also to make education a shared responsibility. Good &amp; Nelson in their article entitled With a Little Help from Our Friends: Private Fundraising and Public Schools in Philadelphia, highlights the practice of educational funding that is mostly carried out by non-profit organizations based on the school environment with various pros and cons and explains what strategies can be taken to support availability of an education budget that involves community participation (Good &amp; Nelson, 2021). This paper will also discuss its practical application in the context of education in Indonesia with various intrigues and conflicts related to public education funding.
Color obstacle detection algorithm using AR drone 2.0 in a controlled environment

This work presents an obstacle detection algorithm using quadrotor AR. Drone 2.0 in a controlled environment with programming in OpenCV, ROS (Robot Operating System), and Python. The algorithm is based on the color object feature to determine it as an obstacle. It is the first step for future work where the drone is allowed to perform autonomous flights and obstacle avoidance. The ROS development environment makes it possible to adjust both the drone's linear and angular speed. The OpenCV management library allows processing the image obtained in real-time thanks to the cv_bridge package. The obstacle detection scenario is simulated using ROS-Gazebo, and then the algorithm is implemented in a real controlled environment. The results obtained were good; an obstacle detection system was successfully implemented using open-source tools.
Using a Gesture Recognition Modeling approach to improve graphical user interface

The gesture recognition technology based on visual detection employs image processing for detection, segmentation, tracking, and recognition of hand gestures. Static and dynamic gesture recognition are the two types of gesture recognition. In this paper, we present our hand gesture interface consisting of three main layers: Hand Gesture Interface, Mapping Gesture, Action, and Graphical User Interface with our approach named IGMA (Improving Graphical User Interface Based on Gesture Recognition Modeling Approach). IGMA organized in two stages: the modeling stage and the code generation stage. The model is Domain Specific Model; it allows action and gesture modeling. At the code generation stage, a specific code generator transforms all models to final source code using a framework specifically designed for a given target platform. Finally, to simplify the implementation of this method, we propose defining a process for improving user interface control based on gesture recognition. We validate our approach by performing experiments on a gastronomy application. The result is an adaptation framework that can guide software engineers in developing Graphical User Interface Based on Gesture Recognition.
A robust VC scheme for securing cloud data storage

Visual Cryptography (VC) was created to encode images into several shares and decode them by aggregating the shares without the use of costly standard cryptanalysis. To enhance the quality and security of visual images in cloud storage, a Robust Collusion Avoidance-based Secure Signific VC (RCA-SSVC) scheme was designed, which creates the collusion-resistance Secret Image (SI) shares and embeds them into the Cover Image (CI) to prevent collusion threats. But it takes a long-time during access for users to recover the real image whenever receiving fake shares kept in the cloud by unauthorized users. Therefore, in this article, a new 2-level data protection scheme is proposed using QR code and RCA-SSVC. In this scheme, the public and private information of users are taken as QR code, which is encoded into sub-pixel blocks of all shares during the share creation phase. Also, a random permutation is applied to the created shares to disrupt the sub-pixels in a single block, which can avoid attackers from guessing the location of the Secret QR (SQR) code’s pixels. During the image restoration step, the restoration process is initiated once the user identity is authenticated by reading QR code information, which is matched with the QR code embedded in the shares. So, image restoration using illegitimate shares during access is prevented. At last, the test outcomes reveal that the RCA-SSVC-QR establishes maximum confidentiality in image access from the cloud storage compared to the classical VC schemes.
Improved median filter to eliminate salt and pepper noise in digital

Digital images are affected by different types of noise, including salt and pepper noise, which negatively affects the image, changing its features and affecting many of its characteristics, which calls for providing an easy way to remove this noise or get rid of it significantly. In this research paper, a new and simple method of deducting salt and pepper noise will be proposed, tested and implemented. The proposed method will be compared with standard filters such as average and median filters. To prove the effectiveness of the proposed method it will be compared with other popular methods dealing with salt and pepper noise such as SM, DWM, MDWM, MDW, TVW, and TS filters.
Significant Impact of 5G Technology worldwide: With special reference to specific industries and Countries

5G address the following major period of versatile telecom morals past the impending 4G guidelines. After 1G, 2G, 3G, and 4G organizations, it is now a new global distant norm. 5G remote technology is designed to deliver multi-gigabit per second (Gbps) maximum data speeds. Pinnacle downlink throughput execution is projected to be exceedingly high with 5G. The goal of this article is to identify, systematize, and generalize major of 5G technology. In the coming years, 5G networks will be widely deployed. Farmers might use real-time data to monitor, track, and automate their agricultural operations. At home and at the stadium, sports fans could have more influence over their viewing experience. With peak performance of 20 Gbps, 5G claims to be 20 times faster than 4G.It is assisting in the transformation of a variety of Industrial Internet of Things (IIoT) applications. For the first time, new experiences such as augmented reality will become a reality. 5G technology is a fifth-generation invention with a wide range of applications. By 2026, the number of 5G cell phone users is predicted to surpass 3 billion. Many outdated gadgets will need to be replaced because they do not support 5 G.
Towards Automating Cryptographic Hardware Implementations: A Case Study of HQC

While hardware implementations allow the production of highly efficient and performance-oriented designs, exploiting features such as parallelization, their longer time to code and implement often bottlenecks rapid prototyping. On the other hand, high-level synthesis (HLS) tools allow for faster experimentation of software code to a hardware platform while demonstrating a reasonable extrapolation of the expected hardware behavior. In this work, we attempt to show a rapid prototyping of the well known HQC algorithm, using HLS, and show how with a modification of certain parameters, varying degrees of comparable results can be obtained. These results, in turn, could be used as a guide for HDL (Hardware Description Language)-RTL (Register-transfer Level) developers to enhance their designs and better prototyping time in the future. Additionally, we also demonstrate that it is possible to benefit from HQC’s versatility; by achieving a low hardware footprint whilst also maintaining good performances, even on low-cost FPGA devices, which we demonstrate on the well-known Artix-7 xc7a100t-ftg256-1.
Electronic resources and its utilities in libraries – future aspects and impact

This paper focuses on the various types of e-Assets. Today, the openness of e-resources in an academic library is wellknown. It furthermore analyzes the inspiration driving the use of e-Resources, the benefits, subject consideration status and overall client achievements that are looked at by clients while getting to e-resources, and the impact of e-resources on clients.
Didactic evolution of similarity detection software : the example of Compilatio

Since 2005, Compilatio has been offering tools to help detect and prevent plagiarism. Users of similarity detection software were initially attracted by the ability to track down cheaters. They are now more aware of the tools and services offered to create an environment that encourages the adoption of integrity and citizenship values, especially digital ones. They are aware that plagiarism is not a passing evil to be eradicated, but a deep-seated temptation that each individual must learn to overcome. The technology used to help teachers spot cheating has also evolved. The approach was initially syntactic, comparing texts formally to detect similarities. It then became semantic, using socalled artificial intelligence techniques to find similarities between different words with the same meaning. The issues related to plagiarism prevention illustrate how technology and pedagogy can be used together to train individuals for their future professional and civic life.
Identification and analysis of low creators in Bilibili video based on multivariate statistics

Bilibili video website has grown into a giant video platform. With the anime culture that can attract the younger generation, Bilibili has built a large-scale user creation platform. To stimulate users’ creative inspiration, Bilibili issued several plans to provide corresponding rewards for the content produced by video creators, attracting more and more people to participate in the creative party. In this context, many excellent works were born, but at the same time, there are also works with mixed qualities in the video, i.e., “low-innovation” works. "Low-innovation" works hinder personal development and have a bad impact on the production climate of the platform. First, this paper uses the principal component analysis algorithm to pre-process the user data of Bilibili to improve the efficiency of the algorithm. Based on the K-means clustering algorithm, it analyzes and identifies "low-innovation" users. According to the analysis results, it sets different incentive plans for different types of user groups and plays a positive role in the video quality of Bilibili.
Parameter Estimation of Induction Motors using Hybrid GWO-CS Algorithm

This study investigates a hybrid algorithm between Grey Wolf Optimization (GWO) and Cuckoo Search (CS) algorithms to find the parameters of induction motors. The parameters of the induction motor have been estimated by using the data supplied by the manufacturer. The problem for parameter estimation of the induction motor is formulated as an optimization problem. Then, the optimization problem is solved by using GWO and hybrid algorithm based on GWO and CS algorithms for the estimation of induction motor parameters. Numerical results show that both algorithms are capable of solving the optimization problem for finding the parameters of induction motor. Also, two algorithms and other algorithms such as Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog-Leaping Algorithm (SFLA), and Modified Shuffled Frog-Leaping Algorithm (MSFLA) are compared for the problem. The results show that the hybrid GWO-CS algorithm gives a smaller objective value and closer torque value to the manufacturer’s data than the GWO algorithm and several algorithms for motor 1. Hybrid GWO-CS algorithm gives nearly the same results with GWO algorithm for motor 2.
A critique on decentralized finance from a social, political, and economic perspective

Throughout the ages, social change has largely been brought as a reaction against an establishing entity or idea. Communism, for instance, was a reaction against the industrial revolution that exacerbated the exploitative nature of capitalism. Contemporarily, we are living through perhaps one of the most critical turning points in finance that began as a reaction against the solipsistic nature of Wall Street bankers prior to the 2008 financial crisis. This new movement exists within the robust ecosystem of decentralized finance (DeFi). Throughout the past decade, innovation within DeFi has grown exponentially, such that literature on its social, political, and economic effects is yet to be well-understood. Thus, this research paper intends to deconstruct the underlying socio-economic systems surrounding DeFi in order to understand, analyze, and critique its fundamental values and assumptions. The primary philosophical concern is whether decentralized finance can be a suitable substitute to the current financial system via its notions of personal financial freedom.
Gradify: Analysis of twitter account using classification algorithm

With technology's increasing capabilities, social media has become the largest pool of data from which it can extract public opinion and begin to gather informative data on the success or failure of a brand, product, or marketing campaign in the eyes of the public. People share their experiences, opinions, and daily activities on social media, which results in enormous amounts of online data that attract developers to carry out data mining and analysis. Thus, there is a necessity for social media screening to obtain results that can be used for analysis. Twitter is an online networking site driven by tweets, which are 140-character limited messages. Thus, the character limit enforces the use of hashtags for text classification. Currently, around 5500–6000, tweets are published every second, which results in approximately 561.6 million tweets per day. Performing sentiment analysis of tweets can help us to determine the polarity and inclination of a vast population toward a specific topic, term, or entity. The applications of such analysis can easily be observed during public elections, movie promotions, brand endorsements, and many other fields. This proposed system uses a Naïve Bayes classifier to determine the tweets based on sentiment. In the implemented system, tweets are collected, and sentiment analysis is performed on them. Based on the sentiment analysis results, a few suggestions can be provided to the user. The primary aim is to provide a method for analyzing sentiment scores based on grades. This paper reports on the design of sentiment analysis, extracting vast numbers of tweets. Results classify users' perceptions via tweets into positive and negative categories. Secondly, it discusses various techniques to carry out a sentiment analysis on Twitter data in detail.
Language detection using multinomial naïve bayes algorithm

In this multilingual world, automatic detection of written or spoken language using Language Identification (LID) technology is a boon in the global communication with people using different languages in different countries. For simplicity and for the purpose of this research, the process of automatically identifying the language(s) from a document is thought of as LID. Lot of ongoing research projects are in the field of Natural Language Processing (NLP) that uses LID as a part of NLP. This field exploits several algorithms evolved in the field of computer science, individually or in combination to achieve accuracy in identifying a language. Among the different approaches adopted in LID,NaïveBayes Classification n-gram text processing seems to be promising.This paper proposes the concept for categorising multiple language texts using Naïve Bayesian algorithms using Machine Learning approaches. Using techniques from existing researches, this paper proposes a way to recognize multilingual documents and calculate the relative proportions of these languages.
Supplementary Data from Subclassification and Individual Survival Time Prediction from Gene Expression Data of Neuroblastoma Patients by Using CASPAR

Supplementary Data from Subclassification and Individual Survival Time Prediction from Gene Expression Data of Neuroblastoma Patients by Using CASPAR
Related Article from Disentangling the Myeloma Web

Related Article from Disentangling the Myeloma Web
Supplementary Methods from &lt;i&gt;CSN1&lt;/i&gt; Somatic Mutations in Penile Squamous Cell Carcinoma

&lt;p&gt;Supplementary methods&lt;/p&gt;
Spatial Graph Convolution Neural Networks for Water Distribution Systems

We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.