Reviewerly/exHarmony huggingface dataset:
Research on Gender Prediction for Social Media User Profiling by machine learning method

This paper aims to propose a gender prediction method that integrates social media users' sentiment. This method is used to predict the gender attributes of users, so as to realize the research on the construction method of social media user portraits. Previous studies on gender prediction have done little analysis of sentiments. This paper has used the idea of transfer learning to analyze users' sentiments and integrated sentimental features into the existing machine learning, and hence, shows superior performance in terms of accuracy as compared to other methods. This paper mainly uses machine learning method to realize the construction of social media user profiling. The gender attribute is studied. Firstly, feature extraction is carried out for text data of media users. Then the idea of transfer learning is used to analyze the user's sentiment and integrate the sentiment characteristics into the existing machine learning. Finally, five prediction methods, i.e. Logistic Regression(LR), Naïve Bayes(NB), k-Nearest Neighbor(KNN), Random Forest(RF) and Support Vector Machines(SVM) is used to predict the gender of the fused sentiments. The results show that The gender prediction effect after sentiment fusion is better than that before. and the accuracy is increased by about 2.1% on average.
Task Definition and Integration For Scientific-Document Writing Support

With the increase in the number of published academic papers, growing expectations have been placed on research related to supporting the writing process of scientific papers.Recently, research has been conducted on various tasks such as citation worthiness (judging whether a sentence requires citation), citation recommendation, and citation-text generation.However, since each task has been studied and evaluated using data that has been independently developed, it is currently impossible to verify whether such tasks can be successfully pipelined to effective use in scientificdocument writing.In this paper, we first define a series of tasks related to scientific-document writing that can be pipelined.Then, we create a dataset of academic papers that can be used for the evaluation of each task as well as a series of these tasks.Finally, using the dataset, we evaluate the tasks of citation worthiness and citation recommendation as well as both of these tasks integrated.The results of our evaluations show that the proposed approach is promising.
Tools, Techniques and Underground Networks of Yahoo-Boys in Ibadan City, Nigeria

Despite the fact that the online criminal activities of Nigerian cyberfraudsters popularly known as the Yahoo-boys has attracted tremendous scholarly attention, little empirical information exists on their operational tools, techniques and underground networks. Hence, this study was motivated by the need to fill this observed gap. Social learning theory was adopted as theoretical guide while in-depth interview was principally deployed for data collection. Snowball sampling technique was utilized for the selection of 11 Yahoo-boys operating in Ibadan city. The results showed that Yahoo-boys who became wealthy through cyber fraud perpetration were being imitated by their peers who saw them as role models. Two major categories of operational tools were generally deployed by the Yahoo-boys for crime commission and illicit cash flows. Underground online forums, foreign criminal contacts and abroad-based criminal associates constituted the major sources of their operational tools and positively reinforced them towards cyber fraud. Yahoo-boys invested huge capital in the procurement of operational tools because they positively defined cyber fraud as a profitable business. There is the need for global law enforcement agencies and relevant international cybercrime-fighting institutions to constantly review and analyze the latest tools and techniques being employed by cyberfraudsters to effectively curtail their illegal activities.
Machine learning in eigensubspace for network path identification and flow forecast

This paper emphasizes the joint time-frequency interpretation of eigensubspace representation for network statistics as features for identification and tracking traffic flows based on the link level activity. Eigencoefficients (frequency domain feature) and eigenvector components (time domain feature) are jointly utilized to quantify their combined significance on the representation of each link data (each component of the link traffic vector) in the eigensubspace. The joint time-frequency method is employed to analyze the traffic data obtained from the Internet2 network. It is shown that the analysis with link-level resolution brings advantages for network traffic engineering applications. A machine learning method is investigated to identify network paths using eigenanalysis of link statistics as the feature set. The merit of the method is validated by experimental studies of the network scenarios considered in the paper. Eigenvectors and eigenflows in the subspace are jointly used as factors (features) for linear regression to forecast the network link traffic. It is demonstrated that the eigensubspace based auto-regressive order two, AR (2), predictor is superior to the time-domain based predictor to forecast the link level traffic of a network.
A Practical Study on Using Computer Technology to Assist College English Teaching

Abstract With the development of science and technology, traditional teaching is also innovating and researching. At present, computers are widely used in different industries. Nowadays, colleges and universities in China are changing the traditional teaching mode to the teaching mode supplemented by computer. College English, as a compulsory basic subject course for every major in college, is a teaching mode supplemented by computer technology, which changes the previous teaching mode and makes college English teaching classroom one of the favorite classes for college students. This paper explores and studies the teaching mode, teaching method and teaching curriculum of college English supplemented by computer.
Engaging Rural Communities in Mexico: Educating youth and their families during a pandemic

Collaboration can be the key to bringing innovative educational programs to recipients who can benefit the most.
A Portable Implementation of RANLUX++

High energy physics has a constant demand for random number generators (RNGs) with high statistical quality. In this paper, we present ROOT’s implementation of the RANLUX++ generator. We discuss the choice of relying only on standard C++ for portability reasons. Building on an initial implementation, we describe a set of optimizations to increase generator speed. This allows to reach performance very close to the original assembler version. We test our implementation on an Apple M1 and Nvidia GPUs to demonstrate the advantages of portable code.
Relationship between knowledge management practices and performance - a segmentation study in Indian private sector

This study proposes a comprehensive model that establishes relationships between antecedents of effective knowledge management (KM) and organisational performance. An attempt is made to identify the probable KM segments and performance variation across segments. An instrument was designed, and seven hypotheses were proposed. Data was collected from 127 private sector managers. Partial least squares structural equation modelling (PLS-SEM) was used to test hypotheses which indicated that while all the hypotheses had the desired positive sign, five of them were significant. This paper presents empirical evidence of KM in enhancing organisational performance. Cluster analysis resulted in three KM segments labelled as - pessimistic, marginally optimistic and highly optimistic. Further, both organisational and financial performance significantly varied among the three segments. This study provides strong evidence that thoughtful planning and implementation is the starting point for enhancing performance. This model can be applied to similar organisations for identifying KM maturity among employee clusters.
A Span-based Model for Joint Entity and Relation Extraction with Relational Graphs

Extracting related entity pairs from natural language texts is essential to constructing knowledge graphs. However, prior work is limited by the long distance between entities and cannot accurately extract entities and relationships in the case of relatively complex overlap. To address these limitations, we present Span-RG, a span-based relation extraction model which uses graph convolutional networks to jointly learn entities and relations. First, a boundary prediction task is designed to determine whether a word is an entity boundary, which enhances the span representation to obtain a high quality candidate entity spans. Then, we improve GCN and constructe a span-based entity relationship graph, where the edge is the relationship and the node is the span. We evaluate Span-RG on three public datasets: ACE05, NYT and WebNLG. Experimental results demonstrate that the performance of the model is significantly better than the baseline method.
FORECASTING SYSTEM FOR PASSENGER, AIRPLANE, LUGGAGE AND CARGO, USING ARTIFICIAL INTELLIGENCE METHOD - BACKPROPAGATION NEURAL NETWORK AT JUANDA INTERNATIONAL AIRPORT

Juanda International Airport is the third busiest airport, after Soekarno Hatta International Airport and Ngurah Rai International Airport. Because the number of Airplane, passengers, luggage and cargo at Juanda International Airport is increasing every year, it is important to improve infrastructure facilities and services, and all facilities at Juanda Airport. In this research, it was designed and built a forecasting system for Airplane, passenger, luggage and cargo. This research is expected to be a consideration in increasing the readiness of infrastructure and services, and all facilities at Juanda Airport. In addition, this system is also expected to be one of the decision supporting system for the management. This system uses one of the Artificial Intelligent methods, Backpropagation Artificial Neural Network. It is known in previous research that Backpropagation is a method of artificial neural networks with the best performance in pattern recognition, or forecasting. The forecasting system has two main processes, the training process and the forecasting process.
Machine Learning Approaches for Human Activity Recognition Based on Multimodal Body Sensors

Human Activity Recognition (HAR) has become a subject of high interest within many fields. In this work, the target is to build a classifier with high accuracy, so two approaches are used to build a classifier. The first approach represented by model (A), and the second represented by the model (B), both models are built using four machine learning classification algorithms, which are Random Forest (RF), Naïve Bayes (NB), Support Vector Machines (SVM), and Multilayer perceptron (MLP). Model (A) depends on a single classifier for recognizing 12 activities and Model (B) depends on applying the ensemble technique and build in two steps for recognizing the same activities. The experimental comparison result shows that model (B) improved the accuracy of model (A), as the accuracy is increased with 3.6% in Random Forest, 4.7% in Multilayer perceptron, and 23.2% in Naïve Bayes but for Support Vector Machines, model (A) achieved higher accuracy than model (B).
Time-Sensitive Adaptive Model for Adult Image Classification

Images play an important role in modern internet communications, but not all of the images shared by the users are appropriate, and it is necessary to check and reject the inappropriate ones. Deep neural networks do this task perfectly, but it may not be necessary to use maximum power for all images. Many easier-to-identify images may be classified at a lower cost than running the full model. Also, the pressure on the system varies from time to time, so an algorithm that can produce the best possible results for different budgets is very useful. For this purpose, a deep convolutional neural network with the ability to generate several outputs from its various layers has been designed. Each output can be considered as a classifier with its own cost and accuracy. A selector is then used to select and combine the results of these outputs to produce the best possible result in the specified time budget. The selector uses a reinforcement learning model, which, despite the time-consuming learning phase, is fast at execution time. Our experiments on challenging social media images dataset show that the proposed model can reduce the processing time by 32 % by sacrificing only 1.4 % of accuracy compared to the VGG-f network. Also, using different metrics such as F1-score and AUC (the Area Under the Curve in the accuracy vs. time budget chart), the superiority of the proposed model at different time budgets over the base model is shown.
METHODS OF DECODING DATA USING BIOLOGICAL RESEARCH AND ARTIFICIAL INTELLIGENCE IN CULTURE PRACTICE

The article is a comprehensive analysis of projects aimed at studying AI technologies and culture interaction. The author examines the specifics and uniqueness of art works created through AI-technologies using examples of projects from “ThoughtWorks Arts Global Research”, “Innovation Laboratory of New Technologies”, “Isolation Foundation” and “IZONE Creative Association”. The article analyzes the principle of selection of materials, algorithmic analysis of data, the interdependence of digital data received from the user's brain impulses with audiovisual content, the possibility of instant data processing in the process of creating an artistic product. The author explores the principles of tracking brain function and decoding human genetic data, which are used to create art projects. The article assesses the potential that AI possesses and explains the conditions necessary for the implementation of AI-technology in culture. As a result of the study, the author revealed that through algorithmic analysis it is possible to transform digital data into a system of expressive signs of visual and sound arts, to broadcast the received audiovisual content. The author finds out that through these technologies it is possible to create interactive art forms (interactive film, installations, immersive presentations, etc.).
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems

Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few. A substantial amount of studies are devoted to studying the convergence behavior of their stochastic gradient-type algorithms. In contrast, there is relatively little work on their generalization, i.e., how the learning models built from training examples would behave on test examples. In this paper, we provide a comprehensive generalization analysis of stochastic gradient methods for minimax problems under both convex-concave and nonconvex-nonconcave cases through the lens of algorithmic stability. We establish a quantitative connection between stability and several generalization measures both in expectation and with high probability. For the convex-concave setting, our stability analysis shows that stochastic gradient descent ascent attains optimal generalization bounds for both smooth and nonsmooth minimax problems. We also establish generalization bounds for both weakly-convex-weakly-concave and gradient-dominated problems.
Appendable-Block Blockchains

Blockchain has emerged as a technology that can change the way people and systems interact, providing mechanisms that ensure integrity and ownership of the data produced without reliance on a trusted third-party. Appendable-block blockchain is a novel instantiation that suits for solutions that require a high transaction throughput. Appendable-block blockchains focus on data produced by nodes instead of a relation (transaction) between two entities. This new kind of blockchain can improve how data are stored and managed in distributed systems. This chapter introduces the notion of appendable-block blockchain and exemplifies its applicability in multiple practical domains. Additionally, the authors provide a discussion on the security aspects of this new blockchain. Finally, the chapter presents current issues and possible future directions for appendable-block blockchains.
Research on Path Planning of Manipulator

In order to further realize the flexibility, intelligence and human-machine friendliness, the robot must have the ability of flexible motion planning.This paper mainly introduces the general steps of manipulator path planning and common path planning algorithms.First, the path planning of the manipulator is reviewed from three aspects: environment modeling, path search, and path smoothing.In addition, compared various manipulator path planning algorithms, such as RRT algorithm, ant colony algorithm, genetic algorithm, etc.Finally, the development trend of path planning technology is summarized and analyzed.
Rapid prediction of earthquake ground shaking intensity using raw waveform data and a Convolutional Neural Network

To produce a rapid prediction of earthquake generated ground motions, on a short timeline after earthquake origin time, seismological community has been developing earthquake early warning systems, which seek to very rapidly (few seconds) detect earthquakes and provide predictions of ground motions at selected target points, for the purpose of providing early warning at those points.
A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance

Stochastic Gradient Descent (SGD) is a popular tool in training large-scale machine learning models. Its performance, however, is highly variable, depending crucially on the choice of the step sizes. Accordingly, a variety of strategies for tuning the step sizes have been proposed, ranging from coordinate-wise approaches (a.k.a. ``adaptive'' step sizes) to sophisticated heuristics to change the step size in each iteration. In this paper, we study two step size schedules whose power has been repeatedly confirmed in practice: the exponential and the cosine step sizes. For the first time, we provide theoretical support for them proving convergence rates for smooth non-convex functions, with and without the Polyak-Łojasiewicz (PL) condition. Moreover, we show the surprising property that these two strategies are \emph{adaptive} to the noise level in the stochastic gradients of PL functions. That is, contrary to polynomial step sizes, they achieve almost optimal performance without needing to know the noise level nor tuning their hyperparameters based on it. Finally, we conduct a fair and comprehensive empirical evaluation of real-world datasets with deep learning architectures. Results show that, even if only requiring at most two hyperparameters to tune, these two strategies best or match the performance of various finely-tuned state-of-the-art strategies.
A Survey on Secure Ranked Keyword Search over Outsourced Encrypted Cloud Data

In this paper we present a survey on keyword based searching algorithms. Various searching techniques are used for retrieving the encrypted data from cloud servers. This survey work involves a comparative study of these keyword based searching algorithms. It concludes that till now multi-keyword ranked search MRSE scheme is the best methodology for searching the encrypted data.
Software Defect Prediction Using Metaheuristic Algorithms and Classification Techniques

From the start of software development, Software Defect Prediction (SDP) is a significant and emerging challenge in the field of software engineering. Without an accurate model for predicting flaws in software, a product may be released in an unsatisfactory condition which might lead to expensive post-developmental activities. Thus, the prediction of software defects in the early stages has become a primary interest in the field of software engineering. Several defect prediction approaches that rely on software metrics have been proposed. Support Vector Machine (SVM), Naive Bayes (NB), Bagging, Neural network, and decision tree (DS) classifiers are known to perform well in predicting bugs. In the prediction of software bugs, this study explored some selected metaheuristics algorithms for feature selection, namely; Firefly Algorithm (FA) and Wolf Search Algorithm (WSA). The Support Vector Machine (SVM) as well as Random Forest (RF) was used as classifiers. This study experiments the proposed models on the publicly available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction. Thus, it was discovered that metaheuristics optimization for attribute reduction can produce outstanding results in the prediction of software defects.
Sketch-and-Fill Network for Semantic Segmentation

Recent efforts in semantic segmentation using deep learning framework have made notable advances. While achieving high performance, however, they often require heavy computation, making them impractical to be used in real world applications. There are two reasons that produce prohibitive computational cost: 1) heavy backbone CNN to create high resolution of contextual information and 2) complex modules to aggregate multi-level features. To address these issues, we propose the computationally efficient architecture called “Sketch-and-Fill Network (SFNet)” with a three-stage Coarse-to-Fine Aggregation (CFA) module for semantic segmentation. In the proposed network, lower-resolution contextual information is first produced so that the overall computation in the backbone CNN is largely reduced. Then, to alleviate the detail loss of the lower-resolution contextual information, the CFA module forms global structures and fills fine details in a coarse-to-fine manner. To preserve global structures, the contextual information is passed without any reduction to the CFA module. Experimental results show that the proposed SFNet achieves significantly lower computational loads while delivering comparable or improved segmentation performance with state-of-the-art methods. Qualitative results show that our method is superior to state-of-the-art methods in capturing fine detail while keeping global structures on Cityscapes, ADE20K and RUGD benchmarks.
Correction to: A sequential homotopy method for mathematical programming problems

A correction to this paper has been published: https://doi.org/10.1007/s10107-021-01668-5