Reviewerly/exHarmony huggingface dataset:
Designing a Recommender System for Articles Using Implicit Feedback

The main motive of this work is to build a recommender system for articles read online on a website. There are many methods in the modern machine learning era which could be deployed to make models for this recommendation system. The dataset used here was taken from DeskDrop, an Internal Communications Platform developed by CI and T. The dataset is split into two parts, namely shared articles and user interactions, and combined to build the model. Collaborative filtering and matrix factorization techniques have been used. The algorithms used in this recommender system are Bayesian personalized ranking (BPR), alternating least squares (ALS), and logistics matrix factorization (LMF). The performance of these algorithms shows a high true positive rate and a better accuracy in terms of recommendation as compared to basic collaborative filtering algorithms.
NumPy and SciPy

NumPy is the foundational module for numeric computation in Python. Its primary data structure, the NumPy n-dimensional array, can be considered the Python equivalent of a MATLAB matrix. SciPy is a collection of modules built upon NumPy to solve a wide range of computational problems in clustering, differential equations, image processing, interpolation, linear algebra, linear programming, optimization, sparse matrices, statistics, and symbolic mathematics, among others. NumPy and SciPy together span the capabilities of the core MATLAB product plus functions from many Toolboxes.
Astigmatic Projection

Although perspective/orthogonal transformations are standard projections used in computer graphics, they may cause problems in cases such as image synthesis for 3D displays.On these occasions, it is more suitable to adopt astigmatic projection,which has different focal lengths in the x- and y-directions.This paper presents the basic properties of astigmatic projection. We first show that a straight line is projected on toa curve that can be expressed as a second order rational Bezier curve. Taking advantage of the convex hull property,a simple subdivision algorithm is presented for efficient tessellation. The distortion of the projected curves was analyzedand we found that the maximum distortion occurs at a corner of the viewing frustum on the near plane.We also discuss interpolation schemes during scan-conversion and propose an interpolation schemespecific to astigmatic projection.Finally, we describe implementation on a GPU utilizing tessellation shaders and show an application to 3D displays.
The Impact of Kubernetes on Development

The last five years can safely be termed a Kubernetes Tsunami in the IT world. Kubernetes has been around since 2014, and it conquered not only the service catalogs of the major cloud providers but also most data centers around the world. Looking at the statistics reveals that if you want to run workloads in containers at scale, there is actually no other container orchestration tool around. In a report from Red Hat from 2021 asking organizations which container orchestration tool they use, you would still see mention of things like Mesosphere and Docker Swarm, but without a notable share and only with news coverage talking about “end-of-life.”
Mitosis detection from histological images using handcrafted features and artificial neural network

Mitosis is defined as the rapid division of cells and its count is relevant to predict the grading of breast cancer. Since manual mitosis detection is time consuming and prone to errors, a fast and accurate detection approach is proposed using handcrafted features with artificial neural network (ANN). This method includes three steps: 1) image pre-processing involves conversion on RGB image to red-channel; 2) segmentation which is done using fuzzy C-means clustering and handcrafted features are extracted; 3) classification in which both random forest classifier and ANN are ensemble to predict the outcome. The system was tested with Mitos-Atypia14 dataset and an accuracy of 91.6% is obtained.
Energy-Efficient Federated Learning in IoT Networks

This paper investigated the problem of energy-efficient transmission resource allocation for Federated learning (FL). By processing data on the heterogeneous devices and uploading the model updates to the server, federated learning facilitates a large-scale model training with lower latency. Although FL has many promising advantages, the number of involved devices is limited by the communication and device battery resources. Therefore, it is rational to select devices due to its importance. This joint device selection and resource allocation problem is formulated to minimize the total energy cost and maximize the training accuracy. This optimization problem is a mixed integer nonlinear programming (MINLP), which is solved by a penalty dual decomposition (PDD) method. The closed-form expression solution shows that devices with more importance and less energy cost are more likely to be selected. The experiments show that the proposed algorithm has a desired performance and outperforms the random selection and full selection benchmarks.
Managing Threads

We have all experienced that moment when running some process such as copying files between directories or launching a new instance of an application causes a program to lag for just a moment and, in some cases, to freeze completely. We are then forced to either wait for the current task to complete or Ctrl+Alt+Delete our way to freedom. When you are creating GUIs, you should be aware of how to handle and have foresight about avoiding these situations.
A Study of Building Peripheral Product Recommendation System

Although the current recommendation system has been very mature and widely used, the recommendation system of peripheral products has been relatively seldom explored. This paper studies the analysis based on the historical behavior data of a large number of users, as well as the classification and processing of the information of the peripheral products purchased by the users, and finally recommends the peripheral products of the most popular products of the related products to the users, which effectively solves the problem that the users are searching again Peripheral products of related products, which also improve the shopping experience of users’ secondary consumption and reduce users’ purchase of counterfeit and shoddy products that increase their visibility by swiping orders. This study uses the Python language. Benefiting from the powerful spark, the system can handle large data sets. The results of this experiment show that the system can achieve high accuracy in large data sets.
Financial Information Management Under the Background of Big Data

With the constant renewal of the knowledge age, economic globalization is intensifying. Big data is the inevitable outcome of the development of information technology to a certain extent, which integrates various Internet technologies and brings many conveniences to people’s lives and work. Massive data not only brings difficulties to management and maintenance, but also provides great potential value. As an important development field of social and economic construction, it plays an important role in promoting the progress of Chinese society and the improvement of people’s material level. The application of big data technology in financial management is an inevitable trend and need of the development of modern society, which makes new development and breakthrough in the financial field. Based on big data and its characteristics, this paper provides an important way to solve big data in financial industry. With the application of science and technology, financial information management based on big data has been realized, which is helpful to promote the continuous progress of financial industry.
How to Use a Script or Function

While scripts are just listings of code stored outside of R, functions are objects of the function mode and are stored in the workspace. Most functions require specific kinds of arguments, which must be input into the function correctly. For example, if a function calls for a matrix and a data frame is input, the function will return an error. Since external tables are often read into the R workspace as data frames, using a data frame for a matrix is quite a common error. This chapter covers an example of using a script to do a simple mining of Twitter tweets, as well as how to call a function, use arguments in a function, and access the output of a function.
UAV Target Tracking Algorithm Based on Kernel Correlation Filter

At present, there are still some problems in the tracking of ground targets by UAV, such as the poor tracking effect when the targets are moving rapidly, rotating and the target size is small. Therefore, this paper proposes an improved kernel correlation filtering algorithm. Firstly, the target is segmented adaptively according to the length-width ratio, and the maximum response is calculated by using the fusion feature, which is obtained by extracting the HOG feature and CN feature of each sub-block. Secondly, the position filter is used to locate the target, and the size filter estimates the size of the target. Finally, a re-detection mechanism is introduced to judge whether to update the filter based on the APCE value. The experimental results show that in the process of UAV target tracking, the improved algorithm can effectively reduce the influence of external interference on the tracking effect and improve the tracking effect.
Improved Stereo Matching Algorithm Based on Adaptive Grid for Fixed-Wing UAVs

Modern unmanned aerial vehicles (UAVs) rely on binocular ranging modules to complete tasks such as obstacle avoidance, 3D reconstruction, and terminal strikes. However, the limited computing resources and high flight speed all put forward requirements for the real-time performance of the ranging algorithm. Thus, in this paper, we focus on how to make the algorithm dynamically allocate computing resources according to the changes of the UAV attitude to improve the system efficiency. To this end, we propose an improved semi-global matching method based on adaptive grid for fixed-wing UAVs. Experimental results demonstrate that the proposed method can effectively adapt to the changes in the speed and attitude of the UAV, and improve the real-time performance of the ranging module.
WebSocket Push

WebSocket support is represented by the version 2.3 introduced <f:websocket> tag, the PushContext interface, and the @Push annotation. It is built on top of the JSR-356 WebSocket specification, introduced in Java EE 7. JSR-356 is even natively supported in Tomcat since 7.0.27 and in Jetty since 9.1.0.
A Network-Based Rating Mechanism Against False-Name Attack

Rating system is a commonly used approach to evaluate a product or service online. It often simply averages the ratings from all users. However, users can manipulate the rating by creating many fake ids to submit non-truthful ratings. In this paper, we propose a weighted rating system underpinned by the connections between users, where the connections can reflect their friendship. Under this rating system, we prove that a user cannot create fake ids to manipulate the evaluation towards her favor. Moreover, the system also represents all ratings, i.e., each single rating will affect the aggregated rating.
Human Behaviors Classification Using Deep Learning Technique

Human behaviors is an action performed by human. There are various types of human behaviors such as running, walking, jumping, sitting and the others complex movement. In this paper, human behaviors video-based classification using Long Short Term Memory (LSTM) model with multiple layers were proposed to classify the human behaviors. A pre-trained pose estimation model, OpenPose was used to extract the body key points from the Berkeley Multimodal Human Action Database, MHAD database. Six activities, jumping, jumping jacks, punching, waving with two hands, waving with right hand and clapping hands of MHAD database were used for the training and testing. The individual frame of MHAD database will group into 32 window width. Dataset had been increased by creating the 26 of 32 frame overlapping. The performance of 2 layers LSTM model, 3 layers LSTM model, 4 layers LSTM model without dropout layers and 4 layers LSTM model with dropout layers were evaluated and compared. Result shows that 4 layers LSTM model with dropout layers had better performance as compared to 2 layers LSTM model, 3 layers LSTM model and 4 layers LSTM model without dropout layers reached the testing accuracy of 95.86%. With adding of dropout layers in the LSTM model with 4 layers, generalization performance in training process had been increased.
Attention Word Embedding Network-Based Lightweight Automatic Essay Scoring Model

In recent years, automatic essay scoring technology has received more and more attention in the fields of education and research, and its purpose is to use natural language processing technology to automatically analyze and score the essay. In this paper, we proposes a lightweight automatic essay scoring model based on attention word embedding network. Using randomly initialized and self-trained word embedding and attention/max/mean pooling operation, we are able to score essays automatically. Our models match or outperform optimal QWK (Quadratic Weighted Kappa) performance on all eight datasets using significantly fewer parameters compared to baseline methods.
Application of VR to Educational System of Korea

Since the breakout of Covid-19, the number of online classes skyrocketed as conducting in-person classes in school has been discouraged. This led to the sudden shift into the adaption of online classes, blurring the line between traditional in-person classes and modern technology. In the Republic of Korea, the nation where it is known for its speed of Internet and high rate of digital natives amongst its citizen, there have been numerous attempts to incorporate virtual reality (VR) into the existing curriculum but has not been getting satisfactory results back. Through interviews and research, this paper tries to assess the current position of VR in the marketplace and suggest possible solutions that can support the expansion of the system into the school. The main purpose of this essay is to analyze whether Google Arts and Culture, one of the most accessible VR assimilated educational platforms, can be blended into the Korean education programs.
Digital Safety in Rail Transport—Basic Assumptions

Purpose: The aim of this chapter is to present key assumptions to clarify the determinants of rail transport digital safety. Design/Methodology/Approach: A critical literature review with theoretical and practical reflections aiming to explain the interdisciplinary nature of digital safety management in rail transport has been adopted as a methodology for this chapter. Findings: This chapter identifies key theoretical approaches to understanding digital safety issues in relation to the life cycle of railway systems, as well as the role of software and hardware in digital safety management processes. Cybersecurity was also identified as a key challenge for today’s railways. Research limitations/implications: The limitations of the analytical and evaluation study result from the difficulty in identifying those issues, which are of the utmost importance when it comes to fully clarifying the issue of digital safety in view of a wide range of approaches to this issue. Practical implications: The practical nature of the content described is manifested in critical analysis and examples and explaining factors responsible for rail transport digital safety. Originality/Value: The originality of this chapter is an attempt to explain the relationship between safety and security aspects.
Analysis of Sensors in the Classification of the Brazilian Sign Language

The use of hands during communication is generally a natural way of communication and allows the transmission of messages. However, in order to communicate with deaf people using sign language, it is necessary to know the rules of sign language. In this way, tools and technologies for communication between deaf people and listeners are very important to improve the social inclusion of the deaf community. The wearable hand devices have been used to identify sign language gestures. In this regard, the purpose of this paper is to analyze the contribution of these sensors, individually and in groups, in the classification of characters of Brazilian sign language alphabet. The wearable hand device used is composed of five flex sensors, two contact sensors, and a three-axis inertial sensor (accelerometer and gyroscope). For the dataset, five volunteers executed 26 alphabet characters. For the classification, each gesture window was divided into three parts: construction period, gesture period and gesture relaxation period. Thus, 28 gesture patterns were considered for classification. The used classifier was the Multilayer Perceptron Neural Network (MLP-NN). In the individual sensor analysis, the flex-sensors obtained the highest accuracy rate (79.0%), followed by the accelerometer (41.7%), the gyroscope (32.1%), the contact sensor 2 (7.1%), and the contact sensor 1 (6.9%). In the analysis of the sensors in groups, the set of all sensors was the one that obtained the highest accuracy rate (96.1%).
The Effect of Microblogging on the Career Planning Ability of Vocational High School Students

This study aims to identify the application of microblogging to improve the career planning skills of vocational high school students.This research is a quasi-experimental research design using a nonequivalent control group design model.The population of this study was all students of class XII SMKN Kalibaru totaling 445 students.The sampling technique used cluster random sampling with one control class and one experimental class.Collecting data using a questionnaire method with a Likert scale modified into 5 answer choices.The scale used is a career planning ability scale of 50 items that are valid and reliable.Data analysis carried out was descriptive statistical analysis, normality test, homogeneity test, Paired Sample t-Test and continued with Independent Sample t-Test.The results of the independent sample test showed the acquisition of a sig (2-tailed) score is 0.000 < 0.05, it means the difference in the average score of students' career planning abilities between the group that was given guidance and counseling services using microblogging and the group that was not provided with microblogging service.The difference between the average pre-test and post-test of the control group is 4.67.While the difference in the average value of the experimental group before being given career services using microblog media and after the service was provided was 25.43.The results showed that microblogging was effective for improving the career planning skills of vocational high school students.Career guidance and counseling carried out by guidance and counseling teachers can help students to have career-related information and help make career planning.Microblogging media is a liaison between guidance and counseling teachers and students, making them closer and relevant to the lives of students who often access social media.
Recursive Named Entity Recognition

Named entity recognition (NER) seeks to locate and classify named entities into predefined categories (persons, organizations, brandnames, sports teams, etc.). NER is often considered as one of the main modules designed to structure a text. We describe our system which is characterized by (1) the use of limited resources, and (2) the embedding of results from other modules such as coreference resolution and relation extraction. The system is based on the output of a dependency parser that adopts an iterative execution flow that embeds results from other modules. At each iteration, candidate categories are generated and are all considered in subsequent iterations. The main advantage of such a system is to select the best candidate only at the end of the process, taking into account all the elements provided by the different modules. Another advantage is that the system does not need a large amount of resources. The system is compared to state-of-the-art academic and industrial systems and obtains the best results.
A Study on Clustering Facebook Multimedia Based on Metadata—A Comparative Analysis

Facebook is an open source access for billions of users to get interact and share information, which leads to complexity while storing day-to-day information on the cloud. Hence it becomes a necessity to group the similar data in particular Clusters using data mining techniques and algorithms. And an unsupervised approach, clustering is used for grouping the instances. The present paper represents three different Cluster algorithms: Expectation Maximization, Simple K-means, and Hierarchical Clusterer to group the extracted Meta Data attribute’s instances in respective Clusters. This research is conducted on a cosmetic company’s Facebook page’s Meta Data that contains several attributes among, “type” Meta Data is considered to examine its instances in different clusters. Expectation Maximization (EM), Simple K-Means, and Hierarchical Clusterer algorithms’ results comparison are carried out and analyzed in detail to represent the best suitable cluster algorithm among considered three algorithms which can correctly group all possible instances in respective cluster which is the ultimate goal of knowledge discovery.
Development of a Conceptual Model of the Digital Ecosystem of Students Based on the Transformation of the Electronic Information and Educational Environment of the Transport University

The article examines the process of transformation of the electronic information and educational environment of the transport university into a digital ecosystem based on the penetration of artificial intelligence technologies. The digital ecosystem of the transport university is studied, based on the principles of a systematic approach, as a set of ecosystems of different users, according to a closed and open scheme. The digital ecosystem of students is studied as a combination of elements that are homogeneous in terms of the level of training, the functioning of which is aimed at forming student's competencies within a separate educational program and providing basic and additional services at the request of the main users. The list of additional services that are elements of the structure of the student's digital ecosystem is determined on the basis of a certain sample of students of various courses in the areas of undergraduate and postgraduate studies in the field of economics and information technology, in order to cover different professional orientation and track possible changes in needs depending on student maturity. Based on the selected set of basic and additional services, a conceptual model of the student's digital ecosystem is formed.
Research on Semi-automatic Coronary Artery Centerline Extraction Based on Deep Learning

In recent years, Cardio vascular disease (CVD) has become a major cause of premature morbidity and mortality in our population, and the prevention and treatment of CVD have become urgent. Currently, the mainstream method to diagnose coronary artery disease is Coronary Computed Tomography Angiography (CCTA). The processing and analysis of CCTA images can help doctors understand the connection and trend of a coronary artery. Aiming at the problem that the radius error of the centerline obtained by the traditional semi-automatic coronary centerline extraction network is large, and it is easy to extend to the non-coronary tissue at the coronary ostium and end, this paper proposes a new three-dimensional convolutional neural network: MST-Net (Multi-Scale Tracking Net), through the multi-scale feature extraction layer, enables the network to extract more local and global features, to obtain more accurate predictions in the coronary centerline extraction task.