Reviewerly/exHarmony huggingface dataset:
Predicting Class Label Using Clustering-Classification Technique: A Comparative Study

Among different techniques, algorithms and applications of Data Mining, predicting the class label of unlabeled objects(undefined class label) is a crucial term in the field. The most common approaches in this area is the use of classification technique (DT, Bayes, SVM, KNN and others) that represent what is known as supervised learning. However, in many cases no target class labels and the boundaries are available to perform the prediction, so the new approach Clustering-classification technique is used.&#x0D; The work in this paper presents a survey of the most common researches conducted in this field and discuss their experiments, the algorithms they used, the types of data they utilized, the data sizes used, and the results they discovered.&#x0D; According to the results, applying the clustering techniques before classification improved classification accuracy and reduced experiment execution time. The Cluster Classifier was proven to be a suitable approach to summarize data by some of the researchers. It achieves a summarization rate of over 50%, which represents a considerable reduction in the size of the test datasets..&#x0D; The findings of the researches indicated that, in addition to feature selection and feature extraction, data preprocessing (handled missing data and effective outlier detection techniques) enhanced the classifier performance and accuracy while reducing the classification error.&#x0D;
Vert.X for Kotlin Restful API

Kotlin as well as other programming languages can be applied to writing service APIs. This chapter presents a new framework called Vert.X. It is considered as one of the powerful frameworks. It can support the implementation of many services including Restful API. Kotlin is used as the main programming language of Android mobile. When deploying Mobile, Restful API is used to interact with data on the server, so how to create Restful APIs is very important. Using Vert.X will help programmers save a lot of time, and Vert.X is considered to be very optimal when deploying services. This chapter will demonstrate the knowledge of how to build and deploy Restful APIs using Vert.X and demonstrate how to write code in Kotlin to call these APIs and how Postman platform interacts the API. This chapter includes brief of Restful API, overview Vert.X, creating Restful API by Kotlin in Vert.X, calling API in Postman, and authentication for Restful API. At the end of the chapter, there are exercises to help readers improve their programming skills using Vert.X to create Restful API.
String Processing

In all programming languages, string processing is extremely important. This chapter will present how to declare strings and summarize the commonly used functions of strings, such as indexOf, lastIndexOf, contains, substring, replace, trim, compare, plus, split, upper case and lowercase functions, etc. Each function has sample code along with detailed explanations that make it easy for readers to understand how to use basic string functions. In this chapter, the book will present some of the most commonly used functions in the string. At the end of the chapter there are exercises to help readers improve the skills in string processing.
Data Types and Variables

In the process of programming, understanding data types and variable declaration plays an important role in software development. When choosing the right data type and managing variables well, this will help the software to be optimized and memory, making the customer experience for the software better. The chapter will present the data types of the Kotlin language, data types will be grouped some category to make it easy for learners to remember and compare, data explicit conversion and implicit conversion are mentioned. Also, important operators for data types covered in this chapter include unary operators, basic arithmetic, assignment, comparison, Logic, increment and decrement operators, etc. This chapter also guides how to input data into the software from the keyboard to help readers easily test the cases during the programming process. For each content, this chapter provides simple and easy-to-understand sample code for easy access, and at the end of the chapter there are practice exercises to improve programming skills.
DIGITAL CULTURE DEVELOPMENT OF FUTURE VOCATIONAL TEACHERS DURING PROFESSIONAL TRAINING

The article actualizes the problem of forming the digital culture of future vocational teachers in the process of professional training, which is a system of views, ideas, standards, information and communication skills and abilities as an integral part of the modern information society. In this study, digital culture is considered as a new social system that determines the experience and opportunities for professional growth for future vocational teachers in the process of vocational training. Digital technologies and networked environments have brought new practices, opportunities and threats, and the digital culture sector mustfind appropriate ways to work in this new reality. The paper presents: basic digital competencies that future vocational teachers should possess in the process of vocational training; stages of formation of digital culture of future vocational teachers in the process of vocational training; methods related to changing approaches to the process of implementing teaching and work of future vocational teachers in the process of vocational training; model of digital culture of future vocational teachers in the process of vocational training and its implementation in educational.
The Lecturers and Students’ Voice about Digital Reading

This research attempts to determine whether digital reading has the same effect as traditional reading on students’ reading skill and to explain the lecturers and the students’ perceptions of digital reading. The writer applied mixed-method research. Sixty students participated as a research sample. Ten lecturers were also involved in this research. The instruments of this research were reading tests, questionnaires, and structured interviews. Reading tests were analyzed by using SPSS 22.0. The questionnaires were analyzed quantitatively using Microsoft Excel, and the structured interview was analyzed qualitatively. The writer found that digital and traditional reading does not affect students’ reading skills. It was evidenced by the t-test value being higher than the alpha (0.40 &gt; 0.05). In contrast to many lecturers’ opinions, students do not prefer digital reading because they need more explanations from their lecturer. Meanwhile, the lecturers revealed that digital reading is useful for ongoing virtual learning. They also were confident in their ability to employ these digital readings. Both lecturers and students agree that the availability of technological tools is the key constraint they must deal with. This research hoped to give meaningful information to the government so that online readers can be facilitated with high-quality online reading materials based on their needs, interests, and behavior.
Mobile Multiple Sink Path Planning for Large-Scale Sensor Networks Based on Hyper-Heuristic Artificial Bee Colony Algorithm

Large-scale wireless sensor networks consists a terrific amount of nodes, a wide range of deployment, extended data transmission time, and large network energy consumption. To solve the above problems, a mobile multiple Sink path planning based on hyper-heuristic artificial bee colony algorithm is proposed. The artificial bee colony algorithm is used as the high-level strategy. In view of the changes in the network operation process, namely the number of nodes and energy changes, the design of three stages of the artificial bee colony algorithm is used to choose and manipulate the low-level heuristic operator. The selected lower layer operator set plans the path of each mobile sink nodes. Compared with other famous meta-heuristic algorithms, it is proved that the proposed hyper-heuristic algorithm is an effective, efficient and robust algorithm, which can effectively solve the path planning problem in view of multiple sink nodes, reduce network delay and reduce network energy consumption.
How the Technological Epoch and COVID-19 Silenced Indigenous Knowledge in the University Curriculum

Chapter 3 discusses how South Africa’s capitalist neoliberal agenda for higher education, where the focus is on the shift from a knowledge economy to a digital economy, will choke the life of indigenous knowledge out of the university curriculum. To support this claim, the chapter first discusses the impact of the core neoliberal ideals on the university curriculum landscape. Second, drawing on the scholarly work of Martin Heidegger and his anticipation of the spirit of the time in the technological epoch, the chapter shows how humans in this era will be viewed as a heap of fungible raw materials, resources or standing reserve (Bestand) awaiting optimisation. The focus in this epoch will be on minimising expense and maximising profit. To do so requires greater control over people to create a disciplined society to optimise them as resources. Furthermore, in the technological age, knowledge is subject to the claims of the market, where the focus will be exclusively on the knowledge that has a legitimate value in and impact on the technological epoch. From this perspective, we will argue that Western knowledge will take centre stage, and in the process, little room will be left for the inclusion of indigenous knowledge in the university curricula. We conclude the chapter by showing how the medical responses from government during the COVID-19 pandemic dismissed and disregarded the voices and efforts of indigenous healers in the fight against the pandemic. All these developments illustrate the government’s power to impose its neoliberal agenda on the university that will ultimately be to the detriment of indigenous knowledge.
Artificial Cognitive Computing for Smart Communications, 5G and Beyond

Artificial Intelligence (AI) is computer intelligence that manifests itself in “cognitive” capabilities that people identify with other brains. AI employs various technologies including Deep Learning, Machine Learning and Natural Language Processing. The self-learning systems are utilizing pattern recognition, natural language processing and data mining to replicate the person's brain functions are called cognitive computing. Cloud-based communication has bolstered this by delivering vital communication services. However, due to restricted capacities and a need for low latency, high reliability, and a good user experience, providing a cloud-based environment and intensive data processing algorithms are insufficient. Cognitive computing is considered a branch of computer science that simulates human cognitive processes. As a result, when cognitive science skills are combined with communications and existing systems may be improved, resulting in higher accuracy and lower latency. We have gone through cognition-based communications in depth in this study, which blends smart communication technologies and intelligent computing based on AI. Following is an overview of the cognitive computing and its evolution. Then, combining networking, analytics, and cloud computing, a systematic and comprehensive framework for using cognition in communication is provided.
An End-to-end Framework to Harness Knowledge Graphs for Building Better Models from Data

Readily available data and software tools have turned “data analytics” into a game anyone can play. But genuine, serious modeling and analysis demands prudence; novice model builders may have inadequate knowledge (of either the domain or methods) to make judicious decisions particularly on which input features should be used to learn good models. Consequently, the practice, so called “garbage in, garbage out”, would be entertained. This work introduces an end-to-end model building framework that makes use of background and semantic knowledge in the form of a knowledge graph to automatically determine the relevance of features from certain structural information, helping produce models with semantically pertinent features rather than mere correlations or (potentially spurious) patterns within the data. This proposed framework enables model builders (even non-experts) to build models from data informed by knowledge latent within any knowledge graph. We detail techniques used in our implementation by making use of an existing knowledge graph. To validate our approach, two models were built from a given dataset using our system, and then prediction errors of these models were examined. The results show that the proposed framework helps in improving model generalization, leading to a better prediction performance of learned models on unseen examples.
Low-Light Image Restoration Using Dehazing-Based Inverted Illumination Map Enhancement

This paper proposes an algorithm to restore low-light images by enhancement of its inverted illumination map using dehazing. The proposed algorithm follows the Retinex theory and decomposes the low-light input image into reflectance and illumination components. The illumination map is then inverted and enhanced using dehazing-type algorithm and gamma correction. The enhanced map is again inverted back to obtain the enhanced illumination, and finally, it is combined with the original reflectance to output an enhanced image. The enhanced images obtained using the proposed algorithm have improved contrast and visual quality. The proposed method is analyzed qualitatively on various standard images used in literature and compared quantitatively with several benchmark techniques. It is found that the proposed algorithm is superior over various other benchmarks.
Holistic Evaluation Framework for VR Industrial Training

Effectiveness of Virtual Reality (VR) training in the industrial training is still a controversial issue among industrialists and experts, despite the fact that VR is capable and powerful. To emphasize the ability of industrial VR training, we propose a holistic evaluation framework that proofs effectiveness of using VR technology in industrial training. The framework includes the process of VR content preparation, data collection methods, and data evaluation methods. We collected the trainees’ activity data within the VR system and analyzed it with ANCOVA and Cosine Similarity methods. The results showed that the use of VR technology in industrial training could improve the trainees’ skills significantly.
A Robust Cursor Activity Control (RCAC) with Iris Gesture and Blink Detection Technique

Utilization of computer technology becomes highly difficult and frequently impossible for those with very little or no arm movement. In this study, we suggest an iris-based cursor control system designed specifically for people with physical disabilities. For many years, eye gaze localization has been a particularly prominent area of study. The most sophisticated efforts in this area include Tobi Eyes, Gaze Pointer, and Face Detection using Viola-Jones, although they all have drawbacks including infrared ray health risks and accuracy divergence due to shaky frames. We have created a model to get around these obstacles in our job. In addition, we've designed a hardware design that combines a camera with a wearing cap. Our prototype RCAC makes it easier to execute mouse motions in accordance with the position of the iris. We were 89.8% accurate with our mouse positioning over the user screen during the entire experiment. The entire system was designed for optimal user comfort with the least amount of setup time.
A Novel Autoencoder for Task-Driven Object Segmentation

Salient object segmentation has attracted increasing attention in recent years. Though a lot of works based on autoencoder have been made, these methods can only recognize and segment one class of objects, but cannot segment the other classes of objects in the same image. To address this issue, this paper proposes a novel autoencoder to perform task-driven object segmentation, in which a control signal is added to the decoder to determine which class of objects need to be segmented. What’s more, we establish a Bayesian rule in the decoder, in which the control signal is set as the prior, and the latent features learned in encoder is transferred to corresponding layer of decoder as observation, thus the posterior probability of each object with respect to the specific-class can be calculated, and the objects belonging to this class can be segmented. This proposed method is evaluated for task-driven salient object segmentation on a several benchmark datasets, including DUT-OMRON, ECSSD, DUTS-TE, etc. Experimental results show that our approach tends to segment accurate, detailed and complete objects, and improves the performance compared with the previous state-of-the-arts.
“Gongzhu” Strategy Based on Convolutional Neural Network

“Gongzhu” is a very interesting and challenging poker game, which belongs to the incomplete information game. The game is divided into two stages: card-showing and card-playing. The behavior of card-showing determines the strategy of card-playing, and the whole game process is highly reversible. This paper proposes a deep learning-based game algorithm of “Gongzhu”. According to the functional characteristics, the network structure of card-showing and card-playing are designed respectively. The two networks are both constructed by Convolutional Neural Network (CNN). Using the real game information of 11,000 high-level human players to generate train set and test set proportionally, the two CNN networks are trained and tested respectively. The fitting accuracy between the result predicted by the agent constructed by this algorithm and the labeled data of human players is used as the evaluation indicator. The experimental results show that the fitting accuracy of card-showing and card-playing networks can reach 88.4% and 71.4% respectively. The agent is able to generate reasonable strategies for card-showing and card-playing.
U-YOLO: Improved YOLOv5 for Small Object Detection on UAV-Captured Images

Small object detection on drone-captured images is a recently popular and challenging task. From the drone’s perspective, the object scale varies significantly, and tiny objects lack distinguishable appearance information in complex backgrounds, which leads to undesired effects. To solve the issue mentioned above, we propose U-YOLO based on the original YOLOv5 model. We first extend the multi-scale feature fusion network and add a detection head for tiny objects. Secondly, we integrate the convolutional block attention model (CBAM) in the detection head to focus on the critical region of the feature map. Lastly, an attention feature fusion module based on contextual information is designed to combine local and global contextual details of small objects and attention mechanisms to enhance the multi-scale feature fusion capability. Experiments on dataset VisDrone2021-DET show that U-YOLO not only improves the detection performance on drone-captured scenarios but also has a real-time detection speed. Compared to baseline model (YOLOv5s), the mAP result of U-YOLO is increased from 19.07 to 24.1%, and the detection speed is 39FPS. It provides a good balance between detection accuracy and speed, promoting the progress of small object detection algorithms on UAV platforms.
How to Improve the Quality of Academic Conversations with the Help of Human-Computer Interaction System

It aims to explore how to extract a set of popular academic discussion models based on professional and amateur dialogues from the perspectives of interpersonal communication, human-computer interaction and computer batch processing. The method is as follows: 1. An integrated interdisciplinary theoretical model of the expert himself is unambiguously expressed; 2. The stubborn or stubborn people encountered several major confusions in the cross-border knowledge of ancient and modern Chinese and foreign in the process of trying to make the so-called common-sense paraphrase. 3. The two sides break through many difficulties, that is, a series of cognitive ambiguities or possible misunderstandings, and try to use the intelligent system to study clear word charts to achieve specific cognitive calculation and system analysis. The result is: with the help of several original knowledge maps, the higher education goals of a first-class research university such as advanced knowledge exploration and high-level talent training are applied to the process of socialized cognition, and more satisfactory positive results have been obtained. Its significance is that it not only opens up an online and offline way to popularize intellectual fusion, but also creates a standardized way to explore the active learning and thinking of the people and seek academic advancement.
Squeeze-and-Excitation Block Based Mask R-CNN for Object Instance Segmentation

Deep learning-based methods have taken center stage in image recognition, such as AlexNet and deep learning-based method. At present, Image recognition based on deep learning has been widely used in agriculture, factory automation, automated driving, medical fields and so on. In the fields of automated driving and medical care, the accuracy of the image recognition directly affects human lives. For these reasons, the importance of improving the accuracy of image recognition is clear. In this paper, we focus on instance segmentation tasks. The method used is Mask R-CNN, which is the basis of current state-of-the-art methods. The network structure based on ResNet, and we tried to improve the accuracy by adding Squeeze-and-Excitation Block (SE Block). According to the result of experiments, it is proved that this method has certain advantages for object instance segmentation.
Predictive Generation Trajectory Clustering Model for Google Online Learning: OGMM

The learner-oriented teaching methodology through which the familiarity of cracking an undetermined problem which is restricted for stimulated content is performed. Learners gain knowledge of a subject in a deep manner. The PBL method does not concentrate for explaining the problems by means of a given answer, other than it allows additional desirable abilities and attributes to be shaped. This engages the achievement of details, improved synchronization and link the assembly and communication. Pedagogic systems have been exaggerated universally by the COVID-19 pandemic, and this pandemic is responsible to the complete shutting down of schools, universities and colleges. Online learning comprises courses delivered 100% interactive by postsecondary institutions, except massively open online courses (MOOCs). Compared to conventional courses that work as a brick-and-mortar devoting for school house, online learning or virtual classes offered over the Internet. The approach helps learners to improve the abilities and turns it to opportunity by doing practice. It improves condemnatory assessment, retrieval of anthologized and facilitates continuous knowledge surrounded by a team. This study also explores the downside of PBL by using the online platform with its benefits.
Personality Detection Using Signature Analysis

Every human is having unique personality. Personality plays vital role for a human to be placed in dream position always. Different research work proposed different automated personality prediction system based on different measures like face, behaviour, attitude, GAIT and handwritten signature. Predicting personality using handwritten signature is a difficult problem. Signature has different features to assess the pattern and predict. In this paper, we propose a personality prediction system based on handwritten signature pattern using neural network. We have analysed various features, classify and extracted their values from signatures in the corpus. We devise a computational strategy for minimising mistakes in each class feature value selection for the entire system. We have also conducted a statistical significance test by computing alpha value and improved the accuracy of our proposed system.