Reviewerly/exHarmony huggingface dataset:
The Exploration of Constructing College English Multimedia Network Teaching Mode

With the maturity of multimedia technology, modern teaching methods and reforms have become the main content of my country's education reform. While the traditional English classroom teaching in our country has successfully met the new environment and opportunity of the computer Internet, it is also facing severe challenges. Although there are many researches on the construction of college English multimedia network teaching mode, the English teaching method is slightly single. This article uses the methods of literature analysis and sample survey, combined with the characteristics of multimedia technology, analyzes the role of multimedia network technology in English classrooms, and further illustrates the importance of multimedia network teaching methods in college English teaching. The analysis results show that 23.1% of people think that the factor that affects the development of multimedia online English teaching is the degree of leadership attention, followed by 19.6% of people think it is the teaching management system.
Managing ICT: Administration, Evaluation, and Policymaking

This chapter deals with ICT platforms, software to choose (Do’s and Don’ts), evaluating ICT with special reference to principles of multimedia evaluation, and ICT Policy. For ICT Policy, the areas that are specifically explored include, safeguarding and maximizing the potential of ICT resources. The chapter ends with an example of ICT Policy for guiding the teachers on managing an ICT framework within the school.
Unbalanced Data Classification Algorithm Based on Hybrid Sampling and Ensemble Learning

Due to the inherent characteristics of unbalanced data sets, the classification results are often affected by a large number of categories, resulting in the problem of low prediction accuracy of a few classes in the classifier. This paper proposes an unbalanced data classification algorithm based on hybrid sampling and ensemble learning—ESBagging. Firstly, the undersampling algorithm ENN is used to eliminate the samples affecting the classification decision boundary, and then the SMOTE algorithm is used to further mitigate the reduced data sets to make the data sets reach the balanced state. Finally, the Bagging ensemble learning framework is used to extract subdata sets from the data sets, and then multiple basic classifiers are obtained based on these sub-data sets, Then, a relatively better prediction model is obtained by combining the classification results of the basic classifier. Taking 10 groups of KEEL imbalanced data set as experimental data, ESBagging, BalancedBagging, and SMOTEBagging were compared. The experimental results show that ESBagging obtains the highest evaluation index in 5 groups, 7 groups, and 7 groups of data sets under F1-measure, G-mean, and AUC criteria respectively. It can be seen that the proposed algorithm has better classification performance on unbalanced data.
Video Call-Based Real-Time Crime Reporting System

Most of the crimes that happen these days don't reach the police either because nobody reports them or there is no real time proof. The citizens don't have the real-time images, videos related to the crime, and hence, it goes unreported. This chapter presents a real-time video call-based crime reporting system, where the crime victim or an eye witness can start an end-to-end encrypted video call with the police present in the nearest police station. A data science approach is used to find the nearest police station. Once the police officer picks up the call, he will get the location of the crime and nearest route to reach the crime spot. Police can even catch the criminals or handle the crime while it is happening. After the video call ends, a recording of that call will also be available to the police.
PrimeNet: Adaptive Multi-Layer Deep Neural Structure for Enhanced Feature Selection in Early Convolution Stage

The colossal depths of the deep neural network sometimes suffer from ineffective backpropagation of the gradients through all its depths, whereas the strong performance of shallower multilayer neural structures proves their ability to increase the gradient signals in the early stages of training, which easily gets backpropagated for global loss corrections. Shallow neural structures are always a good starting point for encouraging the sturdy feature characteristics of the input. In this research, a shallow, deep neural structure called PrimeNet is proposed. PrimeNet is aimed to dynamically identify and encourage the quality visual indicators from the input to be used by the subsequent deep network layers and increase the gradient signals in the lower stages of the training pipeline. In addition to this, the layer-wise training is performed with the help of locally generated errors, which means the gradient is not backpropagated to previous layers, and the hidden layer weights are updated during the forward pass, making this structure a backpropagation free variant. PrimeNet has obtained state-of-the-art results on various image datasets, attaining the dual objective of: (1) a compact dynamic deep neural structure, which (2) eliminates the problem of backward-locking. The PrimeNet unit is proposed as an alternative to traditional convolution and dense blocks for faster and memory-efficient training, outperforming previously reported results aimed at adaptive methods for parallel and multilayer deep neural systems.
Combining Minimal Surface Model with DGCNN for Natural Language Understanding

Fine-tuning large pre-trained models is an adequate transfer mechanism in Natural Language Processing (NLP). However, it is challenging to serve in resource-restricted devices due to being computationally expensive. To reduce the trainable parameters of models while maintaining accuracy, we propose a novel geometry deep learning approach named Minimal Surface Model on DGCNN (MSM-D). In MSM-D, the minimal surface model is introduced as an adapter layer to convert the word vector into high-dimensional manifold data, an input to the point cloud model. MSM - D is empirically adequate and achieves close or even better performance of BERT-base on GLUE benchmark while being 5.7x smaller on inference.
“Literacy from Python”

This pilot case study, which outlines an introductory computer programming project, using Python, builds on the international success of the “Literacy from Scratch” teaching model [1–3]. Its aim is to contextualise the teaching of Python through a cross-curricular teaching and learning model, rather than through teaching Python elements separately. It therefore recommends the development of elementary coding skills by pupils, aged from 10 to 14 years, through the creation of very short stories or scripts, using Python strings. Each story chapter of the project further introduces worked examples of additional Python programming elements such as Turtle graphics and times-tables, within the constructionist framework of a creative writing project [4]. The project is supported by a free website for teachers. Our hope is that this creative approach to coding may encourage the enthusiastic adoption of such a cross-curricular method for teaching and learning computer coding with Python in our schools.
Automatic Information Extraction: A Distant Reading of the Brazilian Historical-Biographical Dictionary

We present some results of applying natural language processing (NLP) techniques in the domain of History, having as object of investigation the Brazilian Historical-Biographical Dictionary (Dicionário Histórico-Biográfico Brasileiro, DHBB). After improving or adding annotation of specific fields, information extraction techniques based on manually derived patterns were applied to three relevant problems: the age of entrance in Brazilian politics, the academic background of Brazilian politicians, and family ties among the political elites.
Semiotic and Thematic Process for Audiovisual Documents Description

The extraction of description is important in audio-visual document retrieval. Although various approaches were proposed, extracting semiotic description that reflect the content knowledge remains a challenging task. This paper proposes an approach for describing external and a semiotic description of audio-visual document. The main objective is to associate a set of semiotic descriptions to each filmic segment in order to facilitate the interrogation of huge quantities of filmic document. The extraction description process relies mainly on the probabilistic LDA model and Dublin Core.To identify the description, a combination method applied both weighing score and position of each extracted theme. Qualitative and quantitative experiments were performed to evaluate the effectiveness of the proposed approach. The performance of the extracted semiotic description is better than that of the descriptions presented in the literature. The good results were obtained due to good choice of the information sources, the adaptation of the LDA and the importance of the identification process.
Drowsiness Estimation of Drivers Using Echo State Networks

Auto-estimation of drowsiness of a vehicle driver is an urgent problem, as more than 15% of traffic accidents are caused by careless driving in Japan. This paper considers drowsiness estimation based on the measurements of the heartbeat, respiration, and the center of pressure on the seat. As the learning model for the prediction, we employ an echo state network (ESN). An ESN is one of the recurrent neural networks (RNNs) that solves the problem of the high computational cost of ordinary RNNs by restricting the tunable parameters. As drowsiness at a time is dependent on the previous time, i.e., the system has dynamics, RNNs will be suitable models for drowsiness estimation. Furthermore, because of their low computational cost, ESNs are a promising candidate model for driver drowsiness estimation. The validity of the proposed method is tested by several persons' data.
Substation Equipment Defect Identification Method Based on Mask R-CNN Algorithm

The structure of power system power network is huge and there are many terminal devices, which is in line with the characteristics of edge computing. It is necessary to use the image recognition algorithm for edge computing to solve the problem of defect recognition of substation equipment. This paper presents a substation equipment defect identification method based on mask r-cnn algorithm. Firstly, the equipment and related defects of substation are introduced. By comparing the defect characteristics and horizontal comparison of algorithms, mask r-cnn is selected as the core algorithm of this paper, and the optimized gfpn network of mask r-cnn is introduced. Finally, the field images are used to test the proposed method to verify its feasibility and accuracy.
Cyber Security Competency Model Based on Learning Theories and Learning Continuum Hierarchy

There is an urgent need for transformative changes in cyber security awareness and training programs to produce individuals and the workforce that can deal with business risks emanating from the prevailing and emerging cyber-attacks. This chapter proposes a cyber security competency model that integrates learning theories (cognitive, affective, and psychomotor), learning continuum hierarchy (awareness and training), and cyber security domain knowledge. Employing literature search of scholarly and practitioner works, together with cyber security standards from governmental and non-governmental organizations, the chapter integrates cyber security domain knowledge, learning theories, and learning continuum hierarchy to design a model of cyber security competencies suitable for use in educating individuals and the general workforce. This theoretical-based approach to designing cyber security awareness and training programs will produce skillful individuals and workforce that can mitigate cyber-attacks in the global business environment.
A Brand-New Method to Solve Rotated Template Matching Using Metaheuristic Algorithms and Ssim

Template matching is a technique used in artificial vision to recognize specific parts within an image, it is used in different areas: Quality control, robotics, even for security by recognition of people or objects. There are metrics in the literature that aid solving this problem, but when image rotation is added to it, it becomes very complex since it increases multiplicatively the number of comparisons that have to be made of the template with the original image. On the other hand, metaheuristic algorithms have been used over the last decades in various areas of engineering to obtain adequate solutions to complex problems. This paper presents a technique to solve the rotated TM problem using SSIM as a metric with metaheuristic algorithms, by using guided search techniques the number of comparisons is reduced. In this work, three different experiments were carried out to compare the efficiency according to the images, the rotation and their use in problems with real photographs. The results showed that the technique is useful without the need for so many comparisons.
Metadata and Semantics Metadata: Contemporary Discussions in the Digital Context

This article discusses the international scientific panorama on semantic metadata using bibliometric indicators and the positioning of Information Science concerning the theme. Bibliographic research and bibliometric studies provided the opportunity to identify indicators of scientific production on semantic metadata to show how this theme is addressed in the international scenario. Bibliometric indicators on semantic metadata are presented as results related to annual production, areas of knowledge, types of documents, main sources of publication and keyword clustering analysis. It can be concluded that the bibliographic production on semantic metadata is still not significant in the Information Science research agendas, which presupposes the development of research that considers theoretical and methodological constructs on semantic metadata in the current technological context.
A Steganography Model Data Protection Method Based on Scrambling Encryption

At present, the image steganography method based on CNN has achieved good results.The trained model and its parameters are of great value.Once leaked, the secret image will be exposed.To protect the security of steganographic network model parameters in the transmission process, an idea based on network model parameter scrambling is proposed in this paper.Firstly, the sender trains the steganography network and extraction network, encrypts the extraction network parameters with the key shared by the sender and the receiver, then sends the extraction network and parameters to the receiver through the public channel, and the receiver recovers them with the key after receiving, to achieve more secure secret communication.In this way, even if the network parameters are intercepted by a third party in the transmission process, the interceptor cannot extract the real secret information.In this paper, the classical Joseph algorithm is used as the scrambling algorithm to scramble the extracted network model parameters of the StegoPNet steganography network.The experimental results show that when the scrambled parameters are used for secret image extraction, a meaningless image independent of the secret image is extracted, it shows that this method can well protect the security of steganography network model.At the same time, this method also has good scalability, and can use a variety of different scrambling algorithms to scramble the parameters.
Rvc: An Reputation and Voting Based Blockchain Consensus Mechanism for Edge Computing-Enabled Iot Systems

By deploying edge servers around devices, edge computing brings computing resources far away from the cloud center close to the Internet of Things (IoT), which reduces latency and promotes the rapid development of IoT. Since edge servers and devices (hereafter referred to as NODEs) are highly scattered, blockchain is becoming one of the most promising solutions to enhance security issues for IoT. In the blockchain, a consensus mechanism determines how to achieve an agreement among nodes, hence it is an essential element for the operation and efficiency of the blockchain. However, due to the exponentially increasing of nodes in IoT, traditional mechanisms consume much more time and finally affect the transaction throughput of the IoT served. In this paper, assisted with edge servers, a Reputation and Voting based Consensus mechanism (RVC) is proposed. To reduce the time consumption of the consensus process, RVC adopts a reputation evaluation algorithm without complex hash calculations to select block proposers, which both consider the behaviors in edge computing and blockchain consensus. To prevent malicious nodes from participating in consensus, a filtering algorithm is designed for RVC, which can detect and filter nodes with malicious behaviors. Simulation results show that, compared with the PoS, PBFT, RAFT, and IBFT, the time consumption of RVC is reduced by 22%, 76.2%, 58.3% and 66.4%, respectively. In terms of successful consensus rate, RVC outperforms above works by 74.8%, 67.4%, 41.4%, and 67.4% respectively. When the network scale and the proportion of malicious nodes change exponentially, RVC shows good scalability in terms of time consumption, successful consensus rate and transaction throughput.
Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting

Fine-tuning attacks are effective in removing the embedded watermarks in deep learning models. However, when the source data is unavailable, it is challenging to just erase the watermark without jeopardizing the model performance. In this context, we introduce Attention Distraction (AD), a novel source data-free watermark removal attack, to make the model selectively forget the embedded watermarks by customizing continual learning. In particular, AD first anchors the model's attention on the main task using some unlabeled data. Then, through continual learning, a small number of lures (randomly selected natural images) that are assigned a new label distract the model's attention away from the watermarks. Experimental results from different datasets and networks corroborate that AD can thoroughly remove the watermark with a small resource budget without compromising the model's performance on the main task, which outperforms the state-of-the-art works.
How to Build Artificial Intelligence Algorithms for Imaging Applications

Recent fervor surrounding the use of artificial intelligence (AI) for radiology applications has been driven by the use of convolutional neural networks to greatly improve performance on a variety of imaging tasks. In this chapter, we give an overview of how to build an AI algorithm for imaging purposes with a focus on convolutional neural networks. We will discuss common types of imaging tasks, basic convolutional neural network components, common neural network architectures used for each type of imaging task, loss functions, and basics of training. We also discuss several more advanced concepts of algorithm design in addition to selection of deep learning libraries and hardware.
Software Cost Estimation and Capability Maturity Model in Context of Global Software Engineering

Software cost estimation is the process of forecasting the effort needed to develop the software system. Global software engineering (GSE) highlights that software development knows no boundaries and majority of the software products and services are developed today by globally-distributed teams, projects, and companies. The problem of cost estimation gets more complex if the discussion is carried out in the context of GSE, which has its own issues. Temporal, cultural, and geographical distance creates communication and software process implementation issues. Traditional software process models such as capability maturity model (CMM) lacks the dynamism to accommodate the recent trends in GSE. The chapter introduces GSE and discusses various cost estimation techniques and different levels of CMM. A couple of GSE-based case studies having CMM-level projects from multiple organizations are studied to analyze the impacts of highly mature processes on effort, quality, and cycle time.
Visualization Enhancement of Parallel Coordinates Plot Based on Fisher Score and Laplacian Score

Over the past decades, high-dimensional data visualization analysis has always been a hot topic in the field of data science. PCP (Parallel Coordinate Plot) is a very commonly utilized tool in the field of data analysis. To be specific, each feature of the dataset can be illustrated in a Cartesian Coordinate System. To complete the recording on one data from a dataset onto the chart, one needs to find the numerical value of each feature belonging to one data on each feature axis and connect those points on each feature axis together. However, when using PCP to deal with and analyze a large amount of data and features, overlapping and crossing between segments would strongly affect the visualization performance of the chart and therefore increase the difficulty of data analysis. To address such issue, this paper presents a visualization enhancement method that can reorder feature axes on the plot and remove unnecessary feature axes automatically. To reorder and remove feature axes automatically in PCPs, we employed the Fisher score and Laplacian score to reorder features based on the corresponding weight. By comparing the visualization result of reordering for each method, features with low priority among the reordering result of both methods can be observed. After using this method on PCP, the visualization performance of PCPs considerably improved, which demonstrates that the methods based on feature selection are beneficial to optimize the PCP performance.
Malicious URL Detection Using Streaming Feature Selection

Malicious uniform resource locators (URLs) are one of the most critical cybersecurity challenges. Attackers are constantly working on new ways to generate malicious URLs. Attackers also constantly update the methods they use to trick systems that detect malicious URLs. This paper proposes a system that can detect these updates using streaming feature selection (SFS) algorithms. First, we built classification models by experimenting with 117 features, using the Online Streaming Feature Selection (OSFS) and Fast-OSFS algorithms. OSFS chose six features, and Fast-OSFS chose sixteen features. We then trained the models using the machine learning algorithms k-nearest neighbors (k-NN), support vector machine (SVM) and artificial neural network (ANN). The results were highly accurate (98.03%) when OSFS with k-NN was used. Additionally, accuracy increased slightly when algorithm Fast-OSFS was used, where Fast-OSFS with k-NN gave an accuracy of (98.73%).
A Comparative Study of Immersive Experiences in Entertainment-Education Systems

New technologies are increasingly being used in the pedagogical field in this technological era, and they are becoming a vital part of learning. Indeed, there is an intensifying need to research new educational strategies and innovative training paths that transform and facilitate learning processes by combining knowledge acquired with the Latin motto ludendo docere. In this context, we conducted a study on teaching strategies in immersive educational environments to understand better their ability to enhance learning by making it more immediate and animated through active participation. In order to compare and evaluate different learning methodologies, three Entertainment-Education systems were proposed and developed: a multi-platform Web App, a multimedia Exhibit, and an eBook. Teachers who utilized our systems were asked to fill out a survey to evaluate them. The results show that the suggested solutions promoted improved awareness and collaboration in group activities, resulting in considerable improvements in teaching and learning processes.