Reviewerly/exHarmony huggingface dataset:
Construction of University Sports Flipped Classroom Based on Sports Skills Learning and Mobile Edge Computing-Driven Neural Network

In order to make students more interested in PE learning in PE teaching, it is necessary for PE educators to change the traditional way of thinking, take students as the main body of PE learning, be guided by teachers themselves, and use mobile edge computing and neural network technology as technologies. It enables students to carry out intelligent physical education independently. Whether the teaching is effective does not mean whether the teacher has completed the teaching content seriously, but whether the students have learned something, or whether the students have learned well. Based on the actual teaching practice of flipped sports classroom, this paper takes the “effectiveness” of sports classroom teaching as the breakthrough point, mainly adopts the research methods of comparative analysis, mobile edge computing, and neural network, and combines relevant domestic and foreign research results on the teaching of sports flipped classroom at home and abroad. An in-depth and comprehensive analysis of the effectiveness was carried out. The research results show that it is reliable to build a university sports flipped classroom with a neural network driven by motor skill learning and mobile edge computing as the carrier.
A Granular Access-Based Blockchain System to Prevent Fraudulent Activities in Medical Health Records

Electronic health record (EHR) systems provide patient health information. EHR faces data security, integrity, and management challenges. Records can be modified by different stakeholder, as it can be used by different users in more than one form. Medical records management is an appropriate application for enabled blockchain records that can be stored, tracked, and managed. In the healthcare sector, a novel system has been presented here using the blockchain concept. Initially, the purpose of our proposed framework is to introduce blockchain innovation for EHR and, in addition, to provide safe electronic record functionality by characterizing granular access rules for the proposed system’s customer. The presented framework has been deployed using blockchain technology and checked for its adaptability, safety, and other several necessary support in the system. So, the blockchain technology is used to create an electronic health record system that must be protected from manipulation and misuse.
Introduction to Artificial Intelligence

Humans can accomplish tasks that scientists are still trying to fathom, and such tasks are hard to write algorithms for. Artificial Intelligence programs are thus written in a way that allows these algorithms to learn from data. This makes data quality crucial to the performance of the algorithm. Data exploration and investigation are a must for Artificial Intelligence developers to identify appropriate charts, present data to visualize its core characteristics, and tell stories observed.
Introduction to iOS Forensics

This chapter will provide you with an overview of iOS devices such as iPhones and iPads, as well as an overview of the operating systems and file systems they run. There are many forensic tools that are used in forensic science; these tools are able to handle all forensic process activities. Digital forensic tools for data extraction are categorized into three types: manual, logical, and physical. You will learn after completing this chapter about various third-party tools used for iOS forensics, and you will be able to answer questions about three important topics: the first topic aims to find the difference between acquisition and backup, the second measures and checks the effect of jailbreaking on an iOS device, and the third illustrates the comparison between third-party tools during forensic analysis process.
An Extension of the Neural Additive Model for Uncertainty Explanation of Machine Learning Survival Models

A method for the local and global uncertainty explanations of machine learning survival models by censored data in the framework of survival analysis is proposed. The method aims to select features of an object, which significantly impact on uncertainty of a survival model prediction corresponding to the object. The survival model is viewed as the black box, that is, only the input and the corresponding output are known. The first basic idea behind the method is to approximate the prediction uncertainty by the prediction uncertainty of the extended semi-parametric Cox proportional hazards model using the well-known generalized additive model. The second idea is to apply the neural additive model for getting the Cox approximation and to train the network in accordance with a specific loss function that takes into account the peculiarities of survival models. Results of the uncertainty explanation are represented in the form of shape functions which show the impact or contribution of every feature on the prediction provided by the black-box model. An algorithm implementing the method is presented. Numerical examples illustrate the proposed method by using real datasets including the German Breast Cancer Study Group 2 dataset and the Monoclonal Gammopathy dataset.
The Integrating Face-to-Face Learning, Distance Learning Technologies and M-Learning Technologies: Effectiveness

The process of higher education digitalization is driven by global transition to a digital economy and society. Building a digital economy and digital education are significant priorities of state policy in the Russian Federation. Mobile learning is a modern educational trend that allows knowledge to be acquired anywhere, anytime using portable devices. It has attracted the attention of many researchers from different disciplines who are aware of the high potential of mobile technology to enhance learning. In this research paper we will focus on the issue of improving learning outcomes using mobile technologies. An example will be the work in English as a second language classes in short-term language courses. Information technologies in teaching foreign languages have shown their effectiveness. The technical and psychological readiness of learners to use mobile technologies in learning is analyzed as well. In this research paper, the effectiveness criteria were defined as: providing learners with additional conditions for self-realization; mastering new ways of comprehending life, culture, history in the university and urban space around them, diversity of communication between teacher and students, individualization of the educational process; expanding the arsenal of learning tools, preparedness of pedagogical developments with the use of mobile learning. Teachers and students should no longer be limited to being able to teach and learn at a particular place and time. Mobile Learning and wireless technology will become an everyday part of learning, both inside and outside the auditorium. With the education standardization, mobile technology may be a chance to maintain the personalized approach to learning and bring to life the adage that the whole world is an auditorium. Conclusions and a discussion of these outcomes are offered as well as some inferences and speculation regarding the future of M-Learning in the classroom and beyond.
An effective method for clustering-based web service recommendation

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.
Analysis of Signal Noise Reduction Techniques

The basis of modern communication is elimination of unwanted irregular fluctuations that accompany a transmitted signal but are not part of it, also called noise. The noise tends to obscure important or useful information. Thus, the reduction of signal corruption caused due to additive white Gaussian noise (AWGN) becomes very important in order to establish best transmission of signals. Hence, it is essential to reduce this noise and also recognize the best techniques. This paper is a comparative analysis between three popular noise reduction techniques that are used to filter signals for any distortions that may occur. The analysis of the filter is performed using MATLAB which has the ability to easily simulate filters using the in-build functions. The paper lists three different methods to eliminate the effects of noise in signals. The three methods employed make use of the discrete Fourier transformation (DFT) technique, Gaussian filter method, and least mean square (LMS) algorithm. The final results are drawn based on the correlation coefficient as well as the bit error rate of the original and the filtered signal, and final analysis is drawn.
Fall Detection Using Multimodal Data

In recent years, the occurrence of falls has increased and has had detrimental effects on older adults. Therefore, various machine learning approaches and datasets have been introduced to construct an efficient fall detection algorithm for the social community. This paper studies the fall detection problem based on a large public dataset, namely the UP-Fall Detection Dataset. This dataset was collected from a dozen of volunteers using different sensors and two cameras. We propose several techniques to obtain valuable features from these sensors and cameras and then construct suitable models for the main problem. The experimental results show that our proposed methods can bypass the state-of-the-art methods on this dataset in terms of accuracy, precision, recall, and F1-score.
TRHyTE: Temporal Knowledge Graph Embedding Based on Temporal-Relational Hyperplanes

Temporal Knowledge Graph Embedding (TKGE) aims at encoding evolving facts with high-dimensional vectorial representations. Although a representative hyperplane-based TKGE approach, namely HyTE, has achieved remarkable performance, it still suffers from several problems including (i) ignorance of latent temporal properties and diversity of relations; (ii) neglect of temporal dependency between adjacent hyperplanes; (iii) inefficient static random negative sampling method; (iv) incomplete testing on partial time information. To address these issues, we propose TRHyTE, a novel Temporal-Relational Hyperplane based TKGE model, which defines three typical properties, including interval, open-interval, and instantaneousness temporal, for relations and correspondingly constructs three relational sub-KGs, supporting distinguishing learning for facts. Within each sub-KG, TRHyTE transforms entities into relation space first, and then explicitly projects transformed entities and relations into temporal-relational hyperplanes to learn time-relation-aware embeddings. Moreover, Gate Recurrent Unit is leveraged to simulate TKG evolution so as to capture temporal dependency between adjacent hyperplanes. Additionally, we develop a dynamic negative samples mechanism for robust training. In testing phase, an expand-and-best-merge strategy is crafted to realize a complete testing on all valid time intervals. Extensive experiments on two well-known benchmarks verify the effectiveness of our proposals.
Small-Scale Aware SSD: Improvement for Small-scale Pedestrians Detection

In the field of autonomous driving, pedestrian detection in areas of interest requires fast detection speed and high accuracy, which is of great significance for ensuring the safety of pedestrians. In this study, MobileNet was selected as the backbone network of the SSD model to reduce the parameter amount and forward propagation time. After feature fusion of the Conv4_3 layer and Fc7 layer of the SSD, the RFB-s module is added and the RFB module is added after MobileNet to improve the detection accuracy of small-scale pedestrians. The experimental results on the Caltech pedestrian data set show that compared with the original algorithm, the accuracy rate is increased by 11.29%, and the amount of calculation is reduced by 80%.
Data-Driven Fuzzy Target-Side Representation for Intelligent Translation System

The encoder–decoder framework has been widely used in various practical artificial intelligence cyber-physical systems, including intelligent translation systems. The decoding process in such a framework usually demands the target-side representation, which is often learned by an autoaggressive decoder to simulate the target context information at the current time-step. However, the autoaggressive decoder only captures the previously generated partial target fragment and fails in simulating the global contextual information. In this article, we propose a new data-driven fuzzy context representation strategy to simulate the global target information. Specifically, we design two fuzzy methods to the global target contextual information, which are bag-of-words of target language generated via a softmax layer from the source-side representation and whole target sentence retrieved from the translation memory according to the source-side representation. Both methods facilitate the autoaggressive decoder to handle the global target context at the current time-step, thereby learning a more effective context vector for the generation of target translation. Extensive experiments on two machine translation tasks demonstrated that the proposed method achieved 3% improvement of BLEU score over a strong baseline.
Improving Core Path Reasoning for the Weakly Supervised Knowledge Base Question Answering

Core Path Reasoning (CPR) is an essential part of the knowledge base question answering (KBQA), which determines whether the answer can be found correctly and indicates the reasonableness of the path. The lack of effective supervision of the core path in weakly supervised KBQA faces great challenges in finding the correct answer through long core paths. Furthermore, even if the correct answer is found, its path might be spurious that is not semantically relevant to the question. In this paper, we focus on solving the CPR problem in weakly supervised KBQA. We introduce a CPR model that aligns questions and paths in a step-by-step reasoning manner from explicit text semantic matching and implicit knowledge bases structure matching. Additionally, we propose a two-stage learning strategy to alleviate the spurious path problem efficiently. We first find relatively correct paths and then use hard Expectation-Maximization to learn the best matching path iteratively. Extensive experiments on two popular KBQA datasets demonstrate the strong competitiveness of our model compared to previous state-of-the-art methods, especially in solving long path and spurious path problem.
Positive Unlabeled Learning with a Sequential Selection Bias

In important domains from video stream analytics to human context recognition, datasets are only partially-labeled. Worse yet, the labels are often applied sequentially, as annotators choose labels frame-by-frame or timestep-by-timestep in sequence. With labels not collected independently, this results in sequential bias in the labeling. Unfortunately, current state-of-the-art methods for partially labeled data are rendered ineffective under sequential bias. In this work, we propose a novel solution to tackling this open sequential bias problem, called DeepSPU. DeepSPU recovers missing labels by constructing a model of the sequentially biased labeling process itself. This labeling model is then learned jointly with the prediction model that infers the missing labels in an iterative training process. Further, we regulate this training using a theoretically-justified cost functions that prevent our model from converging to incorrect but low-cost solution. Our experimental studies demonstrate that DeepSPU consistently outperforms the state-of-the-art methods by over 10% on a rich variety of real-world datasets.
Deep Image Classification Model Based on Dual-Branch

The performance of deep learning in the field of computer vision is better than traditional machine learning technology, and the image classification problem is one of the most prominent research topics. The methods of computer vision are used in industry production. We proposed an image classification model which segments the image at different scales on the basis of Deep-ViT to obtain image information of different scales. When this model is applied to the classification of tube head shapes, the accuracy rate can reach more than 90%, and towards the classification of tube head materials, the accuracy is about 98%.
Sentiment analysis using global vector and long short-term memory

Tweet sentiment analysis is a Deep Learning study that is beneficial for automatically determining public opinion on a certain topic. Using the Long Short-Term Memory (LSTM) algorithm, this paper aims to proposes a Twitter analysis technique that divides Tweets into two categories (positive and negative). The Global Vector (GloVe) word embedding score is used to rate many selected words as network input. GloVe converts words into vectors by building a corpus matrix. The GloVe outperforms its prior model, owing to its smaller vector and corpora sizes. GloVe has a higher accuracy than the model word embedding word2vec, Continuous Bag of Word(CBoW), and word2vec Skip-gram. The preprocessed term variation was conducted to test the performance of sentiment classification. The test results show that this proposed method has succeeded in classifying with the best results with an accuracy of 95.61%.
Secured Different Disciplinaries in Electronic Medical Record based on Watermarking and Consortium Blockchain Technology

The Electronic Medical Record (EMR) is a valuable source of medical data intelligence in ehealth systems.The watermarking techniques have been used to authenticate the owner and protect the EMR from illegal copying.The existing distributive strategies, successfully operated to secure the EMR, are found to be inadequate.Blockchain technology, mainly, is employed by a sharing database that allows the digital crypto-currency.It rapidly leads to the magnified expectations acme.In this excitement, the use of consortium adopting the technology based on Blockchain, in the EMR structure, is found improving.This type of consortium adds an immutable share with a translucent record of the entire business and it is accomplished with responsibility, along with faith and transparency.The combination of watermarking and Blockchain technology provides a singular chance to promote a secured, trustworthy electronic documents administration to share with the e-records system.The authors, in this article, present their views on consortium Blockchain technology which is incorporated in the EMR system.The ledger, used for the distribution of the block structure, has team healthcare models based on dissimilar multiple image watermarking techniques.
A two-stage text detection approach using gradient point adjacency and deep network

Scene text detection is a pivotal problem in computer vision and image processing research. In this paper, a fair attempt is made to design a simple yet effective text detection method in Indic script environment. At first, a fine-scale edge-map is generated from the original image, and subsequently, adaptive clustering is applied to form clusters of edge-points based on their spatial density. Foreground objects are extracted with the help of cluster boundaries and considered as prospective text proposals. Such text proposals are fed to a deep convolutional neural network for learning and prediction as text and non-texts. Finally, true-text components are aggregated as localised final texts of the original image. The proposed method is evaluated on two benchmark datasets viz. ICDAR 2017-MLT and ICDAR 2013 born image, and obtained results are found to surpass some other state-of-the-art methods, which demonstrates its effectiveness in scene and digital environments.
EREBA

Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determining the robustness of a DNN with respect to its energy consumption (energy robustness) is relatively unexplored compared to accuracy-based robustness. This work investigates the energy robustness of Adaptive Neural Networks (AdNNs), a type of energy-saving DNNs proposed for many energy-sensitive domains and have recently gained traction. We propose EREBA, the first black-box testing method for determining the energy robustness of an AdNN. EREBA explores and infers the relationship between inputs and the energy consumption of AdNNs to generate energy surging samples. Extensive implementation and evaluation using three state-of-the-art AdNNs demonstrate that test inputs generated by EREBA could degrade the performance of the system substantially. The test inputs generated by EREBA can increase the energy consumption of AdNNs by 2,000% compared to the original inputs. Our results also show that test inputs generated via EREBA are valuable in detecting energy surging inputs.
Fast Implementation of the Rivest-Shamir-Adleman (RSA) Algorithm with Robust Packet Data Loss Detection Function

Encryption is the process of protecting information from unauthorized parties by converting such information into an unreadable form. Packet data is a method of transferring data, broken into bits called packets which travels over a network. Packet losses occur when packets fail to reach their destination devices as a result of network congestion, faulty routers or as a result of an attack. Encryption is the standard method for making a communication private. In sending a private message to another user, it is first encrypted (termed encipher), the intended recipient alone knows how to correctly decrypt (decipher) the message. There are several algorithms developed for the purpose of encryption which provides data security and integrity. This paper proposes the use of the Rivest-Shamir-Adleman (RSA) algorithm to implement a system for encrypting text files of any length (by breaking long messages into valid blocks and encrypting each block) capable of being transmitted using a Simple Mail Transfer Protocol (SMTP). Probable primes, 3048 bits in length are generated to be used in the generation of public, private key pairs for encryption and decryption. The proposed scheme is better because the route taken during transmission of data is recorded and packet losses are also checked for during transmission of encrypted files as compared to known state-of-the-art schemes.