Reviewerly/exHarmony huggingface dataset:
Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

At present, a patient's demography, such as name, age, and gender are stored separately from the acquired electrocardiogram (ECG) signal. This multiple storage mechanisms can create a severe threat to the reliability of diagnostics if the link between the demography data and the ECG signal breaks, either intentionally or unintentionally. This issue has become more prominent in recent years due to the use of a large number of wearable devices for physiological signal collection, especially in remote or non-clinical settings. In order to address this problem, in this paper, we propose a novel mechanism to embed patient's information within an ECG signal without degrading the accuracy of the physiological information contained in the ECG signal. In this work, a methodology is presented to find the less-significant region of the ECG signal. Then, the patient information is hidden in this region by modifying the selected discrete cosine transform (DCT) coefficients of the signal using our proposed embedding and decoding algorithms. Moreover, the patient information hidden in the ECG signal is able to resist filtering attack, such as high-pass filtering, which generally occur with the ECG signal processing. This is achieved via the use of error buffers in the embedding algorithm. The proposed mechanism can extract the embedded patient information, either in the presence or without the filtering attack. Moreover, a specifically designed synchronization sequence is added to identify the patient data embedded regions of the ECG signal at the decoding end. Further, as a security measure, the embedded patient details are scrambled using a secret key to protect the privacy of the patient. Our evaluation demonstrates the usefulness of the proposed methodology in successfully embedding the patient information without distorting the important medical information in an ECG signal.
The Training and Use of ICT in Teaching Perceptions of Melilla’s (Spain) Mathematics Teachers

Currently, information and communication technology (ICT) has acquired an important role in education. The incorporation of ICT by mathematics teachers has been a challenge as it requires an improvement in their knowledge of the use of new technologies. We aimed to examine the most relevant items of the indicators related to teaching mathematics and ICT, to estimate the relationships between teachers individually and by clusters, and to analyze the incidence of age variables, teaching experience, and gender in this study. A total of 73 high school teachers were included in this cross-sectional study. A validated questionnaire was used to assess mathematics and teaching practice, use, resources, and domain of ICT. Principal component analysis (PCA) was applied to determine the associations among variables of the present study. The application of ICT to teaching was associated with the use of ICT resources in the classroom and indicated that teachers made more choice in selecting a variety of software than electing different hardware devices. Teachers combined technology with educational platforms to improve student learning in mathematics. The general perception of mathematics teachers about their digital competence does not match with models employed in the classroom. Gender was not an influencing factor.
School + Family Community Learning Model of PE Course under COVID-19 Epidemic Situation

Influenced by COVID-19 epidemic, students cannot learn PE course offline, and online live streaming becomes a main method under the epidemic situation. How to guarantee students’ learning quality at home and communication among students, parents and teachers is a matter of concern. Besides, traditional PE teaching model also has many defects, and students’ dependence on the teacher is strong. On this basis, the school + family community learning model was constructed in this study based on big data technique and online teaching under the COVID-19 epidemic situation. This model takes the collaboration among students, parents and teachers as the core and key, aims to improve teachers’ online teaching effect and students’ online learning quality, including four stages: connection stage, adaptation stage, proficiency stage and integration stage. In addition, this model was applied in PE teaching in combination of cooperative learning. Meanwhile, the main first-level evaluation indexes of the model were developed, including teaching and resource platform, infrastructure, environment and layout, IoT application, intelligent recording and playing, training scheme, intelligent control and perception. The teaching effect was verified by the questionnaire method, and the result demonstrates that the teaching model improves both teachers’ online teaching capability and students’ online learning quality.
TZ-MRAS: A Remote Attestation Scheme for the Mobile Terminal Based on ARM TrustZone

With the widespread use of mobile embedded devices in the Internet of Things, mobile office, and edge computing, security issues are becoming more and more serious. Remote attestation, one of the mobile security solutions, is a process of verifying the identity and integrity status of the remote computing device, through which the challenger determines whether the platform is trusted by discovering an unknown fingerprint. The remote attestation on the mobile terminal faces many security challenges presently because there is a lack of trusted roots, devices are heterogeneous, and hardware resources are strictly limited. To ARM’s mobile platform, we propose a mobile remote attestation scheme based on ARM TrustZone (TZ-MRAS), which uses the highest security authority of TrustZone to implement trusted attestation service. Compared with the existing mobile remote attestation scheme, it has the advantages of wide application, easy deployment, and low cost. To defend against the time-of-check-to-time-of-use (TOC-TOU) attack, we propose a probe-based dynamic integrity measurement model, ProbeIMA, which can dynamically detect unknown fingerprints that generate during kernel and process execution. Finally, according to the characteristics of the improved dynamic measurement model, that is, the ProbeIMA will expand the scale of the measurement dataset, an optimized stored measurement log construction algorithm based on the locality principle (LPSML) is proposed, which has the advantages of shortening the length of the authentication path and improving the verification efficiency of the platform configuration. As a proof of concept, we implemented a prototype for each service and made experimental evaluations. The experimental results show the proposed scheme has higher security and efficiency than some existing schemes.
Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolution Neural Network

Deep learning methods have achieved amazing results in sequential input, prediction and image classification. In this study, we propose image transformation of time series crude oil price by incorporating 2-D Directed Acyclic Graph to Convolutional Neural Network (DAG) based on image processing properties. Crude oil price time series is converted into 2-D images, utilizing 10 distinctive technical indicators. Geometric Brownian Motion was utilized to produces data for a 10-day time span. Thus, 10x10 sized 2-D images are constructed. Each image is then labelled as Buy or Sell depending on the returns of the time series. The results show that integrating DAG with CNN improves the prediction accuracy by 14.18%. DAG perform best with an accuracy of 99.16%, sensitivity of 100% and specificity of 99.19%. COVID-19 has negatively affected Nigeria crude oil price which indicates a downward trend of crude oil price. The study recommends poly-cultural economy of Nigeria economy for national development of the nation.
Fault-Tolerant Privacy-Preserving Data Aggregation for Smart Grid

In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:math>) threshold privacy-preserving data aggregation scheme named (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:math>)-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>n</mml:mi></mml:math> smart meters (SMs) through the threshold secret sharing algorithm. When <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi>k</mml:mi></mml:math> or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>k</mml:mi></mml:math> SMs cannot recover the key. Thus, our solution still works functionally even if up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mi>n</mml:mi><mml:mo>−</mml:mo><mml:mi>k</mml:mi></mml:math> SMs fail; also, it resists attacks from the collusion of less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mi>k</mml:mi></mml:math> SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.
A Secure and Privacy‐Preserving Watermark Based Medical Image Sharing Method

The patients' medical image data are one of the most important data in e-health. Medical image data usually play a crucial role in disease diagnosis and implicate unpredictable potential values for improving diagnostic methods and adjusting diagnostic results. To exploit their incredible potential values, medical images need to be shared among different hospitals, medical institutions and insurance companies and others. But how to securely and effectively share these medical image data becomes a challenging problem. In this paper, we proposed to combine encryption and digital watermark technology to achieve a secure and privacy-preserving medical image sharing method. The QR code image of the concatenation of authoritative diagnosis results and the hash of the original medical image is generated as the watermark image. The Discrete cosine transform (DCT) and Inverse DCT (IDCT) algorithms are utilized to embed the watermark image. As the watermarked medical images are desensitized, they are stored to a smart contract based blockchain, such as Ethereum, to achieve secure and fair sharing between data owners and users. The experimental results show that the proposed method can resist several attacks meanwhile it is efficient in medical image sharing.
The Effect of Valence and Arousal on Virtual Agent's Designs in Quiz Based Multimedia Learning Environment

Virtual agents are animated life-like characters generally used in virtual learning environments to facilitate learning tasks.With virtual agent, students can hold meaningful interactions throughout the learning process for more effective cognition.Hence, the effectiveness of virtual agent in term of promoting positive emotions is very much related to character realism influence.The level of realism of virtual agent may cause distress to the users, especially when the character mimics like human which was based on the uncanny valley phenomenon.For that, four different realism designs of virtual agents (realistic, semi-realistic, stylized and cartoon-like agents) in Quiz based Multimedia Learning Environment (Q-MLE) had been developed as experimental items and tested as experimental items to analyze emotions in the dimension of valence and arousal.Quasi-experimental design was used to answer the research questions derived and the data obtained was analysed using ANOVA and post hoc.The experiment was carried out on 600 electric engineering students from seven polytechnics of Malaysia.The experiment was carried out with 600 Electrical Engineering students from seven polytechnics in Malaysia.Students were divided into four different groups where each group consisted of 150 students who underwent four different realism designs of the virtual agents respectively.It has been found that the four different realism designs of virtual agents fall at high affective state with high arousal and positive valence.Consequently, the designed virtual agents escaped from uncanny valley effect.
Diagnosis of Breast Cancer Using Improved Machine Learning Algorithms Based on Bayesian Optimization

Breast cancer is one of the most common types of cancer and is the second main cause of cancer death in females.Early detection of breast cancer is crucial for the survival of a patient as well as for the quality of life throughout cancer treatment.The aim of this study is to develop improved machine learning models for early diagnosis of breast cancer with high accuracy.In this context, a performance comparison of machine learning algorithms including Support Vector Machines, Decision Trees, Naive Bayes, K-Nearest Neighbor, and Ensemble Classifiers was performed on a dataset consisting of routine blood analysis combined with anthropometric measurements to diagnose breast cancer.Neighborhood component analysis was applied as a feature selection method to reveal relevant biomarkers that can be used in breast cancer prediction.In order to assess the performance of each proposed classifier model, two different data division procedures such as hold-out and 10-fold cross-validation were employed.Bayesian Optimization algorithm was applied to all classifiers for the maximizing the prediction accuracy.Different performance criteria such as accuracy, precision, sensitivity, specificity, and Fmeasure were used to measure the success of each classifier.Experimental results showed that the Bayesian optimization-based K-Nearest Neighbor performs better than other machine learning algorithms under the hold-out data division protocol with an accuracy of 95.833%.The results obtained in this study may provide a new perspective on the application of improved machine learning techniques for the early detection of breast cancer.
Application of Computer in Online Teaching of Professional Courses

Due to the ongoing public health crisis, computer online teaching has become a new normal. It is very meaningful to explore how to effectively apply computer in online teaching. In this paper, the online teaching situation of teachers and students in a college was surveyed and analyzed. Then, DingTalk, the most popular online teaching platform among the respondents, was introduced in details. Next, the online teaching situation was tracked, and the learning situation was evaluated in terms of pre-class preparation, in-class teaching, and after-class homework. After that, the satisfaction with the course design and teaching effect was surveyed. The results show that the students felt satisfied with the course design, but regarded practical teaching a weak link in online teaching; the students highly recognized the work attitude, teaching ability, and interactive ability of their teachers, yet calling for better innovation awareness among teachers. The research results provide a good reference for computer-based online teaching of professional courses.
Coal Classification Method Based on Improved Local Receptive Field-Based Extreme Learning Machine Algorithm and Visible–Infrared Spectroscopy

In the process of using coal, if the type of coal cannot be accurately determined, it will have a significant impact on production efficiency, environmental pollution, and economic loss. At present, the traditional classification method of coal mainly relies on technician's experience. This requires a lot of manpower and time, and it is difficult to automate. This paper mainly studies the application of visible-infrared spectroscopy and machine learning methods in coal mine identification and analysis to provide guidance for coal mining and production. This paper explores a fast and high-precision method for coal identification. In this paper, for the characteristics of high dimensionality, strong correlation, and large redundancy of spectral data, the local receptive field (LRF) is used to extract the advanced features of spectral data, which is combined with the extreme learning machine (ELM). We improved the coyote optimization algorithm (COA). The improved coyote optimization algorithm (I-COA) and local receptive field-based extreme learning machine (ELM-LRF) are used to optimize the structure and training parameters of the extreme learning machine network. The experimental results show that the coal classification model based on the network and visible-infrared spectroscopy can effectively identify the coal types through the spectral data. Compared with convolutional neural networks (CNN algorithm) and principal component analysis (PCA algorithm), LRF can extract the spectral characteristics of coal more effectively.
PREPARATION OF FUTURE TEACHERS FOR USING DIGITAL TECHNOLOGIES IN THE PROCESS OF EARLY FOREIGN LANGUAGE TEACHING

The article considers one of the most urgent components of professional teacher training – a digital pedagogical competence. In the process of doing a research work it has been proved that the future teacher should be ready and able to use digital technologies for teaching children and establishing pedagogical cooperation with foreign colleagues. A future teacher should master a methodology of searching on the Internet and a methodology of safe work with a digital content. The article presents a research work with participating the third-year students of specialties «Primary Education» and «Preschool Education» of Pedagogical Institute in Borys Grinchenko Kyiv University. For the purpose of the study, a questionnaire was proposed to the students to make self-diagnostics of information literacy by themselves. The task was to point out if the students` being able to master a methodology of searching the necessary information in the Internet and a methodology of safe work with a digital content, and students` readiness to use digital technologies in the process of early teaching of foreign languages. The analysis of the results of the questionnaire makes it possible to state that the students of the above mentioned specialties possess the information literacy. But they need to be trained in methodology of teaching foreign languages: how to use digital technologies in self-learning and teaching children. The facilities of forming future teachers` foreign communicative competence within the discipline «Foreign Language with Teaching Methodology» have been updated. During the academic year special digital technologies have been used, including lexical online simulators, educational videos and interactive games for lexical and grammatical material and so on. According to the results of the research work, it was pointed out the further ways of forming readiness of the bachelors who study within these specialties to use digital technologies in the process of early teaching of foreign languages. The special necessity of future teachers` pedagogical cooperation with both pupils and colleagues are underlined.
1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise

In this study we analyze the signal classification performances of various classifiers for deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of signal to noise ratio (SNR) levels (-40dB to +20dB). The traditional electronic support measure (ESM) systems require high SNR for radar signal classification. LPI (low probability of intercept) radar signals that are received by ESM systems are usually corrupted by noise. So, we demonstrate through extensive simulations that it is possible to achieve high classification performance at low SNR levels providing that the underlying radar signals are known in advance. MF bank classifier, 1D Convolutional Neural Networks (CNNs) and the minimum distance classifier using spectral-domain features (the skewness, the kurtosis, and the energy of the dominant frequency) have been derived for the radar signal classification and their performances have been compared with each other and with the optimal classifier.
All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers

Contact discovery allows users of mobile messengers to conveniently connect with people in their address book.In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods.Our study of three popular mobile messengers (WhatsApp, Signal, and Telegram) shows that, contrary to expectations, largescale crawling attacks are (still) possible.Using an accurate database of mobile phone number prefixes and very few resources, we have queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal.For Telegram we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service.We present interesting (crossmessenger) usage statistics, which also reveal that very few users change the default privacy settings.Regarding mitigations, we propose novel techniques to significantly limit the feasibility of our crawling attacks, especially a new incremental contact discovery scheme that strictly improves over Signal's current approach.Furthermore, we show that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal of mobile phone numbers.For this, we also propose a significantly improved rainbow table construction for non-uniformly distributed inputs that is of independent interest.
Objective Image Quality Measures for Disparity Maps Evaluation

The evaluation of disparity (range) maps includes the selection of an objective image quality (or error) measure. Among existing measures, the percentage of bad matched pixels is commonly used. However, it requires a disparity error tolerance and ignores the relationship between range and disparity. In this research, twelve error measures are characterized in order to provide the bases to select accurate stereo algorithms during the evaluation process. Adaptations of objective quality measures for disparity maps’ accuracy evaluation are proposed. The adapted objective measures operate in a manner similar to the original objective measures, but allow special handling of missing data. Additionally, the adapted objective measures are sensitive to errors in range and surface structure, which cannot be measured using the bad matched pixels. Their utility was demonstrated by evaluating a set of 50 stereo disparity algorithms known in the literature. Consistency evaluation of the proposed measures was performed using the two conceptually different stereo algorithm evaluation methodologies—ordinary ranking and partition and grouping of the algorithms with comparable accuracy. The evaluation results showed that partition and grouping make a fair judgment about disparity algorithms’ accuracy.