Reviewerly/exHarmony huggingface dataset:
School Principals’ Beliefs, and Perceptions About the Use of Digital Technologies in the Management of the School Organization

This study is based on the scientific consensus and the consensus among Portuguese policy-makers, at the most diverse levels, on the importance that digital technologies have in school management and administration, at the operational and communicational level. Fourteen school principals of schools in the central region of Portugal answered a questionnaire designed to collect their perceptions about the incorporation of technologies in the management of the school and its use in the functioning of the school organization in the pedagogical, cultural, administrative, and financial-patrimonial levels. The obtained results corroborate previous studies in which school principals believe that information and communication technologies use has a great importance in school. However, with the exception of the cultural dimension, in which there is a large consensus among respondents, we observed significant variations in the directors’ responses. The results suggest that the use of digital technologies in management acts cannot be held hostage by standardized guidelines but must respond to the challenges and needs of each specific school.
A Research on Comprehensive Training Platform on Android for Software Engineering in Qinghai Minzu University

With respect to the training program of software engineering specialty, this paper puts forward the comprehensive training platform of Chinese character dictation competition based on Android, and clarifies the purpose and main contents of the comprehensive training platform. Based on the hardware and software facilities of the campus, the C / S model training platform, the use of Android integrated training platform to achieve the Chinese character writing, clearing, timing and other functions, managers of the entire process of the game management, including the participating teams and players, the administrator can simultaneously obtain the client input Chinese characters, and display on the big screen, the judges then give the results after the score and statistical display. The whole integrated training platform is light and practical.
Remote Learning Amidst the Pandemic: Measuring the Relationships of Internet Usage and Cybersecurity Awareness of Primary School Students in Malaysia

In Malaysia, remote learning has been implemented since the pandemic started in March 2020, in which students used online platform tools to replace face-to-face learning at school. However, the new way of learning that depends on the Internet may expose the students to cyberattacks. As the students are young, they are a vulnerable group that could be victims. Furthermore, the high Internet utilisation among young students requires them to have a good cybersecurity awareness to protect themselves from potential threats. Therefore, a model comprising nine online activities and seven components of cybersecurity awareness was proposed, leading to sixty hypotheses. This model was tested through an online survey among seven thousand four hundred seventy-three primary school students in Malaysia aged between seven to twelve years. The Pearson Chi-squared correlation was employed for the hypotheses test. The results suggested that relationships are found in almost all online activities and the studentsâ€™ cybersecurity awareness. The findings of this study are expected to help related agencies in Malaysia plan and design cybersecurity awareness programmes for primary school students with appropriate focus and content.
HuntFlow: Search the Arithmetic Vulnerability in Ethereum Smart Contract

With the development of blockchain technology, smart contracts have been applied in more and more industries. Smart contracts are immutable once deployed, so unaudited smart contracts can easily cause economic losses. Arithmetic vulnerabilities usually refer to integer overflow vulnerability in smart contracts, and integer calculations in smart contracts are usually related to the transfer of digital assets. However, most of the existing smart contract auditing methods are inefficient and cannot be applied to the rapidly developing blockchain technology. To address these problems mentioned above, we propose a lightweight Ethereum smart contract arithmetic vulnerability detection model called HuntFlow, which can automatically detect the arithmetic vulnerabilities in the smart contract on a lightweight computer without expert knowledge. We first obfuscate 142 original opcodes into a set of 28 vulnerability features based on the characteristics of arithmetic vulnerabilities. And then we use Long-Short-Term-Memory (LSTM) network with attention mechanism for training and testing. The experimental result shows that the vulnerability features is better than original opcode for detecting arithmetic vulnerabilities. Moreover, the detection time of HuntFlow is greatly shortened than traditional tools, it costs only 0.028 s for each smart contract.
FMNet: A novel hybrid face mask detection using deep learning

Face Mask recognition has developed as an extremely admired issues in several domains such as image processing, computer vision and artificial intelligence. Several novel approaches are being developed through deep learning based Convolutional Neural Network model to detect the face mask identification. In this paper, we propose “FMNet” model which indicates Face Mask detection Neural Network model for recognition of face mask person on publicly available face masks images as resources. Furthermore, we applied image preprocessing, feature extraction have performed using CNN, applied FMNet model for face mask recognition from images, and finally perform classification technique to categorize the images as person “wearing masks” and “not wearing masks”. Our experimental outcome generates better performance in finding face mask by achieving accuracy as 99.7%. Moreover, comparing our proposed algorithm results with pre-trained models namely VGG 16 attains 99.6%. This system has capability to perform in real time application creates it applicable to identify people in airports, buses, schools etc.
Segmentation of Tumor Region from Mammogram Images Using Deep Learning Approach

Breast cancer is most common among different cancers that affect women and it is the second main cause for cancer deaths in women all over world. In order to increase the survival rate breast cancer must be detected and treated the earliest. Enhancement of mammograms is much needed in order to improve contrast of images which further helps in better segmentation. In this paper, method for segmentation using U-NET after enhancing the mammogram image with CLAHE (Contrast limited Adaptive Histogram Equalization) is proposed. A comparision analysis is carried out between proposed method and mammogram image segmentation without enhancement, it is found that enhanced mammogram image provides better segmentation results. Experimentation is done using publicly available DDSM (Digital Database for Screening Mammography) dataset.
Home Automation and Machine Learning Models for Health Monitoring

Medical surveillance has been constantly linked to hospitals and infirmaries. However, the recent increase in demand for health assistance, especially with the current covid-19 pandemic, has made it clear that relying on placing patients on hospitals for surveillance is deprecated. In the same context, this paper strives to illuminate the significance of using trending paradigms such as home automation and artificial intelligence to better advance and modernize healthcare systems. Accordingly, the main contribution of this study is a demonstration of a novel smart home architecture and an evaluation of machine-learning algorithms aimed at predicting a health condition severity based on the patient data gathered from several sensors and wearables. In respect to the need of providing a real time alerting system, several classification algorithms are highlighted with their advantages in mitigating the remote health monitoring problematic. The results assessment of the machine learning algorithms emphasizes the convenience of using artificial intelligence for health monitoring regardless of time and place constraints.
Dealing with Imbalanced Data

Class imbalance all around presents in real-world applications, which has brought more curiosity from different fields. While emphasising on accuracy for performance evaluation, studying from unbalanced data may produce unproductive outcomes. Cost-sensitive, sampling, ensemble approach and other hybrid methodologies have all been used in the past to address this imbalance problem. In machine learning, the ensemble approach is used to increase the accuracy of single base classifiers by aggregating numerous of them. To handle the issues due to imbalanced data, ensemble algorithms have to be formed specifically. Several performance assessing functions showed that the ensemble method outperformed the other techniques. In this article, different methods are described to handle imbalanced datasets with the special description of SMOTE with the ensemble method. The complexity of the ensemble model is defined. The clustering methods are also used to manage the issues due to imbalanced datasets.
WiEdge: Edge Computing for Audio Sensing Applications With Accurate Wireless Link Prediction

Audio sensing applications on embedded and mobile devices have recently enjoyed increasing popularity. Their performance can be significantly improved by edge computing which offloads computation-intensive tasks to edge servers through wireless links. The quality of wireless links is essential to offloading performance. However, existing edge computing solutions can hardly predict the link quality accurately and efficiently in a dynamic wireless environment, resulting in less optimal offloading decisions and unsatisfied user-perceived Quality of Experience (QoE). In this article, we present WiEdge, a distributed edge computing framework for audio sensing applications with accurate wireless link prediction. By combining cross-layer information extracted from recently received WiFi beacons, TCP-level statistics, and the past throughput observations, WiEdge can predict the throughput of wireless links accurately and efficiently in the near future. Based on the prediction, WiEdge makes optimal offloading decisions for QoE maximization. We formulate the offloading decision problem as a stochastic optimal control problem and propose an efficient solution based on model predictive control from the control-theoretic perspective. We implement WiEdge and evaluate its performance extensively in three representative real-world scenarios. Results show that WiEdge achieves high prediction accuracy and improves average normalized QoE by 2%, 11%, and 40% in three different scenarios, compared with state-of-the-art approaches.
Swarm Enhanced Attentive Mechanism for Sequential Recommendation

Recommendation system facilitates users promptly obtaining the information they need in this age of data explosion. Research on recommendation models have recognized the importance of integrating user historical behavior sequence into the model to alleviate the matrix sparsity. Although deep learning algorithm with attentive mechanism exhibits competitive performance in sequential recommendation, the searching for optimal attentive factors still lack effectiveness. In this work, we redesign the sequential recommendation model by employing swarm intelligence for optimization in the attentive mechanism thus to improve the algorithm accuracy. We conduct extensive comparative experiments to evaluate performance of four swarm intelligence algorithms and traditional recommendation methods. Our work is the first attempt to integrate swarm intelligence into sequential recommendation algorithm. Experimental results confirmed the superior performance on AUC score of the proposed approach.
Character Input Interface Design for Effective Use of Motion Sensors on Smartwatches

In recent years, the opportunity to operate smartwatches has increased owing to the increase in the number of people using smartwatches. The main input methods for smartwatches are touch and voice; however, there is/are these input methods present a few problems. In this study, we focus on gesture input, which involves detection via hand and arm movements. It can be discriminated by the accelerometer and gyroscope in a smartwatch. Previously, we attempted to discriminate finger movements; however, each finger movement was difficult to distinguish. This implies that discriminating gestures using motion sensors is challenging. Therefore, we herein propose side-tap, dial-rotation, and band-rotation operations, which can be effectively performed in a motion sensor. The purposes of these operations are to tap the side of the device, twist the dial, and twist the wrist, respectively. The characteristic waveforms of these operations can be obtained using the accelerometer and gyroscope in the smartwatch. In addition, the proposed operation offers a sufficient number of selections for the character input. The waveforms of the proposed operations are analyzed, and a discrimination algorithm is developed and evaluated. First, each motion is performed 10 times consecutively; subsequently, each motion is randomly performed 10 times. The results show that the average discrimination rate is 97.5%. Therefore, the proposed motion is useful as a new input method for smartwatches. In the future, to use the proposed motion as a character input, an appropriate keyboard design is necessitated. Therefore, we develop a keyboard design that enables users to input characters intuitively using the proposed motion.
Portfolio Optimization Based on Quantum HHL Algorithm

Modern financial problems are essentially complex mathematical problems. The most important and common problems are combinatorial optimization problems in finance, such as common stock investment, financial management, etc. Among such problems, linear system solving problems are of this type. The core of the problem. Now with the development of machine learning and artificial intelligence technology, it has been well applied in the financial industry, but with the advent of the era of big data, massive amounts of data have brought huge challenges to the solution of classic solving methods and computing resources. Lloyd et al. proposed an HHL algorithm for solving linear systems. Under some assumptions, this algorithm can solve linear equations with exponential acceleration compared to classical algorithms. It is a key step in current quantum machine learning algorithms. More classic algorithms can achieve exponential acceleration. In this paper, the use of HHL algorithm to solve combinatorial optimization problems in finance has been researched and tested. The problem is described as a quadratic programming problem with equality constraints and the corresponding program design is carried out, verified and the final system state is carried out. The results are measured, and the results are analyzed. It is found that compared with the classical solution method, the HHL algorithm can be used to solve such combinatorial optimization problems, and the approximate solution obtained is in good agreement with the exact solution, which can be applied well. The solution process of such combinatorial optimization problems can be accelerated, and quantum computing can be better applied to such problems.
A new method in establishing quantitative relationships between manuscripts of the New Testament

Abstract New Testament textual critics have for decades calculated the similarities between the manuscripts in a similar manner, using collations and variation units. This conventional methodology requires enormous amounts of time and manual work. Here is proposed a new method that does not require these preprocessing steps, enabling the establishment of quantitative relationships using manuscript transcriptions only. This is achieved by applying a technique called shingling, where the manuscript transcriptions are turned in a computerized manner into smaller pieces called tokens or k-grams. Then, a string metric is used to calculate the similarities between the tokenized strings. This method is efficient, meaning that it allows critics to consider all textual evidence in each manuscript tradition. At the same time, it returns similarity values that are compatible with those of conventional approaches.
Discrete Mayfly Algorithm for Spherical Asymmetric Traveling Salesman Problem

The asymmetric traveling salesman problem (ATSP), as a branch of the traveling salesman problem, aims to find a group of circuits when a salesman travels all cities at the minimum cost and returns to the starting city under the condition of different round-trip costs between the two cities. Mayfly algorithm (MA) is a swarm-based metaheuristic algorithm, which has been applied to many fields successfully. In this paper, we arrange all cities on the sphere. A discrete mayfly algorithm (DMA) for solving spherical asymmetric traveling salesman problem is proposed. The algorithm applies inver-over operator, crossover operator and 3-opt operator to simplify the parameters of the algorithm, enrich the diversity of the population and enhance the local search ability. By experimenting, analyzing and comparing the effects of DMA and other metaheuristic algorithms in twelve scales, this study proves that the proposed algorithm is superior to others for the spherical ATSP, especially in high-dimensional cases.
Secure Evaluation of Discrete Sine Transform in Homomorphic Encrypted Domain

Discrete sine transform (DST) is widely used in compression encoding, image processing, and speech enhancement. In cloud computing, outsourcing plaintext data to the cloud has the risk of private data leakage. In this paper, we study how to realize data computation outsourcing with privacy protection. We propose a scheme to implement DST in the encrypted domain that uses the scaling method to represent decimals. To improve the accuracy, we also propose a new scheme to implement high precision encrypted domain DST. We approximate complex numbers with high precision using integer coefficient polynomials whose independent variable is a unit root. With this representation, we can perform DST in the encryption domain with high precision. We conducted experiments to verify the effectiveness and efficiency. The two schemes have the advantages of high speed and high precision, respectively.
Enabling Data Security in Electronic Voting System Using Blockchain

Electronic voting is essential in the digital world for voters, who will make their choice of vote through smartphones or computers. Ballot composition, ballot casting, ballot recording, and tabulation are the four steps in the election process. Electronic voting technology aims to speed up ballot counting, minimize the expense of hiring employees to tally ballots manually and increase accessibility for disabled voters. Results can be reported and published within a short period. The major challenge in electronic voting is to maintain data confidentiality, verify data integrity, and ensure authenticity in the overall process. In this paper, blockchain-enabled electronic voting has been proposed, addressing security challenges such as confidentiality, integrity, and authenticity. The proposed work implementation results show the effectiveness of blockchain-based electronic voting scheme.
Terahertz Super-Resolution Nondestructive Detection Algorithm Based on Edge Feature Convolution Network

Much research has been conducted to improve the defect-detection rate and detection accuracy of the imaging technology used in terahertz nondestructive testing.Due to the power limit of light sources and noise interference in terahertz equipment, images have low resolution and fuzzy defect edges.Hence, improving the resolution is crucial for detecting defects.We designed an edge detection network structure based on a traditional deep neural network.Besides, we devised a node-fusing strategy to train the network.It demonstrates significant improvement of the resolution of the terahertz defect contour.A quartz fiber composites with embedded defects was tested with our network.The results showed that the proposed super-resolution reconstruction algorithm improves resolution, particularly on the edges of defect contours.
Software Reuse Enabled by Machine Learning Based Source Code Analysis: A Case for Automated Classification of OpenSource Software with Respect to Requirements

Software reuse is now a central topic in software engineering in order to increase productivity, system security and development speed. In this paper we illustrate a preparatory work for the purpose of creating a complete expert system in the field of software reuse. The objectives of the work are related to the ability to extract the requirements from software documentation and mapping of the software artifacts to the requirements. The automatic classification of the requirements was done using natural language processing techniques, such as Bert a pre-trained bidirectional model. The association of codes towards the requirements was done through the implementation of convolutional neural networks.
Missile: Multi-Cue Shape-Aware Transferable Imbalance Learning for Effective Graph-Based Api Recommendation

Application Programming Interface (API) recommendation based on graphs is a meaningful task in the context of software engineering and data visualization. However, this task was previously undefined until a recently published paper utilizing a deep learning-based method, coined it as Plot2API. Compared to general image classification methods, this dedicated approach introduces a semantic parsing module to exploit the semantic relevant visual information and yields better performance. However, its performance declines sharply in unbalanced datasets, thus limiting its generalizability. To address this issue, we propose a method named MultI-cue Shape-aware tranSfer-able Imbalance LEarning (MISSILE), consisting of three major components: Cross-Language Shape-Aware Plot Transfer Learning (CLSAPTL), Cross-Language API Semantic Similarity-based Data Augmentation (CLASSDA), and Balanced Multi-Label Softmax Loss (BMLSL). CLSAPTL guides the model to learn the graphs’ class hierarchy and thereby enabling the model to learn more transferable visual features. Given that a graph can be associated with multiple APIs, CLASSDA leverages the samples of APIs with semantically similar names to assist in feature learning. Finally, BMLSL alleviates the imbalances between positive and negative samples during training. Extensive experimental results on two datasets shows that MISSILE improves the performance of classic CNNs along with the state-of-the-art method, demonstrating its effectiveness.