Reviewerly/exHarmony huggingface dataset:
Teacher's perceptions, institutional challenges, and educational sustainability during Covid-19 in Ecuador

The Covid-19 pandemic has affected global educational systems, especially in developing countries. Therefore, governments and educational administrators have adopted contingency measures to maintain the functioning and the sustainability of their national educational systems. These measures involved the use of technologies and have enabled a significant amount of teachers to continue working with their students around the globe. This article aims to analyze the perception of Ecuadorian teachers about the contingency measures taken at the governmental and institutional levels while facing the COVID-19 crisis. Also, several aspects regarding the teachers' perceptions about their technological skills, access to electronic devices, and implications of teleworking in their mental health and performance are also analyzed. A cross-sectional study was carried out with 3,183 Ecuadorian teachers working at all levels of education. An online questionnaire which obtained a reliability of 0.84 in Cronbach's Alpha was used. The results show that despite the high levels of uncertainty, most teachers agreed with the measures that had been taken by the Government, the Ministry of Education as well as their institutions and reported feeling comfortable with teleworking. However, the lack of technological resources in rural areas and the lack of trust in the effectiveness of online classes are aspects that need to be analyzed before considering the viability of long-term online education in Ecuador.
Financial Performance Management System and Wireless Sharing Network Optimization of Listed Enterprises under BPNN

The study aims to respond to the difficulties in establishing an index system in financial performance management and ensuring the security of wireless sharing network and the local extremum and slow convergence speed of the traditional BP neural network (BPNN). First, Levenberg–Marquardt (LM) is used to optimize BPNN, and an improved BPNN is proposed. Second, the financial performance evaluation system of listed companies based on BPNN is constructed. Finally, a wireless network infusion detection system based on the improved BPNN is proposed and tested by constructing datasets and a real test environment. The results show that (1) the financial performance evaluation system of listed enterprises constructed can evaluate the financial performance of listed enterprises with fewer errors. It is easy to operate, and it has high accuracy and the abilities of self-learning and self-adaptation; (2) Wireless Infusion Detection System (WIDS) based on the improved BP algorithm has a high detection rate and a low error rate. The study provides important technical support for listed enterprises to improve the financial performance management level and market competitiveness and strengthen the security protection of networks.
A New Hybrid Automated Security Framework to Cloud Storage System

The need for cloud storage grows day after day due to its reliable and scalable nature. The storage and maintenance of user data at a remote location are severe issues due to the difficulty of ensuring data privacy and confidentiality. Some security issues within current cloud systems are managed by a cloud third party (CTP), who may turn into an untrustworthy insider part. This paper presents an automated Encryption/Decryption System for Cloud Data Storage (AEDS) based on hybrid cryptography algorithms to improve data security and ensure confidentiality without interference from CTP. Three encryption approaches are implemented to achieve high performance and efficiency: Automated Sequential Cryptography (ASC), Automated Random Cryptography (ARC), and Improved Automated Random Cryptography (IARC) for data blocks. In the IARC approach, we have presented a novel encryption strategy by converting the static S-box in the AES algorithm to a dynamic S-box. Furthermore, the algorithms RSA and Twofish are used to encrypt the generated keys to enhance privacy issues. We have evaluated our approaches with other existing symmetrical key algorithms such as DES, 3DES, and RC2. Although the two proposed ARC and ASC approaches are more complicated, they take less time than DES, DES3, and RC2 in processing the data and obtaining better performance in data throughput and confidentiality. ARC outperformed all of the other algorithms in the comparison. The ARC’s encrypting process has saved time compared with other algorithms, where its encryption time has been recorded as 22.58 s for a 500 MB file size, while the DES, 3DES, and RC2 have completed the encryption process in 44.43, 135.65, and 66.91 s, respectively, for the same file size. Nevertheless, when the file sizes increased to 2.2 GB, the ASC proved its efficiency in completing the encryption process in less time.
Deep Recurrent Neural Network with Multi-Scale Bi-directional Propagation for Video Deblurring

The success of the state-of-the-art video deblurring methods stems mainly from implicit or explicit estimation of alignment among the adjacent frames for latent video restoration. However, due to the influence of the blur effect, estimating the alignment information from the blurry adjacent frames is not a trivial task. Inaccurate estimations will interfere the following frame restoration. Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the information from unaligned neighboring frames for better video deblurring. Specifically, we build a Multi-scale Bi-directional Propagation (MBP) module with two U-Net RNN cells which can directly exploit the inter-frame information from unaligned neighboring hidden states by integrating them in different scales. Moreover, to better evaluate the proposed algorithm and existing state-of-the-art methods on real-world blurry scenes, we also create a Real-World Blurry Video Dataset (RBVD) by a well-designed Digital Video Acquisition System (DVAS) and use it as the training and evaluation dataset. Extensive experimental results demonstrate that the proposed RBVD dataset effectively improve the performance of existing algorithms on real-world blurry videos, and the proposed algorithm performs favorably against the state-of-the-art methods on three typical benchmarks. The code is available at https://github.com/XJTU-CVLAB-LOWLEVEL/RNN-MBP.
Identity-Based Cloud Storage Auditing for Data Sharing With Access Control of Sensitive Information

Remote data integrity auditing ensures the integrity of cloud storage. In practice, cloud users may not want their sensitive data to be exposed to others. Thus, it is meaningful to investigate how to realize data sharing with sensitive information hiding in cloud storage auditing. Up to now, cloud storage has been proven to achieve the sensitive information hiding property through a third-party sanitizer dedicated to sanitize user data, which leads to high outlays on purchasing and maintaining a special server. To meet this challenge, we design a novel cloud storage auditing protocol to support sensitive information hiding without the need of a third-party sanitizer. In addition, our scheme allows data owners to enable or disable other users to access their sensitive information with the help of the cloud that dose not deviate from the agreement during access control. To be specific, only after receiving the delegations from the data owner, the users can compute the valid warrants that can pass the access verification of the cloud. The proposed protocol is built on identity-based cryptography, thus avoiding the complex certificate management. We validate the advantages of the proposed protocol through massive theoretical analysis and experimental results.
Prediction of Solar Irradiance over the Arabian Peninsula: Satellite Data, Radiative Transfer Model, and Machine Learning Integration Approach

Data from a moderate resolution imaging spectroradiometer instrument onboard the Terra satellite along with a radiative transfer model and a machine learning technique were integrated to predict direct solar irradiance on a horizontal surface over the Arabian Peninsula (AP). In preparation for building appropriate residual network (ResNet) prediction models, we conducted some exploratory data analysis (EDA) and came to some conclusions. We noted that aerosols in the atmosphere correlate with solar irradiance in the eastern region of the AP, especially near the coastlines of the Arabian Gulf and the Sea of Oman. We also found low solar irradiance during March 2016 and March 2017 in the central (~20% less) and eastern regions (~15% less) of the AP, which could be attributed to the high frequency of dust events during those months. Compared to other locations in the AP, high solar irradiance was recorded in the Rub Al Khali desert during winter and spring. The effect of major dust outbreaks over the AP during March 2009 and March 2012 was also noted. The EDA indicated a correlation between high aerosol loading and a decrease in solar irradiance. The analysis showed that the Rub Al Khali desert is one of the best locations in the AP to harvest solar radiation. The analysis also showed the ResNet prediction model achieves high test accuracy scores, indicated by a mean absolute error of ~0.02, a mean squared error of ~0.005, and an R2 of 0.99.
Parallel border tracking in binary images using GPUs

Abstract Border tracking in binary images is an important kernel for many applications. There are very efficient sequential algorithms, most notably, the algorithm proposed by Suzuki et al., which has been implemented for CPUs in well-known libraries. However, under some circumstances, it would be advantageous to perform the border tracking in GPUs as efficiently as possible. In this paper, we propose a parallel version of the Suzuki algorithm that is designed to be executed in GPUs and implemented in CUDA. The proposed algorithm is based on splitting the image into small rectangles. Then, a thread is launched for each rectangle, which tracks the borders in its associated rectangle. The final step is to perform the connection of the borders belonging to several rectangles. The parallel algorithm has been compared with a state-of-the-art sequential CPU version, using two different CPUs and two different GPUs for the evaluation. The computing times obtained show that in these experiments with the GPUs and CPUs that we had available, the proposed parallel algorithm running in the fastest GPU is more than 10 times faster than the sequential CPU routine running in the fastest CPU.
New Feature Selection Algorithm Based on Feature Stability and Correlation

The analysis of a large amount of data with high dimensionality of rows and columns increases the load of machine learning algorithms. Such data are likely to have noise and consequently, obstruct the performance of machine learning algorithms. Feature selection (FS) is one of the most essential machine learning techniques that can solve the above-mentioned problem. It tries to identify and eliminate irrelevant information as much as possible and only maintain a minimum subset of appropriate features. It plays an important role in improving the accuracy of machine-learning algorithms. It also reduces computational complexity, run time, storage, and cost. In this paper, a new feature selection algorithm based on feature stability and correlation is proposed to select the effective minimum subset of appropriate features. The efficiency of the proposed algorithm was evaluated by comparing it with other state-of-the-art dimensionality reduction (DR) algorithms using benchmark datasets. The evaluation criteria included the size of the minimum subset, the classification accuracy, the F-measure, and the area under curve (AUC). The results showed that the proposed algorithm is the pioneer in reducing a given dataset with high predictive accuracy.
Adversarial Attacks to API Recommender Systems: Time to Wake Up and Smell the Coffee?

Recommender systems in software engineering provide developers with a wide range of valuable items to help them complete their tasks. Among others, API recommender systems have gained momentum in recent years as they became more successful at suggesting API calls or code snippets. While these systems have proven to be effective in terms of prediction accuracy, there has been less attention for what concerns such recommenders’ resilience against adversarial attempts. In fact, by crafting the recommenders’ learning material, e.g., data from large open-source software (OSS) repositories, hostile users may succeed in injecting malicious data, putting at risk the software clients adopting API recommender systems. In this paper, we present an empirical investigation of adversarial machine learning techniques and their possible influence on recommender systems. The evaluation performed on three state-of-the-art API recommender systems reveals a worrying outcome: all of them are not immune to malicious data. The obtained result triggers the need for effective countermeasures to protect recommender systems against hostile attacks disguised in training data.
A trusted de-swinging k-anonymity scheme for location privacy protection

Abstract K-anonymity has been gaining widespread attention as one of the most widely used technologies to protect location privacy. Nevertheless, there are still some threats such as behavior deception and service swing, since utilizing distributed k-anonymity technology to construct an anonymous domain. More specifically, the coordinate of the honest node will be a leak if the malicious nodes submit wrong locations coordinate to take part in the domain construction process. Worse still, owing to service swing, the attacker increases the reputation illegally to deceive honest nodes again. To overcome those drawbacks, we propose a trusted de-swinging k-anonymity scheme for location privacy protection. Primarily, we introduce a de-swinging reputation evaluation method (DREM), which designs a penalty factor to curb swinging behavior. This method calculates the reputation from entity honesty degree, location information entropy, and service swing degree. Besides, based on our proposed DREM, a credible cloaking area is constructed to protect the location privacy of the requester. In the area, nodes can choose some nodes with a high reputation for completing the construction process of the anonymous domain. Finally, we design reputation contracts to calculate credit automatically based on smart contracts. The security analysis and simulation results indicate that our proposed scheme effectively resists malicious attacks, curbs the service swing, and encourages nodes to participate honestly in the construction of cloaking areas.
Asymmetric double-image encryption via wavelength multiplexing

In this paper, we propose an asymmetric double-image encryption via wavelength multiplexing. First, a novel iterative phase retrieval algorithm, to the best of our knowledge, is developed to encode two images into one complex-valued function via wavelength multiplexing. Then, the function is encoded by equal modulus decomposition (EMD). This cryptosystem not only retains the advantages of EMD but also reduces the number of public keys so as to enhance the resistance to the amplitude-phase retrieval algorithm (APRA). In the decryption, two high-quality decrypted images can be obtained with their corresponding wavelengths. To the best of our knowledge, this is the first time that wavelength multiplexing is used to achieve high-quality two-image encryption and decryption. Numerical simulation results show the effectiveness and robustness of this new method.
Impact of financial technologies on economic development: Theories, methods and analysis

This research focuses on an analytical review of the scientific literature to select appropriate methods and evaluate the impact of financial technologies on economic growth.Since non-cash payments can be affected by various factors, there is a great variety of studies relating to them in the scientific literature.However, existing scientific papers reflecting on the economic growth and noncash payments do not determine the cause-effect relationship trends in different periods.Current study intends to fill this gap.Using an original method based on correlation analysis, the relationship of indicators in different periods is calculated with the use of the SPSS software.The data is collected for period of 2004-2019, during which the reviewed countries were actively developing and increasing their financial and technological capacities.Analysis results largely indicate an increase in the number of bank branches in many of the reviewed countries.Further, we
Improving the Competence of Future Vocational Education Teachers based on Modular-Rating Education

The article discusses the role of the significance of innovative software and teaching in improvement of the efficiency of learning. And the article discusses ways of correcting the design, value and definition of innovative software and didactic systems of higher education, еvaluation of knowledge of students on the basis of module-rating system, development of model of improvement of students' competence based on module-rating technology of training and definition of pedagogical conditions for its implementation, improvement of the methodological support for implementation of the model of improving competence based on module-rating system, Case-Problem-Method, Case-Incident-Method, based on indicators and criteria for determining the level of development of cognitive competence in future vocational education teachers organization of experimental works.
Visual Servoing in Autoencoder Latent Space

Visual servoing (VS) is a common way in robotics to control a robot motion usinginformation acquired by a camera. This approach requires to extract visual information from the image to design the control law. The resulting servo loop is built in order to minimize an error expressed in the image space. We consider a direct visual servoing (DVS) from whole images. We propose a new framework to perform VS in the latent space learned by a convolutional autoencoder. We show that this latent space avoids explicit feature extraction and tracking issues and provides a good representation, smoothing the cost function of the VS process. Besides, our experiments show that this unsupervised learning approach allows us to obtain, without labelling cost, an accurate end-positioning, often on par with the best DVS methods in terms of accuracy but with a larger convergence area.
Providing upgrade plans for third-party libraries: a recommender system using migration graphs

During the development of a software project, developers often need to upgrade third-party libraries (TPLs), aiming to keep their code up-to-date with the newest functionalities offered by the used libraries. In most cases, upgrading used TPLs is a complex and error-prone activity that must be carefully carried out to limit the ripple effects on the software project that depends on the libraries being upgraded. In this paper, we propose EvoPlan as a novel approach to the recommendation of different upgrade plans given a pair of library-version as input. In particular, among the different paths that can be possibly followed to upgrade the current library version to the desired updated one, EvoPlan is able to suggest the plan that can potentially minimize the efforts being needed to migrate the code of the clients from the library's current release to the target one. The approach has been evaluated on a curated dataset using conventional metrics used in Information Retrieval, i.e., precision, recall, and F-measure. The experimental results show that EvoPlan obtains an encouraging prediction performance considering two different criteria in the plan specification, i.e., the popularity of migration paths and the number of open and closed issues in GitHub for those projects that have already followed the recommended migration paths.
Critical Technical Awakenings

Starting with Philip E. Agre's 1997 essay on “critical technical practice”, we consider examples of writings from computer science where authors describe “waking up” from a previously narrow technical approach to the world, enabling them to recognize how their previous efforts towards social change had been ineffective. We use these examples first to talk about the underlying assumptions of a technology-centric approach to social problems, and second to theorize these awakenings in terms of Paulo Freire's idea of critical consciousness. Specifically, understanding these awakenings among technical practitioners as examples of this more general phenomenon gives guidance for how we might encourage and guide critical awakenings in order to get more technologists working effectively towards positive social change.
Path Planning Based on NURBS for Hyper-Redundant Manipulator Used in Narrow Space

Hyper-redundant manipulators with multiple degrees of freedom have special application prospects in narrow spaces, such as detection in small spaces in aerospace, rescue on-site disaster relief, etc. In order to solve the problems of complex obstacle avoidance planning and inverse solution selection of a hyper-redundant robot in a narrow space, a cubic B-spline curve based on collision-free trajectory using environmental edge information is planned. Firstly, a hyper-redundant robot composed of four pairs of double UCR (Universal-Cylindrical-Revolute) parallel mechanisms (2R1T, 2 Rotational DOFs and 1 Translation DOF) in series to realize flexible obstacle avoidance motion in narrow space is designed. The trajectory point envelope of a single UCR and the workspace of a single pair of UCR in Cartesian space based on the motion constraint boundaries of each joint are obtained. Then, the constraint control points according to the edge information of the obstacle are obtained, and the obstacle avoidance trajectory in the constrained space is planned by combining the A* algorithm and cubic B-spline algorithm. Finally, a variety of test scenarios are built to verify the obstacle avoidance planning algorithm. The results show that the proposed algorithm reduces the computational complexity of the obstacle avoidance process and enables the robot to complete flexible obstacle avoidance movement in the complex narrow space.
<i>RSC Advances</i> Editorial: retraction of falsified manuscripts

Permissions Request permissions RSC Advances Editorial: retraction of falsified manuscripts RSC Adv., 2021, 11, 4194 DOI: 10.1039/D1RA90009A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content.