Reviewerly/exHarmony huggingface dataset:
The Quality of Assessment during Covid-19 Online Education in the Eyes of Students: Tishk International University Case

Measurement and evaluation are an indispensable elements of educational processes.As a result of this, how learning takes place is revealed, and the question of what changes should be made in the future depends on the result of this.The criteria for measurement and evaluation were designed in accordance with face-to-face education.In the conditions of online education in the Covid-19 process, some problems arose regarding measurement and evaluation.With the Likert scale questionnaire, we applied to our students, it was investigated how healthy the teacher assessments were during the term.In this study, it was revealed that the students had problems with measurement, evaluation, and teacher assessment in this period.
The Design and Implementation of EFL Teaching Resource Database Based on AI Technology

Teaching Resource Database is of great help to EFL learning, especially in this information age. Nevertheless, traditional EFL Teaching Resource Database has some deficiencies, such as underuse of existing resources, inefficiency in information search, insufficiency in personalized service, and inconvenience in resource management. These drawbacks become hinderance to its utilization, as well as unfavorable factor for EFL learning. However, the EFL Teaching Resource Database Based on AI Technology can make a much better performance in terms of information retrieval, aggregation, processing and resource management and application. With the blessing of AI technology, the database is more intelligent and efficient than the traditional one. It can enhance learners’ interest and self-learning of English, strengthen co-construction of EFL resources, facilitate personalized use and promote communication among users. This is an example of how AI technology facilitates EFL learning, but more experiments and remedies are needed for its further improvement and it will be polished through more practice.
Determining quantum topological semion code decoder performance and error correction effectiveness with reinforcement learning

Quantum error correction technology is a vital method to eliminate noise during the operation of quantum computers. To solve the problem caused by noise, in this paper, reinforcement learning is used to encode defects of Semion codes, and the experience replay technique is used to realize the design of decoder. Semion codes are quantum topological error correction codes with the same symmetry group <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> as Kitaev toric codes, we used the topological characteristics of error correction codes to map qubits to multi-dimensional space, and error correction accuracy of the decoder is calculated to be 77.5%. Calculate the threshold of topological quantum Semion code, depending on the code distance, resulting in different thresholds, p threshold = 0.081574 when the code distance is d = 3, 5, 7 and threshold p threshold = 0.09542 when the code distance is d = 5, 7, 9. And we design the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mi mathvariant="double-struck">Q</mml:mi></mml:math> -network to optimize the cost of quantum circuit gates and compare the size of the cost reduction under different thresholds. Reinforcement learning is an important method for designing Semion code decoders and optimizing numerical values, providing more general error models and error correction codes for future machine engineering decoders.
A Reconfigurable Modular Vehicle Control Strategy Based on an Improved Artificial Potential Field

The reconfigurable modular vehicle group can transform into different configurations according to different requirements to be competent for various tasks and scenarios and to facilitate the utilization of robots in unstructured scenarios. Efficient and effective reconfiguration strategies and path planning are essential for improving the performance of modular vehicle groups. First, a multi-sensing four-wheel-drive Mecanum vehicle was built, which was equipped with UWB positioning (based on wireless carrier communication technology), communication based on ESP8266 modules, ultrasonic ranging and a magnetic structure. Second, concerning the indoor storage environment, a UWB two-way bilateral ranging and positioning system was designed, and the experimental accuracy for positioning could reach ±0.1 m. Third, a path planning strategy based on the improved artificial potential field method was adopted. According to the target configuration as well as the obstacle avoidance requirements, the motion space was converted into a gravitational field and a repulsive force field, and the vector superposition of the gradients was used for the path planning of each vehicle in turn. Depending on the reconfiguration command and the connection matrix, the magnetic structure would strengthen or disconnect the vehicle group configuration. Finally, the vehicle reconfiguration from the stochastic dispersion state to the target configuration and the transition between different configurations were accomplished using the proposed strategy in both simulations and experiments.
Theoretical Study of Optimizing Rugged Landscapes with the cGA

Estimation of distribution algorithms (EDAs) provide a distribution-based approach for optimization which adapts its probability distribution during the run of the algorithm. We contribute to the theoretical understanding of EDAs and point out that their distribution approach makes them more suitable to deal with rugged fitness landscapes than classical local search algorithms. Concretely, we make the OneMax function rugged by adding noise to each fitness value. The cGA can nevertheless find solutions with $$n(1-\varepsilon )$$ many 1s, even for high variance of noise. In contrast to this, RLS and the (1+1) EA, with high probability, only find solutions with $$n(1/2+o(1))$$ many 1s, even for noise with small variance.
Lightweight Gesture Pose Estimation Based on CPM Algorithm

With the experiential enhancement of artificial intelligence products, gesture estimation, as a classic computer vision task, has a wide range of application scenarios. Aiming at the current network model that needs to be lightweight in mobile smart products, this paper designs a lightweight gesture pose estimation model based on the CPM (Convolutional Pose Machine) multi-stage human pose estimation network. A comparative experiment based on the RHD open-source data set was conducted to compare and analyze the lightweight CPM gesture estimation model while ensuring accuracy while effectively reducing the amount of model parameters, which provides a basis for the development of real-time mobile terminal gesture pose estimation.
Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company

Industry 4.0 refers to the increasing tendency towards automation and data exchange in technologies like Big Data and AI. The existence of technology means telecommunication companies have to adapt. Therefore, it takes great people so that the company can continue to survive. The problem that companies often face in hiring great people is that it costs a lot and takes a long time to recruit. Predictive analysis can assist in identifying system issues and solutions. This study aims to develop predictive analytics that can improve recruitment screening based on CVs and find the best predictive model for the company to reduce costs and long recruitment cycles using technology. The authors built an analytical prediction model in four stages: data collection, data preprocessing, model building, and model evaluation. This technique uses Random Forest and Naive Bayes classification algorithms. Both systems properly predicted more data sets with 70% accuracy, 70% precision, and a recall rate above 80%. Compared between the two techniques, Random Forest outperforms Naive Bayes for this predictive model. A lot of people are talking about predictive analytics for hiring, but there aren't many data mining frameworks that can help to find rules based on the CVs of people who have worked for companies before.Keywords: Recruitment, Human Resource, HR Analytic, Predictive Analytic, Random Forest, Naïve Bayes
Comparison of Satellite-Based and Ångström–Prescott Estimated Global Horizontal Irradiance under Different Cloud Cover Conditions in South African Locations

The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradiance (GHI) for stations in South Africa. The daily GHI from four satellites (namely SOLCAST, CAMS, NASA SSE, and CMSAF SARAH) and the Ångström–Prescott (AP) model are evaluated by validating them against ground observation data from eight radiometric stations located in all six macro-climatological regions of South Africa, for the period 2014-19. The evaluation is carried out under clear-sky, all-sky, and overcast-sky conditions. CLAAS-2 cloud fractional coverage data are used to determine clear and overcast sky days. The observed GHI data are first quality controlled using the Baseline Surface Radiation Network methodology and then quality control of the HelioClim model. The traditional statistical benchmarks, namely the relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) provided information about the performance of the datasets. Under clear skies, the estimated datasets showed excellent performance with maximum rMBE, rMAE, and rRMSE less than 6.5% and a minimum R2 of 0.97. In contrast, under overcast-sky conditions there was noticeably poor performance with maximum rMBE (24%), rMAE (29%), rRMSE (39%), and minimum R2 (0.74). For all-sky conditions, good correlation was found for SOLCAST (0.948), CMSAF (0.948), CAMS (0.944), and AP model (0.91); all with R2 over 0.91. The maximum rRMSE for SOLCAST (10%), CAMS (12%), CMSAF (12%), and AP model (11%) was less than 13%. The maximum rMAE for SOLCAST (7%), CAMS (8%), CMSAF (8%), and AP model (9%) was less than 10%, showing good performance. While the R2 correlations for the NASA SSE satellite-based GHI were less than 0.9 (0.896), the maximum rRMSE was 18% and the maximum rMAE was 15%, showing rather poor performance. The performance of the SOLCAST, CAMS, CMSAF, and AP models was almost the same in the study area. CAMS, CMSAF, and AP models are viable, freely available datasets for estimating the daily GHI at South African locations with quantitative certainty. The relatively poor performance of the NASA SSE datasets in the study area could be attributed to their low spatial resolution of 0.5° × 0.5° (~55 km × 55 km). The feasibility of the datasets decreased significantly as the proportion of sky that was covered by clouds increased. The results of the study could provide a basis/data for further research to correct biases between in situ observations and the estimated GHI datasets using machine learning algorithms.
Intelligent detection method for substation insulator defects based on CenterMask

With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing speed and accuracy of defect detection. Based on the end-to-end learning paradigm, this paper proposes an intelligent detection method for substation insulator defects based on CenterMask. First, the backbone network VoVNet is improved according to the residual connection and eSE module, which effectively solves the problems of deep network saturation and gradient information loss. On this basis, an insulator mask generation method based on a spatial attentiondirected mechanism is proposed. Insulators with complex image backgrounds are accurately segmented. Then, three strategies of pixel-wise regression prediction, multi-scale features and centerness are introduced. The anchor-free single-stage target detector accurately locates the defect points of insulators. Finally, an example analysis is carried out with the substation inspection image of a power supply company in a certain area to verify the effectiveness and robustness of the proposed method.
FedMMD: Heterogenous Federated Learning based on Multi-teacher and Multi-feature Distillation

Federated distillation, a new algorithmic paradigm in Federated learning, enables clients to train different network architectures.In federated distillation, students can learn information from others by distilling the average prediction of clients on the public server data without sacrificing their individual data privacy. However, the method only using the average soft labels as the teacher of all students suffers from client-draft, especially when the local data is heterogeneous. The soft labels are average classification score among models. In this paper, we propose a new framework FedMMD(Federated Learning based on Multi-teacher and Multi-feature Distillation), which is robust to different data distributions across clients. The FedMMD extends the aggregation phase and the distillation phase in the FD training procedure. In contrast to approaches sharing the same teacher across all students, FedMMD assigns various teachers to each student required to take multiple independent distillations. Due to each model can be separately regarded as a teacher, FedMMD solves the problem that the shared teacher only has an average performance caused by the average soft labels. Meanwhile, in each distillation, instead of using the models&#x2019; average soft labels on the public data as the teacher, FedMMD introduces a combination of intermediate representations and soft labels as targets of distillation to learn more information details of teachers. Our extensive experiments on both public datasets (CIFAR10 and MINIST) demonstrate the outperformance of the proposed method. Especially in the heterogeneous data, FedMMD improves the accuracy in the non-IID (Independent and Identically Distributed) regime by 20%.
Deduplication Data Technologies Impact on the Backup Systems in Intellectual Property Organizations

The scientific paper presents experimental research on testing deduplication technologies for data saved in centralized backup systems in IP organizations. This research details the impact of implementing data deduplication technology on backup and restore processes in the context of sustained growth in data volumes. The authors consider important to highlight all data backup mechanisms in order to ensure the management of scheduled backup sessions in accordance with the provisions of the ISO27001:2018 standard. The study focuses on comparing the performance of disk backup systems with SAS technology with and without the implementation of deduplication technologies. The basis of the study regarding the impact of the development of data deduplication mechanisms on the performance of centralized backup systems in these organizations is the analysis of the results of dynamic simulations. The authors consider that these particularly important aspects are presented in detail and with an applied research background can be an important contribution in the approached field.
OCR Error Correction for Vietnamese OCR Text with Different Edit Distances

Candidate word generation by character edit operations is an important method that has been employed in many OCR error correction approaches. In this paper, we study how character edit distances impact the performance of OCR error correction. We propose the algorithm of generating correction candidates with different edit distances. Correction candidates for both non-word and real-word errors are considered. The candidates are scored and ranked based on linguistic features and edit probability. The experiments are tested on the VNOnDB database used in the Vietnamese online handwritten text recognition competition (VOHTR 2018). We evaluate the error correction performance on different edit distances in terms of two error metrics, character error rate (CER) and word error rate (WER). It is shown that the edit distances of 1 and 2 obtain better correction results instead of higher edit distances.
Signal detection and modulation recognition based on convolutional neural networks

In modern electronic countermeasures systems, accurate detection of unknown user signals and identification of signal modulation mode are essential for signal demodulation and communication countermeasures. Energy detector is a traditional unknown signal detection method, but it has SNR-wall problem, and it cannot distinguish different types of signals. In this paper, to address the problems encountered by traditional methods, we first propose a signal detector based on neural networks. It uses the time domain waveform information of the signal to achieve good detection performance, and it can work without relevant prior knowledge. Furthermore, the effects of different modulation modes and SNR on performance are studied. Finally, a signal classifier based on deep learning is proposed, which can achieve good classification performance for detected signals.
High-speed and Low-area Implementations of a Generic and Parallel Integral Image Architecture

Nowadays many applications of computer vision and image processing are based on Integral IMage (IIM) algorithm. Since the first use of IIM by Viola-Jones in the face detection algorithm, its computation is still a real challenge and receive a great attention in the research area especially in the field of real time applications. The IIM is greedy in hardware resources where the total number of additions increases with the image size and requires frequent memory accesses which result in the bottleneck effect when realized with CPU. To improve the processing efficiency, we propose a generic parallel/pipeline architectures with and without memory based on dual direction (by columns then by rows) or two levels of data flow oriented in IIM computing architecture. An interesting characteristic of the proposed architectures is the genericity of the calculation, according to the degree of parallelism (the maximum column size and the maximum row size that can be calculated in a clock cycle), the quantization of image values, on the one hand and on the other hand these architectures can be used in two functional modes. Based on the synthesis results of the implementation using Altera Quartus prime lite edition targeting an Intel/Altera Cyclone V - (FPGA), the proposed architecture achieves a high-throughput and low-area, when compared to the state-of-the-art methods. More particularly, for 480 × 640 image size, the proposed IIM architecture involves 765 logic registers, 643 slice LUT, and just 13,7 kbits and it operates at a maximum frequency of 182.28 MHz. These results show that this approach is one of the best candidates for portable applications that require high speed processing and low hardware resources usage.
Multi-expert and multi-criteria evaluation of Online Education Factors: A fuzzy AHP approach

Abstract : COVID-19 has highly impacted industry, agriculture, services sector as well as education sector all over the world. The countries have seen a complete lockdown, and it has badly affected students' lives in the education sector. Almost more than 32 crores of learners are unable to move to schools or colleges in India. The solution to overcome the offline education crisis is to move to online platforms. But, the effectiveness of online platforms for teaching is a big challenge. The most important thing in teaching is achieving the satisfaction level of students. The literature shows many factors impact satisfaction level, and these factors are ICT orientation, Big-Five Personality Dimensions, Instructor Quality, and Course Design. These factors are having subfactors four, five, seven, and six, respectively. The current study targets to prioritize the factors by using the fuzzy AHP approach. The factors are pritorized based on their normalized weight. To gain depth insights, thesub-factors are also prioritized, and they are ranked relatively as well as globally. Relatively means to figure out the important and least sub-factor from the corresponding factor, globally means to rank each sub-factor among all identified factors. The results show that BF is the most important and CD is the leastimportant factor for achieving students satisfaction level. Looking at relative weights, NE and LQ are the most important factors among BF and CD, respectively. After considering global weights, PI and AD are the most and least important sub-factors, respectively. Keywords: Online Classes, Fuzzy AHP, Instructor Quality, ICT Orientation, COVID 19
A Robust Adversarial Network-Based End-to-End Communications System with Strong Generalization Ability Against Adversarial Attacks

We propose a novel defensive mechanism based on a generative adversarial network (GAN) framework to defend against adversarial attacks in end-to-end communications systems. Specifically, we utilize a generative network to model a powerful adversary and enable the end-to-end communications system to combat the generative attack network via a minimax game. We show that the proposed system not only works well against white-box and black-box adversarial attacks but also possesses excellent generalization capabilities to maintain good performance under no attacks. We also show that our GAN-based end-to-end system outperforms the conventional communications system and the end-to-end communications system with/without adversarial training.
CCENet: Cascade Class-Aware Enhanced Network for High-Resolution Aerial Imagery Semantic Segmentation

Semantic segmentation of high-resolution aerial images is a challenging task on account of inter-class homogeneity and intra-class heterogeneity of land cover. Recent works have sought to mitigate this issue by exploiting pixel-wise global contextual information using self-attention mechanism. However, the existing attention-based methods usually generate inaccurate object boundary segmentation results, as the self-attention model is embedded in high-level features with low resolution due to prohibitively computational complexity. Moreover, existing attention-based models ignore class-wise contextual information from intermediate results, which leads to undesirable feature separability. To obtain discriminative feature as well as generate accurate segmentation boundaries, we present a novel segmentation framework, named Cascade Class-aware Enhanced Network (CCENet) for high resolution aerial imagery. The proposed CCENet predicts segmentation results on multiple stages, and the result of the previous stage is used to refine object boundary details for the latter stage. To exploit the class-aware prior information in previous stage, we propose a lightweight Class-aware Enhanced Module (CaEM) to grab the class-aware contextual dependencies. Specifically, CaEM first extract a set of class representation of the land covers by Global Class Pooling (GCP) block, then reconstruct enhanced feature using Class Relation Measurement (CRM), which alleviates the inter-class homogeneity and intra-class heterogeneity of ground objects in feature space. Quantitative and qualitative experimental results on three publicly available datasets demonstrate the superiority of our CCENet over other state-of-the-art methods in the items of high labelling accuracy and computation efficiency.