Reviewerly/exHarmony huggingface dataset:
Dehazing algorithm for unmanned surface vessel based on GAN-U-Net++ network

The autonomous perception of unmanned surface vessels in the natural sea environment has improved significantly; however, it is adversely affected by haze. Improving the U-Net network can retain and restore spatial information effectively and improve image dehazing. However, many dehazing algorithms that use the U-Net network as the main architecture face problems, such as distorted images and blurred contours of small distant objects. To overcome these limitations, we propose a generative adversarial network (GAN)-U-Net++ network that uses the U-Net++ network with a coordinate attention mechanism as the main part of a GAN generator. We used the coordinate attention mechanism to perform horizontal and vertical pixel level pooling to improve the attention on small targets in hazy images. The addition of the edge preservation loss function and image gradient loss function to small targets improved the sharpness of their edges. A Markov discriminator was used to evaluate the authenticity of the images. Several experiments were conducted to compare the proposed method with existing dehazing algorithms. The structural similarity value and peak signal-to-noise ratio increased by 15.74% and 29.14%, respectively.
Deep Learning-Based English-Chinese Translation Research

Neural machine translation (NMT) has been bringing exciting news in the field of machine translation since its emergence. However, because NMT only employs single neural networks to convert natural languages, it suffers from two drawbacks in terms of reducing translation time: NMT is more sensitive to sentence length than statistical machine translation and the end-to-end implementation process fails to make explicit use of linguistic knowledge to improve translation performance. The network model performance of various deep learning machine translation tasks was constructed and compared in English-Chinese bilingual direction, and the defects of each network were solved by using an attention mechanism. The problems of gradient disappearance and gradient explosion are easy to occur in the recurrent neural network in the long-distance sequence. The short and long-term memory networks cannot reflect the information weight problems in long-distance sequences. In this study, through the comparison of examples, it is concluded that the introduction of an attention mechanism can improve the attention of context information in the process of model generation of the target language sequence, thus translating restore degree and fluency higher. This study proposes a neural machine translation method based on the divide-and-conquer strategy. Based on the idea of divide-and-conquer, this method identifies and extracts the longest noun phrase in a sentence and retains special identifiers or core words to form a sentence frame with the rest of the sentence. This method of translating the longest noun phrase and sentence frame separately by the neural machine translation system, and then recombining the translation, alleviates the poor performance of neural machine translation in long sentences. Experimental results show that the BLEU score of translation obtained by the proposed method has improved by 0.89 compared with the baseline method.
Application of Clustering Algorithm in English Proficiency Evaluation under the Framework of Big Data

With the advancement of science and technology as well as the continual improvement of big data analysis technology, the accuracy of traditional data information classification has declined, making it impossible to assess English ability effectively. A competency evaluation model for college English teaching vacancies is built using this information and the big data architecture. The ability of the big data information model is evaluated and feature information of ability constraints is extracted using the predefined constraint parameter index analysis model. Simultaneously, the K-means clustering algorithm is used to cluster and integrate a series of index parameters of English ability using big data, and the English teaching resource allocation plan is completed in accordance with this, allowing for the scientific evaluation of English teaching ability. The results of the studies show that the clustering method utilized in the context of big data can aid in the evaluation of English competence. In the experiment, four test cycles of English teaching skills were set up, and the effectiveness of the English evaluation techniques described in this paper and two classical cluster evaluation methods were compared and tested. The research shows that using the method described in this paper to evaluate English teaching skills can significantly improve the full utilization of data.
Blockchain Technology-Based Electronic Payment Strategy for City Mobile Pass Cards

Currently, there has been a paradigm shifted in the storing of electronic payment records (EPRs) on mobile distributed systems, wherein mobile devices are coupled with cloud computing to ease financial data transfers between consumers and stakeholders. This enhanced architecture allows low-cost online payment services with high versatility and EPR accessibility. Nevertheless, for e-payment platforms, such a new paradigm poses worries about data privacy and network security. A difficult challenge is how to reliably disseminate EPRs across mobile users while maintaining high-security security standards in the mobile cloud. This paper investigates and demonstrates the electronic payment approach of urban mobile pass cards’ dependability and excellence. Furthermore, on a mobile cloud platform, our technology integrates blockchain with the decentralized interplanetary file system (IPFS). We specifically create a reliable access control method on smart contracts to ensure secure EPR sharing across various clients and stakeholders. Our empirical results indicate that our concept provides a viable solution for data transfers on mobile clouds while protecting critical payment information from potential attacks. When compared to previous data sharing models, the system evaluation and security analysis show performance gains in lightweight access control architecture, minimal network latency with excellent security and data confidentiality levels.
A lightweight hardware‐efficient recurrent network for video super‐resolution

Recent studies in the field of video super-resolution (VSR) have taken great advantage of neural networks and achieved remarkable performance. However, current neural network-based VSR methods have the following two limitations: (1) sophisticated models usually have high computational overhead and require large caches to store intermediate feature maps and parameters. (2) Bidirectional RNN-based methods cannot perform VSR until a complete video sequence is available because of the back-propagation of hidden states. The above two shortcomings make the existing VSR models hard to achieve real-time VSR. To reduce the computational complexity of the network, the authors propose a lightweight hardware-efficient recurrent neural network model, LERN, which is combined with depthwise separable convolution and pixel unshuffle. An SRA block is also introduced to help the model get better VSR performance. In order to wisely exploit video inter-frame information without causing the back propagation of hidden states, the authors introduce a new recurrent architecture that is optimized for video sequences. Besides, the authors design a quantization algorithm to find the optimal fixed-point representations for each layer. Experimental results demonstrate that LERN can achieve excellent performance with low overhead.
An effective LRTC model integrated with total <b>α</b> ‐order variation and boundary adjustment for multichannel visual data inpainting

Restoring damaged multichannel visual data with high loss ratio is quite a challenging task. To address this problem, an effective LRTC (low-rank tensor completion) model integrated with total α-order variation (TVα) in the fractional bounded variation space BVα is proposed to perform superior fractional-in-space regularization. Based on using LR constraint to restore global patterns, TVα regularization is integrated to exploit nonlocally-correlated information on each channel to infer the lost data and simultaneously effectively deal with complex details due to the powerful fractional calculus. Then, a nonlocal fractional regularization strategy for multi-dimensional data and an effective numerical optimization method are creatively designed to solve this problem. Two novel fractional derivative matrix approximations are derived and applied to the first two unfolding modes of the tensor respectively to conveniently solve the fractional regularization subproblem by using an element-wise shrinkage-thresholding operation. In addition, boundary extension and adjustment strategy are designed for the unfolded matrices to alleviate the influence of inaccurate boundary conditions in fractional derivative computations. Experiments are conducted to illustrate its performance and efficiency for YUV video, RGB and HSI restoration, especially its ability to effectively recover complex structures and the details of multi-component visual data with relatively high missing rate.
A Comparative Study of Methods for the Visualization of Probability Distributions of Geographical Data

Probability distributions are omnipresent in data analysis. They are often used to model the natural uncertainty present in real phenomena or to describe the properties of a data set. Designing efficient visual metaphors to convey probability distributions is, however, a difficult problem. This fact is especially true for geographical data, where conveying the spatial context constrains the design space. While many different alternatives have been proposed to solve this problem, they focus on representing data variability. However, they are not designed to support spatial analytical tasks involving probability quantification. The present work aims to adapt recent non-spatial approaches to the geographical context, in order to support probability quantification tasks. We also present a user study that compares the efficiency of these approaches in terms of both accuracy and usability.
Towards Proactive Surveillance through CCTV Cameras under Edge-Computing and Deep Learning

Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. The traditional closed-circuit television (CCTV) surveillance and control system requires human intervention to detect such crime incidents. The purpose of this research is to develop a real-time automatic weapon carrier detection system that may be used with CCTV cameras and surveillance systems. The goal is to alarm and alert the security officials to take proactive action to prevent violent activities. In deep learning literature, region-based classifiers (R-FCN and Faster R-CNN) and regression-based detectors (Yolo invariant) are being used as promising object detection methods. Although region-based classifiers are accurate, they lack the speed of detection required for real-time detection, whereas regression-based detectors (for example, YoloV4 invariant) are fast enough for real-time detection, but lack accuracy. The method applied in this study relies on Yolov4 to quickly detect anomalies, followed by R-FCN to boost detection accuracy by filtering out any false positives. A weapon dataset comprising 4430 locally and internationally available weapon photos with a 70–30 split ratio is used to train and test the system, which is subsequently evaluated using a live surveillance camera system. This hybrid system achieved a 90% accuracy with a low false positive rate, as well as 94% precision, 86% recall, and 89% F1 score. Our results prove that the proposed hybrid system is useful for proactive real-time surveillance to alarm the existence of a suspicious weapon carrier in a surveillance area.
Development of a Mathematical Model to Size the Photovoltaic and Storage Battery Based on the Energy Demand Pattern of the House

Solar energy is used in buildings worldwide. However, because the efficiency of photovoltaic power generation varies with environmental fluctuations, it is difficult to control. Therefore, electricity generation from photovoltaics is often poorly matched to the electricity load of the house. The use of storage batteries and photovoltaic panels can effectively improve the stability of the energy supply; however, it also introduces the problem of higher initial costs. Generally, a larger photovoltaic area and battery capacity can lead to higher costs and more renewable energy; therefore, to determine a suitable size of photovoltaic and storage battery for a house, the energy demand of the house must also be considered. The traditional method for sizing photovoltaics and storage batteries mainly considers the daily average electricity demand and the useful area for installing photovoltaics. To size the photovoltaic in a more precise manner, we propose a mathematical model (nonlinear programming) for selecting a relatively optimal solution for the photovoltaic area, battery capacity, and photovoltaic installation angle by considering the hourly and annual demands for electricity from the grid. To validate this mathematical method, a detached house in Zhouzhi county, Shaanxi province, China, was selected to size the devices by the proposed method. Results show that the device sizes determined using the proposed mathematical model are significantly smaller than those determined using the traditional method, without suffering a significant increase in the demand for electricity from the grid. According to our economic analysis, although the proposed method for sizing devices reduces the device cost payback period by half compared to that of the traditional method, the payback period for the devices sized using the proposed method is still 10.6 years. The extremely low electricity price in China may contribute to the extended payback period and thus discourage residents from installing renewable energy devices.
Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

In order to overcome the problem that learners and teachers cannot find instructional resources to meet their needs and information overload in the massive resources, this article proposes and designs a music instructional resource management platform based on DCNN. This article expounds the overall goal, design principle, overall structure, and interface design of the system. At the same time, the whole construction process of a music instructional resources integration system based on DCNN is discussed in detail from the aspects of configuration of development environment, localization of platform interface, and realization of main functions of the system. In addition, through the demand analysis tool, the demand of college music instructional resources management is analyzed in detail and deeply, and the demand document is formed. This article makes an in-depth study on the categories of music instructional resources and summarizes the resource classification methods that are in line with the actual instructional activities. The experiments show that the accuracy of the proposed algorithm is improved by about 6% compared with the fuzzy clustering algorithm. At the same time, the stability of this system can reach 96.14%. This system is rich in functions and easy to use and can provide a feasible scheme for the management of instructional resources in various disciplines.
Influence Maximization With Visual Analytics

In social networks, individuals' decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; (ii) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms.
Addressing discourse differences in the writing of Russian engineering students and international researchers

Virtually all researchers understand the requirement of presenting their studies in peer-reviewed English-medium journals. Russian scientific writers understand this necessity too; however, evidence suggests that these particular researchers are under-performing relative to similar non-native English speakers. The considerable challenge Russians face centers on articulating their ideas in English in the way that meets the norms and expectations of their international discourse community. That is, their writing is characterized as wordy, cumbersome, too academese, and syntactically complex. These issues need to be addressed in the early stages of developing writing skills. In this study, we address discourse differences between the scientific writing of Russian engineering students and that of international experts. Using the computational linguistics tools Coh-Metrix and Gramulator, we compare a corpus of students’ manuscripts with a similar corpus of experts’ published papers. We focused on six conceptual categories: readability, writing quality, cohesion, syntax, word choice, and genre purity. The overall results suggest that student writing differs significantly for multiple characteristics of text and discourse. A discriminant analysis provided a model that successfully predicts group membership and helps identify the most important issues in Russian student writing. Measures such as noun phrase density, genre purity, word age of acquisition, and variance in sentence length were found to be significant positive predictors of Russian student writing, whereas lexical diversity, adversative/contrastive connectives, adverbial phrase density, and word concreteness were significant positive predictors of expert writing. Our analysis allowed us to provide guidance for instructors, materials designers, as well as for technological assessment tools.
Non-Deep Active Learning for Deep Neural Networks

One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative unlabeled images, conventional methods use deep neural networks with a large number of computation nodes and long computation time, but we propose a non-deep neural network method that does not require any additional training for unlabeled image selection. The proposed method trains a task model on labeled images, and then the model predicts unlabeled images. Based on this prediction, an uncertainty indicator is generated for each unlabeled image. Images with a high uncertainty index are considered to have a high information content, and are selected for annotation. Our proposed method is based on a very simple and powerful idea: select samples near the decision boundary of the model. Experimental results on multiple datasets show that the proposed method achieves higher accuracy than conventional active learning methods on multiple tasks and up to 14 times faster execution time from 1.2 × 106 s to 8.3 × 104 s. The proposed method outperforms the current SoTA method by 1% accuracy on CIFAR-10.
Optimization of Network Home Management System Based on Big Data

Network home has become a research hotspot in today’s society, and it can improve the comfort, safety, and convenience of people’s lives. The traditional network home model only makes certain actions to the home system according to people’s instructions, and it is difficult to realize the intelligence of network home. This also limits the security and convenience of an online home. This study makes full use of the advantages of big data technology in processing nonlinear data, and applies the convolutional neural network (CNN) method and long and short-term memory (LSTM) neural network method to the network home system. CNN can be used to extract people’s behavior information, and LSTM can be used to extract people’s speech features. CNN method can establish the relationship between people’s behavior information, speech information, and network home management system. At the same time, this research mainly analyzes the lighting system, home appliance system, security system, and floor heating system in the network home system. The results show that the CNN-LSTM method has high accuracy in predicting the four systems of network home. The largest prediction error is only 2.78%, and this part of the error comes from the prediction of the home appliance system. The smallest prediction error is only 0.98%.
Modeling Subjective Affect Annotations with Multi-Task Learning

In supervised learning, the generalization capabilities of trained models are based on the available annotations. Usually, multiple annotators are asked to annotate the dataset samples and, then, the common practice is to aggregate the different annotations by computing average scores or majority voting, and train and test models on these aggregated annotations. However, this practice is not suitable for all types of problems, especially when the subjective information of each annotator matters for the task modeling. For example, emotions experienced while watching a video or evoked by other sources of content, such as news headlines, are subjective: different individuals might perceive or experience different emotions. The aggregated annotations in emotion modeling may lose the subjective information and actually represent an annotation bias. In this paper, we highlight the weaknesses of models that are trained on aggregated annotations for modeling tasks related to affect. More concretely, we compare two generic Deep Learning architectures: a Single-Task (ST) architecture and a Multi-Task (MT) architecture. While the ST architecture models single emotional perception each time, the MT architecture jointly models every single annotation and the aggregated annotations at once. Our results show that the MT approach can more accurately model every single annotation and the aggregated annotations when compared to methods that are directly trained on the aggregated annotations. Furthermore, the MT approach achieves state-of-the-art results on the COGNIMUSE, IEMOCAP, and SemEval_2007 benchmarks.