Reviewerly/exHarmony huggingface dataset:
LRF-SRNet: Large-Scale Super-Resolution Network for Estimating Aircraft Pose on the Airport Surface

The introduction of various deep neural network architectures has greatly advanced aircraft pose estimation using high-resolution images. However, realistic airport surface monitors typically take low-resolution (LR) images, and the results of the aircraft pose estimation are far from being accurate enough to be considered acceptable because of long-range capture. To fill this gap, we propose a brand-new, end-to-end low-resolution aircraft pose estimate network (LRF-SRNet) to address the problem of estimating the pose of poor-quality airport surface surveillance aircraft images. The method successfully combines the pose estimation method with the super-resolution (SR) technique. Specifically, to reconstruct high-resolution aircraft images, a super-resolution network (SRNet) is created. In addition, an essential component termed the large receptive field block (LRF block) helps estimate the aircraft's pose. By broadening the neural network's receptive field, it enables the perception of the aircraft's structure. Experimental results demonstrate that, on the airport surface surveillance dataset, our method performs significantly better than the most widely used baseline methods, with AP exceeding Baseline and HRNet by 3.1% and 4.5%.
A Blockchain-Based Authentication and Authorization Scheme for Distributed Mobile Cloud Computing Services

Authentication and authorization constitute the essential security component, access control, for preventing unauthorized access to cloud services in mobile cloud computing (MCC) environments. Traditional centralized access control models relying on third party trust face a critical challenge due to a high trust cost and single point of failure. Blockchain can achieve the distributed trust for access control designs in a mutual untrustworthy scenario, but it also leads to expensive storage overhead. Considering the above issues, this work constructed an authentication and authorization scheme based on blockchain that can provide a dynamic update of access permissions by utilizing the smart contract. Compared with the conventional authentication scheme, the proposed scheme integrates an extra authorization function without additional computation and communication costs in the authentication phase. To improve the storage efficiency and system scalability, only one transaction is required to be stored in blockchain to record a user's access privileges on different service providers (SPs). In addition, mobile users in the proposed scheme are able to register with an arbitrary SP once and then utilize the same credential to access different SPs with different access levels. The security analysis indicates that the proposed scheme is secure under the random oracle model. The performance analysis clearly shows that the proposed scheme possesses superior computation and communication efficiencies and requires a low blockchain storage capacity for accomplishing user registration and updates.
NRSTRNet: A Novel Network for Noise-Robust Scene Text Recognition

Abstract Scene text recognition (STR) has been widely applied in industrial and commercial fields. However, existing methods still face challenges when processing text images with defects such as low contrast, blur, low resolution, and insufficient illumination. These defects are common in actual situations because of diverse text backgrounds in natural scenes and limitations in shooting conditions. To address these challenges, we propose a novel network for noise-robust scene text recognition (NRSTRNet), which comprehensively suppresses the noise in the three critical steps of STR. Specifically, in the text feature extraction stage, NRSTRNet enhances the text-related features through the channel and spatial dimensions and disregards some disturbances from the non-text area, reducing the noise and redundancy in the input image. In the context encoding stage, fine-grained feature coding is proposed to effectively reduce the influence of previous noisy temporal features on current temporal features while simultaneously reducing the impact of partial noise on the overall encoding by sharing contextual feature encoding parameters. In the decoding stage, a self-attention module is added to enhance the connections between different temporal features, thereby leveraging the global information to obtain noise-resistant features. Through these approaches, NRSTRNet can enhance the local semantic information while considering the global semantic information. Experimental results show that the proposed NRSTRNet can improve the ability to characterize text images, enhance stability under the influence of noise, and achieve superior accuracy in text recognition. As a result, our model outperforms SOTA STR models on irregular text recognition benchmarks by 2% on average, and it is exceptionally robust when applied to noisy images.
Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.
Efficiency Assessment of Five Types of Photovoltaic Modules Installed on a Fixed and on a Dual-Axis Solar-Tracked Platform

A solution to increase the electrical output of the photovoltaic systems relies on solar tracking mechanisms that increase the amount of received solar energy. The experimental results obtained during a monitoring period of one year are comparatively presented in the paper for five types of photovoltaic modules installed on a fixed platform (as reference) and on a dual-axis solar tracking platform in the Renewable Energy Systems and Recycling R&amp;D Centre of the Transilvania University of Brasov, Romania. The influence of the solar-tracking mechanism and the meteorological conditions specific to the four seasons during the monitoring period on the output of the analysed photovoltaic technologies are discussed in the paper. The solar tracking increases by 28% the amount of the yearly received solar energy and by 29.6% the electrical energy output of the entire PV platform. The solar conversion efficiency of the tracked PV platform is slightly increased (14.34%) when compared with the fixed one (14.17%). When assessing the influence of solar tracking on each type of PV, the results show that the CIGS PV module has the highest relative energy gain (34%) followed by CIS (30.8%), m-Si (30.6%), p-Si (27.3%) and CdTe (23.4%) PV modules.
The bumpy paths of online sleuthing: Exploring the interactional accomplishment of familiarity, evidence, and authority in online crime discussions

Much of today’s public discourse on crime cases take place on online platforms, as long chains of high-speed posts: speculations, analyses, and laments, as well as ironic, sarcastic, and derogatory comments. These give excellent (and yet risky) possibilities to engage in homemade investigation, with other posters as instant reviewers and audiences. In this article, we explore the interactional origin of case-related familiarity, evidence and authority in crime discussions on the Swedish platform Flashback. Through Internet data and interviews, we show how online sleuths interact digitally with one another so that familiarity with the case is performed, leads and evidence suggested, and investigative authority recognized. We argue that an interactionist and ethnographic approach is needed to uncover such recurring processes in online crime case discussions. The accomplishment of sleuthing is highly dependent on others’ shifting responses, and is, therefore, a “bumpy” path.
An Automatic Counting Algorithm for Topographic Maps Based on Videos for Map Management

Topographic maps comprise a rich data source for the processing and analysis of geographic information. However, the management of paper topographic maps is still dominated by an inefficient manual method, and there is no counting equipment for topographic maps that is suitable for narrow map warehouse passages. The existing algorithms relating to paper counting require paper hardness and cannot directly obtain the internal information of topographic maps. To address the above problems, using a map inventory machine transformed from a paddle-type paper counting machine to perform a preliminary count and obtain the video data of the counting progress, an automatic counting algorithm for topographic maps for map management is proposed in this study. First, the periodic feature changes of all the targets and each individual target in the videos are analyzed. Second, a deformable parts model accelerated by fast feature pyramids and by reducing the region of interest is used for the object detection of mechanical wheels. The grayscale variance function is selected to quantify the quality change in the images. Finally, a fusion window counting model is constructed with the adaptive window and fixed window methods. The experimental results show that the average counting accuracy of the model is about 95%, which can verify the initial counting results of the machine and provide high-quality keyframes for the subsequent sheet designation recognition.
Attentional Extractive Summarization

In this work, a general theoretical framework for extractive summarization is proposed—the Attentional Extractive Summarization framework. Although abstractive approaches are generally used in text summarization today, extractive methods can be especially suitable for some applications, and they can help with other tasks such as Text Classification, Question Answering, and Information Extraction. The proposed approach is based on the interpretation of the attention mechanisms of hierarchical neural networks, which compute document-level representations of documents and summaries from sentence-level representations, which, in turn, are computed from word-level representations. The models proposed under this framework are able to automatically learn relationships among document and summary sentences, without requiring Oracle systems to compute the reference labels for each sentence before the training phase. These relationships are obtained as a result of a binary classification process, the goal of which is to distinguish correct summaries for documents. Two different systems, formalized under the proposed framework, were evaluated on the CNN/DailyMail and the NewsRoom corpora, which are some of the reference corpora in the most relevant works on text summarization. The results obtained during the evaluation support the adequacy of our proposal and suggest that there is still room for the improvement of our attentional framework.
Identifying Cannabis Use Risk Through Social Media Based on Deep Learning Methods

Cannabis is the most used drug around the world with the highest risks and associated criminal problems in many countries. This research describes the process of classifying online posts to identify cannabis use problems and their associated risks as early as possible. We annotated 11,008 online posts, which we used to build robust classification models. We tested classical and deep learning classifiers. Different CNN- and RNN-based models proved to be promising approaches to detect cannabis use posts. Our system can be used by authorities (such as parents or doctors) to monitor cannabis use-related posts. It could raise an alarm to the relevant authorities to take necessary interventions to analyze the cannabis use risks associated with the posts. To the best of our knowledge, this is the first study that uses deep learning methods successfully to detect cannabis use from any text or online posts. We tested our deep learning models on the SubUse-Cann unseen dataset which contains 17,099 tweets. It is an imbalanced dataset with only 6.9% positive cannabis use tweets. Our CNN-based model performed the best with an accuracy of 95.25% and an F1-score of 92.83% for classifying cannabis use.
Digital Surveillance Trends and Chinese Influence in Light of the COVID-19 Pandemic

Abstract Countries across the world expanded digital surveillance strategies in response to the COVID-19 pandemic. As the pandemic occurred contemporaneously with a global trend toward greater digital repression, commentators advanced the notion that China would use the health crisis to promote a technology-enabled form of authoritarian governance abroad. This article surveys the evidence for these claims by first examining the literature on the increase of digital surveillance associated with China and then presenting three case studies from developing countries with varying responses to the COVID-19 pandemic. The selected countries – Brazil, South Africa and Vietnam – used surveillance technology as part of their pandemic response and have either been influenced by Chinese approaches or adopted Chinese technology in recent years. Examining these case studies allows us to better understand claims regarding China's role in the general spread of digital surveillance and the interplay between Chinese state objectives and local political environments. Crucially, we illustrate how China's engagement in digital governance abroad is heavily contingent on domestic environments. Against a backdrop of China's growing influence in global digital governance, the effects observed in these case studies of Chinese surveillance models and technology proliferating through pandemic management are diffuse and contextualised by local factors.
Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System

Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, semi-supervised 3D object detection (SSOD-3D) has emerged as a technique to alleviate the shortage of labeled samples. However, it is still a challenging problem for SSOD-3D to learn 3D object detection from noisy pseudo labels. In this paper, to dynamically filter the unreliable pseudo labels, we first introduce a self-paced SSOD-3D method SPSL-3D. It exploits self-paced learning to automatically adjust the reliability weight of the pseudo label based on its 3D object detection loss. To evaluate the reliability of the pseudo label in accuracy, we present prior knowledge based SPSL-3D (named as PSPSL-3D) to enhance the SPSL-3D with the semantic and structure information provided by a LiDAR-camera system. Extensive experimental results in the public KITTI dataset demonstrate the efficiency of the proposed SPSL-3D and PSPSL-3D.
Digital Soil Mapping of Cadmium: Identifying Arable Land for Producing Winter Wheat with Low Concentrations of Cadmium

Intake of cadmium (Cd) via vegetable food poses a possible health risk. Cereals are one of the major sources of Cd, and the Cd concentration in the soil has a great effect on the levels in the grain. The aim of the study was to produce decision support for identification of areas suitable for low-Cd winter wheat production in the form of a detailed digital soil map covering an important agricultural region in southern Sweden. A two-step approach was used: (1) we increased the number of soil Cd observations by combining two sets of soil samples, one with laboratory Cd analyses (304 samples) and one with predicted Cd from a portable x-ray fluorescent (PXRF) sensor (2097 samples); and (2) a digital soil mapping (DSM) model (gradient boosting regression) was calibrated on all 2401 soil samples to create a soil Cd concentration map using a number of covariates, of which airborne gamma ray data was identified as the most important. In the first step, cross-validation of the PXRF model obtained a model efficiency (E) of 0.82 and mean absolute error (MAE) of 0.08 mg kg−1. The DSM model had an E of 0.69 and MAE of 0.11 mg kg−1. The map of predicted soil Cd concentrations were compared against 307 winter wheat (Triticum aestivum L.) grain samples with laboratory-analyzed Cd concentrations. Areas in the map with low soil Cd concentrations had a high frequency of lower grain Cd concentrations. The map thus seemed to have potential for finding areas suitable for production of low-Cd winter wheat; e.g., for baby food.
A Tent Lévy Flying Sparrow Search Algorithm for Wrapper-Based Feature Selection: A COVID-19 Case Study

The “Curse of Dimensionality” induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big data and improve accuracy. As a recently proposed algorithm, the Sparrow Search Algorithm (SSA) shows its advantages in the FS tasks because of its superior performance. However, SSA is more subject to the population’s poor diversity and falls into a local optimum. Regarding this issue, we propose a variant of the SSA called the Tent Lévy Flying Sparrow Search Algorithm (TFSSA) to select the best subset of features in the wrapper-based method for classification purposes. After the performance results are evaluated on the CEC2020 test suite, TFSSA is used to select the best feature combination to maximize classification accuracy and simultaneously minimize the number of selected features. To evaluate the proposed TFSSA, we have conducted experiments on twenty-one datasets from the UCI repository to compare with nine algorithms in the literature. Nine metrics are used to evaluate and compare these algorithms’ performance properly. Furthermore, the method is also used on the coronavirus disease (COVID-19) dataset, and its classification accuracy and the average number of feature selections are 93.47% and 2.1, respectively, reaching the best. The experimental results and comparison in all datasets demonstrate the effectiveness of our new algorithm, TFSSA, compared with other wrapper-based algorithms.
Accurate object tracking by aligning and refining multiple predictions in Siamese networks

Siamese trackers, coupled with an efficient cross-correlation layer and benefiting from potent convolutional network technology, draw continuous interest in the field of visual object tracking. However, previous Siamese trackers may suffer from inconsistency of predictions that results in degraded performance during tracking: most of the prevalent Siamese networks employ two parallel branches for different subtasks whereas the corresponding outputs may mismatch with each other to some extent. To attack this issue, we advance a two-stage Siamese tracker named SiamPA for accurate object tracking. It employs center-based anchor-free heads in the first stage for preliminary predictions, meanwhile taking the carefully designed Prediction Alignment and Refinement module (PAR) as the second stage to refine the first-stage output. The PAR module is designed for Alignment and Refinement of multi-branch prediction, which works subtly in a mini-Siam manner. It is equipped with two different prediction branches: one used to align the multiple predictions induced in the first stage and the other to adjust coordinates of proposals. Extensive experiments are conducted to demonstrate the effectiveness of our SiamPA, showing that it achieves favorable performance on several prevalent benchmark datasets. Particularly, SiamPA achieves desirable performance while running at 67 FPS, which is far beyond real-time speed.
Revival of Classical Algorithms: A Bibliometric Study on the Trends of Neural Networks and Genetic Algorithms

The purpose of our bibliometric research was to capture and analyze the trends of two types of well-known classical artificial intelligence (AI) algorithms: neural networks (NNs) and genetic algorithms (GAs). Symmetry is a very popular international and interdisciplinary scientific journal that cover six major research subjects of mathematics, computer science, engineering science, physics, biology, and chemistry which are all related to our research on classical AI algorithms; therefore, we referred to the most innovative research articles of classical AI algorithms that have been published in Symmetry, which have also introduced new advanced applications for NNs and Gas. Furthermore, we used the keywords of “neural network algorithm” or “artificial neural network” to search the SSCI database from 2002 to 2021 and obtained 951 NN publications. For comparison purposes, we also analyzed GA trends by using the keywords “genetic algorithm” to search the SSCI database over the same period and we obtained 878 GA publications. All of the NN and GA publication results were categorized into eight groups for deep analyses so as to investigate their current trends and forecasts. Furthermore, we applied the Kolmogorov–Smirnov test (K–S test) to check whether our bibliometric research complied with Lotka’s law. In summary, we found that the number of applications for both NNs and GAs are continuing to grow but the use of NNs is increasing more sharply than the use of GAs due to the boom in deep learning development. We hope that our research can serve as a roadmap for other NN and GA researchers to help them to save time and stay at the cutting edge of AI research trends.