Reviewerly/exHarmony huggingface dataset:
A Large-scale Behavioural Analysis of Bots and Humans on Twitter

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this article, we perform a comparative analysis of the usage and impact of bots and humans on Twitter—one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task, we assign “bot” and “human” ground-truth labels to the dataset and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis, we draw clear differences and interesting similarities between the two entities.
Objective Video Quality Assessment Combining Transfer Learning With CNN

Nowadays, video quality assessment (VQA) is essential to video compression technology applied to video transmission and storage. However, small-scale video quality databases with imbalanced samples and low-level feature representations for distorted videos impede the development of VQA methods. In this paper, we propose a full-reference (FR) VQA metric integrating transfer learning with a convolutional neural network (CNN). First, we imitate the feature-based transfer learning framework to transfer the distorted images as the related domain, which enriches the distorted samples. Second, to extract high-level spatiotemporal features of the distorted videos, a six-layer CNN with the acknowledged learning ability is pretrained and finetuned by the common features of the distorted image blocks (IBs) and video blocks (VBs), respectively. Notably, the labels of the distorted IBs and VBs are predicted by the classic FR metrics. Finally, based on saliency maps and the entropy function, we conduct a pooling stage to obtain the quality scores of the distorted videos by weighting the block-level scores predicted by the trained CNN. In particular, we introduce a preprocessing and a postprocessing to reduce the impact of inaccurate labels predicted by the FR-VQA metric. Due to feature learning in the proposed framework, two kinds of experimental schemes including train-test iterative procedures on one database and tests on one database with training other databases are carried out. The experimental results demonstrate that the proposed method has high expansibility and is on a par with some state-of-the-art VQA metrics on two widely used VQA databases with various compression distortions.
Edge Detection of High-Voltage Porcelain Insulators in Infrared Image Using Dual Parity Morphological Gradients

A new edge detection operator based on dual parity morphological gradients is proposed, in order to accurately segment a porcelain insulator string in an infrared image before the identification and diagnosis of deteriorated insulators by the extracted thermal information. When applied to the double-shed insulators, the proposed detector is capable of effectively eliminating the blurred region between the upper and lower sheds as well as preventing over-segmentation by combining both odd and even structural elements. Compared with the multiscale morphological gradient algorithm, this technique reduces the size of the maximum structural elements to optimize the computational complexity. A quantitative evaluation using PSNR, IoU, and FPR indexes shows that the new technique outperforms other common edge detectors. In addition, the experiments on thousands of real infrared images demonstrate the detector to be highly effective despite its low complexity.
CrowdTracking: Real-Time Vehicle Tracking Through Mobile Crowdsensing

Traditionally, vehicle tracking is accomplished using predeployed video camera networks, which relies on stationary cameras and searches for the target vehicle from videos. In this paper, we develop CrowdTracking, i.e., a crowd tracking system that people can collaboratively keep track of the moving vehicle by taking photographs, especially in areas where video cameras are deficient. In other words, the underlying support of CrowdTracking is mobile crowdsensing. Several novel ideas underpin CrowdTracking. First, the vehicle can be rapidly localized by using both photographing contexts (including the location and the shooting direction) of the photographer and the road network. Second, the moving speed of the vehicle can be estimated according to two localization results and the trajectory will be predicted. As a result, through continuously collecting photographs of the moving vehicle on different roads, the vehicle can be tracked and localized almost in real time. Through precisely localizing the specified vehicle, two optimization objectives are met: 1) maximizing the tracking coverage to the vehicle's actual trajectory and 2) minimizing the number of participants who are assigned vehicle-tracking tasks. We evaluate the localization method with a real dataset and report about 6 m error. We also evaluate the vehicle-tracking method of CrowdTracking using a synthetic data set and experimental results validate its effectiveness and efficiency.
A Novel Image Encryption Scheme Based on 7D Hyperchaotic System and Row-column Simultaneous Swapping

In this paper, a novel image encryption algorithm is proposed based on a sevendimensional (7D) hyperchaotic system and simultaneous row-column swapping.First, the 7D hyperchaotic system is introduced.The SHA-512 hash function is applied in order to generate the system parameters and initial values of the 7D hyperchaotic system.Seven real numbers are transformed in order to form three new sequences.The new sequences are used for the scrambling and diffusion operations.Then, the scrambling matrix is formed, and a plain image is subjected to the permutation process.Finally, the diffusion method is performed on the scrambled image, and the cipher image is ultimately obtained.The experimental results reveal that the proposed scheme has good performance.This scheme has large secret keys and is highly sensitive to the plain images and initial keys.This method could also resist many kinds of attacks.The proposed method is superior with respect to its security and encryption performance compared with some other methods.
Optimizing echo state network with backtracking search optimization algorithm for time series forecasting

The echo state network (ESN) is a state-of-the art reservoir computing approach, which is particularly effective for time series forecasting problems because it is coupled with a time parameter. However, the linear regression algorithm commonly used to compute the output weights of ESN could usually cause the trained network over-fitted and thus obtain unsatisfactory results. To overcome the problem, we present four optimized ESNs that are based on the backtracking search optimization algorithm (BSA) or its variants to improve generalizability. Concretely, we utilize BSA and its variants to determine the most appropriate output weights of ESN given that the optimization problem is complex while BSA is a novel evolutionary algorithm that effectively unscrambles optimal solutions in complex spaces. The three BSA variants, namely, adaptive population selection scheme (APSS)–BSA, adaptive mutation factor strategy (AMFS)–BSA, and APSS&AMFS–BSA, were designed to further improve the performance of BSA. Time series forecasting experiments were performed using two real-life time series. The experimental results of the optimized ESNs were compared with those of the basic ESN without optimization, and the two other comparison approaches, as well as the other existing approaches. Experimental results showed that (a) the results of the optimized ESNs are more accurate than that of basic ESN and (b) APSS&AMFS–BSA–ESN nearly outperforms basic ESN, the three other optimized ESNs, the two comparison approaches, and other existing optimization approaches.
Salient Object Detection Using Cascaded Convolutional Neural Networks and Adversarial Learning

Salient object detection has received much attention and achieved great success in last several years. It is still challenging to get clear boundaries and consistent saliencies, which can be considered as the structural information of salient objects. A popular solution is to conduct some post-processes (e.g., conditional random field (CRF)) to refine these structural information. In this paper, a novel cascaded convolutional neural networks (CNNs) based method is proposed to implicitly learn these structural information via adversarial learning for salient object detection (we termed the proposed method as CCAL). A cascaded CNNs model is first designed as a generator G, which consists of an encoder-decoder network for global saliency estimation and a deep residual network for local saliency refinement. It is hard to explicitly learn such structural information due to the limitation of frequently-used pixel-wise loss functions. Instead, a discriminator D is then designed to distinguish the real salient maps (i.e., ground truths) from the fake ones produced by G, based on which an adversarial loss is introduced to optimize G. G and D are trained in a fully end-to-end fashion by following the strategy of conditional generative adversarial networks to make G well learn the structural information. At last, G is able to produce high quality salient maps without requiring any post-process to fool D. Experimental results on eight benchmark datasets demonstrate the effectiveness and efficiency (about 17 fps on graphics processing unit (GPU)) of the proposed method for salient object detection.
Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data

To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user's experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.
Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost based demodulator is developed, which employs the $k$-Nearest Neighbor (KNN) as a weak classifier to form a strong combined classifier. Finally, experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood (MLD) based demodulator.
Super-resolution land cover mapping by deep learning

Super-resolution mapping (SRM) is a technique to estimate a fine spatial resolution land cover map from coarse spatial resolution fractional proportion images. SRM is often based explicitly on the use of a spatial pattern model that represents the land cover mosaic at the fine spatial resolution. Recently developed deep learning methods have considerable potential as an alternative approach for SRM, based on learning the spatial pattern of land cover from existing fine resolution data such as land cover maps. This letter proposes a deep learning-based SRM algorithm (DeepSRM). A deep convolutional neural network was first trained to estimate a fine resolution indicator image for each class from the coarse resolution fractional image, and all indicator maps were then combined to create the final fine resolution land cover map based on the maximal value strategy. The results of an experiment undertaken with simulated images show that DeepSRM was superior to conventional hard classification and a suite of popular SRM algorithms, yielding the most accurate land cover representation. Consequently, methods such as DeepSRM may help exploit the potential of remote sensing as a source of accurate land cover information.
Segment-Based Disparity Refinement With Occlusion Handling for Stereo Matching

In this paper, we propose a disparity refinement method that directly refines the winner-take-all (WTA) disparity map by exploring its statistical significance. According to the primary steps of the segment-based stereo matching, the reference image is over-segmented into superpixels and a disparity plane is fitted for each superpixel by an improved random sample consensus (RANSAC). We design a two-layer optimization to refine the disparity plane. In the global optimization, mean disparities of superpixels are estimated by Markov random field (MRF) inference, and then, a 3D neighborhood system is derived from the mean disparities for occlusion handling. In the local optimization, a probability model exploiting Bayesian inference and Bayesian prediction is adopted and achieves second-order smoothness implicitly among 3D neighbors. The two-layer optimization is a pure disparity refinement method because no correlation information between stereo image pairs is demanded during the refinement. Experimental results on the Middlebury and KITTI datasets demonstrate that the proposed method can perform accurate stereo matching with a faster speed and handle the occlusion effectively. It can be indicated that the "matching cost computation + disparity refinement" framework is a possible solution to produce accurate disparity map at low computational cost.
Efficient Privacy-Preserving Multi-Dimensional Data Aggregation Scheme in Smart Grid

Smart grid, characterized by high efficiency, security, and flexibility, is gradually replacing the traditional power grid. Data aggregation technology is frequently used to avoid user privacy disclosure as a result of power consumption data transmission in the smart grid. However, traditional one-dimensional data aggregation schemes fail to meet the demands of fine-grained analysis. Therefore, this paper proposes an efficient privacy-preserving multi-dimensional data aggregation (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> MDA) scheme in smart grid by virtue of homomorphic encryption and superincreasing sequence. The security analysis indicates that the proposed scheme is proved to be secure in the random oracle model, satisfying all security and privacy requirements. The extensive performance analysis shows that in comparison to the related schemes, the proposed scheme achieves lowest computation and communication costs, thus appropriate for practical applications.
A deep learning ball tracking system in soccer videos

Increasing interest, enthusiasm of sport lovers, and economics involved offer high importance to sports video recording and analysis. Being crucial for decision making, ball detection and tracking in soccer has become a challenging research area. This paper presents a novel deep learning approach for 2D ball detection and tracking (DLBT) in soccer videos posing various challenges. A new 2-stage buffer median filtering background modelling is used for moving objects blob detection. A deep learning approach for classification of an image patch into three classes, i.e. ball, player, and background is initially proposed. Probabilistic bounding box overlapping technique is proposed further for robust ball track validation. Novel full and boundary grid concepts resume tracking in ball_track_lost and ball_out_of_frame situations. DLBT does not require human intervention to identify ball from the initial frames unlike the most published algorithms. DLBT yields extraordinary accurate and robust tracking results compared to the other contemporary 2D trackers even in presence of various challenges including very small ball size and fast movements.
A fault-tolerant non-Clifford gate for the surface code in two dimensions

Fault-tolerant logic gates will consume a large proportion of the resources of a two-dimensional quantum computing architecture. Here we show how to perform a fault-tolerant non-Clifford gate with the surface code; a quantum error-correcting code now under intensive development. This alleviates the need for distillation or higher-dimensional components to complete a universal gate set. The operation uses both local transversal gates and code deformations over a time that scales with the size of the qubit array. An important component of the gate is a just-in-time decoder. These decoding algorithms allow us to draw upon the advantages of three-dimensional models using only a two-dimensional array of live qubits. Our gate is completed using parity checks of weight no greater than four. We therefore expect it to be amenable with near-future technology. As the gate circumvents the need for magic-state distillation, it may reduce the resource overhead of surface-code quantum computation considerably.
Research about the university teaching performance evaluation under the data envelopment method

How to take active and effective measures to evaluate the university scientifically and rationally has been an eternal topic that the educational circles are constantly exploring. Based on the principle of index construction, the current educational performance evaluation index system is improved and a more reasonable evaluation index system is formed. On this basis, taking the sample data in 2017 as an example, the principal component analysis method is used to reduce the dimension of input and output indicators and eliminate the correlation between indicators, and three principal components of input and three principal components of output are obtained. Secondly, data envelopment analysis model is established, and the data processed are analyzed with the help of MATLAB and DEAP2.1 operation software. The efficiency of these 24 colleges and universities is compared to understand the efficiency and differences of each college. Moreover, projection analysis of non-DEA effective DMU is completed and the direction of improvement and the specific adjustment value are pointed out.
Ontologies to interpret remote sensing images: why do we need them?

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.
RETRACTED: A novel and powerful framework based on neutrosophic sets to aid patients with cancer

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. After a thorough investigation, the Editor has concluded post publication that the acceptance of this article was partly based upon the positive advice of two illegitimate reviewer reports. Although purportedly real reviewer accounts, the Editor has concluded that these were not of appropriate, independent reviewers. No evidence was available to the journal that the reviewer accounts were provided by the corresponding author as suggested reviewers during the submission of the article. Apologies are offered to the reviewers whose identity was assumed and to the readers of the journal that this was not detected during the submission process.
Technological revolution, sustainability, and development in <scp>Africa</scp>: Overview, emerging issues, and challenges

Abstract The paper examines the silent technological revolution in sub‐Saharan Africa focusing on emerging issues and challenges. In view of the centrality of technology diffusion in fostering local innovations and economic development in developing countries, it is surprising that our understanding of the challenges and opportunities in scaling‐up technologies remains limited. This paper capitalises on the ongoing silent technological revolution in sub‐Saharan Africa to present an overview of how new technologies have been adopted and utilised to achieve sustainability. The study identified a host of factors such as weak regulatory enforcement systems, lack of financial credit availability, and limited banking services, which have created conditions for technological innovations such as mobile phone‐based banking, mPedigree, “cardiopad,” and M‐PEPEA to emerge. The public policy implications and directions for future research are identified and examined.