Reviewerly/exHarmony huggingface dataset:
Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

A small ISO and a small exposure time are usually used to capture an image in back- or low-light condition which results in an image with negligible motion blur and small noise but looks dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times. The virtual images are first generated via intensity mapping functions (IMFs) which are computed using camera response functions (CRFs) and this is a model-driven approach. Both the virtual images are then enhanced by using a data-driven approach, i.e. a residual convolutional neural network to approach the ground truth images. The model-driven approach and the data-driven one compensate each other in the proposed hybrid learning framework. The final brightened image is obtained by fusing the original image and two virtual images via a multi-scale exposure fusion algorithm with properly defined weights. Experimental results show that the proposed brightening algorithm outperforms existing algorithms in terms of MEF-SSIM metric.
Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image Compression

In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED) based image compression which takes care of the various frequency components present in an image. Specifically, we demonstrate improvements over prior approaches utilizing this framework by introducing: (a) wavelet transform pre-processing for decomposing image into different frequencies for their separate processing (b) a very deep super-resolution network as a decoder of the convolutional autoencoder in order to achieve a good quality decompressed image. The end-to-end learning is performed for four wavelet sub-bands in parallel, minimizing the computational time. The encoder compresses the image by generating the latent space representations, whereas the decoder transforms the latent space to image space. The algorithm has been tested on various standard datasets i.e., ImageNet, Set 5, Set 14, Live 1, Kodak, Classic 5, General 100 and CLIC 2019 dataset. The proposed algorithm clearly exhibited the compression performance improvement of approximately 5%, 5.5%, and 13% in terms of PSNR, PSNRB and SSIM respectively.
Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review

Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.
Applications of AI in classical software engineering

Abstract Although Artificial Intelligence (AI) has become a buzzword for self-organizing IT applications, its relevance to software engineering has hardly been analyzed systematically. This study combines a systematic review of previous research in the field and five qualitative interviews with software developers who use or want to use AI tools in their daily work routines, to assess the status of development, future development potentials and equally the risks of AI application to software engineering. The study classifies the insights in the software development life cycle. The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e.g. for debugging and documentation, b) the structured analysis of big data pools to discover patterns and novel information clusters and c) the systematic evaluation of these data in neural networks. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains. AI to date depends on man-made structures and is mainly reproductive, but the automation of software engineering routines entails a major advantage: Human developers multiply their creative potential when using AI tools effectively.
Broad Learning System Based on Maximum Correntropy Criterion

As an effective and efficient discriminative learning method, broad learning system (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems. However, the standard BLS is derived under the minimum mean square error (MMSE) criterion, which is, of course, not always a good choice due to its sensitivity to outliers. To enhance the robustness of BLS, we propose in this work to adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a correntropy-based BLS (C-BLS). Due to the inherent superiorities of MCC, the proposed C-BLS is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in the Gaussian or noise-free environment. In addition, three alternative incremental learning algorithms, derived from a weighted regularized least-squares solution rather than pseudoinverse formula, for C-BLS are developed. With the incremental learning algorithms, the system can be updated quickly without the entire retraining process from the beginning when some new samples arrive or the network deems to be expanded. Experiments on various regression and classification data sets are reported to demonstrate the desirable performance of the new methods.
PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN

With the popularity of Deep Learning (DL), researchers have begun to apply DL to tackle with encrypted traffic classification problems. Although these methods can automatically extract traffic features to improve the ability of feature engineering of traditional methods like DPI, a large amount of data is still needed to learn the characteristics of various types of traffic. Therefore, the performance of classification model always significantly depends on the quality of datasets. Nonetheless, the building of datasets is a time-consuming and costly task, especially encrypted traffic. Apparently, it is often more difficult to collect a large amount of traffic samples of those unpopular applications than well-known ones, which often leads to the problem of class imbalance between major and minor encrypted applications in datasets. In this paper, we proposed a novel traffic data augmenting method called PacketCGAN using Conditional GAN, which can control the modes of data to be generated. PacketCGAN exploit the benefit of CGAN to generate specified samples with the input of applications’ types as conditional and thereby achieve data balancing. As a proof of concept, three classical DL models including CNN were adopted to classify four types of encrypted traffic datasets augmented by Random Over Sampling (ROS), SMOTE(Synthetic Minority Over-sampling Techinique), vanilla GAN and PacketCGAN respectively using public datasets. The experimental evaluation results demonstrate that DL based encrypted traffic classifier over our new dataset augmented by PacketCGAN can achieve better performance than the other three in terms of encrypted traffic classification.
Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic organisation of existing works following a pipeline, where sub-problems in last stages benefit from the results in previous ones. Models that employ Deep Learning to solve crowd anomaly detection, one of the proposed stages, are reviewed in depth, and the few works that address emotional aspects of crowds are outlined. The importance of bringing emotional aspects into the study of crowd behaviour is remarked, together with the necessity of producing real-world, challenging datasets in order to improve the current solutions. Opportunities for fusing these models into already functioning video analytics systems are proposed.
Scalable and redactable blockchain with update and anonymity

Internet-of-Things (IoT) envisions communications between heterogeneous devices and utilization of the associated data for smart decision making. Blockchain bridges the gap between widely-distributed IoT devices and the need for a universal trust-layer. When applying blockchain for IoT, some concerns can arise. Among them, scalability is a crucial factor because it decides whether blockchain can keep empowering IoT in the long term. According to a recent survey by Ali et al., some newly launched blockchains are suffering from powerful attacks (e.g. 51% attack) when the computing pool is small, and the number of participated nodes is inadequate (USENIX 2016). To ensure the scalability of initially-deployed blockchains, a proper countermeasure is important to be devised. Ateniese et al. proposed the notion of the redactable blockchain (EuroS&P 2017) which allows block history to be rewritten by using chameleon hash. However, the distribution and management of trapdoor key is crucial for the scalability of chameleon hash as well as redactable blockchain. To deal with the above problems, we propose two cryptographic schemes as the new theoretic tools for blockchain redaction: time updatable chameleon hash (TUCH) and linkable-and-redactable ring signature (LRRS). The use of TUCH and LRRS schemes enables redaction to take place scalably and anonymously where the spontaneous ring is generated for redaction, which costs little expenditures to rewrite a block content. Specifically, the redaction will be processed without assigning and splitting trapdoor key among multiple users in a complex way, and achieve transaction anonymity for users. What is more, we briefly instantiate how to build a redactable blockchain with update and anonymity (SRB) with our proposed TUCH and LRRS. While security analysis confirms that our proposals are theoretically secure, the experimental results show that our proposals are efficient for implementation purposes.
Using Variational Auto Encoding in Credit Card Fraud Detection

Machine learning approaches are widely used to analyze and detect the increasingly serious problem of credit card fraud. However, typical credit card datasets present imbalanced classification situations because of severely skewed class distributions. Although researchers have proposed some strategies to deal with these imbalances, disadvantages remain. We propose an oversampling method based on variational automatic coding (VAE), combined with classic deep learning techniques, to solve this problem. The VAE method is used to generate a large amount of diverse cases from minority groups in an imbalanced dataset, which are then used to train the classification network. The proposed method is tested on an open credit card fraud dataset, which contains transactions conducted by European cardholders over two days in September 2013. Experimental results show that the VAE method performs better than synthetic minority oversampling techniques and traditional deep neural network methods. In addition, it outperforms recent oversampling methods based on generative adversarial network (GAN) models. After submitting the extended dataset to the baseline for training, the test of the VAE model performs well on indicators including precision, F-measure, accuracy and specificity. These experimental results suggest that the VAE-based oversampling method can be effectively applied to imbalanced classification problems.
DRSL: Deep Relational Similarity Learning for Cross-modal Retrieval

Cross-modal retrieval aims to retrieve relevant samples across different media modalities. Existing cross-modal retrieval approaches are contingent on learning common representations of all modalities by assuming that an equal amount of information exists in different modalities. However, since the quantity of information among cross-modal samples is unbalanced and unequal, it is inappropriate to directly match the obtained modality-specific representations across different modalities in a common space. In this paper, we propose a new method called Deep Relational Similarity Learning (DRSL) for cross-modal retrieval. Unlike existing approaches, the proposed DRSL aims to effectively bridge the heterogeneity gap of different modalities by directly learning the natural pairwise similarities instead of explicitly learning a common space. DRSL is a deep hybrid framework that integrates the relation networks module for relation learning, capturing the implicit nonlinear distance metric. To the best of our knowledge, DRSL is the first approach that incorporates relation networks into the cross-modal learning scenario. Comprehensive experimental results show that the proposed DRSL model achieves state-of-the-art results in cross-modal retrieval tasks on four widely-used benchmark datasets, i.e., Wikipedia, Pascal Sentences, NUS-WIDE-10K, and XMediaNet.
Self adaptive cuckoo search: Analysis and experimentation

Cuckoo search (CS) algorithm, based on the brood parasitic behaviour of cuckoo species, since its inception, has proved its worth in various fields of research and can be considered as an efficient problem solver. Though CS is a very good algorithm, its performance degrades as the problem complexity increases. So a new version namely self-adaptive CS (SACS) is proposed to improve its performance. The algorithm employs adaptive parameters and hence no parameter tuning is required to be done. Here proportional population reduction based on the fitness of current best and previous best solution is followed. Secondly, in order to improve the exploration and exploitation tendencies, the Gaussian sampling mechanism known as bare-bones variant along with division of population and generations is added. The concept of Weibull distributed probability switching is also added to increase the balance between the exploration and exploitation processes. Further, CS as an extension of differential evolution (DE), genetic algorithm (GA), stability analysis with respect to Von Neumann’s stability criteria are also presented. For performance evaluation, the SACS algorithm is tested on CEC 2017 benchmark problems and compared with respect to self-adaptive DE (SaDE), JADE, success-history based adaptive DE (SHADE), SHADE with linear population size reduction (LSHADE), mean-variance mapping optimization (MVMO), cuckoo version 1.0 (CV1.0), cuckoo version new (CVnew) and others. Experimental results and statistical analysis show that the proposed SACS algorithm performs better than SaDE, JADE, CV1.0, CVnew and CS whereas comparable with respect to LSHADE, SHADE, and MVMO. Convergence profiles further validate the results.
GeFeS: A generalized wrapper feature selection approach for optimizing classification performance

In this paper, we propose a generalized wrapper-based feature selection, called GeFeS, which is based on a parallel new intelligent genetic algorithm (GA). The proposed GeFeS works properly under different numerical dataset dimensions and sizes, carefully tries to avoid overfitting and significantly enhances classification accuracy. To make the GA more accurate, robust and intelligent, we have proposed a new operator for features weighting, improved the mutation and crossover operators, and integrated nested cross-validation into the GA process to properly validate the learning model. The k-nearest neighbor (kNN) classifier is utilized to evaluate the goodness of selected features. We have evaluated the efficiency of GeFeS on various datasets selected from the UCI machine learning repository. The performance is compared with state-of-the-art classification and feature selection methods. The results demonstrate that GeFeS can significantly generalize the proposed multi-population intelligent genetic algorithm under different sizes of two-class and multi-class datasets. We have achieved the average classification accuracy of 95.83%, 97.62%, 99.02%, 98.51%, and 94.28% while reducing the number of features from 56 to 28, 34 to 18, 279 to 135, 30 to 16, and 19 to 9 under lung cancer, dermatology, arrhythmia, WDBC, and hepatitis, respectively.
Event-Triggered Adaptive Fuzzy Fault-Tolerant Control for Stochastic Nonlinear Systems via Command Filtering

This article investigates the command filtering-based event-triggered adaptive fuzzy control problem for a class of stochastic nonlinear systems with stochastic faults and input saturation. The unknown nonlinear functions and system dynamic changes that caused by stochastic faults are approximated by fuzzy logic systems (FLSs). In order to reduce the computational burden, the command filtering design technique is incorporated into the adaptive fuzzy event-triggered control strategy. Finally, the effectiveness of the proposed method is verified by simulation studies, in which the uniform ultimate boundedness of the system is guaranteed and all the signals in the closed-loop system are bounded.
DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection

Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called “Deepfake” produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector.
Multilevel Edge Features Guided Network for Image Denoising

Image denoising is a challenging inverse problem due to complex scenes and information loss. Recently, various methods have been considered to solve this problem by building a well-designed convolutional neural network (CNN) or introducing some hand-designed image priors. Different from previous works, we investigate a new framework for image denoising, which integrates edge detection, edge guidance, and image denoising into an end-to-end CNN model. To achieve this goal, we propose a multilevel edge features guided network (MLEFGN). First, we build an edge reconstruction network (Edge-Net) to directly predict clear edges from the noisy image. Then, the Edge-Net is embedded as part of the model to provide edge priors, and a dual-path network is applied to extract the image and edge features, respectively. Finally, we introduce a multilevel edge features guidance mechanism for image denoising. To the best of our knowledge, the Edge-Net is the first CNN model specially designed to reconstruct image edges from the noisy image, which shows good accuracy and robustness on natural images. Extensive experiments clearly illustrate that our MLEFGN achieves favorable performance against other methods and plenty of ablation studies demonstrate the effectiveness of our proposed Edge-Net and MLEFGN. The code is available at https://github.com/MIVRC/MLEFGN-PyTorch.
Neural Simplex Architecture

We present the Neural Simplex Architecture (NSA), a new approach to runtime assurance that provides safety guarantees for neural controllers (obtained e.g. using reinforcement learning) of autonomous and other complex systems without unduly sacrificing performance. NSA is inspired by the Simplex control architecture of Sha et al., but with some significant differences. In the traditional approach, the advanced controller (AC) is treated as a black box; when the decision module switches control to the baseline controller (BC), the BC remains in control forever. There is relatively little work on switching control back to the AC, and there are no techniques for correcting the AC’s behavior after it generates a potentially unsafe control input that causes a failover to the BC. Our NSA addresses both of these limitations. NSA not only provides safety assurances in the presence of a possibly unsafe neural controller, but can also improve the safety of such a controller in an online setting via retraining, without overly degrading its performance. To demonstrate NSA’s benefits, we have conducted several significant case studies in the continuous control domain. These include a target-seeking ground rover navigating an obstacle field, and a neural controller for an artificial pancreas system.
Fault Estimation for Mode-Dependent IT2 Fuzzy Systems With Quantized Output Signals

The aim of this article is to analyze the problem of fault estimation for mode-dependent interval type-2 fuzzy systems with quantized output measurements. Different from the existing fault estimation methods requiring the observer matching condition, a new fault estimation technique is proposed, wherein a stochastically intermediate variable subject to the information on operating modes is considered, under which a robust observer is constructed to simultaneously estimate the state and faults. Based on the strategy of linear matrix inequality, sufficient conditions are established to ensure that the states of resulting systems are bounded in probability sense. By offering three illustrative examples, in which two of them are practical models, namely, tunnel diode circuit system and Rössler system, the availability and feasibility of the proposed design method are explained.
Resolving the tension between full utilization of contact tracing app services and user stress as an effort to control the COVID-19 pandemic

Although contact tracing apps can be effective for controlling COVID-19, the app usage can be stressful for users. This study identifies countermeasures for users' stress while maximizing full utilization of the apps. This study presents the relationships among the stress factors, users' appraisal, users' emotion focus coping, and the infusion to exert the full potential of the app through a structural equation model. The research model is validated by surveying Health Code app users. Given the results of the study, the contact tracing apps could become a valuable tool to control COVID-19 by removing app users' privacy concerns.