Reviewerly/exHarmony huggingface dataset:
Single Remote Sensing Image Dehazing Using a Dual-Step Cascaded Residual Dense Network

Remote sensing (RS) dehazing is an extremely challenging task since the non-uniform distribution of haze and fog severely degrade the images and difficult to extract features. To address these issues, we propose an end to end Dual-step Cascaded Residual Dense Network called DCRD-Net, which can exactly remove haze from the hazy RS image and precisely restoring the details. The architecture of network contains two cascaded task-driven subnetworks, in order to deal with the coarse and fine haze-relevant features separately. Besides, the Residual Dense Enhancement Block (RDEB) is involved to guide the feature extraction, so that multi-scale information can be used to estimate the local and global features. Further, the Squeeze and Excitation (SE) Block is employed to optimize the RDEB, for the purpose to get the contextual feature and reduce computation complex. Quantitative and qualitative results illustrate that the designed framework outperforms the recent outstanding dehazing methods on promoting haze remove and restoring detailed in the synthetic and real-world RS images under various scenes. To encourage more comparisons, we release our codes on GitHub https://github.com/cxtalk/DCRD-Net.
CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis

In this manuscript, a Chaotic Multi Verse Harris Hawks Optimization algorithm based Deep Kernel Machine Learning Classifier (CMVHHO-DKMLC) method is proposed for medical diagnostics. By using CMVHHO, the feature selection (FS) is done for finding ideal feature subset of medical documents. In the proposed method, to obtain the ideal subset of characteristics for best classification, initially chaotic multi-verse optimization (CMV) was implemented to create first positions, after that Harris Hawks optimization (HHO) was utilized to keep positions informed current population at DKMLC-based search space. Then the proposed model is performed on MATLAB and the performance of proposed method is evaluated with assessment metrics. For demonstrating the efficacy of the proposed system on medical diagnosis, this manuscript carries out a series of comparative studies by testing with two medical data sets, i.e. Wisconsin Breast Cancer Database [40] and PIMA Indian Diabetic Dataset [41]. This proposed CMVHHO-DKMLC classifier provides 1.165% and 0.667% higher accuracy value compared with existing classifier model like against the chaotic multi-swarm whale optimizer-boosted support vector machine (CMWOAFS-SVM) and improved gray wolf optimization-based feature selection wrapped kernel extreme learning machine (IGWO-KELM) for breast cancer diagnosis. Similarly the proposed CMVHHO-DKMLC classifier provides 25.641% and 3.55% higher accuracy value compared with existing classifier model like chaotic multi-swarm whale optimizer-boosted support vector machine (CMWOAFS-SVM) and multi-objective firefly and multi-objective imperialist competitive algorithm optimizer-boosted support vector machine (MOFA-MOICA-SVM) for diabetic diagnosis. Simulation outcomes have demonstrated the dominance of proposed technique over other two competitive methods.
Toward a Secure, Rich, and Fair Query Service for Light Clients on Public Blockchains

The rapid growth of storage overhead on public blockchains has urged the use of light clients that only store a small fraction of blockchain data and rely on other bootstrapped full nodes for data retrievals. Unfortunately, current blockchain light client designs are far from satisfactory. First, outsourcing retrieval requests could raise severe concerns about result correctness and privacy threats. Second, current light clients do not support rich query and enforce fee payments to full nodes. Given that blockchain storage increases day by day, enabling effective rich blockchain queries and fairly compensating full nodes’ ever growing costs has become extremely necessary. In this article, we propose a general and secure paid query framework to simultaneously meet those demands above. Specifically, we leverage the integration of trusted hardware (e.g., Intel SGX) and smart contract as a starting point for building efficient yet secure query processing with fair payments. Then, we further craft several crucial performance and security refinement designs to boost query efficiency and enforce result correctness, and also explore an enclave-facilitated fair settlement mechanism for on-chain cost optimizations. We implement a prototype of our paid query framework and the experimental result has demonstrated its practically affordable cost.
Classification of Breast Ultrasound Images Based on Convolutional Neural Networks - A Comparative Study

Computer aided diagnosis (CAD) helps physicians towards an early characterization of tumors in different biomedical tissues, including Breast. Deep learning (DL) based image classification, especially convolutional neural networks (CNNs), has achieved a noticeable success in automatic differentiation of breast ultrasound (BUS) images through the last few years. In this paper: ten well-known pretrained CNNs classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify BUS images by transfer learning (TL). A dataset of 780 BUS images (133 normal, 437 benign, and 210 malignant) has been utilized in training and validation. A selected 375 BUS images from the dataset have been utilized to evaluate the classification's accuracy of each CNN model after TL process. Ultrasound's speckle noise effect on classification has been studied by applying a simulated multiplicative speckle noise to the mentioned 375 BUS images before being classified by the ten CNN models. Three restoration approaches have been applied to treat speckled BUS images before being classified. Accuracy evaluation results have represented different values for each CNN model over all 375 BUS images through different studied circumstances. The best accuracy's value was for: ResNet101 when the input images were clear with high resolution, SqueezeNet in case of speckled input images, and InceptionResNetV2 in case of restored images using the three applied restoration schemes. So, in case of speckled BUS images, it is recommended to utilize a proper preprocessing step for image enhancement before applying a CNN based classification model (InceptionResNetV2 is preferred). Ten modified and trained CNNs models for BUS images' classification (input image size: 128 by 128 by 3) utilized in this study are available to researchers at: https://www.kaggle.com/mohammedtgadallah/ten-cnns-for-breast-us-images-classification
Fog-enabled secure multiparty computation based aggregation scheme in smart grid

The Smart Grid (SG) is a modern power grid infrastructure for improved efficiency and reliability using modern telecommunications infrastructure, sensing, and measuring technologies. It provides sustainable electricity to customers and enables them to monitor their electricity use. The Fog Nodes (FNs) are deployed between the Cloud Control Center (CCC) and Smart Meters (SMs) to improve efficiency and reliability. The CCC is collecting usage data through FNs at regular intervals from SMs. As SM data contains private customer data, a secure scheme is required to ensure customer privacy during data collection. The usage view behavior of consumers is only possible if internal or external malicious entities collude together. In this paper, we present the Fog-Enabled Secure Multiparty Computation (FESMPC) based aggregation scheme for SG. This paper’s key contributions to the literature on the topic of secure privacy-aware aggregation schemes in SGs are threefold. Firstly , a secure aggregation scheme that preserves data privacy and ensures fault-tolerance is presented. The scheme is robust against the attacks of collusive and False Data Injection (FDI). A collusive attack is managed through enhanced Shamir’s secret scheme. At the same time, privacy during data aggregation is achieved through a Paillier homomorphic encryption scheme. Secondly , detailed security analyses are provided to show that the proposed scheme supports data privacy, source authentication , fault tolerance, and prevents FDI attacks . Thirdly , the paper presents extensive performance evaluations demonstrating that the proposed program scheme is efficient in encryption , aggregation, decryption, and less communication costs than the three existing state-of-the-art programs.
Global optimisation in Hilbert spaces using the survival of the fittest algorithm

Global optimisation problems in high-dimensional and infinite-dimensional spaces arise in various real-world applications such as engineering, economics and finance, geophysics, biology, machine learning, optimal control, etc. Among stochastic approaches to global optimisation, biology-inspired methods are currently very popular in the literature. Bio-inspired approaches imitate natural ecological and evolutionary processes and are reported to be efficient in a large number of practical study cases. However, many bio-inspired methods can possess some vital drawbacks. For example, due to their semi-empirical nature, convergence to the globally optimal solution cannot always be guaranteed. Another major obstacle is that the existing methods often struggle with higher dimensionality of the space of parameters, which results in a slow convergence. Moreover, it is often difficult to adjust the dimensionality of the space of parameters in the corresponding computer code for a practical realisation of the optimisation method. Here, we present a bio-inspired global stochastic optimisation method applicable in Hilbert function spaces. The proposed method is an evolutionary algorithm inspired by the Darwin’s famous idea of survival of the fittest and is, therefore, referred to as the ‘Survival of the Fittest Algorithm’ (SoFA). Mathematically, the convergence of SoFA is a consequence of the fundamental property of localisation of probabilistic measure in a Hilbert space and we rigorously prove the convergence of the introduced algorithm for a generic class of functionals. The approach is simple in terms of practical coding. As an insightful, real-world problem, we apply our method to find the globally optimal trajectory for the daily vertical migration of zooplankton in the ocean and lakes, this phenomenon is considered to be the largest synchronised movement of biomass on Earth. We maximise fitness in a function space derived from a von-Foerster stage-structured population model with biologically realistic parameters. We show that for problems of fitness maximisation in high-dimensional spaces, SoFA provides better performance as compared to some other stochastic global optimisation algorithms. We highlight the links between the new optimisation algorithm and natural selection processes in ecosystems occurring within a population via gradual exclusion of competitive con-specific strains.
Performance Comparison of CNN Models Using Gradient Flow Analysis

Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the locality and correlation of input data using structures in which convolutional layers are successively applied to the input data. In general, the performance of neural networks has improved as the depth of CNNs has increased. However, an increase in the depth of a CNN is not always accompanied by an increase in the accuracy of the neural network. This is because the gradient vanishing problem may arise, causing the weights of the weighted layers to fail to converge. Accordingly, the gradient flows of the VGGNet, ResNet, SENet, and DenseNet models were analyzed and compared in this study, and the reasons for the differences in the error rate performances of the models were derived.
Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automated systems is accompanied by the increasing concerns regarding their ethical implications. Fundamental rights, such as the ones that require the preservation of privacy, do not discriminate based on sensible attributes (e.g., gender, ethnicity, political/sexual orientation), or require one to provide an explanation for a decision, are daily undermined by the use of increasingly complex and less understandable yet more accurate learning algorithms. For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. In particular, we show that it is possible to simultaneously learn from data while preserving the privacy of the individuals thanks to the use of Homomorphic Encryption, ensuring fairness by learning a fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. We test our approach on a widespread but still controversial application, namely face recognition, using the recent FairFace dataset to prove the validity of our approach.
Background concentrations and extent of Cu, As, Co, and U contamination in Baltic Sea sediments

This study established background (pre-industrial) values of copper (Cu), arsenic (As), cobalt (Co), and uranium (U) in Baltic Sea sediments. The indicated background values could help identifying the spatial and temporal anthropogenic loads of these elements (metals and metalloids) in the Baltic Sea. In this study, 137 sediment samples were collected from cores obtained from 13 monitoring stations in the Gulf of Bothnia (Bothnian Bay and Sea) and the entire Baltic Proper. To understand the extent of contamination, we used direct and combined methods to define the geochemical background values as inputs for the geochemical index (Igeo) calculation. The obtained values were then compared with the background values established by the Swedish Environmental Protection Agency. From the direct method, Cu, Co, As, and U had background values of 39, 21.5, 12.4, and 6.3 mg kg−1 DW. Copper and U exhibited concentrations above the background values in surface sediment in the western and eastern Baltic Proper (maximum Igeo indicates moderate contamination). Arsenic was above background concentrations in the Baltic Sea and highest in the Gulf of Bothnia (maximum Igeo indicates strong contamination). Cobalt concentrations were within the range of background values (no contamination).
The Added Value of a Computer‐Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists.From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed.After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05).Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.
A Comparative Review on Homomorphic Encryption for Cloud Security

With the increase in traffic on cloud servers due to the shifting of various services, information security has become a matter of major concern. This paper describes the security issues influencing cloud computing, compares the performance of traditional and the latest security techniques, and focuses on the challenges facing its implementation. Traditional schemes like RSA, ElGamal, and Pailler Algorithms do not allow operations to be carried out on the encrypted data, whereas the Homomorphic Encryption Scheme allows computation in the encrypted state without decrypting it to the original form. Thus, the security is ensured and also, there is no need to share the secured key. Fully Homomorphic Encryption Algorithms like: Gentry’s Encryption Scheme, Dijk-Gentry-Halevi-Vaikunthanathan Scheme (DGHV), Brakerski-Vaikuntanathan Schemes (BGV) and Multi-key fully Homomorphic encryption are advanced techniques that provide security for the cloud are also described.
Automatic Modulation Classification Based on Cauchy-Score Constellation and Lightweight Network Under Impulsive Noise

Automatic modulation classification in the manner of pattern recognition has caught massive attention due to its simplicity in workflow design. Recently, enormous interest has been aroused because of the boosting development of deep learning and numerous network structures, leading to various substantial contributions. However, to the best of our knowledge, current approaches fail to handle two essential concerns simultaneously: the statistical distribution of the noise and the computational complexity due to the network structure. Since these factors play critical roles when facing practical applications, a modified constellation concept based on the Score function of Cauchy distribution is proposed as a robust feature to impulsive noise. Besides, a lightweight structure based on Shuffle Unit and Gated Recurrent Unit is proposed as the recognizer to lower the potential risk of overfitting and cope with the time-consuming problem. Monte-Carlo experiments involving multiple comparison algorithms are executed under different conditions, and results verify the superior performance of the proposed AMC scheme.
Stress Out: Translating Real-World Stressors into Audio-Visual Stress Cues in VR for Police Training

Abstract Virtual Reality (VR) training has become increasingly important for police first responders in recent years. Improving the training experience in such complex contexts requires ecological validity of virtual training. To achieve this, VR systems need to be capable of simulating the complex experiences of police officers ‘in the field.’ One way to do this is to add stressors into training simulations to induce stress similar to the stress experienced in real-life situations, particularly in situations where this is difficult (e.g., dangerous or resource-intensive) to achieve with traditional training. To include stressors in VR, this paper thus presents the concept of so-called ‘stress cues’ for operationalizing stressors to augment training in VR simulations for the context of police work. Considering the level of complexity of police work and training, a co-creation process that allows for creative collaboration and mitigation of power imbalances was chosen to access the police officers’ knowledge and experience. We assert that stress cues can improve the training experience from the trainer’s perspective as they provide novel interaction design possibilities for trainers to control the training experience. E.g., by actively intervening in training and dynamically changing the interaction space for trainees which also improves the trainee’s experience. Stress cues can also improve the trainee’s experience by enabling personalizable and customizable training based on real-time stress measurements and supplementing information for improved training feedback.
Reservoir computing with diverse timescales for prediction of multiscale dynamics

Machine learning approaches have recently been leveraged as a substitute or an aid for physical/mathematical modeling approaches to dynamical systems. To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing (RC) model with diverse timescales by using a recurrent network of heterogeneous leaky integrator (LI) neurons. We evaluate computational performance of the proposed model in two time series prediction tasks related to four chaotic fast-slow dynamical systems. In a one-step-ahead prediction task where input data are provided only from the fast subsystem, we show that the proposed model yields better performance than the standard RC model with identical LI neurons. Our analysis reveals that the timescale required for producing each component of target multiscale dynamics is appropriately and flexibly selected from the reservoir dynamics by model training. In a long-term prediction task, we demonstrate that a closed-loop version of the proposed model can achieve longer-term predictions compared to the counterpart with identical LI neurons depending on the hyperparameter setting.
3D reconstruction of non-textured surface by combining shape from shading and stereovision

For the improvement of robot-assisted minimally invasive surgery, 3D reconstruction of the visual field in surgery is demanding nowadays. This task faces the difficulty that the tissue surfaces in the visual field usually appear to be non-textured. How to reconstruct such kind of surfaces well under this environment is unsolved. Among many vision techniques, Shape From Shading is a unique method which infers surface shape (normal field) based on the reflectance model without requiring any texture. One major limitation is that the accurate surface depth (not shape) is unable to compute when illumination parameters are not calibrated. Stereovision is a long-standing method which estimates depth by establishing the pixel correspondences between two camera images. But it fails to find correspondences when encountered with non-textured surfaces. It is intuitive to combine these two methods to recover depth of non-textured surfaces and calibrate the illumination parameters as well. However, how to bridge these two methods efficiently is challenging. In this paper, the combination of these two methods is accomplished by a 3D matching framework based on Fast Point Feature Histogram (FPFH). We first reconstruct initial surface point clouds of left image and right image using SFS method. The pixel correspondences can be built by aligning SFS calculated point clouds using FPFH 3D descriptors. Therefore, stereo method can reconstruct depth of the surface. Furthermore, we employ a classic variational fusion algorithm which refines SFS-shape and stereovision-depth. Results of the recovered depth are shown through experiments.