Reviewerly/exHarmony huggingface dataset:
Diversification in session-based news recommender systems

Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous and the only available data is sequences of items in anonymous sessions. Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations. In this context, session-based recommenders are able to recommend next items given the sequence of previous items in the active session. Neighborhood-based session-based recommenders has been shown to be highly effective compared to more sophisticated approaches. In this study we propose scenarios to make these session-based recommender systems diversity-aware and to address the filter bubble phenomenon. The filter bubble phenomenon is a common concern in news recommendation systems and it occurs when the system narrows the information and deprives users of diverse information. The results of applying the proposed scenarios show that these diversification scenarios improve the diversity measures in these session-based recommender systems based on four news datasets.
Breast Fine Needle Cytological Classification Using Deep Hybrid Architectures

Diagnosis of breast cancer in the early stages allows to significantly decrease the mortality rate by allowing to choose the adequate treatment. This paper develops and evaluates twenty-eight hybrid architectures combining seven recent deep learning techniques for feature extraction (DenseNet 201, Inception V3, Inception ReseNet V2, MobileNet V2, ResNet 50, VGG16 and VGG19), and four classifiers (MLP, SVM, DT and KNN) for binary classification of breast cytological images over the FNAC dataset. To evaluate the designed architectures, we used: (1) four classification performance criterias (accuracy, precision, recall and F1-score), (1) Scott Knott (SK) statistical test to cluster the developed architectures and identify the best cluster of the outperforming architectures, and (2) Borda Count voting method to rank the best performing architectures. Results showed the potential of combining deep learning techniques for feature extraction and classical classifiers to classify breast cancer in malignant and benign tumors. The hybrid architectures using MLP classifier and DenseNet 201 for feature extraction were the top performing architectures with a higher accuracy value reaching 99% over the FNAC dataset. As results, the findings of this study recommend the use of the hybrid architectures using DenseNet 201 for the feature extraction of the breast cancer cytological images since it gave the best results for the FNAC data images, especially if combined with the MLP classifier.
Generalized quantum-classical correspondence for random walks on graphs

We introduce a minimal set of physically motivated postulates that the Hamiltonian H of a continuous-time quantum walk should satisfy in order to properly represent the quantum counterpart of the classical random walk on a given graph. We found that these conditions are satisfied by infinitely many quantum Hamiltonians, which provide novel degrees of freedom for quantum enhanced protocols, In particular, the on-site energies, i.e. the diagonal elements of H, and the phases of the off-diagonal elements are unconstrained on the quantum side. The diagonal elements represent a potential energy landscape for the quantum walk, and may be controlled by the interaction with a classical scalar field, whereas, for regular lattices in generic dimension, the off-diagonal phases of H may be tuned by the interaction with a classical gauge field residing on the edges, e.g., the electro-magnetic vector potential for a charged walker.
Practical Analysis on Architecture of EfficientNet

Convolutional neural networks (CNNs) are now used in a variety of computer vision applications. However, it is quite hard to adopt them in real-time system due to the problem of increasing model size. Recently, some efficient networks which still have acceptable performance are proposed. Among them, EfficientNet is one of the state-of-the-art architectures. It can be considered a family of network models. EfficientNet could take its place among the state-of-the-art on the ImageNet challenge while still have much fewer parameters and computation cost. But given some of its subtleties, it is more efficient than most of its predecessors. It uses the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation modules (SE modules). This paper investigates the effect of SE modules on the performance of EfficientNet-B0, the fundamental network model in its family, by repositioning/removing the SE modules.
Blockchain and AI-Empowered Social Distancing Scheme to Combat COVID-19 Situations

The COVID-19 pandemic situation has proved to be disastrous for humanity throughout the world.However, during this period, people must take precautions for safety purposes.One of the essential steps towards eliminating or reducing the effect of COVID-19 is maintaining social distancing while in public places.Some people are neglecting the social distancing norms while on the move.Still, no surveillance system exists, which monitors the people's movement for social distancing and securely & efficiently shares the information with the concerned administration department.There also exists no penalty system which forces the people to ensure social distancing.Motivated from the aforementioned facts, in this paper, we present a blockchain and artificial intelligence (AI)-envisioned scheme for monitoring social distancing to combat COVID-19 situations.The proposed scheme uses fast region-based convolutional neural networks (RCNN) and you only look once (YOLO) models for the object (i.e., human) detection through the live video feed captured from the static CCTV cameras as well as lens-equipped drones.Further, the efficient euclidean distance calculation is embedded for calculating the distance between two humans.Blockchain technology ensures the secure and trusted exchange of information between the entities at the physical layer and the administration departments.Blockchain wallets are also used to pay the fine when people do not follow social distance norms.The performance of the proposed scheme is evaluated based on three broad parameters such as (i) human detection and violation identification, (ii) blockchain simulation and analysis, and (iii) network performance comparison.The parameters considered for (i) is confidence score, for (ii) are scalability, hash rate, and simulation interface, and for (iii) are network bandwidth, throughput, packet loss rate, and network latency.By analyzing all the parameters mentioned above, we observe the proposed scheme outperforms the traditional approaches.
U-ASD Net: Supervised Crowd Counting Based on Semantic Segmentation and Adaptive Scenario Discovery

Crowd counting considers one of the most significant and challenging issues in computer vision and deep learning communities, whose applications are being utilized for various tasks. While this issue is well studied, it remains an open challenge to manage perspective distortions and scale variations. How well these problems are resolved has a huge impact on predicting a high-quality crowd density map. In this study, a hybrid and modified deep neural network (U-ASD Net), based on U-Net and adaptive scenario discovery (ASD), is proposed to get precise and effective crowd counting. The U part is produced by replacing the nearest upsampling in the encoder of U-Net with max-unpooling. This modification provides a better crowd counting performance by capturing more spatial information. The max-unpooling layers upsample the feature maps based on the max locations held from the downsampling process. The ASD part is constructed with three light pathways, two of which have been learned to reflect various densities of the crowd and define the appropriate geometric configuration employing various sizes of the receptive field. The third pathway is an adaptation path, which implicitly discovers and models complex scenarios to recalibrate pathway-wise responses adaptively. ASD has no additional branches to avoid increasing the complexity. The designed model is end-to-end trainable. This integration provides an effective model to count crowds in both dense and sparse datasets. It also predicts an elevated quality density map with a high structural similarity index and a high peak signal-to-noise ratio. Several comprehensive experiments on four popular datasets for crowd counting have been carried out to demonstrate the proposed method’s promising performance compared to other state-of-the-art approaches. The proposed model achieves the lowest count error in terms of the MAE in ShanghaiTech Part A, Part B, and Mall datasets with 64.6, 7.5, and 1.8, respectively. Moreover, it achieves the lowest count error in terms of the MSE in ShanghaiTech Part B, UCF CC 50, UCSD, and Mall datasets with 12.4, 217.8, 2.1, 2.2, respectively. In addition, the proposed model accomplishes the best quality density maps on all the utilized datasets. Furthermore, a new dataset with its manual annotations, called Haramain with three different scenes and different densities, is introduced and used for evaluating the U-ASD Net.
Tackling Inter-class Similarity and Intra-class Variance for Microscopic Image-Based Classification

Automatic classification of aquatic microorganisms is based on the morphological features extracted from individual images. The current works on their classification do not consider the inter-class similarity and intra-class variance that causes misclassification. We are particularly interested in the case where variance within a class occurs due to discrete visual changes in microscopic images. In this paper, we propose to account for it by partitioning the classes with high variance based on the visual features. Our algorithm automatically decides the optimal number of sub-classes to be created and consider each of them as a separate class for training. This way, the network learns finer-grained visual features. Our experiments on two databases of freshwater benthic diatoms and marine plankton show that our method can outperform the state-of-the-art approaches for classification of these aquatic microorganisms.
Progressive U-Net residual network for computed tomography images super-resolution in the screening of COVID-19

Thin-slice computed tomography (CT) examination plays an important role in the screening of suspected and confirmed coronavirus disease 2019 (COVID-19) outbreak patients. Therefore, improving the image resolution of COVID-19 CT has important clinical value for the diagnosis and condition assessment of COVID-19. However, the existing single-image super-resolution (SISR) methods mainly increase the receptive field of convolution kernels by deepening and widening the network structure, and adopt the equal processing methods in the airspace and channel domains with different importance, and a large number of computing resources will be wasted on the unimportant features. We propose a progressive U-Net residual network (PURN) for COVID-19 CT images super-resolution (SR) to solve the practicality of existing models, to better extract features, and reduce the number of parameters. First, we design a dual U-Net module (DUM), which can efficiently extract low-resolution (LR) COVID-19 CT images feature. Second, the DUM module first performs up-block three times, and then down-blocks three times in order to learn the interdependence between high-resolution (HR) and LR images more efficiently. Finally, the local skip connection structure is introduced in the DUM module, and the global long skip connection structure is introduced in the reconstruction layer to further enrich the flow of reconstructed HR image information. Experimental results show that our algorithm effectively improves the SR reconstruction effect of COVID-19 CT images, restores its detailed features more sharply, and greatly improves the practicability of the algorithm.
A category attention instance segmentation network for four cardiac chambers segmentation in fetal echocardiography

Fetal echocardiography is an essential and comprehensive examination technique for the detection of fetal heart anomalies. Accurate cardiac chambers segmentation can assist cardiologists to analyze cardiac morphology and facilitate heart disease diagnosis. Previous research mainly focused on the segmentation of single cardiac chambers, such as left ventricle (LV) segmentation or left atrium (LA) segmentation. We propose a generic framework based on instance segmentation to segment the four cardiac chambers accurately and simultaneously. The proposed Category Attention Instance Segmentation Network (CA-ISNet) has three branches: a category branch for predicting the semantic category, a mask branch for segmenting the cardiac chambers, and a category attention branch for learning category information of instances. The category attention branch is used to correct instance misclassification of the category branch. In our collected dataset, which contains echocardiography images with four-chamber views of 319 fetuses, experimental results show our method can achieve superior segmentation performance against state-of-the-art methods. Specifically, using fivefold cross-validation, our model achieves Dice coefficients of 0.7956, 0.7619, 0.8199, 0.7470 for the four cardiac chambers, and with an average precision of 45.64%.
Chaotic particle swarm optimization algorithm with constraint handling and its application in combined bidding model

Particle swarm optimization (PSO) is a widely used intelligent optimization algorithm. The premature convergence and the constraint handling, however, remain problems for the global optimization searching. In this paper, a dual fitness value chaotic PSO algorithm with equality constraint handling (EDFC-PSO) is proposed. The particle mutation process based on the chaotic Cat mapping is introduced to increase the diversity of particles. Equality constraints are solved by parametric equations, and inequality constraints are considered using the dual fitness value. The proposed EDFC-PSO algorithm is tested by several test functions and then compared with other algorithms. Further, the performance of the proposed algorithm is validated by a combined bidding model of a power system. Our EDFC-PSO algorithm has equality constraints satisfied and enhances the global searching capability in both the test functions and the engineering model.
Security testing of web applications: A systematic mapping of the literature

Web application security is a main component of any web-based business. Web applications are subject to attacks from different locations at various levels of scale and complexity. In this context, a large number of testing techniques, tools and frameworks have been proposed by both practitioners and researchers to effectively and efficiently test the security of web applications. As the number of papers increases in the security of web applications and this research area matures, reviewing and getting an overview of this area is getting challenging for a practitioner or a new researcher. Our objective is to summarize the state-of-the-art in web application security testing which could benefit practitioners to potentially utilize that information. We review and structure the body of knowledge related to web application security testing in the form of a systematic literature mapping (SLM). As part of this study, we pose four sets of research questions, define selection and exclusion criteria, and systematically develop and refine a classification schema. The initial pool consisted of 154 articles. Systematic voting was conducted among the authors regarding the inclusion/exclusion of articles. As a result, there were 80 technical articles in our final pool. Accordance with our inclusion and exclusion criteria, the first article was published in 2005 and this review includes all the papers until the end of 2020. During December 2020, January and February 2021, the search phase has been conducted. This review paper provides an overview of web application security testing with different focused headings. These headings cover contribution types, web security testing tools and their sub features, specific questions/features to the security testing such as vulnerability types, system under testing (SUT) focused headings and more. The results of this study would benefit researchers working on web application security testing. Also, it could be useful for developers who discuss application security while they develop web applications. Thanks to this paper, these researchers could utilize the all results and use them to catch the trend of web application security testing and secure development.
Reliable Data Sharing by Certificateless Encryption Supporting Keyword Search Against Vulnerable KGC in Industrial Internet of Things

Outsourcing Industrial Internet of Things (IIoT) data on the cloud extends the diversity of data analysis for decision making with minimized costs in communications and storage. However, it is adverse to the confidentiality of IIoT data from the owner as access control is performed by honest-but-curious platforms. Although the encryption strategy guarantees data security, it hinders deliverance due to its inbred all-or-nothing decryption. Certificateless encryption supporting keyword search eliminates the overhead of certificates and privileges to retrieve the required data through encrypted keyword search. However, most of the prior works are precarious against a malicious key generation center, which exposes data protected by users’ private keys. We design a certificateless secure data sharing by uniting the functionalities of encryption and access control on search. Our technique resists in/outside keyword guessing attacks and sustains on-demand user revocation. Besides, it achieves Girault’s Level-3 security in the standard model. Nonetheless, empirical performance analysis under a suitable scenario exhibits its feasibility compared to the other related schemes.
The linkage between renewable energy potential and sustainable development: Understanding solar energy variability and photovoltaic power potential in Tibet, China

The accurate pattern of systematic assessment of regional-level solar energy resource potential including seasonal variability and annual trends is essential during renewable solar energy project development. Understanding the linkage between solar energy potential and photovoltaic (PV) development is of particular interest to developers and financers. However, a dearth of long-term, high-resolution solar radiation data makes these patterns and linkages challenging, and thus techniques to improve these characterizations are of great value. To improve knowledge of the renewable energy potential and sustainable development, we assessed high-resolution global surface solar radiation (SSR) dataset derived from remote sensing, and explicitly linked spatio-temporal pattern of solar energy potential across different timescales and PV development in Tibet using geographical statistics methods. With the framework, we provided an intuitive SSR mapping and more than 330 kWh/m2 of PV power potential was predicted in most areas of Tibet. We found spatio-temporal heterogeneity of solar energy seasonal variability: The SSR variation is less than 26% in most areas of Tibet in spring and summer, greater than 30% for autumn and winter; while the middle reaches of Yarlung Zangbo River remains at a low level. We also found PV power was not affected by SSR annual trends in most areas of Tibet during 2000–2016, ranging from −2 to 2 kWh/m2 per year. Additionally, we identified the mismatch between solar energy potential and PV development, characterized by a low average PV installed capacity with affluent solar energy potential, unstable power generation and a high light abandonment rate for PV. The main challenges facing long-term strategic development plans for solar PV was also assessed. Framework and findings herein provide a helpful guide to those responsible for making better decisions in some of the key stages of solar energy development at local and regional scale.
Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring

Although recent research has witnessed a significant progress on the video deblurring task, these methods struggle to reconcile inference efficiency and visual quality simultaneously, especially on ultra-high-definition (UHD) videos (e.g., 4K resolution). To address the problem, we propose a novel deep model for fast and accurate UHD Video Deblurring (UHDVD). The proposed UHDVD is achieved by a separable-patch architecture, which collaborates with a multi-scale integration scheme to achieve a large receptive field without adding the number of generic convolutional layers and kernels. Additionally, we design a residual channel-spatial attention (RCSA) module to improve accuracy and reduce the depth of the network appropriately. The proposed UHDVD is the first real-time deblurring model for 4K videos at 35 fps. To train the proposed model, we build a new dataset comprised of 4K blurry videos and corresponding sharp frames using three different smartphones. Comprehensive experimental results show that our network performs favorably against the state-of-the-art methods on both the 4K dataset and public benchmarks in terms of accuracy, speed, and model size.
Multi-view deep forecasting for hourly solar irradiance with error correction

Short-term solar irradiance forecasting is crucial in managing power network operations and solar photovoltaic applications. In this paper, a Multi-view Deep Forecasting method with Error Correction (MvDF_EC) for 1-hour ahead solar forecasting is proposed. MvDF_EC comprises of the Multi-view Deep Forecasting method (MvDF) and a robust Radial Basis Function Neural Network trained via minimizing the Localized Generalization Error for compensating the solar forecasting error of MvDF. MvDF consists of three deep neural networks which learn representations of input data from different views. The three views are 1) the hierarchical local temporal information extracted by the Temporal Convolutional Neural Network (TCN), 2) the key context sequential information captured by the Bi-directional Long Short-Term Memory Neural Network with Temporal Attention (BLSTMattn), and 3) long-term temporal dependencies between local temporal patterns filtered by the Convolutional Gated Recurrent Unit Neural Network (C_GRU). The solar forecasting performance of the proposed MvDF_EC is evaluated with the National Solar Radiation Database. Simulation results show that MvDF_EC yields the most accurate solar prediction compared with the benchmarks including the smart persistence and the state-of-the-art models. The lowest relative Root Mean Square Error values for Maraba and Labelle are 22.08% and 27.40%, respectively in 1-hour ahead solar forecasting.