Reviewerly/exHarmony huggingface dataset:
Segmenting Objects in Day and Night: Edge-Conditioned CNN for Thermal Image Semantic Segmentation

Despite much research progress in image semantic segmentation, it remains challenging under adverse environmental conditions caused by imaging limitations of the visible spectrum, while thermal infrared cameras have several advantages over cameras for the visible spectrum, such as operating in total darkness, insensitive to illumination variations, robust to shadow effects, and strong ability to penetrate haze and smog. These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night. In this article, we propose a novel network architecture, called edge-conditioned convolutional neural network (EC-CNN), for thermal image semantic segmentation. Particularly, we elaborately design a gated featurewise transform layer in EC-CNN to adaptively incorporate edge prior knowledge. The whole EC-CNN is end-to-end trained and can generate high-quality segmentation results with edge guidance. Meanwhile, we also introduce a new benchmark data set named “Segmenting Objects in Day And night” (SODA) for comprehensive evaluations in thermal image semantic segmentation. SODA contains over 7168 manually annotated and synthetically generated thermal images with 20 semantic region labels and from a broad range of viewpoints and scene complexities. Extensive experiments on SODA demonstrate the effectiveness of the proposed EC-CNN against state-of-the-art methods.
Circular intuitionistic fuzzy sets

In this paper an extension of the Intuitionistic Fuzzy Set called Circular Intuitionistic Fuzzy Set is introduced and studied. Some of the relations and operations over C-IFS are established.
Concurrent Entanglement Routing for Quantum Networks

Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.
Predicting Missing Values in Medical Data Via XGBoost Regression

The data in a patient's laboratory test result is a notable resource to support clinical investigation and enhance medical research. However, for a variety of reasons, this type of data often contains a non-trivial number of missing values. For example, physicians may neglect to order tests or document the results. Such a phenomenon reduces the degree to which this data can be utilized to learn efficient and effective predictive models. To address this problem, various approaches have been developed to impute missing laboratory values; however, their performance has been limited. This is due, in part, to the fact no approaches effectively leverage the contextual information 1) in individual or 2) between laboratory test variables.We introduce an approach to combine an unsupervised prefilling strategy with a supervised machine learning approach, in the form of extreme gradient boosting (XGBoost), to leverage both types of context for imputation purposes. We evaluated the methodology through a series of experiments on approximately 8,200 patients' records in the MIMIC-III dataset.The results demonstrate that the new model outperforms baseline and state-of-the-art models on 13 commonly collected laboratory test variables. In terms of the normalized root mean square derivation (nRMSD), our model exhibits an imputation improvement by over 20%, on average.Missing data imputation on the temporal variables can be largely improved via prefilling strategy and the supervised training technique, which leverages both the longitudinal and cross-sectional context simultaneously.
ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions

Adverse weather conditions such as fog, haze, snow, mist and glare create visibility problems for applications of autonomous vehicles. To ensure safe and smooth operations in frequent bad weather scenarios, image dehazing is crucial to any vehicular motion and navigation task on road or air. Moreover, the commonly deployed mobile systems are resource constrained in nature. Therefore, it is important to ensure memory, compute and run-time efficiency of dehazing algorithms. In this manuscript we propose ReViewNet, a fast, lightweight and robust dehazing system suitable for autonomous vehicles. The network uses components like spatial feature pooling, quadruple color-cue, multi-look architecture and multi-weighted loss to effectively dehaze images captured by cameras of autonomous vehicles. The effectiveness of the proposed model is analyzed by exhaustive quantitative evaluation on five benchmark datasets demonstrating its supremacy over other existing state-of-the-art methods. Further, a component-wise ablation and loss weight ratio analysis demonstrates the contribution of each and every component of the network. We also show the qualitative analysis with special use cases and visual responses on distinctive vehicular vision instances, establishing the effectiveness of the proposed method in numerous hazy weather conditions for autonomous vehicular applications.
Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images

The context-based convolutional neural network (CNN) is one of the most well-known CNNs to improve the performance of semantic segmentation. It has achieved remarkable success in various medical image segmentation tasks. However, extracting rich and useful context information from complex and changeable medical images is a challenge for medical image segmentation. In this study, a novel Multi-Receptive-Field CNN (MRFNet) is proposed to tackle this challenge. MRFNet offers the optimal receptive field for each subnet in the encoder-decoder module (EDM) and generates multi-receptive-field context information at the feature map level. Moreover, MRFNet fuses these multi-feature maps by the concatenation operation. MRFNet is evaluated on 3 public medical image data sets, including SISS, 3DIRCADb, and SPES. Experimental results show that MRFNet achieves the outstanding performance on all 3 data sets, and outperforms other segmentation methods on 3DIRCADb test set without pre-training the model.
A Blockchain-based access control scheme with multiple attribute authorities for secure cloud data sharing

Ciphertext-policy attribute-based encryption(CP-ABE) has been widely studied and used in access control schemes for secure data sharing. Since in most of the existing attribute-based encryption methods, all user attributes are managed by a single central authority, it is easy to cause a single point of failure. Therefore, several multi-authority CP-ABE schemes are proposed to manage user attributes by multiple authorities. However, these schemes still do not eliminate the single point of failure in essence or suffer from high computation and communication overhead on data users. In this paper, we propose a Blockchain-based Multi-authority Access Control scheme called BMAC for sharing data securely. Shamir secret sharing scheme and permissioned blockchain (Hyperledger Fabric) are introduced to implement that each attribute is jointly managed by multiple authorities to avoid single point of failure. In addition, we take advantage of blockchain technology to establish trust among multiple authorities and exploit smart contracts to compute tokens for attributes managed across multiple management domains, which reduces communication and computation overhead on the data user side. Moreover, blockchain helps to record the access control process in a secure and auditable way. Finally, we analyze the security of the proposed algorithm. Further analysis and comparison show the performance of the proposed method.
A new one-dimensional chaotic system with applications in image encryption

This paper presents a new and improved one-dimensional sinusoidal chaotic system (I1DS). After experimental verification, its chaotic performance has a larger range of chaotic parameters, higher sensitivity and complexity than the classic chaotic system. Based on I1DS, we propose a new image encryption scheme. First, I1DS is used to generate chaotic sequences to improve the security of the encryption algorithm. Then, the image is subjected to a dynamic bit-shifting recombination operation. Finally, a non-linear diffusion strategy is used to perform a synchronous shuffling diffusion operation (SSDO) for the image. Each pixel of the diffusion operation is highly sensitive to the subtle differences of the previous operation's pixel value, key, and chaotic interference value. Simulation experiment shows that the encryption scheme has a good speed and effect, can resist various cryptanalysis and attacks, and has better security.
Novel Linguistic Steganography Based on Character-Level Text Generation

With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text generation model. Through training, the best evaluated model is obtained as the prediction model of generating stego text. Then, we use the secret information as the control information to select the right character from predictions of the trained character-level text generation model. Thus, the secret information is hidden in the generated text as the predicted characters having different prediction probability values can be encoded into different secret bit values. For the same secret information, the generated stego texts vary with the starting strings of the text generation model, so we design a selection strategy to find the highest quality stego text from a number of candidate stego texts as the final stego text by changing the starting strings. The experimental results demonstrate that compared with other similar methods, the proposed method has the fastest running speed and highest embedding capacity. Moreover, extensive experiments are conducted to verify the effect of the number of candidate stego texts on the quality of the final stego text. The experimental results show that the quality of the final stego text increases with the number of candidate stego texts increasing, but the growth rate of the quality will slow down.
Kernel-Based Density Map Generation for Dense Object Counting

Crowd counting is an essential topic in computer vision due to its practical usage in surveillance systems. The typical design of crowd counting algorithms is divided into two steps. First, the ground-truth density maps of crowd images are generated from the ground-truth dot maps (density map generation), e.g., by convolving with a Gaussian kernel. Second, deep learning models are designed to predict a density map from an input image (density map estimation). The density map based counting methods that incorporate density map as the intermediate representation have improved counting performance dramatically. However, in the sense of end-to-end training, the hand-crafted methods used for generating the density maps may not be optimal for the particular network or dataset used. To address this issue, we propose an adaptive density map generator, which takes the annotation dot map as input, and learns a density map representation for a counter. The counter and generator are trained jointly within an end-to-end framework. We also show that the proposed framework can be applied to general dense object counting tasks. Extensive experiments are conducted on 10 datasets for 3 applications: crowd counting, vehicle counting, and general object counting. The experiment results on these datasets confirm the effectiveness of the proposed learnable density map representations.
Towards Unsupervised Deep Image Enhancement With Generative Adversarial Network

Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to learn an automatic photo enhancer for paired data, which consists of low-quality photos and corresponding expert-retouched versions. However, the style and characteristics of photos retouched by experts may not meet the needs or preferences of general users. In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images. The proposed model is based on single deep GAN which embeds the modulation and attention mechanisms to capture richer global and local features. Based on the proposed model, we introduce two losses to deal with the unsupervised image enhancement: (1) fidelity loss, which is defined as a L2 regularization in the feature domain of a pre-trained VGG network to ensure the content between the enhanced image and the input image is the same, and (2) quality loss that is formulated as a relativistic hinge adversarial loss to endow the input image the desired characteristics. Both quantitative and qualitative results show that the proposed model effectively improves the aesthetic quality of images. Our code is available at: https://github.com/eezkni/UEGAN.
Generative Adversarial Network-Based Full-Space Domain Adaptation for Land Cover Classification From Multiple-Source Remote Sensing Images

The accuracy of remote sensing image segmentation and classification is known to dramatically decrease when the source and target images are from different sources; while deep learning-based models have boosted performance, they are only effective when trained with a large number of labeled source images that are similar to the target images. In this article, we propose a generative adversarial network (GAN) based domain adaptation for land cover classification using new target remote sensing images that are enormously different from the labeled source images. In GANs, the source and target images are fully aligned in the image space, feature space, and output space domains in two stages via adversarial learning. The source images are translated to the style of the target images, which are then used to train a fully convolutional network (FCN) for semantic segmentation to classify the land cover types of the target images. The domain adaptation and segmentation are integrated to form an end-to-end framework. The experiments that we conducted on a multisource data set covering more than 3500 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with 51 560 256×256 high-resolution satellite images in Wuhan city and a cross-city data set with 11 383 256×256 aerial images in Potsdam and Vaihingen demonstrated that our method exceeded the recent GAN-based domain adaptation methods by at least 6.1% and 4.9% in the mean intersection over union (mIoU) and overall accuracy (OA) indexes, respectively. We also proved that our GAN is a generic framework that can be implemented for other domain transfer methods to boost their performance.
Investigating the effect of teaching as a generative learning strategy when learning through desktop and immersive VR: A media and methods experiment

Abstract Immersive virtual reality (IVR) simulations for education have been found to increase affective outcomes compared to traditional media, but the effects on learning are mixed. As reflection has previously shown to enhance learning in traditional media, we investigated the efficacy of appropriate reflection exercises for IVR. In a 2 × 2 mixed‐methods experiment, 89 (61 female) undergraduate biochemistry students learned about the electron transport chain through desktop virtual reality (DVR) and IVR (media conditions). Approximately, half of each group engaged in a subsequent generative learning strategy (GLS) of teaching in pairs (method conditions). A significant interaction between media and methods illustrated that the GLS of teaching significantly improved transfer ( d = 1.26), retention ( d = 0.60) and self‐efficacy ( d = 0.82) when learning through IVR, but not DVR. In the second part of the study, students switched media conditions and the experiment was repeated. This time, significant main effects favoring the IVR group on the outcomes of intrinsic motivation ( d = 0.16), perceived enjoyment ( d = 0.94) and presence ( d = 1.29) were observed, indicating that students preferred IVR after having experienced both media conditions. The results support the view that methods enable media that affect learning and that the GLS of teaching is specifically relevant for IVR. Practitioner Notes What is already known about this topic Previous research has found a media effect with Immersive Virtual Reality (IVR) in education leading to better motivational outcomes compared to less immersive media, but effects on learning outcomes are mixed. There is evidence that Generative Learning Strategies (GLSs) such as summarizing and enacting can increase learning in IVR. There is also evidence that some instructional methods, such as pretraining, may be beneficial for learning in IVR. What this paper adds Evidence that the GLS of teaching improves self‐efficacy, retention and transfer in educational IVR. An interaction effect between media (DVR/IVR) and method (GLS/no‐GLS) on self‐efficacy, retention and transfer supporting the theoretical view that method enables media. No difference in perceived enjoyment, motivation and presence for students who were new to learning through these media (DVR/IVR), but differences became significant when students learned through the other media first with students preferring IVR. Implications for practice and/or policy Since IVR learning experiences can be highly engaging and also cognitively demanding, it is beneficial to introduce reflection exercises after an IVR learning experience to ensure that students reflect over the material and integrate it with their long‐term memory. One effective solution is to engage students in the GLS of teaching after an IVR simulation, thereby prompting them to select relevant information, organize it into a coherent structure and elaborate on it by incorporating it with their existing knowledge.
Efficient anomaly detection in surveillance videos based on multi layer perception recurrent neural network

Surveillance frameworks actualized in true environment are strong in nature. As the environment is uncertain and dynamic, the surveillance turns out to be increasingly perplexing when contrasted with a static and controlled environment. Effective anomaly identification in the video surveillance is a difficult issue because of spilling, video noise, anomalies, and goals. This examination work proposes a background deduction approach dependent on Maximally Stable Extremal Region (MSER) highlight extraction technique with the ongoing profound learning structure of Multi-layer perception recurrent neural network (MLP-RNN) that is fit for distinguishing multiple objects of various sizes by pixel-wise foreground investigating framework. The proposed algorithm takes as information a reference (without anomaly) and an objective edge, both transiently adjusted, and outputs a segmentation guide of same spatial goals where the featured pixels meaning the recognized anomalies, which ought to be all the components not present in the reference outline. Besides, examine the advantages of various remaking strategies to the reestablish unique picture goals and exhibit the improvement of leftover designs over the littler and more straightforward models proposed by past comparable works. The simulation results are shows serious execution in the tried dataset, just as constant handling ability as compared with existing methods.
CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems

The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. Recently, the potential of GAN as a data augmentation method on minority data has been studied. In this paper, we propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. In addition, we propose an ambiguity reduction method using the generated synthetic minority data for the case of multiple similar classes that are degenerating the classification accuracy. The proposed method is demonstrated with five benchmark datasets. The results indicate that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.
Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning

Mammography plays an important role in screening breast cancer among females, and artificial intelligence has enabled the automated detection of diseases on medical images. This study aimed to develop a deep learning model detecting breast cancer in digital mammograms of various densities and to evaluate the model performance compared to previous studies. From 1501 subjects who underwent digital mammography between February 2007 and May 2015, craniocaudal and mediolateral view mammograms were included and concatenated for each breast, ultimately producing 3002 merged images. Two convolutional neural networks were trained to detect any malignant lesion on the merged images. The performances were tested using 301 merged images from 284 subjects and compared to a meta-analysis including 12 previous deep learning studies. The mean area under the receiver-operating characteristic curve (AUC) for detecting breast cancer in each merged mammogram was 0.952 ± 0.005 by DenseNet-169 and 0.954 ± 0.020 by EfficientNet-B5, respectively. The performance for malignancy detection decreased as breast density increased (density A, mean AUC = 0.984 vs. density D, mean AUC = 0.902 by DenseNet-169). When patients’ age was used as a covariate for malignancy detection, the performance showed little change (mean AUC, 0.953 ± 0.005). The mean sensitivity and specificity of the DenseNet-169 (87 and 88%, respectively) surpassed the mean values (81 and 82%, respectively) obtained in a meta-analysis. Deep learning would work efficiently in screening breast cancer in digital mammograms of various densities, which could be maximized in breasts with lower parenchyma density.