Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Single-Image Super-Resolution: A Comprehensive Review

High-fidelity information, such as 4K quality videos and photographs, is increasing as high-speed internet access becomes more widespread and less expensive. Even though camera sensors’ performance is constantly improving, artificially enhanced photos and videos created by intelligent image processing algorithms have significantly improved image fidelity in recent years. Single image super-resolution is a class of algorithms that produces a high-resolution image from a given low-resolution image. Since the advent of deep learning a decade ago, this field has made significant strides. This paper presents a comprehensive review of the deep learning assisted single image super-resolution domain including generative adversarial network (GAN) models that discusses the prominent architectures, models used, and their merits and demerits. The reason behind covering the GAN models is that it is been known to perform better than the conventional deep learning methods given the resources and the time. For real-world applications with noise and other issues that can cause low-fidelity super resolution (SR) images, we examine another solution based on GAN model. This GAN model-based technique popularly known as blind super resolution is more resilient. We examined the various super-resolution techniques by varying image scaling factors (i.e., 2x, 3x, 4x) to measure PSNR and SSIM metrics for the different datasets. PSNR across the different datasets covered in the experimental section shows an average of 14-17 % decrease in the score as we move up the image resolution scale from 2x to 4x. This is observed across all the datasets and for every model mentioned in the experimental section of the paper. The results also show that blind super-resolution outperforms the conventional deep learning methods and the more complex GAN models. GAN models are complex and preferred when the upscale factor is high, while residual and dense models are recommended for smaller upscaling factors. This paper also discusses the applications of image super-resolution, and finally, the paper is concluded with challenges and future directions.
CPG3D: Cross-Modal Priors Guided 3D Object Reconstruction

Three-dimensional reconstruction is a multimedia technology widely used in computer-aided modeling and 3D animation. Nevertheless, it is still hard for reconstruction methods to overcome the 3D geometry missing and the object occlusion in the single-view images. In this paper, we propose a novel method (CPG3D) for reconstructing high-quality 3D shapes from a single image under the guidance of prior knowledge. Using the single-view image as the query, prior knowledge is collected from public 3D datasets, which can compensate for missing 3D geometries and assist the 3D reconstruction network to high fidelity results. Our method consists of three parts: 1) Cross-modal 3D shape retrieval module: This part retrieves related 3D shapes based on 2D images. Here, we apply the pre-trained model to guarantee the correlation between the retrieved 3D shape and the input image. 2) Multimodal information fusion module: We propose a multimodal attention mechanism to handle the information fusing of 2D visual and 3D structural information; 3) Three-dimensional reconstruction module: We propose a novel encoder-decoder network for 3D shape reconstruction. Specifically, we employ the skip connection operation to link the target image's visual information with the 3D model's structural information to enhance the prediction of 3D details. During training, we employ two carefully designed loss functions to lead the multimodal learning to obtain proper modal features. On the ShapeNet and Pix3D datasets, the final experimental results reveal that our method notably increases reconstruction quality and outperforms SOTA methods.
CAP2M..÷Contingent Anonymity Preserving Privacy Method for the Internet of Things Services

The Internet of Things (IoT) consists of a collection of inter-connected devices that are used to transmit data. Secure transactions that guarantee user anonymity and privacy are necessary for the data transmission process. One of the major constraints of IoT Healthcare is the issue of maintaining security. These problems are addressed in this study by the Contingent Anonymity-Preserving Privacy Method (CAP2M), which takes into account the various security needs. The CAP2M method uses the session deployment and user requirements to preserve user privacy. The initial and final privacy settings are modified in the session deployment depending on the session interval and the service provider's recommendation. Federated learning is employed for training privacy settings toward the session's final interval. The suggested approach maintains privacy on various sharing intervals with minimum computation complexity. In the privacy-preserving process, two-factor authentication is used for concealing the sharing and shared ends. The performance is validated using the metrics failure, complexity, and latency.
Teachers as Augmented Reality Designers

As an emergent technology augmented reality (AR) demonstrated a plethora of advantages in foreign language education, promoting motivation, memorization of contents, as well as contextualized learning. However, the majority of teachers are unfamiliar with AR, and their role as designers and facilitators requires further investigation. Moreover, English is the most explored target language, and the study of AR for the teaching of Italian as a foreign language (TIFL) is limited. Therefore, the investigation explored the perceptions of educators on the experience of designing and implementing mobile AR (MAR) for TIFL. In line with other studies, findings showed that, according to teacher perceptions, MAR can promote the overall learning process. However, the study highlighted issues like the availability of open-source MAR platforms and materials specifically designed for language educators. Moreover, the need of ongoing teacher training as well as possible economical and infrastructural limitations in specific social contexts must be considered, before implementing AR on a large-scale.
Multiple-Deep Neural Network Accelerators for Next-Generation Artificial Intelligence Systems

The next generation of artificial intelligence (AI) systems will have multi-deep neural network (multi-DNN) workloads as their core. Large-scale deployment of AI services and integration across mobile devices require additional breakthroughs in the computer architecture front, with processors that can maintain high performance as the number of DNNs increase, giving rise to the topic of multi-DNN accelerator design.
Fundamentals of Quantum Computation and Basic Quantum Gates

Quantum computing plays a major role in modern computation. It can perform operations exponentially faster when compared to classical computation. It has applications in various areas like Secure communication, Drug design, Artificial Intelligence, Cyber security, etc. Thus the researchers and students are showing interest to perform experiments in quantum computing to design novel architectures. But to learn/understand quantum computing, one should have strong knowledge of its basics. Because quantum computing performs operations at the atomic level, so the learners need to understand basic concepts like Qubits, Superposition, Quantum gates, etc. Therefore this chapter gives a clear idea about the basic concepts of quantum computing like Qubits, Superposition, Entanglement, Teleportation, and Quantum gates.
Securing DNN for smart vehicles: an overview of adversarial attacks, defenses, and frameworks

Abstract Recently, many applications have begun to employ deep neural networks (DNN), such as image recognition and safety-critical applications, for more accurate results. One of the most important critical applications of DNNs is in smart autonomous vehicles. The operative principles of autonomous vehicles depend heavily on their ability to collect data from the environment via integrated sensors, then employ DNN classification to interpret them and make operative decisions. The security and the reliability of DNNs raise many challenges and concerns for researchers. One of those challenges currently in the research domain is the threat of adversarial attacks on DNNs. In this survey, we present state-of-the-art research on DNN frameworks, adversarial attacks, and defenses. We discuss each work along with its advantages and limitations and present our thoughts on and future directions for adversarial attacks and defenses.
Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Abstract The high cost of collecting surficial textures has been the bottleneck problem for many decades. To bridge this gap, this study aims to propose a complete framework based on the generative adversarial networks (GANs) architecture for texture regeneration using limited texture data samples. Four variants of the GAN were compared to achieve the best regeneration performance. Prior to running GAN, specifically improved data augmentation and modified sigmoid normalization operations were dedicated to avoiding the loss of texture information and the non‐convergence problem. After running GAN, the estimation metrics were introduced and discussed separately to quantitatively evaluate the rationality and diversity of the regenerated textures. The results demonstrated that the use of improved data augmentation and normalization methods significantly enhanced the regeneration capability of GAN, and more importantly the skewed distribution of surficial heights which is not achievable by present texture regeneration methods was most effectively regenerated by deep regret analytic GAN with well generalization ability. Our study provides a promising choice to generate virtually realistic pavement surface textures.
Boosted Reptile Search Algorithm for Engineering and Optimization Problems

Recently, various metaheuristic (MH) optimization algorithms have been presented and applied to solve complex engineering and optimization problems. One main category of MH algorithms is the naturally inspired swarm intelligence (SI) algorithms. SI methods have shown great performance on different problems. However, individual MH and SI methods face some shortcomings, such as trapping at local optima. To solve this issue, hybrid SI methods can perform better than individual ones. In this study, we developed a boosted version of the reptile search algorithm (RSA) to be employed for different complex problems, such as intrusion detection systems (IDSs) in cloud–IoT environments, as well as different optimization and engineering problems. This modification was performed by employing the operators of the red fox algorithm (RFO) and triangular mutation operator (TMO). The aim of using the RFO was to boost the exploration of the RSA, whereas the TMO was used for enhancing the exploitation stage of the RSA. To assess the developed approach, called RSRFT, a set of six constrained engineering benchmarks was used. The experimental results illustrated the ability of RSRFT to find the solution to those tested engineering problems. In addition, it outperformed the other well-known optimization techniques that have been used to handle these problems.
Exploring an Affective and Responsive Virtual Environment to Improve Remote Learning

Online classes are typically conducted by using video conferencing software such as Zoom, Microsoft Teams, and Google Meet. Research has identified drawbacks of online learning, such as “Zoom fatigue”, characterized by distractions and lack of engagement. This study presents the CUNY Affective and Responsive Virtual Environment (CARVE) Hub, a novel virtual reality hub that uses a facial emotion classification model to generate emojis for affective and informal responsive interaction in a 3D virtual classroom setting. A web-based machine learning model is employed for facial emotion classification, enabling students to communicate four basic emotions live through automated web camera capture in a virtual classroom without activating their cameras. The experiment is conducted in undergraduate classes on both Zoom and CARVE, and the results of a survey indicate that students have a positive perception of interactions in the proposed virtual classroom compared with Zoom. Correlations between automated emojis and interactions are also observed. This study discusses potential explanations for the improved interactions, including a decrease in pressure on students when they are not showing faces. In addition, video panels in traditional remote classrooms may be useful for communication but not for interaction. Students favor features in virtual reality, such as spatial audio and the ability to move around, with collaboration being identified as the most helpful feature.
Randomization-based neural networks for image-based wind turbine fault diagnosis

As the development of wind energy industry, the safe production of wind farms has become an urgent problem. To avoid serious faults and deterioration, building effective diagnostic model for wind turbine (WT) has raised increasing attentions in wind-power industry. However, the challenges like big data of sensors and model construction exist still. In this paper, to achieve better performance and suitable framework, a three channel broad learning system (3-BLS) is proposed for image-based fault diagnosis (FD) on overall WT system. First, multiple sensor series are collected and converted into interpretable RGB images via right-sized sliding window for broader information and grabbing relations; Next, features are extracted in respective RGB channels, and a manual feature layer is added in the 3-BLS, where the structure is temporary non-specific; Finally, with the help of an optimizer, the concrete 3-BLS is auto-built with its structure configured reasonably and the manual features binary-coded and enabled selectively. In addition, an inter-channel attention scheme is formed during 3-BLS dynamic updating process, and several BLS prototypes different in projections are studied. In experiments, the optimized 3-BLS with less parameters got over 10% accuracy gain than adjusted single BLS and achieved over 98% fault detection on actual collected WT data.
RNN Variants vs Transformer Variants: Uncertainty in Text Classification with Monte Carlo Dropout

Language models that can perform linguistic tasks, just like humans, have surpassed all expectations in recent years. Recurrent Neural Networks (RNN) and Transformer Architectures have exponentially accelerated the development of Natural Language Processing. It has drastically affected how we handle textual data. Understanding the reliability and confidence of these models is crucial for developing machine learning systems that can be successfully applied in real life situations. Uncertainty-based quantitative and comparative research between these two types of architectures has not yet been conducted. It is vital to identify confident models in text classification tasks, as the modern world seeks safe and dependable intelligent systems. In this work, the uncertainty of Transformer-based models such as BERT and XLNet is compared to that of RNN variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). To measure the uncertainty of these models, we use dropouts during the inference phase (Monte Carlo Dropout). Monte Carlo Dropout (MCD) has negligible computation costs and helps separate uncertain samples from the predictions. Based on our thorough experiments, we have determined that BERT surpasses all other models utilized in this study.
API Usage Recommendation Via Multi-View Heterogeneous Graph Representation Learning

Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API usage recommendation. Previous studies adopt statistical models or collaborative filtering methods to mine the implicit API usage patterns for recommendation. However, they rely on the occurrence frequencies of APIs for mining usage patterns, thus prone to fail for the low-frequency APIs. Besides, prior studies generally regard the API call interaction graph as homogeneous graph, ignoring the rich information (e.g., edge types) in the structure graph. In this work, we propose a novel method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEGA</i> for improving the recommendation accuracy especially for the low-frequency APIs. Specifically, besides <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">call interaction graph</i> , MEGA considers another two new heterogeneous graphs: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global API co-occurrence graph</i> enriched with the API frequency information and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hierarchical structure graph</i> enriched with the project component information. With the three multi-view heterogeneous graphs, MEGA can capture the API usage patterns more accurately. Experiments on three Java benchmark datasets demonstrate that MEGA significantly outperforms the baseline models by at least 19% with respect to the Success Rate@1 metric. Especially, for the low-frequency APIs, MEGA also increases the baselines by at least 55% regarding the Success Rate@1 score.
Multi-Scale Attention Generative Adversarial Network for Medical Image Enhancement

High quality medical images are not only an important basis for doctors to carry out clinical diagnosis and treatment, but also conducive to downstream tasks such as image analysis. Although many medical image enhancement methods have achieved good results, some of them still have shortcomings in homogenizing illumination distribution and maintaining texture details, and even introduce boundary artifact noise. In order to deal with these problems, this paper proposes a multi-scale attention generative adversarial network (MAGAN) for medical image enhancement, which is suitable for unpaired images. Our MAGAN is trained in the confrontation between two generators and two discriminators. It tries to fuse multi-scale information in feature extraction by establishing feature pyramid, and filters irrelevant activation to highlight important regions based on attention distribution, which is positive for imaging. Moreover, MAGAN strengthens the constraints on the quality of enhanced image from the perspectives of illumination distribution, texture details, deep semantic features and smoothness, so as to improve the enhancement effect. Compared with six state-of-the-art methods, the experimental results show that MAGAN has the most significant image enhancement effect, and also performs best in the downstream task of image segmentation.
An efficient method for mining high occupancy itemsets based on equivalence class and early pruning

Many researchers have been investigating and applying a new trend of data mining, namely high occupancy itemset mining. Frequent itemset mining often returns a large set of itemsets, but businesses need a smaller set of inputs to investigate or send into a recommendation system to quickly make decisions. Applying an occupancy measure to a support-based mining framework will thus bring many benefits for decision support systems, while managers will benefit by having a new method to visualize reports and analyze data more efficiently. Similar to frequent itemset mining, mining high occupancy itemsets can be applied on any transaction database. In this research, we apply additional conditions to eliminate unqualified itemsets and integrate the property of equivalence class to reduce the runtime of the k-itemsets generation process. Moreover, a new theorem is stated and applied to a specific class of databases so that it is not necessary to calculate the upper-bound occupancy, and this speeds up the process as well as reduces memory requirements with regard to generating high occupancy itemsets. We develop two new algorithms, fast high occupancy itemset mining (FHOI) and depth first search (DFS) for high occupancy itemset mining (DFHOI) to solve the problem. Our new algorithms are examined experimentally using different databases to evaluate its performance in term of runtime and memory usage.
Chest X ray image enhancement using deep contrast diffusion learning

The chest x-ray (CXR) is a diagnostic imaging tool that aids in the early detection and diagnosis of lung abnormalities. Due to scattering radiation, the CXR would have poor contrast, and the diagnosis would be difficult. Although many methods exist, deep learning (DL)-based CXR image enhancement remains difficult due to the amount of contrast that needs to be enhanced and the locations where acceptable contrast must be extracted. In order to improve CXR images, a contrast diffusion network is introduced in this paper. The input image is initially placed through a multi-level contrast-limited adaptive histogram Equalization (CLAHE) process, from which the necessary contrast is extracted and sent into the convolutional neural network (CNN)-based residual learning network together with low contrast CXR. To create the enhanced CXR images, the learned contrast features were diffused over the input image. The amount of contrast to be diffused is determined by multiple levels of CLAHE. Various metrics are used to evaluate the enhanced image's quality. Additionally, the enhanced images are submitted to computer-assisted diagnosis, which improves overall classification efficiency. All of the results are based on the Shenzhen, COVID-CXR, and PadChest datasets.
Adversarial co-training for semantic segmentation over medical images

Abundant labeled data drives the model training for better performance, but collecting sufficient labels is still challenging. To alleviate the pressure of label collection, semi-supervised learning merges unlabeled data into training process. However, the joining of unlabeled data (e.g., data from different hospitals with different acquisition parameters) will change the original distribution. Such a distribution shift leads to a perturbation in the training process, potentially leading to a confirmation bias. In this paper, we investigate distribution shift and develop methods to increase the robustness of our models, with the goal of improving performance in semi-supervised semantic segmentation of medical images. We study distribution shift and increase model robustness to it, for improving practical performance in semi-supervised segmentation over medical images.To alleviate the issue of distribution shift, we introduce adversarial training into the co-training process. We simulate perturbations caused by the distribution shift via adversarial perturbations and introduce the adversarial perturbation to attack the supervised training to improve the robustness against the distribution shift. Benefiting from label guidance, supervised training does not collapse under adversarial attacks. For co-training, two sub-models are trained from two views (over two disjoint subsets of the dataset) to extract different kinds of knowledge independently. Co-training outperforms single-model by integrating both views of knowledge to avoid confirmation bias.For practicality, we conduct extensive experiments on challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts (Yu and Wang, 2019; Peng et al., 2020; Perone et al., 2019). We achieve a DSC score of 87.37% with only 20% of labels on the ACDC dataset, almost same to using 100% of labels. On the SCGM dataset with more distribution shift, we achieve a DSC score of 78.65% with 6.5% of labels, surpassing 10.30% over Peng et al. (2020). Our evaluative results show superior robustness against distribution shifts in medical scenarios.Empirical results show the effectiveness of our work for handling distribution shift in medical scenarios.