Reviewerly/exHarmony huggingface dataset:
Restoring Extremely Dark Images in Real Time

A practical low-light enhancement solution must be computationally fast, memory-efficient, and achieve a visually appealing restoration. Most of the existing methods target restoration quality and thus compromise on speed and memory requirements, raising concerns about their real-world deployability. We propose a new deep learning architecture for extreme low-light single image restoration, which despite its fast & lightweight inference, produces a restoration that is perceptually at par with state-of-the-art computationally intense models. To achieve this, we do most of the processing in the higher scale-spaces, skipping the intermediate-scales wherever possible. Also unique to our model is the potential to process all the scale-spaces concurrently, offering an additional 30% speedup without compromising the restoration quality. Pre-amplification of the dark raw-image is an important step in extreme lowlight image enhancement. Most of the existing state of the art methods need GT exposure value to estimate the pre-amplification factor, which is not practically feasible. Thus, we propose an amplifier module that estimates the amplification factor using only the input raw image and can be used "off-the-shelf" with pre-trained models without any fine-tuning. We show that our model can restore an ultrahigh-definition 4K resolution image in just 1 sec. on a CPU and at 32 fps on a GPU and yet maintain a competitive restoration quality. We also show that our proposed model, without any fine-tuning, generalizes well to cameras not seen during training and to subsequent tasks such as object detection.
Neural Descent for Visual 3D Human Pose and Shape

We present deep neural network methodology to reconstruct the 3d pose and shape of people, including hand gestures and facial expression, given an input RGB image. We rely on a recently introduced, expressive full body statistical 3d human model, GHUM, trained end-to-end, and learn to reconstruct its pose and shape state in a self-supervised regime. Central to our methodology, is a learning to learn and optimize approach, referred to as HUman Neural Descent (HUND), which avoids both second-order differentiation when training the model parameters, and expensive state gradient descent in order to accurately minimize a semantic differentiable rendering loss at test time. Instead, we rely on novel recurrent stages to update the pose and shape parameters such that not only losses are minimized effectively, but the process is meta-regularized in order to ensure endprogress. HUND’s symmetry between training and testing makes it the first 3d human sensing architecture to natively support different operating regimes including self-supervised ones. In diverse tests, we show that HUND achieves very competitive results in datasets like H3.6M and 3DPW, as well as good quality 3d reconstructions for complex imagery collected in-the-wild.
Mineral prospectivity mapping by deep learning method in Yawan-Daqiao area, Gansu

Mineral prospectivity mapping is similar to probability prediction using multi-source geological data. However, the complexity of geological phenomena creates difficulties for research. In this study, a deep regression neural network was built to map the mineral prospectivity in the Daqiao Gold Mine in Gansu Province, China. The neural network was trained using multi-source data including geological, geophysical, and geochemical data for the study area. The proposed deep regression neural network reveals the complex relationships between the mineral prospectivity map and geological, geophysical, and geochemical features, improving the prediction results. Moreover, the training dataset does not require classified samples. Training samples with continuous values can help improve the fault tolerance of the training dataset and reduce the uncertainty of positive samples. The experimental results showed that the proposed neural network learned previous expert knowledge related to mineral prospectivity mapping and can be applied to deep regression neural networks to predict and evaluate mineral resources using multiple data sources. The prospectivity map obtained in this study benefits the search for gold mineralization in the study area.
The use of YouTube in developing the speaking skills of Jordanian EFL university students

This study examines the effectiveness of using YouTube videos in teaching the speaking skills among English as a Foreign Language (EFL) students in Jordan. The study sample comprised 80 students attending Oral Skills classes in the English Language and Literature Department at a private university in Jordan. The participants were equally divided into a control group and an experimental group of 40 students each. The experimental group was taught through the use of YouTube videos, while the control group was taught the speaking skills using the traditional approach. A pre-test and a post-test were administered to the two groups. Four TEFL experts were asked to rate the participants’ performance using the IELTS speaking band descriptors, which consist of four main categories: fluency & coherence, lexical resource, grammatical range & accuracy, and pronunciation. The findings showed that the performance of the two groups was improved. However, compared to the traditional group, the experimental group demonstrated a relatively better improvement. The results also showed significant progress in the speaking performance of the students subjected to the YouTube experiment. Of all the four constructs under investigation, pronunciation and fluency & coherence were the most noticeably advanced in the performance of the YouTube experimental group. The present study recommends that YouTube videos be embedded into the EFL classroom to improve students' speaking skills.
Principal Component Analysis-Based Low-Light Image Enhancement Using Reflection Model

In this article, a novel low-light image enhancement (LIME) using reflection model and principal component analysis (PCA) has been proposed. The proposed algorithm works adaptively for dark images based on reflection model and multiscale principle. An input RGB color image is first stretched to correct any type of color distortion and then converted to HSV color space. By using the concept of multiscale theory, the illumination coefficient of the V component is calculated. Then, an image brightness enhancement scheme is employed based on the Fechner principle, which adaptively regulates the parameters of the enhancement function. Further to this, PCA based on image fusion approach is framed to pull out the relevant features from these two images. Finally, the contrast-limited adaptive histogram equalization (CLAHE) model is applied to improve the global contrast. In comparison with other methods, the proposed method gives better outcomes in context of subjective and objective assessments.
DG-Font: Deformable Generative Networks for Unsupervised Font Generation

Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years. However, existing methods for font generation are often in supervised learning. They require a large number of paired data, which is labor-intensive and expensive to collect. Besides, common image-to-image translation models often define style as the set of textures and colors, which cannot be directly applied to font generation. To address these problems, we propose novel deformable generative networks for unsupervised font generation (DG-Font). We introduce a feature deformation skip connection (FDSC) which predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level feature maps from the content encoder. The outputs of FDSC are fed into a mixer to generate the final results. Taking advantage of FDSC, the mixer outputs a high-quality character with a complete structure. To further improve the quality of generated images, we use three deformable convolution layers in the content encoder to learn style-invariant feature representations. Experiments demonstrate that our model generates characters in higher quality than state-of-art methods. The source code is available at https://github.com/ecnuycxie/DG-Font.
CNL-UNet: A novel lightweight deep learning architecture for multimodal biomedical image segmentation with false output suppression

Automatic biomedical image segmentation plays an important role in speeding up disease detection and diagnosis. The rapid development of Deep Learning has shown ground-breaking improvements in this context. However, state-of-the-art networks like U-Net and SegNet often have poor performance on challenging domains. Most of the recent works were domains specific and computationally expensive. This paper proposes a novel lightweight architecture named CNL-UNet for 2D multimodal Biomedical Image Segmentation. The proposed CNL-UNet has a pre-trained encoder enriched with transfer learning techniques to learn sufficiently from the small amount of data. It has modified skip connections to reduce semantic gaps between the corresponding level of the encoder-decoder layer. Furthermore, the proposed architecture is enhanced with a novel Classifier and Localizer (CNL) module. This module provides us with additional classification and localization information with greater accuracy. Fusing this information with the segmentation output, the CNL-UNet can suppress false positives and false negatives. The proposed architcture has comparatively fewer parameters (11.5M) than U-Net (31M), SegNet (29M), and most of the recent works. Thus it is a lightweight architecture and also less prone to overfit. Besides, in the case of simple datasets, the pruned version of the CNL-UNet can be used. We evaluated our proposed architecture on multimodal biomedical image datasets, namely Chest X-ray, Dermoscopy, Microscopy, Ultrasound, and MRI images. The results demonstrate the superior performance of our proposed architecture over most of the existing networks. We have shown that our model can learn quickly, segment precisely, and automatically suppress falsely classified outputs.
MU-GAN: Facial Attribute Editing Based on Multi-Attention Mechanism

Facial attribute editing has mainly two objectives: 1) translating image from a source domain to a target one, and 2) only changing the facial regions related to a target attribute and preserving the attribute-excluding details. In this work, we propose a multi-attention U-Net-based generative adversarial network (MU-GAN). First, we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator, and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decoder with attribute-excluding detail and enhance attribute editing ability. Second, a self-attention (SA) mechanism is incorporated into convolutional layers for modeling long-range and multi-level dependencies across image regions. Experimental results indicate that our method is capable of balancing attribute editing ability and details preservation ability, and can decouple the correlation among attributes. It outperforms the state-of-the-art methods in terms of attribute manipulation accuracy and image quality. Our code is available at https://github.com/SuSir1996/MU-GAN.
Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture

The growth of internet era leads to a major transformation in a storage of data and accessing the applications.One such new trend that promises the endurance is the Cloud computing.Computing resources offered by the Cloud includes the servers, networks, storage, and applications, all as services.With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network.The services offered via the Cloud are such as the infrastructure, software, platform, database and web services.The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption.This key generation in the cryptography process falls into three categories in this research work.In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements.To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment.The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files.In case of images, it resists well against statistical and differential attacks.Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services.
Digitized-counterdiabatic quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a need to design ansatz for better optimization. To this end, we propose a digitized version of QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better ansatz, along with the Hamiltonian and mixing terms, enhancing the global performance. We apply our digitized-counterdiabatic QAOA to Ising models, classical optimization problems, and the P-spin model, demonstrating that it outperforms standard QAOA in all cases we study.
A Survey on Deep Learning Technique for Video Segmentation

Video segmentation-partitioning video frames into multiple segments or objects-plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding scenes in autonomous driving, to creating virtual background in video conferencing. Recently, with the renaissance of connectionism in computer vision, there has been an influx of deep learning based approaches for video segmentation that have delivered compelling performance. In this survey, we comprehensively review two basic lines of research - generic object segmentation (of unknown categories) in videos, and video semantic segmentation - by introducing their respective task settings, background concepts, perceived need, development history, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out open issues in this field, and suggest opportunities for further research. We also provide a public website to continuously track developments in this fast advancing field: https://github.com/tfzhou/VS-Survey.
Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review

Keeping in view of the constraints and challenges with respect to big data analytics along with security and privacy preservation for 5G and B5G applications, the integration of machine learning and blockchain, two of the most promising technologies of the modern era is inevitable. In comparison to the traditional centralized techniques for security and privacy preservation, blockchain uses decentralized consensus algorithms for verification and validation of different transactions which are supposed to become an integral part of blockchain network. Starting with the existing literature survey, we introduce the basic concepts of blockchain and machine learning in this article. Then, we presented a comprehensive taxonomy for integration of blockchain and machine learning in an IoT environment. We also explored federated learning, reinforcement learning, deep learning algorithms usage in blockchain based applications. Finally, we provide recommendations for future use cases of these emerging technologies in 5G and B5G technologies.
Insurance fraud detection with unsupervised deep learning

Abstract The objective of this paper is to propose a novel deep learning methodology to gain pragmatic insights into the behavior of an insured person using unsupervised variable importance. It lays the groundwork for understanding how insights can be gained into the fraudulent behavior of an insured person with minimum effort. Starting with a preliminary investigation of the limitations of the existing fraud detection models, we propose a new variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational autoencoder. Each model's dynamics is discussed to inform the reader on how models can be adapted for fraud detection and how results can be perceived appropriately. Both qualitative and quantitative performance evaluations are conducted, although a greater emphasis is placed on qualitative evaluation. To broaden the scope of reference of fraud detection setting, various metrics are used in the qualitative evaluation.
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate

In this work, we propose an adversarial unsupervised domain adaptation (UDA) method under inherent conditional and label shifts, in which we aim to align the distributions w.r.t. both p(x|y) and p(y). Since labels are inaccessible in a target domain, conventional adversarial UDA methods assume that p(y) is invariant across domains and rely on aligning p(x) as an alternative to the p(x|y) alignment. To address this, we provide a thorough theoretical and empirical analysis of the conventional adversarial UDA methods under both conditional and label shifts, and propose a novel and practical alternative optimization scheme for adversarial UDA. Specifically, we infer the marginal p(y) and align p(x|y) iteratively at the training stage, and precisely align the posterior p(y|x) at the testing stage. Our experimental results demonstrate its effectiveness on both classification and segmentation UDA and partial UDA.
Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers

Abstract Recently, multimodal transformer models have gained popularity because their performance on downstream tasks suggests they learn rich visual-linguistic representations. Focusing on zero-shot image retrieval tasks, we study three important factors that can impact the quality of learned representations: pretraining data, the attention mechanism, and loss functions. By pretraining models on six datasets, we observe that dataset noise and language similarity to our downstream task are important indicators of model performance. Through architectural analysis, we learn that models with a multimodal attention mechanism can outperform deeper models with modality-specific attention mechanisms. Finally, we show that successful contrastive losses used in the self-supervised learning literature do not yield similar performance gains when used in multimodal transformers.
Energy-efficient VM scheduling based on deep reinforcement learning

Achieving data center resource optimization and QoS guarantee driven by high energy efficiency has become a research hotspot. However, QoS information directly sampled from the cloud environment will inevitably be affected by a small amount of structured noise. This paper proposes a deep reinforcement learning model based on QoS feature learning to optimize data center resource scheduling. In the deep learning stage, we propose a QoS feature learning method based on improved stacked denoising autoencoders to extract more robust QoS characteristic information. In the reinforcement learning stage, we propose a multi-power machines (PMs) collaborative resource scheduling algorithm based on reinforcement learning. Extensive experiments show that compared with other excellent resource scheduling strategies, our method can effectively reduce the energy consumption of cloud data centers while maintaining the lowest service level agreement (SLA) violation rate. A good balance is achieved between energy-saving and QoS optimization.
Incorporating domain knowledge for extractive summarization of legal case documents

Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this purpose, several algorithms have been developed specifically for summarizing legal case documents. However, most of the existing algorithms do not systematically incorporate domain knowledge that specifies what information should ideally be present in a legal case document summary. To address this gap, we propose an unsupervised summarization algorithm DELSumm which is designed to systematically incorporate guidelines from legal experts into an optimization setup. We conduct detailed experiments over case documents from the Indian Supreme Court. The experiments show that our proposed unsupervised method outperforms several strong baselines in terms of ROUGE scores, including both general summarization algorithms and legal-specific ones. In fact, though our proposed algorithm is unsupervised, it outperforms several supervised summarization models that are trained over thousands of document-summary pairs.
Transformer-based Text Detection in the Wild

A major limitation to most state-of-the-art visual localization methods is their ineptitude to make use of ubiquitous signs and directions that are typically intuitive to humans. Localization methods can greatly benefit from a system capable of reasoning about a variety of cues beyond low-level features, such as street signs, store names, building directories, room numbers, etc.In this work, we tackle the problem of text detection in the wild, an essential step towards achieving text-based localization and mapping. While current state-of-the-art text detection methods employ ad-hoc solutions with complex multi-stage components to solve the problem, we propose a Transformer-based architecture inherently capable of dealing with multi-oriented texts in images. A central contribution to our work is the introduction of a loss function tailored to the rotated text detection problem that leverages a rotated version of a generalized intersection over union score to properly capture the rotated text regions.We evaluate our proposed model qualitatively and quantitatively on several challenging datasets namely, IC-DAR15, ICDAR17, and MSRA-TD500, and show that it outperforms current state-of-the-art methods in text detection in the wild.
A sentiment analysis-based expert weight determination method for large-scale group decision-making driven by social media data

• A method based on sentiment analysis to evaluate the quality of LSGDM is proposed. • A dynamic expert weight determination method for LSGDM is proposed. • A LSGDM method based on the dynamic weight determination approach is proposed. In the big data era, it is practical and generalizable to use social media data for decision-making. Thus, determining how to effectively use this kind of data to support large-scale group decision-making (LSGDM) is a direction worth studying. In this paper, for the first time, the sentiment analysis of social media data is used to evaluate the quality of LSGDM and then to dynamically determine the weights of experts from the statistical perspective. The proposed method helps improve the objectivity, accuracy, and acceptability of LSGDM. A case application demonstrates the feasibility of this method, and the results of comparative analyses reveal its superior accuracy and stability to a certain extent.