Reviewerly/exHarmony huggingface dataset:
SAR Image Despeckling Convolutional Model with Integrated Frequency Filtration Technique

The presence of unwanted multiplicative speckle noise within synthetic aperture radar (SAR) sensor data largely degrades its quality and challenges the processing of these data. Thus, in order to increase the performance of a SAR sensor data processing system, speckle removal techniques must be adopted. This paper demonstrates a novel framework that can be utilized to remove the presence of unwanted multiplicative speckle-noise within SAR images. The proposed framework is a convolutional neural network (CNN) based despeckling model with an integrated frequency-filtration based detail preservation module realized through skip connection. This configuration ensures maximal removal of speckle-noise from the raw input data with minimal compensation of sharp features present in the data. An extensive comparative analysis is carried out to study the performance of the proposed framework with other state-of-the-art despeckling models. This study has depicted the quantitative as well as visual performance superiority of the proposed model. Experimentation over real standard SAR data also demonstrates the superior performance of the proposed framework over other despeckling models present in the literature.
Image Denoising based on Multi Domain Feature Inspired Convolutional Neural Network

Blind Gaussian denoising is the process of removing noise from an image affected by additive white Gaussian noise (AWGN) of unknown variance. This paper proposes a multi domain feature inspired deep neural network for blindly (noise variance unknown) denoising AWGN corrupted images. The proposed network uses features of the noisy image in both spatial domain as well as frequency (discrete cosine transform) domain. Spatial and frequency domain features are concatenated to get a combined feature map from which we generate the residual noisy image. The residual noise is then subtracted from the input noisy image to obtain the final denoised image. By restricting the number of layers and trainable parameters in the proposed network, we ensure that it is lightweight in nature and provides fast denoising capability. Comparison with classical state-of-the-art methods and recent CNN based denoising approaches show that the proposed network gives superior denoising results both visually and quantitatively. Our code is uploaded at https://github.com/RTSIR/MDFBGDN.
Mitigating Unnecessary Throttling in Linux CFS Bandwidth Control

An operating system needs to fairly allocate shared hardware resources among different applications, and Linux uses Completely Fair Scheduler (CFS) to achieve this goal. To ensure fairness, CFS implements bandwidth control that sets the maximum limit on the resources that a process can use. Setting the upper limit helps CFS to discover badly behaving applications and hard cap them to limit the overall damage to the system and other applications, thereby improving fairness. We observe that, in an effort to ensure fairness, the bandwidth control can unnecessarily throttle processes which results in poor application performance. We investigate the root cause of this limitation and discover that the CPU runtime accounting mechanism of bandwidth control, which tracks if a process has reached it maximum allocated limit, is responsible for it. We also find that the overhead of fair resource allocation in bandwidth control can become significantly high due to the way it is implemented. We propose mechanisms to reduce throttling as well as the overhead. Our experimental results show that the proposed techniques are able to eliminate nearly all throttling, thus providing up to 12% performance gain. Also, our approach reduces the bandwidth control overhead by up to 24x.
Quality Photo Sketch with Improved Deep Learning Structure

Drawing a sketched picture from realistic scene or photo is useful. In this paper, we propose a high-quality sketch generating model using deep convolutional neural network with self-attention structure. In style-transfer investigation, how to balance and retain both information details of input and style are what we want. For sketch drawing, edges or contours are the major components to form a sketch-like image. However, how to choose edges and contours are the major topics for the model to learn. Besides, keeping a small amount of texture and shadow can give a better view of a sketch result. We resolve this problem by proposing an end-to-end jump connection with elementwise multiplication instead of addition to keep texture details of the original input, which gives highlight of edges and contours for a sketch output. Experimental results show that our new design of network surpasses other state-of-the-art models in sketch details.
Exploring the Effects of Silent Data Corruption in Distributed Deep Learning Training

The profound impact of recent developments in artificial intelligence is unquestionable. The applications of deep learning models are everywhere, from advanced natural language processing to highly accurate prediction of extreme weather. Those models have been continuously increasing in complexity, becoming much more powerful than their original versions. In addition, data to train the models is becoming more available as technological infrastructures sense and collect more readings. Consequently, distributed deep learning training is often times necessary to handle intricate models and massive datasets. Running a distributed training strategy on a supercomputer exposes the models to all the considerations of a large-scale machine; reliability is one of them. As supercomputers integrate a colossal number of components, each fabricated on an ever decreasing feature-size, faults are common during execution of programs. A particular type of fault, silent data corruption, is troublesome because the system does not crash and does not immediately give an evident sign of an error. We set out to explore the effects of that type of faults by inspecting how distributed deep learning training strategies cope with bit-flips that affect their internal data structures. We used checkpoint alteration, a technique that permits the study of this phenomenon on different distributed training platforms and with different deep learning frameworks. We evaluated two distributed learning libraries (Distributed Data Parallel and Horovod) and found out Horovod is slightly more resilient to SDCs. However, fault propagation is similar in both cases, and the model is more sensitive to SDCs than the optimizer.
How to ‘measure’ ideas. Introducing the method of cognitive mapping to the domain of ideational policy studies

Decades of study have greatly improved our understanding of the role of ideas in policy-making. General knowledge accumulation, however, has been slow, which may be caused by the limited availability of methods designed to study ideas. This article introduces the method of cognitive mapping (CM) and argues its value for ideational policy sciences. It starts with an overview of the methodological debate in the literature and methods in use, culminating in five requirements a method to analyze ideas should fulfil. Subsequently, the CM technique is introduced. Using a map of the Dutch Prime Minister Rutte and Central Bank Governor Knot, the article shows CM to fulfil four of the requirements as it helps to distinguish different forms of beliefs separately from their impact on policy and other relevant variables. Finally, a probability probe shows that CM-based expectations match Dutch governmental policies quite accurately, attesting to the validity of the method.
4G Signal RSSI Recommendation System for ISP Quality of Service Improvement

4G Signal RSSI Recommendation System is one of the monitoring methods. The usage rate of local users improves the quality of traffic signals to cycle to receive increased traffic. This paper proposed a method for Prediction and the traffic of data rates used within the area at each location. The result of the proposed approach comparing the performance of models was: the RMSE Gradient Boost Tree, Decision Tree, and Random Forest were 0.291, 0.316 and 0.346, respectively. The correlation will be 0.976, 0.971, and 0.966 for Gradient Boost Tree, Decision Tree, and Random Forest, respectively, and the accuracy of Gradient Boost Tree, Decision Tree, and Random Forest were 97.8%, 97.4%, and 97%, respectively. The results of ensemble learning methods, the RMSE, correlation, and accuracy were: 0.312, 0.972, and 97.5%.
Self-explanatory error checking capability for classifier-based Decision Support Systems

The eXplainable Artificial Intelligence field emerged to solve, albeit partially, the need to explain opaque intelligent models. However, even when intelligent decision support systems employ explainability techniques, it is still up to the decision maker to inspect those explanations and to perceive any problems contained in them. This work proposes an approach to imbue classifier-based Decision Support Systems with the capability of self-detecting and explaining inference errors. The hypothesis here is that fostering such self-awareness might improve system use, aiding the Decision Maker in perceiving problems with proposed solutions and thus being able to select better choices. For the studied datasets, experimental results shown that the approach was effective in detecting over 60% of decision inference errors while improving the accuracy in 20% for the best cases, when correcting all detected errors.
Performance Analysis of JPEG XR with Deep Learning-Based Image Super-Resolution

The demand for efficient and high-level image and video codec compression has been widely increasing. Conventional image compression methods such as JPEG XR use a high quantization parameter (QP) to produce a highly compressed file for any given image. However, higher QP has unpleasing artifacts that lead to perceptual quality degradation. A feasible solution to tackle this limitation is to reduce the high-resolution image size by downsampling it before encoding with JPEG XR. Then, the super-resolution algorithm is applied to the resultant low-resolution image to reconstruct the high-resolution result. In this paper, we downsample the input image before JPEG XR. Then, we investigate the performance of integrating deep learning-based FSRCNN super-resolution (SR) with JPEG XR in terms of quality and compressed file size. According to the experimental results, the fusion of image super-resolution and conventional image coding standard outperforms the JPEG XR enmnressinn.
Detection and Correction of Adversarial Examples Based on IPEG-Compression-Derived Distortion

An effective way to defend against adversarial examples (AEs), which are used, for example, to attack applications such as face recognition, is to detect in advance whether an input image is an AE. Some AE defense methods focus on the response characteristics of image classifiers when denoising filters are applied to the input image. However, several filters are required, which results in a large amount of computation. Because JPEG compression of AEs effectively removes adversarial perturbations, the difference between the image before and after JPEG compression should be highly correlated with the perturbations. However, the difference should not be completely consistent with adversarial perturbations. We have developed a filtering operation that modulates this difference by varying their magnitude and positive/negative sign and adding them to an image so that adversarial perturbations can be effectively removed. We consider that adversarial perturbations that could not be removed by simply applying JPEG compression can be removed by modulating this difference. Furthermore, applying a resizing process to the image after adding these distortions enables us to remove perturbations that could not be removed otherwise. The filtering operation will successfully remove the adversarial noise and reconstruct the corrected samples from AEs. We also consider a simple but effective reconstruction method based on the filtering operations. Experiments in which the adversarial attack used was not known to the detector demonstrated that the proposed method could achieve better performance in terms of accuracy with reasonable computational complexity. In addition, the percentage of correct classification results after applying the proposed filter for non-targeted attacks was higher than that of JPEG compression and scaling. These results suggest that the proposed method effectively removes adversarial perturbations and is an effective filter for detecting AEs.
SCQ: Self-Supervised Cross-Modal Quantization for Unsupervised Large-Scale Retrieval

Cross-modal retrieval (e.g. retrieval of relevant images from the query text) from a large-scale database requires much retrieval time. Recently, fast retrieval methods without decreasing accuracy have been widely studied. If we can prepare labels that represent semantics of data, supervised deep quantization is effective for this aim. However, it is difficult to accurately annotate a large amount of data. This paper proposes an unsuper- vised deep quantization method for cross-modal retrieval, namely, Self-supervised Cross-modal Quantization (SCQ). SCQ enables fast retrieval without decreasing accuracy even if we cannot prepare any labels. SCQ aims to train the image quantizer and the text quantizer to quantize semantically similar (dissimilar) images and texts into the same feature vector (different feature vectors) regardless of modalities. To this end, we introduce a novel training scheme that jointly trains the image quantizer and the text quantizer with contrastive learning into SCQ. Specifically, we minimize the weighted sum of contrastive losses between the same and different modalities. This makes it possible to learn the common feature space shared by two modalities without any labels. Experimental results for two public datasets show that SCQ achieved 4.5% higher retrieval performance at most than the state-of-the-art performance.
2D Pose Estimation based Child Action Recognition

We present a graph convolutional network with 2D pose estimation for the first time on child action recognition task achieving on par results with LRCN on a benchmark dataset containing unconstrained environment based videos.
3D Object Tracking with Reduced Cumulative Error on Sparse Point Clouds

In 3D target tracking, the cumulative error generated in the sparse point cloud environment makes tracking more difficult, which is a key issue to track the target stably to allow the lost target to be retrieved. This paper proposes a Bayesian theorem-based 3D single-target tracking algorithm to improve the subsequent tracking performance by reducing the cumulative error caused by low-quality prior information. First, we use Bayes' theorem to classify the prior information to obtain the maximum a posteriori probability, and second, we suppress the proposals with poor prior information to re-duce the cumulative error. Finally, the algorithm proposed in this paper is applied to the dataset, and the study shows an improvement of about 23% in real-time performance over the PTT-Net algorithm and excellent performance in several tracking metrics.
Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach

In this paper, a variety of neural networks are compared, and the optimal CE-Net model is found and improved. It can segment CT images of cerebral hemorrhage, especially for the small and irregular images.
e-Barter: A Mobile-based Trading Platform

Bartering is more than a century-old form of exchanging goods or services among traders that does not involve money as a form of payment. This is no longer a common practice in commerce nowadays but that does not mean that this form of trading is obsolete. Some are still engaged into this form of trading and uses social media as platform for informal barter trade. Given this disconnect between current technology and the barter system has led to the creation of e-Barter, a mobile application that host online auction and trading as its main objective. Prototyping methodology was used during the development and in the process so specific objectives were met such as identifying the features of an electronic barter system and the appropriate platform for the development was identified. E-Barter was validated through user acceptance testing gaining a grand mean of 4.62 interpreted as Highly Acceptable in terms of functionality, usability, reliability, portability, and over-all acceptance of the 20 respondents coming from IT field and prospective users. Considering that the main reason why barter occurred long time ago is due to lack of money, e-Barter can be a support for them to address their needs or resolve the idea of utilizing the things they no longer need and exchange it for something useful without involving money in the transaction. Thus, help them save money for the things that they need to acquire.
Hindi Spoken Conversational System for Healthcare Humanoid

As the world is advancing in robotics, humanising the robot is one of the focused areas. In this work, we show-case our Hindi Spoken Conversational System deployed Mitra robot. The Hindi Spoken Conversational system includes speech recoznition, conversational AI and speech synthesis systems.
A Programmable Photonic Chip for High-dimensional Quantum Computing

Controlling and programming quantum devices to process quantum information by the unit of qudit provides the possibilities for noise-resilient quantum communications, delicate quantum molecular simulations, and efficient quantum computations, showing great potential to enhance the capabilities of qubit-based quantum technologies. Here, we report a programmable high-dimensional quantum processor in a silicon-photonic in-tegrated circuit, which monolithically integrates all the key functionalities and capabilities of initialisation, manipulation, and measurement of the two quantum quart (ququart) states, and demonstrate its enhancement of quantum computational paral-lelism. By reprogramming the processor, we implemented the basic quantum Fourier transform algorithms, all in quaternary, to benchmark the enhancement of quantum parallelism using qudits. The monolithic integration and high programmability have allowed the implementations of more than one million high-fidelity preparations, operations and projections of qudit states in the processor. Our work shows an integrated photonic quantum technology for qudit-based quantum computing with enhanced capacity, accuracy, and efficiency, which could lead to the acceleration of building a large-scale Quantum computer.
EraisNET: An Optical Flow based 3D ConvNET for Erasing Obstructions

Images captured from behind a fence, window, or during rain generally face occlusions. Though prior works have addressed the problems of individually de-raining, reflection, and occlusion removal, a common approach that removes all the obstruction has found little attention in the literature. In this paper, we address the image occlusion problem by proposing a deep learning-based approach wherein the proposed method uses motion differences between two images and extracts important moving features from videos to separate the background and the obstruction. To accomplish this task, a novel 3D-convolution architecture is introduced, which is trained with synthetically blended videos. We have used learned layer-based CNN methods combined with dense-optical flow with generative networks for better output images. Moreover, a dataset for obstruction removal with sequences for reflection and fencing removal is proposed. The proposed approach is well experimented over a different variety of images and is found as a good candidate against state-of-the-art schemes.
Critical Review on Deep Learning and Smart Technologies for Image Super-Resolution

Image super-resolution is an extremely useful way to improve the quality of an image. It is miracle that making use of the current signal processing and deep learning technologies, the image can look much appealing after the super-solution. This review paper is to highlight important techniques, especially to point out recent key contributions to make superior success of super-resolution of the recent years, especially on face super-resolution. We will start with a very brief and quick review on using conventional signal processing and classic learning approaches for super-resolution, and then concentrate on giving the advantages of deep learning, in particular, the recent powerful concepts on using latent vector and facial priors to achieve superior performance. Further topics of discussion include generative adversarial network (GAN), StyleGAN, latent space, facial priors and diffusion models. Our concentration is on the reasons for the success of these techniques. Attractive demonstrations on a few state-of-the-art models, including some of our work, are provided
Taming the Big Data Monster: Managing Petabytes of Data with Multi-Model Databases

With the development of big data technology, the amount of business data that Internet companies need to handle has reached the petabyte level, which poses great pressure on the system processing capacity. For example, the peak order volume of Alibaba's Global Shopping Festival in 2020 reached 583,000 orders per second. Even worse, multi-model data are involved in real business. The inability to perform high-throughput, lowlatency transaction processing can result in a poor user experience that can lead to serious financial losses due to customer churn. Although numerous optimizations have been proposed, they can fail in the face of petabytes of data, or be significantly less effective. In this paper, we propose a novel and practical multi-model big data system that can manage petabytes of data. Particularly, we show three special designs for processing the petabytes of data. First, we perform partition to reduce the amount of unnecessary data to be scanned. Second, we adaptively adopt row storage mode for big tables that are frequently updated and column storage mode for tables that are frequently queried to improve the system efficiency. Third, we conduct compression to accelerate IO access speed. We analyze Alibaba's two real PB-level business scenarios, Double 11 and Zhixingtong, and generate workloads and benchmark accordingly to verify our system. Experiments show that our system can efficiently manage petabyte-scale data in real scenarios, providing high-performance querying of terabyte-scale datasets, and be suitable for various workloads.
Detection of Geometry-based Adversarial Facial Attacks using Error Level Analysis

Despite Machine Learning (ML) and Deep Learning (DL) models in specific have achieved significant success in various applications of computer vision in recent years, they remain vulnerable to carefully crafted, human-invisible adversarial attacks. Adversarial attacks are generated intentionally by adding small perturbations to an input image to mislead the classifier. This paper investigates a new defense method against geometry-based adversarial face attacks that significantly mitigate the impact of such adversarial attacks on image-classification models. We generate geometry-based adversarial face images based on Fast Landmark Manipulation (FLM) method and Error Level Analysis (ELA) is performed to effectively discriminate adversarial face images from clean images. The performance of the proposed defense method is demonstrated on a real-world dataset with different types of classifiers. The experimental results show that our defense method can effectively detect adversarial face images from clean images with 99.75% accuracy.