Reviewerly/exHarmony huggingface dataset:
Image Similarity between Masked and Unmasked Face for Consumer Electronics Applications

Face recognition has become essential as a convenient biometric-based solution for a plethora of different consumer electronics applications, including access control systems, intelligent environments, smartphone authentication systems and so on. Early in 2020, the COVID-19 pandemic caused the widespread use of face masks, which become essential for containing the outbreak. The masks cause a visible alteration in facial appearance, covering almost the 50% of the human face. In this work, an image similarity technique is applied to assess the difference between two images of the same face wearing or not wearing a face mask. Cosine Similarity measure-based Algorithm (CSA) was used to objectively infer the difficulties that modern facial recognition algorithms, based on deep learning techniques, encounter when dealing with a masked face.
Moving Object Detection for Remote Sensing Video with Satellite Jitter

Moving Object Detection in dynamic scenes with satellite jitter is an important issue in remote sensing video processing. Satellite jitter leads to whole image shaking which cause difficulty for moving object detection. This paper proposed a motion vector clustering method to detect moving feature points. Real moving objects whose motion are different to jitter are able to be detected after removing jitter moving feature points. The proposed method increased 69.6% of succeed detected moving objects and reduce 93.6% of fake moving object.
Single Image Fence Removal Using Fast Fourier Transform

Fences extensively cover the image in a variety of shapes, making fence removal from the image is a challenging task. Most fence removal in a single image is performed in two stages: fence area estimation and inpainting as shown in the Fig 1. Conventional fence detection has utilized information such as edge information, luminance information, and fence regularity to deal with various shapes. However, those methods have resulted in missing fences and false detections of structures similar to fences. In this paper, we propose a network that incorporates a module that includes a fast Fourier transform. This allows us to extract global features of fences that extensively cover the image in various shapes. Furthermore, we create a dataset of rectangular fences, which is not available in previous datasets. Experimental results on existing and proposed datasets show that our method achieves better results, both quantitatively and qualitatively, by suppressing missing fences and false positives of other structures.
Adaptive Long-Neck Network With Atrous-Residual Structure for Instance Segmentation

Instance segmentation is an important yet challenging task in the computer vision field. Existing mainstream single-stage solution with parameterized mask representation has designed the neck models to fuse features of different layers; however, the performance of instance segmentation is still restricted to the layer-by-layer transmission scheme. In this article, an instance segmentation framework with an adaptive long-neck (ALN) network and atrous-residual structure is proposed. The long-neck network is composed of two bidirectional fusion units, which are cascaded to facilitate the information communication among features of different layers in top-down and bottom-up pathways. In particular, a new cross-layer transmission scheme is introduced in a top-down pathway to achieve a hybrid dense fusion of multiscale features and weights of different features are learned adaptively according to their respective contributions to promote the network convergence. Meanwhile, a bottom-up pathway further complements the features with more location clues. In this way, high-level semantic information and low-level location information are tightly integrated. Furthermore, an atrous-residual structure is added to the mask prototype branch of instance prediction to capture more contextual information. This contributes to the generation of high-quality masks. The experimental results indicate that the proposed method achieves effective segmentation and the outputted masks match the contours of objects.
Perceptual image hashing using rotation invariant uniform local binary patterns and color feature

Perceptual image hashing is widely used in cloud-based multimedia system for various security purposes, including image integrity authentication and tamper detection. In this chapter, we propose a robust perceptual image hashing scheme by exploiting rotation invariant uniform local binary patterns (LBPP,Rriu2) and color features. In our scheme, input image is first pre-processed to build a normalized image. Next, LBPP,Rriu2 is exploited to build a stable texture feature matrix, and Non-negative Matrix Factorization (NMF) is used for data reduction and for constructing the LBPP,Rriu2-NMF features. Then, color features are generated from the mean value of each color block. Finally, the LBPP,Rriu2-NMF features and the color features are concatenated and quantized to form final image hash, which is referred to the LBPP,Rriu2-NMF-color hashing. Experimental results show that the proposed image hashing scheme has superior robustness and better discrimination capability than most existing methods.
Channelformer: Attention Based Neural Solution for Wireless Channel Estimation and Effective Online Training

In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms in downlink scenarios. The self-attention mechanism is employed to achieve input precoding for the input features before processing them in the decoder. In particular, we implement multi-head attention in the encoder and a residual convolutional neural architecture as the decoder, respectively. We also employ a customized weight-level pruning to slim the trained neural network with a fine-tuning process, which reduces the computational complexity significantly to realize a low complexity and low latency solution. This enables reductions of up to 70% in the parameters, while maintaining an almost identical performance compared with the complete Channelformer. We also propose an effective online training method based on the fifth generation (5G) new radio (NR) configuration for the modern communication systems, which only needs the available information at the receiver for online training. Using industrial standard channel models, the simulations of attention-based solutions show superior estimation performance compared with other candidate neural network methods for channel estimation.
Dependent or Not: Detecting and Understanding Collections of Refactorings

Refactoring is a program transformation to improve the internal structure of a program while preserving its external behavior. Developers frequently apply multiple refactorings that depend on each other to achieve goals such as improving code reusability. Although manually applying a sequence of dependent refactorings is a common practice, existing refactoring recommendation tools treat refactorings in isolation without revealing the dependencies among them to developers. One reason is that these relationships among refactorings are poorly understood. Current approaches treat refactoring recommendations as a strictly ordered sequence limiting developers’ ability to understand, validate, and apply recommended refactorings. To address this gap, this paper describes a theory for reasoning about collections of refactorings through defining an ordering dependency relation among refactorings and organizing collection of refactorings as a set of refactoring graphs. We propose an algorithm for identifying refactoring dependencies and illustrate these concepts with a tool for visualizing such refactoring dependencies and refactoring graphs. Our validation results demonstrate that 43% of the 1,457,873 recommended refactorings from 9,595 projects that we studied are part of dependent refactoring graphs. Furthermore, refactorings are not only commonly involved in dependent relations, but also when applied, dependent refactoring graphs improve all of the quality attribute metrics in our experiments more than individual refactorings.
Introduction

Understanding how people behave and why people behave in such ways is a central topic to information retrieval (IR) as well as a variety of other scientific disciplines concerning human behavior research. There is substantial empirical evidence demonstrating that people are predictably irrational and usually rely on existing beliefs, heuristics, and mental shortcuts, especially when they are making decisions under time pressure and uncertainty. However, features of human biases and bounded rationality are usually ignored or abstracted out from formal user models, which may lead to errors in simulating and predicting search behaviors and challenges in modeling users’ in situ search experiences. In this book, we seek to synthesize the insights regarding bounded rationality from behavioral economics, cognitive psychology, and information seeking and interactive IR (IIR) research and develop, a behavioral economics approach to modeling and supporting boundedly rational users engaging in search interactions. This beginning chapter provides an overview of the background and motivations behind our research and outlines the structure of this book.
Cloud Configuration Optimization for Recurring Batch-Processing Applications

Recognizing the diversity of Big Data analytic jobs, cloud providers offer a wide range of VM instance types or even clusters to cater for different use cases. The choice of cloud configurations can have a significant impact on the response time and running cost of batch-processing applications, which may need to be re-run regularly with cloud-scale resources. However, identifying the best cloud configuration with a low search cost is quite challenging due to i) the large and high-dimensional configuration space, ii) the time-varying cloud service cost (e.g., AWS Spot instances), and iii) job response time variation even given the same configuration. To tackle these challenges, we design and implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> , a system that enables Adaptive Cloud Configuration Optimization for Recurring Data-Intensive Applications. By leveraging recent algorithmic advances in Gaussian Process UCB techniques, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> can unearth the cost-optimal configuration with a deadline constraint (i.e., maximum tolerated running time) under the time-varying cloud service cost. More importantly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> manages to achieve a theoretical performance guarantee, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub-linearly increasing dynamic regret</i> of the job completion cost. Using extensive trace-driven simulations and empirical measurements of our Kubernetes-based implementation, we demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> can identify a near-cost-optimal configuration (i.e., within 10% of the optimum) after fewer than 20 runs from over 7000 candidate choices, which translates to a 2X-speedup and up to 17.9% cost-savings, when comparing to the state-of-the-art approach, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CherryPick</i> .
Circuit Symmetry Verification Mitigates Quantum-Domain Impairments

State-of-the-art noisy intermediate-scale quantum computers require low-complexity techniques for the mitigation of computational errors inflicted by quantum decoherence. Symmetry verification constitutes a class of quantum error mitigation (QEM) techniques, which distinguishes erroneous computational results from the correct ones by exploiting the intrinsic symmetry of the computational tasks themselves. Inspired by the benefits of quantum switch in the quantum communication theory, we propose beneficial techniques for circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the spatio-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain stabilizer formalism to circuit-oriented stabilizers. The applicability and implementational strategies of the proposed techniques are demonstrated by using practical quantum algorithms, including the quantum Fourier transform (QFT) and the quantum approximate optimization algorithm (QAOA).
Formally Modeling Users in Information Retrieval

Formally modeling user behavior and predicting interaction events is essential for evaluating and advancing IR systems. This chapter focuses on the ways in which IR researchers formally model users and their behaviors in IR activities and introduces two main groups of formal models: (1) single-event-focused models, especially basic click models and enhanced versions of click models with additional user characteristics and result factors being represented and parameterized, and (2) models of search interactions, especially the ones underpinning offline evaluation metrics and search phase models. The review on existing models, rational assumptions, and related empirical studies in this chapter will enhance our understanding of the similarities and connections between related models presented in varying ways and serve as a basis for the further investigation and discussion on how to better characterize user traits, especially their biases and cognitive limits, in modeling search sessions and evaluating the performance of IR systems.
Back to the Fundamentals: Extend the Rational Assumptions

In this chapter, we revisit the fundamental formal models of IR and associated simplified assumptions, with the goal of exploring and introducing actionable directions toward which the assumptions can be extended to at least partially cover the triggers and characteristics of bounded rationality. To this end, we first categorize different types of explicit and implicit assumptions into three groups, pre-search, within-search, and post-search, and discuss their conflicts with empirical findings on bounded rationality. Within each group, we discuss possible ways to extend and revise existing rational assumptions, as a key preparation for enhancing formal user models and IR evaluation techniques. When explaining the methods for extending rational assumptions, we also discuss related boundaries and explain the implications for user modeling and evaluation and how these potential boundaries are related to IIR-specific factors.
Speckle-noise filtering based on non-local mean sparse principal component analysis method

Speckle-noise filtering is an extensive and key process in coherent interferometric techniques to obtain important and accurate information from the recorded interferogram or fringe pattern. The speckle-noise inherent to these interferograms, recorded by digital image sensors in these optical techniques, is eliminated by an appropriate image processing method. Since the beginning and further development of these techniques, a wide variety of speckle noise filtering methods and techniques have been proposed. The present research work aims to exploit the performance of the non-local sparse principal component analysis (NLS-PCA) method for speckle noise reduction as applied to the interferometric fringe patterns obtained from digital holographic and digital speckle pattern interferometric techniques. The performance of the NLS-PCA is evaluated on numerical simulations using different types of speckle fringe patterns resulting in the method being highly effective in filtering the speckle noise and providing superior results evaluated in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), Edge preservation Index (EPI), and Equivalent Number of look (ENL), in comparison to widely known methods such as windowed Fourier transform method, Lee filter, Weiner filter. Furthermore, the proposed method maintains the finer details and preserves the fringe edges effectively. The performance of the NLS-PCA method is also demonstrated on experimental speckle fringe patterns and digital holograms.
Multimodal learning of social image representation

In recent years, the representation of social images has achieved significant results in multilabel classification and cross-modal retrieval. However, simply modeling content information may lead to less accurate embeddings, as social images contain both content and social relationships between images in a variety of modalities. Learning low-dimensional, dense, and sequential representations of social networked multimodal data is the goal of social image representation learning. Many practical applications can also be facilitated by these methods. Many tasks are aided by multiview representations learning, such as cross-view classification. Since social images often contain link information in addition to multimodal content, simply adopting data content may lead to suboptimal multiview representations of social images. Social images are often rich in metadata, and there are ways to turn images and their textual descriptions into links between content, which are ultimately handled as multimodal content.
IIITM Faces: an Indian face image database

This chapter addresses the various challenges that are faced during the face attribute detection, especially in the Indian context. There are various datasets, that were collected in unconstrained environment, but, unfortunately, none of them is capable of capturing the emotions in various orientations of the face. Moreover, these datasets have under-representation of Indian faces, because they are scraped out from the various search engines. As a result, various techniques cannot be evaluated on Indian faces. In this work, a new dataset named IIITM Faces is introduced for the scientific community to address the abovementioned challenges. The IIITM Face dataset includes 107 participants, who show 6 facial emotions in 3 different face orientations. On the extension, these images are further labeled on various attributes, such as gender, beard or no beard person, eyeglasses, clothes worn by the subjects, the color of the cloths, density of their hair, and many more. Also, these images are captured in a specific background with a high-resolution camera; the backgrounds can be easily replaced by other backgrounds for simulation purposes if needed. This dataset can be used by the researches working in the field of image processing, computer vision, and pattern recognition for evaluating their works. The database is made available on request to [email protected]; [email protected].
Hardware and software based methods for underwater image enhancement and restoration

Underwater photography is gaining attention in the current context due to many applications in the field of marine research and scientific applications, such as archaeology, geology, underwater exploration, long-distance plumbing, and network links across continents requiring geo-marine bedding and exploration of the ancient shipwreck. But underwater photographs are generally different, have darker colors and lower contrast. The diffusion and absorption of white light into water also has a profound effect on the quality of underwater images. Over the past few decades, restoration methods and underwater enhancements have been proposed to overcome the aforementioned underwater photography issues. However, a thorough and in-depth study of related achievements, limitations, and improvements still needs attention. The current paper reviews a variety of ways to improve and restore underwater images that deal with common underwater issues, including extreme degradation and distortion. It updates the major hardware and software methods considered to enhance or restore underwater images from the past few years. This current chapter focuses on the benefits and key aspects of existing approaches, identifies research gaps, and ultimately draws recommendations for future research in this area. This in-depth research continues to equip researchers with important background information to understand the challenges and prospects in this important field.
Underwater image enhancement: past, present, and future

Underwater image (UI) enhancement is a significant problem that aims to recover the high-frequency detail, which is lost due to several degradation factors, e.g., blur motion, low visibility, poor contrast, and noise. Recently, various real-time imaging applications have been developed for solving a particular problem, such as object detection and recognition; however, they require high-quality images to obtain desirable performance. Therefore it is essential to enhance the quality of UIs before feeding them to a given application. This chapter presents an in-depth study of UI enhancement techniques in three-fold: first, a comprehensive survey of conventional and deep-learning-based models is presented; second, different benchmark datasets and performance measures used for evaluating the UI enhancement techniques in the existing literature are explored; and third, a few of the most promising research challenges in the field of UI enhancement and some future directions in this regard are highlighted.
Comprehensive survey of face super-resolution techniques

Face hallucination is one of the domains of image super-resolution, where super-resolution takes the place of face images. It is an attractive domain of research in the field of digital image processing due to its wide application in various fields, such as surveillance, face recognition, facial expression recognition, etc. In the real-world scenario, captured face images are noisy or small in size due to low-resolution imaging system, noise introduced by the image sensors or motion between the object and sensor. These captured face images are low-resolution images that contain less information, poor quality of features; hence it is very difficult to identify the person and their facial expression. The face super-resolution technique uses upsampling and feature improvement to construct the high-resolution or super-resolution image. This chapter provides a detailed survey of learning-based face hallucination methods and a detailed description of Bayesian, regularization, subspace learning, and deep learning methods. Issues and challenges with the existing super-resolution methods are discussed to highlight the problems and future direction to solve them, which will help the researcher to develop the new face hallucination methods.
Gender Influence on Authorship Pattern: A Case Study

Little is known about the existing gender disparities in library and information science research. The current study aims to analyze research articles from the Annals of Library and information studies journal from a gender perspective. The study looked at the author’s position in relation to their gender, various patterns of collaboration, and if this has affected citations. This study also attempts to determine the male and female authors’ research interests as the first author. The data were gathered from the Scopus database between 2011 and 2019 and were quantified using bibliometrics. Six distinct authorship patterns were recognized. The findings reveal men in first and last author positions constitute the highest research articles and citations indicating male authors to be more influential compared to females. Additionally, the study findings have provided an overview of the current state of women in library and information science research and highlighted areas where improvements can be made.