Reviewerly/exHarmony huggingface dataset:
No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.
A Bridge-based Compression Algorithm for Topological Quantum Circuits

The topological quantum error correction (TQEC) scheme is promising for scalable and reliable quantum computing. A TQEC circuit can be modeled by a three-dimensional diagram, and the implementation resource of a TQEC circuit is abstracted to its space-time volume. Implementing a quantum algorithm with a reasonable physical qubit number and reasonable computation time is challenging for large-scale practical problems. Therefore, minimizing the space-time volume of a TQEC circuit becomes a crucial issue. Previous work shows that bridge compression can greatly compress TQEC circuits, but it was performed only manually. It is desirable to develop automated compression techniques for TQEC circuits to achieve low-overhead, large-scale quantum computations. In this paper, we present the first work that can automatically perform bridge compression on TQEC circuits. Compared with the state-of-the-art method, experimental results show that our proposed algorithm can averagely reduce space-time volumes by 83%.
Generalized Proof of Liabilities

Proof of liabilities (PoL) allows a prover to prove his/her liabilities to a group of verifiers. This is a cryptographic primitive once used only for proving financial solvency but is also applicable to domains outside finance, including transparent and private donations, new algorithms for disapproval voting and publicly verifiable official reports such as COVID-19 daily cases. These applications share a common nature in incentives: it's not in the prover's interest to increase his/her total liabilities. We generalize PoL for these applications by attempting for the first time to standardize the goals it should achieve from security, privacy and efficiency perspectives. We also propose DAPOL+, a concrete PoL scheme extending the state-of-the-art DAPOL protocol but providing provable security and privacy, with benchmark results demonstrating its practicality. In addition, we explore techniques to provide additional features that might be desired in different applications of PoL and measure the asymptotic probability of failure.
Privacy-Preserving Cross-Chain Payment Scheme for Blockchain-Enabled Energy Trading

Recently, blockchain-based transactive energy systems are introduced into energy trading scenarios which efficiently alleviates drawbacks in centralized energy trading systems such as low compatibility, poor flexibility, and single point of failure. Nevertheless, there still exists performance bottlenecks such as privacy leakage and low trading throughput, which limits the wide application of the blockchain-based energy system to the real world. To solve these concerns, this paper proposes a privacy-preserving cross-chain payment scheme for blockchain-based energy trading. We firstly adopt a stealthy communication algorithm to hide data privacy and transaction privacy during energy trading. Then, we apply the optimized-version hash-locking method to construct the cross-chain payment channel for two-way anchored transactions, which enhances the payment efficiency and hides the trading connection between the trading participants. Moreover, we use non-interactive zero-knowledge (NIZK) to ensure privacy-preserving payment verification. The evaluation results demonstrate that our scheme can effectively protect privacy for cross-chain payment in energy trading systems.
Blockchain-Enabled SLA Compliance for Crowdsourced Edge-Based Network Function Virtualization

Network function virtualization (NFV) allows edge devices to host different types of virtual network functions (VNFs) and support Computing in the Network. Therefore, an edge device can abstract multiple VNFs of different infrastructure providers (InPs) as services and serve them at different times to act as a tenant for different InPs. Deploying edge devices is expensive and difficult to manage. Hence, crowdsourced edge devices can be used. To ensure the quality and availability of the services and the privacy of sensitive data of the service stakeholders, it is extremely important to establish a service level agreement (SLA) between an edge device owner and an InP. For monitoring of SLA compliance and enforcing accountability, a trusted platform is required. The traditional NFV management authority cannot be trusted due to its centralized characteristics. In this article, we propose a blockchain-powered framework for the SLA compliance of edge devices offering VNFs as services and introduce a smart contract-driven framework to establish trust in edge-based NFV. We analyze the performance of the proposed framework in the context of a private blockchain network.
Category attention transfer for efficient fine-grained visual categorization

Fine-Grained Visual Categorization (FGVC) aims at distinguishing subordinate-level categories with subtle interclass differences. Although previous research shows the impressive effectiveness of the recurrent multi-attention models and the second-order feature encoding, they often require an enormous amount of both computation and memory space, making them inadequate for mobile applications. This paper proposed a Category Attention Transfer CNN (CAT-CNN) to address the efficiency issue in solving FGVC problems. We transfer part attention knowledge from a very large-scale FGVC network to a small but efficient network to significantly improve its presentation ability. Using the proposed CAT-CNN, the accuracy of the efficient networks, such as ShuffleNet, MobilieNet, and EfficientNet, can be improved by up to 5.7% on the CUB-2011-200 dataset without increasing computation complexity or memory cost. Our experiments show that the proposed CAT-CNN can be applied to multiple structures to enhance their performance. With a single efficient network structure and single inference, the proposed CAT-MobileNet-large-1.0 and the CAT-EfficientNet-b0 can achieve accuracies of 86.5% and 86.7%, respectively, on the CUB-2011-200 dataset, which is close to or better than the results from state-of-the-art methods using large scale networks and multiple inferences, and make FGVC feasible on mobile devices.
Copy-Move Forgery Detection in Videos Using Machine Learning Algorithm

Digital multimedia applications have greatly increased in recent years as a result of improvements in network technology, low-cost multimedia devices, intelligent image or video editing software, and widespread acceptance of digital multimedia coding standards. One of the most difficult aspects of video forensics is establishing whether a video is authenticated or not. When videos are used as fundamental evidence to affect judgments, such as in a court of law, this can be a critical duty. This paper proposes using the statistical feature of noise remnant to detect passive forgeries in a digital video. To find the revamped section of a video by analyzing the temporal correlation of block-level noise remnants. Significant simulation findings are given to show that the approach can accurately identify the altered region and predict alteration parameters with high reliability.
‘All at the tap of a button’: Mapping the food app landscape

Mobile applications (commonly known as ‘apps’) are highly popular forms of software, with hundreds of billions of downloads globally each year. The ways in which the affordances of apps are portrayed on the app store platforms are crucial in sparking consumers’ initial interest. This article presents findings from the ‘Mapping the Food App Landscape’ study. The following two sources of online material were used in this study: (1) descriptions of food-related apps available in the Google Play store; and (2) the lists of the top-most installed free Android apps presented in the App Annie app analytics platform for Australia, Canada, the United Kingdom and the United States. The analytical approach is distinctive in bringing together the key feminist new materialism concepts of affective forces, relational connections and agential capacities with that of the promissory narrative. The study’s findings show that inapp publishers’ efforts to entice users, the Google Play app descriptions presented food apps as solutions to or escapes from the stresses and difficulties of everyday life. These app descriptions promised to generate excitement, fun and pleasure (games apps); configure and support convenient food supply and preparation arrangements (food ordering and delivery, meal planning and recipe apps); offer reassurance and better control over the body and encourage greater embodied self-awareness, health and wellbeing (food-tracking and nutrition apps); and contribute to creative and novel experiments in cooking (recipe apps). These findings map the landscape of food apps and the sociocultural contexts in which they are being created, published and adopted.
SocialNet: Detecting Social Distancing Violations in Crowd Scene on IoT devices

Recently, the COVID-19 pandemic has affected the world and spread in the majority of the countries. To decrease the number of infections, experts suggested people practice social distancing by maintaining a distance of six feet apart. It is hard to monitor this restriction by only a traditional surveillance system. Existing methods used deep learning to tackle this problem by designing a Deep Convolutional Neural Network (DCNN). However, these methods do not accommodate for low-power systems such as Internet-of-Things-based devices. In this paper, we propose SocialNet, a novel network design that can detect violations of social distancing in public crowd scene. SocialNet is composed of two components, (1) The detector backbone and (2) The Autoencoder. In the detector backbone, the network generates the bounding boxes of the human/person category. In the Autoencoder, the network learns to predict a score which represents the violation between each bounding box and the others. The input image is divided into nine patches and each patch goes through the Autoencoder to predict the violation score. The Autoencoder consists of the encoder which produces the latent vector for the decoder network. The decoder network outputs a real-valued vector of length fifteen as scores for each patch in the image. Furthermore, SocialNet uses mixed precision which makes it suitable for low-power devices.
Enhancing the generalizability of predictive models with synergy of data and physics

Wind farm needs prediction models for predictive maintenance. There is a need to predict values of non-observable parameters beyond ranges reflected in available data. A prediction model developed for one machine many not perform well in another similar machine. This is usually due to lack of generalizability of data-driven models. To increase generalizability of predictive models, this research integrates the data mining with first-principle knowledge. Physics-based principles are combined with machine learning algorithms through feature engineering, strong rules and divide-and-conquer. The proposed synergy concept is illustrated with the wind turbine blade icing prediction and achieves significant prediction accuracy across different turbines. The proposed process is widely accepted by wind energy predictive maintenance practitioners because of its simplicity and efficiency. Furthermore, this paper demonstrates the importance of embedding physical principles within the machine learning process, and also highlight an important point that the need for more complex machine learning algorithms in industrial big data mining is often much less than it is in other applications, making it essential to incorporate physics and follow Less is More philosophy.
Deepfake-Image Anti-Forensics with Adversarial Examples Attacks

Many deepfake-image forensic detectors have been proposed and improved due to the development of synthetic techniques. However, recent studies show that most of these detectors are not immune to adversarial example attacks. Therefore, understanding the impact of adversarial examples on their performance is an important step towards improving deepfake-image detectors. This study developed an anti-forensics case study of two popular general deepfake detectors based on their accuracy and generalization. Herein, we propose the Poisson noise DeepFool (PNDF), an improved iterative adversarial examples generation method. This method can simply and effectively attack forensics detectors by adding perturbations to images in different directions. Our attacks can reduce its AUC from 0.9999 to 0.0331, and the detection accuracy of deepfake images from 0.9997 to 0.0731. Compared with state-of-the-art studies, our work provides an important defense direction for future research on deepfake-image detectors, by focusing on the generalization performance of detectors and their resistance to adversarial example attacks.
Quantum Wrapper Networking

We introduce a new concept of Quantum Wrapper Networking that enables control, management, and operation of quantum networks that can co-exist with classical networks while keeping the requirements for quantum networks intact, by utilizing the common network control and management and performance monitoring frameworks.
New Code-Based Cryptosystem Based on Binary Image of Generalized Reed-Solomon Code

McEliece cryptosystem represents a smart open key system based on the hardness of the decoding of an arbitrary linear code, which is believed to be able to resist the advent of quantum computers. But the original McEliece cryptosystem, based on Goppa codes, has just very limited interest in practice, partly because it requires a very large public key. In this paper we propose a new instance of McEliece cryptosystem that reduces a public key size. This cryptosystem is based on images of Generalized Reed-Solomon (GRS) codes. It was shown that in order to break the proposed system, intruder has to solve a decoding problem for a code that is not equivalent to private GRS code.
An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network

Multi-person pose estimation has been gaining considerable interest due to its use in several real-world applications, such as activity recognition, motion capture, and augmented reality. Although the improvement of the accuracy and speed of multi-person pose estimation techniques has been recently studied, limitations still exist in balancing these two aspects. In this paper, a novel knowledge distilled lightweight top-down pose network (KDLPN) is proposed that balances computational complexity and accuracy. For the first time in multi-person pose estimation, a network that reduces computational complexity by applying a "Pelee" structure and shuffles pixels in the dense upsampling convolution layer to reduce the number of channels is presented. Furthermore, to prevent performance degradation because of the reduced computational complexity, knowledge distillation is applied to establish the pose estimation network as a teacher network. The method performance is evaluated on the MSCOCO dataset. Experimental results demonstrate that our KDLPN network significantly reduces 95% of the parameters required by state-of-the-art methods with minimal performance degradation. Moreover, our method is compared with other pose estimation methods to substantiate the importance of computational complexity reduction and its effectiveness.
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-task Reinforcement Learning

In a multi-task reinforcement learning setting, the learner commonly benefits from training on multiple related tasks by exploiting similarities among them. At the same time, the trained agent is able to solve a wider range of different problems. While this effect is well documented for model-free multi-task methods, we demonstrate a detrimental effect when using a single learned dynamics model for multiple tasks. Thus, we address the fundamental question of whether model-based multi-task reinforcement learning benefits from shared dynamics models in a similar way model-free methods do from shared policy networks. Using a single dynamics model, we see clear evidence of task confusion and reduced performance. As a remedy, enforcing an internal structure for the learned dynamics model by training isolated sub-networks for each task notably improves performance while using the same amount of parameters. We illustrate our findings by comparing both methods on a simple gridworld and a more complex vizdoom multi-task experiment.
Mexican Hat Wavelet Transform and Its Applications

In this chapter, we discuss a unique method to time-frequency analysis which gives a centralized way to represent discrete and continuous time-frequency. This serves as a straightforward way to include all possible (countable) discrete and continuous time scales in one model. We consider the Mexican hat wavelet which is one of the basic wavelet functions formulated by the second derivative of Gaussian function to define the Mexican hat wavelet transform (MHWT). Further, the theory of MHWT is implemented to obtain the Mexican hat wavelet Stieltjes transform (MHWST) of a bounded variation function. Some convenient properties of MHWST are also presented. Further, a standard method is introduced for representing functions of class B(m, n). Besides, an integral transform is constructed with the help of the Fourier summation kernel. This construction results in a flexible way to present some conditions that are necessary and sufficient for a function of class B(m, n) to be Mexican hat wavelet and MHWST.
An Improved Depth-Based Camera Model in Binocular Visual System

Due to the problem that the camera model is only effective for a fixed depth of field of view because of the lacking of depth information of the traditional camera model, we propose an improved camera model based on the depth in the binocular visual system. This depth-based camera model improves the stability of the camera's measurement accuracy when the depth of field changes.
Ensuring the Software Quality for Critical Infrastructure Objects

The logic of the software (SW) operation is critical for ensuring the safety of systems of critical infrastructure objects (CIO). When such systems are put into operation, it is the SW what renders majority of the decisions of control with minimization of data entry by operators. For a system that meets all security requirements, it is absolutely essential to ensure good quality SW. CIO designers adhere to industry regulations to create secure systems. This article proposes practice improvements in quality assurance that system designers can use in the SW development process, as well as an algorithm for evaluating software quality.
Amortized Threshold Symmetric-key Encryption

Threshold cryptography enables cryptographic operations while keeping the secret keys distributed at all times. Agrawal et al. (CCS'18) propose a framework for Distributed Symmetric-key Encryption (DiSE). They introduce a new notion of Threshold Symmetric-key Encryption (TSE), in that encryption and decryption are performed by interacting with a threshold number of servers. However, the necessity for interaction on each invocation limits performance when encrypting large datasets, incurring heavy computation and communication on the servers.
Correlation Analysis of Spatio-temporal Arabic COVID-19 Tweets

Since the recent COVID-19 outbreak, several researchers have begun to focus on various difficulties to data mining of social data to study people's reactions to the outbreak. Recent approaches have mostly concentrated on the analysis of social data in the English language. In this study, we present an in-depth social data mining approach to extract Spatio-temporal and semantic insights about the COVID-19 pandemic from the Arabic social data. We developed sentiment-based categorization methods to extract major topics at various location granularities (regions/cities). Besides, we used topic abstraction levels (subtopics and main topics). A correlation-based analysis of Arabic tweets and official health provider data will also be presented. Furthermore, we used occurrence-based and statistical correlation methodologies to create many topic-based analysis mechanisms. Our findings demonstrate a positive association between top subjects (for example, lockdown and vaccine) and the increasing number of COVID-19 new cases, but unfavorable attitudes among Arab Twitter users were generally heightened during this pandemic, on issues such as lockdown, closure, and law enforcement.
Leading with Digital Technologies Governance in the State-Owned Enterprises

By sharing novel research into the practical implications of large-scale digital records, this study aims to analyze the advanced impact of protective digital technologies strategies on state-owned enterprise governance. The study used panel data fixed effect regression model for analyzing the advanced impact of protective data strategies on digital-risk governance. Furthermore, to analyze the persistence of digital-risk and address the endogeneity concern, a dynamic panel model is used, and the results are estimated using GMM technique. This study shows that the protective data strategies are helpful in reducing digital-risk and therefore state-owned enterprises can reduce their digital-risk exposure by adopting innovative record practices. To the best of the author’s knowledge no prior study analyzes the impact of protective data strategies on the digital-risk governance. Therefore, the current research provides a significant contribution in the enterprise information literature regarding digital records and digital-risk governance.
Developing a Game Engine in C# Programming Language

A video game refers to a digital breed of entertainment experienced on various devices, including personal computers, gaming consoles, tablets, and smartphones. For some time now, video games have not been perceived as a form of play solely reserved for children but as the way of spending the leisure time enjoyed by a vast of adults worldwide. As a result, the video games sector has become a behemoth larger than the movie and music industry combined, and it is growing on daily basis. The process of developing video games is more complex than ever which is the reason why every now and then novel game engine is proposed. The objective of this paper is to present features of a game engine that was implemented in C# programming language. The introduced game engine draws on MonoGame library that contains collection of functions and commands thus facilitating the creation of a virtual space and supporting the development of video games. The functionalities and capabilities of the presented game engine are demonstrated on the examples of 2D and 3D video games prototypes.