Reviewerly/exHarmony huggingface dataset:
A restoration method using dual generate adversarial networks for Chinese ancient characters

Ancient books that record the history of different periods are precious for human civilization. But the protection of them is facing serious problems such as aging. It is significant to repair the damaged characters in ancient books and restore their original textures. The requirement of the restoration of the damaged character is keeping the stroke shape correct and the font style consistent. In order to solve these problems, this paper proposes a new restoration method based on generative adversarial networks. We use the shape restoration network to complete the stroke shape recovery and the font style recovery. The texture repair network is responsible for reconstructing texture details. In order to improve the accuracy of the generator in the shape restoration network, we use the adversarial feature loss (AFL), which can update the generator and discriminator synchronously to replace the traditional perceptual loss. Meanwhile, the font style loss is proposed to maintain the stylistic consistency for the whole character. Our model is evaluated on the datasets Yi and Qing, and shows that it outperforms current state-of-the-art techniques quantitatively and qualitatively. In particular, the Structural Similarity has increased by 8.0% and 6.7% respectively on the two datasets.
Defining adaptivity and logical architecture for engineering (smart) self-adaptive cyber–physical systems

Modern cyber–physical systems (CPSs) are embedded in the physical world and intrinsically operate in a continuously changing and uncertain environment or operational context. To meet their business goals and preserve or even improve specific adaptation goals, besides the variety of run-time uncertainties and changes to which the CPSs are exposed—the systems need to self-adapt. The current literature in this domain still lacks a precise definition of what self-adaptive systems are and how they differ from those considered non-adaptive. Therefore, in order to answer how to engineer self-adaptive CPSs or self-adaptive systems in general, we first need to answer what is adaptivity, correspondingly self-adaptive systems. In this paper, we first formally define the notion of adaptivity. Second, within the frame of the formal definitions, we propose a logical architecture for engineering decentralised self-adaptive CPSs that operate in dynamic, uncertain, and partially observable operational contexts. This logical architecture provides a structure and serves as a foundation for the implementation of a class of self-adaptive CPSs. First, our results show that in order to answer if a system is adaptive, the right framing is necessary: the system's adaptation goals, its context, and the time period in which the system is adaptive. Second, we discuss the benefits of our architecture by comparing it with the MAPE-K conceptual model. Commonly accepted definitions of adaptivity and self-adaptive systems are necessary for work in this domain to be compared and discussed since the same terms are often used with different semantics. Furthermore, in modern self-adaptive CPSs, which operate in dynamic and uncertain contexts, it is insufficient if the adaptation logic is specified during the system's design, but instead, the adaptation logic itself needs to adapt and "learn" during run-time.
IDEA-Net: Adaptive Dual Self-Attention Network for Single Image Denoising

Image denoising is a challenging task due to possible data bias and prediction variance. Existing approaches usually suffer from high computational cost. In this work, we propose an unsupervised image denoiser, dubbed as adaptIve Dual sElf-Attention Network (IDEA-Net), to handle these challenges. IDEA-Net benefits from a generatively learned image-wise dual self-attention region where the denoising process is enforced. Besides, IDEA-Net is not only robust to possible data bias but also helpful to reduce the prediction variance by applying a simplified encoder-decoder with Poisson dropout operations on a single noisy image merely. The proposed IDEA-Net demonstrated the outperformance on four benchmark datasets compared with other single-image-based learning and non-learning image denoisers. IDEA-Net also shows an appropriate choice to remove real-world noise in low-light and noisy scenes, which in turn, contribute to more accurate dark face detection. The source code is available at https://github.com/zhemingzuo/IDEA-Net.
Measurement-device-independent quantum key distribution protocol with phase post-selection

Measurement-device-independent quantum key distribution (MDI-QKD) protocol can remove all the loopholes of the detection devices and, thus, has attracted much attention. Based on the technique of single-photon interference, we propose a modified MDI-QKD protocol with phase post-selection. We prove the security of the announcement of the private phases in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mi>X</mml:mi> </mml:mrow> </mml:math> basis and show how to apply the phase post-selection method to the double-scanning four-intensity MDI-QKD protocol. The numerical results show that the phase post-selection method can significantly improve the key rates at all distances. In the double-scanning method, two parameters need to be scanned in the calculation of the final key rate, and the global parameter optimization is pretty time-consuming. We propose an accelerated method that can greatly reduce the running time of the global parameter optimization program. This makes the method practically useful in an unstable channel.
Revolutionizing the Industrial Internet of Things Using Blockchain: An Unified Approach

Blockchain brings some significant changes in many industries implementing a shared, decentralized public ledger. Characteristics like immutability, autonomy, and transparency make this technology desirable to all sectors. Industry 4.0 becomes more effective in building upon a concrete and robust system like blockchain since its integration provides trust, transparency, and sustainability. It can deal with several aspects of the industry like supply chain, tracking products, payments, database management, security, etc. Unlike other systems, there are some issues with company-level implementation and maintenance of blockchain. The solution to these issues can pave the way for blockchain to make industrial management more successful. In this chapter, we present a brief discussion on blockchain and its architecture. Specifically, we describe the classification and consensus mechanisms that are very important in the blockchain network. We also detail the transaction system in the blockchain. Later on, we discuss the fundamentals of Industry 4.0. Furthermore, we provide an explicit discussion on the usage of blockchain in the industry. Finally, we point out some issues that can create possible future research areas in blockchain for industries.
Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification

The large-scale use of surveillance cameras in public spaces raised severe concerns about an individual privacy breach. Introducing privacy and security in video surveillance systems, primarily in person re-identification (re-id), is quite challenging. Event cameras are novel sensors, which only respond to brightness changes in the scene. This characteristic makes event-based vision sensors viable for privacy-preserving in video surveillance. Integrating privacy into the person re-id; this work investigates the possibility of performing person re-id with the event-camera network for the first time. We transform the asynchronous events stream generated by an event camera into synchronous image-like representations to leverage deep learning models and then evaluate how complex the re-id problem is with this new sensor modality. Interestingly, such event-based representations contain meaningful spatial details which are very similar to standard edges and contours. We use two different representations, image-like representation and their transformation to polar coordinates (which carry more distinct edge patterns). Finally, we train a person re-id model on such images to demonstrate the feasibility of performing event-driven re-id. We evaluate the performance of our approach and produce baseline results on two synthetic datasets (generated from publicly available datasets, SAIVT and DukeMTMC-reid).
Knowledge Capture and Replay for Continual Learning

Deep neural networks model data for a task or a sequence of tasks, where the knowledge extracted from the data is encoded in the parameters and representations of the network. Extraction and utilization of these representations is vital when data is no longer available in the future, especially in a continual learning scenario. We introduce flashcards, which are visual representations that capture the encoded knowledge of a network as a recursive function of some predefined random image patterns. In a continual learning scenario, flashcards help to prevent catastrophic forgetting by consolidating the knowledge of all the previous tasks. Flashcards are required to be constructed only before learning the subsequent task, hence, they are independent of the number of tasks trained before, making them task agnostic. We demonstrate the efficacy of flashcards in capturing learned knowledge representation (as an alternative to the original data), and empirically validate on a variety of continual learning tasks: reconstruction, denoising, and task-incremental classification, using several heterogeneous (varying background and complexity) benchmark datasets. Experimental evidence indicates that: (i) flashcards as a replay strategy is task agnostic, (ii) performs better than generative replay, and (iii) is on par with episodic replay without additional memory overhead.
Missing Data Analysis in Regression

Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms. We investigated the actual effects of missing data for regression by analyzing its impact in several publicly available databases implementing popular algorithms like Decision Tree, Random Forests, Adaboost, K-Nearest Neighbors, Support Vector Machines, and Neural Networks. Our goal is to offer a systematic view of how missing data may affect regression results. After exhaustive simulation analyzing eight public datasets from UCI and KEEL (Abalone, Arfoil, Bike, California, Compactiv, Mortage, Wankara and Wine), we concluded that the effect of missing data may be significant. The results obtained showed that K-Nearest Neighbors works better than others in the regression of data that has missing data.
Interval Modeling of Riverol-Pilipovik Water Treatment System

Riverol-Pilipovik (RP) water treatment system is a reverse osmosis-based desalination plant which is represented by a set of transfer functions. At times, there may be variations in the parameters of the system which may change its behavior. In this paper, interval modeling of RP system is done to accommodate the effect of parametric variations. A comparative study of original system with its interval model is shown with the help of step response and impulse response. Also, the characteristics of the response are compared to demonstrate the resemblance of the interval model to the original system.
Cross Encoder-Decoder Transformer with Global-Local Visual Extractor for Medical Image Captioning

Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual Extractor (GLVE) to capture both global features and local features. (2) The Cross Encoder-Decoder Transformer (CEDT) for injecting multiple-level encoder features into the decoding process. GLVE extracts not only global visual features that can be obtained from an entire image, such as size of organ or bone structure, but also local visual features that can be generated from a local region, such as lesion area. Given an image, CEDT can create a detailed description of the overall features by injecting both low-level and high-level encoder outputs into the decoder. Each method contributes to performance improvement and generates a description such as organ size and bone structure. The proposed model was evaluated on the IU X-ray dataset and achieved better performance than the transformer-based baseline results, by 5.6% in BLEU score, by 0.56% in METEOR, and by 1.98% in ROUGE-L.
Political instability and youths unemployment in sub‐Saharan Africa

Abstract The main objective of this study is to analyze the effect of political instability on youths unemployment in sub‐Saharan Africa. Particular interest is devoted to the channels through which political instability affects youths unemployment. The empirical analysis covers a sample of 23 countries for the period 2000–2016. To better characterize the labor market framework in sub‐Saharan Africa, the empirical analysis incorporates the impact of political instability on youths underemployment and labor underutilization. Using panel data techniques, the results show that political instability increases youths unemployment, underemployment, and labor underutilization in sub‐Saharan Africa. The main transmission channels identified are foreign direct investment, domestic investment, gross domestic product, government spending, human capital, and labor market flexibility. The findings confirm the investigated hypothesis. As the main policy implication, policymakers in sub‐Saharan Africa should resolve political instability as a priority toward addressing youths unemployment.
5G cascaded channel estimation using convolutional neural networks

Cascaded channels have been considered in several physical multipath propagation scenarios. However they are subject to phenomena such as multipath scattering, time dispersion and Doppler shift between the different links, which impose great challenges in relation to the channel estimation processing function in the receiver. In this paper we propose to tackle the problem of cascaded channels estimation in the fifth-generation and beyond (5G+) systems using convolutional neural networks (CNNs), without forward error correction (FEC) codes. The results show that the CNN-based framework reaches very close to perfect (theoretical) channel estimation levels, in terms of bit error rate (BER) values, and outperforms the least square (LS) practical estimation, measured in mean squared error (MSE). The benefits of CNN-based wireless cascaded channels estimation are particularly relevant for increasing number of links and modulation order. These findings are further confirmed at the CNN implementation stage on a field programmable gate array (FPGA) platform for a number of realistic quantization scenarios.
A Survey of Data Representation for Multi-Modality Event Detection and Evolution

The rapid growth of online data has made it very convenient for people to obtain information. However, it also leads to the problem of “information overload”. Therefore, how to detect hot events from the massive amount of information has always been a problem. With the development of multimedia platforms, event detection has gradually developed from traditional single modality detection to multi-modality detection and is receiving increasing attention. The goal of multi-modality event detection is to discover events from a huge amount of online data with different data structures, such as texts, images and videos. These data represent real-world events from different perspectives so that they can provide more information about an event. In addition, event evolution is also a meaningful research direction; it models how events change dynamically over time and has great significance for event analysis. This paper comprehensively reviews the existing research on event detection and evolution. We first give a series of necessary definitions of event detection and evolution. Next, we discuss the techniques of data representation for event detection, including textual, visual, and multi-modality content. Finally, we review event evolution under multi-modality data. Furthermore, we review several public datasets and compare their results. At the end of this paper, we provide a conclusion and discuss future work.
Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.
The COVID-19 Outbreak as a Trigger Event for Sinophobic Hate Crimes in the United Kingdom

Abstract We assessed whether the COVID-19 outbreak in the United Kingdom was associated with a rise in sinophobic hate crimes as well as the temporal distribution of victimization rates. A victimization survey (N = 393) showed that following the first known case of COVID-19 in the United Kingdom, Chinese/East Asian persons had a higher likelihood of being hate crime or incident victims than members of other ethnic minority groups. Specifically, victimization reported by Chinese/East Asian participants reached its highest level in March 2020 (before lockdown); it then dropped significantly after an initial relaxation of restrictions in May 2020. Overall, we documented a temporary, potentially slightly delayed hate crime trigger effect of the COVID-19 outbreak.
Secure Storage Model for Digital Forensic Readiness

Securing digital evidence is a key factor that contributes to evidence admissibility during digital forensic investigations, particularly in establishing the chain of custody of digital evidence. However, not enough is done to ensure that the environment and access to the evidence are secure. Attackers can go to extreme lengths to cover up their tracks, which is a serious concern to digital forensics – particularly digital forensic readiness. If an attacker gains access to the location where evidence is stored, they could easily alter the evidence (if not remove it altogether). Even though integrity checks can be performed to ensure that the evidence is sound, the collected evidence may contain sensitive information that an attacker can easily use for other forms of attack. To this end, this paper proposes a model for securely storing digital evidence captured pre- and post-incident to achieve reactive forensics. Various components were considered, such as integrity checks, environment sandboxing, strong encryption, two-factor authentication, as well as unique random file naming. A proof-of-concept tool was developed to realize this model and to prove its validity. A series of tests were conducted to check for system security, performance, and requirements validation, Overall, the results obtained showed that, with minimal effort, securing forensic artefacts is a relatively inexpensive and reliable feat. This paper aims to standardize evidence storage, practice high security standards, as well as remove the need to create new systems that achieve the same purpose.
Realtime Face Mask Detection Using TINY-YOLO V4

In the time of the Covid-19 pandemic there is a need to maintain social distancing and prioritize personal hygiene by the use of face masks and proper sanitary precautions. This although is hard to be monitored and controlled accurately and efficiently, can be done through the use of object detection using convolutional neural networks. This can be done in a way using Tiny-YOLOv4 which is an object detection algorithm that provides lightning-fast detection for many classes of objects without the use of such hardware resources. This project aims to train and test a custom data set using this algorithm to create a highly efficient and accurate face mask detection system that can be easily customized to add additional features such as warning systems, etc. It aims to be a system that can prove to be useful once the pandemic is over as it provides crucial data for the prevention and control of any other possible pandemics that may occur in the future.
Learning Quantum Circuits of Some <i>T</i> Gates

In this paper, we study the problem of learning an unknown quantum circuit of a certain structure. If the unknown target is an $n$-qubit Clifford circuit, we devise an efficient algorithm to reconstruct its circuit representation by using $O(n^2)$ queries to it. For decades, it has been unknown how to handle circuits beyond the Clifford group since the stabilizer formalism cannot be applied in this case. Herein, we study quantum circuits of $T$-depth one on the computational basis. We show that the output state of a $T$-depth one circuit {\textit{of full $T$-rank}} can be represented by a stabilizer pseudomixture with a specific algebraic structure. Using Pauli and Bell measurements on copies of the output states, we can generate a hypothesis circuit that is equivalent to the unknown target circuit on computational basis states as input. If the number of $T$ gates of the target is of the order $O({{\log n}})$, our algorithm requires $O(n^2)$ queries to it and produces its equivalent circuit representation on the computational basis in time $O(n^3)$. Using further additional $O(4^{3n})$ classical computations, we can derive an exact description of the target for arbitrary input states. Our results greatly extend the previously known facts that stabilizer states can be efficiently identified based on the stabilizer formalism.