Reviewerly/exHarmony huggingface dataset:
Single Image Dehazing Based on Contrastive Learning and Transformer

Abstract For the single image dehazing problem, an end-to-end multi-stage dehazing algorithm is designed. The algorithm contains two distinct parts to extract features. For shallow features, texture-level information is mined by stacking pixel and channel attention mechanisms. The proposed method uses multi-head self-attention (MHSA) to capture high-level features. MHSA improves dehazing performance by mining the dependencies of a wide range of abstract information. The superiority of the transformer architecture is extended with cascaded attention mechanisms and convolutions to improve feature extraction capabilities. Multilayer perceptron (MLP) is used in the decoding stage to equalize the context information. Furthermore, a contrastive loss function that introduces multiple negative samples and correction terms is proposed. The correction term is generated according to the difference between the precise and blurred images, which can enhance the training effect when dealing with different concentrations of dehaze. The training result of this loss function assists the model in approximating clear images and staying away from blurry images. According to the experimental results compared with other methods under the same conditions, the proposed method achieves good results in both subjective visual effects and objective evaluation indicators. The proposed contrast loss function also improves the dehazing performance of the algorithm.
Radar Communication Jamming Signal Recognition Based on the Attention Mechanism

Abstract In modern battlefields, radar radiation sources detect targets by transmitting high-power modulated signals. In order to protect the operational effectiveness of the radar, there is a higher requirement for radar communication anti-jamming signal recognition capability. The accurate and fast identification of radar anti-jamming signals is the prerequisite for taking effective anti-jamming measures, which will directly affect the operational effectiveness of radar. At present, the recognition method based on the neural network can automatically extract signal features and obtain good recognition results. Because radar emitter signals have a strong correlation in time sequence, the LSTM network is used in this paper. For better feature extraction and classification of signals, this paper constructs the LSTM attention network, combines the attention mechanism with the LSTM recognition network, and proves the effectiveness of radar communication jamming signal recognition network based on the attention mechanism through experiments.
Research on the angular velocity estimation method based on the state augmentation method

Abstract In the maneuvering target tracking research due to the unpredictability of the target’s maneuvering, it makes it difficult to establish an accurate target motion model to reflect the real motion trajectory of the target. Based on the determination of the parametric maneuver model and the radar measurement conditions of the target, the joint estimation method of the motion state and the maneuver parameters is designed to obtain the optimal estimation of the motion state and the maneuver parameters based on the optimal evaluation criteria. Currently, in the descriptive category of the state space model, the state augmentation method is obtained to solve the joint estimation of the motion state and maneuver parameters based on the estimation criterion [1]. Therefore, the angular velocity estimation method of the state augmentation method is used to consider the estimated value of the target angular velocity and the estimated variance comprehensively to improve the convergence speed of target tracking, minimize the error between the filtered angular velocity and the real angular velocity of the maneuvering target, and finally achieve the purpose of improving the target tracking accuracy.
The introduction of distance learning into the system of vocational education as a process of self-organization and management of students’ personal resources

The article examines issues related to distance learning programs, with a particular focus on e-learning, and highlights the limitations and challenges associated with their implementation. Lessons learned from developing distance courses show that factors such as profiling participants before starting training, technology friendliness for the user, accommodating different learning styles, encouraging and rewarding online exchanges, commitment of course moderators, variety of learning materials, and blended learning approaches contribute to the success of e-learning programs. The article concludes that enhancing the skills of researchers from developing countries through e-learning programs can increase the accessibility of learning for those who live and work in remote and conflict-prone areas, and bring together a network of professionals to interact and share experiences on common problems and solutions.
Towards Proactive Risk-Aware Cloud Cost Optimization Leveraging Transient Resources

Low-cost transient resources such as Amazon's Elastic Compute Cloud (EC2) Spot instances can be opportunistically leveraged to reduce the ongoing costs of cloud applications. However, they are susceptible to unilateral revocations by the vendor making them a risky proposition for long-running applications with strict performance requirements. It is challenging to effectively balance the cost savings that transient resources provide with the associated revocation risk which, if realised, can impact application performance. To address this challenge, we propose an approach for risk-aware cloud cost optimization that is inspired by the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">portfolio diversification</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contract diversification</i> mitigates the revocation risk by procuring the required compute capacity as a mixed portfolio of transient and non-transient resources. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Resource diversification</i> further diversifies the risk by using multiple transient resource types. Using our approach, consumers can leverage contract and resource diversification to proactively (re-)configure their application's resource portfolio to handle workload and resource price fluctuations while minimizing ongoing cost and keeping ongoing revocation risk within tolerable limits. Simulative evaluation using three real-world workload traces and Amazon's EC2 offerings demonstrate that our proposed approach can achieve meaningful cost savings compared to the baseline costs, while significantly reducing the portfolio's exposure to revocation risk.
Fast Hardware Implementation for Extended GCD of Large Numbers in Redundant Representation

The extended greatest common divisor (XGCD) of large numbers is a commonly used and computing-intensive operation in cryptography. The growing demands for high-speed cryptography applications urge for fast XGCD implementation. However, it is challenging to design fast architectures for XGCD because of its complex operations and tight data dependency. In this brief, we propose a low-latency and high-efficiency architecture for XGCD by using a modified extended <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -ary algorithm in redundant data representation. We modify the original <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -ary algorithm by adopting a parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> to reduce computing delay and applying a hardware-friendly method to Bézout coefficients. The redundant signed digit (RSD) representation is selected to avoid carry propagation and achieve high clock frequency. We devise high-parallel and low-latency architectures for the proposed algorithms with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=2, 4$ </tex-math></inline-formula> , and 8. The designs are coded in SystemVerilog and synthesized using TSMC 28-nm CMOS technology. The implementation results show that for the data bit width of 1024, the design with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=8$ </tex-math></inline-formula> achieves the shortest latency among different values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> , which is about 1.5x faster than the prior art and only needs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$472~ns$ </tex-math></inline-formula> .
Threats to Journalists from the Consumer Internet of Things

Threats associated with the consumer Internet of Things (IoT) may particularly inhibit the work and wellbeing of journalists, especially because of the danger of technological surveillance and the imperative to protect confidential sources. These issues may have knock-on effects on societal stability and democratic processes if press freedom is eroded. Still, journalists remain unaware of potential IoT threats, and so are unable to incorporate them into risk assessments or to advise their sources. This shows a clear gap in the literature, requiring immediate attention. This article therefore identifies and organises distinctive and novel threats to journalism from the consumer IoT. The article presents a novel conceptualisation of threats to the press in six categories: regulatory gaps, legal threats, profiling threats, tracking threats, data and device modification threats and networked device threats. Each of the threats in these categories includes a description and hypothetical consequences that include real-life ways in which IoT devices can be used to inhibit journalistic work, building on interdisciplinary literature analysis and expert interviews. In so doing, this article synthesises technical information about IoT device capabilities with human security and privacy requirements tailored to a specific at-risk population: journalists. It is therefore important for cyber science scholarship to address the contemporary and emerging risks associated with IoT devices to vulnerable groups such as journalists. This exploratory conceptualisation enables the evidence-based conceptual evolution of understandings of cyber security risks to journalists.
Near-Ultrasonic Covert Channels Using Software-Defined Radio Techniques

Traditional cybersecurity practices rely on computers only communicating through well-defined expected channels. If malware was developed to use covert channels, such as one created using ultrasonic sound, then this could bypass certain security measures found in computer networks. This paper aims to demonstrate the viability of acoustic covert channels by creating a low-bandwidth ultrasonic frequency channel utilising software-defined radio (SDR) techniques. Previous work was evaluated to identify the strengths and weaknesses of their implementations. Software-defined radio techniques were then applied to improve the performance and reliability of the acoustic covert channel. The proposed implementation was then evaluated over a range of hardware and compared to previous implantations based on the attributes of their throughput, range, and reliability. The outcome of this research was an ultrasonic covert channel implemented in GNU Radio. The proposed implementation was found to provide 47% higher throughput than previous work while using less signal bandwidth. Utilising software-defined radio techniques improves the performance of the acoustic covert channels over previous implementations. It is expected that this technique would be effective in an office environment, but less effective in high security or server environments due to the lack of audio equipment available in these spaces.
Reading Multilevel 2-D Barcodes Using a Machine Learning Approach

This article addresses the reading problem of multilevel 2-D barcodes over a print-and-capture (PC) channel. The prior reading schemes have different limitations to hinder their applications, e.g., suffering from quantization error, being sensitive to the predetermined decision boundaries, and being sensitive to the selection of initial parameters. In this article, we introduce a machine learning approach to address the above limitations using a new ensemble clustering (EC) algorithm. Based on the new EC algorithm, we propose two reading schemes of a multilevel 2-D barcode. Specifically, the first proposed scheme is named the EC reading scheme. In the EC reading scheme, we introduce a weighted ensemble mechanism to assign different weights to different base clustering results. Then, we propose the second scheme, named the enhanced EC (EEC) reading scheme, to further improve the reading performance with the help of the reference symbols. We implement our approach and conduct extensive performance comparisons through an actual excremental platform under various multilevel 2-D barcodes and various capturing devices. From experimental results, we observe that both proposed reading schemes have better performance than the prior reading schemes. Moreover, the EEC reading scheme has better performance than the EC reading scheme, and their performance gap becomes more apparent as the distortion of a PC channel increases.
Blockchain-Based Cardinal E-Voting System Using Biometrics, Watermarked QR Code and Partial Homomorphic Encryption

A robust democracy cannot be concn. Therefore, we propose a privacy-preserving, cost-effective and verifiable blockchain-based electronic score voting scheme. Our framework is premised on a watermarked quick response (QR) code-based secure identification mechanism for creating a tamper-resistant biometric voter ID card. Two watermarked image shares, namely, owner and master shares, are created through XOR-based visual cryptography (VC) which are managed by the ID owner and a semi-trusted constituency authority (CA). Election Commission officer (ECO) commences the cryptographic system setup by distributing the secret key shares amongst all the CAs. First, voters show their share for their details verification and biometric authentication. If successful, they use the modified ElGamal homomorphic encryption to enable additive computations on their score votes while ensuring their secrecy. The integrity and validity of the encrypted vote are ensured through a non-interactive partial knowledge range proof (NIPKRP) based on the Pederson commitment protocol. On the counting day, the vote ciphertext en route to CA is subjected to progressive aggregation. This intra-constituency level computation follows the range voting paradigm. Then, CAs decrypt the resultant encrypted ballots and determine the winner for their respective constituencies after comparing the obtained scores. Accordingly, each CA creates and transmits an encrypted binary vector to ECO for pluralistic inter-constituency aggregation. Finally, ECO uses its secret key to decrypt the final encrypted aggregate vector to determine the majority winning party. If at least two maximum scores or seat counts come out to be identical, most significant bit (MSB) is given priority to break the tie. CAs can collaborate to verify the results by using their secret shares. Our proposed scheme facilitates secure, tamperproof and decentralized score aggregation for e-voting with a suitable tie-breaker. The efficacy and usability of the scheme have been reported with experimental results and security proofs.
Criteria for Realistic and Expedient Scenarios for Tabletop Exercises on Cyber Attacks Against Industrial Control Systems in the Petroleum Industry

Digitalization of the petroleum industry entails a greater interconnection between Information Technology (IT) and Industrial Automation and Control Systems (IACS), and has led to an increased attack surface. To mitigate the consequences of incidents and to ensure a safe operation, the industry uses preparedness exercises. Previously, these exercises have concerned safety-related incidents. Today, digitalization requires the industry to also exercise security incidents, especially incidents that are directed towards IACS. While the need for more detailed guidelines in the area of cyber security and IACS has been explicitly called for by the industry, few guidelines are currently available. We aimed to lessen this shortcoming by investigating descriptions of events to use in exercises, known as scenarios. This project investigated what characterizes a scenario to be realistic and expedient for preparedness exercises on cyber attacks against IACS in the petroleum industry, with a focus on tabletop exercises. Based on data collected through interviews, a list of criteria that characterize such scenarios was created. The list was validated and approved by respondents from two different operator companies. The results highlight the importance of basing the scenario on today’s threat landscape, making the scenarios plausible, and design the scenario such that it leads to a challenging tabletop exercise which also gives a sense of empowerment for the participants.
Neutralizing Adversarial Machine Learning in Industrial Control Systems Using Blockchain

The protection of critical national infrastructures such as drinking water, gas, and electricity is extremely important as nations are dependent on their operation and steadiness. However, despite the value of such utilities their security issues have been poorly addressed which has resulted in a growing number of cyberattacks with increasing impact and huge consequences. There are many machine learning solutions to detect anomalies against this type of infrastructure given the popularity of such an approach in terms of accuracy and success in detecting zero-day attacks. However, machine learning algorithms are prone to adversarial attacks. In this paper, an energy-consumption-based machine learning approach is proposed to detect anomalies in a water treatment system and evaluate its robustness against adversarial attacks using a novel dataset. The evaluations include three popular machine learning algorithms and four categories of adversarial attack set to poison both training and testing data. The captured results show that although some machine learning algorithms are more robust against adversarial confrontations than others, overall, the proposed anomaly detection mechanism which is built on energy consumption metrics and its associated dataset are vulnerable to such attacks. To this end, a blockchain approach to protect the data during the training and testing phases of such machine learning models is proposed. The proposed smart contract is deployed in a public blockchain test network and their costs and mining time are investigated.
Keyword Extractor for Contrastive Learning of Unsupervised Sentence Embedding

Contrastive learning has been widely applied to learning unsupervised sentence embedding. One major method is unsupervised SimCSE, which only uses random dropouts as noise to build positive pairs for contrastive learning. But token-level matching does not always properly represent the similarity between texts. Sentence-level semantic representation based on keyword-level could enhance the similarity matching performance between texts. To emphasize the contribution of keywords in sentence representation, we propose KESimCSE, which not only includes the constrastive learning of sentence embedding but also adds KL divergence of two elements to the loss function. One element is the dot product of each token embedding and the [CLS] embedding of the BERT outputs, as [CLS] generally captures the semantics of the whole sentence. The other element is the weight list of all tokens obtained through keyword extraction method. Our experiments show that the averaged Spearman’s correlation of KESimCSE on semantic textual similarity tasks raises to 77.21%, which outperforms unsupervised SimCSE by nearly 1.30%.
Beyond Natural Language Processing: Building Knowledge Graphs to Assist Scientists Understand COVID-19 Concepts

To combat COVID-19, scientists must digest the vast amount of relevant biomedical knowledge in the literature to understand disease mechanisms and related biological functions. Nearly 3,000 scientific papers are published on PubMed every day. This knowledge bottleneck has resulted in severe delays in developing COVID-19 vaccines and drugs. Our research produces a hierarchy of knowledge concepts related to COVID-19, designed to assist scientists in answering questions and generating summaries. It aims to discover scientific and comprehensive knowledge to extract fine-grained multimedia elements (i.e., physical and visual structures, relational events and events, and chemical knowledge). Our project is toward one step in natural language understanding: detailed contextual sentences, subgraphs, and knowledge subgraphs are the first time to be automatically generated, and relations and coreferences of COVID-19 mentions will be sketched. Extensive results show that our method outperforms other state-of-the-art methods. In addition, we have published the generated knowledge graph on Google Drive1 and released the source in the Github2.
Kill Two Birds with One Stone: Solving Deblurring And Super Resolution with One Neural Network

Computed tomography (CT) is a three-dimensional imaging modality that uses X-ray beams to produce cross-sectional information of an object or a patient. CT suffers from multiple inherent limitations of image quality. The purpose of this study is to propose three deep learning models to address two of the common limitations, namely image blurring and limited spatial resolution. Two of the models, residual dense network (RDN) and Sharp U-Net are first seen their application in these medical imaging tasks. Moreover, we incorporate the advantages of these two methods to design a novel neural network architecture named Sharp RDN. The performance of these methods was benchmarked on two data sets through both qualitative and quantitative comparisons. The data sets were AAPM simulated data and TCIA clinical data. The metrics for the quantitative measure were PSNR, SSIM, and RMSE. Experimental results showed that both RDN and Sharp RDN drastically improved the corrupted image quality by giving a 65% improvement in the deblurring task, and 32% improvement in the SR task. The performance of Sharp U-Net was poorer than RDN and Sharp RDN, indicating that U-Net-type architectures are not suitable for these image tasks.