Reviewerly/exHarmony huggingface dataset:
Digital Twins as Foundation for Augmented Reality Applications in Aerospace

Augmented reality techniques can be used to support system modeling and industrial operations at different levels, enabling designers and engineers to augment their real environment with relevant virtual content. In aerospace, these techniques are tightly coupled to digital twins. Together, they can enhance scarce operational resources, facilitating skill transfer and knowledge retention. It is possible to define the term digital twin based on conceptual data models as used in model-based system engineering. In this definition, a conceptual data model is used to accompany a product as an unique evolving system model through the whole lifecycle, starting from virtual abstractions and progressing toward a virtual replication of the real-world entity. Throughout the whole lifecycle, digital twins help to analyze or predict system behavior for improving decision-making and avoiding cost-expensive prototyping. In this chapter, we discuss how digital twin representations can leverage on augmented reality approaches and provide an overview of how model-based system engineering can help to maintain information consistency through the different phases of the product lifecycle. In this context, we address different aspects related to the use of augmented reality approaches for digital twins in aerospace, such as overlay precision, interaction, data visualization, and remote collaboration. Our example applications take different phases of the product lifecycle into account, from creation to operation.
Performance Comparison of Different Machine Learning Techniques for Early Prediction of Breast Cancer using Wisconsin Breast Cancer Dataset

A significant health issue, cancer is becoming more prevalent globally and is a leading cause of mortality. Recent studies have shown that breast cancer is one of the most prevalent cancer type, particularly among women. Early detection can increase the chances of survival for those with breast cancer and lower treatment cost. However, there are drawbacks to the early diagnosis methods utilized in today’s healthcare systems. These include the need for substantial human resources, long-term effects, and difficult access to these services for everybody. For early breast cancer diagnosis, technologies that are simple to use, yield reliable findings compared to scientific methodologies, and are available to everyone are required. Artificial Intelligence techniques enable the early diagnosis of breast cancer. This study aims to classify benign and malignant breast cancer image features. Artificial Neural Network, Support Vector Machine and Random Forest algorithms were used to classify features obtained from images. Experiments were performed on the Wisconsin Breast Cancer dataset. Experimental evaluation shows that 99% of the most successful results were achieved with the Artificial Neural Network algorithm. According to experimental findings, the classification technique can identify breast cancer in its early stages. The findings of the study are expected to shed on light new researches for investigation into breast cancer early detection.
NIDN: Medical Code Assignment via Note-Code Interaction Denoising Network

Clinical records are files that contain detailed information about a patient's health status. Clinical notes are typically complex, and the medical code space is large, so medical code assignment from clinical text is a long-standing challenge. The traditional manual coding method is inefficient and error-prone. Incorrect coding may lead to adverse consequences. With machine learning and computer hardware development, the deep neural network model has been widely applied in the medical care domain. However, noise in lengthy documents, complex code association, and the imbalanced class problem urgently need to be solved. Therefore, we propose a Note-code Interaction Denoising Network (NIDN). We exploit the self-attention mechanism to identify the most relevant context of the medical code in the clinical document. We leverage the label attention mechanism to learn code-specific text representation. We utilize the correlation between labels in multi-task learning to assist the model in prediction. To better learn from lengthy texts and improve the performance of long-tail distribution, we develop a denoising module to reduce the influence of noise in medical code prediction. Experimental results show that our proposed model outperforms competitive baselines on a real-world MIMIC-III dataset.
Deep learning-driven MIMO: Data encoding and processing mechanism

Large-scale Multiple-Input Multiple Output (MIMO) is the key technology of 5G communication. However, dealing with physical channels is a complex process. Machine learning techniques have not been utilized commercially because of the limited learning capabilities of traditional machine learning algorithms. We design a deep learning hybrid precoding scheme based on the attention mechanism. The method mainly includes channel modeling and deep learning encoding two modules. The channel modeling module mainly describes the problem formally, which is convenient for the subsequent method design and processing. The model design module introduces the design framework, details, and main training process of the model. We utilize the attention layer to extract the eigenvalues of the interference between multiple users through the output attention distribution matrix. Then, according to the characteristics of inter-user interference, the loss minimization function is used to study the optimal precoder to achieve the maximum reachable rate of the system. Under the same condition, we compare our proposed method with the traditional unsupervised learning-based hybrid precoding algorithm, the TTD-based (True-Time-Delay, TTD) phase correction hybrid precoding algorithm, and the deep learning-based method. Additionally, we verify the role of attention mechanism in the model. Extensive simulation results demonstrate the effectiveness of the proposed method. The results of this research prove that deep learning technology can play a driving role in the encoding and processing of MIMO with its unique feature extraction and modeling capabilities. In addition, this research also provides a good reference for the application of deep learning in MIMO data processing problems.
AMSFF-Net: Attention-Based Multi-Stream Feature Fusion Network for Single Image Dehazing

In this paper, an end-to-end convolutional neural network is proposed to recover haze-free image named as Attention-Based Multi-Stream Feature Fusion Network (AMSFF-Net). The encoder-decoder network structure is used to construct the network. An encoder generates features at three resolution levels. The multi-stream features are extracted using residual dense blocks and fused by feature fusion blocks. AMSFF-Net has ability to pay more attention to informative features at different resolution levels using pixel attention mechanism. A sharp image can be recovered by the good kernel estimation. Further, AMSFF-Net has ability to capture semantic and sharp textural details from the extracted features and retain high-quality image from coarse-to-fine using mixed-convolution attention mechanism at decoder. The skip connections decrease the loss of image details from the larger receptive fields. Moreover, deep semantic loss function emphasizes more semantic information in deep features. Experimental findings prove that the proposed method outperforms in synthetic and real-world images.
Offset-Based BBB-Secure Tweakable Block-ciphers with Updatable Caches

A nonce-respecting tweakable blockcipher is the building-block for the OCB authenticated encryption mode. An XEX-based TBC is used to process each block in OCB. However, XEX can provide at most birthday bound privacy security, whereas in Asiacrypt 2017, beyond-birthday-bound (BBB) forging security of OCB3 was shown in [14]. In this paper we study how at a small cost we can construct a nonce-respecting BBB-secure tweakable blockcipher. We propose the $$\textsf {OTBC\text {-}3}$$ construction, which maintains a cache that can be easily updated when used in an OCB-like mode. We show how this can be used in a BBB-secure variant of OCB with some additional keys and a few extra blockcipher calls but roughly the same amortised rate.
A Proposal for Device Independent Probabilistic Quantum Oblivious Transfer

In this paper, we propose a novel Probabilistic Quantum Oblivious Transfer (also known as Quantum Private Query or QPQ) scheme with full Device-Independent (DI) certification. To the best of our knowledge, this is the first time we provide such a full DI-QPQ scheme using EPR pairs. Our proposed scheme exploits the self-testing of shared EPR pairs along with the self-testing of projective measurement operators in a setting where the client and the server do not trust each other. To certify full device independence, we exploit a strategy to self-test a particular class of POVM elements that are used in the protocol. Further, we provide formal security analysis and obtain an upper bound on the maximum cheating probabilities for both the dishonest client as well as the dishonest server.
Parallel Isogeny Path Finding with Limited Memory

The security guarantees of most isogeny-based protocols rely on the computational hardness of finding an isogeny between two supersingular isogenous curves defined over a prime field $$\mathbb {F}_q$$ with q a power of a large prime p. In most scenarios, the isogeny is known to be of degree $$\ell ^e$$ for some small prime $$\ell $$ . We call this problem the Supersingular Fixed-Degree Isogeny Path (SIPFD) problem. It is believed that the most general version of SIPFD is not solvable faster than in exponential time by classical as well as quantum attackers. In a classical setting, a meet-in-the-middle algorithm is the fastest known strategy for solving the SIPFD. However, due to its stringent memory requirements, it quickly becomes infeasible for moderately large SIPFD instances. In a practical setting, one has therefore to resort to time-memory trade-offs to instantiate attacks on the SIPFD. This is particularly true for GPU platforms, which are inherently more memory-constrained than CPU architectures. In such a setting, a van Oorschot-Wiener-based collision finding algorithm offers a better asymptotic scaling. Finding the best algorithmic choice for solving instances under a fixed prime size, memory budget and computational platform remains so far an open problem. To answer this question, we present a precise estimation of the costs of both strategies considering most recent algorithmic improvements. As a second main contribution, we substantiate our estimations via optimized software implementations of both algorithms. In this context, we provide the first optimized GPU implementation of the van Oorschot-Wiener approach for solving the SIPFD. Based on practical measurements we extrapolate the running times for solving different-sized instances. Finally, we give estimates of the costs of computing a degree- $$2^{88}$$ isogeny using our CUDA software library running on an NVIDIA A100 GPU server.
autoTimeSVD++: A Temporal Hybrid Recommender System Based on Contractive Autoencoder and Matrix Factorization

Matrix factorization is one of the successful approaches used largely in Recommender systems to provide recommendations to users based on their historical preferences. In recent years, many approaches based on deep learning, such as autoencoders, were used alone or combined with other methods to extract non-linear relationships between items. But most of these models are static and do not capture dynamic changes regarding the rating process which is dynamic and may change over time. In this paper, we propose a new hybrid model autoTimeSVD++ which combines timeSVD++ and autoencoder to extract item side information including time effect. The experimental results show that the proposed model achieves competitive results compared to many baselines models.
IRIS Advanced Threat Intelligence Orchestrator- A Way to Manage Cybersecurity Challenges of IoT Ecosystems in Smart Cities

This paper provides an overview of the Advanced Threat Intelligence Orchestrator in assisting organizations and society’s first responders in managing, prioritizing, and sharing information related to cyber security incidents. In order to accomplish this, the capabilities and benefits of security, orchestration, automation, and response (SOAR) systems, on which Orchestrator is based, were promoted. The results of this survey conducted as part of the IRIS EU-funded project to protect Internet of Things (IoT) and Artificial Intelligence (AI)-driven ICT-enabled systems from cyber threats and attacks on their privacy facilitating SOC/CSIRTs/CERTs. In this context, the tool is explored in methods of orchestrating and automating cyber security processes and routines. The open-source tool that was chosen for the creation of Advanced Threat Intelligence Orchestrator was SHUFFLE. SHUFFLE gives a wide variety of functionalities as it can be integrated with numerous tools and APIS. Furthermore, the provision of schematic workflows with action steps makes the stakeholders’ interface more intuitive.
Automated Extraction and Representation of Citation Network: A CJEU Case-Study

Although with some discrepancy, both in common law and in civil law systems, previous judgments play an important role with respect to future decisions. Traditional legal methodologies usually involve the use of manual rather than automatic keyword search mechanisms to retrace the steps of the judicial decision-making. However, these methods are generally highly time-consuming and can be subject to different types of biases. In this work, we present an automated extraction pipeline to map and structure citations in rulings regarding fiscal state aids in the case-law of the Court of Justice of the European Union. In particular, by exploiting the available XML data in the EUR-Lex platform, we built an end-to-end parser based on a set of regular expressions and heuristics, which is able to iteratively extract all citations, finally creating a hierarchical structure of citations with their contextual information at the paragraph level. Such data structure can be projected into a graphical representation, enabling useful visualization and exploration features and insights, such as the diachronic study of the development of specific citations and legal principles over time. Our work suggests how the exploitation and analysis of citation networks through automated means can provide significant tools to support traditional legal methodologies.
Trace Element Assemblages of Pseudomorphic Iron Oxyhydroxides of the Pobeda-1 Hydrothermal Field, 17°08.7′ N, Mid-Atlantic Ridge: The Development of a Halmyrolysis Model from LA-ICP-MS Data

Halmyrolysis, as one of the global processes of alteration of seafloor hydrothermal sediments, needs to be recognized in terms of mineral and trace element evolution to elaborate new criteria for metallogenic and geoecological forecasts with respect to ocean exploration. The purpose of this paper is to explain trace elements’ behavior during the halmyrolysis of sulfide deposits. This task is resolved using an LA-ICP-MS analysis of iron oxyhydroxides (IOHs) on examples of oxidized pyrrhotite-rich diffusers of the ultramafic-hosted Pobeda-1 hydrothermal field (Mid-Atlantic Ridge). The IOHs formed after the sulfides were enriched in seawater-derived trace elements (Na, K, Mg, Ca, Sr, P, U, Mo, V, REE, Cr). Six trace element assemblages (TEAs) are statistically recognized for the IOHs. TEA-I (Cu, In, Sn, Bi, Se, Te) is inherited from chalcopyrite, isocubanine and bornite microinclusions. TEA-II is typical of Zn sulfides (Zn, Cd, Sb, Tl, Ag) interacted with seawater (Mg, U, Mo, Ni, Na, K) and hydrothermal fluid (Eu). TEA-III (Ca, Sr, Cu, Si, Se, P, As) reflects the inclusions of aragonite, opal, atacamite and possibly native selenium, while P and As occur as absorbed oxyanion groups on IOHs or Ca–Fe hydroxyphosphates. TEA-IV (Al, Ga, Ge, Tl, W, Ti ± Mn, Co, Ba) indicates the presence of minor clays, Co-rich Mn oxyhydroxides and barite. TEA-V with Pb and V is closely related to TEA-VI with REEs except for Eu. The halmyrolysis of sulfides includes two stages: (i) oxidation of S(II) of primary sulfides and the formation of supergene sulfides, which scavenge the redox-sensitive elements (e.g., U, Mo, Ni, Eu), and (ii) oxidation of Fe (II) to Fe (III) and absorption of most elements of TEAs III, IV, V and VI by IOHs.
MDA-Based Approach for Blockchain Smart Contract Development

Blockchain smart contracts can support the decentralisation of business processes, but due to smart contracts’ specifics, their development is a complicated process. Introducing model-driven development principles in smart contract development can facilitate requirement specification, design, and implementation activities. This paper presents a model-driven development method MDAsmartCD (Model-Driven Architecture-based Smart Contract Development) to alleviate smart contract development by supporting the complete MDA life cycle, covering the definition of Computation-Independent Model, Platform-Independent Model, and two instances of Platform-Specific Models. In MDAsmartCD, model transformations (model-to-model and model-to-text) are used to produce smart contract code in the Hyperledger Fabric platform Go and the Ethereum platform Solidity programming languages. The method application was demonstrated by implementing the smart contract for the hackathon solution and executing the generated Solidity and Go smart contracts in the workflow of issuing certificates for hackathon participants. During the execution of the workflow, both deployed smart contracts behaved identically and recorded analogous results in respective blockchain data storages. This demonstrated that the MDAsmartCD method enables the generation of compilable and executable smart contract code, ready for deployment on a blockchain platform.
An Enhanced LBPH Approach to Ambient-Light-Affected Face Recognition Data in Sensor Network

Although combining a high-resolution camera with a wireless sensing network is effective for interpreting different signals for image presentation on the identification of face recognition, its accuracy is still severely restricted. Removing the unfavorable impact of ambient light remains one of the most difficult challenges in facial recognition. Therefore, it is important to find an algorithm that can capture the major features of the object when there are ambient light changes. In this study, face recognition is used as an example of image recognition to analyze the differences between Local Binary Patterns Histograms (LBPH) and OpenFace deep learning neural network algorithms and compare the accuracy and error rates of face recognition in different environmental lighting. According to the prediction results of 13 images based on grouping statistics, the accuracy rate of face recognition of LBPH is higher than that of OpenFace in scenes with changes in ambient lighting. When the azimuth angle of the light source is more than +/−25° and the elevation angle is +000°, the accuracy rate of face recognition is low. When the azimuth angle is between +25° and −25° and the elevation angle is +000°, the accuracy rate of face recognition is higher. Through the experimental design, the results show that, concerning the uncertainty of illumination angles of lighting source, the LBPH algorithm has a higher accuracy in face recognition.
Testing management scenarios for the North Sea ecosystem using qualitative and quantitative models

Abstract The complexities of ecosystem-based management require stepwise approaches, ideally involving stakeholders, to scope key processes, pressures, and impact in relation to sustainability and management objectives. Use of qualitative methods like Fuzzy Cognitive Mapping (FCM) with a lower skill and data threshold than traditional quantitative models afford opportunity for even untrained stakeholders to evaluate the present and future status of the marine ecosystems under varying impacts. Here, we present the results applying FCM models for subregions of the North Sea. Models for the southern North Sea, Skagerrak, Kattegat, and the Norwegian Trench were developed with varying level of stakeholder involvement. Future scenarios of increased and decreased fishing, and increased seal biomass in the Kattegat, were compared with similar scenarios run on two quantitative ecosystem model. Correspondence in response by the models to the same scenarios was lowest in the southern North Sea, which had the simplest FCM model, and highest in Norwegian Trench. The results show the potential of combining FCM and quantitative modelling approaches in integrated ecosystem assessments (IEAs) and in future ecosystem-based management advice, but to facilitate such comparisons and allow them to complement and enhance our IEAs, it is important that their components are aligned and comparable.
A rapid and accurate gamma compensation method based on double response curve fitting for high-quality fringe pattern generation

The gamma effect of structured light illumination devices and camera will yield undesired nonlinear phase errors both in fringe projection system and fringe reflection system. The existing nonlinear correction methods can be classified into two categories: actively modifying the fringe patterns before their projection, or passively compensating the phase error after the fringe patterns are captured. The former needs to capture lots of fringe patterns in advance, while the latter occupies measurement time. In this paper, a rapid and robust active gamma compensation method based on double response curve fitting is proposed for the first time. In the proposed method, the appropriate pre-coding gamma value is determined jointly by the two constructed response curves, so the correction results will not get worse when the number of projected grayscale images decreases and no more than 4 frames of grayscale images are required. Experimental results verified that this method is fast, accurate and easy to implement. With the proposed technique, high-accuracy measurement can be achieved with the conventional 3-step phase-shifting algorithm.
Offensive language identification in dravidian languages using MPNet and CNN

Social media has effectively replaced traditional forms of communication and marketing. As these platforms allow for the free expression of ideas and facts through text, images, and videos, there exists a significant need to screen them to safeguard people and organisations from objectionable information directed at them. Our work aims to categorise code-mixed social media comments and posts in Tamil, Malayalam, and Kannada into offensive or not offensive at different levels. We present a multilingual MPNet and CNN fusion model for detecting offensive language content directed at an individual (or group) in low-resource Dravidian languages at different levels. Our model is capable of handling data that has been code-mixed, such as Tamil and Latin scripts. The model was successfully validated on the datasets, achieving offensive language detection results better than those of other baseline models with weighted average F1-score of 0.85, 0.98, and 0.76, and performed better than the baseline models EWDT, and EWODT by 0.02, 0.02, 0.04 for Tamil, Malayalam, and Kannada respectively.