Reviewerly/exHarmony huggingface dataset:
Generating a full spherical view by modeling the relation between two fisheye images

Abstract Full spherical views provide advantages in many applications that use visual information. Dual back-to-back fisheye cameras are receiving much attention to obtain this type of view. However, obtaining a high-quality full spherical view is very challenging. In this paper, we propose a correction step that models the relation between the pixels of the pair of fisheye images in polar coordinates. This correction is implemented during the mapping from the unit sphere to the fisheye image using the equidistant fisheye projection. The objective is that the projections of the same point in the pair of images have the same position on the unit sphere after the correction. In this way, they will also have the same position on the equirectangular coordinate system. Consequently, the discontinuity between the spherical views for blending is minimized. Throughout the manuscript, we show that the angular polar coordinates of the same scene point in the fisheye images are related by a sine function and the radial distance coordinates by a linear function. Also, we propose employing a polynomial as a geometric transformation between the pair of spherical views during the image alignment since the relationship between the matching points of pairs of spherical views is not linear, especially in the top/bottom regions. Quantitative evaluations demonstrate that using the correction step improves the quality of the full spherical view, i.e. IQ MS-SSIM, up to 7%. Similarly, using a polynomial improves the IQ MS-SSIM up to 6.29% with respect to using an affine matrix.
Financial development impact on domestic investment: does income level matter?

Financial development significantly bolsters a country’s economic growth and resilience. Despite increasing focus on the relationship between financial development and economic growth, few studies examine the impact of financial development on domestic investment across countries’ income levels. Therefore, this study employs the system Generalized Method of Moments estimator and the Pooled Mean Group estimator to investigate this relationship, utilizing a panel of 152 countries from 1980 to 2021. The empirical findings affirm that financial development positively influences investment performance until a specific threshold over time. However, while increasing financial development benefits investment, further deepening the financial sector may eventually diminish its impact on domestic investment. Specifically, the benefit of investment growth remains valid only up to a threshold of 0.5147, beyond which it becomes a hindrance. In the short run, financial development changes do not substantially impact investment. Additionally, the marginal effect of financial development on investment is more pronounced in low- and middle-income countries. These empirical findings provide valuable reference for enhancing financial development to foster investment growth.
Quantum Implementation of the SAND Algorithm and Its Quantum Resource Estimation for Brute-Force Attack

The SAND algorithm is a family of lightweight AND-RX block ciphers released by DCC in 2022. Our research focuses on assessing the security of SAND with a quantum computation model. This paper presents the first quantum implementation of SAND (including two versions of SAND, SAND-64 and SAND-128). Considering the depth-times-width metric, the quantum circuit implementation of the SAND algorithm demonstrates a relatively lower consumption of quantum resources than that of the quantum implementations of existing lightweight algorithms. A generalized Grover-based brute-force attack framework was implemented and employed to perform attacks on two versions of the SAND algorithm. This framework utilized the g-database algorithm, which considered different plaintext-ciphertext pairs in a unified manner, reducing quantum resource consumption. Our findings indicate that the SAND-128 algorithm achieved the NIST security level I, while the SAND-64 algorithm fell short of meeting the requirements of security level I.
A Tool Kit for Relation Induction in Text Analysis

Distances derived from word embeddings can measure a range of gradational relations—similarity, hierarchy, entailment, and stereotype—and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation induction, and demonstrate how such techniques can complement dictionary methods as unsupervised, deductive methods.
AM‐MulFSNet: A fast semantic segmentation network combining attention mechanism and multi‐branch

Abstract In order to balance accuracy and real‐time performance in semantic segmentation, this paper proposes a real‐time semantic segmentation algorithm model based on attention mechanism and multi‐branch feature fusion using Fast convolutional neural network model (Fast‐SCNN). In this method, the spatial detail feature enhancement branch is introduced to enhance spatial detail features firstly. Then, through rational design of fusion module, the feature information of each branch is optimized to achieve better fusion of deep and shallow features. At the end of the feature fusion module, an adaptive feature enhancement focus module is introduced to capture the interdependence between remote pixels. The experimental results show that the proposed algorithm achieves 71.55% segmentation accuracy on Cityscapes dataset, the reasoning speed FPS is 97.6 frames/s, and the number of parameters is 1.39 M, which verifies the effectiveness of the network model constructed by the algorithm. Code is available at https://github.com/ccchhheeennn/model .
The benefits and costs of explainable artificial intelligence in visual quality control: Evidence from fault detection performance and eye movements

Abstract Visual inspection tasks often require humans to cooperate with artificial intelligence (AI)‐based image classifiers. To enhance this cooperation, explainable artificial intelligence (XAI) can highlight those image areas that have contributed to an AI decision. However, the literature on visual cueing suggests that such XAI support might come with costs of its own. To better understand how the benefits and cost of XAI depend on the accuracy of AI classifications and XAI highlights, we conducted two experiments that simulated visual quality control in a chocolate factory. Participants had to decide whether chocolate molds contained faulty bars or not, and were always informed whether the AI had classified the mold as faulty or not. In half of the experiment, they saw additional XAI highlights that justified this classification. While XAI speeded up performance, its effects on error rates were highly dependent on (X)AI accuracy. XAI benefits were observed when the system correctly detected and highlighted the fault, but XAI costs were evident for misplaced highlights that marked an intact area while the actual fault was located elsewhere. Eye movement analyses indicated that participants spent less time searching the rest of the mold and thus looked at the fault less often. However, we also observed large interindividual differences. Taken together, the results suggest that despite its potentials, XAI can discourage people from investing effort into their own information analysis.
ArtProtect: Blockchain and NFC‐based anti‐counterfeit system for physical art

Abstract Counterfeit artwork presents a significant risk to copyright holders and the economy. Without expertise in art, it is not straightforward to distinguish an artwork counterfeit from a genuine piece. This work designs and implements a reliable solution that enables users with near‐field communication‐enabled Android smartphones to verify artwork authenticity by accessing its respective certificate of authenticity stored in the Ethereum blockchain. To represent a physical artwork, the artwork image and metadata are stored in an inter‐planetary file system and minted as an ERC721 non‐fungible token to a smart contract deployed in Ethereum Rinkeby Testnet. The mobile app ArtProtect was developed to generate certificate of authenticity based on each non‐fungible token fetched through OpenSea Testnet API. Users access this information by scanning an near‐field communication tag embedded into the artwork. The content of the tag is signed by the respective artwork's artist and responsible agent by using Ethereum signing with their Ethereum wallet accounts. Through testing and analysis, the implemented work is secure, tamper‐evident, usable, flexible, and inexpensive to be applied to a real‐world scenario.
Riesz Networks: Scale-Invariant Neural Networks in a Single Forward Pass

Abstract Scale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is typically achieved by data augmentation. However, when presented with a scale far outside the range covered by the training set, neural networks may fail to generalize. Here, we introduce the Riesz network, a novel scale- invariant neural network. Instead of standard 2d or 3d convolutions for combining spatial information, the Riesz network is based on the Riesz transform which is a scale-equivariant operation. As a consequence, this network naturally generalizes to unseen or even arbitrary scales in a single forward pass. As an application example, we consider detecting and segmenting cracks in tomographic images of concrete. In this context, ‘scale’ refers to the crack thickness which may vary strongly even within the same sample. To prove its scale invariance, the Riesz network is trained on one fixed crack width. We then validate its performance in segmenting simulated and real tomographic images featuring a wide range of crack widths. An additional experiment is carried out on the MNIST Large Scale data set.
Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project

Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.
Chronoclasm: Real-Time Data Animation

Chronoclasms are crises of time that occur when practices and concepts of time come into contradiction. The clash between the sovereign authority of the clock that is the material heartbeat of every computer (and every network) and the multidimensional flow of subatomic interactions enables another time to emerge, the time we call history. Temporal concepts and practices emerge into this third, historical form of time in forms that are themselves historically mutable. Close attention to techniques of animation, specifically solar, surveillance, and financial visualizations and their MPEG codec and generative adversarial network infrastructures, can give privileged entry to analysis of chronoclasm as crisis and as the underpinning structure of time under capital.
A comprehensive framework for quantitative risk assessment of organizational networks using FAIR-modified attack trees

Attack trees are a widely used method for threat modeling and analyzing cyber-attacks in organizational networks. Assessing the risk associated with each individual node of an attack tree is crucial for understanding the overall risk of the attack. This article presents a comparative study of different threat modeling methods and risk assessment approaches in organizational networks. The article also presents a novel comprehensive approach for quantifying risk assessment of organizational networks based on attack trees modified according to the factor analysis of information risk (FAIR) approach. Our results demonstrate the effectiveness of the novel approach in capturing the unique characteristics of different assets and their dependencies in an attack tree, leading to quantitative risk assessment.
INTEGRATION OF DIGITAL MEANS IN THE FINANCIAL SPHERE: THE POTENTIAL OF CLOUD COMPUTING, BLOCKCHAIN, BIG DATA AND AI

The integration of digital means in the financial sphere nowadays is not just a necessity, but also a strategic step that determines the further development of this industry. The modern world of finance bets on high technologies, namely on such key areas as Cloud Computing, Blockchain, Big Data and Artificial Intelligence (AI). The purpose of our article is to conduct an in-depth analysis of the potential of Cloud Computing, Blockchain, Big Data and Artificial Intelligence (AI) technologies in the context of their impact on the financial sphere. The object of our article is the financial sphere, including banking, the investment sector, insurance, and other component industries that interact with finance. As a result of the conducted research: the advantages and disadvantages of the integration of digital means in the financial sphere are considered; the potential of Cloud Computing, Blockchain, Big Data and AI in the financial sphere was investigated; it is emphasized that all these technologies are already changing the financial sphere and have great potential for further development. Thanks to them, financial institutions can be more competitive, respond faster to changes in the market, and provide more innovative services for customers. Therefore, the integration of these digital means into the financial sphere is a necessity and a key direction for the further development and modernization of the industry. Cloud Computing, Blockchain, Big Data, and Artificial Intelligence (AI) technologies enable financial institutions to increase efficiency, reduce costs, and improve customer service.The challenges of implementing new technologies in the financial sector include the absence of a unified regulatory framework, high costs associated with transitioning to digital platforms, and resistance from traditional players. These challenges can be addressed by creating clear regulations, providing financial support to companies transitioning to digital technologies, and fostering partnerships between innovative startups and large financial institutions for collaborative development and implementation of technological solutions.
Model Explanation via Support Graphs

Abstract In this note, we introduce the notion of support graph to define explanations for any model of a logic program. An explanation is an acyclic support graph that, for each true atom in the model, induces a proof in terms of program rules represented by labels. A classical model may have zero, one or several explanations: when it has at least one, it is called a justified model. We prove that all stable models are justified, whereas, for disjunctive programs, some justified models may not be stable. We also provide a meta-programming encoding in Answer Set Programming that generates the explanations for a given stable model of some program. We prove that the encoding is sound and complete, that is, there is a one-to-one correspondence between each answer set of the encoding and each explanation for the original stable model.
Dynamic phasor measurement algorithm based on high-precision time synchronization

Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach—the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model’s characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals.
On time-memory trade-offs for password hashing schemes

A password hashing algorithm is a cryptographic method that transforms passwords into a secure and irreversible format. It is used not only for authentication purposes but also for key derivation mechanisms. The primary purpose of password hashing is to enhance the security of user credentials by preventing the exposure of plaintext passwords in the event of a data breach. As a key derivation function, password hashing aims to derive secret keys from a master key, password, or passphrase using a pseudorandom function. This review focuses on the design and analysis of time-memory trade-off (TMTO) attacks on recent password hashing algorithms. This review presents a comprehensive survey of TMTO attacks and recent studies on password hashing for authentication by examining the literature. The study provides valuable insights and strategies for safely navigating transitions, emphasizing the importance of a systematic approach and thorough testing to mitigate risk. The purpose of this paper is to provide guidance to developers and administrators on how to update cryptographic practices in response to evolving security standards and threats.
Artificial intelligence: reflecting on the past and looking towards the next paradigm shift

Artificial intelligence (AI) has undergone major advances over the past decades, propelled by key innovations in machine learning and the availability of big data and computing power. This paper surveys the historical progress of AI from its origins in logic-based systems like the Logic Theorist to recent deep learning breakthroughs like Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer 3 (GPT-3) and Large Language Model Meta AI (LLaMA). The early rule-based systems using handcrafted expertise gave way to statistical learning techniques and neural networks trained on large datasets. Milestones like AlexNet and AlphaGo established deep learning as a dominant AI approach. Transfer learning enabled models pre-trained on diverse corpora to excel at specialised downstream tasks. The scope of AI expanded from niche applications like playing chess to multifaceted capabilities in computer vision, natural language processing and dialogue agents. However, current AI still needs to catch up to human intelligence in aspects like reasoning, creativity, and empathy. Addressing limitations around real-world knowledge, biases, and transparency remains vital for further progress and aligning AI with human values. This survey provides a comprehensive overview of the evolution of AI and documents innovations that shaped its advancement over the past six decades.
Commonsense knowledge in cognitive robotics: a systematic literature review

One of the big challenges in robotics is the generalization necessary for performing unknown tasks in unknown environments on unknown objects. For us humans, this challenge is simplified by the commonsense knowledge we can access. For cognitive robotics, representing and acquiring commonsense knowledge is a relevant problem, so we perform a systematic literature review to investigate the current state of commonsense knowledge exploitation in cognitive robotics. For this review, we combine a keyword search on six search engines with a snowballing search on six related reviews, resulting in 2,048 distinct publications. After applying pre-defined inclusion and exclusion criteria, we analyse the remaining 52 publications. Our focus lies on the use cases and domains for which commonsense knowledge is employed, the commonsense aspects that are considered, the datasets/resources used as sources for commonsense knowledge and the methods for evaluating these approaches. Additionally, we discovered a divide in terminology between research from the knowledge representation and reasoning and the cognitive robotics community. This divide is investigated by looking at the extensive review performed by Zech et al. (The International Journal of Robotics Research, 2019, 38, 518–562), with whom we have no overlapping publications despite the similar goals.
Wind turbine fault detection and isolation robust against data imbalance using KNN

Abstract Due to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attention. In this paper, using the wind turbine SCADA data, an effective FDI scheme is proposed using the K ‐Nearest Neighbors (KNN) classifier. The operational data set is labeled by the status and warning data sets, and the labeled operational data set, after eliminating invalid data, feature selection, and standardization, is used for training and validation of the FDI model. Data imbalance, which is common in real data sets, does not affect the performance of the proposed method, hence there is no need for data balancing methods in this algorithm and the performance is not deteriorated by occurring false alarms. Therefore, the proposed method has provided impressive performance in FDI compared with previous research on this data set. Also, many of the fault classes addressed in this paper were not considered in previous works on this data set.