Reviewerly/exHarmony huggingface dataset:
Efficient 2D Tikhonov smoothness regularization with recursive filtering

In this work, we describe an implementation of the 2D Tikhonov regularization filter which scales linearly with the input signal’s size. In the homogeneous case, we propose a novel algorithm to decompose the filter’s 2D kernel as a sum of axis-aligned Gaussians. Our algorithm uses symmetries of the kernel to provide a fast computation of the Gaussian decomposition in the frequency domain, where the 2D Tikhonov kernel has a closed-form expression. The convolution with each Gaussian is then computed using linear-time separable recursive filtering. In the non-homogeneous case, we also decompose the 2D problem as a series of iterated linear-time separable recursive filters, which can be combined with the Biconjugate Gradient Stabilized method for fast convergence. In this way, a fast solution to the 2D Tikhonov regularization problem is obtained.
Exploring aesthetic procedural noise for crafting model-agnostic universal adversarial perturbations

The research on universal adversarial perturbations (UAPs) is significant to trustworthy deep learning. To disentangle the UAPs with the training data dependency and the target model dependency, the exploration of procedural noise functions is a feasible method. However, the current procedural adversarial noise attack method has several characteristics like visually significant anisotropy and gradient artifacts that may impact the stealthiness of adversarial examples. This study proposes a novel model-free and data-free UAP method based on the procedural noise functions with two variants: Simplex noise attack and Worley noise attack. The attack method can achieve deceit on the neural networks with a more aesthetic rendering effect. A detailed empirical study is provided to validate the effectiveness of the proposed attack method. The extensive experiments show that the UAPs generated by the proposed method achieve considerable attack performance on the ImageNet dataset and the CIFAR-10 dataset. Moreover, this study implements the performance evaluation and robustness analysis of existing defense methods against the proposed UAPs. It has the potential to enhance research on the robustness of neural networks in real applications. The code is available at https://github.com/momo1986/adversarial_example_simplex_worley.
Hierarchical active learning for defect localization in 3D systems

AbstractAbstractAim: Advanced sensing and imaging is capable to retrieve rich information of complex systems, which can be integrated with underlying physics to develop a personalized simulation framework to discern system internal property for defect localization. Simulation-based defect localization involves modeling the heterogeneous 3D physical systems and calibrating spatial-varying model parameters. In this paper, we aim to develop an effective simulation-based active learning framework for defect localization in complex 3D systems.Methods: We develop a Hierarchical Gaussian Process (GP)-based Active Learning (HGPAL) framework to estimate the spatial-varying model parameters for reliable defect localization in complex 3D systems. We first develop a GP regression model on the 2D embedding of a 3D geometry to capture the geometric information. Second, we propose a Hierarchical GP-based active learning approach composed of master GP and sub-GP modeling to account for the system heterogeneity and further estimate the spatial-varying model parameter for defect localization.Results: We evaluate the performance of the proposed HGPAL framework in a 3D body-heart system to identify and localize infarct in the heart. Numerical experiments demonstrate the effectiveness of our HGPAL framework for 3D defect localization.Conclusion: This study successfully developed and tested an HGPAL framework for defect localization in complex 3D systems. The results suggest that this approach can efficiently and accurately localize defects in 3D systems, demonstrating its potential for practical applications in precision engineering system design and healthcare treatment planning.Keywords: Gaussian processhierarchical optimizationsimulation model calibrationcardiac electrophysiology modeling Disclosure statementNo potential conflict of interest was reported by the author(s).Consent and approvalThis does not apply to this work as no human subjects were involved.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
The Facets of Intangible Heritage in Southern Chinese Martial Arts: Applying a Knowledge-driven Cultural Contact Detection Approach

Investigating the intangible nature of a cultural domain can take multiple forms, addressing, for example, the aesthetic, epistemic, and social dimensions of its phenomenology. The context of Southern Chinese martial arts is of particular significance, as it carries immaterial components of all these aspects: The technical and stylistic framework of a martial art system; the imagery associated to movements; and the transmission of knowledge orally, practically, or through influence, are but examples of intangible characteristics that can and should be captured, not unlike cultural artifacts. The latter case—the one of formalizing cultural influence through its various forms of evidenceis emblematic as well as largely untrodden ground. A previous attempt at detecting cultural influence computationally was made in the context of Roman archaeology, though the binding of that early effort with the domain model was tight; also, there has hardly been any prior dedicated effort to model the martial arts domain through ontologies. In this article, we present the realization of the full cycle of a computational approach to investigating cultural contact in Southern Chinese martial arts. The entire approach is predicated upon the usage of standards and techniques of the Semantic Web and formal knowledge. Starting from a modular domain ontology, which models martial arts independently of the goal of capturing cultural influence, we perform knowledge extraction from archival material from the Hong Kong Martial Arts Living Archive and generate a dataset of the results modeled after said ontology. Then, we combine the resulting knowledge base with a rule model that represents ways to infer knowledge of potential contact between cultures based on the evidence present in the knowledge base. The results offer an insight into how an inference-based computational model can be applied to detect interesting facts even in the as-yet underexplored domain of intangible cultural heritage. The implemented workflow shows that the full-cycle employment of semantic technologies can offer the ground truth required for largely different approaches, such as statistical and machine learning ones, to operate.
Smart embedded health monitoring system and secure electronic health record (EHR) transactions using blockchain technology

The proposed work integrates a smart embedded health monitoring system with IoT. The data that are sensed and analyzed from the system are secured using blockchain technology and are transmitted and further analyzed. Blockchains are used in various sectors. Mainly they are welcomed for their secure transactions and the privacy that we can ensure through it. In this paper, our proposed system explored its uses in the healthcare sector. The health sector is where information is of privacy concern for patients and needed information for doctors treating them. Blockchain is the best-suited solution for Identity and Access Management (IAM) needed in the healthcare sector. As blockchains have different types named public and private blockchains, we recommend private blockchains be implemented where no anyone can make transactions and mine the transactions. Patients, doctors, and hospital organizations care for their privacy, and authorized access to others’ data, is the reason for the choice of the private blockchain. Adding to the IAM, blockchain also accounts for flexibility, decentralization, and an immutable ledger. Those operations are authenticated and authorized by a blockchain admin who has no relationship with the entities that exist in the network. The owner of the data is given the sole control of permitting access to their data. Our system asks patients for permission to be given to any doctor who is requesting access to those data. Also, patients can permit a doctor they want within the network. Hyperledger uses no online currencies and hence no miners to wait for. This is important as it reduces the waiting time for a transaction to be verified. REST API is used for collecting information requested from the blockchain and presenting it to the end-users. The proposed system is very unique to the date in which a dedicated smart embedded system is developed to monitor the vital parameters of the patient; once the abnormality is sensed, it will be spontaneously alerted and the connected recording system is activated after the sensors read the deviations very accurately and are unique than conventional methods. Our system provides a secure, reliable, and IAM-included network in a webpage that can be accessed across all devices.
A Workpiece-Dense Scene Object Detection Method Based on Improved YOLOv5

Aiming at the problem of detection difficulties caused by the characteristics of high similarity and disorderly arrangement of workpieces in dense scenes of industrial production lines, this paper proposes a workpiece detection method based on improved YOLOv5, which embeds a coordinate attention mechanism in the feature extraction network to enhance the network’s focus on important features and enhance the model’s ability to pinpoint targets. The pooling structure of the space pyramid has been replaced, which reduces the amount of calculation and further improves the running speed. A weighted bidirectional feature pyramid is introduced in the feature fusion network to realize efficient bidirectional cross-scale connection and weighted feature fusion, and improve the detection ability of small targets and dense targets. The SIoU loss function is used to improve the training speed and further improve the detection performance of the model. The average accuracy of the improved model on the self-built artifact dataset is improved by 5% compared with the original model and the number of model parameters is 14.6MB, which is only 0.5MB higher than the original model. It is proved that the improved model has the characteristics of high detection accuracy, strong robustness and light weight.
Email Security Issues, Tools, and Techniques Used in Investigation

The email system is a globally distributed communication infrastructure service that involves multiple actors playing different roles to ensure end-to-end mail delivery. It is an indispensable method of communicating that is changing how people share data and information. As a result, it facilitates effective and efficient communication, especially in business, as well as convenience, accessibility, and replication. Today, email can send more than just text files; it can also send audio, video, photos, and other files with extensions. With email becoming ubiquitous in all aspects of our lives, enhancing its security, operating procedures, and forensic investigation has become essential. The purpose of this paper is to review some real email forensic incidents and the tools and techniques that have been proposed. A discussion of the major threats to email as well as techniques to mitigate them will also be provided. A comparison study was made of several techniques and analysis tools used in email forensics. In addition, this paper compares the available software tools for email forensics based on factors such as language interface, user interface, programming language, creation of image files, calculation of hash value, cost, and advantages.
Data curation as anticipatory generification in data infrastructure

Data curation is crucial for data reusability. New possibilities for digital data sharing are an urgent concern for data curators, who must keep historical datasets and present data collections always ready to meet unknown future data needs. This calls for a more nuanced understanding of the temporal horizons of data curation in Information Systems research. Based on a qualitative interpretive case study of data management in an environmental monitoring infrastructure, we characterise three data curation practices to support data reuse. These practices follow three interleaving temporal perspectives: retrospective (by upgrading historical datasets), present-oriented (by monitoring ongoing data collections), and future-looking (by disseminating data). We conceptualise this work as anticipatory generification, involving continuous and temporally oriented data curation to maintain data sufficiently open-ended to anticipate future data reusability. Anticipatory generification is essential for the sustainable evolution of environmental data infrastructures. Our study contributes to the Information Systems literature by further theorising the temporal perspectives of data infrastructures and providing additional insight into how the future is anticipated in practice.
Factors Affecting Acceptance of Social Robots Among Prospective Users

Abstract Rapid technological and scientific developments in the robotics field have led to the expansion of the use of service robots in domestic environments. The purpose of the study reported in this paper is to identify the factors that determine people’s psychology, attitude, and intention to use a social robotic system. A new conceptual framework is developed and empirically tested, using data from 347 people, performing Structural Equation Modeling analysis. The proposed framework incorporates various factors related to hedonic attitudes, utilitarian attitudes, normative beliefs, control beliefs, and personality traits. The results reveal predominantly the positive impact of normative beliefs and utilitarian and hedonic attitudes but, also, the negative impact of control beliefs on people’s intention to use social robot systems. Consequently, it is concluded that future clients are not only influenced by society’s general attitudes, perceptions, and prejudices towards technology but, also, by the functional benefits of social robots.
Cognitive effort in human translation and machine translation post-editing processes

Abstract Due to the inaccuracies of Machine Translation (MT), Post-Editing (PE) is inevitable. This poses questions over whether human effort to polish an MT is worthwhile or whether it would be more efficient to translate manually. However, to date, fewer attempts have been made to compare the cognitive effort in the PE process and the sub-phases (orientation, drafting, and revision) of PE with that of Human Translation (HT). To fill this gap, the current study aims to investigate and compare cognitive effort in HT and PE processes in translation from Chinese to English. Data were collected via eye-tracking and keyboard-logging approaches from 25 participants recruited to fulfil three HT and three PE tasks respectively. The comparison of cognitive effort was made from the processes of HT and PE, and their different sub-phases. The study reveals a significant difference in cognitive effort, orientation duration, and drafting duration between HT and PE.
A self-learning Monte Carlo tree search algorithm for robot path planning

This paper proposes a self-learning Monte Carlo tree search algorithm (SL-MCTS), which has the ability to continuously improve its problem-solving ability in single-player scenarios. SL-MCTS combines the MCTS algorithm with a two-branch neural network (PV-Network). The MCTS architecture can balance the search for exploration and exploitation. PV-Network replaces the rollout process of MCTS and predicts the promising search direction and the value of nodes, which increases the MCTS convergence speed and search efficiency. The paper proposes an effective method to assess the trajectory of the current model during the self-learning process by comparing the performance of the current model with that of its best-performing historical model. Additionally, this method can encourage SL-MCTS to generate optimal solutions during the self-learning process. We evaluate the performance of SL-MCTS on the robot path planning scenario. The experimental results show that the performance of SL-MCTS is far superior to the traditional MCTS and single-player MCTS algorithms in terms of path quality and time consumption, especially its time consumption is half less than that of the traditional MCTS algorithms. SL-MCTS also performs comparably to other iterative-based search algorithms designed specifically for path planning tasks.
Image Forgery Detection Using Integrated Convolution-LSTM (2D) and Convolution (2D)

Digital forensics and computer vision must explore image forgery detection and their related technologies. Image fraud detection is expanding as sophisticated image editing software becomes more accessible. This makes changing photos easier than with the older methods. Convolution LSTM (1D) and Convolution LSTM (2D) + Convolution (2D) are popular deep learning models. We tested them using the public CASIA.2.0 image forgery database. ConvLSTM (2D) and its combination outperformed ConvLSTM (1D) in accuracy, precision, recall, and F1-score. We also provided a related work on image forgery detection models and methods. We also reviewed publicly available datasets used in picture forgery detection research, highlighting their merits and drawbacks. Our investigation revealed the state of picture fraud detection and the deep learning models that worked well. Our work greatly impacts fraudulent photo detection. First, it highlights how important deep learning models are for picture forgery detection. Second, ConvLSTM (2D) + Conv (2D) detect image forgeries better than ConvLSTM (1D). Finally, our dataset analysis and proposed integrated approach help research construct more effective and accurate picture forgery detection systems.
Gaussian quantum illumination via monotone metrics

Quantum illumination is to discern the presence or absence of a low reflectivity target, where the error probability decays exponentially in the number of copies used. When the target reflectivity is small so that it is hard to distinguish target presence or absence, the exponential decay constant falls into a class of objects called monotone metrics. We evaluate monotone metrics restricted to Gaussian states in terms of first-order moments and covariance matrix. Under the assumption of a low reflectivity target, we explicitly derive analytic formulae for decay constant of an arbitrary Gaussian input state. Especially, in the limit of large background noise and low reflectivity, there is no need of symplectic diagonalization which usually complicates the computation of decay constants. First, we show that two-mode squeezed vacuum (TMSV) states are the optimal probe among pure Gaussian states with fixed signal mean photon number. Second, as an alternative to preparing TMSV states with high mean photon number, we show that preparing a TMSV state with low mean photon number and displacing the signal mode is a more experimentally feasible setup without degrading the performance that much. Third, we show that it is of utmost importance to prepare an efficient idler memory to beat coherent states and provide analytic bounds on the idler memory transmittivity in terms of signal power, background noise, and idler memory noise. Finally, we identify the region of physically possible correlations between the signal and idler modes that can beat coherent states.
Encoder–decoder-based CNN model for detection of object removal by image inpainting

In multimedia forensics, several methods have been developed for the authentication of digital images. However, the detection and localization of removed objects from an image has always been a challenging problem. Image forgery, for the removal of objects, can be done in many ways. Among them, image inpainting performs object removal and fills the empty region with surrounding patches. The clues of inpainted region are visually imperceptible. Till date, limited work has been done for image inpainting detection. Hence, a convolutional neural network-based model for the detection of inpainted regions in an image is presented in this research. A hybrid encoder–decoder-based architecture is proposed, where a segment of DenseNet-121 architecture is adopted as an encoder. The primary goal of this architecture is to use spatial maps to explore the distinguishing features between inpainted and uninpainted regions. Inpainted image dataset created by using the exemplar-based image inpainting method is used to train and validate the proposed model. The performance of the proposed model is evaluated using various performance metrics. Experimental results show that the proposed model outperformed existing methods for a variety of inpainted images.
Network effects and store-of-value features in the cryptocurrency market

It is important to determine the network effects and store-of-value feature of cryptocurrencies due to the argument that it could be considered as a new ‘asset class’. Current studies on cryptocurrencies' network effects mainly focused on using Metcalfe's Law to evaluate the relationship between cryptocurrency prices and the squared number of active wallets addresses. In terms of cryptocurrencies' store-of-value features, previous studies primarily compared daily volatility of limited number of popular cryptocurrencies to Gold. Extant studies are also based on out-of-date data. This research extends the literature by using up-to-date daily data of a sample of the top 100 cryptocurrencies covering 2010–2023 to explore the network effects and the store of value characteristics of a wide range of cryptocurrencies. Firstly, we used nonlinear regression models to examine the relationship between cryptocurrency prices and active wallets addresses, the number of transactions and circulations. Secondly, to deepen our understanding of the store-of-value features of cryptocurrencies, we used a combination of GARCH models and time series analysis to explore the volatility in the daily returns of the sampled cryptocurrencies. Findings indicate that at least one of the network factors (i.e., active wallets addresses, the number of transactions, and number of circulation supply) have a significant effect on cryptocurrency prices. The study also finds that stable coins have comparable daily volatility as Gold, while only mature cryptocurrencies, such as PAXG, Bitcoin, Ethereum, BNB and LINK, demonstrate strong correlation with Gold. Bitcoin also showed a high positive time-series correlation with 24 of the 42 cryptocurrencies. Findings from this study provide important insights to investors, market analysts, regulators and other stakeholders on the marketisation and the store of value potentials of cryptocurrencies.