Reviewerly/exHarmony huggingface dataset:
Euler-Maruyama-Based Data-Driven State Restoration and Parameter Adaptation in Stochastic Neural Fields with Finite Signal Transmission Rate

In this paper, we propose a data-driven parameters’ adaptation technique for Stochastic Dynamic Neural Field (SDNF) models with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">finite</i> signal transmission rate modeled by a distance-dependent delay. Our approach integrates nonlinear Bayesian filtering methods into mathematical neuroscience by establishing the state-space representation for the SDNF models with the delays. This allows to formulate the filtering problem in order to reconstruct the average membrane potential from incomplete data available from measurement devices. Additionally, when the state-space model is set up, the unknown system parameters can be estimated in a systematic way, for example, by using the method of maximum likelihood. In this paper, we derive for the first time the SDNF-oriented adaptive Extended Kalman filter (EKF) with the space-dependent delays in order to calibrate the SDNF models and to reconstruct the average membrane potential from incomplete data collected. The main benefit of the novel methodology is that both problems – the state and parameter estimation – are solved in parallel. The numerical experiments are provided to illustrate a performance of the novel methodology.
Boundary-Semantic Collaborative Guidance Network With Dual-Stream Feedback Mechanism for Salient Object Detection in Optical Remote Sensing Imagery

With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention.However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps.To address these issues, we propose a novel method named boundarysemantic collaborative guidance network (BSCGNet) with dualstream feedback mechanism.First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth.Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication.Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism.Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years.Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.
A novel complex-valued convolutional network for real-world single image dehazing

Previous deep learning-based single image dehazing methods are implemented by using real-valued convolutional neural network (RV-CNN), which ignore the phase information of the image, and tend to cause models to perform well on synthetic datasets but low generalization on real-world datasets. In this paper, we propose a novel Complex-Valued convolutional Dehazing Network (CVD-Net), which considers both amplitude and phase information of image for haze removal in real-world scenes. Specifically, we construct complex-valued transformation module, including complex-valued convolutional layer and residual block embedded into the middle of the network to extract features of image efficiently, and also design a complex-valued selected fusion module (CVSF) and complex-valued attention module (CVAM) to promote the interaction between different scale features for preserving image detailed information. Both qualitative and quantitative experimental results show that the proposed CVD-Net can effectively remove the haze, and has good generalization in real-world hazy images.
FalconNet: Factorization for the Light-Weight ConvNets

Designing light-weight CNN models with little parameters and Flops is a prominent research concern. However, three significant issues persist in the current light-weight CNNs: i) the lack of architectural consistency leads to redundancy and hindered capacity comparison, as well as the ambiguity in causation between architectural choices and performance enhancement; ii) the utilization of a single-branch depth-wise convolution compromises the model representational capacity; iii) the depth-wise convolutions account for large proportions of parameters and Flops, while lacking efficient method to make them light-weight. To address these issues, we factorize the four vital components of light-weight CNNs from coarse to fine and redesign them: i) we design a light-weight overall architecture termed LightNet, which obtains better performance by simply implementing the basic blocks of other light-weight CNNs; ii) we abstract a Meta Light Block, which consists of spatial operator and channel operator and uniformly describes current basic blocks; iii) we raise RepSO which constructs multiple spatial operator branches to enhance the representational ability; iv) we raise the concept of receptive range, guided by which we raise RefCO to sparsely factorize the channel operator. Based on above four vital components, we raise a novel light-weight CNN model termed as FalconNet. Experimental results validate that FalconNet can achieve higher accuracy with lower number of parameters and Flops compared to existing light-weight CNNs.
Leveraging a novel NFT-enabled blockchain architecture for the authentication of IoT assets in smart cities

The concept of smart city architecture requires a comprehensive solution that can combine real-time response applications for cyber-physical systems. However, the architecture faces challenges that can obstruct the operations in terms of systems, processes, and data flow as far as the breach risk is concerned. Though the field has been researched with the existence of centralized and distributed architectures to support smart cities. Research gaps regarding security concerns, platform assistance, and resource management continue to persist. This research article presents a novel blockchain-based architecture that proposes expansion in the non-fungible tokens (NFTs) to cater to the expansion of IoT-enabled smart assets. It enables NFTs to employ fog computing for all users and smart devices connected to a fog node in a cyber-physical system. The proposed expansion suggested in Non-Fungible Tokens (NFTs) for IoT assets representation in a cyber-physical system, provides devices and user identification and authentication functionality. The proposed NFT architecture has been designed to provide a smart city solution for cyber-physical systems that ensures robust security features (such as CIA) by introducing new attributes and functions for Owner, User, Fog, and IoT device/s authentication. The validation and rigor of the security services, efficiency, and latency have been achieved by deployments on private and public ledgers. The efficiency, and cost-effectiveness of the suggested functions and components have been evaluated in terms of evaluation cost and time complexity which resulted in promising results, obtained and validated on a testnet. The evaluation cost for the devised mint component was approximately 81%, and devised approve() was approximately 23% more efficient than other solutions.
Furthering the Development of Virtual Agents and Communication Robot Devices through the Consideration of the Temporal Home

This paper extends current research on Human–Robot Interaction (HRI) within Human–Computer Interaction (HCI), focusing on how future virtual agents and communication robots can support the temporal structures and routines within the home. We recruited representatives from 15 households with varied compositions, ranging from single inhabitants to full nest families. Drawing upon P.G. Wodehouse’s The Inimitable Jeeves as an inspiration, the methodology sought to imitate the relationship between a recruitment agency worker (researcher) and an employer (participant) seeking to hire a personal assistant (e.g., a virtual agent or communication robot device) for their home. A ‘household audit’ comprising a guided household tour and an architectural survey was conducted to ascertain the nuanced spatiotemporal routines within the home. The study analysed the responses of participants using the Labovian narrative schema, a traditional method in linguistics research. The findings were then examined through the lens of Reddy et al.’s temporal features of work to understand how domestic work unfolds within the home from a temporal perspective. We argue that the temporal concepts discussed by Reddy et al. provided valuable insights into the temporal dynamics of everyday activities and could inform the design of virtual agents and communication robotic devices to fulfil their roles as domesticated ‘personal assistants’.
Harmonising knowledge for safer materials via the “NanoCommons” Knowledge Base

In mediaeval Europe, the term “commons” described the way that communities managed land that was held “in common” and provided a clear set of rules for how this “common land” was used and developed by, and for, the community. Similarly, as we move towards an increasingly knowledge-based society where data is the new oil, new approaches to sharing and jointly owning publicly funded research data are needed to maximise its added value. Such common management approaches will extend the data’s useful life and facilitate its reuse for a range of additional purposes, from modelling, to meta-analysis to regulatory risk assessment as examples relevant to nanosafety data. This “commons” approach to nanosafety data and nanoinformatics infrastructure provision, co-development, and maintenance is at the heart of the “ NanoCommons ” project and underpins its post-funding transition to providing a basis on which other initiatives and projects can build. The present paper summarises part of the NanoCommons infrastructure called the NanoCommons Knowledge Base. It provides interoperability for nanosafety data sources and tools, on both semantic and technical levels. The NanoCommons Knowledge Base connects knowledge and provides both programmatic (via an Application Programming Interface) and a user-friendly graphical interface to enable (and democratise) access to state of the art tools for nanomaterials safety prediction, NMs design for safety and sustainability, and NMs risk assessment, as well. In addition, the standards and interfaces for interoperability, e.g., file templates to contribute data to the NanoCommons , are described, and a snapshot of the range and breadth of nanoinformatics tools and models that have already been integrated are presented Finally, we demonstrate how the NanoCommons Knowledge Base can support users in the FAIRification of their experimental workflows and how the NanoCommons Knowledge Base itself has progressed towards richer compliance with the FAIR principles.
Simulating lossy Gaussian boson sampling with matrix-product operators

Gaussian boson sampling, a computational model that is widely believed to admit quantum supremacy, has already been experimentally demonstrated and is claimed to surpass the classical simulation capabilities of even the most powerful supercomputers today. However, whether the current approach limited by photon loss and noise in such experiments prescribes a scalable path to quantum advantage is an open question. To understand the effect of photon loss on the scalability of Gaussian boson sampling, we analytically derive the asymptotic operator entanglement entropy scaling, which relates to the simulation complexity. As a result, we observe that efficient tensor network simulations are likely possible under the ${N}_{\text{out}}\ensuremath{\propto}\sqrt{N}$ scaling of the number of surviving photons ${N}_{\text{out}}$ in the number of input photons $N$. We numerically verify this result using a tensor network algorithm with $\mathrm{U}(1)$ symmetry, and we overcome previous challenges due to the large local Hilbert-space dimensions in Gaussian boson sampling with hardware acceleration. Additionally, we observe that increasing the photon number through larger squeezing does not increase the entanglement entropy significantly. Finally, we numerically find the bond dimension necessary for fixed accuracy simulations, providing more direct evidence for the complexity of tensor networks.
FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.
A Multireservoir Echo State Network Combined with Olfactory Feelings Structure

As a special form of recurrent neural network (RNN), echo state networks (ESNs) have achieved good results in nonlinear system modeling, fuzzy nonlinear control, time series prediction, and so on. However, the traditional single-reservoir ESN topology limits the prediction ability of the network. In this paper, we design a multireservoir olfactory feelings echo state network (OFESN) inspired by the structure of the Drosophila olfactory bulb, which provides a new connection mode. The connection between subreservoirs is transformed into the connection between each autonomous neuron, the neurons in each subreservoir are sparsely connected, and the neurons in different subreservoirs cannot communicate with each other. The OFESN greatly simplifies the coupling connections between neurons in different libraries, reduces information redundancy, and improves the running speed of the network. The findings from the simulation demonstrate that the OFESN model, as introduced in this study, enhances the capacity to approximate sine superposition function and the Mackey–Glass system when combined. Additionally, this model exhibits improved prediction accuracy by 98% in some cases and reduced fluctuations in prediction errors.
Fighting Hate and Hate Speech: Raising Anti-Hate Awareness through Critical Analysis of Popular Cultural Texts on an Undergraduate Course

Central to the understanding of hate is an apprehension of the complexities of various hate-motivated social attitudes, which include Othering and the production of social, economic, and political hierarchies of domination. While hate speech is endemic both online and offline in contemporary society, Korean youths have difficulties recognizing its structural forces. The present study aims to offer an instructional model of a college-level course for identifying and countering hate in everyday life. As participants in this course, students read popular cultural texts thematizing hate, wrote critical reviews, and held group discussions to develop anti-hate critical thinking and raise awareness about online hate speech and hate-motivated social behavior. They showed significant progress in the surveys, which measured anti-hate critical thinking, as well as during the course, as they proceeded from observing and identifying hate speech to formulating and articulating proactive strategies to challenge it. This study provided an opportunity for college students to develop good citizenship in reading hate speech and representations of hate in popular cultural texts with a critical eye, and to reflect on the problem of hate in society.
The Perrey Archive: a story about collecting earthquakes and eruptions

The Perrey Archive, a unique historical collection of texts and manuscripts on earthquakes and volcanic eruptions of the past, is preserved in the Fondo Sismico of the library of the Società Napoletana di Storia Patria. This collection is the result of the tireless research of French scientist Alexis Perrey (1807–1882), who is regarded as one of the founders of seismology. The article illustrates what it means to "collect" earthquakes and eruptions, as well as other extreme events and how Perrey built his collection over the course of roughly 40 years, and it describes the role that the catalogs created by Perrey played for historical seismology. Finally, in addition to the value and significance of Perrey's intellectual enterprise for the history of science, the article discusses the different aspects of archives and archiving for sciences. Photographic composition of some volumes from the Seismic Collection of the Società Napoletana di Storia Patria, photo by Claudio Novelli.
How cryptomarket communities navigate marketplace structures, risk perceptions and ideologies amid evolving cryptocurrency practices

Cryptomarkets are increasingly requiring users to purchase products with Monero (a ‘privacy coin’) to further obfuscate the digital trail of money compared to conventional cryptocurrencies (e.g. Bitcoin). This is the first study to explore how cryptomarket communities are used to facilitate norms and behaviours to expedite these emerging cryptocurrency practices. Through a qualitative analysis of Monero threads in a Reddit cryptomarket community (3451 total posts), this research illustrates how online communities often underpin the adoption of new technologies in cryptomarkets. The findings reveal: how the online community functions, adapts, and fails to support cryptocurrency transitions; the appraisal and contestation of cryptocurrency risks; and the ideological drivers and symbolic resources used to align community practices to adopt Monero. This research contributes to an understanding of the processes that underpin the constant evolution of online illicit markets as human and non-human elements are constantly re-assembled.
The Measurement Problem in Statistical Signal Processing

Discussing quantum theory foundations, von Neumann noted that the measurement process should not be regarded in terms of a temporal evolution. A reason for their claim is the insurmountability of the gap between reversible and irreversible processes. The time operator formalism that goes beyond such a gap is an adequate framework to elaborate the measurement problem. It considers signals to be stochastic processes, regardless of whether they correspond to variables or distribution densities. Signal processing that utilizes statistical properties to perform tasks is statistical signal processing. The hierarchy of the measurement process is indicated by crossing between states and devices, which implies an evolution in the temporal domain. The concept has been generalized to an open system by the use of duality in frame theory.
Non‐fungible token‐based health record marketplace

Abstract With an increasing affinity towards patient‐centric care, sharing real‐time sensitive data for collaboration between multiple parties with finer access control becomes critical. Most existing studies based on the blockchain technology in the medical field discuss various application scenarios and security aspects, without focusing on data ownership, secure data sharing, or finer access control. In this work, a non‐fungible token (NFT)‐based system is proposed to implement a health record marketplace. The system leverages the NFT technology to provide dual ownership along with finer access control and efficiency in data sharing. The advantage of permissioned blockchain along with InterPlanetary File System (IPFS) are taken for off‐chain data storage to improve security and efficiency. Because price determination is critical in the market, Stackelberg game theory is utilized to determine pricing strategies for both data owners and consumers. Also, to efficiently achieve finer access control, a popularity‐based adaptive NFT management scheme using reinforcement learning is proposed. Simulation experiments are carried out to demonstrate accuracy and efficiency of our proposed schemes.
Resolving power imbalances in construction payment using blockchain smart contracts

Purpose This research aims to develop a blockchain smart contract–enabled framework to resolve power imbalance problems in construction payment. Design/methodology/approach This research adopts a design science research method to develop the blockchain smart contract–enabled framework. The authors then develop a prototype system. Finally, the authors evaluate its performance in solving power imbalance-induced payment problems. Findings The results show that the prototype system can resolve power imbalance problems in construction payment by allowing project participants to make transparent and decentralized decisions that are self-enforceable by blockchain smart contracts. Research limitations/implications This study provides theoretical explanations for how blockchain smart contracts can resolve power imbalances in construction payment; based on that, it proposes a novel blockchain smart contract–enabled method to rebalance the power of stakeholders in construction payment. Thus, it contributes to the body of knowledge on blockchain technology and construction payment. Practical implications This study moves beyond a conceptual framework and develops a practical blockchain smart contract system for resolving power imbalances in construction payment, strengthening construction project members' confidence in using blockchain technology. Social implications The proposed blockchain smart contract–enabled solution helps mitigate negative social impacts associated with late payment and non-payment. Furthermore, the research maximizes trust among participants in payment processes to inspire collaborative culture in the construction industry. Originality/value This paper introduces a novel blockchain smart contract integrated method, allowing project stakeholders to resolve power imbalance problems in construction payment through decentralized decision-making.