Reviewerly/exHarmony huggingface dataset:
Antrag: Annular NTRU Trapdoor Generation

In this paper, we introduce a novel trapdoor generation technique for Prest’s hybrid sampler over NTRU lattices. Prest’s sampler is used in particular in the recently proposed Mitaka signature scheme (Eurocrypt 2022), a variant of the Falcon signature scheme, one of the candidates selected by NIST for standardization. Mitaka was introduced to address Falcon’s main drawback, namely the fact that the lattice Gaussian sampler used in its signature generation is highly complex, difficult to implement correctly, to parallelize or protect against side-channels, and to instantiate over rings of dimension not a power of two to reach intermediate security levels. Prest’s sampler is considerably simpler and solves these various issues, but when applying the same trapdoor generation approach as Falcon, the resulting signatures have far lower security in equal dimension. The Mitaka paper showed how certain randomness-recycling techniques could be used to mitigate this security loss, but the resulting scheme is still substantially less secure than Falcon (by around 20 to 50 bits of CoreSVP security depending on the parameters), and has much slower key generation. Our new trapdoor generation techniques solves all of those issues satisfactorily: it gives rise to a much simpler and faster key generation algorithm than Mitaka’s (achieving similar speeds to Falcon), and is able to comfortably generate trapdoors reaching the same NIST security levels as Falcon as well. It can also be easily adapted to rings of intermediate dimensions, in order to support the same versatility as Mitaka in terms of parameter selection. All in all, this new technique combines all the advantages of both Falcon and Mitaka (and more) with none of the drawbacks.
Cryptographic Smooth Neighbors

We revisit the problem of finding two consecutive B-smooth integers by giving an optimised implementation of the Conrey-Holmstrom-McLaughlin “smooth neighbors” algorithm. While this algorithm is not guaranteed to return the complete set of B-smooth neighbors, in practice it returns a very close approximation to the complete set but does so in a tiny fraction of the time of its exhaustive counterparts. We exploit this algorithm to find record-sized solutions to the pure twin smooth problem, and subsequently to produce instances of cryptographic parameters whose corresponding isogeny degrees are significantly smoother than prior works. Our methods seem well-suited to finding parameters for the SQISign signature scheme, especially for instantiations looking to minimise the cost of signature generation. We give a number of examples, among which are the first parameter sets geared towards efficient SQISign instantiations at NIST’s security levels III and V.
Injection-Secure Structured and Searchable Symmetric Encryption

Recent work on dynamic structured and searchable symmetric encryption has focused on achieving the notion of forward-privacy. This is mainly motivated by the claim that forward privacy protects against adaptive file injection attacks (Zhang, Katz, Papamanthou, Usenix Security, 2016). In this work, we revisit the notion of forward-privacy in several respects. First, we observe that forward-privacy does not necessarily guarantee security against adaptive file injection attacks if a scheme reveals other leakage patterns like the query equality. We then propose a notion of security called correlation security which generalizes forward privacy. We then show how correlation security can be used to formally define security against different kinds of injection attacks. We then propose the first injection-secure multi-map encryption scheme and use it as a building block to design the first injection-secure searchable symmetric encryption (SSE) scheme. Towards achieving this, we also propose a new fully-dynamic volume-hiding multi-map encryption scheme which may be of independent interest.
Research on the Application of Layered Architecture in Computer Software Development

The objective of this study is to examine the application of layered technologies in computer software development. As software scale and complexity continue to increase, the adoption of appropriate software architecture becomes crucial. Layered architecture, a commonly employed software design pattern, enables effective organization and management of software systems, enhancing their maintainability, scalability, and reusability. This paper explores the fundamental principles and advantages of layered architecture and presents its specific applications in computer software development, such as client-server architecture, MVC architecture, and microservices architecture. Through analysis and comparison of relevant case studies, the suitability and effectiveness of layered technologies in different contexts are evaluated. Lastly, the study concludes with an overview of the application prospects of layered technologies and suggests future research directions.
Role of Artificial Intelligence in Business Dynamics

Artificial intelligence (AI) has appeared as an innovative technology that is restructuring the business operations in maximum business sectors. This chapter highlights the role of AI in business mainly attached with its functions like operations, marketing, finance, selling, R&amp;D, etc. Further, this piece of literature will also discuss its adaptability along with its current potential and future prospects. Artificial intelligence (AI) is an innovative area which is found to be very useful for commercial and domestic purposes. The blend of AI in industry and business can enhance the capacity of production and marketing. Making use of this AI is also providing a competitive edge to many companies. Apart from marketing, it also has the ability to modernize business with inventive ideas. It also acts as a modern tool for complex problem solving and decision making. Hence, the authors will discuss the growth of business using AI aids and its role in functional part of the business.
Quantum Key Distribution for Critical Infrastructures: Towards Cyber-Physical Security for Hydropower and Dams

Hydropower facilities are often remotely monitored or controlled from a centralized remote control room. Additionally, major component manufacturers monitor the performance of installed components, increasingly via public communication infrastructures. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility's control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes become vulnerable as more advances are made in computer technologies, such as quantum computing. In contrast, quantum key distribution (QKD) and other quantum cryptographic protocols are not based upon a computational problem, and offer an alternative to symmetric cryptography in some scenarios. Although the underlying mechanism of quantum cryptogrpahic protocols such as QKD ensure that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. In QKD, when the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.
The effects of virtual reality travel on satisfaction and visiting intention utilizing an extended stimulus-organism-response theory: perspectives from Thai tourists

Purpose This study investigates how virtual reality (VR) travel attributes (e.g. sense and quality of information) influence spectators' flow experience, how emotion and past experience affect enjoyment and examines the impact of flow experience and enjoyment on satisfaction and booking or visiting intention. Design/methodology/approach The VR tour stimuli were fabricated using scenic views from the National Aquarium in the USA. Participants were equipped with Matterport VR and audio headsets and started their virtual travel. Once the participants completed their VR tours, they were asked to complete the questionnaire. Using the stimulus-organism-response theory, 303 valid responses were analyzed using partial least square structural equation modeling (PLS-SEM). Findings The results showed that the sense and quality of information in VR travel positively and significantly impacted the flow experience. Moreover, emotions and past experiences positively and significantly influenced the enjoyment of VR travel. Similarly, flow experience and enjoyment positively and significantly affect satisfaction. However, satisfaction with VR-related tourism experiences negatively affects users' bookings and visiting intentions. Practical implications This study concludes that, from Thai tourists' perspectives, virtual travel should be used as a solution only during the pandemic because, in the long term it can cause a loss to the business chain in the tourism industry. Originality/value To the best of the authors' knowledge, no prior research has examined the influence of past experiences and emotions on satisfaction with VR travel.
Navigating the Future of the Internet of Things: Emerging Trends and Transformative Applications

This editorial navigates the transformative landscape of emerging technologies within the Internet of Things (IoT), aiming to unravel their interconnected impact and humanistic implications across diverse domains. As scholarly voyagers in the realm of technological innovation, this paper delineates the synergistic interplay between blockchain integration, edge computing, Artificial Intelligence (AI)/Machine Learning (ML), digital twins, and IoT-driven smart cities. Each section of this editorial unravels the significance and applications of these technologies: blockchain fortifies IoT security, edge computing enables real-time decision-making, AI/ML augments device intelligence, digital twins refine simulations, and IoT-driven smart cities encapsulate these advancements for societal betterment. Beyond technical expositions, this narrative aspires to humanize scholarly discourse, weaving ethical considerations, interdisciplinary collaborations, and societal implications into the fabric of technological advancement. The editorial concludes by synthesizing these insights, advocating for a more connected, sustainable, and ethically informed future. Through this scholarly expedition, the aim is to inspire dialogue, stimulate interdisciplinary collaborations, and chart a course toward a future where technology converges harmoniously with societal enhancement.
API Management

Customers today want access to enterprise data and services through a variety of digital devices and channels. To meet customer expectations, enterprises need to open their assets in an agile, flexible, secure, and scalable manner. APIs form the window into an enterprise’s data and services. They allow applications to easily communicate with each other using a lightweight protocol like HTTP. Developers use APIs to write applications that interact with the back-end system. Once an API has been created, it must be managed using an API management platform.
An energy-aware negotiation protocol for live migration of virtual machines

The energy consumption of information systems, especially scalable systems like clouds is required to be reduced to realise green computing systems. In an approach, the energy consumption of servers is decreased by the migration of virtual machines to more energy-efficient guest servers. We take a distributed approach that each server autonomously negotiates with other servers on the live migration of virtual machines. In this paper, we newly propose a migration negotiation for virtual machines (MNVM) protocol for doing the negotiation on the migration of virtual machines among servers. We propose NRB and NEA algorithms to do the energy-efficient live migration of virtual machines by taking advantage of the MNVM protocol. In the evaluation, the total energy consumption of the servers and the average execution time of processes are shown to be smaller in the NRB and NEA algorithms than non-migration algorithms.
reVISit: Supporting Scalable Evaluation of Interactive Visualizations

reVISit is an open-source software toolkit and framework for creating, deploying, and monitoring empirical visualization studies. Running a quality empirical study in visualization can be demanding and resource-intensive, requiring substantial time, cost, and technical expertise from the research team. These challenges are amplified as research norms trend towards more complex and rigorous study methodologies, alongside a growing need to evaluate more complex interactive visualizations. reVISit aims to ameliorate these challenges by introducing a domain-specific language for study set-up, and a series of software components, such as UI elements, behavior provenance, and an experiment monitoring and management interface. Together with interactive or static stimuli provided by the experimenter, these are compiled to a ready-to-deploy web-based experiment. We demonstrate reVISit’s functionality by re-implementing two studies — a graphical perception task and a more complex, interactive study. reVISit is an open-source community project, available at https://revisit.dev/.
Rule-Augmented Artificial Intelligence-empowered Systems for Medical Diagnosis using Large Language Models

In this paper, we investigate the enhancement of Artificial Intelligence (AI) technologies in healthcare and the better understanding of medical literature with the use of Large Language Models (LLMs) and Natural Language Processing (NLP). Specifically, we introduce a rule-augmented AI-empowered system which incorporates a rule-based decision system, the ChatGPT application programming interface (API), and other external machine learning and analytical APIs to offer diagnostic suggestions to patients. The complexities of patient healthcare experiences, including doctor-patient interactions, understanding levels, treatment procedures, and preventive care, are considered. We illustrate how a diagnostic process typically integrates various strategies depending on various factors. To digitize the greatest portion of the process, we propose and illustrate the use of LLMs for humanizing the communication process and investigating ways to reduce burdens and costs in primary healthcare. We also outline a theoretical decision model for evaluating the use of technological components from external sources versus building them from scratch. The paper is structured into sections detailing background theories and context, our proposed and implemented rule-augmented AI-empowered system, as well as a system test in a corresponding use case. Finally, the paper key findings are presented, which contribute valuable insights for future work in this field.
A lightweight YOLOv7 insulator defect detection algorithm based on DSC-SE

As the UAV(Unmanned Aerial Vehicle) carrying target detection algorithm in transmission line insulator inspection, we propose a lightweight YOLOv7 insulator defect detection algorithm for the problems of inferior insulator defect detection speed and high model complexity. Firstly, a lightweight DSC-SE module is designed using a DSC(Depthwise Separable Convolution) fused SE channel attention mechanism to substitute the SC(Standard Convolution) of the YOLOv7 backbone extraction network to decrease the number of parameters in the network as well as to strengthen the shallow network’s ability to obtain information about target features. Then, in the feature fusion part, GSConv(Grid Sensitive Convolution) is used instead of standard convolution to further lessen the number of parameters and the computational effort of the network. EIoU-loss(Efficient-IoU) is performed in the prediction head part to make the model converge faster. According to the experimental results, the recognition accuracy rate of the improved model is 95.2%, with a model size of 7.9M. Compared with YOLOv7, the GFLOPs are reduced by 54.5%, the model size is compressed by 37.8%, and the accuracy is improved by 4.9%. The single image detection time on the Jetson Nano is 105ms and the capture rate is 13FPS. With guaranteed accuracy and detection speed, it meets the demands of real-time detection.
Performance Analysis of Blockchain-Enabled Security and Privacy Algorithms in Connected and Autonomous Vehicles: A Comprehensive Review

Strategic investment(s) in vehicle automation technologies led to the rapid development of technology that revolutionised transport services and reduced fatalities on a scale never seen before. Technological advancements and their integration in Connected Autonomous Vehicles (CAVs) increased uptake and adoption and pushed firmly for the development of highly supportive legal and regulatory and testing environments. However, systemic threats to the security and privacy of technologies and lack of data transparency have created a dynamic threat landscape within which the establishment and verification of security and privacy requirements proved to be an arduous task. In CAVs security and privacy issues can affect the resilience of these systems and hinder the safety of the passengers. Existing research efforts have been placed to investigate the security issues in CAVs and propose solutions across the whole spectrum of cyber resilience. This paper examines the state-of-the-art in security and privacy solutions for CAVs. It investigates their integration challenges, drawbacks and efficiencies when coupled with distributed technologies such as Blockchain. It has also listed different cyber-attacks being investigated while designing security and privacy mechanism for CAVs.
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification

This paper presents a novel reference architecture for blockchain-enabled federated learning (BCFL), a state-of-the-art approach that amalgamates the strengths of federated learning and blockchain technology. We define smart contract functions, stakeholders and their roles, and the use of interplanetary file system (IPFS) as key components of BCFL and conduct a comprehensive analysis. In traditional centralized federated learning, the selection of local nodes and the collection of learning results for each round are merged under the control of a central server. In contrast, in BCFL, all these processes are monitored and managed via smart contracts. Additionally, we propose an extension architecture to support both cross-device and cross-silo federated learning scenarios. Furthermore, we implement and verify the architecture in a practical real-world Ethereum development environment. Our BCFL reference architecture provides significant flexibility and extensibility, accommodating the integration of various additional elements, as per specific requirements and use cases, thereby rendering it an adaptable solution for a wide range of BCFL applications. As a prominent example of extensibility, decentralized identifiers (DIDs) have been employed as an authentication method to introduce practical utilization within BCFL. This study not only bridges a crucial gap between research and practical deployment but also lays a solid foundation for future explorations in the realm of BCFL. The pivotal contribution of this study is the successful implementation and verification of a realistic BCFL reference architecture. We intend to make the source code publicly accessible shortly, fostering further advancements and adaptations within the community.
Metaverse for Healthcare: Technologies, Challenges, and Vision

The continuous enhancement of living conditions imposes higher requirements for medical and healthcare services. Although improved to a certain extent, there still exist critical challenges in current medical pattern, such as the shortage of medical resources, inefficient medical treatment, and limited medical technology level. The metaverse can offer a novel mechanism to address these problems in traditional healthcare domain, and thus, to enhance the quality of medical services. Generally, the metaverse is a dynamic feedback system that facilitates the collaboration and coexistence between the virtual and physical worlds. By fostering collaboration and evolution between intelligent agents in the virtual world, knowledge of this interdependence can be reconstructed in the digital realm. This allows problems existed in the real world to be abstracted and represented in the digital space, where models can be established and computational experiments can be conducted. The outcomes obtained can dynamically guide or control the execution of strategies in the real world, with real-world execution results serving as dynamic data inputs to continually update the virtual world's model. In addition, this paper summarizes the current research status of different healthcare application scenarios for metaverse, highlights the challenges and vision, and aims to inspire further research in this field.
KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing

Text-conditioned image editing is a recently emerged and highly practical task, and its potential is immeasurable. However, most of the concurrent methods are unable to perform action editing, i.e. they can not produce results that conform to the action semantics of the editing prompt and preserve the content of the original image. To solve the problem of action editing, we propose KV Inversion, a method that can achieve satisfactory reconstruction performance and action editing, which can solve two major problems: 1) the edited result can match the corresponding action, and 2) the edited object can retain the texture and identity of the original real image. In addition, our method does not require training the Stable Diffusion model itself, nor does it require scanning a large-scale dataset to perform time-consuming training.
The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication.
Quantum algorithms for the computation of quantum thermal averages at work

Recently, a variety of quantum algorithms have been devised to estimate thermal averages on a genuine quantum processor. In this paper, we consider the practical implementation of the so-called Quantum-Quantum Metropolis Algorithm. As a test bed for this purpose, we simulate a basic system of three frustrated quantum spins and discuss its systematics, also in comparison with the Quantum Metropolis Sampling algorithm.