Embodied Machine Learning

Abstract: Machine learning becomes more prevalent in specialized domains such as medicine and biology every year, but domain expert trust in machine learning continues to lag behind. Researchers have explored increasing rational trust in AI but little research exists focusing on systems that foster affective and normative trust between domain experts and data scientists who create the models. Tools like Project Jupyter have attempted to bridge this gap between data scientists and domain experts, but failed to see uptake in applied fields or to promote collaboration through co-located synchronous work. To address this we present a proof-of-concept tabletop interactive machine learning system for synchronous, co-located model fine tuning. We tested our system with biology experts and data scientists on a cell biology dataset. Results show that our system promotes interactions between domain experts, data scientists, and the model-in-training and fosters domain expert affective and normative trust in the resulting AI model.


LensLeech: On-Lens Interaction for Arbitrary Camera Devices

Abstract: Cameras provide a vast amount of information at high rates and are part of many specialized or general-purpose devices. This versatility makes them suitable for many interaction scenarios, yet they are constrained by geometry and require objects to keep a minimum distance for focusing. We present the LensLeech, a soft silicone cylinder that can be placed directly on or above lenses. The clear body itself acts as a lens to focus a marker pattern from its surface into the camera it sits on. This allows us to detect rotation, translation, and deformation-based gestures such as pressing or squeezing the soft silicone. We discuss design requirements, describe fabrication processes, and report on the limitations of such on-lens widgets. To demonstrate the versatility of LensLeeches, we built prototypes to show application examples for wearable cameras, smartphones, and interchangeable-lens cameras, extending existing devices by providing both optical input and output for new functionality.


KaavadBits: Exploring Tangible Interactive Storytelling of Branching Narratives through a Kaavad-inspired Installation

Abstract: This work explores branching narratives through KaavadBits, a tabletop art installation embodying the kaavadiya-jajmaan or the narrator-patron perspective of kaavad baanchana, the Indian storytelling tradition of reciting the kaavad. For diegetic worldbuilding of tales from Panchatantra, a compilation of ancient Indian animal fables, the narrator takes the physical form of a tree, with which the audience interacts using tokens for a seamless multi-modal storytelling experience. Building on the related explorations, we propose a novel design that immerses the audience through choice, character and question-based interactions. We discuss the insights from a pilot user study and directions for future work. Through this paper, we aim to strike consequential tangible, technological and narrative explorations into various lesser-known traditional forms of storytelling that may inspire new interaction techniques, ultimately preserving the intangible heritages.


Take Me to the Dancing: Experiencing Presence of Remote Friend at Our Own Physical Space while Dancing with Him or Her

Abstract: The experience of dancing with remote friends can provide the opportunity to deepen intimacy in distance. However, existing remote dancing platforms have proven insufficient in conveying the presence of friends in our physical atmosphere. In this paper, we explore ' how can we design the presence of a remote friend' during the dance. To achieve this objective we employed a visual design approach called "sharing the same ground".Our experimental prototype enables two individuals to engage in dancing within their respective physical space, while simultaneously visualizing their footsteps movement to each other in real-time. Using motion sensors to capture foot position and rotation, we project calculated footsteps visuals on the floor to facilitate dancing. Through qualitative analysis, we concluded that the prototype establishes the strong presence of friends engaged in remote dancing.


Port-based entanglement teleportation via noisy resource states

Abstract: Abstract Port-based teleportation (PBT) represents a variation of the standard quantum teleportation and is currently being employed and explored within the field of quantum information processing owing to its various applications. In this study, we focus on PBT protocol when the resource state is disrupted by local Pauli noises. Here, we fully characterise the channel of the noisy PBT protocol using Krauss representation. Especially, by exploiting the application of PBT for entanglement distribution necessary in realizing quantum networks, we investigate entanglement transmission through this protocol for each qubit considering noisy resource states, denoted as port-based entanglement teleportation (PBET). Finally, we derive upper and lower bounds for the teleported entanglement as a function of the initial entanglement and the noises. Our study demonstrates that quantum entanglement can be efficiently distributed by protocols utilizing large-sized resource states in the presence of noise and is expected to serve as a reliable guide for developing optimized PBET protocols. To obtain these results, we address that the order of entanglement of two qubit states is preserved through the local Pauli channel, and identify the boundaries of entanglement loss through this teleportation channel.


Robust sensitive-information de-identification framework based on relative-position estimation of objects in closed-circuit television videos

Abstract: Closed-circuit television (CCTV) videos provide important data for intelligent control systems; however, sensitive information such as human faces and vehicle license plates must be de-identified by a de-identification framework before such videos are used. Previous research has mainly focused on enhancing the performance of de-identification methods, and only a few are devoted to detecting sensitive information. To address this issue, this study proposes an artificial intelligence-based sensitive information de-identification framework. The framework detects objects and utilizes their structural information to estimate the relative position of sensitive data, followed by the de-identification process. To enhance the recall of the detection module, this study analyzes difficult-to-detect faces and license plates in CCTV videos and proposes relative position estimation methods. These methods minimize the possibility of sensitive information leakage while reducing the number of locations of sensitive information that need to be annotated. When evaluated using actual urban CCTV videos, the recall of the framework was 84.63%, which is 33.01% higher than that of the conventional algorithm.


Local quantum Fisher information and Jensen-Shannon coherence dynamics of two-spin-qubits XYZ-Heisenberg state

Abstract: In this study, we investigate the impact of the XYZ-DM-Heisenberg model on quantum resources, including local quantum Fisher information, concurrence, and Jensen-Shannon coherence, in the presence of intrinsic decoherence. By exploring various spin-exchange interactions, with a focus on the role of the y-DM interaction, we reveal how these interactions affect quantum correlations and coherence dynamics. We find that the system transitions from separability to the generation of quantum properties when spin interactions are introduced. The specific parameter choices significantly influence the dynamic map of quantum functions. Notably, strong y-DM interaction, in combination with equal spin interaction, leads to pronounced fluctuations, while weak DM interaction results in sudden death and rebirth in the x direction. Additionally, ferro- and anti-ferromagnetic regimes impact the quantum functions differently. In the presence of intrinsic decoherence, fluctuations decrease, and coherence remains robust compared to the local quantum Fisher information and entanglement, reducing loss.


Digitally Empowering Women Through Community Radio and Mobile Storytelling in India

Abstract: In the post-COVID-19 era, digital empowerment has become significant, especially with reference to media messages and communication technologies being used to financially empower citizens. As past histories of media are leaving behind a legacy of analog and archaic forms of media content creation, new technologies are building the ‘media archaeology' of past traditions, heritages, epistemologies to generate meanings, and dialogue for new means of storytelling, thereby preserving heritage in ‘digital formats'. The study has some broader mission and objectives with more specific research questions: What are the key themes of digital literacy among community radio practitioners? Which theoretical frameworks can define digital literacy? What are the different tools of digital empowerment? Is there an emerging ‘empowering relationship' among women content creators and consumers?


An AUV collision avoidance algorithm in unknown environment with multiple constraints

Abstract: Ensuring the safety of Autonomous Underwater Vehicles (AUVs) is paramount for successfully executing their marine operations. Grounded in partial observation, motion characteristics, and safety distance constraints of AUVs navigating in uncharted environments, this study proposes a collision avoidance algorithm to guarantee AUV safety. The algorithm integrates the Dubins Improved Hybrid A* (DIHA*) algorithm and the Fuzzy Heading Avoidance (FHA) algorithm. The DIHA*-FHA algorithm dissects collision avoidance paths into target-oriented global paths and collision avoidance-oriented local paths. Subsequently, algorithmic functions are invoked based on different types of obstacle information. The components are delineated as follows: (1) We are employing the DIHA* algorithm to compute the optimal path solution aligning with the actual motion characteristics of the AUV, leveraging known prior obstacle information in the environment to guide the AUV toward the target. (2) Utilizing the FHA algorithm, grounded in a fuzzy controller, to swiftly respond to obstacle information detected by the AUV during its operation in an unknown environment. This involves calculating a local smooth path that maintains a safe distance from obstacles, facilitating effective collision avoidance. Ultimately, this study substantiates the superior performance of the DIHA*-FHA algorithm in real-time, path quality, and mitigation of local minima issues under multiple constraints in unknown environments, as compared to other algorithms. Experimental results underscore that this algorithm enhances the collision avoidance capability of AUVs in unknown environments with multiple constraints, playing a pivotal role in ensuring AUV safety in practical maritime operations.


Predominant fracture gold in the Jiaodong gold province, China: Constraints on gold mineralization processes

Abstract: The Jiaodong Peninsula, the largest gold province in China, hosts proven gold resources of ∼ 5,500 t. Knowledge of the accurate occurrence of gold and its formation mechanism is key to a better understanding of the mineralization process of these deposits. In this study, we combined field-emission scanning electron microscopy, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), electron backscattering diffraction (EBSD), and high-resolution X-ray computed tomography (micro-CT) to investigate the predominant gold occurrence type and its formation processes in the Sanshandao and Xincheng giant gold deposits. Microscopic observations confirmed that visible gold in the two-dimensional mode occurred as fracture gold and inclusions in the pyrite. However, high-resolution micro-CT revealed that almost all visible gold in the three-dimensional mode was connected to the fracture, suggesting that the previously so-called gold inclusions were actually fracture gold. The LA-ICP-MS results showed that the pyrite contained low concentrations of invisible gold (<1 ppm). Thus, fracture gold is the predominant type in the Jiaodong gold province. The EBSD analyses showed that the neighboring pyrite fragments shared a similar crystallographic orientation, and these fragments could be assembled into individual grains. The in-situ fracturing texture resulted from structural activities and/or hydraulic fracturing. By combining petrographic, crystallographic, and geochemical analyses with geological information, the mineralization process of the Jiaodong gold deposits can be summarized as follows. When the gold-bearing fluid reacted with the wall rocks, intensive fluid–rock interactions led to the precipitation of massive amounts of pyrite. Subsequent structural activities and/or hydraulic fracturing broke the pyrite and triggered fluid boiling. Boiling induced gold saturation and precipitation in the cracks of the fractured pyrite. Thus, massive ore fluids with low gold concentrations can also form world-class gold deposits via the synergistic mechanisms of intensive fluid–rock interaction and fluid boiling.


Network mechanism for generating genuinely correlative Gaussian states

Abstract: Abstract Generating a long-distance quantum state with genuine quantum correlation (GQC) is one of the most essential functions of quantum networks to support quantum communication. Here, we provide a deterministic scheme for generating multimode Gaussian states with certain GQC (including genuine entanglement). Efficient algorithms of generating multimode states are also proposed. Our scheme is useful for resolving the bottleneck in generating some multimode Gaussian states and may pave the way towards real world applications of preparing multipartite quantum states in current quantum technologies.


Enhanced model-driven web application development with code generation using deep learning technique

Abstract: Advancements in modern technology have led to an endless reliance on the Internet. This has created a great demand for the fast and accurate development of web applications. Web development has benefitted from programming approaches such as Model-Driven Web Engineering (MDWE). MDWE allows developers to choose pre-defined models and utilize them for their requirements. This kind of structural blueprinting, called wireframing, enables efficiency in software development. However, these techniques are seldom understood by people without a technological background. Hence, much of the coding central to a project remains the responsibility of a few tech-educated people. This work proposes an approach that offloads some of the typing to a machine-based code generator. This has been achieved by pairing MDWE methods with Deep Learning capabilities. This ensures a less coding-intensive web development methodology that can be utilized even by non-web developers. This work makes several contributions to improving overall MDWE methods.


Privacy preservation using optimized Federated Learning: A critical survey

Abstract: FL is a futuristic research topic that enables cross-sectoral training in ML systems in various organizations with some privacy restrictions. This review article establishes the extensive review of FL with different privacy-preserving techniques and the obstacles involved in the existing privacy-preserving model. This review is initiated by providing the background of FL and provides an overview of the technical details of the component involved in FL. Then it provides a brief review of the around 75 articles related to privacy-preserving in the FL-enabled techniques. Compared to the other survey articles this presented review article provides a brief analysis of the different privacy terms utilized in FL. The categorization of the privacy preservation models in FL highlights the significance of the model and the obstacles that limit the application of the particular privacy preservation model in real-time application. Further, this review articles ensure the details about the year of publishing, performance metrics analyzed in different articles along with their achievements. The limitation experienced in each category of the privacy-preserving technique is elaborated briefly, which assists future researchers to explore more privacy-preserving models in FL.


International trade privacy data management system combining Internet of Things blockchain

Abstract: Currently, international trade (Int’l Trade) often faces risks such as privacy data leakage and transaction security during transactions, which seriously affect the protection of privacy data in trade. Therefore, this article used the Internet of Things and blockchain to attempt to address these risk issues and improve the security of privacy data. By analyzing the security threats in Int’l Trade transactions, this article used additive aggregation functions to analyze the protection effect of privacy data and studied the core issues of Int’l Trade privacy data. This article analyzed the privacy protection model using the Internet of Things blockchain and proposed corresponding Int’l Trade privacy data protection strategies. Among the five surveyed enterprises, data audit and data backup were all above the standard value, while data query only exceeded the standard value for enterprises 2, 3, 4, and 5. The average abnormal value of the enterprise in data anomalies decreased by 0.06 compared to the standard value. Through experimental testing, it has been found that Internet of Things blockchain can effectively improve the privacy data protection effect of Int’l Trade and promote the convenience of Int’l Trade transactions.


Optimization of artificial intelligence cloud computing in information management design

Abstract: In order to promote the construction of enterprise informatization, the author studied the adaptive optimization method of Efficient Management Information System (EMIS). The author proposes to combine the fuzzy C-means algorithm to form a server clustering algorithm, and adds an improved Drosophila optimization algorithm to overcome the problems of slow Rate of convergence of GRNN and easy to fall into the minimum, and the cloud platform collects 23 performance indicators, the output results of the coordinated evolutionary algorithm are analyzed by the neighborhood rough set analysis of algorithms to select features to avoid the curse of dimensionality problem. The experimental results indicate that, compared with existing research results, the algorithm proposed by the author has increased its speed by 1.43, 3.22, and 3.72 times, respectively; In terms of convergence steps, they have also been reduced by 1.61, 5, and 6 times respectively, and when running the algorithm, the computer’s memory and CPU usage are controlled at around 50%, without affecting normal functionality. This proves that the task scheduling of the cloud platform is more balanced, and indirectly proves the accuracy of the algorithm’s clustering effect.


A Survey on AutoML Methods and Systems for Clustering

Abstract: Automated Machine Learning (AutoML) aims to identify the best-performing machine learning algorithm along with its input parameters for a given dataset and a specific machine learning task. This is a challenging problem, as the process of finding the best model and tuning it for a particular problem at hand is both time-consuming for a data scientist and computationally expensive. In this survey, we focus on unsupervised learning, and we turn our attention on AutoML methods for clustering. We present a systematic review that includes many recent research works for automated clustering. Furthermore, we provide a taxonomy for the classification of existing works, and we perform a qualitative comparison. As a result, this survey provides a comprehensive overview of the field of AutoML for clustering. Moreover, we identify open challenges for future research in this field.


Open-set domain adaptation for scene classification using multi-adversarial learning

Abstract: Domain adaptation methods are able to transfer knowledge across different domains, tackling multi-sensor, multi-temporal or cross-regional remote sensing scenarios as they do not rely on labels or annotations in the target domain. However, most of the previous studies have focused on closed-set domain adaptation, based on the assumption that the source and target domains share identical class labels. Real-world scenarios are typically more complex, and the model could potentially encounter novel classes that are not previously included in the source domain, commonly referred to as "unknown" classes. Here we investigate the open-set domain adaptation scenario in the field of remote sensing scene classification, where there is a partial overlap between the label space of the target domain and that of the source domain. To deal with this problem, we propose a novel open-set domain adaptation method for scene classification using remote sensing images, which is named Multi-Adversarial Open-Set Domain Adaptation Network (MAOSDAN). Our proposed MAOSDAN consists of three main components. First, we employ an attention-aware Open Set BackPropagation (OSBP) to better distinguish the "unknown" and "known" samples for the target domain. Then, an auxiliary adversarial learning is designed for mitigating the negative transfer effect that arises from forcefully aligning the "unknown" target sample in network training. Finally, we adopt an adaptive entropy suppression to increase the probability of samples and prevent some samples from being mistakenly classified. Our proposed MAOSDAN achieves an average score of 75.07% in three publicly available remote sensing datasets, which significantly outperforms other open-set domain adaptation algorithms by attaining 4.52∼17.15%. In addition, MAOSDAN surpasses the baseline deep learning model with 18.12% improvement. A comprehensive experimental evaluation demonstrates that our MAOSDAN shows promising prospects in addressing practical and general domain adaptation scenarios, especially in scenarios where the label set of the source domain is a subset of the target domain.


FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams

Abstract: Given a stream of financial transactions between traders in an e-market, how can we accurately detect fraudulent traders and suspicious behaviors in real time? Despite the efforts made in detecting these fraudsters, this field still faces serious challenges, including the ineffectiveness of existing methods for the complex and streaming environment of e-markets. As a result, it is still difficult to quickly and accurately detect suspected traders and behavior patterns in real-time transactions, and it is still considered an open problem. To solve this problem and alleviate the existing challenges, in this article, we propose FiFrauD, which is an unsupervised, scalable approach that depicts the behavior of manipulators in a transaction stream. In this approach, real-time transactions between traders are converted into a stream of graphs and, instead of using supervised and semi-supervised learning methods, fraudulent traders are detected precisely by exploiting density signals in graphs. Specifically, we reveal the traits of fraudulent traders in the market and propose a novel metric from this perspective, i.e., graph topology, time, and behavior. Then, we search for suspicious blocks by greedily optimizing the proposed metric. Theoretical analysis demonstrates upper bounds for FiFrauD's effectiveness in catching suspicious trades. Extensive experiments on five real-world datasets with both actual and synthetic labels demonstrate that FiFrauD achieves significant accuracy improvements compared with state-of-the-art fraud detection methods. Also, it can find various suspicious behavior patterns in a linear runtime and provide interpretable results. Furthermore, FiFrauD is resistant to the camouflage tactics used by fraudulent traders.


Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing

Abstract: Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.


Service Robots Roll Forward

Abstract: Transitioning from classic 'programmed automation' to modern 'AI-powered autonomy.'


A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

Abstract: A dandelion algorithm (DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA, which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained; while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection (CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.


UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Abstract: Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation, which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units (RSU) or unmanned aerial vehicles (UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning (MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization (MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers (e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.


Multi-UAVs Collaborative Path Planning in the Cramped Environment

Abstract: Due to its flexibility and complementarity, the multi-UAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue (SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles (UAVs) in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search (ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes. The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.


Sparse Reconstructive Evidential Clustering for Multi-View Data

Abstract: Although many multi-view clustering (MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects, which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm (SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional human-readable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides, SRMVEC delivers effectiveness on benchmark datasets by out-performing some state-of-the-art methods.


Noise resistance: a key factor in the metrological applications of highly entangled multiqubit states

Abstract: The advent of quantum entanglement has revolutionized metrology, enabling the development of ultra-precise measurement techniques that surpass the limitations of classical systems. However, the delicate nature of entangled states makes them vulnerable to various noise sources, significantly impeding their metrological utility. To address this challenge, we delve into the intricate relationship between noise and the metrological usefulness of highly entangled multiqubit systems. By studying the impact of various factors, we aim to unravel the fundamental limits of quantum metrology and devise strategies to enhance the resilience of quantum systems against noise. Our investigation reveals that increasing the number of qubits in an entangled state can significantly enhance its noise robustness, particularly for certain entangled states. Furthermore, we uncover the surprising advantage of utilizing different local operators, surpassing the robustness offered by the conventional identical-operator approach. Additionally, by employing a technique that utilizes multiple copies of the quantum state, we identified many states with latent metrological usefulness and demonstrated their hidden noise robustness. While these strategies significantly enhance the robustness of many studied states, it is crucial to note that generalizations may not apply universally. Therefore, validating these findings for each specific state is essential before practical implementation. Our findings proved that by rigorously assessing noise susceptibility and adopting appropriate strategies, we can achieve substantial gains in precision and noise robustness. This paves the way for the development of more robust and reliable quantum metrological techniques, offering valuable insights across a wide range of applications, from fundamental physics to cutting-edge technologies like quantum computing, sensing, and communication.


Blockchain-Based Smart Contracts

Abstract: Blockchains are decentralized, distributed, and immutable ledgers that can execute contracts on the blockchain, called smart contracts. Blockchain-based smart contracts have different challenges, opportunities, and applications in various fields like cryptocurrency systems, real estate, agriculture, logistics, and fire industry. The codes that can be executed and which operate on top of a blockchain to facilitate, implement, and enforce agreements between numerous parties in the absence of a trusted third party are called smart contracts. This chapter uses a taxonomy to classify different processes, such as comparing and contrasting the strengths and weaknesses of various methods and identifying research gaps and unanswered questions. The chapter also outlines some promising research guidelines for blockchain-based smart contracts and discusses the benefits and challenges. Researchers and practitioners in the field of blockchain-based smart contracts may find this chapter useful because it offers an overview of current and emerging developments in the field.


The Intersection of AI Technology and Intellectual Property Adjudication in Supply Chain Management

Abstract: In modern Nigerian supply chain management, the adjudication of intellectual property rights has gained paramount importance. With the rapid advancement of technology, the integration of AI-powered analytics has emerged as a promising resolution for settling disputes and safeguarding intellectual property rights. This chapter highlights the multifaceted role of AI-driven analytics in the adjudication process, exploring its impact on improving efficiency, precision, and impartiality in resolving disputes related to intellectual property within the Nigerian supply chain. The authors suggest that comprehensive ethical standards, data privacy regulations, and transparency protocols should be established by the government, stakeholders, and law enforcement agencies to mitigate potential biases, ensure data integrity, and ensure adherence to ethical and legal norms in AI technologies.


Blockchain Technology Through Bitcoin and Ethereum

Abstract: With a focus on the two most important cryptocurrencies, Bitcoin and Ethereum, this chapter explains the significance of blockchain in today's environment and analyzes its implementation in both networks as well as investigates how they work, describing their transactions, unique characteristics, benefits, and necessities, noting how Ethereum addressed some of Bitcoin's shortcomings with the introduction of smart contracts. They both depended on the proof of work (PoW) consensus mechanism at first, which Ethereum has moved to proof of stake (PoS) in its new upgrade as Ethereum 2.0. They examine the benefits and differences of these consensus protocols and other ones that deserve consideration and shed light on how they both have dealt with their challenges and drawbacks. Moreover, they analyze the prospects and likely improvements in blockchain technology, as they hope to illustrate the potential of blockchain along with Bitcoin and Ethereum to spark future innovation and address contemporary difficulties through their capabilities and network applications.


Virtual Education Modality and Digital Competence in University Students

Abstract: This chapter considers students from different Panamanian universities after the experience in the 2021-2022 academic year. There are important considerations about digital competence and professional progression, gender, age, and attitude influencing the virtual modality. Their behaviors and attitudes are also influenced by their competencies, going beyond a consideration referred solely to the rational approach of the study. 253 students participated in the study, most of whom were women (74.7 percent). The results evidently reveal the massive use and application of information and communication technologies in the university classroom. That technological tools put the focus on the curricula of universities and intervene in the degree of competence of their students. The digital skills present in university students are linked to daily activity, to systematic use in their subjects. This allows the assessment of competence, and an approach to the evaluation of digital competences that are not specific to a specialty, but are also generic competences.


All-optical spin valve effect in nonlinear optics

Abstract: More than three decades after the inception of electron spin-based information encoding inspired by nonlinear electro-optic devices, we present a complementary approach: nonlinear optical devices directly inspired by spintronics. We theoretically propose an all-optical spin-valve device and a spin-dependent beam splitter, where the optical pseudospin is a superposition of signal and idler beams undergoing a sum-frequency generation process inside a 2D nonlinear photonic crystal. We delve into the operation of these devices, examining key properties such as the transmission angle and splitting ratio, optically controlled by the pump beam. Our findings open new avenues for both classical and quantum optical information processing in the frequency domain.


Climate‐ and Technology‐Dependent Performance Loss Rates in a Large Commercial Photovoltaic Monitoring Dataset

Abstract: Detailed knowledge of the performance of photovoltaic (PV) systems over their full lifetime is invaluable for evaluating their profitability and sustainability. PV systems are known to have gradually declining performance; for long‐term yield assessments, typically a performance loss rate (PLR) of −0.5% yr −1 is assumed. Herein, monitoring data of thousands of commercial PV systems is evaluated to determine how performance loss trends and PLR values are affected by operational climate, PV module technology, and other parameters. Using performance ratio (PR) measurements from almost 6000 PV systems, the long‐term trends in PR and overall PLR value are evaluated. Subsequently, the relation between climate, performance loss, and PLR for three different climate classification schemes is evaluated. Finally, how long‐term performance loss trends and estimated PLR values differ for PV module technology and other parameters are evaluated. The results show that in general, PV systems that have operated now for at least 3 but up to 25 years show a PLR of roughly −1% yr −1 , indicating substantially higher performance loss compared to typical estimates used for new PV systems. Climate by itself does not seem to be the leading factor that determines overall performance loss trends or PLR values in the dataset analyzed.


Long-term balanced allocation via thinning

Abstract: In the two-thinning balls-and-bins model, an overseer is provided with uniform random allocation of m balls into n bins in an on-line fashion. The overseer may reject the allocation of each ball, in which case it is placed into a new bin, drawn independently, uniformly at random. The purpose of the overseer is to reduce the maximum load, that is, the difference between the maximum number of balls in a single bin and the average number of balls among all bins. We provide tight estimates for three quantities: the lowest achievable maximum load at a given time m, the lowest achievable maximum load uniformly over the entire time interval [m]:={1,2,…,m} and the lowest achievable typical maximum load over the interval [m], that is, a load which upper-bounds 1−o(1) portion of the times in [m]. In particular, for m polynomial in n and sufficiently large, we provide an explicit strategy, which achieves a typical maximum load of (logn)1/2+o(1), asymptotically the same as that can be achieved at a single time m. In contrast, we show that no strategy can achieve better than Θ(logn loglogn) maximum load for all times up to time m.


Blockchain Integration in Upstream Oil and Gas

Abstract: With its complex exploration and production processes, extensive information exchange and stringent safety standards, the upstream oil and gas industry is embracing cutting-edge technologies to maximize operational efficiency and improve data reliability. Blockchain technology has risen to prominence as an appealing solution offering decentralized, trustworthy, and immutable information management capabilities. An outline of the upstream oil and gas industry is provided to demonstrate its role and impact on societal development. The fundamental principles of blockchain are then discussed, emphasizing its potential to revolutionize data handling, transactions, and smart contracts. The chapter also provides examples of some of the current operational projects in the upstream oil and gas industry that are exploring its potential uses and the challenges associated with its implementation. Through the insights presented in this chapter, oil and gas companies and key stakeholders can utilize blockchain technology to boost business performance and enhance efficiency.


Blockchain in Healthcare

Abstract: Blockchain technology has emerged as a solution to tackle challenges in the healthcare sector, such as fragmented data vulnerabilities in security and a lack of transparency. In this chapter, the authors provide an overview of blockchain and its potential benefits for the healthcare industry. These include collaboration, improved integrity and security of data, enhanced efficiency, empowerment of patients, and advancements in supply chain management. However, implementing blockchain faces obstacles like scalability issues, the absence of interoperability standards uncertainties regarding regulatory aspects, and usability concerns. To ensure the adoption of technology in healthcare, the authors propose a strategic roadmap for both short-term implementation at individual hospitals and long-term transformation of insurance and billing processes through smart contract automation. This requires integrating with existing IT systems training for staff members addressing ethical considerations effectively and customizing approaches based on regional requirements.


MTLink: Adaptive multi-task learning based pre-trained language model for traceability link recovery between issues and commits

Abstract: Traceability links between issues and commits (issue-commit links recovery (ILR)) play a significant role in software maintenance tasks by enhancing developers’ observability in practice. Recent advancements in large language models, particularly pre-trained models, have improved the effectiveness of automated ILR. However, these models’ large parameter sizes and extended training time pose challenges in large software projects. Besides, existing methods often overlook the association and distinction among artifacts, leading to the generation of erroneous links. To mitigate these problems, this paper proposes a novel link recovery method called MTLink. It utilizes multi-teacher knowledge distillation (MTKD) to compress the model and employs an adaptive multi-task strategy to reduce information loss and improve link accuracy. Experiments are conducted on four open-source projects. The results show that (i) MTLink outperforms state-of-the-art methods; (ii) The multi-teacher knowledge distillation maintains accuracy despite model size reduction; (iii) The adaptive multi-task tracing method effectively handles confusion caused by similar artifacts and balances each task. In conclusion, MTLink offers an efficient solution for ILR in software traceability. The code is available at https://zenodo.org/records/10321150.


Artificial intelligence capabilities, dynamic capabilities and organizational creativity: contributing factors to the United Arab Emirates Government’s organizational performance

Abstract: Purpose This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations. Design/methodology/approach The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing. Findings Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link. Practical implications The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance. Originality/value There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.


Motion-Based Wave Inference With Neural Networks: Transfer Learning From Numerical Simulation to Experimental Data

Abstract: Abstract The directional wave spectrum, which describes the distribution of wave energy along frequencies and directions, can be estimated from the measured motions of a vessel subjected to a particular sea condition by resorting to the wave-buoy analogy. Several methods have been proposed to address the inverse estimation problem; recently, machine learning techniques have been assessed as further alternatives. However, it may be difficult to gather large datasets of in-service motion responses and the associated sea states to train effective data-driven models. In this work, an encoder–decoder neural network is trained with the synthetic responses of a station-keeping platform supply vessel (PSV) to estimate the directional wave spectrum. This estimation model is directly applied to perform wave inference from motion data of wave basin tests with a small-scale model of the same vessel. Furthermore, fine-tuning is also used to incorporate experimental data into the neural network model. Results show a satisfactory match between estimated and measured values, both with respect to the energy distribution and the integral spectrum parameters, indicating that the proposed approach can be employed to obtain data-driven wave inference models when there is little or no availability of measured motion records and the corresponding sea conditions.


Implementing quantum anonymous multi-party ranking: the influence and application of the law of large numbers

Abstract: Abstract Secure multi-party ranking is not only a pivotal component within the domain of secure multi-party computation but also holds extensive real-world applicability, given its efficacy in safeguarding the integrity and privacy of data. In this paper, we propose a verifiable quantum anonymous multi-party ranking protocol, which leverages the power of multi-particle entangled states in conjunction with the law of large numbers. The protocol achieves the secure ranking of participants’ private data while verifying their identities through the assistance of a semi-honest third party. We prove that the protocol is resistant to different types of attacks from internal or external attackers. In addition, the proposed protocol employs qubits as the information carriers, which improves the feasibility of the protocol. We demonstrate the feasibility of the protocol by using the online analog quantum computer of IBM Corporation placed on the cloud. Our research represents an innovative initiative that merges the realms of quantum cryptography and statistical analysis to address pertinent practical challenges.


A lightweight multi-target ship tracking model based on Yolov7

Abstract: Abstract Ship multi-target tracking has long been a topic that has attracted researchers from different fields. Currently, the following challenges still exist in ship multi-target tracking. Ships obscuring each other leads to an increase in misdetections and omissions in the algorithm. Some algorithms have a large number of parameters and computations, which are not favourable for deployment into devices. As a result, we present a novel multi-target tracking technique that combines the Yolov7 detector with Kalman filtering. First, the retrieval of fine ship details in the video is accomplished by employing the CNNS(Convolutional Neural Networks) + Transformer + CNNS architecture. Second, we introduce a novel lightweight module known as Light-SPP, which aims to integrate ship features. Finally, Wise-iou Loss, which increases the predictability of ship position, is shown as the detector’s localization loss function. The experimental results show that the number of parameters and computation of the model decrease by 11.0% and 17.7% respectively, and the continuous tracking accuracy and tracking and positioning accuracy improve by 17.7% and 7.4% respectively. In summary, our proposed ship multi-target tracking algorithm mainly solves the problems of low tracking accuracy and large computational volume, and has high engineering application prospects in the field of water transport.


A robust multi-chaotic remote sensing image encryption scheme based on RNA and immune algorithms

Abstract: Abstract Remote sensing images have been widely used in the military and other areas because of their rich perceptional data. This makes their visual security critical for practical usage. To address this challenge, an enhanced image encryption scheme is proposed. In the scrambling phase, n bands of remote sensing images undergo Arnold double-bit-level permutation. This reduces not only the pixel correlation in each image plane but also between each frequency band.To enhance security, an RNA crossover rule (RNACMO) is introduced. The RNA image is divided into RNA single strands of different lengths using chaotic sequences, and different crossover methods, including single-point and uniform, are adaptively selected according to the number of RNA single strands. RNACMO significantly improves the security level of the scheme. An improved immune algorithm (IIA) is exploited to optimize chaotic function sequences, which improves the chaotic property of the scheme. In experiments, the proposed algorithm achieves average values of 99.6094% for NPCR, 33.4635% for UACI, and 26.7712% for BACI in encrypted remote sensing images, indicating stronger security and better resilience against attacks compared with other encryption algorithms for remote sensing images.


Development of QR Code

Abstract: QR code, an abbreviation for Quick Response code, was developed and published three decades ago. It was originally designed for production and logistics industries with advantageous characters: 1) high-speed readability, 2) flexibility for reading angles, and 3) robust readability against noise. In this paper, structure of code and encoding/decoding process will be introduced.


How to Measure Cybersecurity and Why Heuristics Matter

Abstract: In this article, we continue our lessons learned from medical science and explore possibilities to measure cybersecurity and apply heuristics.


Open Science for Fostering Seismologic Science Literacy

Abstract: This paper reports on an attempt to introduce “open science” into the field of seismology to improve disaster literacy in seismology and related fields. The term “open science” has a variety of meanings, but the core idea is of diverse stakeholders “doing science collaboratively.” This study reports on four pieces of action research to improve seismology-related disaster literacy through the idea of “open science,” which focuses on “doing science collaboratively” with the active participation of citizens (non-experts) and researchers (experts). In particular, this study focuses on the “citizen science” perspective because disaster literacy, first and foremost, refers to the disaster literacy of the general public. Specifically, we improved earthquake literacy by operating the Abuyama Earthquake Observatory Science Museum; promoting citizen-participatory earthquake and tsunami evacuation drills by developing and implementing Nige-tore, a smartphone app to support tsunami evacuation drills; promoting the Minna de Honkoku project to decipher historical materials related to natural disasters through citizen involvement; and conducting a trial “open/citizen science”-oriented seismologic-data observation program, the Manten Project, for inland earthquake research.


Open-Door Events of the Sakurajima Volcano Observatory, Kyoto University in the Last Ten Years

Abstract: Sakurajima Volcano Observatory, with a history exceeding 60 years, is recognized by researchers in Japan and overseas for conducting frontier research centering on Sakurajima, the most active volcano in Japan. It is owned by Kyoto University and represents the university in the surrounding communities. This paper reviews the open-door events, which have been conducted yearly for the last ten years as a part of a university-wide effort by Kyoto University. They make use of the facilities at Sakurajima Volcano Observatory, which aims to enhance the understanding of volcanoes among the general public in collaboration with geo-guides as “boundary workers.”


Enhancing willingness to communicate in English among Chinese students in the UK: the impact of MALL with Duolingo and HelloTalk

Abstract: Abstract Over the past decade, mobile-assisted language learning technologies, such as smartphone applications, have become increasingly prominent in language learning; however, empirical research on how the use of mobile apps can influence students’ willingness to communicate (WTC) is scarce. This study assessed two mobile apps with distinct language learning approaches: Duolingo, grounded in grammar-translation and behaviourist principles, and HelloTalk, based on social constructivist theory and communicative language learning. The research examined the impact of these apps on Chinese students’ WTC in the UK. A mixed-methods approach, including a quasi-experimental design, was adopted, involving 67 International Foundation Year Chinese students from a Confucius Heritage Culture background. The participants were randomly assigned to two experimental groups (Duolingo, N = 33; HelloTalk, N = 34) and used one app for five weeks. Quantitative data were collected through pre- and post-treatment using a WTC scale and an English media usage frequency questionnaire. Qualitative data were obtained through semi-structured interviews. Results indicated that both apps enhanced WTC, with Duolingo exhibiting a slightly stronger effect. Participants displayed greater WTC with emotionally and socially connected individuals, while WTC with strangers remained lowest. Factors such as usage time, communication goals, and frequency of use influenced participants’ WTC with various groups of people.


Embracing intelligent machines: A qualitative study to explore the transformational trends in the workplace

Abstract: Purpose With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines. Design/methodology/approach We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected. Findings This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies. Originality/value This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.


Intelligent System Detection of Dead Victims at Natural Disaster Areas Using Deep Learning

Abstract: Disaster is the occurrence or sequence of occurrences that endangers and disrupts people’s lives and livelihoods due to natural and/or non-natural as well as human elements, including fatalities, property loss, environmental harm, and psychological effects. In addition to concentrating on the victims’ safety and their own safety, the search and rescue (SAR) team plays a significant part in this evacuation operation. Based on these issues, this study examined how to use a drone equipped with electronic equipment to search for victims on the ground to speed up the evacuation process at natural disaster sites, assisting the evacuation process and enhancing the safety of the SAR team. The drone carries a near-infrared camera and GPS. The images captured by the camera provide the parameters for classifying victims using deep learning. The system has been implemented by sampling data from human poses resembling the position of the victims’ bodies from natural disasters. From the experimental results, the system can detect objects with high accuracy, that is, 99% in both static and dynamic conditions. The best model results were obtained at a height of 2 meters with a low error percentage.


GISPLIT: High-performance global solar irradiance component-separation model dynamically constrained by 1-min sky conditions

Abstract: The separation of global horizontal irradiance (GHI) into its direct and diffuse components is necessary in a variety of applications, most specially in solar energy utilization, where knowledge of direct normal irradiance (DNI) is of paramount importance. Here a novel and efficient model, referred to as GISPLIT, is presented to perform this task accurately, using time series of measured data at 1-min resolution. To better describe the radiative effects of different cloud situations, the model takes advantage of a preliminary classification of the sky conditions into six sky types. An empirical submodel is assigned to each sky class to split GHI into its components, using a limited number of predictors that are related to GHI’s magnitude and variability, and to coincident estimates of the clear-sky irradiance components. Those submodels are trained and validated using rigorously quality-assessed measurements from 120 radiometric stations over all continents and all five major Köppen-Geiger (KG) climate classes, totaling ≈64 million valid data points. Four model versions are evaluated using training data for either all KG climate regions combined or conditioned by KG climate, and either with or without additional support from machine learning. The validation of the four versions suggests that the conditioning by KG climate does not add any significant benefit over the “all-climates” training approach and that, overall, the model version trained with data from all KG climates combined and supported by machine learning generally predicts DNI with the best RMSE results at unseen sites, although with little difference over the other versions.


Exploring the uncharted territories: a structured literature review on cryptocurrency accounting and auditing

Abstract: Purpose The objective of this study is to systematically review the current body of literature in order to gain insights into the progress of research in accounting and auditing of cryptocurrencies, while also highlighting the associated risks and identifying gaps for future exploration. Design/methodology/approach To achieve this, a structured literature review was carried out, presenting a thorough and critical assessment of the available studies focused on cryptocurrencies within the accounting and auditing domain. Findings The analysis reveals that the majority of the research has concentrated on the reporting and measurement aspects of cryptocurrencies, neglecting the auditing aspect. Regarding the methodology, future investigations should incorporate both theoretical and empirical manners to address this gap. Various spheres require further exploration, as they have the potential to significantly impact practitioners and academics. Originality/value The significance of this paper lies in its comprehensive examination of the existing literature, synthesizing and organizing information pertaining to accounting and auditing considerations of crypto transactions. Moreover, it provides valuable insights into best practices and prompts identifying avenues for further research in this field.


Digital transformation and integration of artificial intelligence in financial institutions

Abstract: Purpose Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience. Design/methodology/approach The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots. Findings The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions. Originality/value The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.


Economics of incident response panels in cyber insurance

Abstract: Cyber insurance is becoming a popular cyber risk management tool. Beyond pure financial risk transfer, prior theoretical works anticipated that cyber insurance would influence the mitigation measures employed by policyholders, such as by excluding losses caused by security mismanagement or by offering premium discounts for security controls. Empirical literature has shown cyber insurance is ineffective at influencing pre-breach security levels; however, it has also identified how insurers indemnify the cost of a team of post-breach providers with expertise spanning legal, technical, and communications. Our work models the peculiarities of the institution, the panel, that triages incidents and assigns firms. In particular, we model the incomplete aspect of this contract in which policyholders may be assigned a less efficient firm, which can be interpreted as a bait and switch. At the same time, our context for the bait and switch is business-to-business (B2B) and differs from the usual understanding of the phenomenon as an upsell. Consequently, new managerial implications arise on the insurer-side of the market. We characterise the conditions under which policyholders accept their insurer's hotline recommendation for incident response under the incomplete contract. We additionally show how panels can mitigate the adverse selection problem with respect to policyholders' losses by including providers of differentiated efficiency.


A systematic literature review of knowledge graph construction and application in education

Abstract: In the dynamic landscape of modern education, the search for improved pedagogical methods, enriched learning experiences, and empowered educators remains a perpetual pursuit. In recent years, a remarkable technological innovation has asserted its dominance in education: Knowledge Graphs (KGs). These structured representations of knowledge are increasingly proving to be indispensable tools, fostering advancements driven by the growing recognition of their essential role in enriching personalised learning, curriculum design, concept mapping, and educational content recommendation systems. In this paper, a systematic literature review (SLR) has been conducted to comprehensively examine KG construction methodologies and their applications across five key domains in education. In each examined study, we highlight the specific KG functionalities, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations. This paper distinguishes itself by offering a broad overview of KGs in education, analyzing state-of-the-art methodologies, and identifying research gaps and limitations, paving the way for future advancements.


Teaching with Augmented Reality Using Tablets, Both as a Tool and an Object of Learning

Abstract: In this paper, we present a novel approach to utilizing tablets in chemistry education. In the context of education for sustainable development, we utilize tablets as objects of learning to address the lithium-ion battery. In addition, we used tablets as learning tools by making use of augmented reality technology. This way, we have created an innovative digital learning scenario that corresponds to a 3-h laboratory. Evaluation took place during the implementation of nonformal student laboratories. Results show positive effects in interest to working with tablets as a tool and an object of learning.


Overview of temporal action detection based on deep learning

Abstract: Abstract Temporal Action Detection (TAD) aims to accurately capture each action interval in an untrimmed video and to understand human actions. This paper comprehensively surveys the state-of-the-art techniques and models used for TAD task. Firstly, it conducts comprehensive research on this field through Citespace and comprehensively introduce relevant dataset. Secondly, it summarizes three types of methods, i.e., anchor-based, boundary-based, and query-based, from the design method level. Thirdly, it summarizes three types of supervised learning methods from the level of learning methods, i.e., fully supervised, weakly supervised, and unsupervised. Finally, this paper explores the current problems, and proposes prospects in TAD task.


A Semantic Tree-Based Fast-Moving Object Trajectory Tracking Algorithm for Table Tennis

Abstract: Table tennis is a popular sport around the world. A key technology in table tennis education and analysis system is reconstructing the trajectory of the fast-moving ball from videos. Typically the table tennis ball is too small and barely visible in the video, making it difficult to be recognized directly by detection models like YOLO. However, table tennis balls usually has obvious motion features, which are usually not found in similar false targets. It inspired the authors to first find all candidate targets and then use the motion features of table tennis ball to select them out. In this article, the authors propose a tree-based algorithm named T-FORT to track the ball and reconstruct its trajectory. Specifically, they consider all the possible objects in a tree-framework, and identify the real target by integrating visual features and moving patterns. The authors conduct a set of experiments on three datasets to evaluate the effectiveness and performance of the proposed algorithm. The experimental results show that the proposed method is more precise than existing algorithms, and is robust in various scenarios.


Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

Abstract: Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms.


Rate distortion optimization for adaptive gradient quantization in federated learning

Abstract: Federated Learning (FL) is an emerging machine learning framework designed to preserve privacy. However, the continuous updating of model parameters over uplink channels with limited throughput leads to a huge communication overload, which is a major challenge for FL. To address this issue, we propose an adaptive gradient quantization approach that enhances communication efficiency. Aiming to minimize the total communication costs, we consider both the correlation of gradients between local clients and the correlation of gradients between communication rounds, namely, in the time and space dimensions. The compression strategy is based on rate distortion theory, which allows us to find an optimal quantization strategy for the gradients. To further reduce the computational complexity, we introduce the Kalman filter into the proposed approach. Finally, numerical results demonstrate the effectiveness and robustness of the proposed rate-distortion optimization adaptive gradient quantization approach in significantly reducing the communication costs when compared to other quantization methods.


The role of political hate speech in promoting extreme ideologies and manipulating public discourse for social approval in Pakistan

Abstract: Abstract The proliferation of politically motivated hate speech towards rival politicians has been rapidly increasing in Pakistan. However, the literature on political hate speech and its association with extreme ideologies is under‐theorized in the Pakistani context. We have utilized van Dijk's manipulative discourse and Walther's social approval theory to structure political hate speech and its consequences. The data was based on two Pakistani mainstream politicians’ public speeches and partisans’ comments available on YouTube. Findings reveal that politicians commonly exploit discursive categories, such as name‐calling, offensive metaphors and sarcasm, in their hate speech to dehumanize, ridicule and threaten their rivals. Their hateful slurs showcase extreme ideologies, such as polarization, intolerance, threat and violence. They use the hateful epithets to manipulate partisans, to sell fear of being betrayed and enslaved to the military and foreign powers, and to obtain, in exchange, audience and consent. The study theorizes that Pakistani mainstream politicians deliberately sensationalize hatred in their public speeches to harangue their rivals and foment extreme ideologies for the purpose of increasing their audience.


Property probes: Live exploration of program analysis results

Abstract: We present property probes, a mechanism for helping a developer explore partial program analysis results in terms of the source program interactively while the program is edited. A node locator data structure is introduced that maps between source code spans and program representation nodes, and that helps identify probed nodes in a robust way, after modifications to the source code. We have developed a client–server based tool CodeProber supporting property probes, and argue that it is very helpful in debugging and understanding program analyses. We have evaluated our tool on several languages and analyses, including a full Java compiler and a tool for intraprocedural dataflow analysis. Our performance results show that the probe overhead is negligible even when analyzing large projects.


Distributed Learning in the IoT–Edge–Cloud Continuum

Abstract: The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.


A soft voting ensemble learning approach for credit card fraud detection

Abstract: With the advancement of e-commerce and modern technological development, credit cards are widely used for both online and offline purchases, which has increased the number of daily fraudulent transactions. Many organizations and financial institutions worldwide lose billions of dollars annually because of credit card fraud. Due to the global distribution of both legitimate and fraudulent transactions, it is difficult to discern between the two. Furthermore, because only a small proportion of transactions are fraudulent, there is a problem of class imbalance. Hence, an effective fraud-detection methodology is required to sustain the reliability of the payment system. Machine learning has recently emerged as a viable substitute for identifying this type of fraud. However, ML approaches have difficulty identifying fraud with high prediction accuracy, while also decreasing misclassification costs due to the size of the imbalanced data. In this research, a soft voting ensemble learning approach for detecting credit card fraud on imbalanced data is proposed. To do this, the proposed approach is evaluated and compared with numerous sophisticated sampling techniques (i.e., oversampling, undersampling, and hybrid sampling) to overcome the class imbalance problem. We develop several credit card fraud classifiers, including ensemble classifiers, with and without sampling techniques. According to the experimental results, the proposed soft-voting approach outperforms individual classifiers. With a false negative rate (FNR) of 0.0306, it achieves a precision of 0.9870, recall of 0.9694, f1-score of 0.8764, and AUROC of 0.9936.


Sublinear Scaling in Non-Markovian Open Quantum Systems Simulations

Abstract: An exact algorithm to calculate process tensors---compact representations of environmental influences---provides a scaling advantage over previous algorithms and enables tackling problems in open quantum systems that are currently out of reach.


Efficient color image steganography based on new adapted chaotic dynamical system with discrete orthogonal moment transforms

Abstract: In today's rapidly evolving digital landscape, safeguarding the secure transmission of sensitive information is paramount. Traditional steganography methods, while effective at data concealment, have grown vulnerable to brute force attacks in our age of powerful computing. This paper addresses the pressing need to bolster data transmission security. To address the challenges posed by limited key space and security concerns associated with 1-D chaotic maps in data transmission, we introduce a novel 1-D chaotic map, the Multi-Parameter Chebyshev Map (MCM). The MCM boasts four control parameters, each with a broad chaotic range and high chaos level, significantly enhancing security. Furthermore, we present an innovative steganography technique for color images that leverages the MCM in conjunction with discrete moment transforms. This method chaotically embeds the secret message within the moment coefficients of a cover color image using MCM and quantization rules. The expansive chaotic range and high chaos level of the MCM substantially reinforce the security of this approach. The considered moment transforms include Tchebichef, Krawtchouk, Hahn, Charlier, Meixner, Shmaliy, Racah, and Dual-Hahn. The performance of the proposed steganography method is thoroughly evaluated and compared with other state-of-the-art methods in terms of hiding capacity and image quality in both spatial and transform domains. The quantization of moment coefficients demonstrates its superior performance. This method holds crucial implications for practical applications requiring secure communication of color images, such as military and medical contexts. The combination of MCM and moment transforms in this method presents a promising avenue for future research in the field of information hiding.


Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration

Abstract: This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address the existing models’ rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post), with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to assess our models’ performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their potential for both academic and practical applications in the field.


Unveiling Cryptocurrency Impact on Financial Markets and Traditional Banking Systems: Lessons for Sustainable Blockchain and Interdisciplinary Collaborations

Abstract: The advent of cryptocurrencies and blockchain technology has sparked a revolutionary shift in the financial sector. This study sets out on a wide-ranging investigation to understand the nuanced dynamics, repercussions, and potential future paths of this shifting environment in the UK and USA. The primary goals of the research are to examine how cryptocurrencies affect financial markets and conventional banking systems; to examine how blockchain technology might be used in the financial sector; to assess policy and regulatory considerations; and to predict and plan for the future. This research digs into how cryptocurrencies have revolutionized the banking and finance sectors. Analysis of adoption rates, market volatility, and integration methods sheds light on the changing position of cryptocurrencies in investment portfolios, reconfiguration of asset classes, and coping mechanisms of conventional financial institutions. When looking at the financial sector as a whole, the transformational potential of blockchain technology becomes clear. The advent of DeFi, smart contracts, and asset tokenization offers new prospects to improve financial transactions, increase transparency, and broaden participation in the investment market. The research analyzes cryptocurrencies and blockchain technology from a policy and regulatory perspective. The delicate balancing act between stimulating innovation and guaranteeing consumer protection, market integrity, and financial stability is highlighted by a comparison of the regulatory methods adopted in the United Kingdom and United States, as well as proposals from international organizations. The research identifies potential future paths for these technologies and their implications. Opportunities and challenges that will influence the future of finance emerge, with a focus on central bank digital currencies (CBDCs), sustainable blockchain solutions, and interdisciplinary collaborations. As this deep dive comes to a close, the transformational power of cryptocurrencies and blockchain technology is highlighted. It sheds light on the forces that are altering the structures of the world’s financial markets, conventional banking structures, and regulatory frameworks. The findings and critical assessment stress the need for well-considered choices, ethical innovation, and interdisciplinary cooperation in order to succeed in an ever-changing environment. To further democratize access, improve transparency, and reshape the economic fabric of our planet, the future of finance resides at the confluence of tradition and innovation, where cryptocurrencies and blockchain technology exist.


Multi-Channel Graph Convolutional Networks for Graphs with Inconsistent Structures and Features

Abstract: Graph convolutional networks (GCNs) have attracted increasing attention in various fields due to their significant capacity to process graph-structured data. Typically, the GCN model and its variants heavily rely on the transmission of node features across the graph structure, which implicitly assumes that the graph structure and node features are consistent, i.e., they carry related information. However, in many real-world networks, node features may unexpectedly mismatch with the structural information. Existing GCNs fail to generalize to inconsistent scenarios and are even outperformed by models that ignore the graph structure or node features. To address this problem, we investigate how to extract representations from both the graph structure and node features. Consequently, we propose the multi-channel graph convolutional network (MCGCN) for graphs with inconsistent structures and features. Specifically, the MCGCN encodes the graph structure and node features using two specific convolution channels to extract two separate specific representations. Additionally, two joint convolution channels are constructed to extract the common information shared by the graph structure and node features. Finally, an attention mechanism is utilized to adaptively learn the importance weights of these channels under the guidance of the node classification task. In this way, our model can handle both consistent and inconsistent scenarios. Extensive experiments on both synthetic and real-world datasets for node classification and recommendation tasks show that our methods, MCGCN-A and MCGCN-I, achieve the best performance on seven out of eight datasets and the second-best performance on the remaining dataset. For simpler graph structures or tasks where the overhead of multiple convolution channels is not justified, traditional single-channel GCN models might be more efficient.


Locality-Based Action-Poisoning Attack against the Continuous Control of an Autonomous Driving Model

Abstract: Various studies have been conducted on Multi-Agent Reinforcement Learning (MARL) to control multiple agents to drive effectively and safely in a simulation, demonstrating the applicability of MARL in autonomous driving. However, several studies have indicated that MARL is vulnerable to poisoning attacks. This study proposes a ’locality-based action-poisoning attack’ against MARL-based continuous control systems. Each bird in a flock interacts with its neighbors to generate the collective behavior, which is implemented through rules in the Reynolds’ flocking algorithm, where each individual maintains an appropriate distance from its neighbors and moves in a similar direction. We use this concept to propose an action-poisoning attack, based on the hypothesis that if an agent is performing significantly different behaviors from neighboring agents, it can disturb the driving stability of the entirety of the agents. We demonstrate that when a MARL-based continuous control system is trained in an environment where a single target agent performs an action that violates Reynolds’ rules, the driving performance of all victim agents decreases, and the model can converge to a suboptimal policy. The proposed attack method can disrupt the training performance of the victim model by up to 97% compared to the original model in certain setting, when the attacker is allowed black-box access.


Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments

Abstract: This research addresses the sensor placement optimization problem (SPOP) in the industrial sector, aiming to enhance operational efficiency and safety through the strategic deployment of sensors. The focus is on optimizing the locations of thermal cameras and motion sensors, with a dual objective of maximizing coverage and minimizing redundancy in the production hall. To solve this challenge, a method based on the application of the nature-inspired bat algorithm was employed. The study reveals noteworthy findings, emphasizing the proficiency of the bat algorithm (BA) in optimizing the placement of thermal cameras and motion sensors. Numeric outcomes demonstrate the algorithm’s effectiveness in maximizing machine coverage while minimizing sensor usage within a real-world industrial environment. These results underscore the versatility and reliability of the BA, establishing it as a valuable tool for addressing complex optimization tasks in industrial settings.


Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

Abstract: Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment.


Specific Emitter Identification Based on Attractor Feature Space of System under Blind Equalization

Abstract: In the process of the individual identification of radiation sources, the effective extraction of fine features of target radiation sources can be regarded as being crucial for the subsequent individual identification. However, in the complex electromagnetic environment, the effective extraction of the radiation source features is still facing great challenges. To solve this problem, we propose an algorithm for constructing the attractor feature space of the radiation source system based on blind equalization to solve this problem. Firstly, we use blind equalization to process the target signal. Secondly, we use the phase-space reconstruction technique to construct the system attractor feature space of the target signal processed, and explore the adaptive relationship between feature-space-embedding dimensions, the delay time and the neural network, finding the optimal values. Finally, the feature space is used as the input of the neural network for the subsequent individual discrimination decision. Experimentally, it is proved that our proposed algorithm improves the individual recognition rate of the target radiation source under the complex electromagnetic environment to a certain extent, which is of practical application value.


Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Abstract: The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.


Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

Abstract: The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs.


Data Augmentation for Regression Machine Learning Problems in High Dimensions

Abstract: Machine learning approaches are currently used to understand or model complex physical systems. In general, a substantial number of samples must be collected to create a model with reliable results. However, collecting numerous data is often relatively time-consuming or expensive. Moreover, the problems of industrial interest tend to be more and more complex, and depend on a high number of parameters. High-dimensional problems intrinsically involve the need for large amounts of data through the curse of dimensionality. That is why new approaches based on smart sampling techniques have been investigated to minimize the number of samples to be given to train the model, such as active learning methods. Here, we propose a technique based on a combination of the Fisher information matrix and sparse proper generalized decomposition that enables the definition of a new active learning informativeness criterion in high dimensions. We provide examples proving the performances of this technique on a theoretical 5D polynomial function and on an industrial crash simulation application. The results prove that the proposed strategy outperforms the usual ones.