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Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework's integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy. [ABSTRACT FROM AUTHOR]

As generative NLP can now produce content nearly indistinguishable from human writing, it is becoming difficult to identify genuine research contributions in academic writing and scientific publications. Moreover, information in machine-generated text can be factually wrong or even entirely fabricated. In this work, we introduce a novel benchmark dataset containing human-written and machine-generated scientific papers from SCIgen, GPT-2, GPT-3, ChatGPT, and Galactica, as well as papers co-created by humans and ChatGPT. We also experiment with several types of classifiers—linguistic-based and transformer-based—for detecting the authorship of scientific text. A strong focus is put on generalization capabilities and explainability to highlight the strengths and weaknesses of these detectors. Our work makes an important step towards creating more robust methods for distinguishing between human-written and machine-generated scientific papers, ultimately ensuring the integrity of scientific literature. [ABSTRACT FROM AUTHOR]

By utilizing smart contracts, which are essentially scripts that are anchored in a decentralized manner on blockchains or other similar infrastructures, it is possible to make the execution of predetermined procedures visible to the outside world. The programmability of previously unrealized assets, such as money, and the automation of previously manual business logic are both made possible by smart contracts. This revelation inspired us to analyze smart contracts in blockchain technologies written in English between 2012 and 2022. The scope of research is limited to the journal. Reviews, conferences, book chapters, theses, monographs, and interview-based works, and also articles in the press, are eliminated. This review comprises 252 articles over the last ten years with "Blockchain", "block-chain", "smart contracts", and "smart contracts" as keywords. This paper discusses smart contracts' present status and significance in blockchain technology. The gaps and challenges in the relevant literature have also been discussed, particularly emphasizing the limitations. Based on these findings, several research problems and prospective research routes for future study that will likely be valuable to academics and professionals are identified. [ABSTRACT FROM AUTHOR]

In South Korea, the rapid proliferation of smartphones has led to an uptick in messenger phishing attacks associated with electronic communication financial scams. In response to this, various phishing detection algorithms have been proposed. However, collecting messenger phishing data poses challenges due to concerns about its potential use in criminal activities. Consequently, a Korean phishing dataset can be composed of imbalanced data, where the number of general messages might outnumber the phishing ones. This class imbalance problem and data scarcity can lead to overfitting issues, making it difficult to achieve high performance. To solve this problem, this paper proposes a phishing messages classification method using Biased Discriminant Analysis without resorting to data augmentation techniques. In this paper, by optimizing the parameters for BDA, we achieved exceptionally high performances in the phishing messages classification experiment, with 95.45% for Recall and 96.85% for the BA metric. Moreover, when compared with other algorithms, the proposed method demonstrated robustness against overfitting due to the class imbalance problem and exhibited minimal performance disparity between training and testing datasets. [ABSTRACT FROM AUTHOR]

In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases. [ABSTRACT FROM AUTHOR]

Quantum information mobilizes the description of quantum systems, their states, and their behavior. Since a measurement postulate introduced by von Neumann in 1932, if a quantum system has been prepared in two different mixed states represented by the same density operator ρ , these preparations are said to have led to the same mixture. For more than 50 years, there has been a lack of consensus about this postulate. In a 2011 article, considering variances of spin components, Fratini and Hayrapetyan tried to show that this postulate is unjustified. The aim of the present paper is to discuss major points in this 2011 article and in their reply to a 2012 paper by Bodor and Diosi claiming that their analysis was irrelevant. Facing some ambiguities or inconsistencies in the 2011 paper and in the reply, we first try to guess their aim, establish results useful in this context, and finally discuss the use or misuse of several concepts implied in this debate. [ABSTRACT FROM AUTHOR]

Cloud providers offer various purchasing options to enable users to tailor their costs according to their specific requirements, including on-demand, reserved instances, and spot instances. On-demand and spot instances satisfy short-term workloads, whereas reserved instances fulfill long-term instances. However, there are workloads that fall outside of either long-term or short-term categories. Consequently, there is a notable absence of services specifically tailored for medium-term workloads. On-demand services, while offering flexibility, often come with high costs. Spot instances, though cost-effective, carry the risk of termination. Reserved instances, while stable and less expensive, may have a remaining period that extends beyond the duration of users' tasks. This gap underscores the need for solutions that address the unique requirements and challenges associated with medium-term workloads in the cloud computing landscape. This paper introduces a new cloud broker that introduces IaaS services for medium-term workloads. On one hand, this broker strategically reserves resources from providers, and on the other hand, it interacts with users. Its interaction with users is twofold. It collects users' preferences regarding commitment term for medium-term workloads and then transforms the leased resources based on commitment term, aligning with the requirements of most users. To ensure profitability, the broker sells these services utilizing an auction algorithm. Hence, in this paper, an auction algorithm is introduced and developed, which treats cloud services as virtual assets and integrates the depreciation over time. The findings affirm the lack of services that fulfill medium workloads while ensuring the financial viabilty and profitability of the broker, given that the estimated return on investment (ROI) is acceptable. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to evaluate six different models: VGG16, VGG19, ResNet18, ResNet50, ResNet101, and Vit. This paper compares the performance of these models based on three metrics: accuracy, precision, and F1 score. The results showed that the VGG19 model, combined with the proposed method, achieved the highest average accuracy of 96.67%. [ABSTRACT FROM AUTHOR]

As it concerns remote laboratories (RLs) for teaching microcontroller programming, the related literature reveals several common characteristics and a common architecture. Our search of the literature was constrained to papers published in the period of 2020–2023 specifically on remote laboratories related to the subject of teaching microcontroller programming of the Arduino family. The objective of this search is to present, on the one hand, the extent to which the RL platform from the Hellenic Mediterranean University (HMU-RLP) for Arduino microcontroller programming conforms to this common architecture and, on the other hand, how it extends this architecture with new features for monitoring and assessing users' activities over remote labs in the context of pervasive and supervised learning. The HMU-RLP hosts a great number of experiments that can be practiced by RL users in the form of different scenarios provided by teachers as activities that users can perform in their self-learning process or assigned as exercises complementary to the theoretical part of a course. More importantly, it provides three types of assessments of the code users program during their experimentation with RLs. The first type monitors each action users perform over the web page offered by the RL. The second type monitors the activities of users at the hardware level. To this end, a shadow microcontroller is used that monitors the pins of the microcontroller programmed by the users. The third type automatically assesses the code uploaded by the users, checking its similarity with the prototype code uploaded by the instructors. A trained AI model is used to this end. For the assessments provided by the HMU-RLP, the experience API (xAPI) standard is exploited to store users' learning analytics (LAs). The LAs can be processed by the instructors for the students' evaluation and personalized learning. The xAPI reporting and visualization tools used in our prototype RLP implementation are also presented in the paper. We also discuss the planned development of such functionalities in the future for the use of the HMU-RLP as an adaptive tool for supervised distant learning. [ABSTRACT FROM AUTHOR]

The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily on well-constructed datasets. This paper addresses the critical DDoS dataset creation and evaluation domain, focusing on the cloud network. After conducting an in-depth analysis of 16 publicly available datasets, this research identifies 15 shortcomings across various dimensions, emphasizing the need for a new approach to dataset creation. Building upon this understanding, this paper introduces a new public DDoS dataset named BCCC-cPacket-Cloud-DDoS-2024. This dataset is meticulously crafted, addressing challenges identified in previous datasets through a cloud infrastructure featuring over eight benign user activities and 17 DDoS attack scenarios. Also, a Benign User Profiler (BUP) tool has been designed and developed to generate benign user network traffic based on a normal user behavior profile. We manually label the dataset and extract over 300 features from the network and transport layers of the traffic flows using NTLFlowLyzer. The experimental phase involves identifying an optimal feature set using three distinct algorithms: ANOVA, information gain, and extra tree. Finally, this paper proposes a multi-layered DDoS detection model and evaluates its performance using the generated dataset to cover the main issues of the traditional approaches. [ABSTRACT FROM AUTHOR]

The Flutter framework with Dart programming allows developers to effortlessly build applications for both web and mobile from a single codebase. It enables efficient conversions to native codes for mobile apps and optimized JavaScript for web browsers. Since utilizing a wide range of widgets in Flutter ensures consistent experiences on various devices for users, it becomes crucial in programming education by providing a unified environment for learning app development while reducing the need for platform-specific knowledge. However, the setup of the Flutter environment is challenging for novice students due to its multiple steps, such as installing dependencies and configuring environments. To support independent learning for these students, it is essential to simplify the setup by providing user-friendly instructions and automated tools. In this paper, we present a Docker-based environment for Flutter app developments across Windows, Linux, and Mac through Visual Studio Code, ensuring a unified learning experience. This paper aims to simplify complex configurations and address the obstacles encountered by students when initiating Flutter projects. For the evaluation, we prepared three simple Flutter projects along with the setup environment in a Docker container. Then, we asked 24 Master's students at Okayama University, Japan, to install the environment and modify the source codes in the projects independently by following the given instructions. The results show that all the students successfully completed the assignments, which confirms the efficiency and validity of our proposal. [ABSTRACT FROM AUTHOR]

The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi-skilled workers, and targeting profit and carbon emissions. In contrast to common approaches in reinforcement learning that typically employ weighting strategies to solve multi-objective problems, our approach innovatively incorporates non-dominated ranking directly into the reward function. The exploration of Pareto frontier solutions or better solutions is moderated by comparing performance between solutions and dynamically adjusting rewards based on the occurrence of repeated solutions. The experimental results show that the multi-objective Advantage Actor-Critic algorithm based on Pareto optimization exhibits superior performance in terms of metrics superiority in the comparison of six experimental cases of different scales, with an excellent metrics comparison rate of 70%. In some of the experimental cases in this paper, the solutions produced by the multi-objective Advantage Actor-Critic algorithm show some advantages over other popular algorithms such as the Deep Deterministic Policy Gradient Algorithm, the Soft Actor-Critic Algorithm, and the Non-Dominated Sorting Genetic Algorithm II. This further corroborates the effectiveness of our proposed solution. [ABSTRACT FROM AUTHOR]

Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine learning practices. In order to evaluate the pros and cons of the machine-learning-based approach, we replicated a research study that uses a deep neural network model for intrusion detection. The results of our replicated research study expose several systematic problems with the used datasets and evaluation methods. In our experiments, a signature-based intrusion detection system with a minimal setup was able to outperform the tested model even under small traffic changes. Testing the replicated neural network on a new dataset recorded in the same environment with the same attacks using the same tools showed that the accuracy of the neural network dropped to 54%. Furthermore, the often-claimed advantage of being able to detect zero-day attacks could not be seen in our experiments. [ABSTRACT FROM AUTHOR]

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works. [ABSTRACT FROM AUTHOR]

Recent technological developments have enabled computers to identify and categorize facial expressions to determine a person's emotional state in an image or a video. This process, called "Facial Expression Recognition (FER)", has become one of the most popular research areas in computer vision. In recent times, deep FER systems have primarily concentrated on addressing two significant challenges: the problem of overfitting due to limited training data availability, and the presence of expression-unrelated variations, including illumination, head pose, image resolution, and identity bias. In this paper, a comprehensive survey is provided on deep FER, encompassing algorithms and datasets that offer insights into these intrinsic problems. Initially, this paper presents a detailed timeline showcasing the evolution of methods and datasets in deep facial expression recognition (FER). This timeline illustrates the progression and development of the techniques and data resources used in FER. Then, a comprehensive review of FER methods is introduced, including the basic principles of FER (components such as preprocessing, feature extraction and classification, and methods, etc.) from the pro-deep learning era (traditional methods using handcrafted features, i.e., SVM and HOG, etc.) to the deep learning era. Moreover, a brief introduction is provided related to the benchmark datasets (there are two categories: controlled environments (lab) and uncontrolled environments (in the wild)) used to evaluate different FER methods and a comparison of different FER models. Existing deep neural networks and related training strategies designed for FER, based on static images and dynamic image sequences, are discussed. The remaining challenges and corresponding opportunities in FER and the future directions for designing robust deep FER systems are also pinpointed. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research paper was to analyse the counterstrategies to mitigate cybersecurity challenges using organisational learning loops amidst major crises in the Higher Education and Research Sector (HERS). The authors proposed the learning loop framework revealing several counterstrategies to mitigate cybersecurity issues in HERS. The counterstrategies are explored, and their implications for research and practice are discussed. Methodology: The qualitative methodology was adopted, and semi-structured interviews with cybersecurity experts and top managers were conducted. Results: This exploratory paper proposed the learning loop framework revealing introducing new policies and procedures, changing existing systems, partnership with other companies, integrating new software, improving employee learning, enhancing security, and monitoring and evaluating security measures as significant counterstrategies to ensure the cyber-safe working environment in HERS. These counterstrategies will help to tackle cybersecurity in HERS, not only during the current major crisis but also in the future. Implications: The outcomes provide insightful implications for both theory and practice. This study proposes a learning framework that prioritises counterstrategies to mitigate cybersecurity challenges in HERS amidst a major crisis. The proposed model can help HERS be more efficient in mitigating cybersecurity issues in future crises. The counterstrategies can also be tested, adopted, and implemented by practitioners working in other sectors to mitigate cybersecurity issues during and after major crises. Future research can focus on addressing the shortcomings and limitations of the proposed learning framework adopted by HERS. [ABSTRACT FROM AUTHOR]

In the realm of Parkinson's Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient's missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions. [ABSTRACT FROM AUTHOR]

This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one "real" smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics. [ABSTRACT FROM AUTHOR]

The richness and complexity of consent present challenges to those aiming to make related contributions to computer information systems (CIS). This paper aims to support consent-related research in CIS by simplifying the understanding of existing literature and facilitating the framing of future consent management research. Firstly, it outlines existing consent management research and shows how it relates to the literature in law and ethics. Secondly, it presents some fundamental explanations and definitions that must be considered for further contributions to the consent management literature. Thirdly, it identifies five types of consent-related stances often taken in the consent management literature and explains each in some detail. Fourth, it explains one of the identified types of stances (i.e., the disciplinary stance) by expanding on the links between consent as a legal construct and its ethical counterpart. Fifth, considering another of the identified types of stances (i.e., the theoretical stances normally adopted in the consent management literature), the paper presents the key requirements for legally and ethically effective consent management based on three prominent theories. Sixth, it presents the identified types of stances in a conceptual model, contending that the model is novel, relevant, understandable, and useful. [ABSTRACT FROM AUTHOR]

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring. [ABSTRACT FROM AUTHOR]

The proliferation of misinformation presents a significant challenge in today's information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the intent to cause harm. In some instances, individuals unwittingly share misinformation, driven by a desire to assist others without thorough research. However, there are also situations where misinformation involves negligence, or even intentional manipulation, with the aim of shaping the opinions and decisions of the target audience. Another key factor contributing to misinformation is its alignment with individual beliefs and emotions. This alignment magnifies the impact and influence of misinformation, as people tend to seek information that reinforces their existing beliefs. As a starting point, some 56 papers containing 'misinformation detection' in the title, abstract, or keywords, marked as "articles", written in English, published between 2016 and 2022, were extracted from the Web of Science platform and further analyzed using Biblioshiny. This bibliometric study aims to offer a comprehensive perspective on the field of misinformation detection by examining its evolution and identifying emerging trends, influential authors, collaborative networks, highly cited articles, key terms, institutional affiliations, themes, and other relevant factors. Additionally, the study reviews the most cited papers and provides an overview of all selected papers in the dataset, shedding light on methods employed to counter misinformation and the primary research areas where misinformation detection has been explored, including sources such as online social networks, communities, and news platforms. Recent events related to health issues stemming from the COVID-19 pandemic have heightened interest within the research community regarding misinformation detection, a statistic which is also supported by the fact that half of the papers included in top 10 papers based on number of citations have addressed this subject. The insights derived from this analysis contribute valuable knowledge to address the issue, enhancing our understanding of the field's dynamics and aiding in the development of effective strategies to detect and mitigate the impact of misinformation. The results spotlight that IEEE Access occupies the first position in the current analysis based on the number of published papers, the King Saud University is listed as the top contributor for the misinformation detection, while in terms of countries, the top-5 list based on the highest contribution to this area is made by the USA, India, China, Spain, and the UK. Moreover, the study supports the promotion of verified and reliable sources of data, fostering a more informed and trustworthy information environment. [ABSTRACT FROM AUTHOR]

A bilinear map whose domain and target sets are identical is called a self-bilinear map. Original self-bilinear maps are defined over cyclic groups. Since the map itself reveals information about the underlying cyclic group, the Decisional Diffie–Hellman Problem (DDH) and the computational Diffie–Hellman (CDH) problem may be solved easily in some specific groups. This brings a lot of limitations to constructing secure self-bilinear schemes. As a compromise, a self-bilinear map with auxiliary information was proposed in CRYPTO'2014. In this paper, we construct this weak variant of a self-bilinear map from generic sets and indistinguishable obfuscation. These sets should own several properties. A new notion, One Way Encoding System (OWES), is proposed to summarize these properties. The new Encoding Division Problem (EDP) is defined to complete the security proof. The OWES can be built by making use of one level of graded encoding systems (GES). To construct a concrete self-bilinear map scheme, Garg, Gentry, and Halvei(GGH13) GES is adopted in our work. Even though the security of GGH13 was recently broken by Hu et al., their algorithm does not threaten our applications. At the end of this paper, some further considerations for the EDP for concrete construction are given to improve the confidence that EDP is indeed hard. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across a variety of domains. However, this popularity has attracted the attention of malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines the tactics that adversaries use to leverage ChatGPT in a variety of cyberattacks. Attackers pose as regular users and manipulate ChatGPT's vulnerability to malicious interactions, particularly in the context of cyber assault. The paper presents illustrative examples of cyberattacks that are possible with ChatGPT and discusses the realm of ChatGPT-fueled cybersecurity threats. The paper also investigates the extent of user awareness of the relationship between ChatGPT and cyberattacks. A survey of 253 participants was conducted, and their responses were measured on a three-point Likert scale. The results provide a comprehensive understanding of how ChatGPT can be used to improve business processes and identify areas for improvement. Over 80% of the participants agreed that cyber criminals use ChatGPT for malicious purposes. This finding underscores the importance of improving the security of this novel model. Organizations must take steps to protect their computational infrastructure. This analysis also highlights opportunities for streamlining processes, improving service quality, and increasing efficiency. Finally, the paper provides recommendations for using ChatGPT in a secure manner, outlining ways to mitigate potential cyberattacks and strengthen defenses against adversaries. [ABSTRACT FROM AUTHOR]

With the advancement of IT technology, intelligent devices such as autonomous vehicles, unmanned equipment, and drones are rapidly evolving. Consequently, the proliferation of defense systems based on these technologies is increasing worldwide. In response, the U.S. Department of Defense is implementing the RMF (Risk Management Framework) to ensure the cybersecurity of defense systems and conducting cybersecurity T&E (test and evaluation) concurrently. However, RMF and cybersecurity T&E conducted during the acquisition phase of defense systems often result in fragmented cybersecurity assessments, excluding the operational environment of the defense systems. This omission fails to account for the complex network integration, data exchange functionalities, and mission-specific requirements in actual cyber attack scenarios. For these reasons, vulnerabilities in defense systems that remain unidentified during the acquisition phase can potentially pose significant cybersecurity threats during operational phases, necessitating substantial costs and efforts for remediation. Therefore, this paper proposes a mission-based cybersecurity T&E model using a Multi-Cyber Range to effectively apply these two systems in a practical manner. The Multi-Cyber Range integrates independently operated cyber ranges into a network to expand the evaluation environment, which better reflects the mission environment of defense systems. The proposed model's effectiveness is validated using a cyber attack simulation system targeting a virtualized arbitrary defense system. This paper not only presents an enhanced model for mission-based cybersecurity T&E, but also contributes to the advancement of cybersecurity T&E methodologies by providing a concrete application process. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI research resulted in diverse scholars possessing significant challenges in designing proper evaluation methods. This paper proposes a novel framework of a three-layered top-down approach on how to arrive at an optimal explainer, accenting the persistent need for consensus in XAI evaluation. This paper also investigates a critical comparative evaluation of explanations in both model agnostic and specific explainers including LIME, SHAP, Anchors, and TabNet, aiming to enhance the adaptability of XAI in a tabular domain. The results demonstrate that TabNet achieved the highest classification recall followed by TabPFN, and XGBoost. Additionally, this paper develops an optimal approach by introducing a novel measure of relative performance loss with emphasis on faithfulness and fidelity of global explanations by quantifying the extent to which a model's capabilities diminish when eliminating topmost features. This addresses a conspicuous gap in the lack of consensus among researchers regarding how global feature importance impacts classification loss, thereby undermining the trust and correctness of such applications. Finally, a practical use case on medical tabular data is provided to concretely illustrate the findings. [ABSTRACT FROM AUTHOR]

A measurement performed on a quantum system is an act of gaining information about its state. However, in the foundations of quantum theory, the concept of information is multiply defined, particularly in the area of quantum reconstruction, and its conceptual foundations remain surprisingly under-explored. In this paper, we investigate the gain of information in quantum measurements from an operational viewpoint in the special case of a two-outcome probabilistic source. We show that the continuous extension of the Shannon entropy naturally admits two distinct measures of information gain, differential information gain and relative information gain, and that these have radically different characteristics. In particular, while differential information gain can increase or decrease as additional data are acquired, relative information gain consistently grows and, moreover, exhibits asymptotic indifference to the data or choice of Bayesian prior. In order to make a principled choice between these measures, we articulate a Principle of Information Increase, which incorporates a proposal due to Summhammer that more data from measurements leads to more knowledge about the system, and also takes into consideration black swan events. This principle favours differential information gain as the more relevant metric and guides the selection of priors for these information measures. Finally, we show that, of the symmetric beta distribution priors, the Jeffreys binomial prior is the prior that ensures maximal robustness of information gain for the particular data sequence obtained in a run of experiments. [ABSTRACT FROM AUTHOR]

Named Data Networking (NDN) stands out as a prominent architectural framework for the future Internet, aiming to address deficiencies present in IP networks, specifically in the domain of security. Although NDN packets containing requested content are signed with the publisher's signature which establishes data provenance for content, the NDN domain still requires more holistic frameworks that address consumers' identity verification while accessing protected contents or services using producer/publisher-preapproved authentication servers. In response, this paper introduces the NDN One-Time Authentication (NDNOTA) framework, designed to authenticate NDN online services, applications, and data in real time. NDNOTA comprises three fundamental elements: the consumer, producer, and authentication server. Employing a variety of security measures such as single sign-on (SSO), token credentials, certified asymmetric keys, and signed NDN packets, NDNOTA aims to reinforce the security of NDN-based interactions. To assess the effectiveness of the proposed framework, we validate and evaluate its impact on the three core elements in terms of time performance. For example, when accessing authenticated content through the entire NDNOTA process, consumers experience an additional time overhead of 70 milliseconds, making the total process take 83 milliseconds. In contrast, accessing normal content that does not require authentication does not incur this delay. The additional NDNOTA delay is mitigated once the authentication token is generated and stored, resulting in a comparable time frame to unauthenticated content requests. Additionally, obtaining private content through the authentication process requires 10 messages, whereas acquiring public data only requires two messages. [ABSTRACT FROM AUTHOR]

This document is a preface to a special issue on computational linguistics and natural language processing. It highlights the increasing presence of intelligent robots in various aspects of our lives, such as customer service, manufacturing, and security. The document emphasizes the importance of robots being able to communicate using human language, which requires the study of computational linguistics and natural language processing. The special issue includes papers on topics such as linguistic profiling, higher-order logical representations, deciphering scripts, and distinguishing between human-written and machine-generated texts. The document concludes by expressing gratitude to the reviewers and contributors of the special issue. [Extracted from the article]

This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors amidst the growing complexity of projects and the increasing integration of AI technologies. The methodology employed involves the utilization of three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors, such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the influential factors identified via MICMAC provide insights for prioritizing strategic interventions. The implications of this study extend to informing decision-makers in the civil engineering domain about effective strategies for leveraging AI in their cost management practices. By adopting a systematic MCDM approach, stakeholders can enhance project outcomes while optimizing resource allocation and mitigating financial risks. [ABSTRACT FROM AUTHOR]

Situation awareness (SA) describes an individual's understanding of their surroundings and actions in the near future based on the individual's comprehension and understanding of the surrounding inputs. SA measurements can be applied to improve system performance or human effectiveness in many fields of study, including driving. However, in some scenarios drivers might need to drive in unfamiliar traffic regulations (UFTRs), where the traffic rules and vehicle configurations are a bit different from what the drivers are used to under familiar traffic regulations. Such driving conditions require drivers to adapt their attention, knowledge, and reactions to safely reach the destination. This ability is influenced by the degree of handedness. In such tasks, mixed-/left-handed people show better performance than strong right-handed people. This paper aims to explore the influence of the degree of handedness on SA when driving under UFTRs. We analyzed the SA of two groups of drivers: strong right-handed drivers and mixed-/left-handed drivers. Both groups were not familiar with driving in keep-left traffic regulations. Using a driving simulator, all participants drove in a simulated keep-left traffic system. The participants' SA was measured using a subjective assessment, named the Participant Situation Awareness Questionnaire PSAQ, and performance-based assessment. The results of the study indicate that mixed-/left-handed participants had significantly higher SA than strong right-handed participants when measured by performance-based assessment. Also, in the subjective assessment, mixed-/left-handed participants had significantly higher PSAQ performance scores than strong right-handed participants. The findings of this study suggest that advanced driver assistance systems (ADAS), which show improvement in road safety, should adapt the system functionality based on the driver's degree of handedness when driving under UFTRs. [ABSTRACT FROM AUTHOR]

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry. [ABSTRACT FROM AUTHOR]

This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution. [ABSTRACT FROM AUTHOR]

The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by analyzing user behavior datasets within a time retrospective window. It integrates a pre-trained Domain Large Model to represent user behavior data across multiple modalities, thereby constructing a high-generalization implicit behavior feature spectrum for users. In response to the tendency of existing fake user behavior mining to neglect time-series features, this study introduces an improved network called Sequence Interval Detection Net (SIDN) based on Sequence to Sequence (seq2seq) to characterize time interval sequence behaviors, achieving strong expressive capabilities for detecting fake behaviors within the time window. Ultimately, the amalgamation of latent behavioral features and explicit characteristics serves as the input for spectral clustering in detecting fraudulent users. The experimental results on Weibo real dataset demonstrate that the proposed model outperforms the detection utilizing explicit user features, with an improvement of 27.0% in detection accuracy. [ABSTRACT FROM AUTHOR]

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose. [ABSTRACT FROM AUTHOR]

Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES leverages Bayesian methods, dropout techniques, and an active learning framework to dynamically adjust the number of epochs during the training of the detection model, preventing overfitting while enhancing model accuracy and reliability. Our solution takes a set of Application Programming Interfaces (APIs), representing ransomware behavior as input we call "UA-DES-DBN". The method incorporates uncertainty and calibration quality measures, optimizing the training process for better more accurate ransomware detection. Experiments demonstrate the effectiveness of UA-DES-DBN compared to more conventional models. The proposed model improved accuracy from 94% to 98% across various input sizes, surpassing other models. UA-DES-DBN also decreased the false positive rate from 0.18 to 0.10, making it more useful in real-world cybersecurity applications. [ABSTRACT FROM AUTHOR]

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions. [ABSTRACT FROM AUTHOR]

This paper considers a resource allocation problem involving servers and mobile users (MUs) operating in a serverless edge computing (SEC)-enabled Internet of Things (IoT) network. Each MU has a fixed budget, and each server is powered by the grid and has energy harvesting (EH) capability. Our objective is to maximize the revenue of the operator that operates the said servers and the number of resources purchased by the MUs. We propose a Stackelberg game approach, where servers and MUs act as leaders and followers, respectively. We prove the existence of a Stackelberg game equilibrium and develop an iterative algorithm to determine the final game equilibrium price. Simulation results show that the proposed scheme is efficient in terms of the SEC's profit and MU's demand. Moreover, both MUs and SECs gain benefits from renewable energy. [ABSTRACT FROM AUTHOR]

History is a subject that students often find uninspiring in school education. This paper explores the application of social VR metaverse platforms in combination with interactive, nonlinear web platforms designed for immersive storytelling to support learning about a sensitive historical event, namely the Asia Minor Catastrophe. The goal was to design an alternative method of learning history and investigate if it would engage students and foster their independence. A mixed-methods research design was applied. Thirty-four (n = 34) adult participants engaged in the interactive book and VR space over the course of three weeks. After an online workshop, feedback was collected from participants through a custom questionnaire. The quantitative data from the questionnaire were analyzed statistically utilizing IBM SPSS, while the qualitative responses were coded thematically. This study reveals that these two tools can enhance historical education by increasing student engagement, interaction, and understanding. Participants appreciated the immersive and participatory nature of the material. This study concludes that these technologies have the potential to enhance history education by promoting active participation and engagement. [ABSTRACT FROM AUTHOR]

For the significant distortion problem caused by the special projection method of equi-rectangular projection (ERP) images, this paper proposes an omnidirectional image super-resolution algorithm model based on position information transformation, taking SwinIR as the base. By introducing a space position transformation module that supports deformable convolution, the image preprocessing process is optimized to reduce the distortion effects in the polar regions of the ERP image. Meanwhile, by introducing deformable convolution in the deep feature extraction process, the model's adaptability to local deformations of images is enhanced. Experimental results on publicly available datasets have shown that our method outperforms SwinIR, with an average improvement of over 0.2 dB in WS-PSNR and over 0.030 in WS-SSIM for ×4 pixel upscaling. [ABSTRACT FROM AUTHOR]

While new technologies are expected to revolutionise and become game-changers in improving the efficiency and practices of our daily lives, it is also critical to investigate and understand the barriers and opportunities faced by their adopters. Such findings can serve as an additional feature in the decisionmaking process when analysing the risks, costs, and benefits of adopting an emerging technology in a particular setting. Although several studies have attempted to perform such investigations, these approaches adopt a qualitative data collection methodology, which is limited in terms of the size of the targeted participant group and is associated with a significant manual overhead when transcribing and inferring results. This paper presents a scalable and automated framework for tracking the likely adoption and/or rejection of new technologies from a large landscape of adopters. In particular, a large corpus of social media texts containing references to emerging technologies was compiled. Text mining techniques were applied to extract the sentiments expressed towards technology aspects. In the context of the problem definition herein, we hypothesise that the expression of positive sentiment implies an increase in the likelihood of impacting a technology user's acceptance to adopt, integrate, and/or use the technology, and negative sentiment implies an increase in the likelihood of impacting the rejection of emerging technologies by adopters. To quantitatively test our hypothesis, a ground truth analysis was performed to validate that the sentiments captured by the text mining approach were comparable to the results provided by human annotators when asked to label whether such texts positively or negatively impact their outlook towards adopting an emerging technology. The collected annotations demonstrated comparable results to those of the text mining approach, illustrating that the automatically extracted sentiments expressed towards technologies are useful features in understanding the landscape faced by technology adopters, as well as serving as an important decisionmaking component when, for example, recognising shifts in user behaviours, new demands, and emerging uncertainties. [ABSTRACT FROM AUTHOR]

The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic summaries have been proposed in this direction that involve first selecting the most important nodes and then linking them, trying to extract the most useful subgraph out of the original graph. However, the current state of the art systems use costly centrality measures for identifying the most important nodes, whereas even costlier procedures have been devised for linking the selected nodes. In this paper, we address both those deficiencies by first exploiting embeddings for node selection, and then by meticulously selecting approximate algorithms for node linking. Experiments performed over two real-world big KGs demonstrate that the summaries constructed using our method enjoy better quality. Specifically, the coverage scores obtained were 0.8, 0.81, and 0.81 for DBpedia v3.9 and 0.94 for Wikidata dump 2018, across 20%, 25%, and 30% summary sizes, respectively. Additionally, our method can compute orders of magnitude faster than the state of the art. [ABSTRACT FROM AUTHOR]

Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs. [ABSTRACT FROM AUTHOR]

Spatial and temporal uncertainties are found in data for many critical applications. This paper describes the use of interval-based representations of some spatial and temporal information. Uncertainties in the information can arise from multiple sources in which degrees of support and non-support occur in evaluations. This motivates the use of intuitionistic fuzzy sets to permit the use of the positive and negative memberships to capture these uncertainties. The interval representations will include both simple and complex or nested intervals. The relationships between intervals such as overlapping, containing, etc. are then developed for both the simple and complex intervals. Such relationships are required to support the aggregation approaches of the interval information. Both averaging and merging approaches to interval aggregation are then developed. Furthermore, potential techniques for the associated aggregation of the interval intuitionistic fuzzy memberships are provided. A motivating example of maritime depth data required for safe navigation is used to illustrate the approach. Finally, some potential future developments are discussed. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Things (IoT) into business process management (BPM) aims to increase the automation level, efficiency, transparency, and comprehensibility of the business processes taking place in the physical world. The IoT enables the seamless networking of physical devices, allowing for the enrichment of processes with real-time data about the physical world and, thus, for optimized process automation and monitoring. To realize these benefits, the modeling of IoT-aware processes needs to be appropriately supported. Despite the great attention paid to this topic, more clarity is needed about the current state of the art of corresponding modeling solutions. Capturing IoT characteristics in business process models visually or based on labels is essential to ensure effective design and communication of IoT-aware business processes. A clear discernibility of IoT characteristics can enable the precise modeling and analysis of IoT-aware processes and facilitate collaboration among different stakeholders. With an increasing number of process model elements, it becomes crucial that process model readers can understand the IoT aspects of business processes in order to make informed decisions and to optimize the processes with respect to IoT integration. This paper presents the results of a large user study (N = 249) that explored the perception of IoT aspects in BPMN 2.0 process models to gain insights into the IoT's involvement in business processes that drive the successful implementation and communication of IoT-aware processes. [ABSTRACT FROM AUTHOR]

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations. [ABSTRACT FROM AUTHOR]

This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative Adversarial Networks (GANs) and Bayesian inference. The framework utilizes a Conditional GAN (C-GAN) to realistically impute missing values in the time series data. Subsequently, Bayesian inference is employed to quantify the uncertainty associated with the forecasts due to the missing data. This combined approach improves the robustness and reliability of forecasting compared to traditional methods. The effectiveness of our proposed method is evaluated on a real-world dataset of air pollution data from Mexico City. The results demonstrate the framework's capability to handle missing data and achieve improved forecasting accuracy. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) chirp waveforms are an attractive candidate to be a dual-function signal scheme for the joint radar and communication systems. OFDM chirp signals can not only be employed to transmit communication data through classic phase modulation, but also can perform radar detection by applying linear frequency modulation for subcarriers. However, the performance of the OFDM chirp communication system under the phase noise environment still remains uninvestigated. This paper tries to discuss the influence of phase noise on OFDM chirp communication systems and proposes effective phase noise estimation and compensation methods. We find that the phase noise effect on OFDM chirp communication systems consists of a common phase error (CPE) and an inter-carrier interference (ICI). If not compensated, the performance of the dual-function systems can be seriously degraded. In particular, an exact expression for the signal-plus-interference to noise ratio (SINR) for the OFDM chirp communication system is derived and some critical parameters are analyzed to exhibit the phase noise effects on system performance. Moreover, two low-complexity estimation approaches, maximum likelihood (ML) and linear minimum mean square error (LMMSE), as well as two compensation approaches, the de-correlation and cancellation algorithms, are respectively utilized to eliminate the phase noise impairment. Finally, the phase noise effects and the effectiveness of the compensation approach are verified by extensive numerical results. [ABSTRACT FROM AUTHOR]

Railway infrastructure is generally classified as either fixed or movable infrastructure assets. Failure in any of the assets could lead to the complete shutdown and disruption of the entire system, economic loss, inconvenience to passengers and the train operating company(s), and can sometimes result in death or injury in the event of the derailment of the rolling stock. Considering the importance of the railway infrastructure assets, it is only necessary to continuously explore their behavior, reliability, and safety. In this paper, a proactive multi-criteria decision-making model that is based on an interval-valued intuitionistic fuzzy set and some reliability quantitative parameters has been proposed for the evaluation of the reliability of the infrastructure assets. Results from the evaluation show that the failure mode 'Broken and defective rails' has the most risk and reliability concerns. Hence, priority should be given to the failure mode to avoid a total system collapse. [ABSTRACT FROM AUTHOR]