Reviewerly/exHarmony huggingface dataset:
Optimizing state-discrimination receivers for continuous-variable quantum key distribution over a wiretap channel

Abstract We address a continuous-variable quantum key distribution protocol employing quaternary phase-shift-keying of coherent states and a non-Gaussian measurement inspired by quantum receivers minimizing the error probability in a quantum-state-discrimination scenario. We consider a pure-loss quantum wiretap channel, in which a possible eavesdropper is limited to collect the sole channel losses. We perform a characterization of state-discrimination receivers and design an optimized receiver maximizing the asymptotic secure key rate (SKR), namely the key-rate optimized receiver (KOR), comparing its performance with respect to the pretty good measurement and the heterodyne-based protocol. We show that the KOR increases the SKR for metropolitan-network distances. Finally, we also investigate the implementations of feasible schemes, such as the displacement feed-forward receiver, obtaining an increase in the SKR in particular regimes.
Introduction: Advancing (Im)politeness Studies?

This volume represents a concerted effort by both established and emerging scholars in the field to push the boundaries of research on (im)politeness. By delving into the cultural, emotional, and digital dimensions of (im)politeness across diverse genres, this collection presents a range of approaches that deepen our understanding of this essential phenomenon in everyday human interaction. The introduction offers a succinct synopsis of this volume, followed by an earnest examination, through three confessions, of the obstacles and complexities inherent in advancing (im)politeness studies. Particularly noteworthy is the inclusion of five small poems that seek, in a creative manner, to depict the intricate and ambiguous nature of (im)politeness.
Effective implementation of $$\text{L}{0}$$-regularised compressed sensing with chaotic-amplitude-controlled coherent Ising machines

Coherent Ising machine (CIM) is a network of optical parametric oscillators that can solve large-scale combinatorial optimisation problems by finding the ground state of an Ising Hamiltonian. As a practical application of CIM, Aonishi et al., proposed a quantum-classical hybrid system to solve optimisation problems of [Formula: see text]-regularisation-based compressed sensing. In the hybrid system, the CIM was an open-loop system without an amplitude control feedback loop. In this case, the hybrid system is enhanced by using a closed-loop CIM to achieve chaotic behaviour around the target amplitude, which would enable escaping from local minima in the energy landscape. Both artificial and magnetic resonance image data were used for the testing of our proposed closed-loop system. Compared with the open-loop system, the results of this study demonstrate an improved degree of accuracy and a wider range of effectiveness.
FedRight: An effective model copyright protection for federated learning

Federated learning (FL), an effective distributed machine learning framework, implements model training and meanwhile protects local data privacy. It has been applied to a broad variety of practical areas due to their great performance and appreciable profits. Who really owns the model, and how to protect the copyright has become a real problem. Intuitively, the existing property rights protection methods in centralized scenarios (e.g., watermark embedding and model fingerprints) are possible solutions for FL. But they are still challenged by the distributed nature of FL in aspects of the no data sharing, parameter aggregation, and federated training settings. For the first time we formalize the problem of copyright protection for FL, and propose FedRight to protect model copyright based on model fingerprints, i.e., extracting model features by generating adversarial examples as model fingerprints. FedRight outperforms previous works in four key aspects: (i) Validity - it extracts model features to generate transferable fingerprints to train a detector to verify the copyright of the model. (ii) Fidelity - it is with imperceptible impact on the federated training, thus promises good main task performance. (iii) Robustness - it is empirically robust against malicious attack on copyright protection, i.e., fine-tuning, model pruning and adaptive attacks. (iv) Black-box - it is valid in black-box forensic scenario where only application programming interface calls to the model are available. Extensive evaluations across 3 datasets and 9 model structures demonstrate FedRight's superior fidelity, validity and robustness.
Quantum error correction with fractal topological codes

Recently, a class of fractal surface codes (FSCs), has been constructed on fractal lattices with Hausdorff dimension <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mi>&amp;#x03F5;</mml:mi></mml:math>, which admits a fault-tolerant non-Clifford CCZ gate \cite{zhu2021topological}. We investigate the performance of such FSCs as fault-tolerant quantum memories. We prove that there exist decoding strategies with non-zero thresholds for bit-flip and phase-flip errors in the FSCs with Hausdorff dimension <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mi>&amp;#x03F5;</mml:mi></mml:math>. For the bit-flip errors, we adapt the sweep decoder, developed for string-like syndromes in the regular 3D surface code, to the FSCs by designing suitable modifications on the boundaries of the holes in the fractal lattice. Our adaptation of the sweep decoder for the FSCs maintains its self-correcting and single-shot nature. For the phase-flip errors, we employ the minimum-weight-perfect-matching (MWPM) decoder for the point-like syndromes. We report a sustainable fault-tolerant threshold (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&amp;#x223C;</mml:mo><mml:mn>1.7</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math>) under phenomenological noise for the sweep decoder and the code capacity threshold (lower bounded by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2.95</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math>) for the MWPM decoder for a particular FSC with Hausdorff dimension <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>D</mml:mi><mml:mi>H</mml:mi></mml:msub><mml:mo>&amp;#x2248;</mml:mo><mml:mn>2.966</mml:mn></mml:math>. The latter can be mapped to a lower bound of the critical point of a confinement-Higgs transition on the fractal lattice, which is tunable via the Hausdorff dimension.
A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles

As blockchain technology plays an increasingly important role in the Internet of Vehicles, how to further enhance the data consensus between the areas of the Internet of Vehicles has become a key issue in blockchain design. The traditional blockchain-based vehicle networking consensus mechanism adopts the double-layer PBFT architecture, through the grouping of nodes for first intra-group consensus, and then global consensus. To further reduce delay, we propose a CRMWSL-PBFT algorithm (C-PBFT) for vehicle networking. Firstly, in order to ensure the security of RSU nodes in the network of vehicles and reduce the probability of malicious nodes participating in the consensus, we propose to calculate the reputation of RSU nodes based on multi-weighted subjective logic (CRMWSL) model. Secondly, in order to ensure the efficiency of blockchain data consensus, we improve the consensus protocol of traditional double-layer PBFT, change the election method of the committee and the PBFT consensus process, and improve throughput by reducing the number of consensus nodes. For the committee, we combine the credibility value and hash method to ensure the credibility of nodes, but also to ensure a certain degree of election randomness. For the PBFT consensus process, the regional committee consensus is carried out first, and then the regional master node carries out the global consensus. Through experimental comparison, we show that the C-PBFT significantly reduces consensus time, network overhead, and is scalable for Internet of Vehicles.
Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review and Conceptual Framework

The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue.
Effect of Gratitude Expression of AI Chatbot on Willingness to Donate

AbstractThis study aimed to verify the effect of artificial intelligence (AI) chatbots’ gratitude expression for a fundraising campaign on recipients’ willingness to donate. The mediating effects of self-efficacy and social worth on the relationship between expressions of gratitude and the willingness to donate were explored. The moderating effect of identity disclosure to enhance the effect of expressing gratitude was also investigated. By conducting a 2 (gratitude expression: absent vs. present) × 3(identity cue: text-only vs. robot vs. human) factorial design experiment with 564 American adults, the AI chatbot’s gratitude expression was found to increase the subject’s willingness to donate. The effect of these expressions of gratitude was mediated by self-efficacy rather than by social worth. However, when the AI chatbot provided a human identity cue, not only was the mediating effect of self-efficacy strengthened but the mediating effect of social worth was also significant.Keywords: Gratitude expressionself-efficacysocial worthidentity cueartificial intelligence chatbot Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data used to support the findings of this study are available from the corresponding author upon request.Additional informationFundingThis work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [NRF-2021S1A5C2A02088387].Notes on contributorsMin NamkoongMin Namkoong is a doctoral student at the Department of Media and Communication at Sungkyunkwan University. His research interests include human-computer interaction, public relations, and persuasion.Gain ParkGain Park is an assistant professor at the Department of Journalism and Media Studies at New Mexico State University. Her research interests include human-computer interaction, immersive media, and persuasion.Younjung ParkYounjung Park is a senior researcher of Global Convergence Content Research Center at Sungkyunkwan University. Her research interests include natural language processing, machine learning, human-computer interaction, and interpersonal communication in virtual space.Seyoung LeeSeyoung Lee is an assistant professor of the Department of Media and Communication at Sungkyunkwan University. His research interests include interpersonal communication, human communication, and human-media agent interaction.
Enhancing IoT Security through a Green and Sustainable Federated Learning Platform: Leveraging Efficient Encryption and the Quondam Signature Algorithm

This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront.
Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions

The interest in social media sentiment analysis and opinion mining for public security events has increased over the years. The availability of social media platforms for communication provides a valuable source of information for sentiment analysis and opinion mining research. The content shared across the media gives potential input to the physical environment and social phenomena related to public security threats. The input has been used to: monitor public security threats or emergency events, analyzing sentiment and opinionated data for threat management and the detection of public security threat events using geographic location-based sentiment analysis. However, a systematic survey that describes the trends and latest developments in this domain is unavailable. This paper presents a survey of social media sentiment analysis and opinion mining for public security. This paper aims to: understand the progress of the current state-of-the-art, identify the research gaps, and propose potential future directions. In total, 200 articles published from 2016 to 2023 were considered in this survey. The taxonomy shows the key attributes and limitations of the work presented in the surveyed articles. Subsequently, the potential future direction of work on sentiment analysis in the public security domain is suggested for interested researchers.
Track-Oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking

In this paper, we derive and propose a track-oriented marginal Poisson multi-Bernoulli mixture (TO-MPMBM) filter to address the problem that the standard random finite set filters cannot build continuous trajectories for multiple extended targets.First, the Poisson point process model and the multi-Bernoulli mixture (MBM) model are used to establish the set of birth trajectories and the set of existing trajectories, respectively.Second, the proposed filter recursively propagates the marginal association distributions and the Poisson multi-Bernoulli mixture (PMBM) density over the set of alive trajectories.Finally, after pruning and merging process, the trajectories with existence probability greater than the given threshold are extracted as the estimated target trajectories.A comparison of the proposed filter with the existing trajectory filters in two classical scenarios confirms the validity and reliability of the TO-MPMBM filter.
An Informative Path Planning Framework for Active Learning in UAV-Based Semantic Mapping

Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of imagery to facilitate the interpretation of large-scale complex environments. Commonly used supervised deep learning for segmentation relies on large amounts of pixelwise labeled data, which is tedious and costly to annotate. The domain-specific visual appearance of aerial environments often prevents the usage of models pretrained on publicly available datasets. To address this, we propose a novel general planning framework for UAVs to autonomously acquire informative training images for model retraining. We leverage multiple acquisition functions and fuse them into probabilistic terrain maps. Our framework combines the mapped acquisition function information into the UAV's planning objectives. In this way, the UAV adaptively acquires informative aerial images to be manually labeled for model retraining. Experimental results on real-world data and in a photorealistic simulation show that our framework maximizes model performance and drastically reduces labeling efforts. Our map-based planners outperform state-of-the-art local planning.
An improved heterogeneous graph convolutional network for job recommendation

Job recommendation is crucial in online recruitment platforms due to the overwhelming number of job postings. Job seekers spend considerable time and effort searching for suitable employment. With millions of job seekers browsing job postings daily, the demand for accurate and effective job recommendations is more pressing than ever. To address this challenge, we propose IHGCN, an improved semi-supervised heterogeneous graph convolutional network model for job recommendation. IHGCN aims to provide job recommendations for early job seekers based on their resumes. Firstly, we introduce a novel labeling classification standard specifically tailored to early job seeker resumes. Secondly, we construct a heterogeneous resume graph where each resume is represented as a node. Job recommendation is treated as a multi-classification problem. Thirdly, our IHGCN model learns a node representation from the graph to perform effective job recommendations. To evaluate our model, we conduct experiments using a real-world resume dataset obtained from LinkedIn. The results demonstrate that IHGCN outperforms the baselines by around 10%. This study highlights the benefits of leveraging meta-paths within the Graph Convolutional Network model to address the sparsity problem caused by the one-hot representation of nodes.
Detecting future financial statement fraud using a machine learning model in Indonesia: a comparative study

Purpose This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud. Design/methodology/approach This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision. Findings The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud. Practical implications This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud. Originality/value This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.
BCSoM: Blockchain-based certificateless aggregate signcryption scheme for Internet of Medical Things

The integration of fog computing and blockchain in the Internet of Medical Things (IoMT) domain has wholly transformed the healthcare industry and e-health services. In IoMT, the patient’s personal health data is shared on an open channel that makes the IoMT system an easy target for attackers. Therefore, confidentiality and authentication are the key security requirements for an IoMT system. Although few works have been proposed to provide secure communication in conventional healthcare infrastructures, limited work has been done to achieve data confidentiality and authentication in blockchain-based IoMT under fog environment. Therefore, this work proposes a blockchain-assisted certificateless aggregate signcryption called BCSoM scheme that achieves data confidentiality and device/patient authentication. In the proposed work, first, IoMT devices generate a signcrypted text and transmit it to an aggregator that generates an aggregated signcrypted text and sends it to a receiver fog server. The receiver fog server verifies the signcrypted text through the blockchain and also performs unsigncryption to retrieve the original message. The proposed scheme is proven secure under the Discrete Logarithm (DL) and Computational Diffe–Hellman (CDH) assumptions. Finally, a thorough performance analysis using the Hyperledger Fabric platform and cryptographic libraries shows that the proposed scheme is computationally more efficient when compared to existing works.