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This research paper proposes a hybrid quantum key distribution (QKD)-based framework for secure enterprise communication using post-quantum cryptography (PQC) and smart cards. The paper discusses the limitations of current PQC implementations and the need for a hybrid approach that combines classical non-PQC algorithms with quantum-resistant PQC algorithms to establish secure communication channels. The proposed framework utilizes point-to-point QKD links between quantum devices to establish a shared secret key, which is then used to encrypt and decrypt data using PQC algorithms. Smart cards are used for authentication and key synchronization to ensure the security of end-user devices. The paper also reviews relevant literature on QKD and PQC and discusses future work. The proposed framework offers a cost-effective and secure way for enterprises to communicate with each other. [ABSTRACT FROM AUTHOR]

• Metacognitive potential of children with ADHD syndrome tends to be underestimated in education. • Four conceptions of mind were elicited in phenomenographic interviews analysis. • A metaphor of a mind-machine enabled expression of intuitive, idiosyncratic knowledge. • Children with ADHD are aware of their limitations: inability to inhibit impulsive reactions. • Characteristic overload reaction can lead to a symbolic "turning off" that is often described. The paper explores the concepts of mind in young learners with ADHD syndrome: How do they experience and conceptualize their own minds? To what extend are they able to explain the mental operations activated during learning? Which of these processes do they consider to be the most important in their learning? What are the educational consequences of such perception? The article consists of four parts. The first provides the reader with a short though comprehensive theoretical backgrounds, including a critical review of latest research on the subject. Second part describes the methodology of phenomenographic research conducted with 8-years old students suffering from ADHD syndrom (N = 25). The results of interviews (both on iconic and verbal expressions' levels) constitute the main body of the text. The subjects were asked to imagine their mind being an unusual machine, and then draw and explain how such mind-machine would work while learning. Four conceptions of mind were elicited and described: a/ mind as an untamed mystery; b/ as a memory card; c/ as a processing center, and d/ as a control panel. The results were discuss in terms of their limitations, the possibilities of extending the research on metacognitive potential of children suffering from ADHD syndrom, and consequences to pedagogical practice. The paper is addressed to educators, both scientific researchers and elementary teachers, to help them understand the way students with ADHD perceive and interpret their learning environment and one's own mind's potential. [ABSTRACT FROM AUTHOR]

The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the corresponding financial loss costs billions of dollars. In this paper, we study e-commerce credit card fraud detection, in collaboration with our industrial partner, Worldline. Transactional companies are more and more dependent on machine learning models such as deep learning anomaly detection models, as part of real-world fraud detection systems (FDS). We focus on continual learning to find the best model with respect to two objectives: to maximize the accuracy and to minimize the catastrophic forgetting phenomenon. For the latter, we proposed an evaluation procedure to quantify the forgetting in data streams with delayed feedback: the plasticity/stability visualization matrix. We also investigated six strategies and 13 methods on a real-size case study including five months of e-commerce credit card transactions. Finally, we discuss how the trade-off between plasticity and stability is set, in practice, in the case of FDS. • Fraud detection models must be updated continually to handle new fraud strategies. • They must balance plasticity (learn new patterns) and stability (remember old ones). • We show how to quantify both and discuss the trade-off for fraud detection. • We provide an extensive comparison of six strategies and 13 different models. • We present a real case study based on more than 50 million e-commerce transactions. [ABSTRACT FROM AUTHOR]

An anonymous transit pass system allows passengers to access transport services within fixed time periods, with their privileges automatically deactivating upon time expiration. Although existing transit pass systems are deployable on powerful devices like PCs, their adaptation to more user-friendly devices, such as mobile phones with smart cards, is inefficient due to their reliance on heavy-weight operations like bilinear maps. In this paper, we introduce an innovative anonymous transit pass system, dubbed A n o P a s , optimized for deployment on mobile phones with smart cards, where the smart card is responsible for crucial lightweight operations and the mobile phone handles key-independent and time-consuming tasks. Group signatures with time-bound keys (GS-TBK) serve as our core component, representing a new variant of standard group signatures for the secure use of time-based digital services, preserving users' privacy while providing flexible authentication services. We first constructed a practical GS-TBK scheme using the tag-based signatures and then applied it to the design of AnoPas. We achieve the most efficient passing protocol compared to the state-of-the-art AnoPas/GS-TBK schemes. We also present an implementation showing that our passing protocol takes around 38.6 ms on a smart card and around 33.6 ms on a mobile phone. [Display omitted] • Anonymous transit pass on mobile phones with smart cards. • Practical group signatures with time-bound keys. • Support for automatic revocation and prematurely revocation. • Flexible authentication for time-based digital services. • Privacy protection for users. [ABSTRACT FROM AUTHOR]

A Two-Factor Authentication (2FA) scheme can authenticate a client if the client is able to provide the possession factor (like biometric feature, smart card) and the knowledge factor (like password, secret key) simultaneously. With only one factor, it is hard for an adversary to impersonate the client to pass the authentication, and thus 2FA provides better security than single-factor authentication schemes. However, as far as we know, all existing 2FA schemes do not consider the leakage of server's database, and their authenticity may fail when the database is also compromised (in addition to one factor). Considering numerous reports of database leakage in the real world, it seems imminent to study and design 2FA schemes resilient to database leakage. In this paper, we formalize security models for 2FA schemes by taking database leakage into account. Our security models consider malicious adversaries who can obtain both the client's one authentication factor and the server's database, and have two requirements, authenticity and zero-knowledge. Authenticity ensures that such malicious adversaries cannot impersonate the client to pass the authentication, while zero-knowledge guarantees that such malicious adversaries obtain no information about the client's the other factor. Zero-knowledge is especially important for biometric features (like faces, fingerprints), which are inherent to human beings and can hardly be changed. Then we propose a biometric-based 2FA scheme with biometric feature and secret key serving as the two authentication factors. Our 2FA scheme has three rounds, and we prove its authenticity and zero-knowledge under database leakage in the random oracle model. Notably, our construction makes a novel use of a recent technical advance called robust property-preserving hashing (Boyle et al., ITCS 2019) together with fully homomorphic encryption, to recognize or discern clients by their biometric samplings in a homomorphic and secure way. [ABSTRACT FROM AUTHOR]

Service bottlenecks are a key barrier to building a resilient public transport system. In this paper, we propose a new approach to automatically extract the role of a station in dynamical public transport flow networks based on the emerging role discovery method in network science. The term "role" in this study refers to the distinctive position or function that a station plays within the public transport flow network. Using smart card data from Nanjing public bikesharing agencies, we first construct dynamical public transport flow networks with notions of dynamical graph and edge. We then develop a dynamical algorithm to recursively compute the structural flow characteristics of nodes in passenger flow networks. Non-negative Matrix Factorization is conducted to extract the role memberships from the derived structural feature matrix and interpret each role in terms of measurements with practical values. The network hubs and potential service bottlenecks are then identified based on their operating characteristics and dynamics. Furthermore, the day-to-day and within-day role dynamics of public transport stations over time are unveiled. The results contribute to a better understanding of the interplay between stations in the network, and the identification of roles provides insight for public transport agencies to improve service resilience. [ABSTRACT FROM AUTHOR]

Travel activities influence individual travel location, time, and frequencies. Understanding individual travel regularity under different activities is crucial for individual mobility prediction and urban facilities planning. Existing studies need further improvement as they have not adequately taken into account the impact of activity factors on individual travel patterns. In this paper, we propose an innovative framework for exploring travel regularity and the correlation with urban region attributes at the individual level. The framework's novelty and uniqueness lie in its three layers: (i) A trip pre-processing layer for extracting every complete public transit trip and trip purposes (e.g. commuting activity and non-commuting activity) by combining individual's travel characteristics and public transport network. (ii) An entropy-based travel regularity measurement and K-means based multiple-view clustering layer to assess the extent of individual travel repetition over time for various travel activity and investigate the similarities and differences among users. (iii) A region dependence correlation analysis layer for exploring the correlation between individual travel regularity and regions attributes of two key locations: home and workplace More importantly, by employing the framework, we gained empirical insights based on a large-scale dataset (covering 0.64 million users) collected from public traffic smart cards. For instance, commuters can be categorized into four groups based on the regularity of their commuting activities: Regular Workers (24%), Flex-time Workers (21%), Overtime Workers (35%), and Other Workers (20%). The distribution of these four groups is associated with workplace's administrative characteristics, with a Pearson correlation of 0.503. In addition, individuals can also be classified into two groups based on their travel regularity of non-commuting activities: Limited Visitors (37%) and Active Explorers (63%). The distribution of these individuals is correlated with the coverage ratio of public transit facility in their home locations, showing a Pearson correlation of 0.604. • A framework exploring individual travel regularity under different purpose. • Entropy-Space K-means for travel activity-based individual classification. • Inter-personal and intra-personal variability of individual travel patterns. • Correlation between individual travel regularity and urban region attributes. [ABSTRACT FROM AUTHOR]

Group travel behaviour widely exists in cities, but has not been well investigated by researchers. To fill this gap, this paper develops a co-existence-based methodological framework to systematically explore the spatiotemporal characteristics of group travel behaviour. We apply our framework to a case study of Shanghai, China, using a one-month tranche of metro smart card data. Results show that most travellers perform a small number of group trips, together with a small number of co-travellers. They usually travel in a dyad or triad group and form far more small social communities than large ones. Group travel behaviour is distinctly different from individual travel in terms of both time and space: group travellers are more likely to travel during weekends, on holidays, and in the afternoons and evenings. They also prefer to perform group behaviour near stations located in the city centre or the centres of new towns in suburban areas, and close to attractions and public facilities. The analysis we present has various potential applications such as improving the management of public events and supporting the design of group ticket policy. [ABSTRACT FROM AUTHOR]