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This study introduces an innovative smart impact detection system designed for real-time monitoring of shipment status and path integrity. Leveraging the advanced capabilities of the ESPRESSIF ESP32-S3-MINI-1U-N8 microcontroller, which integrates Wi-Fi, a display, a memory card slot, and accelerometers, this detector represents a significant advancement in shipment tracking technology. The device is engineered to continuously measure impact magnitudes in terms of g-force, and records data when predefined impact thresholds are exceeded. These data are then wirelessly transmitted to a remote server, providing users with the ability to track shipment status and path via a dedicated application. The performance testing revealed impact measurements ranging from −0.5 to 2 g, with occasional peaks reaching approximately 4.5 g, demonstrating the system's sensitivity and reliability in diverse conditions. This smart impact detector not only facilitates continuous monitoring, but also enhances the ability to respond swiftly to potential shipment violations, thus providing a novel solution for ensuring shipment integrity. This research contributes to the field by presenting a comprehensive real-time impact detection system that integrates modern microcontroller technology with effective monitoring capabilities, setting a new benchmark for shipment tracking systems. [ABSTRACT FROM AUTHOR]

In the current 5G vehicle network system, there are security issues such as wireless intrusion, privacy leakage, and remote control. To address these challenges, an improved lightweight anonymous authentication key negotiation scheme based on certificate-less aggregate signatures is proposed and its security and efficiency are analyzed. The result shows that the scheme can offer security attributes including anonymity, traceability, and revocability, as well as effective identity authentication, and it can resist forgery attacks, man-in-the-middle attacks, tampering attacks, and smart card loss attacks. Moreover, compared with similar schemes, it possesses superior security and more efficient computational efficiency and less communication overhead, thereby being more appropriate for high-speed, large-capacity, low-latency, and resource-constrained 5G vehicle network application scenarios. [ABSTRACT FROM AUTHOR]

Despite extensive investigations on urban metro passenger flows, their evolving spatiotemporal patterns with the extensions of urban metro networks have not been well understood. Using Shenzhen as a case study city, our study initiates an investigation into this matter by analyzing the evolving network topology of Shenzhen Metro. Subsequently, leveraging long-term smart card data, we analyze the evolving spatiotemporal patterns of passenger flows and develop an analytical approach to pinpoint the major passenger sources of urban metro congestion. While the passenger travel demand and the passenger flow volumes kept increasing with the extension of the urban metro network, the major passenger sources were very stable in space, highlighting the inherent invariance in the evolution of the urban metro system. Finally, we analyze the impact of population and land use factors on passenger flow contributions of passenger sources, obtaining useful clues to foresee future passenger flow conditions. [ABSTRACT FROM AUTHOR]

Remote data backup technology avoids the risk of data loss and tampering, and has higher security compared to local data backup solutions. However, the data transmission channel for remote data backup is not secure, and the backup server cannot be fully trusted, so users usually encrypt the data before uploading it to the remote server. As a result, how to protect this encryption key is crucial. We design a User-Centric Design (UCD) data backup scheme based on multi-factor authentication to protect this encryption key. Our scheme utilizes a secret sharing scheme to divide the encryption key into three parts, which are stored in the laptop, the smart card, and the server. The encryption key can be easily reconstructed from any two parts with user's private information password, identity and biometrics. As long as the biometrics has enough entropy, our scheme can resist replay attacks, impersonation user attacks, impersonation server attacks, malicious servers and offline password guessing attacks. [ABSTRACT FROM AUTHOR]

Detecting fraudulent activities in credit card transactions can be challenging due to issues like high dimensionality and class imbalance that are often present in the datasets. To address these challenges, data reduction techniques such as data sampling and feature selection have become essential. In this study, we compare four approaches for data reduction: using data sampling alone, employing feature selection alone, applying data sampling followed by feature selection, and using feature selection followed by data sampling. Additionally, we include results using all features. We build classification models using five Decision Tree-based classifiers and Logistic Regression, and evaluate their performance using two performance metrics: the Area Under the Receiver Operating Characteristic Curve (AUC) and the Area under the Precision–Recall Curve (AUPRC). In this work, we adopt ensemble supervised feature selection (SFS) techniques and Random Undersampling (RUS) for data reduction. The experimental results demonstrate that all four data reduction techniques have the potential to improve the performance of classifiers. These results are valuable since the classifiers available are dependent upon application domains, computing environments, and licensing agreements. However, these techniques can be applied independently of all these dependencies. We recommend utilizing the ensemble SFS followed by RUS (SFS–RUS) approach as the preferred data reduction method due to its ability to run feature selection and data sampling in parallel. Additionally, we find that XGBoost and CatBoost outperform other classifiers. [ABSTRACT FROM AUTHOR]

Metro transit is the core of urban transportation, and the mobility analysis of metro ridership can contribute to enhance the overall service level of the metro transit. Researchers studying metro ridership are focused on the spatiotemporal distribution characteristics of the ridership in the underground system of metro station by metro smart card data. However, limited by lack of travel mobility chain of ridership integrity, their activity patterns cannot be used to identify the heterogeneity of metro ridership's origin and transfer travel mode. In our research, we applied full spatiotemporal coverage of mobile phone data to identify the complete travel mobility of metro ridership in the perspective of ground and underground transit. First, the mobility of the boarding and alighting stations was extracted and the order of the transfer station was then extracted. Second, relying on the ridership flow identification method, the aboveground origin and destination of the ridership outside the metro system were extracted, and their transferred traffic mode was identified. The empirical results have shown that our proposed framework can accurately analyze the mobility patterns of metro ridership in an aboveground area and underground station. [ABSTRACT FROM AUTHOR]

This research aims to address the need for monitoring the behavior of organic and inorganic materials in hypergravity conditions. To fulfill this objective, a container with specific features was designed. The container has a box with a lid, measuring 10×10×10 cm, conforming to the 1U volume of the CubeSat standard. It includes four cylindrical spaces to accommodate the sample wells. The container was 3D printed using polylactic acid (PLA) wire. For the electronic components, four ESP32-CAM modules were utilized, with two programmed to capture and upload photos to the cloud, and the other two programmed to capture and store photos on a micro SD memory card. Additionally, four light emitting diodes (LEDs) were incorporated to illuminate the well spaces. The total weight of the container is 450 grams, and it has a maximum wireless upload distance of 10 meters to the cloud. The storage capacity of the SD memory card determines the number of images that can be saved. [ABSTRACT FROM AUTHOR]

This research deals with the critical issue of credit card fraud, a problem that has escalated in the last decade due to the significant increase in credit card usage, largely driven by advances in international trade, e-commerce, and FinTech. With global losses projected to exceed USD 400 billion in the next decade, the urgent need for effective fraud detection systems is apparent. Our study leverages the power of machine learning (ML) and presents a novel approach to credit card fraud detection. We used the European cardholders dataset for model training, addressing the data imbalance issue that often hinders the effectiveness of the learning process. As a key innovative element, we introduce compact data learning (CDL), a powerful tool for reducing the size and complexity of the training dataset without sacrificing the accuracy of the ML system. Comparative experiments have shown that our CDL-adapted feature reduction outperforms various ML algorithms and feature reduction methods. The findings of this research not only contribute to the theoretical foundations of fraud detection but also provide practical implications for the financial sector, which can benefit immensely from the enhanced fraud detection system. [ABSTRACT FROM AUTHOR]

Electronic toll collection system (ETCS) devices on vehicles communicate wirelessly with road head devices at toll stations. ETCS is preferred in countries because it is more convenient and efficient than manual toll collection. However, the scattered and constant innovations in this sector require a bridge between worldwide ETCS usage and advancements and a single page to grasp this sector's developments to ensure further developments. Following a systematic review, this study examined ETCS worldwide and technology adaption considering the scopes, limitations, usages, and related technology of the ever‐developing electronic toll collection system. Existing ETCS technologies have been investigated and synchronized with ingrowing tech disparity to overcome the research synchronization gap. The study also included a bibliometric assessment to identify the important areas of research, technologies most used, degree of knowledge generated, growth/trend of the researches, most well‐known authors, keywords, countries, documents, source of publication, and so on considering time‐to‐time changes. The study provided bibliometric solutions to the gap of lack of synchronization of research worldwide and even ETCS implementation difference within a country, which complicated collaboration with the central system and linked organizations and made it difficult for users to adapt the system. [ABSTRACT FROM AUTHOR]

Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or datasets, there is currently no methodology focused on use cases aligned with the risk-based approach of the European AI Act (AI Act). In this paper, we propose a new framework for the documentation of use cases that we call use case cards, based on the use case modelling included in the Unified Markup Language (UML) standard. Unlike other documentation methodologies, we focus on the intended purpose and operational use of an AI system. It consists of two main parts: firstly, a UML-based template, tailored to allow implicitly assessing the risk level of the AI system and defining relevant requirements, and secondly, a supporting UML diagram designed to provide information about the system-user interactions and relationships. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists. We have validated our proposal with 11 experts with different backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We provide the 5 use case cards used in the co-design and validation process. Use case cards allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI. [ABSTRACT FROM AUTHOR]

Fraudulent activities especially in auto insurance and credit card transactions impose significant financial losses on businesses and individuals. To overcome this issue, we propose a novel approach for fraud detection, combining convolutional neural networks (CNNs) with support vector machine (SVM), k nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT) algorithms. The core of this methodology lies in utilizing the deep features extracted from the CNNs as inputs to various machine learning models, thus significantly contributing to the enhancement of fraud detection accuracy and efficiency. Our results demonstrate superior performance compared to previous studies, highlighting our model’s potential for widespread adoption in combating fraudulent activities. [ABSTRACT FROM AUTHOR]

The Michelangelo effect is a phenomenon that shows a reduction in perceived effort and an improvement in performance among both healthy subjects and patients when completing a motor task related to artistic stimuli, compared to performing the same task with non-artistic stimuli. It could contribute to the efficacy of art therapy in neurorehabilitation. In this study, the possible occurrence of this effect was tested in a cognitive task by asking 15 healthy subjects and 17 patients with a history of stroke to solve a digital version of the classical memory card game. Three different types of images were used in a randomized order: French cards, artistic portraits, and photos of famous people (to compensate for the possible effects of face recognition). Healthy subjects were involved to test the usability and the load demand of the developed system, reporting no statistically significant differences among the three sessions (p > 0.05). Conversely, patients had a better performance in terms of time (p = 0.014) and the number of attempts (p = 0.007) needed to complete the task in the presence of artistic stimuli, accompanied by a reduction in the perceived effort (p = 0.033). Furthermore, artistic stimuli, with respect to the other two types of images, seemed more associated with visuospatial control than linguistic functions. [ABSTRACT FROM AUTHOR]

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]

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Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training. [ABSTRACT FROM AUTHOR]

In Internet of Vehicles (IoV), the secure data transmission between vehicles and transportation infrastructure effectively ensures the safety and fast driving of vehicles, while authentication and key agreement protocols between vehicles and transportation infrastructure (V2I) play an important role in ensuring data security and user privacy. However, how to design a provably secure and lightweight V2I authentication protocol for IoV is a challenge. Recently, Kumar et al. proposed a conditional privacy-preserving and desynchronization-resistant authentication protocol for IoV, but we find that Kumar et al.'s scheme is vulnerable to identity guessing attacks, impersonation attacks, and a lack of session key secrecy once the attacker obtains data stored in smart card through side-channel attacks. We also point out that Kumar et al.'s protocol is vulnerable to Roadside Unit (RSU) captured attacks and lacks perfect forward secrecy. Therefore, we propose an improved V2I authentication protocol for IoV, which uses the Physical Unclonable Function (PUF) to resist RSU captured attacks, and designed a three-factor secrecy strategy to resist side-channel attacks; a conditional privacy-preserving strategy was also adopted to achieve anonymity and malicious user tracking. Furthermore, the proposed protocol is provably secure under the random oracle model and has low computation and communication costs. [ABSTRACT FROM AUTHOR]

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand. [ABSTRACT FROM AUTHOR]

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), protocols with authentication and key agreement functions can enhance the security of the interaction between users and sensor nodes, guaranteeing the security of user access and sensor node information. Existing schemes have various security vulnerabilities and are susceptible to security attacks (e.g., masquerading user, password guessing, internal privilege, and MITT attacks), so they cannot meet the anonymity requirements or achieve forward security. To effectively improve the security performance of WSNs, an elliptic curve cryptography (ECC)-based three-factor authentication and key agreement scheme for WSNs is proposed. The scheme is based on the ECC protocol and combines biometrics, smart card and password authentication technology; uses a challenge/response mechanism to complete the authentication between users, gateways, and sensors; and negotiates a secure session key. The Burrows, Abadi and Needham logic for formal security analysis proves the correctness and security of the scheme, and the informal analysis of multiple known attacks proves that the scheme can resist various attacks and has high security characteristics. The feasibility of the scheme has been analysed and verified with the ProVerif tool. The efficiency analysis results show that the scheme is suitable for resource-constrained WSNs. [ABSTRACT FROM AUTHOR]

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020–22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative. [ABSTRACT FROM AUTHOR]

Ohmic heating is an emerging technology currently in high demand for application in various processes. In this research, a prototype of a multipurpose ohmic heating apparatus was successfully designed, fabricated, and tested. This apparatus was designed based on low-cost and versatile sensors and components available worldwide. Three independent chambers could be operated parallelly and individually with different treatments. Parameter data, i.e., voltage, electrical current, the temperature of heated material and environmental humidity-temperature, could be recorded by an embedded data logging system. The sensor had been tested and validated by comparing all the sensors used with commercial standard instruments. The result showed that all sensors had high measurement accuracy, indicated by very low mean absolute error (MAE) and mean absolute percentage error (MAPE), with R2 > 0.999. The performance test revealed that product temperature could be accurately maintained according to the set point temperature with a deviation value lower than 0.1 °C. The data logging system was able to record and store the parameter data in SD card memory for up to several days without interruption. The prototype of the ohmic heating apparatus could be an effective alternative to process many purposes such as pasteurisation, cooking, warming, and fermentation based on the ohmic heating principle. [ABSTRACT FROM AUTHOR]

To support more complex and robust online services, enterprise-class applications prefer to interconnect multiple servers as the pedestal to enhance the system's interoperability. However, the multiserver architecture always struggles to reconcile the trade-off between convenience and security, leaving users exposed to a variety of network attack threats. Existing security authentication schemes based on the Chebyshev Chaotic Map for multiserver architectures cannot provide three-factor (including password, biometric feature, and smart card) security. Therefore, we propose a novel Physical-Unclonable-Function-based Lightweight Three-Factor Authentication (PUF-LTA) scheme, which can achieve three-factor security. The PUF-LTA scheme mainly includes two components: (1) PUF-assisted registration and (2) lightweight mutual authentication with one-time interaction. During the PUF-assisted registration process, to defend against side-channel attacks on smart cards, the login credentials of users are XORed with the unique identifier generated by the PUF so that the adversary cannot obtain these secret login credentials. During the lightweight mutual authentication process, we combine the Chebyshev polynomial map and symmetric encryption/decryption to negotiate the session key between users and servers, which only needs one interaction. The security performance of PUF-LTA is theoretically proved by leveraging the random oracle model. In contrast with relevant multiserver authentication schemes, PUF-LTA is more efficient and suitable for resource-constrained multiserver environments because it can ensure secure three-factor authentication and support flexible biometrics and password updates with less computation cost. [ABSTRACT FROM AUTHOR]

Wafer probe test plays a crucial role to distinguish the good dies from the remaining defected dies on the wafers via the probe card as the testing signal interface between the tester and the integrated circuits on the fabricated wafers. Unexpected probe card failures that happen during the testing process will affect testing quality, reduce overall equipment efficiency and productivity. In practice, the engineers rely on domain knowledge and the process of trial and error for fault diagnosis and troubleshooting. However, as the IC device features are continuously shrinking with an increasing number and density of the bond pads of the circuits on the wafer, fault diagnosis and troubleshooting for probe card have become complicated and time-consuming. To fill the gap, this study aims to develop a data-driven framework that integrates rough set theory and domain knowledge to derive effective decision rules to enhance the decision quality and efficiency for advanced quality control and smart manufacturing. An empirical study was conducted in a leading semiconductor testing company in Taiwan for validation. The proposed framework can shorten fault diagnosis procedure and enhance productivity, while enhancing probing test integrity to reduce both the producer risk and customer risk. The developed solution is implemented in real setting. [ABSTRACT FROM AUTHOR]

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine Information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme's security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. [ABSTRACT FROM AUTHOR]