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Stabilized quantum-enhanced SIEM architecture and speed-up through Hoeffding tree algorithms enable quantum cybersecurity analytics in botnet detection
Get PDFFor the first time, we enable the execution of hybrid quantum machine learning (HQML) methods on real quantum computers with 100 data samples and real-device-based simulations with 5000 data samples, thereby outperforming the current state of research of Suryotrisongko and Musashi from 2022 who were dealing with 1000 data samples and quantum simulators (pure software-based emulators) only. Additionally, we beat their reported accuracy of 76.8% by an average accuracy of 91.2%, all within a total execution time of 1687 s. We achieve this significant progress through two-step strategy: Firstly, we establish a stable quantum architecture that enables us to execute HQML algorithms on real quantum devices. Secondly, we introduce new hybrid quantum binary classifiers (HQBCs) based on Hoeffding decision tree algorithms. These algorithms speed up the process via batch-wise execution, reducing the number of shots required on real quantum devices compared to conventional loop-based optimizers. Their incremental nature serves the purpose of online large-scale data streaming for domain generation algorithm (DGA) botnet detection, and allows us to apply HQML to the field of cybersecurity analytics. We conduct our experiments using the Qiskit library with the Aer quantum simulator, and on three different real quantum devices from Azure Quantum: IonQ, Rigetti, and Quantinuum. This is the first time these tools are combined in this manner
Thermophysical properties of tetrabutylammonium chloride, paraffin and fatty acids for thermal energy applications
Get PDFInvestigating the thermophysical properties of substances is crucial for using them as phase change materials (PCMs) and heat transfer fluids (HTFs) in thermal energy applications. In this study, the thermophysical properties of three medium-temperature PCMs (around 338 K) and one ionic liquid, tetrabutylammonium chloride ([N 4444 + ][Cl − ]), were evaluated and compared. The commercial PCMs were two fatty acids (OM65 and stearic acid) and one paraffin (RT64HC). The characterised thermophysical properties were the viscosity, density, phase change temperatures, melting and solidification enthalpies, and thermal conductivity for the solid and liquid phases. The uncertainties for each property were calculated, and two empirical equations were obtained from the correlation of viscosity and thermal conductivity data along isotherms. This paper also compared the thermophysical properties of commercial PCMs and HTFs against the ionic liquid, discussing the potential use of the ionic liquid as a thermal energy storage material and HTFs
UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities
Get PDFUnmanned aerial vehicles (UAVs) can be used as drones’ edge Intelligence to assist with data collection, training models, and communication over wireless networks. UAV use for smart cities is rapidly growing in various industries, including tracking and surveillance, military defense, managing healthcare delivery, wireless communications, and more. In traditional machine learning techniques, an enormous amount of sensor data from UAVs must be shared to central storage to perform model training, which poses serious privacy risks and risks of misuse of information. The federated learning technique (FL), which can be applied to UAVs, is a promising means of collaboratively training a global model while retaining local access to sensitive raw data. Despite this, FL is a significant communication burden for battery-constrained UAVs due to local model training and global synchronization frequency. In this article, we address the major challenges associated with UAV-based FL for smart cities, including single-point failure, privacy leakage, scalability, and global model verification. To tackle these challenges, we present a differentially private federated learning framework based on Accumulative Reputation-based Selection (ARS) for the edge-aided UAV network that utilizes blockchains to prevent single-point failures where we switched from central control to decentralized control, Interplanetary File System (IPFS) for off-chain model storage and their respective hash-keys on-chain to ensure model integrity. Due to IPFS, the size of the blockchain will be reduced, and local differential privacy will be applied to prevent privacy leakages. In the proposed framework, an aggregator will be selected based on its ARS score and model verification by the validators. After most validators approve it, it will be available for use. Several parameters are taken into consideration during evaluation, including accuracy, precision, recall, F1-score, and time consumption. It also evaluates the number of edge computers vs test accuracy, the number of edge computers vs time consumption for global model convergence, and the number of rounds vs test accuracy. This is done by considering two benchmark datasets: MNIST and CIFAR-10. The results show that the proposed work preserves privacy while achieving high accuracy. Moreover, it is scalable to accommodate many participants
Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles
Get PDFIntelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fosters the progress of ITS through enabling direct vehicle communication and interaction with infrastructure. However, the penetration rate of V2X vehicles is advancing gradually. Consequently , there will be mixed scenarios on the road, involving both on-board units (OBUs)-equipped and non-equipped vehicles. This results in disparities in communication capabilities, highlighting the need to ensure the efficient and safe operation of vehicles in such mixed scenarios. This paper addresses this challenge by presenting a feasibility analysis and prediction method for lane-changing overtaking maneuvers in mixed scenarios, specifically for vehicles equipped with OBUs. This method assists vehicles in completing overtaking maneuvers by offering a non-binary lane-changing overtaking feasibility index along with corresponding speed guidance. First, vehicle sensors are used to sense the state of surrounding vehicles, addressing any missing sensor data due to occlusions. Moreover, the future driving behavior of the vehicle is taken into account to more accurately predict the future state of the vehicle. Then, a deep reinforcement learning algorithm is deployed to process the hybrid action space to train a lane-changing overtaking model, which also takes into account the influence of the flow of each lane in front of the vehicle, and finally predicts the feasibility of the vehicle performing lane-changing overtaking. Experimental results demonstrate that our method can accurately predict the vehicle's future state and effectively assist the vehicle in completing lane-changing overtaking maneuvers. This research provides strong support for the integration of ITS and V2X technologies
It’s Not UAV, It’s Me: Demographic and Self-Other Effects in Public Acceptance of a Socially Assistive Aerial Manipulation System for Fatigue Management
No full textModern developments in speech-enabled drones and aerial manipulation systems (AMS) enable drones to have social interactions with people, which is important for therapeutic applications involving flight and above-eye-level monitoring in people’s homes, but not everyone will accept drones into their daily lives. Consistently assessing who would accept a socially assistive drone into their home is a challenge for roboticists. An animation-based Mechanical Turk survey (N = 176) found that acceptance of a voice-enabled AMS for fatigue – i.e., physical or mental tiredness in the participant’s life – was higher among younger adults with higher education and longer symptoms of fatigue, suggesting demographics and a need for the task performed by the drone are critical factors for drone acceptance. Participants rated the drone as more acceptable for others than for themselves, demonstrating a self-other effect. A second video-based YouGov survey (N = 404) found that younger adults rated an AMS for managing the symptom of day-to-day fatigue as more acceptable than older adults. The self-other effect was reduced among participants who read a situation with specific versus general phrasing of the AMS’s imagined use, suggesting that it may be caused by an attribution bias. These results demonstrate how analyzing demographics and specifying the wording of technology use can more consistently assess to whom drones for fatigue are acceptable, which is of interest to public opinion researchers and roboticists
The Potential for Workplaces to Provide Social Support for Distressed Infrastructure Workers
Get PDFInfrastructure workers experience high rates of psychological distress and suicide. Social capital (e.g., co-workers, friends, family) and social support (e.g., emotional, practical, informational) help to minimize distress. This study explores how social capital and social support contribute to psychological distress and if accessing social capital to provide social support is different for distressed compared to non-distressed workers. A sample of 220 infrastructure workers recruited online from Canada, the United Kingdom, and the United States of America was used. The study explored social capital (sum and diversity) along with social support and who the workers would approach first for each type of social support. It found that increased social capital was associated with higher distress, whereas lower social support was associated with higher distress. The primary contribution of this research indicates that although distressed infrastructure workers have more social capital available, they may not be obtaining the necessary social support needed from their networks. Also, as some distressed workers indicated they approach work colleagues to receive some types of social support, there may be an opportunity for workplaces to provide social support to co-workers to alleviate the gap in support and help improve psychological well-being
Marker location and knee joint model constraint affect the reporting of overhead squat kinematics in elite youth football players
Get PDFMotion capture systems are used in the analysis and interpretation of athlete movement patterns for a variety of reasons, but data integrity remains critical regardless of the purpose of measurement. The extent to which marker location or constraining degrees of freedom in the biomechanical model impacts on this integrity lacks consensus. Elite youth academy footballers (n=10) performed repeated bilateral overhead squats using a marker-based motion capture system. Kinematic data were calculated using four different marker sets with three degrees of freedom (3DOF) and six degrees of freedom (6DOF) configurations for the three joint rotations of the right knee. Root mean squared error (RMSE) differences between marker sets ranged in the sagittal plane between 1.02 and 4.19 degrees to larger values in the frontal (1.30- 6.39 degrees) and transverse planes (1.33 and 7.97 degrees). The cross-correlation function (CCF) of the knee kinematic time series for all eight marker-sets ranged from excellent for sagittal plane motion (>0.99) but reduced for both coronal and transverse planes (< 0.9). Two-way ANOVA repeated measures for marker sets calculated for all directions at peak squat knee flexion revealed significant differences between marker sets for frontal and transverse planes ( < 0.9). Two-way ANOVA repeated measures for marker sets calculated for all directions at peak squat knee flexion revealed significant differences between marker sets for frontal and transverse planes (p < 0.05). Pairwise transverse plane 6DOF marker set comparisons showed significant differences except between the anterior partial cluster and cluster marker sets. The paired 3DOF comparison revealed a significant difference between two of the four marker sets. The 3DOF and 6DOF model comparisons demonstrated significant differences except for the anterior partial cluster. Marker location and constraining DOF while measuring relatively large ranges of motion in this population are important considerations for data integrity. This was particularly evident in the measurement of frontal and transverse kinematics with implications for future studies using motion capture with athletic populations
Where less is more: Limited feedback in formative online multiple‐choice tests improves student self‐regulation
Get PDFBackground: Formative online multiple‐choice tests are ubiquitous in higher education and potentially powerful learning tools. However, commonly used feedback approaches in online multiple‐choice tests can discourage meaningful engagement and enable strategies, such as trial‐and‐error, that circumvent intended learning outcomes. These strategies will not prepare graduates as self‐regulated learners, nor for the complexities of contemporary work settings. Objectives: To investigate whether providing only a score after formative online multiple‐choice test attempts (score‐only feedback) increases the likelihood of students to engage in self‐regulated learning compared with more directive feedback. Measurable outcomes included deeper learning, collaboration, information seeking, and satisfaction. Methods: Data in this mixed methods study were collected from nursing students through surveys, test results, focus groups, and student discussion board contributions. A quasi‐experimental design was used for quantitative data, and qualitative data were analysed thematically against domains of self‐regulated learning. Results and Conclusions: Students receiving score‐only feedback were more cognitively engaged with the content, collaborated constructively, and sought out richer sources of information. However, it was also associated with lower satisfaction. In this study, minimal feedback created states of uncertainty, which resulted in the activation of self‐regulatory actions. Implications for Practice: Providing overly directive feedback for formative online multiple‐choice tests is conducive to surface‐level learning strategies. By minimising feedback and allowing for extended states of uncertainty, students are more likely to regulate their learning through self‐assessment and problem‐solving strategies, all of which are required by graduates to meet the challenges of real‐world work settings
Performance Analysis of Multiport Antennas in Vehicle-to-Vehicle Communication Channels
Get PDFA holistic performance analysis and classification of multiport antennas (MPAs) is conducted in this paper. We focus on 5.9 GHz vehicle-to-vehicle communications suited to the emerging technology of intelligent transportation systems. Three-dimensional (3-D) uniform/isotropic, directional, and omnidirectional propagation scenarios are considered to account for any wireless environment. The presented analysis can be adapted to any MPA with an arbitrary number of ports, operating in any frequency band, or used in other emerging technologies such as in 5G and beyond 5G communications. On top of the classical key performance metrics (KPMs) in communication theory, i.e., the diversity antenna gain (DAG) and channel capacity (CC), we employ for the first time the energy efficiency as one more KPM capable to characterize performance and classify MPAs, particularly when DAG and CC fail to do so. Computation of the aforementioned KPMs departs from a covariance matrix formulation incorporating all intrinsic features that affect MPA performance, namely, MPA radiation characteristics, MPA termination conditions, and wireless propagation channel attributes. Accordingly, we derive the ideal form of the covariance matrix under the standardized and widely adopted 3-D uniform/isotropic wireless propagation scenario. A good MPA design should be one with a covariance matrix as close as possible to this ideal one. The adopted performance analysis methodology can thus inform the design of optimum MPAs and accordingly, we designed a proof-of-concept box-shaped MPA which shows outstanding performance across all propagation scenarios. It would be wise to conduct similar performance analyses as in this paper before releasing an MPA design
Leadership development in the Hong Kong Civil Service: Accessing social resources through guanxi networks
Get PDFDrawing on guanxi and conservation of resources theory we explore how close personal ties between middle managers who participated in leadership development, constitutes an important social resource in an East Asian public sector context. We contribute to studies exploring the importance of informal leadership development opportunities and techniques, specifically utilised to overcome public sector structural barriers. We gathered qualitative data from 44 middle managers who had completed formal leadership development within the Hong Kong Civil Service. Our data revealed that high quality Superior‐to‐Subordinate Guanxi helped study participants to gain access to important development resources including further developmental opportunities, stretch assignments, developmental support and feedback. High quality Peer‐to‐Peer Guanxi helped them to gain access to peer tacit knowledge, participation in collaborative development projects and positive peer developmental feedback. Our findings inform future leadership development design and are relevant to East Asian public sector context's where guanxi ties are significant
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