Reviewerly/exHarmony huggingface dataset:
Cognitive load and mixed strategies: On brains and minimax

International Game Theory ReviewAccepted Papers No AccessCognitive load and mixed strategies: On brains and minimaxSean Duffy, J. J. Naddeo, David Owens, and John SmithPh.D.Sean Duffy, J. J. Naddeo Search for more papers by this author , David Owens Search for more papers by this author , and John Smith Search for more papers by this author https://doi.org/10.1142/S021919892450004XCited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 4 February 2023 Accepted 9 February 2024 PDF download
Efficiency Improvement to Neural-Network-Driven Optimal Path Planning via Region and Guideline Prediction

Traditional sampling-based algorithms rely on random samples to explore a whole configuration space of robots for optimal path planning, while a uniform sampler impedes the exploration with randomly generated samples, leading to a long calculation time, especially in complex environments. Recently, neural-network-driven methods have attracted wide interest in developing non-uniform sampling to improve the sampling efficiency and reduce the calculation time. A region that contains an optimal path is predicted by neural networks and employed subsequently to biasedly generate samples. This work aims at enhancing the sampling efficiency and reducing the calculation time of the optimal path planning by a novel region and guideline prediction (denoted as RGP) model. We innovatively propose the RGP model with a guideline prediction module to estimate the guideline distributions, which are characterized by the central line of the predicted region. The predicted region and guideline are integrated into a sampling-based algorithm, namely RGP-RRT*, with an adaptively biased sampling strategy to select a proper domain for sampling. Simulations demonstrate the RGP model outperforms other region prediction models in accuracy and robustness. Besides, the RGP-RRT* reliably achieves a 7.2-80.1% reduction in calculation time and a 2.0-58.1% reduction in sample number compared with other neural-network-driven methods. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RTPWDSDM/RGP-RRTstar</uri> .
Rethinking of BERT Sentence Embedding for Text Classification

Abstract Text classification is a fundamental task in NLP that is used in several real-life tasks and applications. Large pre-trained language models such as BERT achieve state-of-the-art performance in several NLP tasks including text classification tasks. Although BERT boosts text classification performance, the common way of using it for classification lacks many aspects of its advantages. This work rethinks the way of using BERT final layer and hidden layers embeddings by proposing different aggregation architectures for text classification tasks such as sentiment analysis and sarcasm detection. This research also proposes different approaches for using BERT as a feature extractor without fine-tuning whose performance surpasses its fine-tuning counterpart. It also proposes promising multi-task learning aggregation architectures to improve the performance of the related classification problems. The experiments of the different architectures show that freezing BERT can outperform fine-tuning it for sentiment analysis. The experiments also show that multi-task learning while freezing BERT boosts the performance of yet hard tasks such as sarcasm detection. The best-performing models achieved new state-of-the-art performance on the ArSarcasm-v2 dataset for Arabic sarcasm detection and sentiment analysis. For multi-task learning and freezing BERT, a new SOTA F1-score of 64.41 was achieved for the sarcasm detection with a 3.47% improvement and near SOTA FPN of 75.78 for the sentiment classification. For single-task learning, a new SOTA FPN of 75.26 was achieved for the sentiment with a 1.81% improvement.
Improving Security in the Internet of Vehicles: A Blockchain-Based Data Sharing Scheme

To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.
Adaptive variational simulation for open quantum systems

Emerging quantum hardware provides new possibilities for quantum simulation. While much of the research has focused on simulating closed quantum systems, the real-world quantum systems are mostly open. Therefore, it is essential to develop quantum algorithms that can effectively simulate open quantum systems. Here we present an adaptive variational quantum algorithm for simulating open quantum system dynamics described by the Lindblad equation. The algorithm is designed to build resource-efficient ansatze through the dynamical addition of operators by maintaining the simulation accuracy. We validate the effectiveness of our algorithm on both noiseless simulators and IBM quantum processors and observe good quantitative and qualitative agreement with the exact solution. We also investigate the scaling of the required resources with system size and accuracy and find polynomial behavior. Our results demonstrate that near-future quantum processors are capable of simulating open quantum systems.
Are we ready for education in Metaverse? PLS-SEM analysis

The objective of this study is to investigate the adoption of met-averse education in Ho Chi Minh City, Vietnam, by employing the Theory of Planned Behavior (TPB) as the foundational theoretical framework. Metaverse education, utilizing immersive and interactive virtual environments for educational objectives, is an innovative and auspicious methodology in the contemporary digital realm. Data collection was conducted via judgmental sampling. Partial least squares structural equation modeling was used for the analysis. The results suggest a significant and positive impact of Consumer Intention (CI) on the practical use (AU) of the met-averse in the field of education. Furthermore, the factors of Perceived Behavioral Control (PBC), Subjective Norm (SN), and Perceived Value strongly influenced Consumer Intention (CI) in a positive manner. Moreover, Emotional Value (EV) and Social Value (SV) had a substantial impact on the Perceived Value (PV) of using the metaverse in educational contexts. Nevertheless, the lack of statistical significance in the relationship between Attitude towards Behavior (ATB) and Consumer Intention (CI) is recognized. This research not only addresses a significant gap in the theory of IS literature but also offers valuable insights for educators, policymakers, and developers. If you really understand the things that make people open to metaverse education, you can make big changes to plans and actions that are used to successfully add metaverse education to the regular school setting, especially in Ho Chi Minh City, Vietnam.
Development of a TPACK Educational Program to Enhance Pre-service Teachers’ Teaching Expertise in Artificial Intelligence Convergence Education

This research focuses on developing an Artificial Intelligence (AI)-based educational program within the Technological Pedagogical Content Knowledge (TPACK) framework to enhance the competency of pre-service teachers in AI convergence education. To assess the effectiveness of this program, South Korean pre-service teachers participated in a study where they engaged in AI convergence TPACK education. Freshman students were divided into two groups: one received traditional AI education, while the other took part in the AI convergence TPACK educational program. An evaluative tool was used to measure the AI-convergence teaching skills of these pre-service teachers. The results of the study revealed that the AI convergence TPACK educational program significantly contributed to developing AI convergence teaching capabilities among the participants. Furthermore, a comparative analysis demonstrated a substantial advantage in the AI convergence education skills of teachers who participated in the TPACK program compared to those who received AI education alone. Notably, the most significant improvements were observed in Pedagogical Content Knowledge (PCK) and TPACK, which were identified as critical components of AI convergence teaching expertise. This research underscores the importance of a tailored and comprehensive approach to training pre-service teachers in the ever-evolving landscape of AI in education. It highlights the effectiveness of the AI convergence TPACK educational program in enhancing pre-service teachers' AI convergence teaching skills and emphasizes the pivotal role of PCK and TPACK in preparing educators for AI-integrated educational environments.
LD-Recognition: Classroom Action Recognition Based on Passive RFID

Classroom learning is one of the main ways for students to acquire knowledge because the class usually has a large number of students and it is difficult for teachers to pay attention to the learning status of each student at the same time. Therefore, mastering the learning status of each student and dealing with it is the key factor to determine the quality of course teaching, and classroom actions can accurately reflect the learning state of students. Based on this, in this article, a passive radio frequency identification (RFID)-based classroom action recognition system LD-recognition is proposed. The system pastes the label on the right side of the desk, and the learning state of the students was judged by recognizing the four movements of raising the left hand, raising the right hand, nodding off, and holding the book. The system uses a multichannel attentional graph convolutional neural network (ATGCN) to deeply learn the phase and signal strength of actions and conduct action recognition. LD-recognition verifies the accuracy of actions from different distances, different experimenters, and different network models. The experimental results show that the recognition accuracy of LD-recognition system is high, reaching 96.9% on average.
High-Speed Design of Post Quantum Cryptography With Optimized Hashing and Multiplication

In this brief, we realize different architectural techniques for improving the performance of post-quantum cryptography (PQC) algorithms when implemented as hardware accelerators on an application-specific integrated circuit (ASIC) platform. Having SABER as a case study, we designed a 256-bit wide architecture geared for high-speed cryptographic applications that incorporates smaller and distributed SRAM memory blocks. Moreover, we have adapted the building blocks of SABER to process 256-bit words. We have also used a buffering technique for efficient polynomial coefficient multiplications to reduce the clock cycle count. Finally, double-sponge functions are combined serially (one after another) in a high-speed KECCAK core to improve the hash operations of SHA/SHAKE. For key-generation, encapsulation, and decapsulation operations of SABER, our 256-bit wide accelerator with a single sponge function is 1.71x, 1.45x, and 1.78x faster than the raw clock cycle count of a serialized SABER design. Similarly, our 256-bit implementation with double-sponge functions takes 1.08x, 1.07x & 1.06x fewer clock cycles compared to its single-sponge counterpart. The studied optimization techniques are not specific to SABER – they can be utilized for improving the performance of other lattice-based PQC accelerators.
The Need For ABET Accreditation of Associate’s Cybersecurity Programs

Abstract Cybersecurity professionals at all levels of preparation are in high demand, with the need still growing rapidly. In response, community colleges have been offering Associate’s programs in cybersecurity for over a decade. The content of such programs has been driven by many factors including the needs of local industry, professional certification requirements for entry-level jobs, and education advancement programs under such organizations as the National Security Agency, the National Institute of Science and Technology (NIST), the National CyberWatch Center, and the Association for Computing Machinery (ACM). A consequence of this diversity of drivers is wide variation in the types of graduates produced, which is not conducive to developing shared expectations, from prospective students to employers. In addition, as a discipline matures, creating standards for educational programs is a professional responsibility of the academic community, as is establishing accreditation criteria to ensure quality is sustained---enter ABET. This paper summarizes the major efforts that led to Associate's degree programs in cybersecurity, along with the motivation to create the first ABET Associate’s Cybersecurity Program Criteria. It discusses the process to develop these cybersecurity criteria, describes the current criteria, and presents the current status of the effort. In essence, this process reflects the maturation of the cybersecurity discipline.
Students’ Self-Perception of Their Entrepreneurial Characteristics

Abstract In recent years, entrepreneurship has been viewed as a necessary component of engineering education to support the development of an innovative workforce. Engineering entrepreneurship education is seen as a means to develop entrepreneurial mindset and skills that are essential for a successful professional life. In an effort to integrate entrepreneurship education into an already dense curriculum, universities and colleges offer a range of entrepreneurship programming from individual classes, certificate programs, and minors and or majors. With these various options, students have several different pathways to entrepreneurship education. However, research has shown that student demographics influence their participation in entrepreneurship programming. Further, self-efficacy, which is the belief in one’s ability, is seen as a key characteristic motivating intent and activity. To continue to understand the factors that affect student engagement in entrepreneurial learning and their development of entrepreneurial skills, we examined students’ self-perceptions and beliefs with respect to demographics and background. Specifically, we looked at students’ self-perception of entrepreneurial self-efficacy (ESE), creative self-efficacy (CSE), and Risk-taking with respect to gender, family background, class standing, major, previous exposure to entrepreneurship classes, and on their self-identification as entrepreneurial. Survey data was collected from 194 students at a large, Midwest public research university. Independent t-tests were used to look for differences in ESE, CSE and Risk-taking with respect to student demographics and background. Results indicate that men score higher in risk-taking than women. Students with entrepreneurs in the family and students in their third year or higher score higher in some aspects of ESE. Students with previous exposure to entrepreneurship classes score higher in CSE. Finally, students that self-identify as entrepreneurial score higher in ESE, CSE, and Risk-Taking. These results indicate that engineering students hold varying entrepreneurial characteristics based on their background and demographics and that the characteristics assessed are more sensitive to students' self- identification as entrepreneurial than their background and demographics. Implications of the results on the development and implementation of entrepreneurship programming for engineers are discussed.
FLSTRA: Federated Learning in Stratosphere

We propose a federated learning (FL) in stratosphere (FLSTRA) system,where a high altitude platformstation (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithmto achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problemis non-convex; thus, we propose an iterative algorithmto solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.
Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc

Abstract The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.
NRP: A Multi-source, Heterogeneous, Automatic Data Collection System for Infants in Neonatal Intensive Care Units

The incidence of premature births continues to rise each year and premature babies require specialized care in neonatal intensive care units (NICUs). The intermittent monitoring of these babies, however, poses challenges for clinicians in terms of visualizing and detecting meaningful diagnostic trends. In order to address this issue, the adoption of continuous, multi-parameter, electronic medical record-keeping methods is promising in terms of leveraging advanced analytics techniques and potentially improving health outcomes. In this article, we present the design and implementation of the Neonates Recording Platform ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> ), a hardware-software tool to be deployed at the bedside in a real NICU environment. The <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> enables data from various sources to be collected, labelled, processed and stored. We conducted tests involving the acquisition of neonates' physiological parameters, synchronized with video recordings, in addition to real-time analysis of body pose, with the capture of up to 33 reference points, and audio files from both the infant and the environment. In <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> , the collected data is organized hierarchically in a portable format and is automatically cleaned and validated, thereby ensuring its usability for healthcare professionals and data scientists. Additionally, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> enables medical staff to configure trials and add customized text or tagging events. A significant contribution of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> platform is the integration of a unique computer vision algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CardMed</i> , which extracts physiological information (such as heart rate, breath rate, and blood oxygenation) directly from any monitoring device. The development of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> involved collaboration with medical staff and data scientists, and evaluation took place at the NICU of the Puerta del Mar University Hospital in Cádiz, Spain.
Metadata and Image Features Co-Aware Semi-Supervised Vertical Federated Learning With Attention Mechanism

Recently, vertical federated learning (VFL) has gained more attention due to its ability to combine various valuable features for better model performance without violating privacy. However, the development of VFL still faces three challenges: 1) insufficient aligned samples, 2) monotone data modality, and 3) simple concatenation-based collaborative training strategy. In this paper, we construct a metadata and image features co-aware VFL model for smart healthcare, from which users can obtain convenient and timely online auxiliary diagnostic services. Furthermore, we propose a metadata and image features co-aware semi-supervised VFL scheme with attention mechanism, which aims to jointly train the VFL models by utilizing both metadata and image data from the entire sample space. Specifically, a generative-adversarial method is designed to train the generator to estimate the missing features, so as to expand the training set and improve the accuracy of model. Moreover, using semi-supervised clustering to forecast pseudo labels for the missing part of the active party, we further boost the performance of the VFL model by increasing the proportion of labeled samples. Meanwhile, inspired by the attention mechanism, we establish an entirely novel VFL training mode that emphasizes the underlying effect of metadata features on image features, which makes metadata features highlight crucial areas in the image features to improve the training effect. Finally, the experimental results confirm that our proposed scheme achieves higher accuracy compared with other related works in the VFL scenario.