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Blockchain based Trustful Spectrum Sensing and Sharing against SSDF attacks 9

Blockchain based Trustful Spectrum Sensing and Sharing again...

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9th Annual international conference on Network and Information Systems for Computers, ICNISC 2023

作者： Yang, Biyan Yang, Liyuan Zhu, Youcun Wei, Yunkai Leng, Supeng School of Information and Communication Engineering China Zhejiang Xiangmanting Biological Technology Co. LTD. China

ISBN: (纸本)9798350306682

The efficiency of the cognitive radio (CR) based spectrum sharing is crucial to improve the utilization of the limited radio spectrum resources. However, efficient use of the spectrum in CR relies on accurate spectrum sensing and trusted spectrum sharing when facing spectrum sensing data falsification (SSDF) from malicious secondary users. Current centralized schemes on this issue generally suffer from heavy communication overhead, long delay and single point failure;while decentralized schemes generally have problem of heavy energy consumption and computing overhead. In this paper, we propose a blockchain based spectrum sensing and trusted sharing (BSSTS) scheme against SSDF attacks, which includes an accuracy based spectrum sensing mechanism and a contribution based spectrum sharing mechanism. In the first mechanism, we form the accurate spectrum sensing problem into an optimization model, and solve the model with a genetic algorithm. Then, in the second mechanism, we propose a lightweight consensus to allocate the available channel slots to the secondary users based on their contributions in the sensing stage. The simulations are conducted to verify the performance of BSSTS. Compared with the benchmark schemes, BSSTS can increase the idle spectrum utilization by up to 20%, and decrease the times of interfering the primary user by up to 66%. © 2023 IEEE.

关键词： cognitive radio

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international conference on Mechanical Design, ICMD 2023

International Conference on Mechanical Design, ICMD 2023

引用

international conference on Mechanical Design, ICMD 2023

ISBN: (纸本)9789819709212

The proceedings contain 176 papers. The special focus in this conference is on Mechanical Design. The topics include: Study of Influence Factors for Disruptive Innovation in Equipment Manufacturing Enterprises;inverse Seeking Method to Find the Force of the Variable Span Outrigger of Truck Crane Based on Analytic Method and Its Algorithm Realization;approach of Adaptive Change for Open Architecture Product Based on Axiomatic Design;Research on the Innovative Design of TRIZ Patent Circumvention Based on Basic-Elements;Design and Load Capacity Research of a Novel Roller-Plate CVT;design Knowledge Graph and User Profiling-Driven Product Innovation Design Problem Solving;a Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction;Research on the Innovation Process Modeling for the Integration of DCC and Function Trimming;an Analysis Method to Identify the Importance of Technical Parameters for complex Products;inter-generational Aging Design of Community Fitness Space and Equipment;optimization Method for the Propagation Path of complex Product Design Change Based on Reinforcement Learning;intelligent cognitive Artistic Style Recognition Model for Mechanical Design;design Principle and Application for Automotive Wiring Corrugated Pipe Cutting Machine;Design of Multi-task Scene Image Acquisition System in a Spacecraft Based on TRIZ;design and data Analysis of Muskmelon Sugar Content Detection System;a Human Digital Twin Based Framework for Human–Robot Hand-Over Task Intention Recognition;design and Application of data-Driven Visualization Platform for Dual Robotic Arms Assembly Process;an Innovative Design Method with Good Operability and Practicality;product Requirements Mining and Innovative Design Based on Extension Theory Using Online Review data.

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An Empirical Evaluation of Live Coding in CS1 23

An Empirical Evaluation of Live Coding in CS1

引用

19th Annual ACM conference on international computing Education Research V.1 (ICER)

作者： Shah, Anshul Hogan, Emma Agarwal, Vardhan Driscoll, John Porter, Leo Griswold, William G. Raj, Adalbert Gerald Soosai Univ Calif San Diego San Diego CA 92093 USA

ISBN: (纸本)9781450399760

Background and Context. Live coding is a teaching method in which an instructor dynamically writes code in front of students in an effort to impart skills such as incremental development and debugging. By contrast, traditional, static-code examples typically involve an instructor annotating or explaining components of pre-written code. Despite recommendations to use live coding and a wealth of qualitative analyses that identify perceived learning benefits of it, there are a lack of empirical evaluations to confirm those learning benefits, especially with respect to students' programming processes. Objectives. Our work aims to provide a holistic, empirical comparison of a live-coding pedagogy with a static-code one. We evaluated the impact of a live-coding pedagogy on three main areas: 1) students' adherence to effective programming processes, 2) their performance on exams and assignments, and 3) their lecture experiences, such as engagement during lecture and perceptions of code examples. Method. In our treatment-control quasi-experimental setup, one lecture group saw live-coding examples while the other saw only static-code ones. Both lecture groups were taught by the same instructor, were taught the exact same content, and completed the same assignments and exams. We collected compilation-level programming process data, student performance on exam and homework questions, and feedback via a survey and course evaluations. Findings. Our findings showed no statistically significant differences between the live-coding and static-code groups on programming process metrics related to incremental development, debugging, and productivity. Similarly, there was no difference between the groups on course performance on assignments and exams. Finally, student feedback suggests that more students in the live-coding group reported that lectures were too fast and failed to facilitate note-taking, potentially mitigating the perceived benefits of live coding. Implications. Live cod

关键词： live coding programming processes course performance student perceptions pedagogical techniques cognitive Apprenticeship

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Ground extraction and road obstacle segmentation method based on point cloud data 3

Ground extraction and road obstacle segmentation method base...

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3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Li, XiaoWei Yang, HuiCheng Feng, HaiHong Xu, ShuQi School of Electrical Engineering Anhui Polytechnic University Wuhu China Wuhu Angong Cognitive Technology Co LTD China

ISBN: (纸本)9781510661301

With the rise of artificial intelligence and other technologies, unmanned driving has become an important direction of future automobile development. The ground extraction of roads is a key task. The traditional method of ground extraction is limited to flat roads and the speed is slow. In view of the under-segmentation problem between the ground point cloud and the multi-objective object point cloud, we propose a ground segmentation algorithm for point cloud data based on multi-region segmentation. In this paper, the ground data can be accurately found by using the method of concentric region division for point cloud data. At the same time, it also has good robustness for uneven road surface, so as to provide a good foundation for the subsequent segmentation of road obstacles. For the extraction of road obstacles, we adopt the DBSCAN clustering method based on sub-region. According to the characteristics of near-density and distant point cloud, we adopt adaptive parameter selection for different regions to improve the accuracy of obstacle extraction. In order to verify the results, the use of KITTI data set on ubuntu18.04 using ROS system to complete the test, can maintain about 28fps processing speed. © 2023 SPIE.

关键词： Extraction

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Predicting Operator cognitive States for Supervisory Human-Autonomy Teaming

Predicting Operator Cognitive States for Supervisory Human-A...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Kintz, Jacob Ryan Buchner, Savannah Lynn Anderson, Allison Paige Clark, Torin Kristofer Ann and H.J. Smead Aerospace Engineering Sciences University of Colorado Boulder BoulderCO United States

ISBN: (纸本)9798350337020

Autonomous systems show promise as teammates for human operators in safety- and performance-critical environments. Humans' cognitive states (like trust, mental workload, and situation awareness) change as they work in demanding environments with autonomous systems. Providing adaptive autonomous systems with information about human teammates' cognitive states is a current gap in human-autonomy teaming research. In this paper we present results from a human-autonomy teaming experiment in which participants completed a spaceflight-relevant task in a supervisory, 'on-the-loop' role. To complete the task, participants worked with an autonomous system that had five distinct modes of autonomy. Our experiment results show that unobtrusive measures (based on actions participants take and eye tracking data) could inform statistical models that accurately predicted three different subjectively reported cognitive states at the same time (RMSE from 11% to 14% of questionnaires' ranges). Our work builds upon previous research and demonstrates that our model-building approach can extend to different human-autonomy teaming scenarios. This also represents the first time that three cognitive states were predicted using unobtrusive measures from the same supervisory human-autonomy teaming task. Our research enables future experiments investigating an autonomous system which adapts according to cognitive state predictions. © 2023 IEEE.

关键词： human space flight human-computer interaction supervisory control

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Characterization Of Physiological Signals Under cognitive Stress 20

Characterization Of Physiological Signals Under Cognitive St...

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20th international conference on Electrical Engineering, computing Science and Automatic Control, CCE 2023

作者： Ruan-Limas, Jennifer Garay-Jiménez, Laura I. Ruiz-Ledezma, Elena F. Instituto Politécnico Nacional Upiita CDMX Mexico Instituto Politécnico Nacional SEPI-UPIITA CDMX Mexico Instituto Politécnico Nacional SEPI-ESCOM CDMX Mexico

ISBN: (纸本)9798350306767

A fusion of wearable device sensors and an electroencephalography system was implemented to identify changes under controlled situations related to stress in the student's population, to assess whether the physiological signals can detect variations in the stress level by a descriptive statistical analysis. The signals used are: movement, blood volume pulse, electrodermal activity, heart rate, temperature and electroencephalography. The analysis was made in volunteers between 18 and 25 years old, who are young adults with at least 10 years of schooling and being apparently healthy. The recording protocol consisted of 3 consecutive stages taken in a single session, which consisted of two math tests (stage 1 and stage 3) with a relaxation period (stage 2) in between. The physiological signal data were segmented per stage and descriptive statistical metrics were calculated per volunteer. For the appropriate proper selection of metrics, they were compared using the Repeated Measures one-way ANOVA method with a = 0.05, using Tukey's multiple comparisons tests. It was found that the heart rate and temperature signals, along with the descriptive statistical metrics, range, and kurtosis, are the best suited for the study to find the differences. Volunteers in stage 3 maintained a state of relaxation equal to or less than that of stage 2. © 2023 IEEE.

关键词： Electroencephalography

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Non-IT Program Students Learn Basic Web Language;The Preliminary Study of Affective and cognitive Response on IVML Prototype 17th

Non-IT Program Students Learn Basic Web Language;The Prelimi...

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17th international conference on Information Technology and Applications, ICITA 2023

作者： Naim, Nur Faraha Mohd Ibrahim, Ag. Asri Ag. Baharum, Aslina Moung, Ervin Gubin Salimun, Carolyn Multimedia Technology Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kampus Antarabangsa Labuan Federal Territory of Labuan Malaysia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia User Experience Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia

ISBN: (纸本)9789819983230

This paper examines the response of affective and cognitive processes under human–computer interaction (HCI) toward interactive video mobile learning known as the IVML prototype. The idea is to determine the potential of the interactive elements in the educational video as the key factor that can influence learners’ engagement and comprehension of the presented topic. This paper aims to measure the level of affective and cognitive response of learners toward the subject learning that are not from their field, not exposed to, or not familiar with. The data analyze based on user experience, user interface, and human–computer interaction. The statistical result may produce a significant outcome that could contribute to the hyper video platform. The findings may confirm the significant role of technical aspects and psychological aspects included in video production style, hence heightening the hyper video platform in teaching and learning activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human computer interaction

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Federated Learning for Privacy-aware cognitive Workload Estimation 23

Federated Learning for Privacy-aware Cognitive Workload Esti...

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2nd international conference on Mobile and Ubiquitous Multimedia, Proceedings of MUM 2023

作者： Fenoglio, Dario Josifovski, Daniel Gobbetti, Alessandro Formo, Mattias Gjoreski, Hristijan Gjoreski, Martin Langheinrich, Marc Università della Svizzera Italiana Lugano Switzerland Faculty of Electrical Engineering and Information Technologies Skopje Macedonia

ISBN: (纸本)9798400709210

Human physiological monitoring has become easily accessible by integrating wearable devices into our lives, providing valuable real-time data. Methods for cognitive Workload (CW) estimation utilize such physiological data to quantify CW during task execution. These methods are crucial for various domains, including mobile healthcare, forecasting human errors, and human-machine interaction. However, accurately estimating CW continues to pose a challenge due to the absence of objective ground truth data, context dependency, and the privacy sensitivity of the data. This study tackled the complex task of estimating CW based on privacy-sensitive data (e.g., eye movement, pupil diameter, blink information, and other physiological signals) using Federated Learning (FL) methods to improve user privacy. We compared the outcomes of the FL models with the more conventional centralized approach on two publicly-available datasets COLET and ADABase, which include data from 75 participants overall. The results highlight the efficacy of FL in collaboratively training a global (person-independent) model. The FL models achieved performances on par with centralized state-of-the-art models while preserving data privacy. Recognizing the importance of privacy in user sensing, FL presents a promising approach that enables wearable sensing applications in privacy-sensitive domains. © 2023 ACM.

关键词： Eye movements

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Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot 27

Deep Reinforcement Learning for Mapless Navigation of Autono...

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27th international conference on System Theory, Control and computing, ICSTCC 2023

作者： Yadav, Harsh Xue, Honghu Rudall, Yan Bakr, Mohamed Hein, Benedikt Rueckert, Elmar Nguyen, Ngoc Thinh Inst. for Robotics and Cognitive Systems University of Lübeck Luebeck Germany Robotics and Automation KION Group AG Hamburg Germany Inst. of Automation Technology Helmut-Schmidt-University Hamburg Germany Inst. for Cyber Physical Systems Montanuniversität Leoben Leoben Austria

ISBN: (纸本)9798350337983

This paper presents a study on the mapless navigation of autonomous mobile robot using Deep Reinforcement Learning in an intralogistics setting. The task is to make an autonomous mobile robot learn to navigate to a goal without prior knowledge of the environment. In this paper, a controller using the Soft Actor-Critic algorithm is designed, trained, and applied for navigating the robot equipped with 360° LiDAR and front camera sensors. The controller is successfully validated in an almost fully observable environment under extensive simulations. Furthermore, we investigate the performance of the proposed controller in a partially observable environment and possible limitations. We use a 3D Temporal Convolution Network for processing the time series image data from visual observations. Besides Partial Observability, we also address the problem of sparse positive rewards in training the Deep Reinforcement Learning algorithm with a combined approach of Automatic Curriculum Learning and Dual Prioritized Experience Replay. © 2023 IEEE.

关键词： Reinforcement learning

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Development of Logistic regression based spectrum sensing algorithm using extreme eigenvalues

Development of Logistic regression based spectrum sensing al...

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2023 international conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Srinu, Sesham Jeremia, Manfred Abisai, F.M.S. University of Namibia Department of Electrical and Computer Engineering Namibia

ISBN: (纸本)9798350339369

Next-generation (NextGen) wireless systems require huge bandwidth for their communication due to the Internet of Things (IoT) and high data rate applications. cognitive radio (CR) technology provides a solution by finding spectrum holes and opportunistically assigning them to NextGen wireless systems. Among various functions of CR, spectrum sensing is a crucial function that can explore spectrum holes over a specified range of the radio spectrum. Developing an efficient sensing method is a focused area of research. Out of numerous sensing algorithms developed, eigenvalue-based sensing has shown superiority. However, the test statistic designed using the ratio of extreme eigenvalues is not a linear function of signal-to-noise ratio, which may not be an efficient measure for signal detection. On the other hand, machine learning algorithms have not been greatly explored in this area, despite being applied to solving many research problems. Hence, this work focuses on developing a less complex and efficient test statistic using a machine learning algorithm. We have considered extreme eigenvalues of a random covariance matrix as training features to model the logistic regression-based detection technique. The results reveal that the proposed method can detect -7 dB signals and outperforms the eigenvalue-based detection method. © 2023 IEEE.

关键词： cognitive radio

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