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Modeling heterogeneous behaviors with different strategies in a terrorist attack

Virtual Reality & Intelligent Hardware 2023年第4期5卷 351-365页

作者： Le BI Tingting LIU Zhen LIU Jason TEO Yumeng ZHAO Yanjie CHAI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Science&Technology Ningbo UniversityCixi 315300China Faculty of Computing&Informatics Universiti Malaysia SabahKota Kinabalu 88000Malaysia

Existing simulations of terrorist attacks do not consider individual *** overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist *** constructed an emotional model that integrated personality and visual perception for *** emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by *** demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

关键词： Terrorist attack simulation computer animation Big Five personality Intelligent decision making Heterogeneous behaviors

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Evaluating Gaze Detection for Children with Autism Using the ChildPlay-R Dataset

Evaluating Gaze Detection for Children with Autism Using the...

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International Conference on Automatic Face and Gesture Recognition

作者： Nursena Boluk Hatice Kose Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

This study focuses on detecting two-dimensional (2D) gaze points in interactions between children both with and without autism and adults. Within the scope of this objective, a novel subset called ChildPlay-R has been created based on the open-source ChildPlay dataset. This new dataset includes explicitly the interactions of children with and without autism with adults in similar uncontrolled environments and relevant 2D gaze points. Moreover, the analyses using the Modified Spatiotemporal Gaze Detection (M-STGD) model, compared to the traditional Spatiotemporal Gaze Architecture (STGA), reveal significant improvements in the Area Under the Curve values and out-of-sample precision. These advancements indicate the M-STGD model's potential to provide a deeper understanding of the social engagement patterns of children with autism. This study contributes to the automatic detection of 2D gaze points in social interactions and supports the social development of children with autism.

关键词： Autism Analytical models Adaptation models Accuracy Two-dimensional displays Face recognition Gesture recognition

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Performance Evaluation of Raspberry Pi Pico for Internet of Things and Its Analysis 4

Performance Evaluation of Raspberry Pi Pico for Internet of ...

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Adi, Puput Dani Prasetyo Kristiyanto, Arip Sopandi, Ajang Nirmala, Irma Tahir, Muhlis Wijaya, Etistika Yuni Department of Telecommunication Bandung Indonesia Faculty of Computer Sciences Pamulang University Department of Information System Indonesia University of Muhammadiyah Department of Informatics Engineering Tangerang Indonesia Tanjungpura University Department of Computer Systems West Borneo Pontianak Indonesia Trunojoyo University Madura Department of Informatics Education Indonesia

ISBN: (纸本)9798350378368

The massive development of the Internet of Things to date continues to develop a lot of small devices that are integrated such as the Wireless Sensor Network, several experts have conducted research using simulation and practice of WSN, one of the powerful embedded system hardware is the Raspberry Pi Pico, which is a small-sized Raspberry Pi such as Mini Arduino or Micro Arduino. The purpose of developing research in the field of Embedded Systems is to strive in terms of power consumption and dynamism in research, which can be placed on drones and also in various research ideas such as medical for patient comfort. Raspberry Pi RP2040 microcontroller uses an ARM Cortex-M0+ dual-core processor, Raspberry Pi Pico works on a voltage of 1.8 - 5.5 V DC, and can be handled with a 3.7 V DC battery. With its small dimensions, Raspberry Pi pico can be applied to various applicative research. © 2024 IEEE.

关键词： ARM processors

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Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Urban Safety Improvement 22

Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Ur...

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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Glucina, Matko Zigulic, Nikola Frank, Domagoj Lorencin, Ivan Matika, Dario Istrian University of Applied Sciences Department of Engineering Pula Croatia University of Rijeka Faculty of Engineering Rijeka Croatia University North Koprivnica Department of Computer Science and Informatics Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical Engineering Varazdin Croatia

ISBN: (纸本)9798350385601

Individual safety in urban and city centers is one of the fundamental rights of every person. Today's innovations open up various possibilities for improving the quality of life and safety, but if they are not applied, they carry the danger of an additional increase in crime or greater insecurity in urban centers. This investigation aims to apply insights into the anticipation of illegal actions by various criminal groups that use illegal means, such as weapons, to get property that does not belong to them. In this research, computer vision and advanced strategies such as YOLOv10 and YOLOv9 are used to compare their properties and performance for improving security in urban and populated areas. The best results were achieved by YOLOv9m with a high percentage of mean average precision in the amount of 0.923, while the worst performance was shown by YOLOv10n with a mean average precision of 0.902. Precision and recall for the YOLOv9 and YOLOv10 models were also taken into account and the results are as follows: 0.984.. 0.993.. 0.979.. and 0.996. © 2024 IEEE.

关键词： Neural networks

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Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

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computers, Materials & Continua 2022年第8期72卷 2963-2980页

作者： Mariem Ayedi Walaa H.ElAshmawi Esraa Eldesouky First Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia MEDIATRON Lab. SUP’COMCarthage UniversityTunis2083Tunisia Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Faculty of Computer Science Misr International UniversityCairoEgypt

Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network *** intriguing design goal for such networks is to achieve balanced energy and spectral resource *** paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource *** proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of *** experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency ***,the network’s lifetime is ***,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,***,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

关键词： Underground wireless sensor networks resource efficiency chaotic theory crossover algorithm salp swarm algorithm

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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eduARM: Web Platform to Support the Teaching and Learning of the ARM Architecture 15

eduARM: Web Platform to Support the Teaching and Learning of...

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15th International Conference on computer Supported Education, CSEDU 2023

作者： Alves, Maria Inês Araújo, António Duarte Lima, Bruno Faculty of Engineering University of Porto Porto Portugal INESC TEC Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal INESC TEC Department of Informatics Engineering Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9789897586415

computer architecture is a prevalent topic of study in informatics and Electrical engineering courses, though students’ overall grasp of this subject’s concepts is many times hampered, mainly due to the lack of educational tools that can intuitively represent the internal behaviour of a CPU. With the evolution of the ARM architecture and its adoption in higher education institutions, the demand for this sort of tool has increased. Educational tools, specifically developed for the ARMv8 processor, are scarce and inadequate for what is necessary in an academic context. In order to contribute towards solving this problem, eduARM, a practical and interactive web platform that simulates how a ARMv8 CPU functions, was developed and is presented through this paper. Since this tool’s main purpose is to aid computer architecture students, contributing to an improvement in their learning experience, it comprises varied concepts of computer architecture and organization in a simple and intuitive manner, such as the internal structure of a CPU, in both its unicycle and pipelined versions, and the effects of executing a set of instructions. As to better understand its value, the developed tool was then validated through a case study with the participation of computer architecture students. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Students

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Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Jeremy, Jason Hidayat, Egi Muhammad Idris Siregar, Simon Trilaksono, Bambang Riyanto Habibullah, Natsir School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Telkom University Faculty of Applied Science Department Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG. © 2023 IEEE.

关键词： Model predictive control

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Integration of Large Language Models into Higher Education: A Perspective from Learners 25

Integration of Large Language Models into Higher Education: ...

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25th International Symposium on computers in Education, SIIE 2023

作者： Zdravkova, Katerina Dalipi, Fisnik Ahlgren, Fredrik Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia Linnaeus University Faculty of Technology Department of Informatics Kalmar Sweden Linnaeus University Faculty of Technology Department of Computer Science and Media Technology Kalmar Sweden

ISBN: (纸本)9798350329315

Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail. © 2023 IEEE.

关键词： Students

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