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74th International Astronautical Congress, IAC 2023

作者： Karim, Andrew Tardif, Lyne Zamudio-Turcotte, M.A. Katherine Department of Physics Physical Engineering and Optics Université Laval 2325 Rue de l'Université QuebecQCG1V 0A6 Canada Montreal School Service Center 5100 Rue Sherbrooke Est MontrealQCH1V 3R9 Canada Department of Computer and Software Engineering Polytechnique Montreal 2500 Chemin de Polytechnique MontrealQCH3T 1J4 Canada

Science, Technology, engineering, and Mathematics (STEM) educational activities often target the younger generation in an attempt to foster their interest towards science, while instructing them about ways to use their creativity to solve real-life problems. However, very few of these activities focus on special needs students, who may present conditions such as attention-deficit disorders, autism, behavioural disorders, intellectual deficiencies, and physical handicaps. The SkyBox and AstroBot framework is a set of activities that are highly customizable in order to be presented to a variety of special needs students. Its main goal is to convey a message of perseverance, as these special needs students often struggle to find interest in school and can benefit from the visit of an undergraduate student in their class, one who is likely to land a career in the space industry. The SkyBox and AstroBot activities have shown significant outreach in the 9 specialized classrooms they were presented to in 2022 and 2023. In every workshop presented, students demonstrated a higher-than-usual level of attention by asking many questions to the animator and by developing some attachment to them. Many students even expressed an interest for a career in the space industry, either as an astronaut, an engineer, or a scientist, after their workshops. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： attention-deficit autism behavioral disorder education STEM

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NDN Collaborative Caching Replacement and Placement Policy Performance Evaluation

NDN Collaborative Caching Replacement and Placement Policy P...

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International Conference on Wireless and Telematics (ICWT)

作者： Eki Ahmad Zaki Hamidi Khibran Muhammad Akbar Ridha Muldina Negara Ivah Puspita Payangan Mutiara Dewi Puspitaningsih Adzka Zahratu Putri As'Ari Jupriyadi Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia School of Electrical Engineering Telkom University Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Named Data Networking (NDN) is an emerging technology that aims to provide rapid and efficient content distribution and retrieval in the network. This paper focuses on the impact of selecting the pair between cache replacement and placement within NDN, an aspect often overlooked in previous research. Four cache placement strategies will be implemented in the initial simulation: Leave Copy Down (LCD), Leave Copy Everywhere (LCE), Prob Cache, and Cache Less for More (CL4M). Each cache placement will be combined with two replacements: least frequently used (LFU) and least recently used (LRU). The effects of these combinations on critical metrics such as cache hit ratio, latency, link load, and path stretch are investigated. The results reveal that LCE combined with LFU demonstrates remarkable enhancements in all measurements over LCE coupled with LRU. These findings underscore the importance of meticulous selection and optimization of cache placement and replacement strategies in NDN environments to augment network efficiency and performance.

关键词： Wireless communication Performance evaluation Collaboration Telematics Liquid crystal displays Optimization

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MOKD: cross-domain finetuning for few-shot classification via maximizing optimized kernel dependence 24

MOKD: cross-domain finetuning for few-shot classification vi...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongduan Tian Feng Liu Tongliang Liu Bo Du Yiu-ming Cheung Bo Han TMLR Group and Department of Computer Science Hong Kong Baptist University TMLR Group University of Melbourne Sydney AI Centre The University of Sydney National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Department of Computer Science Hong Kong Baptist University

In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://***/tmlr-group/MOKD.

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Discrete biological modeling for the immune response to dengue virus

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第1期13卷 214-234页

作者： Khaled A.Al-Utaibi M.Muzamil Ayesha Sohail Fatima Alam Alessandro Nutini Sadiq M.Sait Computer Science and Software Engineering Department University of Ha’il Ha’ilSaudi Arabia Quaid e Azam Medical College BahawalpurPakistan Department of Mathematics Comsats University Islamabad Lahore 54000Pakistan Biology and Biomechanics Department Centro Studi Attivita Motorie via di Tiglio 9455100 LuccaItaly Center for Communications and IT Research Research Institute King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Dengue infection affects more than half of the world’s population,with 1 billion symp-tomatic cases identified per year and several distinct genetic serotypes:DENV 1–*** via the mosquito bite,the dengue virus infects Langerhans ***,B lymphocytes,and mast cells infected with dengue virus produce various cytokines although it is not clear which ones are predominant during DHF disease.A mathemat-ical model of the Dengue virus infection is developed according to complex dynamics determined by many *** from a state of equilibrium that we could define as“virus-free”asymptotically stable with a viral reproduction number lower than one which means a very effective action of the innate immune system:it stops the infectious process,the mathematical analysis of stability in the presence of the virus demonstrates that the proposed model is dynamically *** fever affects more than half of the world’s population,with 1 billion symptomatic cases and multiple genetic serotypes confirmed each year,which simulates a network of interactions between the various populations involved without considering the speeds of the processes in question which are indicated in a separate *** this research,a hybrid approach of petri nets is utilized to connect the discrete models of dengue.

关键词： Modeling scientific computing DENV cytokines petri nets infected cell

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Language-driven Grasp Detection with Mask-guided Attention

Language-driven Grasp Detection with Mask-guided Attention

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tuan Van Vo Minh Nhat Vu Baoru Huang An Vuong Ngan Le Thieu Vo Anh Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Austria Austrian Institute of Technology (AIT) GmbH Austria Imperial College London UK Department of Computer Science & Computer Engineering University of Arkansas USA National University of Singapore Singapore Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we propose a new method for language-driven grasp detection with mask-guided attention by utilizing the transformer attention mechanism with semantic segmentation features. Our approach integrates visual data, segmentation mask features, and natural language instructions, significantly improving grasp detection accuracy. Our work introduces a new framework for language-driven grasp detection, paving the way for language-driven robotic applications. Intensive experiments show that our method outperforms other recent baselines by a clear margin, with a 10.0% success score improvement. We further validate our method in real-world robotic experiments, confirming the effectiveness of our approach.

关键词： Visualization Attention mechanisms Accuracy Service robots Source coding Semantic segmentation Natural languages Grasping Transformers Feature extraction

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Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

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File Type Identification Classifier with Classification-by-Retrieval and One-Shot Learning

SSRN

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SSRN 2024年

作者： Lisker, Simona Butman, Ayelet Dubin, Ran Dvir, Amit Hajaj, Chen Department of Computer Science Holon Institute of Technology Holon 52 Golomb Street POB 305 Holon5810201 Israel Department of Computer Science Ariel Cyber Innovation Center Ariel University Kiryat Hamada St. Ariel4070000 Israel Department of Computer and Software Engineering Ariel Cyber Innovation Center Ariel University Kiryat Hamada St. Ariel4070000 Israel Department of Industrial Engineering and Management Data Science and Artificial Intelligence Research Center Ariel University Kiryat Hamada St. Ariel4070000 Israel

In classical malware detection methodologies, digitalforensics and file carving, file type classification holds a pivotalrole. However, it is still a challenge to accurately categorize an ever-growing diversity of file formats and *** approaches to address the issue have been introducedin recent years. Most approches use hand-crafted features, andsome use a compact neural network architectureapproach with explicit feature extraction. Nonetheless, thesemethods encounter some challenges, including training time fornew file types, dealing with an insufficient number of trainingexamples, the complexity of training, and their accuracylevel. Our paper introduces three novel, content-basedretrieval and identification, file type classificationapproaches, proactively addressing these identified *** three approaches significantly enhance accuracy andprioritize an intuitive method for swift integration of new filetypes using pre-trained models, thereby enhancing speed andreliability in file type detection. The first approach is basedon the principles of Natural Language Processing (NLP) andtransformer architecture. The second approach amalgamates anensemble of statistical features, OOXML files identifier and a pre-trained model usingtransfer *** meticulously evaluated the classification task, achieving an accuracy of 72.4% and 69.2%, respectively, for our first two approaches, surpassing the performance of the state-of-the-art convolutional neural network (CNN) based models described in the literature in terms of accuracy. Finally, our third approach of one-shot learning enables the identification of files with minimal examples, facilitating efficient training in a short period, and demonstrating a remarkable 100% accuracy in several scenarios. © 2024, The Authors. All rights reserved.

关键词： Malware

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Dead-beat Identification for Model Reference Adaptive Control⋆

Dead-beat Identification for Model Reference Adaptive Contro...

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IEEE Conference on Decision and Control

作者： Ali Kashani Ahmad Kalhor Babak Nadjar Araabi Claus Danielson Mechanical Engineering department University of New Mexico NM US Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this work, we propose a novel parameter identifier for linear time-invariant systems, which has the fastest possible convergence in principal (i.e., dead-beat). The identifier applies where states and derivatives of states can be measured. The identifier is applied to the certainty-equivalence (indirect) model reference adaptive control that relaxes the conventional assumptions about open-loop stability and persistent excitation in MRAC. Furthermore, in conventional MRAC the parameter convergence rate is proportional to the tracking error, whereas our proposed adaptation law has dead-beat dynamics. This method is previously applied to a Delta robot. In this paper, we present a theoretical analysis of the method by solving the identifier dynamics to get its closed-form solution and examine the effect of perturbations such as white additive noise, uncertainty, and disturbance. The linearized model of an actuated inverted pendulum with unknown parameters is used as a benchmark example.

关键词： Linear systems Adaptation models Uncertainty Closed-form solutions Perturbation methods Benchmark testing Stability analysis

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Simple In-place Data Augmentation for Surveillance Object Detection

arXiv

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arXiv 2024年

作者： Otgonbold, Munkh-Erdene Batnasan, Ganzorig Gochoo, Munkhjargal Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University United Arab Emirates Department of Electronics Mongolian University of Science and Technology Mongolia

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications. © 2024, CC BY.

关键词： Object detection

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Role of activity-based learning and ChatGPT on students' performance in education

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computers and Education: Artificial Intelligence 2024年 6卷

作者： Al Shloul, Tamara Mazhar, Tehseen Abbas, Qamar Iqbal, Muhammad Ghadi, Yazeed Yasin Shahzad, Tariq Mallek, Fatma Hamam, Habib General Education Department Liwa College Abu Dhabi15222 United Arab Emirates Department of Computer Science Virtual University of Pakistan Lahore51000 Pakistan Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad 44000 Pakistan Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Faculty of Engineering University de Moncton MonctonNBE1A3E9 Canada Hodmas University College Taleh Area Mogadishu Somalia Bridges for Academic Excellence Tunis Tunisia School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa

Purpose: This study investigates the impact of activity-based learning and the utilization of ChatGPT on students' academic performance within the educational framework. Objectives: The study aims to assess the effectiveness of activity-based learning in comparison to traditional methods, while also evaluating the potential benefits and drawbacks of integrating ChatGPT as an educational tool. Methods: The study employs a comparative approach, analyzing the outcomes of students exposed to activity-based learning versus those using conventional methods. Additionally, the study examines the usage of ChatGPT in education through surveys and trials to determine its contribution to personalized feedback, interactive learning, and innovative teaching methods. Results: The findings reveal that activity-based learning enhances students' engagement, motivation, and critical thinking skills. Students participating in activity-based learning demonstrate improved academic achievement, which is attributed to their active involvement and practical application of knowledge. Similarly, the integration of ChatGPT offers novel avenues for interactive learning and individualized assistance, fostering students' understanding and exploration of complex concepts. Conclusion: In conclusion, activity-based learning proves to be a student-centered approach that enhances learning outcomes by fostering active participation and practical engagement. The utilization of ChatGPT in education showcases its potential to enhance educational experiences through interactive conversations and innovative teaching methodologies, despite considerations regarding potential limitations and ethical implications. © 2024 The Authors

关键词： Students

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