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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Dipto Biswas Joon-Min Gil Dept. of Computer Software Engineering Graduate School of Daegu Catholic University Gyeongsan-si South Korea School of Computer Software Engineering Gyeongsan-si South Korea

In this paper, we compare the performance of two popular NLP models, pre-train fine-tuned BERT and BiLSTM with combined CNN, in terms of the classification and recommendation tasks of research papers. We conduct the performance evaluation of these two models with research journal benchmark dataset. Performance results show that the pre-train fine-tuned BERT model is superior to CNN-BiLSTM combined model in terms of classification performance.

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PLCD: Policy Learning for Capped Service Mobility Downtime

PLCD: Policy Learning for Capped Service Mobility Downtime

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International Conference on computer Communications and Networks (ICCCN)

作者： Lusungu J. Mwasinga Syed M. Raza Duc-Tai Le Moonseong Kim Hyunseung Choo Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Dept. of IT Convergence Software Seoul Theological University Bucheon Korea

Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme.

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Inception: Efficiently Computable Misinformation Attacks on Markov Games

arXiv

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arXiv 2024年

作者： McMahan, Jeremy Wu, Young Chen, Yudong Zhu, Xiaojin Xie, Qiaomin Dept. of Computer Sciences University of Wisconsin-Madison United States Dept. of Industrial and Systems Engineering University of Wisconsin-Madison United States

We study security threats to Markov games due to information asymmetry and misinformation. We consider an attacker player who can spread misinformation about its reward function to influence the robust victim player's behavior. Given a fixed fake reward function, we derive the victim's policy under worst-case rationality and present polynomial-time algorithms to compute the attacker's optimal worst-case policy based on linear programming and backward induction. Then, we provide an efficient inception ("planting an idea in someone's mind") attack algorithm to find the optimal fake reward function within a restricted set of reward functions with dominant strategies. Importantly, our methods exploit the universal assumption of rationality to compute attacks efficiently. Thus, our work exposes a security vulnerability arising from standard game assumptions under misinformation. © 2024, CC BY.

关键词： Linear programming

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LIDS: Lightweight Dynamic Scheduling Technique for 6G-enabled Massive LoRa based IoT systems

LIDS: Lightweight Dynamic Scheduling Technique for 6G-enable...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Kumbam, Akshitha Choudhury, Nikumani Nasralla, Moustafa M. Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Pilani Hyderabad India Prince Sultan University Dept. of Communications & Networks Engineering Riyadh Saudi Arabia

ISBN: (纸本)9781665459754

The sixth-generation (6G) wireless communication networks are envisioned to deliver improved Quality of Services (QoS) such as data-rate, latency, localization, etc., compared to 5G services. This demand will mainly arise due to the massive Internet of Things (IoT) connections among billions of embedded devices. It is worth noting that these IoT devices are constrained in nature in terms of memory, computation and most importantly power. Low-power wireless computing necessitates a link-layer technology that considers the aforementioned IoT device requirements such as low-power, low data-rate, long-range and energy efficient reliable communications. Long Range Radio (LoRa) technology has recently become widely popular to facilitate low-power, long-range communication, while other similar technologies struggle to achieve long-range transmissions. The LoRA standard defines several PHY and MAC sub-layer specifications for Low-Power Wide Area Networks (LPWAN) that can be used to build several IoT applications. With 6G communications, an IoT application is expected to consist of hundreds of devices connected at a time, and this will need a proper scheduling mechanism among these devices for optimal performance. The LoRa standard does not define any scheduling mechanism and leaves it to the research community to explore. In this paper, we propose a distributed scheduling mechanism under the supervision of a common LoRa gateway. The proposed mechanism focuses on a Lightweight DynamicScheduling (LIDS) scheme to minimize loss in data transmissions through collisions. The proposed mechanism optimizes the power consumption to achieve the desired level of QoS requirements such as throughput and latency. © 2022 IEEE.

关键词： Internet of things

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Computational Thinking and AI Literacy: A Gender-Based Analysis Among Early Learners

Computational Thinking and AI Literacy: A Gender-Based Analy...

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IEEE Education engineering (EDUCON)

作者： Andrea E. Cotino-Arbelo Jezabel Molina-Gil Carina S. González-González Dept. of Computer and Systems Engineering Women Research Studies Institute (of the University of La Laguna) La Laguna Spain

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

While women remain underrepresented in STEM fields, our study reveals insights from an overlooked frontier: preschool classrooms. Despite decades of global initiatives aimed at increasing female participation in technology fields, gender-interest stereotypes continue to discourage girls from pursuing computer science careers. Our research challenges these barriers by implementing an innovative educational program integrating Computational Thinking (CT) and Artificial Intelligence (AI) literacy. Using a quasi-experimental pretest-posttest design with $N=114$ preschoolers, we assessed the impact of hands-on activities in algorithms, debugging, and control structures through the TechCheck questionnaire. Our findings challenge prevailing assumptions: while initial CT skills showed no gender disparity (boys M=58.8, girls M=53.95, p=,445), post-intervention results revealed girls significantly outperforming boys (girls M=66.11, boys M=51.61, $p=,022$ ). On the contrary, AI literacy assessments showed complete gender parity (p=,995). These results suggest that early childhood education could be the key to dismantling gender barriers in computer science, demonstrating that when learning environments are equitable, girls not only match but can exceed traditional performance expectations. This study opens new pathways for understanding how early educational interventions can reshape the future of gender equity in STEM fields, beginning with our youngest learners.

关键词： computer science Visualization Educational programs Gender equity engineering profession computer architecture Cultural differences Reliability Artificial intelligence engineering education

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Robust texture features for emphysema classification in CT images 28

Robust texture features for emphysema classification in CT i...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Li, Haipeng Mukundan, Ramakrishnan Dept. of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (纸本)9789082797053

In this paper, we propose a novel feature extraction method based on local quinary patterns (LQP), multifractal features and intensity histograms for classifying emphysema into three subtypes in computed tomography images. Compared to local binary patterns, LQP method computes more image local patterns to represent texture features. Multifractal features which enhancing local textures are combined with other features to constitute a feature vector for this classification task. An autoencoder network and principal components analysis are used to reduce the dimensionality of the feature vector before using an SVM classifier. The proposed method is tested on an emphysema database containing 168 annotated regions of interest of three different subtypes. The experimental results demonstrate that our method outperforms most other state-of-the-art approaches with the best classification accuracy of 92.3% using the least dimensionality (15) of the feature vector. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Local binary pattern

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An Ising Machine-Based Hybrid Optimization Method Using Constraint Conversion and Correction

An Ising Machine-Based Hybrid Optimization Method Using Cons...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Kinya Iwata Masashi Tawada Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University Green Computing Systems Research Organization Waseda University

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes an Ising machine-based hy-brid optimization method to solve combinatorial optimization problems, which consists of an approximate formulation process, an annealing process with an Ising machine, and a correction process. The approximate formulation and correction aim to improve the performance of the Ising machine. Computational experiments were conducted on the multi-dimensional knapsack problem (MKP) using an Ising machine. We compare the results with those of the proposed method and the conventional method and demonstrate the effectiveness of the proposed approach.

关键词： Annealing Optimization methods Consumer electronics

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A Variable Step-Size Hybrid Adaptive Nonlinear Filter for Solar Radiation Prediction

A Variable Step-Size Hybrid Adaptive Nonlinear Filter for So...

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International Conference on systems and Informatics (ICSAI)

作者： Y. Xiao L. Ma K. Khorasani Dept. of Management & Information Systems Prefectural University of Hiroshima Hiroshima Japan Dept. of Electrical & Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In this paper, a new hybrid adaptive nonlinear filter (HANF) scheme with variable step sizes (VSS) is proposed for solar radiation prediction. Our methodology consists of a Volterra filter and a functional link artificial neural network (FLANN) filter. The Volterra filter with the first- and second-order kernels is included, that is capable of expressing both linearity and nonlinearity. The FLANN filter can handle the nonlinearity that may be expressed by higher-order exponential terms that the Volterra filter with a limited number of kernels is unable to deal with. The VSSs are introduced in the two filters to allow the HANF to enjoy desirable tracking capability such that the time-varying nonlinearity underlying the nonlinear phenomena can be detected and tracked. The proposed VSS-HANF is applied to a real hourly solar radiation time sequence to confirm its improved prediction performance as compared to its counterpart with fixed step sizes.

关键词： Accuracy Finite impulse response filters Adaptive filters Weather forecasting Linearity Nonlinear filters Artificial neural networks Solar radiation Kernel Informatics

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Design and Development of a Smart Tool for Distribution Transformer sizing Considering Plug-in Electric Vehicles Charging

Design and Development of a Smart Tool for Distribution Tran...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Yussef Afifi Chantel Le Elias Louis Leontis Yuanrong Kyle Zheng Walid G. Morsi Dept. of Electrical Computer and Software Engineering Faculty of Engineering Ontario Tech University Oshawa Canada Faculty of Engineering Ontario Tech Universit Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

The use of electric vehicles (EV) has exponentially grown within the past couple of years and it is expected to increase by 2050 causing a drastic increase in residential demand. Due to such an increase in demand, many existing distribution transformers servicing these residential homes may need either replacement or upgrade in their sizes to account for the home charging of these EVs. Choosing the correct economic transformer’s size helps electric utilities to benefit from such a significant capital investment. In this paper, a Smart Transformer Sizing Tool (STST) was developed to provide the correct economic size of transformers that ensure no overload as well as maintain the voltage quality. The results demonstrating the effectiveness of the proposed tool are presented and conclusions are drawn.

关键词： Economics Plug-in electric vehicles Heat engines Knowledge based systems Voltage Transformers Power industry

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Generalized Stochastic Petri Net Based Simulation of IoT Supported Dynamic Navigation in Teaching Building Evacuation

Generalized Stochastic Petri Net Based Simulation of IoT Sup...

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2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022

作者： Strobing, Jordan Granit, Meghan Wang, Jiacun Zhao, Lin Monmouth University Dept. of Computer Science and Software Engineering NJ United States School of Safety Science and Engineering Xi'an Univ. of Sci. and Tech Xi'an China

ISBN: (数字)9781665498357

ISBN: (纸本)9781665498357

Emergency management and evacuation efficiency is important to ensure the safety of faculty and students in college. Teaching buildings are typically of multiple stories. When classes are in session, a teaching building may have a large number of students inside. In case of an event like a fire, people have to be evacuated as soon as possible. Due to panic, people may not use good judgement to choose optimal evacuation path, which can further cause congestion in a path to an exit. This study attempts to leverage the recent advances in information technology to dynamically guide evacuees. We use generalized stochastic Petri nets (GSPN) to model the evacuation process in a teaching building. The layout of the building, sizes of classrooms and hallways, number of people in each room, and people's decision pattern in choosing a direction to move are all parameters of the model. With simulation we can estimate the evacuation time-span. Moreover, by observing the state of GSPN model, we can analyze the congestion status of each simulated pathway, and based on that we can dynamically notify people to select the right path that lead them to an exit with the least amount of time. © 2022 IEEE.

关键词： Internet of things

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