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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Stylianidis, Nearchos Papadopoulos, Panayiotis Kyriacou, Alexis Eliades, Demetrios G. Panayiotou, Christos Polycarpou, Marios KIOS Research Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Lelantus Innovations Ltd Nicosia Cyprus

Monitoring and control of indoor air quality and thermal comfort conditions have become a key challenge for achieving a desirable and healthy indoor environment. Indoor environmental conditions in large-scale commercial buildings are mainly affected by the Fan-Coil Units (FCUs) of the Heating, Ventilation, and Air-Conditioning (HVAC) system. Investigation of the effect of the FCUs on the indoor environment is essential in improving the indoor air quality. Computational Fluid Dynamics (CFD) modeling offers a realistic representation of the building's indoor environment. In this study, the impact of air recirculation through the FCUs on the indoor environmental conditions has been investigated for a real office space using two different CFD models: a) a detailed model that shows the behavior of the airflow through the FCUs, and b) a simplified model based on considering that each FCU operates as an air inlet-outlet. The comparison between the models and the experimental measurements from the actual room setup shows that the detailed model approaches the experimental measurements, while the simplified model shows a significant deviation. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Computational fluid dynamics

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Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans

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engineering Applications of Artificial Intelligence 2023年第PartB期123卷

作者： Baker, Mohammed Rashad Jihad, Kamal H. Al-Bayaty, Hussein Ghareeb, Ahmed Ali, Hessein Choi, Jun-Ki Sun, Qiancheng Department of Software College of Computer Science and Information Technology University of Kirkuk Iraq Electronic Computer Center University Presidency University of Kirkuk Iraq Department of Electrical Engineering College of Engineering University of Kirkuk Iraq Department of Mechanical Engineering College of Engineering University of Kirkuk Iraq Department of Mechanical Engineering Union College United States Department of Mechanical and Aerospace Engineering University of Dayton United States

Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios. © 2023 Elsevier Ltd

关键词： COVID-19

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How Do Hackathons Foster Creativity? Towards Automated Evaluation of Creativity at Scale 25

How Do Hackathons Foster Creativity? Towards Automated Evalu...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Computer Science Aalborg University Copenhagen Denmark Department of Computer Science Aalborg University Copenhagen Denmark Center for Hybrid Intelligence Department of Management Aarhus University Aarhus Denmark Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400713941

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025 Copyright held by the owner/author(s).

关键词： creativity Hackathons human-centered AI large language models quantitative methods

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Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation

arXiv

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arXiv 2023年

作者： Li, Jin Malialis, Kleanthis Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas. These data are often unlabelled. In this case, identifying infrequent events, such as anomalies, poses a great challenge. This problem becomes even more difficult in non-stationary environments, which can cause deterioration of the predictive performance of a model. To address the above challenges, the paper proposes an autoencoder-based incremental learning method with drift detection (strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of both incremental learning and drift detection. We conduct an experimental study using real-world and synthetic datasets with severe or extreme class imbalance, and provide an empirical analysis of strAEm++DD. We further conduct a comparative study, showing that the proposed method significantly outperforms existing baseline and advanced methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Coordinated Operational Optimization of Water and Power Systems Under Emergency Conditions

SSRN

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SSRN 2024年

作者： Perelman, Gal Shmaya, Tomer Navon, Aviad Vrachimis, Stelios Panteli, Mathaios Eliades, Demetrios G. Ostfeld, Avi Faculty of Civil and Environmental Engineering Technion – Israel Institute of Technology Israel Faculty of Electrical Engineering and Computer Engineering Technion – Israel Institute of Technology Haifa Israel KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

This research explores the integrated management of Water Distribution Systems (WDS) and Power Distribution Systems (PDS) for improved operational efficiency and resilience under extreme scenarios. Traditionally, these systems are operated independently leading to sub-optimal operational efficiency. Alternatively, some studies proposed a joint operation of WDS and PDS but assumed that decisions are made simultaneously by a single decision-maker, which is impractical. This study offers a novel approach for emergency control based on sparse communication between WDS and PDS operators to enhance resilience and improve the integrated systems’ functionality. The primary objective is to minimize the load shedding in the PDS through strategic demand management in the interconnected WDS, demonstrating the potential of collaborative decision-making between these two critical infrastructure systems. The paper highlights the significance of cross-sectoral collaboration, presenting a viable pathway for managing local infrastructure systems under extreme conditions while ensuring uninterrupted service delivery to communities. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Experimental Studies with software Defined Radio Receivers of Aerial Objects On-Board Systems Signals 17

Experimental Studies with Software Defined Radio Receivers o...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Khudov, Hennadii Kostianets, Oleksandr Tahyan, Kristina Makoveichuk, Oleksandr Biernik, Yelyzaveta Shevchenko, Yurii Force University Ivan Kozhedub Kharkiv National Air Radar Troops Tactic Department Kharkiv Ukraine Air Force Scientific Center Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Scientific and Technical University Academician Y. Bugay International Department of Computer Science and Software Engineering Kyiv Ukraine

ISBN: (纸本)9798331520564

The paper proposes experimental studies with software Defined Radio receivers of aerial objects onboard systems signals. It is established that the signals of the following channels are additional sources of signals, for example, of unmanned aerial vehicles: command, telemetric, issuance of targeted information, manual control, satellite navigation. Experimental studies were conducted and signal spectrograms of signals were obtained. The RTL 820T2 model was chosen as the software Defined Radio receiver. For an aerial object coordinates determining that emits the signals, we will use the network of software Defined Radio receivers. So, the resulting measurement error when using the radar and the software Defined Radio network of receivers is determined. We can see that the use of the software Defined Radio network of receivers will increase the overall accuracy of determining the coordinates of an aerial object. © 2024 IEEE.

关键词： Radar receivers

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GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation

arXiv

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arXiv 2025年

作者： Shahin, Nada Ismail, Leila Lab Department of Computer Science and Software Engineering College of Information Technology UAE University Al-Ain United Arab Emirates Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community. © 2025, CC BY.

关键词： Neural machine translation

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Intrusion Detection System Using PSO and DE Algorithms with BP Neural Network 8

Intrusion Detection System Using PSO and DE Algorithms with ...

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8th International Conference on Fuzzy Systems and Data Mining, FSDM 2022

作者： Wang, Ke Guan, Jingwei Luo, Song Guan, Zhi MoE Beijing China Department of Computer Science and Technology Eecs Peking University Beijing China Liangjiang International College Chongqing University of Technology Chongqing China School of Computer Science and Engineering Chongqing University of Technology Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9781643683461

Intrusion detection system (IDS) combines software and hardware to detect network attacks. In this paper, we propose a new intrusion detection method based on an improved BP neural network algorithm. We improve the BP neural network algorithm by combining it with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. We also propose a new framework based on common intrusion detection framework to accommodate our improved BP neural network. The experiments based on the CICIDS2017 dataset show our approach achieves better detection efficiency. © 2022 The authors and IOS Press.

关键词： Particle swarm optimization (PSO)

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Quality of Service (QoS)-Driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital Technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Fused Gromov-Wasserstein graph mixup for graph-level classifications 23

Fused Gromov-Wasserstein graph mixup for graph-level classif...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xinyu Ma Xu Chu Yasha Wang Yang Lin Junfeng Zhao Liantao Ma Wenwu Zhu School of Computer Science Peking University Department of Computer Science and Technology Tsinghua University School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University

Graph data augmentation has shown superiority in enhancing generalizability and robustness of GNNs in graph-level classifications. However, existing methods primarily focus on the augmentation in the graph signal space and the graph structure space independently, neglecting the joint interaction between them. In this paper, we address this limitation by formulating the problem as an optimal transport problem that aims to find an optimal inter-graph node matching strategy considering the interactions between graph structures and signals. To solve this problem, we propose a novel graph mixup algorithm called FGWMixup, which seeks a "midpoint" of source graphs in the Fused Gromov-Wasserstein (FGW) metric space. To enhance the scalability of our method, we introduce a relaxed FGW solver that accelerates FGWMixup by improving the convergence rate from O(t-1) to O(t-2). Extensive experiments conducted on five datasets using both classic (MPNNs) and advanced (Graphormers) GNN backbones demonstrate that FGWMixup effectively improves the generalizability and robustness of GNNs. Codes are available at https://***/ArthurLeoM/FGWMixup.

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