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6th International Conference on Algorithms, Computing and Systems, ICACS 2022

作者： Didachos, Christos Kintos, Dionisis Panagiotis Fousteris, Manolis Gerogiannis, Vassilis C. Son, Le Hoang Kanavos, Andreas Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Patras Greece Department of Digital Systems University of Thessaly Larissa Greece Vnu Information Technology Institute Vietnam National University Hanoi Viet Nam Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules' chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab"approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame. © 2022 ACM.

关键词： Big data

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Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection

arXiv

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arXiv 2024年

作者： Svystun, Serhii Melnychenko, Oleksandr Radiuk, Pavlo Savenko, Oleg Sachenko, Anatoliy Lysyi, Andrii Department of Computer Engineering and Information Systems Khmelnytskyi National University Khmelnytskyi29016 Ukraine Department of Computer Science Khmelnytskyi National University Khmelnytskyi29016 Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil46020 Ukraine Department of Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom26600 Poland

The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model’s accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Semantic Data Representation for Explainable Windows Malware Detection Models

arXiv

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arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

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Utilizing Hybrid P4 Solutions to Enhance 5G gNB with Data Plane Programmability

Utilizing Hybrid P4 Solutions to Enhance 5G gNB with Data Pl...

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Joint IFIP Wireless and Mobile Networking Conference (WMNC)

作者： Mohsen Memarian Andreas Kassler Karl-Johan Grinnemo Sándor Laki Gergely Pongracz Johan Forsman Computer Science Department Karlstad University Karlstad Sweden Deggendorf Institute of Technology Institute of Applied Computer Science Deggendorf Germany Faculty of Informatics ELTE Eötvös Loránd University Budapest Hungary Ericsson Research Budapest Hungary TietoEvry TBD Sweden

ISBN: (数字)9783903176683

ISBN: (纸本)9798331542450

The traditional method of data plane programming involves deploying a single P4 program to a single target. However, different targets have varying capabilities, functionalities, and support for various programming languages beyond P4. Therefore, disaggregating a single data plane program into multiple subprograms that run on different targets can allow us to leverage the strengths of each target, which becomes particularly important in the context of 5G, where some data plane processing functions, such as buffering and retransmission for RLC processing, cannot be effectively expressed in P4. This paper delves into the decomposition of a 5G gNB across a P4-programmable SmartNIC and an x86 server using DPDK-based processing, thus harnessing the strengths of each target. Our evaluation revealed that offloading certain processing to an x86 server can improve throughput by up to 50%, thanks to the scalability of DPDK applications’ performance with the number of CPU cores. However, offloading does introduce a slight increase in latency, so the approach should be adjusted based on the specific needs and available resources.

关键词： Wireless communication computer languages 5G mobile communication Scalability Programming Throughput Servers

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FEASIBILITY OF ASSESSING COGNITIVE IMPAIRMENT VIA DISTRIBUTED CAMERA NETWORK AND PRIVACY-PRESERVING EDGE COMPUTING

arXiv

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arXiv 2024年

作者： Hegde, Chaitra Kiarashi, Yashar Levey, Allan I. Rodriguez, Amy D. Kwon, Hyeokhyen Clifford, Gari D. Department of Biomedical Informatics Emory University School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University AtlantaGA United States Department of Neurology Emory University AtlantaGA United States Department of Biomedical Informatics Emory University Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States

INTRODUCTION: Mild cognitive impairment (MCI) is characterized by a decline in cognitive functions beyond typical age and education-related expectations. Since, MCI has been linked to reduced social interactions and increased aimless movements, we aimed to automate the capture of these behaviors to enhance longitudinal monitoring. METHODS: Using a privacy-preserving distributed camera network, we collected movement and social interaction data from groups of individuals with MCI undergoing therapy within a 1700m2 space. We developed movement and social interaction features, which were then used to train a series of machine learning algorithms to distinguish between higher and lower cognitive functioning MCI groups. RESULTS: A Wilcoxon rank-sum test revealed statistically significant differences between high and low-functioning cohorts in features such as linear path length, walking speed, change in direction while walking, entropy of velocity and direction change, and number of group formations in the indoor space. Despite lacking individual identifiers to associate with specific levels of MCI, a machine learning approach using the most significant features provided a 71% accuracy. DISCUSSION: We provide evidence to show that a privacy-preserving low-cost camera network using edge computing framework has the potential to distinguish between different levels of cognitive impairment from the movements and social interactions captured during group activities. © 2024, CC BY-NC-SA.

关键词： Differential privacy

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Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance

Data Cleaning Process for mHealth Log Data to Inform Health ...

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作者： Savai, Simon Muhindi Hasan, Md Kamrul Kamano, Jemimah Misoi, Lawrence Wakholi, Peter Were, Martin C. Institute of Biomedical Informatics Moi University Eldoret Kenya Department of Computer Science Vanderbilt University TN United States School of Medicine Moi University Eldoret Kenya Moi Teaching and Referral Hospital Eldoret Kenya School of Computing and Informatics Technology Makerere University Kampala Uganda Department of Biomedical Informatics VUMC Nashville TN United States

ISBN: (纸本)9781643682907

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck's framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance. © 2022 The authors and IOS Press.

关键词： Developing countries

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Dynamic Day and Night Cycle Impact in a Serious VR Game

Dynamic Day and Night Cycle Impact in a Serious VR Game

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Virtual Reality (ICVR), IEEE International Conference on

作者： Fardani Annisa Damastuti Kenan Firmansyah Yunifa Miftachul Arif Ali Ridho Barakbah Mochamad Hariadi Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Creative Multimedia Technology Electronic Engineering Polytechnic Institute of Surabaya Surabaya Indonesia Department of Informatics Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Department of Informatics and Computer Engineering Electronic Engineering Polytechnic Institute of Surabaya Surabaya Indonesia Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350364231

ISBN: (纸本)9798350364248

The immersive nature of virtual reality (VR) gaming is significantly improved by the implementation of dynamic environmental systems. This paper focuses on the development and impact of a dynamic day and night cycle in a serious VR game. This feature is intended to simulate a full 24-hour cycle within a 10-minute gameplay session, resulting in a seamless transition between different times of day that impacts both the visual aesthetics and gameplay mechanics. The day and night cycle introduces strategic depth by requiring players to adjust their management strategies according to the time of day, which affects resource availability, operational efficiency, and conditions. This system not only enhances visual fidelity but also directly impacts gameplay, necessitating that player modify their strategies in response to the changing environment. Visual aesthetics are expected to increase by 15%, strategic profundity will be improved by 20%, and player immersion will be enhanced by 25%, according to the research findings. This investigation underscores the importance of natural cycles in enhancing the strategic complexity, interactivity, and realism of virtual reality environments, thereby offering valuable insights for the creation of future virtual reality games.

关键词： Visualization Accuracy Virtual reality Games Immersive experience Hardware Complexity theory

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Opportunistic Routing for Mobile Edge Computing: A Community Detected and Task Priority Aware Approach

SSRN

引用

SSRN 2024年

作者： Wu, Jia Dai, Tingyi Guan, Peiyuan Chen, Ziru Gou, Fangfang Taherkordi, Amir The Department of Informatics University of Oslo Oslo0316 Norway The Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States The School of Computer Science and Technology GuiZhou University Guiyang550025 China The Research Center for Artificial Intelligence Faculty of IT Monash University MelbourneVIC3800 Australia

As the 5G era develops quickly, there has been a significant growth in the amount of network data. Concurrently, traditional routing algorithms are encountering growing challenges. Traditional routing algorithms typically rely on maintaining stable end-to-end paths to facilitate smooth data transmission. However, in wireless multi-hop networks, the mobility of nodes and the dynamic nature of network topology make maintaining stable end-to-end paths exceedingly difficult, if not impossible. Consequently, data transmission efficiency decreases while network overhead increases. To address these challenges, opportunistic routing has emerged. Opportunistic routing is self-organizing networks that rely on chance encounters between nodes to transmit messages, and the store-and-carry forwarding technique is used for data transmission between nodes. Typically, most traditional opportunistic routing algorithms divide nodes into communities. However, such approaches often overlook node characteristics, potentially leading to disparate nodes being assigned to separate communities despite positive correlations. Thus, accurately dividing communities remains a significant challenge. To address this, we propose the Efficient Community Detection Data Transmitting (ECDDT) algorithm. During community division, ECDDT combines social attributes and network topology using mobile edge computing. Subsequently, to expedite task execution, we schedule these tasks in a reasonable manner. Through simulation experiments across four diverse real datasets, we evaluate the algorithm's efficacy. Our findings demonstrate favorable performance in delivery rate and routing overhead. © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study

Data Driven Methods to Predict Vertical Profiles of the Rail...

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International Conference on ICT For Smart Society (ICISS)

作者： Ade Chandra Nugraha Suhono Harso Supangkat IGB Baskara Nugraha Yunendar Aryo Handoko Awan Hermawan Purwadinata Dimas Wisnu Saputro Department of Computer Engineering and Informatics Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Smart City and Community Innovation Center School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Bandung Institute of Technology Bandung Indonesia Managing Director of Infrastructure PT Kereta Api Indonesia Bandung Indonesia Department of Computer Engineering and Informatics Bandung State of Polytechnic Bandung Indonesia

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R^2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains).

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Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mahaveerakannan R Murali Dhar M S Sohan Goswami P. Gopala Krishna K. Jayanthi M. Sathish Kumar Department of Spatial Informatics The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana Department of Computer Science and Engineering Pollachi Institute of Engineering and Technology Coimbatore Pollachi Tamil Nadu Department of Cyber Security Karpagam college of Engineering Coimbatore Tamil Nadu

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method.

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