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25th International computer science and Engineering Conference, ICSEC 2021

作者： Polpinij, Jantima Namee, Khanista Mahasrakham University Faculty of Informatics Intellect Lab Department of Computer Science Mahasarakham Thailand King Mongkut's University of Technology North Bangkok Faculty of Industrial Technology and Management Bangkok Thailand

ISBN: (纸本)9781665411974

Missing data is common in data analytics, including rainfall, and can reduce accuracy through biased estimates that lead to invalid conclusions. Many methods have been proposed to handle missing data but the same method might not be suitable for every dataset. This challenge was taken up here as the presentation of a comparative study of popular methods to handle the issue of missing values. Popular methods of handling missing data values include removal of instances with missing values, arithmetic mean (AM), normal ratio (NR), nearest neighbor (NN) and inverse distance (ID). We proposed a method of estimating missing values called step-5 simple moving average (step-5 SMA). This applies simple moving average (SMA) principles with consideration of the EI Nino-Southern Oscillation (ENSO) phenomenon. After enhancing the training set, our method was used to model monthly rainfall forecasts utilizing two supervised machine learning algorithms as support vector machines (SVM) and k-nearest neighbor (k-NN). We used monthly precipitation data (in mm) gathered between 1953 and 2013 from 26 water measurement stations of the Meteorological Department located in Northeast Thailand. After evaluating by MAE and RMSE, results showed that monthly rainfall forecasters developed by the training set that removed observations with missing values returned the lowest performance. Enhancing the quality of the training set using our step-5 SMA gave a better performance than the other missing value estimation methods. © 2021 IEEE.

关键词： Rain

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Parking Occupancy Prediction and Traffic Assignment in a University Environment 10

Parking Occupancy Prediction and Traffic Assignment in a Uni...

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10th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2022

作者： Farag, Mohamed Hilal, Amr El-Tawab, Samy Arab Academy for Science Technology & Maritime Transport Alexandria Egypt Virginia Tech Transportation Institute Virginia Tech BlacksburgVA United States Informatics Lab Virginia Tech BlacksburgVA United States Alexandria University Department of Computer and Systems Engineering Egypt James Madison University College of Integrated Science & Engineering HarrisonburgVA United States

ISBN: (纸本)9781665464635

The fourth industrial revolution has given rise to large-scale data-driven models like smart cities and Intelligent transportation. Within these models, applications like smart parking have been growing rapidly in research and industry. However, different scenarios and environments (e.g., shopping areas, residential places, and business complexes) can require special handling due to the various factors impacting people's schedules and behavior. In this paper, we provide an initial investigation of traffic assignment based on parking prediction for a mid-size university environment where parking is concentrated in three parking garages around the campus. Our initial investigation includes results for parking prediction using a statistical method and plans for an augmenting study using variations of Neural Networks. On top of the parking prediction layer, we propose an application layer that directs and fuses the model predictions to produce the parking options provided to the application user. The presented investigation can help the university administration in their consideration of building additional garages. © 2022 IEEE.

关键词： Forecasting

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A Deep Learning Framework for Healthy LifeStyle Monitoring and Outdoor Localization

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IEEE Access 2025年

作者： Rafiq, Mehrab Alshammari, Naif S. Alhasson, Haifa F. AlHammadi, Dina Abdulaziz Alshehri, Mohammed Jalal, Ahmad Liu, Hui Air University Department of Computing and AI E-9 Islamabad44000 Pakistan Majmaah University College of Computer and Information Sciences Department of Computer Sciences Majmaah11952 Saudi Arabia Qassim University College of Computer Department of Information Technology Buraydah52571 Saudi Arabia Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences P.O.Box 84428 Riyadh11671 Saudi Arabia King Khalid University Department of Computer Science Abha61421 Saudi Arabia Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Lab Bremen28359 Germany

The green research field of ubiquitous computing has been able to draw and hold academics’ interest for a while. Recognition and localization of human locomotion have also been widely developed as ubiquitous computing applications. Personal safety, behavior analysis, entertainment, and healthcare monitoring all utilize these apps.A key component of several fields, such as robots, sports, healthcare, and security, is human locomotion recognition (HLR). Researchers and engineers have been trying to use the increasing popularity of wearable technology, especially environmental sensors and Inertial Measurement Units (IMUs), to identify and categorize human locomotion activities in an accurate and efficient *** capabilities of smartphones and wearable technology have increased due to advancements in sensing technology. Inertial sensors like gyroscopes and accelerometers are now frequently seen in smartphones. These sensors can now be utilized for a wide range of purposes, while their original purpose was to improve gadget features. Using smartphone IMU, ambient, GPS, and audio sensor data from two publicly available benchmark datasets—the Extrasensory dataset and the Domino dataset—this study proposes a sophisticated approach for human locomotion and localization detection. In the preprocessing stage, a Chebyshev Type 2 filter was used for windowing and segmentation, while a Hamming window was applied. Feature extraction was divided into two parts: for actions, the extracted features included Fast Fourier Transform (FFT), State Space Correlation Entropy (SSCE), Maximum Lyapunov Exponent (MLE), and Auto Regression;for localization-based features, Recursive Feature Elimination (RFE), step count, heading angle, and step length were employed. Kernel Fisher Discriminant Analysis was applied for feature optimization, and a deep neural network was utilized for feature classification. The proposed system achieved an overall classification accuracy of 88.4% on the Extrasen

关键词： Ambient intelligence

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Deep Learning-Driven Resource Allocation for MEC-Enabled UAV Collision Avoidance System

Deep Learning-Driven Resource Allocation for MEC-Enabled UAV...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Khadidja Zairi Bouziane Brik Younes Guellouma Hadda Cherroun LIM Lab. Amar Telidji University Laghouat Algeria Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE DRIVE Laboratory EA1859 University of Burgundy 49 Rue Mademoiselle Bourgeois Nevers France

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The Internet of Unmanned Aerial Vehicles (UAVs) envisions critical services like Collision detection and Avoidance among Vehicles (CAVs). These services are typically implemented at the Multi-access Edge Computing (MEC) to enable ultra-low latency communication, ensuring real-time reactions to prevent collisions. To ensure network coverage and optimal connection of UAVs to the nearest MEC host, the CAV must be deployed across all MEC hosts. However, this may impose additional demands on these hots’ available computational and memory resources. We introduce an Artificial Intelligence empowered framework to optimize virtualized resource allocation to the MEC hosts. The framework harnesses the capabilities of Deep Learning (DL) for twofold pivotal objectives: (i) Forecasting UAV Density: the framework predicts the UAV density that each MEC host must accommodate. This anticipatory insight guides resource allocation, adapting it to the anticipated demand dynamics. (ii) Precision in Virtual Resource Allotment: DL is further instrumental in calibrating the precise quantum of virtual resources requisite for the collision detection application to function optimally. This approach ensures the collision avoidance system’s peak efficiency without unduly taxing MEC host computational capacities. Validation of the proposed framework entails empirical analysis. The experimental outcomes underscore the precision of the prediction model and corroborate the resource allocation framework’s efficacy.

关键词： Wireless communication Computational modeling computer architecture Predictive models Autonomous aerial vehicles Resource management Collision avoidance

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Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future

arXiv

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arXiv 2022年

作者： Klie, Jan-Christoph Webber, Bonnie Gurevych, Iryna Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany School of Informatics University of Edinburgh United Kingdom UKP Lab TU Darmstadt Germany

Annotated data is an essential ingredient in natural language processing for training and evaluating machine learning models. It is therefore very desirable for the annotations to be of high quality. Recent work, however, has shown that several popular datasets contain a surprising amount of annotation errors or inconsistencies. To alleviate this issue, many methods for annotation error detection have been devised over the years. While researchers show that their approaches work well on their newly introduced datasets, they rarely compare their methods to previous work or on the same datasets. This raises strong concerns on methods’ general performance and makes it difficult to asses their strengths and weaknesses. We therefore reimplement 18 methods for detecting potential annotation errors and evaluate them on 9 English datasets for text classification as well as token and span labeling. In addition, we define a uniform evaluation setup including a new formalization of the annotation error detection task, evaluation protocol and general best practices. To facilitate future research and reproducibility, we release our datasets and implementations in an easy-to-use and open source software package.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Open source software

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

arXiv

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arXiv 2025年

作者： Quijano, Andrew Halkidis, Spyros T. Gallagher, Kevin Akkaya, Kemal Samaras, Nikolaos Dept. of Computer Science and Engineering New York University New YorkNY United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece NOVA LINCS NOVA School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University MiamiFL United States

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Side channel attack

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Andrew Quijano Spyros T. Halkidis Kevin Gallagher Kemal Akkaya Nikolaos Samaras Dept. of Computer Science and Engineering New York University New York NY USA Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University Miami FL USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

关键词： Resistance Hands Privacy Protocols Prevention and mitigation Computational modeling Side-channel attacks Data models Timing Decision trees

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Convex Hull-based Algebraic Constraint for Visual Quadric SLAM

arXiv

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arXiv 2025年

作者： Yu, Xiaolong Zhao, Junqiao Song, Shuangfu Zhu, Zhongyang Yuan, Zihan Ye, Chen Feng, Tiantian School of Computer Science and Technology Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Using Quadrics as the object representation has the benefits of both generality and closed-form projection derivation between image and world spaces. Although numerous constraints have been proposed for dual quadric reconstruction, we found that many of them are imprecise and provide minimal improvements to localization. After scrutinizing the existing constraints, we introduce a concise yet more precise convex hull-based algebraic constraint for object landmarks, which is applied to object reconstruction, frontend pose estimation, and backend bundle adjustment. This constraint is designed to fully leverage precise semantic segmentation, effectively mitigating mismatches between complex-shaped object contours and dual quadrics. Experiments on public datasets demonstrate that our approach is applicable to both monocular and RGB-D SLAM and achieves improved object mapping and localization than existing quadric SLAM methods. The implementation of our method is available at https://***/tievtongji/convexhull-based-algebraic-constraint. © 2025, CC BY.

关键词： Semantic Segmentation

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Targeting Requirements Violations of Autonomous Driving Systems by Dynamic Evolutionary Search 36

Targeting Requirements Violations of Autonomous Driving Syst...

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36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021

作者： Luo, Yixing Zhang, Xiao-Yi Arcaini, Paolo Jin, Zhi Zhao, Haiyan Ishikawa, Fuyuki Wu, Rongxin Xie, Tao Peking University Ministry of Education Key Lab. of High-Confidence Software Technologies Beijing China Department of Computer Science and Technology School of EECS Peking University Beijing China National Institute of Informatics Tokyo Japan Xiamen University School of Informatics Xiamen China

ISBN: (纸本)9781665403375

Autonomous Driving Systems (ADSs) are complex systems that must satisfy multiple requirements such as safety, compliance to traffic rules, and comfortableness. However, satisfying all these requirements may not always be possible due to emerging environmental conditions. Therefore, the ADSs may have to make trade-offs among multiple requirements during the ongoing operation, resulting in one or more requirements violations. For ADS engineers, it is highly important to know which combinations of requirements violations may occur, as different combinations can expose different types of failures. However, there is currently no testing approach that can generate scenarios to expose different combinations of requirements violations. To address this issue, in this paper, we introduce the notion of requirements violation pattern to characterize a specific combination of requirements violations. Based on this notion, we propose a testing approach named EMOOD that can effectively generate test scenarios to expose as many requirements violation patterns as possible. EMOOD uses a prioritization technique to sort all possible patterns to search for, from the most to the least critical ones. Then, EMOOD iteratively includes an evolutionary many-objective optimization algorithm to find different combinations of requirements violations. In each iteration, the targeted pattern is determined by a dynamic prioritization technique to give preferences to those patterns with higher criticality and higher likelihood to occur. We apply EMOOD to an industrial ADS under two common traffic situations. Evaluation results show that EMOOD outperforms three baseline approaches in generating test scenarios by discovering more requirements violation patterns. © 2021 IEEE.

关键词： Iterative methods

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Influence of Manual Inter-Observer Variability for the Performance of Deep Learning Models in Semantic Segmentation

Influence of Manual Inter-Observer Variability for the Perfo...

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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Catalin Stoean Nebojsa Bacanin Ruxandra Stoean Leonard Ionescu Alina-Maria Gărău Cristina-Camelia Ghiţescu Department of Computer Science University of Craiova Artificial Intelligence and Machine Learning Team Romanian Institute of Science and Technology Romania Faculty of Informatics and Computing Singidunum University Belgrade Serbia Restoration and Conservation Lab Oltenia Museum History and Archaeology Section Craiova Romania

Deep learning models for semantic segmentation have expanded their application span from general objects to structures of interest in real-world tasks. The current paper discusses one such practical challenge, i.e. the delineation of corrosion compounds on microscope images taken at the surface of unrestored archaeological artefacts. Apart from the complexity of learning irregular shapes, the influence of the human manual annotations on model training becomes pivotal. The aim of the current paper thus becomes to measure the impact of interobserver variability on the segmentation of four copper corrosion compounds. The results support the need for rigorous guidelines in the manual establishment of the ground truth.

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