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Weighting Attributes Based on the Greedy Algorithm Properties

Procedia computer science 2024年 246卷 4883-4892页

作者： Beata Zielosko Urszula Stańczyk Kamil Jabloński University of Silesia in Katowice Institute of Computer Science Bȩdzińska 39 41-200 Sosnowiec Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules.

关键词： Greedy algorithm Weighting attributes Decision rules Rule filtering Authorship attribution

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Leveraging Large Language Models for automatic Smart Contract Generation

Leveraging Large Language Models for Automatic Smart Contrac...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Emanuele Antonio Napoli Fadi Barbàra Valentina Gatteschi Claudio Schifanella Department of Control and Computer Engineering Politecnico di Torino Turin ITALY Department of Computer Science University of Turin Turin ITALY

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

In the rapidly evolving landscape of blockchain technology, smart contracts stand as pivotal instrument for automating and enforcing digital agreements. However, their creation often necessitates specialized programming skills, hindering broader adoption and accessibility. This paper proposes a pipeline that leverages the capabilities of Large Language Models (LLMs) to automate the generation of smart contracts. By harnessing the natural language understanding and generation capabilities of LLMs, our approach aims to make accessible smart contract development to people that are not familiar with this task. The proposed pipeline employs the CO-STAR methodology to optimize prompt creation for high-quality outputs. Moreover, in order to assess the correctness and reliability of the generated smart contracts, we leverage on Slither, one of the most cutting-edge vulnerability detection tools. Furthermore, we propose a benchmarking suite based on metrics such as compilability, vulnerabilities, and presence of comments, among the others, in order to evaluate the effectiveness of the pipeline in terms of consistency of generated smart contracts, LLM's temperature effect, and prompt selection. The results show that our pipeline is able to produce 98.1% of compilable smart contracts, the temperature value has negligible effect on the generated smart contracts, and the CO-STAR methodology produces valuable and consistent outputs with low-impact vulnerabilities.

关键词： Measurement Large language models Instruments Smart contracts Pipelines Programming Software

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A 3d-Pointcloud Based Semi-automatic Private Dataset Generation Method for Specific 6d Pose Estimation Tasks

SSRN

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SSRN 2024年

作者： Lou, Yuanlong Fu, Dongxiang Tian, Ying Department of control science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Deep learning algorithms often require high-quality datasets, but public datasets can be less effective for specific applications like visual robotic arm grasping. Generating private datasets traditionally involves time-consuming manual work or is limited by equipment constraints. To address this problem, a 3D-pointcloud based semi-automatic private dataset generation method for specific object pose detection task is proposed, which has broadly adaptive performance that can be done with only one binocular camera. Firstly, the RGBD data is used to automatically generate labeled point clouds. Then, the labels, which contain the target category and position information as well as the pose information are automatically generated from the point cloud. Only the denoising need simple manual processing, the rest of the steps are operated by the algorithm, so it is called semi-automatic method. Compared to manual labeling, experiments show this semi-automatic method significantly reduces time while maintaining accuracy. © 2024, The Authors. All rights reserved.

关键词： Object detection

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A frequency domain property driven search method for echo state network structures

A frequency domain property driven search method for echo st...

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Intelligent control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Zhaozhao Zhang Bofan Li Yingqin Zhu College Computer Science and Technology Xi'an University of Science and Technology Xi'an China Department of Control Automatic CINVESTAV-IPN(National Polytechnic Institute) Mexico City Mexico

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

To overcome the limitations of conventional echo state networks (ESN), such as redundant connections and insufficient frequency-domain feature extraction, this paper proposes a frequency-domain feature architecture search-based ESN (FS-ESN). Leveraging a frequency response fitness function, FS-ESN dynamically optimizes reservoir structures and incorporates frequency-domain features aligned with target tasks. Experimental results demonstrate that FS-ESN significantly improves feature extraction, adaptability, and predictive performance, outperforming traditional ESN models in task-specific applications.

关键词： Adaptation models Frequency-domain analysis Computational modeling Stochastic processes Echo state networks Predictive models Signal processing Feature extraction Reservoirs Frequency response

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A solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller

A solar tracking system with light-dependent resistors and a...

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Sayeedurrahman Sabir, Bushra Khan, Rashid Ahmed Masood, Dawood Nat, Aslam Aligarh Muslim University Z.H. College of Engineering & Technology Department of Electrical Engineering Aligarh202002 India National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan Sapienza University of Rome Department of Computer Control Management Engineering 00185 Italy

ISBN: (纸本)9798350338003

Solar energy is a clean and simple method of electricity generation. This electricity is utilized for various purposes, ranging from modest electrical gadgets and cars to major enterprises, satellites, and space exploration. The title of this project, "Solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller"alludes to the project's purpose, which is to harvest as much solar energy as possible from a solar panel throughout the day using a specific Sun tracking mechanism. To do this, a mechanism was developed that tracks the sun's position and rotates the solar panel correspondingly so that it receives maximum sunlight at all times of the day. This is accomplished by mounting a stepper motor and a couple of light-dependent resistors (LDRs) on the solar panel, which adjusts its orientation based on the location of the sun. This project has three primary components: the solar panel, the stepper motor, and the LDRs. Microcontroller, sensor, and stepper motor. For this aim, an Arduino UNO microcontroller has been utilized. It accepts an output signal from a sensor as input, processes it, and controls stepper motors based on the supplied program. A stepper motor is attached to the shaft on which a panel is positioned to raise and lower the panel. Since maximal solar rays always strike the surface of the solar panel module, it is possible to obtain maximum power output, increasing its total efficiency as compared to the static panel. © 2023 IEEE.

关键词： Microcontrollers

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Disentangled Feature Distillation for Light Field Super-Resolution with Degradations

Disentangled Feature Distillation for Light Field Super-Reso...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhou, Linjie Gao, Wei Li, Ge Yuan, Hui Zhao, Tiesong Yue, Guanghui School of Electronic and Computer Engineering Peking University Shenzhen China School of Control Science and Engineering Shandong University Jinan China Fuzhou University Department of Communication Engineering Fuzhou China School of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350313154

Light field (LF) super-resolution has achieved remarkable results with the assumption of only downsampling. However, real-world LF scenes contain multiple degradation effects, which makes it difficult for existing methods to deal with hybrid distortions. In this paper, we propose a disentangled feature distillation framework for LF super-resolution with degradations. To reduce the learning difficulty, we propose a feature disentanglement mechanism to split the mixed reconstruction for both super-resolution and denoising into two single task learning processes. We also propose a feature enhancement strategy via knowledge distillation to transfer prior feature of each single reconstruction to our task of mixed reconstruction. Finally, the separate restored representations are fused to reconstruct a clean high-resolution LF. Experiments demonstrate the superior performance of our framework for different scale factors and noise levels. Additionally, our approach can also obtain excellent performance for joint super-resolution and deblurring, showing its gencralization for practical LF super-resolution applications. © 2023 IEEE.

关键词： Distillation

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Combining Global and Local Merges in Logic-based Entity Resolution 20

Combining Global and Local Merges in Logic-based Entity Reso...

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20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023

作者： Bienvenu, Meghyn Cima, Gianluca Gutiérrez-Basulto, Víctor Ibáñez-García, Yazmín Univ. Bordeaux CNRS Bordeaux INP LaBRI UMR 5800 France Department of Computer Control and Management Engineering Sapienza University of Rome Italy School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9781956792027

In the recently proposed LACE framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: all occurrences of those entity references (possibly across multiple database tuples) are deemed equal and can be merged. By contrast, a local form of merge is often more natural when identifying pairs of data values, e.g. some occurrences of 'J. Smith' may be equated with 'Joe Smith', while others should merge with 'Jane Smith'. This motivates us to extend LACE with local merges of values and explore the computational properties of the resulting formalism. © 2023 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved

关键词： Knowledge representation

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Wirelessly Actuated Rotation-free Magnetic Motor

Wirelessly Actuated Rotation-free Magnetic Motor

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

作者： Umur Ulas Harman Ahmed Hafez Cameron Duffield Zihan Zhao Luke Dixon Daniela Rus Shuhei Miyashita Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Insigneo Institute for in Silico Medicine

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper addresses the challenge of actuating millimetre-sized motors, which are wirelessly driven by external magnetic fields. Traditional approaches, relying on rotating magnetic fields, often inadvertently cause the entire robot – especially if it is small and lightweight – to rotate, instead of a specified shaft in the motor. To overcome this issue, our study introduces a novel mechanism that leverages symmetrically configured magnetic motors to cancel out the torques, thus preventing unwanted rotation of the robot. This is achieved by utilizing a magnetic field along a single axis to induce rotational movement. The design features two millimetre-sized rotating magnets that interact to achieve a 90 ◦ rotation, complemented by an external magnetic field that accomplishes the remaining 270 ◦ , thus completing a full rotation. Furthermore, we demonstrate that applying a perpendicularly oriented magnetic field can inversely affect the motor’s rotation direction. A proof-of-concept experiment employing this mechanism successfully actuated a gripper in a water tank while it is free-floating, showcasing its potential for enhancing robotic applications at the sub-centimeter scale, where the small net torque of a miniature motor is essential.

关键词： Water Shafts Torque Gears Net zero Storage management Motors Permanent magnets Magnetic fields Grippers

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CONVERGENCE ANALYSIS OF LINEARIZED q PENALTY METHODS FOR NONCONVEX OPTIMIZATION WITH NONLINEAR EQUALITY CONSTRAINTS

arXiv

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arXiv 2025年

作者： El Bourkhissi, Lahcen Necoara, Ion Department of Automatic Control and Systems Engineering National University of Science and Technology Politehnica Bucharest Romania Department of Automatic Control and Systems Engineering National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

In this paper, we consider nonconvex optimization problems with nonlinear equality constraints. We assume that the objective function and the functional constraints are locally smooth. To solve this problem, we introduce a linearized q penalty based method, where q ∈ (1, 2] is the parameter defining the norm used in the construction of the penalty function. Our method involves linearizing the objective function and functional constraints in a Gauss-Newton fashion at the current iteration in the penalty formulation and introduces a quadratic regularization. This approach yields an easily solvable subproblem, whose solution becomes the next iterate. By using a novel dynamic rule for the choice of the regularization parameter, we establish that the iterates of our method converge to an ǫ-first-order solution in O(1/ǫ2+(q−1)/q) outer iterations. Finally, we put theory into practice and evaluate the performance of the proposed algorithm by making numerical comparisons with existing methods from literature. © 2025, CC BY.

关键词： Numerical methods

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Infrared Multi-Object Detection Using Deep Learning

Infrared Multi-Object Detection Using Deep Learning

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Electrical engineering (ICEENG), 12th International Conference on

作者： Hossam Aboalia Sherif Hussein Alaaeldin Mahmoud Optoelectronics and Automatic Control Systems Department Military Technical College Cairo Egypt Computer Engineering and Artificial Intelligence Department Military Technical College Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

computer vision has gained great regard in the past few years and one of the most important branches of this field is object detection. It was usual to detect objects using visible images that need good illumination conditions to obtain high performance, but this type of images exhibits unsatisfactory attitude in bad environmental conditions and darkness situations. Other alternatives are emerging, such as infrared images which depend on the infrared radiation emitted or reflected from objects. Infrared imaging systems can be used in complete darkness, so the problem of visible images can be overcome. There are many object detection models which take images as an input and predict location and class type for each element in the image. So, a comparative study using deep learning based infrared object detection models (YOLOv3-4GB, YOLOv5s, YOLOv7) was performed. The models were trained on Forward Looking Infrared (FLIR) dataset that contains 3 classes (person-car-bicycle). The YOLOv7 model attained the best performance and achieved Mean Average Precision (mAP) of 82.4%.

关键词： YOLO Training Deep learning Image resolution Transfer learning Noise Neural networks

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