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International computer sciences and Information Technologies (CSIT)

作者： Irina Kalinina Aleksandr Gozhyj Victoria Vysotska Eugene Malakhov Victor Gozhyj Iryna Tregubova Intelligent Information Systems Department Petro Mohyla Black Sea National University Mykolayiv Ukraine Department of Information Systems and Networks Institute of Computer Science and Information Technologies National University “Lviv Polytechnic” Lviv Ukraine Department of Mathematical Methods of Computer Systems of Odessa National Mechnikov University Odesa Ukraine Department of Computer Science State University of Intelligent Technologies and Telecommunications Odesa Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article describes and investigates the systematic methodology of data analysis and preprocessing for solving classification problems. The methodology combines the following groups of methods on the basis of a systemic approach: methods of processing data gaps, methods of processing anomalous values, methods of feature generation, methods of identifying nonlinearities and non-stationarity, and normalization methods. Methods of character generation were studied. Methods of feature selection and generation are divided into three main groups: filtering methods, wrapping methods, and embedded methods. Since feature selection plays a crucial role in machine learning, increasing model performance and reducing computational costs, the paper proposes a combined feature selection method that includes the step-by-step use of both filtering and wrapping methods. The method consists of five steps to efficiently select the most relevant features of a data set. It offers a better approach to feature selection. This results in improved model performance with fewer features and reduced computational cost. The creation of a red wine classification system was considered for the experimental verification of the system methodology of analysis and pre-processing of data. The Red Wine Quality dataset was used. The purpose of the classification is to identify factors associated with the risk of untimely delivery of previous orders to customers and information about recipients of goods. To solve the task of wine quality classification, simulations were carried out using various algorithms. The effectiveness of the system approach to solving problems of analysis and preprocessing of data to solve problems of classification was proved.

关键词： Data analysis Systematics Filtering Computational modeling Machine learning Feature extraction Computational efficiency Classification algorithms Wrapping Information technology

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Modular Sensor Integration into Soft Robots using Stretchable Wires for Nuclear Infrastructure Inspection and Radiation Spectroscopy

Modular Sensor Integration into Soft Robots using Stretchabl...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Calder Wilson Joseph Karam Callen Votzke Farhan Rozaidi Camille J. Palmer Ross L. Hatton Matthew L. Johnston School of Electrical Engineering and Computer Science Corvallis OR USA Collaborative Robotics and Intelligent Systems Institute Corvallis OR USA School of Nuclear Science & Engineering Oregon State University Corvallis OR USA

Soft robots are uniquely suited for applications in inspection, search and rescue, and exploration in confined and unstructured environments. Leveraging the benefits of these soft robots will increasingly require the integration of equally compliant electronic components for sensing, actuation, control, and computation to maintain mechanical conformability at the system level. In this work, we demonstrate the integration of modular electronic sensors into a robotic snake platform using stretchable interconnects. Aimed at applications in nuclear infrastructure inspection, included sensors include visual perception and on-board radiation spectroscopy. In addition to high mechanical compliance, the stretchable interconnect enables physical separation of analog sensors and digital electronics for use in radiation environments. We present details of stretchable electronics fabrication and integration, standalone validation of integrated sensors, and field test results from a simulated nuclear infrastructure inspection task.

关键词： Mechanical sensors Fabrication Spectroscopy Visualization Wires Soft robotics Inspection

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A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves

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Information Processing in Agriculture 2024年第2期12卷 1-11页

作者： Polimena, Stefano Pio, Gianvito Cefola, Maria Palumbo, Michela Ceci, Michelangelo Attolico, Giovanni Department of Computer Science University of Bari Aldo Moro Via E. Orabona 4 Bari70125 Italy Via Ariosto 25 Rome00185 Italy Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council of Italy via Amendola 122/D Bari70126 Italy Department of Knowledge Technologies Jožef Stefan Institute Jamova cesta 39 1000 Ljubljana Slovenia Institute of Sciences of Food Production National Research Council of Italy c/o CS-DAT Via Michele Protano Foggia71121 Italy Department of Science of Agriculture Food and Environment University of Foggia Via Napoli 25 Foggia71122 Italy

The perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RSE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications. © 2024 The Author(s)

关键词： Ammonia

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The Iterative Compromise Ranking Analysis (ICRA) - The New Approach to Make Reliable Decisions

The Iterative Compromise Ranking Analysis (ICRA) - The New A...

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Special Sessions in the Advances in Information systems and Technologies Track, AIST 2022 and 17th Conference on Information systems Management, ISM 2022 held as part of the Federated Conference on computer science and Information systems, FedCSIS 2022

作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Shekhovtsov, Andrii Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

In the field of multi-criteria decision-making, compromise is often sought because it is highly desirable for decision-making. However, over the years, many methods have been developed for decision-making, between which discrepancies in the final rankings are often present. For this reason, it is worth noting the possibility of a compromise between different multi-criteria decision-making methods. One such solution is the Iterative Compromise Ranking Analysis (ICRA), which, by means of an iterative evaluation of the preferences of alternatives, leads to a compromise between the methods under consideration. This work presents an example of a solution to a theoretical decision problem, for which five methods were used: TOPSIS, VIKOR, MARCOS, MABAC and EDAS. In addition, an empirical analysis of the compromise solution was carried out to check the effect of parameters on the number of iterations needed to reach a compromise and the differences between the rankings proposed by the methods and the compromise ranking. The work showed that this is an interesting tool that can find its use in the field of multi-criteria decision-making as well as can be used to analyze the behaviour of multi-criteria decision-making methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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A Sustainable Approach for Determining Compromise Ranking Based on Intuitonistic Fuzzy Score Functions

A Sustainable Approach for Determining Compromise Ranking B...

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作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Więckowski, Jakub Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

Many real-world decision-making problems require some degree of uncertainty to be taken into account. For purpose of representing such problems, intuitionistic fuzzy sets are used, however, most well-known multi-criteria decision-making methods operate in a crisp environment. In this paper, we present an assessment of score functions that are used to convert fuzzy numbers into crisp ones. Five score functions were selected to assess their usefulness and effectiveness. Those functions were used to transform the theoretical fuzzy decision matrix problems into a crisp environment for evaluating alternatives using the Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) method. In addition, the compromise between score functions is presented and compared with the other results. The research showed that score functions are useful tools when dealing with problems in an uncertain environment and might prove helpful for decision-makers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Smart Manufacturing Integration of Heterogeneous Information systems via OPC UA. A Case Study

Smart Manufacturing Integration of Heterogeneous Information...

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Automatics and Informatics (ICAI), International Conference

作者： Plamen Vasilev Ani Boneva Veronika Ivanova Tsetso Tsvetanov Department of Industrial Automation University of Chemical Technology and Metallurgy Sofia Bulgaria Department of Communication and Computer Systems Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Robotized and Mechatronics Intelligent Systems Institute of Robotics Bulgaria Academy of Sciences Sofia Bulgaria

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

Modern automation, digitalization, virtualization, and intelligent production impose new criteria and approaches to process management. The operation and performance of any system and device depend on how it is managed. The article describes Smart Manufacturing Integration of Heterogeneous Information systems via OPC UA. The main goal is the development of heterogeneous information systems with real applications, with which reliable and up-to-date information can be exchanged between systems of different hierarchical levels. The goal was achieved with the following steps i) building the research infrastructure ii) analyzing the information compatibility and challenges in integrating systems from different hierarchical levels, and iii) implementing the integration by means of the OPC-UA standard communication protocol. The setup has been tested under laboratory conditions. The obtained results are shown.

关键词： Performance evaluation Protocols Automation Process control Production Topology Reliability Virtualization Standards Smart manufacturing

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3D Facial Expressions through Analysis-by-Neural-Synthesis

3D Facial Expressions through Analysis-by-Neural-Synthesis

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： George Retsinas Panagiotis P. Filntisis Radek Daněček Victoria F. Abrevaya Anastasios Roussos Timo Bolkarr Petros Maragos Institute of Robotics Athena Research Center Maroussi Greece MPI for Intelligent Systems Tübingen Germany Institute of Computer Science (ICS) Foundation for Research & Technology - Hellas (FORTH) Greece School of Electrical & Computer Engineering National Technical University of Athens Greece

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While existing methods for 3D face reconstruction from in-the-wild images excel at recovering the overall face shape, they commonly miss subtle, extreme, asymmetric, or rarely observed expressions. We improve upon these meth-ods with SMIRK (Spatial Modeling for Image-based Reconstruction of Kinesics), which faithfully reconstructs expres-sive 3D faces from images. We identify two key limitations in existing methods: shortcomings in their self-supervised training formulation, and a lack of expression diversity in the training images. For training, most methods employ differentiable rendering to compare a predicted face mesh with the input image, along with a plethora of additional loss functions. This differentiable rendering loss not only has to provide supervision to optimize for 3D face geom-etry, camera, albedo, and lighting, which is an ill-posed optimization problem, but the domain gap between ren-dering and input image further hinders the learning pro-cess. Instead, SMIRK replaces the differentiable rendering with a neural rendering module that, given the ren-dered predicted mesh geometry, and sparsely sampled pix-els of the input image, generates a face image. As the neural rendering gets color information from sampled im-age pixels, supervising with neural rendering-based reconstruction loss can focus solely on the geometry. Further it enables us to generate images of the input identity with varying expressions while training. These are then utilized as input to the reconstruction model and used as supervision with ground truth geometry. This effectively augments the training data and enhances the generalization for di-verse expressions. Our qualitative, quantitative and partic-ularly our perceptual evaluations demonstrate that SMIRK achieves the new state-of-the art performance on accurate expression reconstruction. For our method's source code, demo video and more, please visit our project webpage: https://***/smirk/.

关键词： Training Geometry Solid modeling Three-dimensional displays Accuracy Face recognition Training data

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Fuzzy Approach for the Temporary Logistics Hubs' Selection Planning in Disaster Region 11

Fuzzy Approach for the Temporary Logistics Hubs' Selection P...

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11th IEEE International Conference on intelligent systems, IS 2022

作者： Sirbiladze, Gia Kacprzyk, Janusz Lozano, Jose Antonio Ghvaberidze, Bezhan Midodashvili, Bidzina Matsaberidze, Bidzina Iv. Javakhishvili Tbilsi State University Department of Computer Sciences Tbilisi Georgia Intelligent Systems Laboratory Systems Research Institute Polish Academy of Sciences Warsaw Poland University of the Basque Country Department of Computer Science and Artificial Intelligence Donostia-San Sebastian Spain

ISBN: (纸本)9781665456562

Two-stage fuzzy methodology for the optimal planning of selection of temporary logistics hubs (TLHs) is developed. At the first stage, a q-rung orthopair fuzzy TOPSIS approach is developed to form and present expert knowledge about opening temporary logistics hubs. Fuzzy TOPSIS-based approach allows to determine the order of opening of TLHs and to provide post-disaster decision-making. At the second stage, by help of the built fuzzy TOPSIS aggregation a new objective function is given. By use of the built criterion TLHs' total identification level of the order of establishment can be maximized, and together with the criterion concerning minimization of the selected TLHs' quantity, poses a multi-objective facility location set covering problem. The offered approach is demonstrated by the example of temporary logistics y. The analysis of the results obtained revealed the importance of taking into account the opinions of multiple decision-makers for ensuring coordination in service delivery in disaster region. © 2022 IEEE.

关键词： Multiobjective optimization

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Object Detection Methods for a Robot Soccer

Object Detection Methods for a Robot Soccer

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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Ramir Sultanov Roman Lavrenov Shifa Sulaiman Yang Bai Mikhail Svinin Evgeni Magid Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Information Science and Engineering Department Ritsumeikan University Biwako-Kusatsu Campus Shiga Japan

Soccer tournaments between robot teams are being held around the world to encourage robotic research. An accurate detection and tracking of a ball position, an identification of robots and objects during a game are crucial for robot teams. In this paper, object detection methods for identifying robots and a ball during a robot soccer game are presented. Standard OpenCV library functions were adjusted for a ball and robots' detection. Simulation studies were carried out in Gazebo environment to compare a performance of various methods discussed in this paper.

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Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection

arXiv

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arXiv 2024年

作者： Rahman, Md Abdur Wu, Fan Cuzzocrea, Alfredo Ahamed, Sheikh Iqbal Dept. of Intelligent Systems and Robotics University of West Florida FL United States Department of Computer Science Tuskegee University TuskegeeAL United States iDEA Lab University of Calabria Rende Italy Department of Computer Science Marquette University WI United States

Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt is vulnerable or not, we follows two approaches: 1) a pre-trained LLM, and 2) a fine-tuned LLM. Then, we conduct a thorough analysis and comparison of the classification performance. Firstly, we use pre-trained XLM-RoBERTa model to detect prompt injections using test dataset without any fine-tuning and evaluate it by zero-shot classification. Then, this proposed work will apply supervised fine-tuning to this pre-trained LLM using a task-specific labeled dataset from deepset in huggingface, and this fine-tuned model achieves impressive results with 99.13% accuracy, 100% precision, 98.33% recall and 99.15% F1-score thorough rigorous experimentation and evaluation. We observe that our approach is highly efficient in detecting prompt injection attacks. © 2024, CC BY.

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