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Automatics and Informatics (ICAI), International Conference

作者： Rostislav Kandilarov Yordan Yordanov Department of Machine Science Machine Elements Engineering Graphics and Physics Faculty of Transport University of Ruse “Angel Kanchev” Ruse Bulgaria Computer Systems and Technologies Faculty of Electrical Engeneering Electronics and Automation University of Ruse “Angel Kanchev” Ruse Bulgaria

In this paper various BLE protocols for communication between remote or Android applications and intelligent dual channel (cool and warm white) LED lamps were reverse engineered. Three protocols were fully decoded and several partially. Replay attack is tested and confirmed working. Demo C++ library was developed and a proof-of-concept firmware for esp32 based development board was used to verify the decoding and the attacks applicability.

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Performance evaluation for ranking-based discretisation 24

Performance evaluation for ranking-based discretisation

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Baron, Grzegorz Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes' values by nominal categories. Typically all real-valued features are subjected to transformations, regardless of their characteristics. The paper presents research on discretisation executed with a discerning approach. To all available attributes, feature selection mechanisms were employed, in the form of rankings that order variables based on their importance. Exploiting this discovered knowledge on attributes, discretisation was then driven by a ranking, and either highest or lowest ranking features were selected for transformation. The influence of selective discretisation on the performance of classification systems was studied for three popular inducers. The procedure was employed in the field of stylometry, and a task of authorship recognition, considered as a binary classification with balanced classes. The experiments show that discretisation based on importance of features can lead to better performance than in the case of transformations applied to all attributes. © 2020 The Authors. Published by Elsevier B.V.

关键词： Feature Selection

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Analysis of the Technology of Transfering a Three-Dimensional Model from Cad Format to the Control Code for 3D Printing

Analysis of the Technology of Transfering a Three-Dimensiona...

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2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020

作者： Kholodilov, A.A. Faleeva, E.V. Kholodilova, M.V. Far Eastern State Transport University Department of Computer Science and Computer Graphics Khabarovsk Russia Computing Center of the Far Eastern Branch Russian Academy of Sciences Khabarovsk Russia

ISBN: (纸本)9781728169514

The paper analyzes the technology for translating a three-dimensional model from a CAD format into a control g-code for subsequent three-dimensional printing. The main stages of the process are considered, the principles of mathematical modeling at the stages are highlighted, recommendations for possible improvement of the software part of the 3D printing technology are indicated. © 2020 IEEE.

关键词： 3D printers

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An Attack on Facial Soft-biometric Privacy Enhancement

arXiv

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arXiv 2021年

作者： Osorio-Roig, Dailé Rathgeb, Christian Drozdowski, Pawel Terhörst, Philipp Štruc, Vitomir Busch, Christoph Department of Computer Science Hochschule Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research Darmstadt Germany Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

In the recent past, different researchers have proposed privacy-enhancing face recognition systems designed to conceal soft-biometric attributes at feature level. These works have reported impressive results, but generally did not consider specific attacks in their analysis of privacy protection. We introduce an attack on said schemes based on two observations: (1) highly similar facial representations usually originate from face images with similar soft-biometric attributes;(2) to achieve high recognition accuracy, robustness against intra-class variations within facial representations has to be retained in their privacy-enhanced versions. The presented attack only requires the privacy-enhancing algorithm as a black-box and a relatively small database of face images with annotated soft-biometric attributes. Firstly, an intercepted privacy-enhanced face representation is compared against the attacker’s database. Subsequently, the unknown attribute is inferred from the attributes associated with the highest obtained similarity scores. In the experiments, the attack is applied against two state-of-the-art approaches. The attack is shown to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90%. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Categorization, Detection, and Prevention of Ransomware Attack: A Review

Categorization, Detection, and Prevention of Ransomware Atta...

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International Conference on Image Information Processing

作者： Priyank Pandey Paras Jain Ashutosh Gupta Vishan Kumar Gupta Himanshu Jindal Avdhesh Gupta Department of CSE Graphics Era Deemed to be University Dehradun India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India Department of CSE Sir Padampat Singhania University Udaipur Rajasthan India Department of Computer Science Amity University Punjab Mohali India Department of CSE Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Ransomware is a type of malware that targets users like government and business organizations. This malware stops the user from accessing their own data or resource and asks for ransom in return for getting their access back to the resource. It usually locks the screen or locks a particular resource. In recent years this attack has been a flourishing business for the attackers and a huge loss to other parties. In this work, we are giving an overview of ransomware attacks and discussing a detailed review of methodologies present to avoid, analyze, and detect the attack and to protect the user from this attack. By implementing some early preventions and techniques to combat these malicious software’s, this study would be the ideal opportunity to coordinate and extend research efforts in the future. Here, we have generated a table for current methodology, its outcomes, and future trends of ransomware. In the last, we reviewed 30 papers, which contained machine learning based methods, results and analysis for the categorization, detection, and prevention of various ransomware attacks.

关键词： Reviews Government Machine learning Information processing Turning Market research Ransomware

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Ai-Assisted Character Design in Medical Storytelling with Stable Diffusion

SSRN

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SSRN 2024年

作者： Mittenentzwei, Sarah Garrison, Laura A. Budich, Beatrice Lawonn, Kai Dockhorn, Alexander Preim, Bernhard Meuschke, Monique Department of Simulation and Graphics University of Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Informatics University of Bergen Thormøhlens gate 55 Bergen5020 Norway Faculty of Mathematics and Computer Science University of Jena Ernst-Abbe-Platz 2 Jena07743 Germany Institute of Artificial Intelligence Leibniz University Hannover30167 Germany

Data-driven storytelling has experienced significant growth in recent years to become a common practice in various application areas, including healthcare. In the realm of medical narratives, characters play a pivotal role in connecting audiences with data and conveying complex medical information in an engaging manner that may influence positive behavioral and lifestyle changes on the part of the viewer. However, the process of designing characters that are both informative and engaging remains a challenge. In this paper, we propose an AI-assisted pipeline for character design in the context of data-driven medical stories. Stable Diffusion, a deep learning text-to-image model, is a crucial component of our pipeline for converting data into visual representations of characters. This reduces the time and artistic expertise required to create characters that accurately reflect the underlying data, even in time-oriented studies such as cohort studies. We evaluate the characters in a crowd-sourced study according to their authenticity to the underlying data, resemblance across different time steps, and compare static images with AI-driven animations. Study participants were able to recognize many aspects of the underlying data in our characters. Adjusting the weight and age of a character remains a challenge, as many facial features tend to change that make the character almost unrecognizable. While study participants preferred the static images, our evaluation shows the potential of animating the characters. © 2024, The Authors. All rights reserved.

关键词： Pipelines

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Immersive Situated Analysis of Dams' Behavior

Immersive Situated Analysis of Dams' Behavior

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International Conference on graphics and Interaction (ICGI)

作者： Nuno Verdelho Trindade Pedro Leitão Daniel Gonçalves Sérgio Oliveira Alfredo Ferreira Graphics and Interaction Area INESC-ID Instituto Superior Técnico University of Lisbon Lisbon Portugal Computer Science and Engineering Department Instituto Superior Técnico University of Lisbon Lisbon Portugal Concrete Dams Department Laboratório Nacional de Engenharia Civil Lisbon Portugal

Dam safety control is a complex and challenging task that involves the analysis, monitoring, and behavior prediction of the various components of dams. Its primary objective is to ensure the safety and optimal performance of these structures. This undertaking involves the abstraction and interpretation of extensive structural, hydraulic, and geotechnical data, typically acquired through sensor networks embedded within the dam. This study introduces DamVR, a virtual reality tool for situated dam safety control data visualization. This tool allows dam engineers and other industry professionals to analyze the fluctuation of structural behavior over time. Its immersive environment contains a realistic representation of the dam structure, networks of sensors, and surroundings. It aims to frame the data within the visual context of the object of the analysis. This contextualization is intended to improve analytical reasoning and decision-making. The usability of DamVrwas evaluated by a group of domain experts and compared to an existing augmented reality tool in similar tasks. The results indicate that DamVris a usable and effective dam safety control data visualization tool.

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Kvc-Ongoing: Keystroke Verification Challenge

SSRN

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SSRN 2024年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Acien, Alejandro Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Großmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskóla, Przemys law Lab Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Germany Department of Computer Science Technical University of Darmstadt Germany Universidad de Buenos Aires Argentina Verigram LLC Singapore Citix Kazakhstan Regensburg University Of Applied Sciences Germany Division of Radiology and Biomedical Engineering The University of Tokyo Japan National University of Singapore Singapore University of Moratuwa Sri Lanka NASK – National Research Institute Poland

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects’ age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab. © 2024, The Authors. All rights reserved.

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RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture

arXiv

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arXiv 2024年

作者： Pan, Xiaoyu Zheng, Bowen Jiang, Xinwei Zeng, Zijiao Kou, Qilong Wang, He Jin, Xiaogang State Key Lab of CAD & CG Zhejiang University China ZJU-Tencent Game and Intelligent Graphics Innovation Technology Joint Lab China Tencent Games Digital Content Technology Center China Co. LTD China Department of Computer Science UCL Centre for Artificial Intelligence University College London United Kingdom

Optical motion capture (MoCap) is the "gold standard" for accurately capturing full-body motions. To make use of raw MoCap point data, the system labels the points with corresponding body part locations and solves the fullbody motions. However, MoCap data often contains mislabeling, occlusion and positional errors, requiring extensive manual correction. To alleviate this burden, we introduce RoMo, a learning-based framework for robustly labeling and solving raw optical motion capture data. In the labeling stage, RoMo employs a divide-and-conquer strategy to break down the complex full-body labeling challenge into manageable subtasks: alignment, full-body segmentation and part-specific labeling. To utilize the temporal continuity of markers, RoMo generates marker tracklets using a K-partite graph-based clustering algorithm, where markers serve as nodes, and edges are formed based on positional and feature similarities. For motion solving, to prevent error accumulation along the kinematic chain, we introduce a hybrid inverse kinematic solver that utilizes joint positions as intermediate representations and adjusts the template skeleton to match estimated joint positions. We demonstrate that RoMo achieves high labeling and solving accuracy across multiple metrics and various datasets. Extensive comparisons show that our method outperforms state-of-the-art research methods. On a real dataset, RoMo improves the F1 score of hand labeling from 0.94 to 0.98, and reduces joint position error of body motion solving by 25%. Furthermore, RoMo can be applied in scenarios where commercial systems are inadequate. The code and data for RoMo are available at https:// ***/non-void/RoMo. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion capture

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Assessing quality of decision reducts 24

Assessing quality of decision reducts

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Stanczyk, Urszula Zielosko, Beata Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bedzinska 39 Sosnowiec41-200 Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution. © 2020 The Authors. Published by Elsevier B.V.

关键词： Rough set theory

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