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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ilaria Torre Erik Lagerstedt Nathaniel Dennler Katie Seaborn Iolanda Leite Éva Székely Department of Computer Science and Engineering Chalmers University of Technology Sweden School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden School of Informatics University of Skövde Sweden Department of Computer Science University of Southern California USA Department of Industrial Engineering and Economics Tokyo Institute of Technology Japan

When deploying robots, its physical characteristics, role, and tasks are often fixed. Such factors can also be associated with gender stereotypes among humans, which then transfer to the robots. One factor that can induce gendering but is comparatively easy to change is the robot’s voice. Designing voice in a way that interferes with fixed factors might therefore be a way to reduce gender stereotypes in human-robot interaction contexts. To this end, we have conducted a video-based online study to investigate how factors that might inspire gendering of a robot interact. In particular, we investigated how giving the robot a gender-ambiguous voice can affect perception of the robot. We compared assessments (n=111) of videos in which a robot’s body presentation and occupation mis/matched with human gender stereotypes. We found evidence that a gender-ambiguous voice can reduce gendering of a robot endowed with stereotypically feminine or masculine attributes. The results can inform more just robot design while opening new questions regarding the phenomenon of robot gendering.

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Empirical Study on Memory Allocation Patterns in GUI-Based Applications

Empirical Study on Memory Allocation Patterns in GUI-Based A...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Alexandre B. Ferreira Rivalino Matias Caio A. R. dos Santos Department of Informatics Federal Institute of Technology Sao Paulo SP Brazil School of Computer Science Federal University of Uberlandia Uberlandia MG Brazil School of Computer Science Federal University of Uberlandia Monte Carmelo MG Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The ubiquity of dynamic memory allocations in computer programs makes the comprehension of their prevalent patterns of major importance. Previous studies have discussed patterns of memory allocations in different categories of applications. In this paper, we investigate these patterns for GUI-based applications. We analyzed 16 real-world applications built on top of two widely adopted frameworks, GTK+ and Qt. The target applications were selected due to their similarities in terms of look and feel and functionalities. We found that most of their memory allocation patterns were compatible with prevalent patterns observed in non-GUI applications. Surprisingly, on average, Qt-based applications showed allocation sizes twice larger than applications using GTK+, which underscores the significant impact of framework selection on memory allocation sizes. Two distinct patterns of allocation paths were observed in GTK+ applications and Qt applications, which indicate how these applications and the related frameworks behave in terms of memory allocations.

关键词： Measurement Dynamic scheduling Resource management Cybernetics Graphical user interfaces

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PLS-SEM Approach and Sentiment Analysis for Identifying Significant Factors of Customer Satisfaction

PLS-SEM Approach and Sentiment Analysis for Identifying Sign...

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Data Science and Its Applications (ICoDSA), International Conference on

作者： Marastika Wicaksono Aji Bawono Diana Purwitasari Mauridhi Hery Purnomo Departement of Technology Management Sepuluh Nopember Institute of Technology Surabaya East Java Departement of Informatics Engineering Sepuluh Nopember Institute of Technology Surabaya East Java Departement of Electrical Engineering Departement of Computer Engineering Sepuluh Nopember Institute of Technology Surabaya East Java

Reviews have a direct impact on customer satisfaction. The aim of this study is to dissect and analyze a collection of 775 negative and 557 positive comment reviews, drawn from four distinct e-commerce platforms. By classifying these remarks into positive and negative sentiments, this research endeavors to illuminate underlying trends permeating these marketplaces. The research methodology employed involves field observations of online shopping experiences, utilizing data derived from 254 e-commerce customers. These data were collected via validated questionnaires and subsequently analyzed using the partial least squares structural equation modeling approach, employing the lavaan r library within the R programming environment. The questionnaire results produced a rating scale from 1 to 5, categorizing responses from “very satisfied” to “less satisfied”, effectively illustrating both positive and negative commentary. The field comment data collected was coordinated with comment data extracted from four marketplace trading accounts. This data comment customer was analyzed using a range of comparative models such as k-nearest neighbors, multinomial naive bayes, stochastic gradient descent, and decision trees to conduct sentiment analysis. The findings reveal that the naive bayes method generates the greatest accuracy in sentiment analysis, registering an accuracy value of 0.886. Moreover, the analysis executed through r programming indicates that the e-service quality model yields the most robust results, reflected by an adjusted r-square value of 0.885. This study exerts a notable impact on service quality, as evidenced by a coefficient value of 0.865 and a perceived reputation score of 0.162.

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POLYNOMIAL-TIME RECOGNITION AND MAXIMUM INDEPENDENT SET IN BURLING GRAPHS

arXiv

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arXiv 2024年

作者： Rzążewski, Pawel Walczak, Bartosz Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Institute of Informatics University of Warsaw Warsaw Poland Department of Theoretical Computer Science Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland

A Burling graph is an induced subgraph of some graph in Burling’s construction of triangle-free high-chromatic graphs. We provide a polynomial-time algorithm which decides whether a given graph is a Burling graph and if it is, constructs its intersection model by rectangular frames. That model enables a polynomial-time algorithm for the maximum independent set problem in Burling graphs. As a consequence, we establish Burling graphs as the first known hereditary class of graphs that admits such an algorithm while not being χ-bounded. Copyright © 2024, The Authors. All rights reserved.

关键词： Polynomial approximation

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Improved detection mechanism in MANETs using adaptive IP tracing mechanism

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International Journal of Intelligent Networks 2020年 1卷 102-106页

作者： Mamade, Bayisa Kune Dhas, C. Suresh Gnana School of Informatics and Electrical Engineering Institute of Technology Ambo University Ethiopia Department of Computer Science Institute of Technology Ambo University Ethiopia

The network has enabled enormous volumes of services without any restrictions, different users need to have access to the service provided are more focused by malicious users. It is imperative to identify malicious entry or access to services, hence this paper discusses the various aspects of malicious access due to DDoS attacks and on how to detect it also this method uses the decisions made by hackers to manage the Traceback information science. In this paper, we consider the flash crowds based on the difference between DDoS attacks, a flash crowd is used to mitigate the DDoS attack. There is a tendency to see information on flux entropy variations on routers’ short-term and store them. Once the victim has been aware of a DDoS attack, the victim starts pushback tracing. The initial Traceback rule identifies its upstream routers where attacks have taken place and transmits the requests for Traceback to the upstream connected routers. If the flow rate is low, the low detection rule is initiated to trace the attackers and therefore the study is evaluated based on the instances where the packets are attacked by malicious users. The evaluation is conducted based on whether the packet traversed is malicious or normal and finally the accuracy of IP Traceback techniques and time complexity are tested. © 2020 The Author(s)

关键词： Entropy

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements 36

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Somogyi, Árpád Tettamanti, Tamás Varga, Pál Szalay, Zsolt Baranyai, Dániel Lovas, Tamás Budapest University of Technology and Economics Fac. of Civil Engineering Dept. of Photogrammetry and Geoinformatics Budapest Hungary Institute for Computer Science and Control The Eötvös Loránd Research Network Systems and Control Laboratory Budapest Hungary Budapest University of Technology and Economics Fac. of Electrical Engineering and Informatics Dept. of Telecommunications and Media Informatics Budapest Hungary Budapest University of Technology and Economics Fac. of Transportation Eng. and Vehicle Eng. Dept. of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665477161

One major goal of digital twin technology applied in the Architecture, Engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Identification for Tree-Shaped Structural Causal Models in Polynomial Time

arXiv

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arXiv 2023年

作者： Gupta, Aaryan Bläser, Markus Department of Computer Science & Engineering Indian Institute of Technology Bombay Mumbai India Saarland University Saarland Informatics Campus Saarbrücken Germany

Linear structural causal models (SCMs) are used to express and analyse the relationships between random variables. Direct causal effects are represented as directed edges and confounding factors as bidirected edges. Identifying the causal parameters from correlations between the nodes is an open problem in artificial intelligence. In this paper, we study SCMs whose directed component forms a tree. Van der Zander et al. (AISTATS’22, PLMR 151, pp. 6770–6792, 2022) give a PSPACE-algorithm for the identification problem in this case, which is a significant improvement over the general Gröbner basis approach, which has doubly-exponential time complexity in the number of structural parameters. In this work, we present a randomized polynomial-time algorithm, which solves the identification problem for tree-shaped SCMs. For every structural parameter, our algorithms decides whether it is generically identifiable, generically 2-identifiable, or generically unidentifiable. (No other cases can occur.) In the first two cases, it provides one or two fractional affine square root terms of polynomials (FASTPs) for the corresponding parameter, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

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SONICSIM: A CUSTOMIZABLE SIMULATION PLATFORM FOR SPEECH PROCESSING IN MOVING SOUND SOURCE SCENARIOS

arXiv

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arXiv 2024年

作者： Li, Kai Sang, Wendi Zeng, Chang Yang, Runxuan Chen, Guo Hu, Xiaolin Department of Computer Science and Technology Institute for AI BNRist Tsinghua University Beijing100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing100084 China National Institute of Informatics Japan Beijing100010 China

Systematic evaluation of speech separation and enhancement models under moving sound source conditions requires extensive and diverse data. However, real-world datasets often lack sufficient data for training and evaluation, and synthetic datasets, while larger, lack acoustic realism. Consequently, neither effectively meets practical needs. To address this issue, we introduce SonicSim, a synthetic toolkit based on the embodied AI simulation platform Habitat-sim, designed to generate highly customizable data for moving sound sources. SonicSim supports multi-level adjustments—including scene-level, microphone-level, and source-level—enabling the creation of more diverse synthetic data. Leveraging SonicSim, we constructed a benchmark dataset called SonicSet, utilizing LibriSpeech, Freesound Dataset 50k (FSD50K), Free Music Archive (FMA), and 90 scenes from Matterport3D to evaluate speech separation and enhancement models. Additionally, to investigate the differences between synthetic and real-world data, we selected 5 hours of raw, non-reverberant data from the SonicSet validation set and recorded a real-world speech separation dataset, providing a reference for comparing SonicSet with other synthetic datasets. For speech enhancement, we utilized the real-world dataset RealMAN to validate the acoustic gap between SonicSet and existing synthetic datasets. The results indicate that models trained on SonicSet generalize better to real-world scenarios compared to other synthetic datasets. Code is publicly available at https://***/SonicSim/. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech enhancement

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Realizing Predictive Maintenance in Production Machinery Through Low-Cost IIoT Framework and Anomaly Detection: A Case Study in a Real-World Manufacturing Environment

Realizing Predictive Maintenance in Production Machinery Thr...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Gabriel Filios Stylianos Karatzas Marios Krousarlis Sotiris Nikoletseas Stefanos H. Panagiotou Paul Spirakis Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece Civil Engineering Department University of Patras Greece Department of Computer Science University of Liverpool UK

Digital transformation and predictive maintenance are still some of the key challenges faced by the industrial sector as it moves towards Industry 4.0. However, the lack of resources, experienced personnel, and the absence of digital-savvy culture in manufacturing companies, often hinder the adoption of the required technologies. In this context, maintenance is a required process to ensure the smooth and continuous operation of the production, but to this day, engineers are still required to physically inspect equipment on site. By applying digital technologies, a predictive maintenance framework will enable the industry to maximize the life of the equipment and to act on time in order to prevent failures and thus reduce costs and optimize maintenance efficiency. In this effort, integrating such technologies and the required modern computing devices for monitoring legacy industrial equipment remains a challenge. Therefore, in this paper, we present a low-cost IIoT system for an AI-based predictive maintenance of machinery in a real-world industrial environment. In specific, a data collection system, consisting of a Raspberry Pi device and several sensors (namely, an accelerometer, temperature sensors and a microphone), was designed and developed. The collected data was then utilized to feed a deep-learning LSTM-autoencoder model for anomaly detection through a semi-supervised learning approach. Despite the small amount of data collected, the model is able to effectively detect anomalies. Finally, ways in which the existing system can be improved are proposed.

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Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

Fine-grained Domain Adaptive Crowd Counting via Point-derive...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongtuo Liu Dan Xu Sucheng Ren Hanjie Wu Hongmin Cai Shengfeng He School of Computer Science South China University of Technology Institute of Informatics University of Amsterdam Department of Computer Science and Engineering Hong Kong University of Science and Technology School of Computing and Information Systems Singapore Management University

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle domain-invariant crowd and domain-specific background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a weakly-supervised manner. Based on the derived segmentation, we design a crowd-aware domain adaptation mechanism consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer (CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide crowd features transfer across domains beyond background distractions. The CDA module dedicates to regularising target-domain crowd density generation by its own crowd density distribution. Our method outperforms previous approaches consistently in the widely-used adaptation scenarios.

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