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Fine Tuning Vision Transformer Model for Facial Emotion Recognition: Performance Analysis for Human-Machine Teaming

Fine Tuning Vision Transformer Model for Facial Emotion Reco...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Sanjeev Roka Danda B. Rawat Department of Electrical Engineering and Computer Science Howard University Washington DC USA

Facial Emotion Recognition (FER) has become essential in various domains, including robotic systems, affective computing, emotion-triggered intelligent agents, and human-computer interaction for human-machine teaming. Although Convolutional Neural Network (CNN)-based models were popular for facial emotion classification, Transformer-based models have shown better performance in computer vision tasks such as image classification, semantic segmentation, and object detection. In this study, we explore the performance of the Vision Transformer model on a publicly available large FER dataset called AffectNet, which provides a realistic representation of emotions “in the wild.” We fine-tuned the model for the emotion classification task based on facial expressions. We achieved an accuracy of 64.48% on the Affectnet validation set, outperforming many other methods that use only transformer models. Further, we explore how they can be used for Human-Machine Teaming particularly in vehicular systems to improve driver safety, comfort, and experience.

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Frameworks for SNNs: A Review of Data science-Oriented Software and an Expansion of SpykeTorch 1

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24th international conference on Engineering Applications of Neural Networks (EANN)

作者： Manna, Davide L. Vicente-Sola, Alex Kirkland, Paul Bihl, Trevor J. Di Caterina, Gaetano Univ Strathclyde Neuromorph Sensor Signal Proc NSSP Lab Glasgow G1 1XW Lanark Scotland Air Force Res Lab AFRL Wright Patterson AFB OH USA

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

the Neuromorphic (NM) field has seen significant growth in recent years, especially in the development of Machine Learning (ML) applications. Developing effective learning systems for such applications requires extensive experimentation and simulation, which can be facilitated by using software frameworks that provide researchers with a set of ready-to-use tools. the NM technological landscape has witnessed the emergence of several new frameworks in addition to the existing libraries in neuroscience fields. this work reviews nine frameworks for developing Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. We emphasize the availability of spiking neuron models and learning rules to more easily direct decisions on the most suitable frameworks to carry out different types of research. Furthermore, we present an extension to the SpykeTorch framework that enables users to incorporate a broader range of neuron models in SNNs trained with Spike-Timing-Dependent Plasticity (STDP). the extended code is made available to the public, providing a valuable resource for researchers in this field.

关键词： frameworks spiking neural networks spiking neurons neuromorphic software machine learning unsupervised learning

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Experimental Validation of an Interface for a Human-Robot Interaction Within a Collaborative Task 1

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Proceedings of the 8th international conference on Interactive Collaborative Robotics, ICR 2023

作者： Mustafin, Maksim Chebotareva, Elvira Li, Hongbing Magid, Evgeni Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlyovskaya st. Kazan420111 Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Minhang Shanghai200240 China Tikhonov Moscow Institute of Electronics and Mathematics HSE-University 34 Tallinn st. Moscow123458 Russia

ISBN: (数字)9783031431111

ISBN: (纸本)9783031431104

this paper presents a prototype of a non-contact UR robot based Virtual control (UR-VC) system for collaborative robots of the UR family, which is based on computer vision techniques and a virtual interaction interface. A control method involved specific hand movements within a field of view of a web camera, which was connected to a laptop with the running UR-VC system. We present the UR-VC system and the results of an experimental validation. To inquire if the UR-VC system is comfortable and user-friendly for an interaction with collaborative robots and to study opportunities for a further development and expansion directions of the system, we designed a test case that simulates a joint product assembly in a collaborative workspace. the constructed collaborative workspace included the UR3e robot, the laptop with the running UR-VC system and assembly parts for a collaborative task. 24 participants were involved in the experiments. First, the participants learned how to control the robot using the UR-VC system. After the training, all participants successfully controlled the robot using the proposed interface for performing the collaborative task. Participants’ experience of operating the robot was analyzed via surveys, their unconstrained comments and video recordings of the experiments. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

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Empowering Vulnerability Prioritization: A Heterogeneous Graph-Driven Framework for Exploitability Prediction 24th

Empowering Vulnerability Prioritization: A Heterogeneous Gra...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Yin, Jiao Chen, Guihong Hong, Wei Wang, Hua Cao, Jinli Miao, Yuan Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC3011 Australia School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China School of Cyber Security Guangdong Polytechnic Normal University Guangdong Guangzhou510000 China Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia

ISBN: (纸本)9789819972531

With the increasing number of software vulnerabilities being disclosed each year, prioritizing them becomes essential as it is challenging to patch all of them promptly. Exploitability prediction plays a crucial role in assessing the severity of vulnerabilities and determining their prioritization. Most existing works on exploitability prediction focus on building predictive models based on features extracted from individual vulnerabilities, neglecting the relationships between vulnerabilities and their contextual information. Only a few studies have explored using homogeneous graph-based techniques to enhance performance in this domain. this paper proposes a novel heterogeneous graph-driven framework for enhancing vulnerability exploitability prediction. the framework comprises two heterogeneous graph feature extraction technique streams: topological feature concatenation and node embedding based on heterogeneous graph neural networks (HGNN). Experimental results demonstrate that both streams, leveraging heterogeneous graph-based features, significantly improve the performance of exploitability prediction compared with using features extracted from individual vulnerabilities. Specifically, the two streams achieve 5.44% and 2.06% improvement in the F1 score, respectively. the data and codes are available on Github (https://***/happyResearcher/HG-VEP ) to facilitate reproducibility and further research in this field. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Improving Problem Decomposition and Regulation in Distributed Multi-Agent Path Finder (DMAPF) 24th

Improving Problem Decomposition and Regulation in Distribute...

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24th international conference on Principles and Practice of Multi-Agent systems (PRIMA)

作者： Pianpak, Poom Tran Cao Son New Mexico State Univ Las Cruces NM 88003 USA

ISBN: (纸本)9783031212024;9783031212031

Distributed Multi-Agent Path Finder (DMAPF) is a novel distributed algorithm to solve the Multi-Agent Path Finding (MAPF) problem, where the objective is to find a sequence of movements for agents to reach their assigned locations without colliding with obstacles, which include other agents. the idea of DMAPF is to decompose a given MAPF problem into smaller sub-problems, then solve them in parallel. It has been shown that DMAPF can achieve higher scalability compared to centralized methods. this paper addresses two problems in the previous works. First, the previous works only divide problem maps in a simple, rectangular manner. this can create sub-problems with unbalanced numbers of locations in their maps when the shape of the original map is not rectangular or when the obstacles are not uniformly distributed. Having sub-problems that vary in sizes diminishes the effectiveness of parallelism. Second, the idea of DMAPF is to have agents move across sub-problems until they reach the sub-problems that contain their goals, but the previous works do not have a mechanism to regulate the number of agents residing in the sub-problems, thus it may fail to find the solution when a sub-problem is overcrowded. To mitigate the problems, we introduce (i) a method to decompose MAPF problems with balanced numbers of vertices;and (ii) a mechanism to regulate the number of agents in sub-problems. We also improve the performance of the Answer Set Programming (ASP) encoding, that was used in previous DMAPF implementations to solve MAPF sub-problem instances, by eliminating unnecessary parameters. the results show that the new solver scales better and is more efficient than the previous versions.

关键词： Multi agent systems

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Path-KGE: Preference-Aware Knowledge Graph Embedding with Path Semantics for Link Prediction 24th

Path-KGE: Preference-Aware Knowledge Graph Embedding with P...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Yang, Liu Zhao, Jie Long, Jun Huang, Jincai Wang, Zidong Chen, Tingxuan School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China

ISBN: (纸本)9789819972531

Knowledge graph embedding (KGE) aims to transfer entities and relations into low-dimensional vector space while preserving underlying semantics in knowledge graph (KG). Some existing models directly learn embeddings based on original triples, while others integrate external information to enhance the limited semantic information in KG. However, most of them ignore latent user preferences in their interactions with real-world KG applications, not even considering the relational semantics implied in historical interaction data, which are practical and essential for many downstream tasks like link prediction. To address these issues, we propose a novel preference-aware knowledge graph embedding with path semantics model Path-KGE, which learns semantic information of relation paths and integrates it into embedding process. First, we mine multi-hop relations with frequent and temporal characteristics as semantic relation paths in user interaction data, to obtain implicit user preference features. Second, we design a path importance function to distinguish the semantic impacts of user preferences on distinct relation paths. Finally, we utilize weight scores as long-term constraints to learn preference-aware embeddings, so that the representation vectors of entity pairs connected by preferred relation paths can remain close distances in vector space as in reality. the experimental results show that our model outperforms state-of-the-art translation-based models on link prediction and triple classification tasks. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Web Page Segmentation: A DOM-Structural Cohesion Analysis Approach 24th

Web Page Segmentation: A DOM-Structural Cohesion Analysis Ap...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Huynh, Minh-Hieu Le, Quoc-Tri Nguyen, Vu Nguyen, Tien Katalon Inc. Ho Chi Minh City Viet Nam University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam University of Texas at Dallas RichardsonTX United States

ISBN: (纸本)9789819972531

Web page segmentation is a fundamental technique applied in information retrieval systems to enhance web crawling tasks and information extraction. Its objectives are to gain deep insights from crawling results and to extract the main content of a webpage by disregarding the irrelevant regions. Over time, several solutions have been proposed to address the segmentation problem using different approaches and learning strategies. Among these, the structural cue, which is a characteristic of the DOM tree, is widely utilized as a primary factor in segmentation models. In this paper, we propose a novel technique for web page segmentation using DOM-structural cohesion analysis. Our approach involves generating blocks that represent groups of DOM subtrees with similar tag structures. By analyzing the cohesion within each generated block and comparing detailed information such as types, attributes, and visual cues of web page elements, the approach can effectively maintain or reconstruct the segmentation layout. Additionally, we employ the Canny algorithm to optimize the segmentation result by reducing redundant spaces, resulting in a more accurate segmentation. We evaluate the effectiveness of our approach using a dataset of 1,969 web pages. the approach achieves 64% on the FB3 score, surpassing existing state-of-the-art methods. the proposed DOM-structural cohesion analysis has the potential to improve web page segmentation and its various applications. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Websites

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the advantage of smart manufacturing systems using cloud and RAMI 4.0

The advantage of smart manufacturing systems using cloud and...

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international Carpathian control conference (ICCC)

作者： Elisabeta Mihaela Ciortea Faculty of Computer Science and Engineering University "1 Decembrie 1918" of Alba Iulia Alba Iulia Romania

the article presents an empirical analysis of the benefits of cloud manufacturing, the need to consider the Reference Architecture Model Industry 4.0 (RAMI 4.0), and, last but not least, IoT applications. Using high-performance and complex platforms, it is possible to modernize automation, tracking, and control systems, with the condition of completing optimization systems to increase performance. the article attempts a theoretical approach to cloud systems that has implications in production systems. RAMI offers an analysis of the entire production system, a system that can be analyzed, interpreted and ensures optimal functioning through intervention. Cloud manufacturing provides an overview of the entire production system with all resources used, but also an overview of actions to optimize the production system. In order to be able to draw the most relevant conclusions in real-time, we simulated such a system using Petri nets. Petri nets are chosen because they are easier to interpret, provide real-time information, and are relatively easy to modify the system proposed for simulation, so that decisions can be made as quickly as possible without disrupting the production system. this article focuses more on the relationship between cloud manufacturing and RAMI 4.0. the advantages of this approach are computational speed and storage capacity without investing in additional configurations, user data synchronization, and data processing. the downside is that it wants to find a solution for data security, which leads to mistrust among users. the case study is applied to a module of the production system because the system is complex and can provide easy-to-interpret results for the decision-making unit.

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Efficient Model for Probabilistic Web Resources Under Uncertainty 24th

Efficient Model for Probabilistic Web Resources Under Uncer...

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Omri, Asma Benslimane, Djamal Omri, Mohamed Nazih MARS Research Laboratory University of Sousse Sousse Tunisia University Claude Bernard Lyon 1 LIRIS Research Laboratory Villeurbanne France

ISBN: (纸本)9783031482311

Individuals, organizations, and connected objects produce and publish vast data on the web. these data are then combined into mashups to produce new valuable data. However, combining data from different sources may lead to data uncertainty because these sources contain heterogeneous, contradictory, or incomplete information. In expert and intelligent systems, the word "uncertainty" is related to working with inaccurate data, imprecise and incomplete information, and unreliability of results that can lead to irrational decisions. An expert and intelligent system can consider all the uncertain information, and manage web services and web resources based on the best possible answer rather than on the quality of the exact answer to manage such problems. this paper proposes a new model to define, compute, and interpret uncertain web resources in the context of classical hypertext navigation and evaluation of data queries. the experimental study as well as the analysis of the results, which we carried out on different corpora, confirmed the usefulness of our approach. It provides the effect of the treatment of uncertainty on the execution time and the importance of calculating the confidence level of the response returned to the user. the analysis of the IT execution time shows that the time required for the composition of the services by our approach is negligible, as compared to that of the other approach studied. the impact of the variation in the number of nodes of type "mux" and type "ind" were also evaluated. Our algorithm checks all possibilities in polynomial time and can adapt to many possibilities of multiplexing values. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Intelligent systems

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LiDAR and SfM-MVS Integrated Approach to Build a Highly Detailed 3D Virtual Model of Urban Areas 9

LiDAR and SfM-MVS Integrated Approach to Build a Highly Deta...

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9th international conference on Geographical Information systems theory, Applications and Management, GISTAM 2023

作者： Grasso, Nives Spadavecchia, Claudio Di Pietra, Vincenzo Belcore, Elena DIATI Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy

ISBN: (纸本)9789897586491

the three-dimensional reconstruction of buildings, road infrastructures, service networks, and cultural heritage in urban environments is relevant for many market segments and numerous functions in the management and coordination of public authorities. these stakeholders are showing increasing interest in modern acquisition and reconstruction technologies for digital models typical of the geomatic and computer vision disciplines. In this context, it is essential to methodically exploit the potential of active and passive instruments and apply multi-sensor integration techniques, to obtain metrically accurate and high-resolution products. this study proposes a multi-sensor and multi-scale approach for high-resolution 3D model reconstruction focused on a city portion of Turin (Italy). We performed an integrated survey based on LiDAR and photogrammetric techniques, both aerial and terrestrial. then we produced a set of 3D digital products for (i) promoting the historical and artistic heritage through Virtual Reality (VR) applications, (ii) supporting the restoration of Baroque buildings, and (iii) providing advanced analysis concerning the alteration of the urban road system. the final output describes in detail the architectural elements investigated (e.g., 9,480,000 tringles to define the mesh of a statue). It emphasizes the need for deepening sensor integration and data fusion. Copyright © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Data integration

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