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Generation Gap or Diffusion Trap? How Age Affects the Detection of Personalized AI-Generated Images 8th

Generation Gap or Diffusion Trap? How Age Affects the Detect...

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Luedemann, Rene Schulz, Alexander Kuhl, Ulrike Bielefeld Univ Machine Learning Grp Inspiration 1 D-33619 Bielefeld Germany

ISBN: (纸本)9783031838446;9783031838453

AI-generated content, particularly artificially generated image data, presents a fascinating challenge as it blurs the line between reality and fabrication in digital media. This study explores the ability of different age groups to distinguish between real and AI-generated images, using a custom model based on Stable Diffusion to create personalized synthetic images of the same individual. Participants (N = 112) showed an overall accuracy of 87.01%, with younger individuals outperforming older ones in both detection accuracy and confidence. Older participants also took longer to make their decisions, indicating either an age-related decline in processing speed, or a more careful and deliberate analysis of the presented content. These findings highlight the importance of improving digital literacy across age groups and developing robust detection tools to better equip users to navigate an increasingly AI-driven digital landscape. In the interest of reproducible research, the entire code, images, and data is available at: https://***/ukuhl/GenGapCHIRA2024.

关键词： Stable diffusion Generative AI User study User behaviour analysis Age-related differences

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RoBERTa-wwm-CBA: A Mental Disease Identification Model Based on RoBERTa-wwm and Hybrid Neural Networks 5th

RoBERTa-wwm-CBA: A Mental Disease Identification Model Based...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Xu, Hongkui Guo, Xubin Zhao, Jingzheng Shandong Jianzhu Univ Jinan 250101 Peoples R China Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Recent years have witnessed a escalating prevalence and severity of mental health challenges among the general populace, a trend corroborated by a multitude of research studies. Traditional diagnostic methodologies for mental illness typically involve hospital-based psychological counseling. However, certain individuals confront deeply ingrained stigmatized views, which can impede the timely detection and intervention of mental health issues due to their nuanced and often latent nature. In response to these challenges, this investigation introduces an innovative text classification model termed RoBERTa-wwm-CBA (RoBERTawwm-CNN-BiLSTM-Attention), which combines the powerful feature extraction capabilities of the RoBERTa-wwm language model with a hybrid neural network architecture. This model is designed to effectively identify potential psychological distress in individuals seeking counseling by mining text features across various levels of granularity. The RoBERTa-wwm-CBA model leverages the RoBERTa-wwm's pre-trained expertise to seize the nuanced features within text data. It incorporates a CNN layer to extract local semantic features from the text, integrates these with broader contextual information through a BiLSTM layer, and applies an attention mechanism to ascribe differential importance to different features. This facilitates the derivation of a holistic text vector representation. Empirical evaluations on the Emotional First Aid Dataset have revealed that the RoBERTa-wwm-CBA model achieves superior performance in terms of accuracy, precision, recall, and F1 score, substantially enhancing the efficiency and accuracy of mental illness recognition.

关键词： Psychological Counseling Text Classification RoBERTa-wwm CNN BiLSTM

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Quantum-Enabled (QeSVM) Facial Expression Identification of Autistic Children 4th

Quantum-Enabled (QeSVM) Facial Expression Identification of ...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Abirami, S. P. Chandar, B. Misha Coimbatore Inst Technol CIT Dept Comp Sci Coimbatore Tamil Nadu India VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati Andhra Pradesh India

ISBN: (纸本)9783031837920;9783031837937

Autism Spectrum Disorder (ASD) is a special neurodevelopmental condition identified in children with challenges in social communication, restricted interest, and repetitive behaviour traits. Early detection and identification of ASD is vital for effective intervention, yet traditional diagnostic methods often result in delayed diagnoses due to the subtlety and variability of early symptoms. Facial expression analysis is recently been looked upon as a significant approach for early detection, given the atypical facial expression patterns frequently observed in children with ASD. Recent advancements in machine learning (ML) and deep learning (DL) have improved the accuracy of facial expression recognition, enabling automated systems to detect these subtle differences more reliably. This research explores the possible incorporation of quantum computing principles, particularly through quantum support vector machines (QeSVMs), into the analysis of facial expressions in autistic children. Quantum computing offers unique advantages in processing high-dimensional data, allowing for more efficient and accurate pattern recognition in comparison to conventional classical methods. By leveraging quantum-enhanced machine learning techniques, this study aims to improve the early identification of ASD through facial expression analysis, providing a novel and potentially transformative approach to ASD diagnosis. The findings suggest that QeSVMs, when applied to facial expression data, can enhance classification accuracy and computational efficiency with huge scope for effective early detection strategies for ASD. This paper is one such effort that claims to be a growing field of knowledge. This novel research contributes to the growing body of knowledge of effectively integrating quantum computing and autism diagnosis, offering insights that could lead to better outcomes for children with ASD through earlier and more reliable detection methods.

关键词： Autism Spectrum Disorder Quantum Computing Landmark Identification Geometric Features Early Intervention

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Enhancing Human - Robot Collaboration in the Industry 5.0 Framework with Physics-Informed Neural Networks: Application to Collision Detection 5th

Enhancing Human - Robot Collaboration in the Industry 5.0 Fr...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Ciampi, Francesco G. Diallo, Thierno M. L. Mhenni, Faida Patalano, Stanislao Choley, Jean-Yves Univ Naples Federico II Dept Ind Engn I-80125 Naples Italy ISAE Supmeca Quartz Lab EA7393 3 Rue Fernand Hainaut F-93400 St Ouen France

ISBN: (纸本)9783031807596;9783031807602

In the context of rapidly evolving technology and increased attention to social and environmental dimensions, the industrial sector is transitioning from Industry 4.0 to Industry 5.0. This new paradigm emphasizes human centrality in highly automated environments, necessitating the exploration of collaboration mechanisms between humans and robots. This study investigates the application of Physics-Informed Neural Networks (PINNs) to enhance Human-Robot Collaboration (HRC). PINNs integrates the traditional data-driven approach based on machine learning models with a prior physical knowledge of the system, providing a valuable solution when data are scarce or physical models are too complex or incomplete. The study first focuses on the main aspects of interest in the field of HRC through a state-of-the-art analysis, evaluating the application of Physics-Informed Neural Networks (PINNs) in HRC and robotics. It then delves into the definition of PINNs and their implementation. Finally, as a proof of concept, the model is applied to a case study concerning collision detection in a 6 DoF robotic arm. This is achieved by predicting the joint currents and comparing them with the measured values to identify the contribution due to external forces such as collisions. The results demonstrate that the PINNs model outperforms a traditional neural network, achieving an average error below 10%. Additionally, the collision detection application shows an f1_score of 0.80, indicating strong performance.

关键词： Industry 5.0 Human-robot collaboration Collision detection Physics-informed neural networks Physics-informed machine learning

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Sound Blocks VR: An Accessible Virtual Reality Musical Instrument 8th

Sound Blocks VR: An Accessible Virtual Reality Musical Instr...

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Gioiosa, Marta Avanzini, Federico Ludovico, Luca Andrea Brambilla, Susanna Ripamonti, Laura Univ Milan Lab Music Informat LIM Dept Comp Sci Milan Italy Univ Milan Playlab inNovat Games PONG Dept Comp Sci Milan Italy

ISBN: (纸本)9783031838446;9783031838453

In recent years, immersive technologies such as augmented and virtual reality have gained significant popularity and found applications across various research domains. This paper focuses on the adoption of such technologies for musical purposes, particularly in enhancing accessibility in musical performance. In the domain of Digital Musical Instruments, extensive efforts have been made to develop accessible designs and interfaces for music creation. Concerning Virtual Reality Musical Instruments (VRMIs), one notable challenge is the discomfort stemming from using controllers to interact with objects in the virtual world, specifically for people with a physical disability. In fact, the design of these applications often incorporates objects that require precise control. The proposed solution is an accessible VRMI, called Sound Blocks VR, which accommodates both controller-based and hand-tracking interactions to address the needs of individuals with physical disabilities (especially motor-impaired users) or, in general, who feel uncomfortable using controllers. Where available, the passthrough feature can also be exploited. The usability of Sound Blocks VR was evaluated using the System Usability Scale through interviews with musicians and nonmusicians. The average usability score computed on 17 participants is 87.94 out of 100, which indicates that participants found the experience intuitive and user-friendly. Moreover, some qualitative questions were posed to investigate specific aspects of the experience design.

关键词： Digital Musical Instruments Virtual Reality Accessibility Motor Impairment

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Is Local Sustainable? Exploring Consumers' Perceptions Towards Short Food Supply Chains in Greece 5th

Is Local Sustainable? Exploring Consumers' Perceptions Towar...

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5th 5th Olympus - International Conference on Supply Chain

作者： Nanos, Ioannis Giannakou, Evmorfia Skylogiannis, Vasileios Zoumpeloulis, Apostolos Panagiotis Stafyla, Amalia Int Hellen Univ Katerini Greece

ISBN: (纸本)9783031693502;9783031693519

The present study aims to explore the issue of sustainability and short food supply chains, focusing on the attitudes and perceptions of consumers towards them. The objectives of the research are to identify and measure the perceptions of consumers about the social, economic, environmental and cultural sustainability dimensions of SFSCs and to explore the relationship between these perceptions and purchase behavior. This is attempted through quantitative research on a sample size of 768 Greek consumers with the use of an online survey, based on previous relevant studies found in the literature. The findings show that consumers' attitude towards short food supply chains is positive, although the purchase frequency is not very high and the amount of money spent is rather small. Moreover, consumers agree that SFSCs have sustainability dimensions, with more evident the economic, followed by cultural. Finally, the perceptions of consumers about sustainability dimensions influence positively their attitudes towards SFSCs, but they do not seem to influence significantly their purchasing behavior yet. The outcomes of the research contribute to the scientific knowledge on a field that is gaining attention and importance among academics and consumers.

关键词： Short Food Supply Chains Sustainability Food Supply Chain

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Indicators for the Evaluation of Agri-Food Supply Chains Under Industry 5.0 Parameters

Indicators for the Evaluation of Agri-Food Supply Chains Und...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Garcia, Nayeli Lema, Freddy Rosero-Mantilla, Cesar Sanchez, Carlos Tubon-Nunez, Edith Morales, Luis Cordova, Patricio Siguenza-Guzman, Lorena Sigcha, Erik Tigre, Franklin Univ Tecn Ambato Ambato Ecuador Escuela Politec Nacl Quito Ecuador Univ Cuenca Dept Comp Sci Cuenca Ecuador

ISBN: (纸本)9783031834318;9783031834325

The growing challenges of the agro-industrial sector have motivated companies to seek ways to increase their competitiveness through the implementation of Industry 5.0. This new industrial era involves not only the integration of advanced technologies but also the adoption of sustainable practices, the promotion of resilience, and a human-centered approach at all stages of supply chains. This transformation not only optimizes efficiency and product quality but also ensures a positive impact on the environment. The present research reviews the indicators for the evaluation of agri-food supply chains under the parameters of Industry 5.0. The key criteria have been considered, including Environmental, Business Model, Quality and Profitability, Flexibility, IoT, Risk Management, Human Rights and Welfare, and Human-Centered Technology. The aforementioned indicators will enable an organization undergoing examination to evaluate the status of its operations in order to facilitate preparation for a disruptive event that may impact the supply chain. The selection of the indicators was done through a systematic literature review, which considered 96 articles in English and Portuguese published between 2019 and 2023. The analysis of the documents was performed using the *** software version 2019, considering the 4Ws (when, who, where, and what) and the four research questions elaborated using the PICO strategy. The research results provide definitions of the main elements of the 5.0 supply chain, as well as the criteria considered for the selection of relevant indicators. A total of twenty-one indicators for the agri-food supply chain were subjected to a review based on the three essential elements and eight key criteria. The indicators thus identified represent a valuable tool for improving the resilience and sustainability of agribusiness operations in an increasingly challenging environment.

关键词： Resilience sustainability human focus COVID-19

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JSMBox-A Runtime Monitoring Framework for Analyzing and Classifying Malicious JavaScript 33rd

JSMBox-A Runtime Monitoring Framework for Analyzing and Clas...

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33rd International Conference on Software and Data Engineering

作者： Phung, Phu H. Varghese, Allen Wang, Bojue Zhao, Yu Yu, Chong Univ Dayton Dept Comp Sci Intelligent Syst Secur Lab 300 Coll Pk Ave Dayton OH 45469 USA Univ Cincinnati Dept Comp Sci 2901 Woodside Dr Cincinnati OH 45221 USA Univ Cincinnati Dept Elect & Comp Engn Cincinnati OH USA

ISBN: (纸本)9783031752001;9783031752018

In recent years, there has been a notable increase in the prevalence of malicious websites, leading to a majority of cyber-attacks and data breaches. Malicious websites often incorporate JavaScript code to execute attacks on web browsers. Despite existing methodologies documented in the literature, the analysis and detection of malicious JavaScript pose significant challenges due to the dynamic nature of JavaScript and the use of advanced evasion techniques. These challenges motivate the need for an innovative and efficient approach to comprehensively analyze the code to identify its malicious intent. In this paper, we introduce a monitoring approach for analyzing JavaScript code, which can capture all of the code's features at runtime. Our method leverages the security reference monitor technique to mediate JavaScript security-sensitive executions, including function calls and property accesses. Therefore, the proposed method can capture behaviors at runtime regardless of how the code is written, even with recent advanced evasion techniques like WebAssembly diversification. We have implemented our approach as a JavaScript dynamic analysis framework called JSMBox in aChromium-based browser extension. Our experiments demonstrated that JSMBox is capable of effectively countering sophisticated evasion techniques found in modern malicious JavaScript code, including WebAssembly diversification. We have also evaluated the framework's ability to classify malicious behaviors based on a large-scale raw dataset comprising about 20,000 malicious and benign webpages. Our developed tool automatically launches the browser to execute these webpages, records JavaScript code execution events, and captures their execution frequency as extracted features. We have tested the extracted dataset with various machine-learning models, yielding promising experimental results that confirm the effectiveness of our approach and achieve a high accuracy rate.

关键词： JavaScript Dynamic Analysis Runtime Monitoring Maliciousness Classification

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A Novel Approach of SHA-3-512bits Using Keccak Technique Based on Sponge Function Implementation on FPGA 5th

A Novel Approach of SHA-3-512bits Using Keccak Technique Bas...

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5th International Conference on Computing science, Communication and Security

作者： Lakshmi, K. Janshi Sreenivasulu, G. Sri Venkateswara Univ Coll Engn Dept Elect & Commun Engn Tirupati Andhra Pradesh India

ISBN: (纸本)9783031751691;9783031751707

SHA-3-512bits offers stronger cryptographic security than SHA1, SHA 2, and *** paper describes a SHA-3-512bits hash function utilizing the Sponge-based Keccak Algorithm. Four alternate messages digest lengths of 512 bits, 384 bits, 256 bits, and 224 bits are allowed by the SHA3 hash algorithm, depending on the hash function in use. The SHA3-512 function operates on a single instance, processing input messages at a rate of 72 bytes per 1096 cycles at 316.25 MHz and a 1600 bits data path with a round-iterative structure. The SHA-3-512bits is verified by VIVADO 2017.2 version software with hardware implementation on FPGA ARTIX-7 device. The proposed method consumed power is 192.334W and occupied an area of 2493 LUTs, Flip-Flops 1876 and IOB's 17.65% of ARTIX-7 FPGA device and became cost-effective device used ARTEX-7.

关键词： Cryptography Security SHA-3-512 Keccak Sponge function VIVADO FPGA Artix-7

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DRLN: Disentangled Representation Learning Network for Multimodal Sentiment Analysis 5th

DRLN: Disentangled Representation Learning Network for Multi...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Hou, Jingming Omar, Nazlia Tiun, Sabrina Saad, Saidah He, Qian Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43650 Selangor Malaysia Guilin Univ Elect Technol State & Local Joint Engn Res Ctr Satellite Nav & Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Multimodal sentiment analysis (MSA) involves the analysis of human emotions by integrating emotional features from text, audio, and visual modalities. Previously, a considerable amount of research had commonly focused on fusing these three modalities equally, neglecting the importance of text modality and the influence of modal heterogeneity on fusion. To address this issue, we propose the Disentangled Representation Learning Network (DRLN) which decomposes representations from different modalities into separate sub-spaces centered around text modality to capture similarity and dissimilarity features among modalities. Meanwhile, we utilize contrastive learning loss and reconstruction loss to better learn representations within each sub-space. Finally, extensive experiments on two benchmark datasets demonstrate that our model outperforms state-of-the-art methods on various metrics.

关键词： Multimodal Sentiment Analysis Multimodal Representation Learning Contrastive Learning Text-Centric

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