18,647 research outputs found
Vision- and tactile-based continuous multimodal intention and attention recognition for safer physical human-robot interaction
Employing skin-like tactile sensors on robots enhances both the safety and
usability of collaborative robots by adding the capability to detect human
contact. Unfortunately, simple binary tactile sensors alone cannot determine
the context of the human contact -- whether it is a deliberate interaction or
an unintended collision that requires safety manoeuvres. Many published methods
classify discrete interactions using more advanced tactile sensors or by
analysing joint torques. Instead, we propose to augment the intention
recognition capabilities of simple binary tactile sensors by adding a
robot-mounted camera for human posture analysis. Different interaction
characteristics, including touch location, human pose, and gaze direction, are
used to train a supervised machine learning algorithm to classify whether a
touch is intentional or not with an F1-score of 86%. We demonstrate that
multimodal intention recognition is significantly more accurate than monomodal
analyses with the collaborative robot Baxter. Furthermore, our method can also
continuously monitor interactions that fluidly change between intentional or
unintentional by gauging the user's attention through gaze. If a user stops
paying attention mid-task, the proposed intention and attention recognition
algorithm can activate safety features to prevent unsafe interactions. We also
employ a feature reduction technique that reduces the number of inputs to five
to achieve a more generalized low-dimensional classifier. This simplification
both reduces the amount of training data required and improves real-world
classification accuracy. It also renders the method potentially agnostic to the
robot and touch sensor architectures while achieving a high degree of task
adaptability.Comment: 11 pages, 8 figures, preprint under revie
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos
In recent years, distracted driving has garnered considerable attention as it
continues to pose a significant threat to public safety on the roads. This has
increased the need for innovative solutions that can identify and eliminate
distracted driving behavior before it results in fatal accidents. In this
paper, we propose a Signal-Based anomaly detection algorithm that segments
videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to
precisely estimate the start and end times of an anomalous driving event. In
the phase of anomaly detection and analysis, driver pose background estimation,
mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM
classifier was applied to candidate anomalies to detect and classify final
anomalies. The proposed method achieved an overlap score of 0.5424 and ranked
9th on the public leader board in the AI City Challenge 2023, according to
experimental validation results
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
Multi-modal Facial Affective Analysis based on Masked Autoencoder
Human affective behavior analysis focuses on analyzing human expressions or
other behaviors to enhance the understanding of human psychology. The CVPR 2023
Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to
providing high-quality and large-scale Aff-wild2 for the recognition of
commonly used emotion representations, such as Action Units (AU), basic
expression categories(EXPR), and Valence-Arousal (VA). The competition is
committed to making significant strides in improving the accuracy and
practicality of affective analysis research in real-world scenarios. In this
paper, we introduce our submission to the CVPR 2023: ABAW5. Our approach
involves several key components. First, we utilize the visual information from
a Masked Autoencoder(MAE) model that has been pre-trained on a large-scale face
image dataset in a self-supervised manner. Next, we finetune the MAE encoder on
the image frames from the Aff-wild2 for AU, EXPR and VA tasks, which can be
regarded as a static and uni-modal training. Additionally, we leverage the
multi-modal and temporal information from the videos and implement a
transformer-based framework to fuse the multi-modal features. Our approach
achieves impressive results in the ABAW5 competition, with an average F1 score
of 55.49\% and 41.21\% in the AU and EXPR tracks, respectively, and an average
CCC of 0.6372 in the VA track. Our approach ranks first in the EXPR and AU
tracks, and second in the VA track. Extensive quantitative experiments and
ablation studies demonstrate the effectiveness of our proposed method
Economia colaborativa
A importância de se proceder à análise dos principais desafios jurÃdicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutÃvel, correspondendo à necessidade de se fomentar a segurança jurÃdica destas práticas, potenciadoras de crescimento económico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domÃnio, com o objetivo de identificar os problemas jurÃdicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.
O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202
Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Comedians without a Cause: The Politics and Aesthetics of Humour in Dutch Cabaret (1966-2020)
Comedians play an important role in society and public debate. While comedians have been considered important cultural critics for quite some time, comedy has acquired a new social and political significance in recent years, with humour taking centre stage in political and social debates around issues of identity, social justice, and freedom of speech. To understand the shifting meanings and political implications of humour within a Dutch context, this PhD thesis examines the political and aesthetic workings of humour in the highly popular Dutch cabaret genre, focusing on cabaret performances from the 1960s to the present. The central questions of the thesis are: how do comedians use humour to deliver social critique, and how does their humour resonate with political ideologies? These questions are answered by adopting a cultural studies approach to humour, which is used to analyse Dutch cabaret performances, and by studying related materials such as reviews and media interviews with comedians. This thesis shows that, from the 1960s onwards, Dutch comedians have been considered ‘progressive rebels’ – politically engaged, subversive, and carrying a left-wing political agenda – but that this image is in need of correction. While we tend to look for progressive political messages in the work of comedians who present themselves as being anti-establishment rebels – such as Youp van ‘t Hek, Hans Teeuwen, and Theo Maassen – this thesis demonstrates that their transgressive and provocative humour tends to protect social hierarchies and relationships of power. Moreover, it shows that, paradoxically, both the deliberately moderate and nuanced humour of Wim Kan and Claudia de Breij, and the seemingly past-oriented nostalgia of Alex Klaasen, are more radical and progressive than the transgressive humour of van ‘t Hek, Teeuwen and Maassen. Finally, comedians who present absurdist or deconstructionist forms of humour, such as the early student cabarets, Freek de Jonge, and Micha Wertheim, tend to disassociate themselves from an explicit political engagement. By challenging the dominant image of the Dutch comedian as a ‘progressive rebel,’ this thesis contributes to a better understanding of humour in the present cultural moment, in which humour is often either not taken seriously, or one-sidedly celebrated as being merely pleasurable, innocent, or progressively liberating. In so doing, this thesis concludes, the ‘dark’ and more conservative sides of humour tend to get obscured
Open Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture
Classification of AI-manipulated content is receiving great attention, for
distinguishing different types of manipulations. Most of the methods developed
so far fail in the open-set scenario, that is when the algorithm used for the
manipulation is not represented by the training set. In this paper, we focus on
the classification of synthetic face generation and manipulation in open-set
scenarios, and propose a method for classification with a rejection option. The
proposed method combines the use of Vision Transformers (ViT) with a hybrid
approach for simultaneous classification and localization. Feature map
correlation is exploited by the ViT module, while a localization branch is
employed as an attention mechanism to force the model to learn per-class
discriminative features associated with the forgery when the manipulation is
performed locally in the image. Rejection is performed by considering several
strategies and analyzing the model output layers. The effectiveness of the
proposed method is assessed for the task of classification of facial attribute
editing and GAN attribution
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