7,173 research outputs found
Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work
The increasing prevalence of Artificial Intelligence (AI) in safety-critical
contexts such as air-traffic control leads to systems that are practical and
efficient, and to some extent explainable to humans to be trusted and accepted.
The present structured literature analysis examines n = 236 articles on the
requirements for the explainability and acceptance of AI. Results include a
comprehensive review of n = 48 articles on information people need to perceive
an AI as explainable, the information needed to accept an AI, and
representation and interaction methods promoting trust in an AI. Results
indicate that the two main groups of users are developers who require
information about the internal operations of the model and end users who
require information about AI results or behavior. Users' information needs vary
in specificity, complexity, and urgency and must consider context, domain
knowledge, and the user's cognitive resources. The acceptance of AI systems
depends on information about the system's functions and performance, privacy
and ethical considerations, as well as goal-supporting information tailored to
individual preferences and information to establish trust in the system.
Information about the system's limitations and potential failures can increase
acceptance and trust. Trusted interaction methods are human-like, including
natural language, speech, text, and visual representations such as graphs,
charts, and animations. Our results have significant implications for future
human-centric AI systems being developed. Thus, they are suitable as input for
further application-specific investigations of user needs
Automated Exploration of Reaction Network and Mechanism via Meta-dynamics Nanoreactor
We developed an automated approach to construct the complex reaction network
and explore the reaction mechanism for several reactant molecules. The
nanoreactor type molecular dynamics was employed to generate possible chemical
reactions, in which the meta-dynamics was taken to overcome reaction barriers
and the semi-empirical GFN2-xTB method was used to reduce computational cost.
The identification of reaction events from trajectories was conducted by using
the hidden Markov model based on the evolution of the molecular connectivity.
This provided the starting points for the further transition state searches at
the more accurate electronic structure levels to obtain the reaction mechanism.
Then the whole reaction network with multiply pathways was obtained. The
feasibility and efficiency of this automated construction of the reaction
network was examined by two examples. The first reaction under study was the
HCHO + NH3 biomolecular reaction. The second example focused on the reaction
network for a multi-species system composed of dozens of HCN and H2O compounds.
The result indicated that the proposed approach was a valuable and effective
tool for the automated exploration of reaction networks
FedForgery: Generalized Face Forgery Detection with Residual Federated Learning
With the continuous development of deep learning in the field of image
generation models, a large number of vivid forged faces have been generated and
spread on the Internet. These high-authenticity artifacts could grow into a
threat to society security. Existing face forgery detection methods directly
utilize the obtained public shared or centralized data for training but ignore
the personal privacy and security issues when personal data couldn't be
centralizedly shared in real-world scenarios. Additionally, different
distributions caused by diverse artifact types would further bring adverse
influences on the forgery detection task. To solve the mentioned problems, the
paper proposes a novel generalized residual Federated learning for face Forgery
detection (FedForgery). The designed variational autoencoder aims to learn
robust discriminative residual feature maps to detect forgery faces (with
diverse or even unknown artifact types). Furthermore, the general federated
learning strategy is introduced to construct distributed detection model
trained collaboratively with multiple local decentralized devices, which could
further boost the representation generalization. Experiments conducted on
publicly available face forgery detection datasets prove the superior
performance of the proposed FedForgery. The designed novel generalized face
forgery detection protocols and source code would be publicly available.Comment: The code is available at https://github.com/GANG370/FedForgery. The
paper has been accepted in the IEEE Transactions on Information Forensics &
Securit
Urbanised forested landscape: Urbanisation, timber extraction and forest care on the Vișeu Valley, northern Romania
By looking at urbanisation processes from the vantage point of the forest, and the ways in which it both constitutes our living space while having been separated from the bounded space of the urban in modern history, the thesis asks: How can we (re)imagine urbanisation beyond the limits of the urban? How can a feminine line of thinking engage with the forest beyond the capitalist-colonial paradigm and its extractive project? and How can we “think with care” (Puig de la Bellacasa 2017) towards the forest as an inhabitant of our common world, instead of perpetuating the image of the forest as a space outside the delimited boundaries of the city? Through a case study research, introducing the Vișeu Valley in northern Romania as both a site engaged in the circulation of the global timber flow, a part of what Brenner and Schmid (2014) name “planetary urbanisation”, where the extractive logging operations beginning in the late XVIIIth century have constructed it as an extractive landscape, and a more than human landscape inhabited by a multitude of beings (animal, plant, and human) the thesis argues towards the importance of forest care and indigenous knowledge in landscape management understood as a trans-generational transmission of knowledge, that is interdependent with the persistence of the landscape as such. Having a trans-scalar approach, the thesis investigates the ways in which the extractive projects of the capitalist-colonial paradigm have and still are shaping forested landscapes across the globe in order to situate the case as part of a planetary forest landscape and the contemporary debates it is engaged in. By engaging with emerging paradigms within the fields of plant communication, forestry, legal scholarship and landscape urbanism that present trees and forests as intelligent beings, and look at urbanisation as a way of inhabiting the landscape in both indigenous and modern cultures, the thesis argues towards viewing forested landscapes as more than human living spaces. Thinking urbanisation through the case of the Vișeu Valley’s urbanised forested landscape, the thesis aligns with alternate ways of viewing urbanisation as co-habitation with more than human beings, particularly those emerging from interdisciplinary research in the Amazon river basin (Tavares 2017, Heckenberger 2012) and, in light of emerging discourses on the rights of nature, proposes an expanded concept of planetary citizenship, to include non-human personhood
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
This paper introduces InternVid, a large-scale video-centric multimodal
dataset that enables learning powerful and transferable video-text
representations for multimodal understanding and generation. The InternVid
dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M
video clips accompanied by detailed descriptions of total 4.1B words. Our core
contribution is to develop a scalable approach to autonomously build a
high-quality video-text dataset with large language models (LLM), thereby
showcasing its efficacy in learning video-language representation at scale.
Specifically, we utilize a multi-scale approach to generate video-related
descriptions. Furthermore, we introduce ViCLIP, a video-text representation
learning model based on ViT-L. Learned on InternVid via contrastive learning,
this model demonstrates leading zero-shot action recognition and competitive
video retrieval performance. Beyond basic video understanding tasks like
recognition and retrieval, our dataset and model have broad applications. They
are particularly beneficial for generating interleaved video-text data for
learning a video-centric dialogue system, advancing video-to-text and
text-to-video generation research. These proposed resources provide a tool for
researchers and practitioners interested in multimodal video understanding and
generation.Comment: Data and Code:
https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVi
KYT2022 Finnish Research Programme on Nuclear Waste Management 2019–2022 : Final Report
KYT2022 (Finnish Research Programme on Nuclear Waste Management 2019–2022), organised by the Ministry of Economic Affairs and Employment, was a national research programme with the objective to ensure that the authorities have sufficient levels of nuclear expertise and preparedness that are needed for safety of nuclear waste management.
The starting point for public research programs on nuclear safety is that they create the conditions for maintaining the knowledge required for the continued safe and economic use of nuclear energy, developing new know-how and participating in international collaboration.
The content of the KYT2022 research programme was composed of nationally important research topics, which are the safety, feasibility and acceptability of nuclear waste management.
KYT2022 research programme also functioned as a discussion and information-sharing forum for the authorities, those responsible for nuclear waste management and the research organizations, which helped to make use of the limited research resources. The programme aimed to develop national research infrastructure, ensure the continuing availability of expertise, produce high-level scientific research and increase general knowledge of nuclear waste management
Colour technologies for content production and distribution of broadcast content
The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
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
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Cracks and keyhole pores are detrimental defects in alloys produced by laser
directed energy deposition (LDED). Laser-material interaction sound may hold
information about underlying complex physical events such as crack propagation
and pores formation. However, due to the noisy environment and intricate signal
content, acoustic-based monitoring in LDED has received little attention. This
paper proposes a novel acoustic-based in-situ defect detection strategy in
LDED. The key contribution of this study is to develop an in-situ acoustic
signal denoising, feature extraction, and sound classification pipeline that
incorporates convolutional neural networks (CNN) for online defect prediction.
Microscope images are used to identify locations of the cracks and keyhole
pores within a part. The defect locations are spatiotemporally registered with
acoustic signal. Various acoustic features corresponding to defect-free
regions, cracks, and keyhole pores are extracted and analysed in time-domain,
frequency-domain, and time-frequency representations. The CNN model is trained
to predict defect occurrences using the Mel-Frequency Cepstral Coefficients
(MFCCs) of the lasermaterial interaction sound. The CNN model is compared to
various classic machine learning models trained on the denoised acoustic
dataset and raw acoustic dataset. The validation results shows that the CNN
model trained on the denoised dataset outperforms others with the highest
overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC
score (98%). Furthermore, the trained CNN model can be deployed into an
in-house developed software platform for online quality monitoring. The
proposed strategy is the first study to use acoustic signals with deep learning
for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin
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