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| 2. |
- Damaschke, Peter, 1963, et al.
(författare)
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Editing simple graphs
- 2014
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Ingår i: Journal of Graph Algorithms and Applications. - : Journal of Graph Algorithms and Applications. - 1526-1719. ; 18:4, s. 557-576
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Tidskriftsartikel (refereegranskat)abstract
- We study the complexity of turning a given graph, by edge editing, into a target graph whose critical-clique graph is any fixed graph. The problem came up in practice, in an effort of mining huge word similarity graphs for well structured word clusters. It also adds to the rich field of graph modification problems. We show in a generic way that several variants of this problem are in SUBEPT. As a special case, we give a tight time bound for edge deletion to obtain a single clique and isolated vertices, and we round up this study with NP-completeness results for a number of target graphs.
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| 3. |
- Damaschke, Peter, 1963, et al.
(författare)
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Editing the simplest graphs
- 2014
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319046570 ; 8344, s. 249-260
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Konferensbidrag (refereegranskat)abstract
- We study the complexity of editing a graph into a target graph with any fixed critical-clique graph. The problem came up in practice, in mining a huge word similarity graph for well structured word clusters. It also adds to the rich field of graph modification problems. We show in a generic way that several variants of this problem are in SUBEPT. As a special case, we give a tight time bound for edge deletion to obtain a single clique and isolated vertices, and we round up this study with NP-completeness results for a number of target graphs.
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| 4. |
- Ekblom, Ebba, et al.
(författare)
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EFFGAN: Ensembles of fine-tuned federated GANs
- 2022
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Konferensbidrag (refereegranskat)abstract
- Decentralized machine learning tackles the problemof learning useful models when data is distributed amongseveral clients. The most prevalent decentralized setting todayis federated learning (FL), where a central server orchestratesthe learning among clients. In this work, we contribute to therelatively understudied sub-field of generative modelling in theFL framework.We study the task of how to train generative adversarial net-works (GANs) when training data is heterogeneously distributed(non-iid) over clients and cannot be shared. Our objective isto train a generator that is able to sample from the collectivedata distribution centrally, while the client data never leaves theclients and user privacy is respected. We show using standardbenchmark image datasets that existing approaches fail in thissetting, experiencing so-called client drift when the local numberof epochs becomes to large and local parameters drift too faraway in parameter space. To tackle this challenge, we proposea novel approach namedEFFGAN: Ensembles of fine-tunedfederated GANs. Being an ensemble of local expert generators, EFFGAN is able to learn the data distribution over all clientsand mitigate client drift. It is able to train with a large numberof local epochs, making it more communication efficient thanprevious works
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| 5. |
- Ericsson, David, et al.
(författare)
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Adversarial representation learning for private speech generation
- 2020
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Konferensbidrag (refereegranskat)abstract
- As more data is collected in various settingsacross organizations, companies, and countries,there has been an increase in the demand of userprivacy. Developing privacy preserving methodsfor data analytics is thus an important area of research. In this work we present a model basedon generative adversarial networks (GANs) thatlearns to obfuscate specific sensitive attributes inspeech data. We train a model that learns to hidesensitive information in the data, while preservingthe meaning in the utterance. The model is trainedin two steps: first to filter sensitive informationin the spectrogram domain, and then to generatenew and private information independent of thefiltered one. The model is based on a U-Net CNNthat takes mel-spectrograms as input. A MelGANis used to invert the spectrograms back to rawaudio waveforms. We show that it is possible tohide sensitive information such as gender by generating new data, trained adversarially to maintainutility and realism.
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| 6. |
- Fallahi, Sara, 1985-, et al.
(författare)
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Financing solutions for circular business models : Exploring the role of business ecosystems and artificial intelligence
- 2023
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Ingår i: Business Strategy and the Environment. - : John Wiley and Sons Ltd. - 0964-4733 .- 1099-0836. ; 32:6
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Tidskriftsartikel (refereegranskat)abstract
- The circular economy promotes a transition away from linear modes of production and consumption to systems with circular material flows that can significantly improve resource productivity. However, transforming linear business models to circular business models posits a number of financial consequences for product companies as they need to secure more capital in a stock of products that will be rented out over time and therefore will encounter a slower, more volatile cash flow in the short term compared to linear direct sales of products. This paper discusses the role of financial actors in circular business ecosystems and alternative financing solutions when moving from product-dominant business models to Product-as-a-Service (PaaS) or function-based business models. Furthermore, the paper demonstrates a solution where state-of-the-art artificial intelligence (AI) modeling can be incorporated for financial risk assessment. We provide an open implementation and a thorough empirical evaluation of an AI-model, which learns to predict residual value of stocks of used items. Furthermore, the paper highlights solutions, managerial implications, and potentials for financing circular business models, argues the importance of different forms of data in future business ecosystems, and offers recommendations for how AI can help mitigate some of the challenges businesses face as they transition to circular business models. © 2022 The Authors.
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| 7. |
- Korneliusson, Marie, 1993, et al.
(författare)
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Generative Modelling of Semantic Segmentation Data in the Fashion Domain
- 2019
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Ingår i: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). ; , s. 3169-3172
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Konferensbidrag (refereegranskat)abstract
- In this work, we propose a method to generatively model the joint distribution of images and corresponding semantic segmentation masks using generative adversarial networks. We extend the Style-GAN architecture by iteratively growing the network during training, to add new output channels that model the semantic segmentation masks. We train the proposed method on a large dataset of fashion images and our experimental evaluation shows that the model produces samples that are coherent and plausible with semantic segmentation masks that closely match the semantics in the image.
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| 8. |
- Kågebäck, Mikael, 1981, et al.
(författare)
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Extractive Summarization using Continuous Vector Space Models
- 2014
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Ingår i: Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC) EACL, April 26-30, 2014 Gothenburg, Sweden. - 9781937284947 ; , s. 31-39
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Konferensbidrag (refereegranskat)abstract
- Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring similarity. We evaluate different compositionsfor sentence representation on a standard dataset using the ROUGE evaluation measures. Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization.
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| 9. |
- Listo Zec, Edvin, et al.
(författare)
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Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration
- 2023
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Ingår i: FL 2022. - Cham : Springer Nature. ; , s. 59-71
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Konferensbidrag (refereegranskat)abstract
- We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks. We study both covariate and label shift, and our contribution is an algorithm which for each client finds beneficial collaborations based on a similarity estimate for the local task. Our method does not rely on hyperparameters which are hard to estimate, such as the number of client clusters, but rather continuously adapts to the network topology using soft cluster assignment based on a novel adaptive gossip algorithm. We test the proposed method in various settings where data is not independent and identically distributed among the clients. The experimental evaluation shows that the proposed method performs better than previous state-of-the-art algorithms for this problem setting, and handles situations well where previous methods fail.
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| 10. |
- Martinsson, John, et al.
(författare)
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A Novel Method for Smart Fire Detection Using Acoustic Measurements and Machine Learning : Proof of Concept
- 2022
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Ingår i: Fire technology. - : Springer. - 0015-2684 .- 1572-8099. ; 58, s. 3385-
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Tidskriftsartikel (refereegranskat)abstract
- Fires are a major hazard resulting in high monetary costs, personal suffering, and irreplaceable losses. The consequences of a fire can be mitigated by early detection systems which increase the potential for successful intervention. The number of false alarms in current systems can for some applications be very high, but could be reduced by increasing the reliability of the detection system by using complementary signals from multiple sensors. The current study investigates the novel use of machine learning for fire event detection based on acoustic sensor measurements. Many materials exposed to heat give rise to acoustic emissions during heating, pyrolysis and burning phases. Further, sound is generated by the heat flow associated with the flame itself. The acoustic data collected in this study is used to define an acoustic sound event detection task, and the proposed machine learning method is trained to detect the presence of a fire event based on the emitted acoustic signal. The method is able to detect the presence of fire events from the examined material types with an overall F-score of 98.4%. The method has been developed using laboratory scale tests as a proof of concept and needs further development using realistic scenarios in the future. © 2022, The Author(s).
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