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Université de Fribourg

APCNN: Tackling Cclass imbalance in relation extraction through aggregated piecewise convolutional neural networks

Smirnova, Alisa ; Audiffren, Julien ; Cudre-Mauroux, Philippe

In: 2019 6th Swiss Conference on Data Science (SDS), 2019, p. 63–68

One of the major difficulties in applying distant supervision to relation extraction is class imbalance, as the distribution of relations appearing in text is heavily skewed. This is particularly damaging for the multi-instance variant of relation extraction. In this work, we introduce a new model called Aggregated Piecewise Convolutional Neural Networks, or APCNN, to address this problem....

Université de Fribourg

Nodesketch: highly-efficient graph embeddings via recursive sketching

Yang, Dingqi ; Rosso, Paolo ; Li, Bin ; Cudre-Mauroux, Philippe

In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, p. 1162–1172

Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix factorization. However, these techniques...

Université de Fribourg

Fast and automatic assessment of fall risk by coupling machine learning algorithms with a depth camera to monitor simple balance tasks

Dubois, Amandine ; Mouthon, Audrey ; Sivagnanaselvam, Ranjith Steve ; Bresciani, Jean-Pierre

In: Journal of NeuroEngineering and Rehabilitation, 2019, vol. 16, no. 1, p. 71

Falls in the elderly constitute a major health issue associated to population ageing. Current clinical tests evaluating fall risk mostly consist in assessing balance abilities. The devices used for these tests can be expensive or inconvenient to set up. We investigated whether, how and to which extent fall risk could be assessed using a low cost ambient sensor to monitor balance tasks.Method:...

Consortium of Swiss Academic Libraries

SCOOP: A Real-Time Sparsity Driven People Localization Algorithm

Golbabaee, Mohammad ; Alahi, Alexandre ; Vandergheynst, Pierre

In: Journal of Mathematical Imaging and Vision, 2014, vol. 48, no. 1, p. 160-175

Université de Fribourg

Swisslink: high-precision, context-free entity linking exploiting unambiguous labels

Prokofyev, Roman ; Luggen, Michael ; Difallah, Djellel Eddine ; Cudré-Mauroux, Philippe

In: Proceedings of the 13th International Conference on Semantic Systems, 2017, p. 65–72

Webpages are an abundant source of textual information with manually annotated entity links, and are often used as a source of training data for a wide variety of machine learning NLP tasks. However, manual annotations such as those found on Wikipedia are sparse, noisy, and biased towards popular entities. Existing entity linking systems deal with those issues by relying on simple statistics...

Université de Fribourg

Are meta-paths necessary?: revisiting heterogeneous graph embeddings

Hussein, Rana ; Yang, Dingqi ; Cudre-Mauroux, Philippe

In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018, p. 437–446

The graph embedding paradigm projects nodes of a graph into a vector space, which can facilitate various downstream graph analysis tasks such as node classification and clustering. To efficiently learn node embeddings from a graph, graph embedding techniques usually preserve the proximity between node pairs sampled from the graph using random walks. In the context of a heterogeneous graph,...

Université de Fribourg

Privacy-preserving social media data publishing for personalized ranking-based recommendation

Yang, Dingqi ; Qu, Bingqing ; Cudré-Mauroux, Philippe

In: IEEE Transactions on Knowledge and Data Engineering, 2019, vol. 31, no. 3, p. 507–520

Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users' online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from...