@inproceedings{holzhuter2022segmentation,
  abstract = {3D object classification is involved in many computer vision pipelines such as autonomous driving or robotics. However, the irregular format of 3D data makes it challenging to develop suitable deep learning architectures. This paper proposes CompointNet, a graph convolutional network architecture, which performs 3D object classification by means of part decomposition. Our model consumes a 3D point cloud in the form of a part graph which is constructed from segmented 3D shapes. The model learns a global descriptor by hierarchically aggregating neighbourhood information using simple graph convolutions. To capture both local and global information, a global classification method processing each point separately is combined with our part graph based approach into a hybrid version of CompointNet. We compare our approach to several state-of-the art methods and demonstrate competitive performance. Particularly, in terms of per class accuracy, our hybrid approach outperforms the compared methods. The proposed hybrid variants achieve a high classification accuracy, while being much more efficient than those benchmark models with a comparable performance. The conducted experiments show that part based approaches levering structural information about a 3D object, indeed, can improve the classification performance of 3D deep learning models.},
  author = {Holzhüter, Clara and Teich, Florian and Wörgötter, Florentin},
  booktitle = {International Conference on Computer Vision Theory and Applications (VISAPP)},
  doi = {10.5220/0010778100003124},
  interhash = {cd68224558c95823bb19e025c7e0717f},
  intrahash = {4b97b241db951d0c437b84119fbc3623},
  pages = {290--298},
  title = {Segmentation Improves 3D Object Classification in Graph Convolutional Networks},
  url = {https://www.scitepress.org/Papers/2022/107781/},
  year = 2022
}

@article{guo2022lwtool,
  abstract = {Pressure mapping smart textile is a new type of sensing modality that transforms the pressure distribution over surfaces into digital ”image” and ”video”, that has rich application scenarios in Human Activity Recognition (HAR), because all human activities are linked with force change over certain surfaces. To speed up its application exploration, we propose a toolkit named LwTool for the data processing, including: (a) a feature library, including 1830 ready-to-use temporal and spatial features, (b) a hierarchical feature selection framework that automatically picks out the best features for a new application from the feature library. As real-time processing capability is important for instant user feedback, we emphasize not only on good recognition result but also on reducing time cost when selecting features. Our library and algorithms are validated on Smart-Toy and Smart-Bedsheet applications, an 89.7% accuracy for Smart-Toy and an 83.8% accuracy for Smart-Bedsheet can be achieved (10-fold cross-validation) using our feature library. Adopting the feature selection algorithm, the processing speed is increased by more than 3 times while maintaining high accuracy for both two applications. We believe our method could be a general and powerful toolkit in building real-time recognition software systems for pressure mapping smart textile.},
  author = {Guo, Tao and Huang, Zhixin and Cheng, Jingyuan},
  doi = {10.1016/j.sysarc.2021.102387},
  interhash = {01b9f1cdeeafe42cd1446c74d8d03ed7},
  intrahash = {3fe807ea45ddc66b1d321ae17aa51911},
  journal = {Pervasive and Mobile Computing},
  pages = 101540,
  publisher = {Elsevier},
  title = {LwTool: A data processing toolkit for building a real-time pressure mapping smart textile software system},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S1574119222000013},
  volume = 80,
  year = 2022
}

@article{duran2017pioneering,
  abstract = {The bipartite network representation of the drug–target interactions (DTIs) in a biosystem enhances understanding of the drugs’ multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared—using standard and innovative validation frameworks—with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory—initially detected in brain-network topological self-organization and afterwards generalized to any complex network—is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug–target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.},
  author = {Durán, Claudio and Daminelli, Simone and M., Thomas Josephine and Haupt, V. Joachim and Schroeder, Michael and Cannistraci, Carlo Vittorio},
  doi = {10.1093/bib/bbx041},
  interhash = {bb3e0cc35d1f80cd4fa36045584bdd01},
  intrahash = {6ad5135848655b42291dabdcce235bc8},
  journal = {Briefings in Bioinformatics},
  number = 6,
  pages = {1183--1202},
  title = {Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory},
  url = {https://academic.oup.com/bib/article/19/6/1183/3769276},
  volume = 19,
  year = 2017
}

@article{daminelli2015common,
  abstract = {Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.},
  author = {Daminelli, Simone and Thomas, Josephine Maria and Durán, Claudio and Cannistraci, Carlo Vittorio},
  doi = {10.1088/1367-2630/17/11/113037},
  interhash = {10bfcbfdcb73e2515e01823f8a9160ed},
  intrahash = {730d06703bf0c68c697aa36e43cd1b99},
  journal = {New Journal of Physics},
  number = 11,
  pages = 113037,
  publisher = {IOP Publishing},
  title = {Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks},
  url = {https://iopscience.iop.org/article/10.1088/1367-2630/17/11/113037},
  volume = 17,
  year = 2015
}

@article{muscoloni2017machine,
  abstract = {Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem. Here, we show that intelligent machines for unsupervised recognition and visualization of similarities in big data can also infer the network angular coordinates of the hyperbolic model according to a geometrical organization that we term "angular coalescence." Based on this phenomenon, we propose a class of algorithms that offers fast and accurate "coalescent embedding" in the hyperbolic circle even for large networks. This computational solution to an inverse problem in physics of complex systems favors the application of network latent geometry techniques in disciplines dealing with big network data analysis including biology, medicine, and social science.},
  author = {Muscoloni, Alessandro and Thomas, Josephine Maria and Ciucci, Sara and Bianconi, Ginestra and Cannistraci, Carlo Vittorio},
  codeurl = {https://github.com/biomedical-cybernetics/coalescent_embedding},
  doi = {10.1038/s41467-017-01825-5},
  interhash = {2cbdb2ccaa548549f5aaf2c208626769},
  intrahash = {1cb52c31ed6124032758e52c12ba60af},
  journal = {Nature Communications},
  number = 1,
  pages = 1615,
  publisher = {Springer},
  title = {Machine learning meets complex networks via coalescent embedding in the hyperbolic space},
  url = {https://doi.org/10.1038/s41467-017-01825-5},
  volume = 8,
  year = 2017
}