RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and Practice

Over the years, RDF streaming was explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming solutions, due to the lack of a common language. This work attempts to address this critical research gap, by systematizing RDF stream types present in the literature in a novel taxonomy. The proposed RDF Stream Taxonomy (RDF-STaX) is embodied in an OWL 2 DL ontology that follows the FAIR principles, making it readily applicable in practice. Extensive documentation and additional resources are provided, to foster the adoption of the ontology. Two realized use cases are presented, demonstrating the usefulness of the resource in discussing research works and annotating streaming datasets. Another result of this contribution is the novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The aim of RDF-STaX is to address a real need of the community for a better way to systematize and describe RDF streams. The resource is designed to help drive innovation in RDF streaming, by fostering scientific discussion, cooperation, and tool interoperability.

Enriching language models with graph-based context information to better understand textual data

A considerable number of texts encountered daily are somehow connected with each other. For example, Wikipedia articles refer to other articles via hyperlinks, scientific papers relate to others via citations or (co)authors, while tweets relate via users that follow each other or reshare content. Hence, a graph-like structure can represent existing connections and be seen as capturing the "context" of the texts. The question thus arises if extracting and integrating such context information into a language model might help facilitate a better automated understanding of the text. In this study, we experimentally demonstrate that incorporating graph-based contextualization into BERT model enhances its performance on an example of a classification task. Specifically, on Pubmed dataset, we observed a reduction in error from 8.51% to 7.96%, while increasing the number of parameters just by 1.6%. Our source code: https://github.com/tryptofanik/gc-bert

Towards Edge-Cloud Architectures for Personal Protective Equipment Detection

Detecting Personal Protective Equipment in images and video streams is a relevant problem in ensuring the safety of construction workers. In this contribution, an architecture enabling live image recognition of such equipment is proposed. The solution is deployable in two settings -- edge-cloud and edge-only. The system was tested on an active construction site, as a part of a larger scenario, within the scope of the ASSIST-IoT H2020 project. To determine the feasibility of the edge-only variant, a model for counting people wearing safety helmets was developed using the YOLOX method. It was found that an edge-only deployment is possible for this use case, given the hardware infrastructure available on site. In the preliminary evaluation, several important observations were made, that are crucial to the further development and deployment of the system. Future work will include an in-depth investigation of performance aspects of the two architecture variants.

Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations

Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training. Until now, the majority of research has been devoted to "core issues", such as adaptation of machine learning algorithms to FL, data privacy protection, or dealing with the effects of uneven data distribution between clients. This contribution is anchored in a practical use case, where FL is to be actually deployed within an Internet of Things ecosystem. Hence, somewhat different issues that need to be considered, beyond popular considerations found in the literature, are identified. Moreover, an architecture that enables the building of flexible, and adaptable, FL solutions is introduced.

StatMix: Data augmentation method that relies on image statistics in federated learning

Availability of large amount of annotated data is one of the pillars of deep learning success. Although numerous big datasets have been made available for research, this is often not the case in real life applications (e.g. companies are not able to share data due to GDPR or concerns related to intellectual property rights protection). Federated learning (FL) is a potential solution to this problem, as it enables training a global model on data scattered across multiple nodes, without sharing local data itself. However, even FL methods pose a threat to data privacy, if not handled properly. Therefore, we propose StatMix, an augmentation approach that uses image statistics, to improve results of FL scenario(s). StatMix is empirically tested on CIFAR-10 and CIFAR-100, using two neural network architectures. In all FL experiments, application of StatMix improves the average accuracy, compared to the baseline training (with no use of StatMix). Some improvement can also be observed in non-FL setups.

Using adversarial images to improve outcomes of federated learning for non-IID data

One of the important problems in federated learning is how to deal with unbalanced data. This contribution introduces a novel technique designed to deal with label skewed non-IID data, using adversarial inputs, created by the I-FGSM method. Adversarial inputs guide the training process and allow the Weighted Federated Averaging to give more importance to clients with 'selected' local label distributions. Experimental results, gathered from image classification tasks, for MNIST and CIFAR-10 datasets, are reported and analyzed.

Ontology Reuse: the Real Test of Ontological Design

Reusing ontologies in practice is still very challenging, especially when multiple ontologies are involved. Moreover, despite recent advances, systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology, are investigated from the perspective of a practical use case. Special scrutiny is given to cross-ontology references, which are vital for combining ontologies. Diverse methods to detect the issues are proposed, including natural language processing and network analysis. Moreover, several suggestions for improving ontologies and their quality assurance processes are presented. It is argued that while the advancing automatic tools for ontology quality assurance are crucial for ontology improvement, they will not solve the problem entirely. It is ontology reuse that is the ultimate method for continuously verifying and improving ontology quality, as well as for guiding its future development. Many issues can be found and fixed only through practical and diverse ontology reuse scenarios.

Applying machine learning to predict behavior of bus transport in Warsaw, Poland

Nowadays, it is possible to collect precise data describing movements of public transport. Specifically, for each bus (or tram) geoposition data can be regularly collected. This includes data for all buses in Warsaw, Poland. Moreover, this data can be downloaded and analyzed. In this context, one of the simplest questions is: can a model be build to represent behavior of busses, and predict their delays. This work provides initial results of our attempt to answer this question.

Practical Aspects of Zero-Shot Learning

One of important areas of machine learning research is zero-shot learning. It is applied when properly labeled training data set is not available. A number of zero-shot algorithms have been proposed and experimented with. However, none of them seems to be the "overall winner". In situations like this, it may be possible to develop a meta-classifier that would combine "best aspects" of individual classifiers and outperform all of them. In this context, the goal of this contribution is twofold. First, multiple state-of-the-art zero-shot learning methods are compared for standard benchmark datasets. Second, multiple meta-classifiers are suggested and experimentally compared (for the same datasets).