2021
Autores
Beck, D; Morgado, L; Lee, M; Gutl, C; Dengel, A; Wang, MJ; Warren, S; Richter, J;
Publicação
2021 7TH INTERNATIONAL CONFERENCE OF THE IMMERSIVE LEARNING RESEARCH NETWORK (ILRN)
Abstract
The interdisciplinary field of immersive learning research is scattered. Combining efforts for better exploration of this field from the different disciplines requires researchers to communicate and coordinate effectively. We call upon the community of immersive learning researchers for planting the Knowledge Tree of Immersive Learning Research, a proposal for a systematization effort for this field, combining both scholarly and practical knowledge, cultivating a robust and ever-growing knowledge base and methodological toolbox for immersive learning. This endeavor aims at promoting evidence-informed practice and guiding future research in the field. This paper contributes with the rationale for three objectives: 1) Developing common scientific terminology amidst the community of researchers; 2) Cultivating a common understanding of methodology, and 3) Advancing common use of theoretical approaches, frameworks, and models.
2021
Autores
Rocha, A; Costa, A; Oliveira, MA; Aguiar, A;
Publicação
ERCIM NEWS
Abstract
iReceptor Plus will enable researchers around the world to share and analyse huge immunological distributed datasets, from multiple countries, containing sequencing data pertaining to both healthy and sick individuals. Most of the Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data is currently stored and curated by individual labs, using a variety of tools and technologies.
2021
Autores
Cunha, M; Mendes, R; Vilela, JP;
Publicação
COMPUTER SCIENCE REVIEW
Abstract
Due to the pervasiveness of always connected devices, large amounts of heterogeneous data are continuously being collected. Beyond the benefits that accrue for the users, there are private and sensitive information that is exposed. Therefore, Privacy-Preserving Mechanisms (PPMs) are crucial to protect users' privacy. In this paper, we perform a thorough study of the state of the art on the following topics: heterogeneous data types, PPMs, and tools for privacy protection. Building from the achieved knowledge, we propose a privacy taxonomy that establishes a relation between different types of data and suitable PPMs for the characteristics of those data types. Moreover, we perform a systematic analysis of solutions for privacy protection, by presenting and comparing privacy tools. From the performed analysis, we identify open challenges and future directions, namely, in the development of novel PPMs. (C) 2021 The Authors. Published by Elsevier Inc.
2021
Autores
Areias, M; Rocha, R;
Publicação
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
Abstract
Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and scalable hash trie map design that fully supports the concurrent search, insert and remove operations on hash maps. To the best of our knowledge, our proposal is the first that puts together the following characteristics: (i) be lock free; (ii) use fixed size data structures; and (iii) maintain the access to all internal data structures as persistent memory references. Our design is modular enough to allow different types of configurations aimed for different performances in memory usage and execution time and can be easily implemented in any type of language, library or within other complex data structures. We discuss in detail the key algorithms required to easily reproduce our implementation by others and we present a proof of correctness showing that our proposal is linearizable and lock-free for the search, insert and remove operations. Experimental results show that our proposal is quite competitive when compared against other state-of-the-art proposals implemented in Java.
2021
Autores
Brandao, A; Mendes, R; Vilela, JP;
Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XIX, IDA 2021
Abstract
Privacy is becoming a crucial requirement in many machine learning systems. In this paper we introduce an efficient and secure distributed K-Means algorithm, that is robust to non-IID data. The base idea of our proposal consists in each client computing the K-Means algorithm locally, with a variable number of clusters. The server will use the resultant centroids to apply the K-Means algorithm again, discovering the global centroids. To maintain the client's privacy, homomorphic encryption and secure aggregation is used in the process of learning the global centroids. This algorithm is efficient and reduces transmission costs, since only the local centroids are used to find the global centroids. In our experimental evaluation, we demonstrate that our strategy achieves a similar performance to the centralized version even in cases where the data follows an extreme non-IID form.
2021
Autores
Sayers, D; Sousa-Silva, R; Höhn, S; Ahmedi, L; Allkivi-Metsoja, K; Anastasiou, D; Benuš, Š; Bowker, L; Bytyçi, E; Catala, A; Çepani, A; Chacón-Beltrán, R; Dadi, S; Dalipi, F; Despotovic, V; Doczekalska, A; Drude, S; Fort, K; Fuchs, R; Galinski, C; Gobbo, F; Gungor, T; Guo, S; Höckner, K; Láncos, PL; Libal, T; Jantunen, T; Jones, D; Klimova, B; Korkmaz, EE; Maucec, MS; Melo, M; Meunier, F; Migge, B; Mititelu, VB; Névéol, A; Rossi, A; Pareja-Lora, A; Sanchez-Stockhammer, C; Sahin, A; Soltan, A; Soria, C; Shaikh, S; Turchi, M; Yildirim Yayilgan, S;
Publicação
Abstract
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