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1. Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering

   https://doi.org/10.1145/3591359  

   Ao, Jing; Cheng, Zehui; Chirkova, Rada; Kolaitis, Phokion G. (June 2023, Journal of Data and Information Quality)

   We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains. 

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2. Activity recognition in wearables using adversarial multi-source domain adaptation

   https://doi.org/https://doi.org/10.1016/j.smhl.2020.100174  

   Chakma, A; Faridee, A; Khan, H; Roy, N (March 2021, Smart health)

   null (Ed.)

   Human activity recognition (HAR) from wearable sensors data has become ubiquitous due to the widespread proliferation of IoT and wearable devices. However, recognizing human activity in heterogeneous environments, for example, with sensors of different models and make, across different persons and their on-body sensor placements introduces wide range discrepancies in the data distributions, and therefore, leads to an increased error margin. Transductive transfer learning techniques such as domain adaptation have been quite successful in mitigating the domain discrepancies between the source and target domain distributions without the costly target domain data annotations. However, little exploration has been done when multiple distinct source domains are present, and the optimum mapping to the target domain from each source is not apparent. In this paper, we propose a deep Multi-Source Adversarial Domain Adaptation (MSADA) framework that opportunistically helps select the most relevant feature representations from multiple source domains and establish such mappings to the target domain by learning the perplexity scores. We showcase that the learned mappings can actually reflect our prior knowledge on the semantic relationships between the domains, indicating that MSADA can be employed as a powerful tool for exploratory activity data analysis. We empirically demonstrate that our proposed multi-source domain adaptation approach achieves 2% improvement with OPPORTUNITY dataset (cross-person heterogeneity, 4 ADLs), whereas 13% improvement on DSADS dataset (cross-position heterogeneity, 10 ADLs and sports activities). 

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3. Domain Generalization via Multidomain Discriminant Analysis

   Shoubo Hu, Kun Zhang (January 2019, Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI) 2019)

   Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains. This problem is ubiquitous in practice since the distributions of the target data may rarely be identical to those of the source data. In this paper, we propose Multidomain Discriminant Analysis (MDA) to address DG of classification tasks in general situations. MDA learns a domain-invariant feature transformation that aims to achieve appealing properties, including a minimal divergence among domains within each class, a maximal separability among classes, and overall maximal compactness of all classes. Furthermore, we provide the bounds on excess risk and generalization error by learning theory analysis. Comprehensive experiments on synthetic and real benchmark datasets demonstrate the effectiveness of MDA. 

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4. Graph Matching and Pseudo-Label Guided Deep Unsupervised Domain Adaptation

   https://doi.org/https://doi.org/10.1007/978-3-030-01424-7_34  

   Das, Debasmit; Lee, C.S. George (October 2018, 2018 27th International Conference on Artificial Neural Networks)

   The goal of domain adaptation is to train a high-performance predictive model on the target domain data by using knowledge from the source domain data, which has different but related data distribution. In this paper, we consider unsupervised domain adaptation where we have labelled source domain data but unlabelled target domain data. Our solution to unsupervised domain adaptation is to learn a domain- invariant representation that is also category discriminative. Domain- invariant representations are realized by minimizing the domain discrepancy. To minimize the domain discrepancy, we propose a novel graph- matching metric between the source and target domain representations. Minimizing this metric allows the source and target representations to be in support of each other. We further exploit confident unlabelled target domain samples and their pseudo-labels to refine our proposed model. We expect the refining step to improve the performance further. This is validated by performing experiments on standard image classification adaptation datasets. Results showed our proposed approach out-perform previous domain-invariant representation learning approaches. 

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5. Conflict-Based Steiner Search for Multi-Agent Combinatorial Path Finding.

   Ren, Zhongqiang; Rathinam, Sivakumar; Choset, Howie (January 2022, Proceedings of Robotics: Science and Systems)

   NA (Ed.)

   Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collisionfree paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e. multi-target sequencing). To solve the problem, we leverage the well-known Conflict-Based Search (CBS) for MAPF and propose a novel framework called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the conflict resolving strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in multi-target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. Our extensive tests verify the advantage of CBSS over baseline approaches in terms of computing shorter paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. We also evaluate CBSS with several MCPF variants, which demonstrates the generality of our problem formulation and the CBSS framework. 

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