The object navigation task requires robots to understand the semantic regularities in their environments. However, existing modular object navigation frameworks rely on instance segmentation models trained at fixed camera height viewpoints, limiting generalization performance and increasing labeling costs for new height viewpoints. To tackle this issue, we propose a semi-supervised method that transfers knowledge from a source height to a target height, minimizing the need for additional labels. Our approach introduces three key innovations: i) a projection policy to enhance the teacher model's detection capabilities at the target height, ii) a dynamic weight mechanism that emphasizes high-confidence pseudo-labels to reduce overfitting, and iii) a prototype contrast transferring method to transfer knowl-edge effectively. Experiments on the Habitat- Matterport 3D (HM3D) dataset show our method outperforms state-of-the-art semi-supervised techniques, improving both segmentation accuracy and navigation performance. The code is available at: https://github.com/FreeformRobotics/TransferKnowledge.

Recently, Biomedical Question Answering (BQA) has attracted growing attention due to its application value and technical challenges. Most existing works treat it as a semantic matching task that predicts answers by computing confidence among questions, options and evidence sentences, which is insufficient for scenarios that require complex reasoning based on a deep understanding of biomedical evidences. We propose a novel model termed Hierarchical Representation-based Dynamic Reasoning Network (HDRN) to tackle this problem. It first constructs the hierarchical representations for biomedical evidences to learn semantics within and among evidences. It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems. Against the existing state-of-the-art model, the proposed model significantly improves more than 4.5%, 3% and 1.3% on three mainstream BQA datasets, PubMedQA, MedQA-USMLE and NLPEC. The ablation study demonstrates the superiority of each improvement of our model. The code will be released after the paper is published.

Automatically and accurately identifying user intents and filling the associated slots from their spoken language are critical to the success of dialogue systems. Traditional methods require manually defining the DOMAIN-INTENT-SLOT schema and asking many domain experts to annotate the corresponding utterances, upon which neural models are trained. This procedure brings the challenges of information sharing hindering, out-of-schema, or data sparsity in open domain dialogue systems. To tackle these challenges, we explore a new task of automatic intent-slot induction and propose a novel domain-independent tool. That is, we design a coarse-to-fine three-step procedure including Role-labeling, Concept-mining, And Pattern-mining (RCAP): (1) role-labeling: extracting key phrases from users’ utterances and classifying them into a quadruple of coarsely-defined intent-roles via sequence labeling; (2) concept-mining: clustering the extracted intent-role mentions and naming them into abstract fine-grained concepts; (3) pattern-mining: applying the Apriori algorithm to mine intent-role patterns and automatically inferring the intent-slot using these coarse-grained intent-role labels and fine-grained concepts. Empirical evaluations on both real-world in-domain and out-of-domain datasets show that: (1) our RCAP can generate satisfactory SLU schema and outperforms the state-of-the-art supervised learning method; (2) our RCAP can be directly applied to out-of-domain datasets and gain at least 76% improvement of F1-score on intent detection and 41% improvement of F1-score on slot filling; (3) our RCAP exhibits its power in generic intent-slot extractions with less manual effort, which opens pathways for schema induction on new domains and unseen intent-slot discovery for generalizable dialogue systems.

Detecting communities plays a great important role in sociology, biology and computer science, disciplines where systems are often modeled as graphs. Such inherent community structures make us deeply understand about the networks and therefore have drawn significant interests among researchers. This paper describes a probabilistic community detection algorithm by modeling topic on sampling sub graphs. In this algorithm, the communities are modeled as latent topic variables of an LDA topic model and the vertices of sampling sub graphs are drawn from these topics with different probabilities. This paper also proposes a sub graph sampling algorithm and explores its impact on community detection performance. Our algorithm is evaluated by extensive experiments using many computer-generated artificial graphs and real-world networks. The results show that our algorithm is effective in detecting probabilistic community.

As information continues to grow at an explosive rate, more and more heterogeneous network data sources are coming into being. While OLAP (On-Line Analytical Processing) techniques have been proven effective for analyzing and mining structured data, unfortunately, to our best knowledge, there are no OLAP tools available that are able to analyze multi-dimensional heterogeneous networks from different perspectives and with multiple granularities. Therefore, we have developed a novel HMGraph OLAP (Heterogeneous and Multi-dimensional Graph OLAP) framework for the purpose of providing more dimensions and operations to mine multi-dimensional heterogeneous information network. After information dimensions and topological dimensions, we have been the first to propose entity dimensions, which represent an important dimension for heterogeneous network analysis. On the basis of this notion, we designed HMGraph OLAP operations named (Rotate and Stretch for entity dimensions, which are able to mine relationships between different entities. We then proposed the HMGraph Cube, which is an efficient data warehousing model for HMGraph OLAP. In addition, through comparison with common strategies, we have shown that the optimizations we have proposed deliver better performance. Finally, we have implemented a HMGraph OLAP prototype, LiterMiner, which has proven effective for the analysis of multi-dimensional heterogeneous networks.