Predicting potential facts in the future, Temporal Knowledge Graph (TKG) extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of ...facts. Intuitively, facts (events) that happened at different timestamps have different influences on future events, which can be attributed to a hierarchy among not only facts but also relevant entities. Therefore, it is crucial to pay more attention to important entities and events when forecasting the future. However, most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts, which may be affected by the diversity and variability of the evolution, and they might fail to attach importance to facts that matter. Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data, which is considered to be a solution for modelling hierarchical relations among facts. To this end, we propose ReTIN, a novel model integrating real‐time influence of historical facts for TKG reasoning based on hyperbolic geometry, which provides low‐dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG. Considering both real‐time and global features of TKG boosts the adaptation of ReTIN to the ever‐changing dynamics and inherent constraints. Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines. The ablation study further supports the value of exploiting temporal information.
Not wearing a safety helmet can result in serious injuries on construction sites, but the cause of such unsafe behavior is not yet well understood. This paper describes a practical field study to ...find the factors that influence non-helmet use on construction sites using a real-time tracking system (the Eye on Project or EOP), which is developed by our research team to provide an objective record of helmet use; and which overcomes the deficiencies of traditional recording methods (i.e. self-reporting). The application of association rules then combines designated risk factors and finds the causality patterns of non-helmet use. Based on the analyses, this paper develops a method of evaluating workers' risk level involving the intensity of multiple risk factors.
•The Eye on Project (EOP) for Non-helmet use identified on construction method is developed.•Individual characteristics influence non-helmet use.•The causality patterns of non-helmet use are found by association rules.•A method of evaluating workers' risk level involving the intensity of multiple risk factors is developed.
Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE). Recently, with powerful representation for pairwise text–image inputs via early interaction, ...the accuracy of vision–language (VL) transformers has outperformed existing methods for text–image retrieval. However, when the same paradigm is used for inference, the efficiency of the VL transformers is still too low to be applied in a real cross-modal SE. Inspired by the mechanism of human learning and using cross-modal knowledge, this paper presents a novel Vision–Language Decomposed Transformer (VLDeformer), which greatly increases the efficiency of VL transformers while maintaining their outstanding accuracy. By the proposed method, the cross-model retrieval is separated into two stages: the VL transformer learning stage, and the VL decomposition stage. The latter stage plays the role of single modal indexing, which is to some extent like the term indexing of a text SE. The model learns cross-modal knowledge from early-interaction pre-training and is then decomposed into an individual encoder. The VLDeformer requires only small target datasets for supervision and achieves both 1000+ times acceleration and less than 0.6% average recall drop. VLDeformer also outperforms state-of-the-art visual-semantic embedding methods on COCO and Flickr30k.11Code is available at .
•Address the efficiency–accuracy trade-off between one/two stream CMR paradigms.•Combine the two stream models’ efficiency and one stream models’ accuracy.•Significantly outperforms two stream methods with similar efficiency.•Achieves 1000+ times acceleration with less than 0.6% average recall drop.
Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-embeddings of word did ...not explore the power of the unsupervised word embedding sufficiently.
In this paper, we discuss a capsule network-based approach, taking advantage of capsule’s potential for recognizing highly overlapping features and dealing with segmentation. We propose a capsule network-based method to decompose the unsupervised word embedding of an ambiguous word into context specific sense embedding, called CapsDecE2S. In this approach, the unsupervised ambiguous embedding is fed into capsule network to produce its multiple morpheme-like vectors, which are defined as the basic semantic language units of meaning. With attention operations, CapsDecE2S integrates the word context to reconstruct the multiple morpheme-like vectors into the context-specific sense embedding. To train CapsDecE2S, we propose a sense matching training method. In this method, we convert the sense learning into a binary classification that explicitly learns the relation between senses by the label of matching and non-matching. The CapsDecE2S was experimentally evaluated on two sense learning tasks, i.e., word in context and word sense disambiguation. Results on two public corpora Word-in-Context and English all-words Word Sense Disambiguation show that, the CapsDecE2S model achieves the new state-of-the-art for the word in context and word sense disambiguation tasks. The source code can be downloaded from the Github page11https://github.com/Gdls/CapsDecE2S..
This study proposes a proactive struck-by risk detection method for workers proximal to the laydown area. This approach regards the laydown area as a radiation source, and proactive struck-by risk ...detection is formulated as a location-based estimate of radiation quantity received by the worker. The position probability grid is adopted to track the worker and model their movement. The movement pattern model, which is learned from the trajectory records of the workers in the same trade, reflects the movement preferences of different trades, and the randomness model represents the randomness of worker movement. The worker's next location can be predicted by using the tracked location and movement model, and the struck-by risk can then be estimated. The method's potential for integration into currently available proximity warning systems is also demonstrated.
•A proactive struck-by risk detection method for construction workers is developed.•The method is achieved through the prediction of worker's next location.•The prediction of next location is distinguished by the workers movement features.•The workers movement features were modeled by movement patterns and randomness.
This paper presents an approach to Alzheimer's disease (AD) diagnosis from spontaneous speech using an end-to-end ASR-enhanced neural network. Under the condition that only audio data are provided ...and accurate transcripts are unavailable, this paper proposes a system that can analyze utterances to differentiate between AD patients, healthy controls, and individuals with mild cognitive impairment. The ASR-enhanced model comprises automatic speech recognition (ASR) with an encoder-decoder structure and the encoder followed by an AD classification network. The encoder takes a Mel spectrogram as input and transforms it into high-level acoustic features that correlate with AD. The classification network then maps intermediate acoustic features to three categories. In the training phase, the AD classification and speech recognition tasks are optimized simultaneously. Experimental results obtained from an AD recognition dataset of Chinese spontaneous speech 1 illustrate the effectiveness of integrating ASR into AD diagnosis in an end-to-end manner. Further, our model has low dependency on accurate ASR transcripts. This work achieved accuracy scores of 89.1% and 82.6% for long and short utterance tracks, respectively.
Pervasive computing environments (PvCE) are embedded with interconnected smart devices which provide users with services desired. To meet requirements of users, smart devices with different kinds of ...functions may need to be associated together to provide the service described in the user requirement, which is called service composition. As the service composition environment may be dynamic and large scale, centralized service composition algorithm is usually inefficient due to message cost. On the other hand, a decentralized approach, which employs pre-determined coordinators to search and compose service, may have high cost as well. In this paper, we discuss a localized approach for service composition based on the Ubiquitous Interacting Object (UIO) model we have proposed earlier. UIO is an abstraction of physical devices in PvCE with ability to find and collaborate with other devices through exposing their capabilities as services. In our localized service composition algorithm (LASEC), UIOs collaborate with each other in a bottom-up, localized manner to compose required service without requiring global knowledge. To solve the problem of blind compositions in LASEC, we propose a novel mechanism called Alien-information-based Acknowledging (A-Ack), in which a UIO decides on collaborating with another UIO only after obtaining some additional information from the collaboration candidate. Specifically, this information refers to ability of a given UIO to compose another part of the service. Proposed LASEC is message-efficient and quality-guaranteed. Extensive simulations of LASEC as well as existing decentralized and pull-based centralized algorithms have been conducted. The results show the relatively low communication cost and composition time of LASEC. Moreover, we demonstrate feasibility of our approach with a prototype implementation.
The presentation of a boy with an acutely painful scrotum is often associated with a history of trauma. Two recent patients with a history of scrotal trauma and an enlarged hemiscrotum were found on ...surgical exploration to have unusual causes for the scrotal enlargement. In the first case, there was bleeding from omentum in an incarcerated inguinal hernia, and in the second, there was bleeding into a scrotal cystic lymphangioma. Fewer than 40 cases of scrotal lymphangioma have been reported.
Open-domain table question answering aims to provide answers to a question by retrieving and extracting information from a large collection of tables. Existing studies of open-domain table QA either ...directly adopt text retrieval methods or consider the table structure only in the encoding layer for table retrieval, which may cause syntactical and structural information loss during table scoring. To address this issue, we propose a syntax- and structure-aware retrieval method for the open-domain table QA task. It provides syntactical representations for the question and uses the structural header and value representations for the tables to avoid the loss of fine-grained syntactical and structural information. Then, a syntactical-to-structural aggregator is used to obtain the matching score between the question and a candidate table by mimicking the human retrieval process. Experimental results show that our method achieves the state-of-the-art on the NQ-tables dataset and overwhelms strong baselines on a newly curated open-domain Text-to-SQL dataset.
Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. In recent years, there have been a lot of works on table QA, but there is a lack of ...comprehensive surveys on this research topic. Hence, we aim to provide an overview of available datasets and representative methods in table QA. We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods. Moreover, as table QA is still a challenging task for existing methods, we also identify and outline several key challenges and discuss the potential future directions of table QA.
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