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Tighter Bounds of Speedup Factor of Partitioned EDF for Constrained-Deadline Sporadic Tasks 42

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Tighter Bounds of Speedup Factor of Partitioned EDF for Cons...

42nd IEEE Real-Time systems Symposium, RTSS 2021

作者： Liu, Xingwu Chen, Zizhao Han, Xin Sun, Zhenyu Guo, Zhishan School of Mathematical Sciences Dalian University of Technology China SK-Lab of Media Convergence Production Technology and Systems Chinaso Inc School of Software Technology Dalian University of Technology China SK-Lab of Computer Architecture ICT CAS University of CAS Beijing China Department of Electrical and Computer Engineering University of Central Florida United States

ISBN: (纸本)9781665428026

Even though earliest-deadline-first (EDF) is optimal in terms of uniprocessor schedulability, it is co-NP-hard to precisely verify uniprocessor schedulability for constrained-deadline task sets. The most efficient way to solve this problem in polynomial time is via a partially linear approximation of the demand bound function. Such approximation leads to a simple uniprocessor schedulability testing with speedup factor ρ. Such a result further leads to Deadline-Monotonic Partitioned-EDF on multi-processors with speedup factor of 1 + ρ − 1/m (where m is the number of processors). The current state of the art results indicate that ρ is within the range [1.5, 14/9]. Especially, it has been a conjecture that ρ = 1.5. This paper improves the range of ρ to (1.5026, 1.5380). The improved lower bound disproves the conjecture of lower bound 1.5. A novel technique is to construct an auxiliary function that is larger than the approximate demand bound function but keeps the supremum ρ unchanged. It solves the dilemma that beating the lower bound 1.5 requires extremely large task sets, while the large size makes it difficult to check the schedulability. This technique not only enables us to disprove 1.5 by a task set of only eight tasks, but also sheds light on future work in transferring/downsizing task sets and deriving utilization bound based tests for various workload abstraction models, such as DAG tasks. ©2021 IEEE

关键词： Polynomial approximation

Neural Machine Translation with Contrastive Translation Memories

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Neural Machine Translation with Contrastive Translation Memo...

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Cheng, Xin Gao, Shen Liu, Lemao Zhao, Dongyan Yan, Rui Wangxuan Institute of Computer Technology Peking University China Center for Data Science Peking University China Tencent AI Lab School of Computer Science and Technology Shandong University China State Key Laboratory of Media Convergence Production Technology and Systems China China Gaoling School of Artificial Intelligence Renmin University of China China

Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios. Different from previous works that make use of mutually similar but redundant translation memories (TMs), we propose a new retrieval-augmented NMT to model contrastively retrieved translation memories that are holistically similar to the source sentence while individually contrastive to each other providing maximal information gains in three phases. First, in TM retrieval phase, we adopt a contrastive retrieval algorithm to avoid redundancy and uninformativeness of similar translation pieces. Second, in memory encoding stage, given a set of TMs we propose a novel Hierarchical Group Attention module to gather both local context of each TM and global context of the whole TM set. Finally, in training phase, a Multi-TM contrastive learning objective is introduced to learn salient feature of each TM with respect to target sentence. Experimental results show that our framework obtains improvements over strong baselines on the benchmark datasets. © 2022 Association for Computational Linguistics.

关键词： Neural machine translation

Relation-Aware Multi-hop Reasoning forVisual Dialog 10th

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Relation-Aware Multi-hop Reasoning forVisual Dialog

10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Zhao, Yao Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China State Key Lab of Media Convergence Production Technology and Systems Beijing China

ISBN: (纸本)9783030884796

Visual dialog is a multi-modal task that requires a dialog agent to answer a series of progressive questions grounded in an image. In this paper, we propose Relation-aware Multi-hop Reasoning Network (i.e. R2N for short) for visual dialog tasks, which can perform multi-hop reasoning during visual co-reference resolution process in a recurrent way. At each hop, in order to fully understand the visual scene in the image, a Relation-aware Graph Attention Network is used, which encodes each image into graphs with multi-type inter-object relations via a graph attention mechanism. Moreover, we find that the auxiliary clustering mechanism on answer candidates is conducive to model’s performance. We evaluate R2N on VisDial v1.0 dataset. Experimental results on the VisDial v1.0 dataset demonstrate that the proposed model is effective and outperforms compared models. © 2021, Springer Nature Switzerland AG.

关键词： Computers

End-to-End Streaming Customizable Keyword Spotting Based on Text-Adaptive Neural Search 1

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Yang, Baochen Guo, Jiaqi Li, Haoyu Xi, Yu Zhuo, Qing Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China State Key Laboratory of Media Convergence Production Technology and Systems Beijing China AISpeech Ltd. Suzhou China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

Streaming keyword spotting (KWS) is an important technique for voice assistant wake-up. While KWS with a preset fixed keyword has been well studied, test-time customizable keyword spotting in streaming mode remains a great challenge due to the lack of pre-collected keyword-specific training data and the requirement of streaming detection output. In this paper, we propose a novel end-to-end text-adaptive neural search architecture with a multi-label trigger mechanism to allow any pre-trained ASR acoustic model to be effectively used for fast streaming customizable keyword spotting. Evaluation results on various datasets show that our approach significantly outperforms both traditional post-processing baseline and the neural search baseline, meanwhile achieving a 44x search speedup compared to the traditional post-processing method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Customizable keyword Keyword spotting Streaming Text-adaptive search

WebSRC: A Dataset for Web-Based Structural Reading Comprehension

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WebSRC: A Dataset for Web-Based Structural Reading Comprehen...

2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021

作者： Chen, Xingyu Zhao, Zihan Chen, Lu Jiabao, J.I. Zhang, Danyang Luo, Ao Xiong, Yuxuan Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai Jiao Tong University Shanghai China State Key Lab of Media Convergence Production Technology and Systems Beijing China

ISBN: (纸本)9781955917094

Web search is an essential way for humans to obtain information, but it's still a great challenge for machines to understand the contents of web pages. In this paper, we introduce the task of structural reading comprehension (SRC) on web. Given a web page and a question about it, the task is to find the answer from the web page. This task requires a system not only to understand the semantics of texts but also the structure of the web page. Moreover, we proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset. WebSRC consists of 400K question-answer pairs, which are collected from 6.4K web pages. Along with the QA pairs, corresponding HTML source code, screenshots, and metadata are also provided in our dataset. Each question in WebSRC requires a certain structural understanding of a web page to answer, and the answer is either a text span on the web page or yes/no. We evaluate various baselines on our dataset to show the difficulty of our task. We also investigate the usefulness of structural information and visual features. Our dataset and baselines have been publicly available. © 2021 Association for Computational Linguistics

关键词： Websites

Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion

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arXiv 2022年

作者： Chen, Xiang Zhang, Ningyu Li, Lei Deng, Shumin Tan, Chuanqi Xu, Changliang Huang, Fei Si, Luo Chen, Huajun Zhejiang University AZFT Joint Lab for Knowledge Engine Hangzhou Innovation Center Zhejiang Hangzhou China Alibaba Group Zhejiang Hangzhou China State Key Laboratory of Media Convergence Production Technology and Systems Beijing China

Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system. Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction. However, different tasks and modalities require changes to the model architecture, and not all images/objects are relevant to text input, which hinders the applicability to diverse real-world scenarios. In this paper, we propose a hybrid transformer with multi-level fusion to address those issues. Specifically, we leverage a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks. Moreover, we propose multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine-grained correlation-aware fusion modules. We conduct extensive experiments to validate that our MKGformer can obtain SOTA performance on four datasets of multimodal link prediction, multimodal RE, and multimodal NER. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue

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arXiv 2022年

作者： Chen, Zhi Liu, Yuncong Chen, Lu Zhu, Su Wu, Mengyue Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems Beijing China AISpeech Co. Ltd. Suzhou China

This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD). Unlike chit-chat dialogue models, task-oriented dialogue models fulfill at least two task-specific modules: dialogue state tracker (DST) and response generator (RG). The dialogue state consists of the domain-slot-value triples, which are regarded as the user's constraints to search the domain-related databases. The large-scale task-oriented dialogue data with the annotated structured dialogue state usually are inaccessible. It prevents the development of the pretrained language model for the task-oriented dialogue. We propose a simple yet effective pretraining method to alleviate this problem, which consists of two pretraining phases. The first phase is to pretrain on large-scale contextual text data, where the structured information of the text is extracted by the information extracting tool. To bridge the gap between the pretraining method and downstream tasks, we design two pretraining tasks: ontology-like triple recovery and next-text generation, which simulates the DST and RG, respectively. The second phase is to fine-tune the pretrained model on the TOD data. The experimental results show that our proposed method achieves an exciting boost and get competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Ontology

TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages

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arXiv 2022年

作者： Zhao, Zihan Chen, Lu Cao, Ruisheng Xu, Hongshen Chen, Xingyu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China Shanghai Jiao Tong University Shanghai China State Key Laboratory of Media Convergence Production Technology and Systems China

Recently, the structural reading comprehension (SRC) task on web pages has attracted increasing research interests. Although previous SRC work has leveraged extra information such as HTML tags or XPaths, the informative topology of web pages is not effectively exploited. In this work, we propose a Topological Information Enhanced model (TIE), which transforms the token-level task into a tag-level task by introducing a two-stage process (i.e. node locating and answer refining). Based on that, TIE integrates Graph Attention Network (GAT) and Pre-trained Language Model (PLM) to leverage the topological information of both logical structures and spatial structures. Experimental results demonstrate that our model outperforms strong baselines and achieves state-of-the-art performances on the web-based SRC benchmark WebSRC at the time of writing. The code of TIE will be publicly available at https://***/X-LANCE/TIE. Copyright © 2022, The Authors. All rights reserved.

关键词： Websites

DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning

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arXiv 2022年

作者： Chen, Zhi Ji, Jiabao Chen, Lu Liu, Yuncong Ma, Da Chen, Bei Wu, Mengyue Zhu, Su Dong, Xin Ge, Fujiang Miao, Qingliang Lou, Jian-Guang Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems Beijing China Microsoft Research Asia China AISpeech Co. Ltd. Suzhou China

Building a universal conversational agent has been a long-standing goal of the dialogue research community. Most previous works only focus on a small set of dialogue tasks. In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks. To achieve this goal, a large-scale well-annotated dialogue dataset with rich task diversity (DialogZoo) is collected. We introduce a framework to unify all dialogue tasks and propose novel auxiliary self-supervised tasks to achieve stable training of DFM on the highly diverse large scale DialogZoo corpus. Experiments show that, compared with models of the same size, DFM can achieve state-of-the-art or competitive performance on very rich cross-domain downstream dialogue tasks. This demonstrates that DFM largely extends the ability of unified dialogue pre-trained model. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset