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Adaptive Token Selection and Fusion Network for Multimodal Sentiment Analysis 1

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Li, Xiang Lu, Ming Guo, Ziming Zhang, Xiaoming School of Cyber Science and Technology Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Multimodal sentiment analysis aims to predict human sentiment polarity with multiple modalities. Most existing methods focus on directly integrating original modal features into multimodal fusion, ignoring the redundancy and heterogeneity across modalities. In this paper, we propose a simple but efficient Adaptive Token Selection and Fusion Network (ATSFN) to mitigate the effect of redundancy and heterogeneity. ATSFN employs adaptive trainable tokens to extract unimodal informative tokens and perform dynamic multimodal token fusion. Specifically, we first integrate critical information from original features into adaptive selection tokens through token selection transformers. Sentiment features flow through these smaller sequences of tokens to capture important information while reducing redundancy. Next, we introduce a token fusion transformer to fuse multimodal features dynamically. It adaptively estimates the unique contribution of each modality to sentiment tendencies through learnable fusion tokens. Experiments on two benchmark datasets demonstrate that our proposed approach achieves competitive performance and significant improvements. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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TANET: Thread-Aware Pretraining for Abstractive Conversational Summarization

TANET: Thread-Aware Pretraining for Abstractive Conversation...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Yang, Ze Wang, Liran Tian, Zhoujin Wu, Wei Li, Zhoujun State Key Lab of Software Development Environment Beihang University Beijing China Meituan Beijing China

ISBN: (纸本)9781955917766

Although pre-trained language models (PLMs) have achieved great success and become a milestone in NLP, abstractive conversational summarization remains a challenging but less studied task. The difficulty lies in two aspects. One is the lack of large-scale conversational summary data. Another is that applying the existing pre-trained models to this task is tricky because of the structural dependence within the conversation and its informal expression, etc. In this work, we first build a large-scale (11M) pretraining dataset called RCSUM, based on the multi-person discussions in the Reddit community. We then present TANET, a thread-aware Transformerbased network. Unlike the existing pre-trained models that treat a conversation as a sequence of sentences, we argue that the inherent contextual dependency among the utterances plays an essential role in understanding the entire conversation and thus propose two new techniques to incorporate the structural information into our model. The first is thread-aware attention which is computed by taking into account the contextual dependency within utterances. Second, we apply thread prediction loss to predict the relations between utterances. We evaluate our model on four datasets of real conversations, covering types of meeting transcripts, customer-service records, and forum threads. Experimental results demonstrate that TANET achieves a new state-of-the-art in terms of both automatic evaluation and human judgment. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Natural language processing systems

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CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers

CQR-SQL: Conversational Question Reformulation Enhanced Cont...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Xiao, Dongling Chai, Linzheng Zhang, Qian-Wen Yan, Zhao Li, Zhoujun Cao, Yunbo Tencent Cloud Xiaowei State Key Lab of Software Development Environment Beihang University Beijing China

Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependency, such as co-reference, ellipsis and user focus change. In this paper, we propose CQR-SQL, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit schema and decouple contextual dependency for multi-turn SQL parsing. Specifically, we first present a schema enhanced recursive CQR method to produce domain-relevant self-contained questions. Secondly, we train CQR-SQL models to map the semantics of multi-turn questions and auxiliary self-contained questions into the same latent space through schema grounding consistency task and tree-structured SQL parsing consistency task, which enhances the abilities of SQL parsing by adequately contextual understanding. At the time of writing, our CQR-SQL achieves new state-of-the-art results on two context-dependent text-to-SQL benchmarks SPARC and COSQL. © 2022 Association for Computational Linguistics.

关键词： Semantics

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IoTAEG: Automatic Exploit Generation of IoT Devices 4

IoTAEG: Automatic Exploit Generation of IoT Devices

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2023 4th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2023

作者： Wang, Yu Li, Zhoujun Zhang, Yipeng Zhai, You Beihang University State Key Lab of Software Development Environment Beijing China North China University of Technology School of Information Science and Technology Beijing China

ISBN: (纸本)9798350308877

Automatic exploit generation (AEG) refers to the process of automatically finding the path in the program that can trigger vulnerabilities and generate exploits. Generally speaking, the process of finding vulnerabilities needs to apply fuzzing and symbolic execution techniques. Existing AEG generally targets executables for regular Linux and Windows platforms, but no AEG for vulnerable Internet of Things (IoT) devices. In response to this situation, we propose the exploit generation system IoTAEG, which automatically detects stack overflow vulnerabilities in the firmware of IoT devices, and automatically generates and exploits the generation system based on the stack overflow vulnerabilities. IoTAEG uses the mature fuzzing software AFL++ to detect vulnerabilities in IoT devices, uses the crashed input found by AFL++ to construct a symbolic state through symbolic execution, dynamically analyzes the constructed symbolic state, and detects whether there are exploitable vulnerabilities. If the above vulnerabilities exist, different exploit generation strategies will be adopted for different protection mechanisms, and some protection mechanisms such as Address space layout randomization (ASLR) and Non-eXecute (NX) will be bypassed. For some difficult-to-exploit cases, IoTAEG uses advanced stack overflow exploitation methods to generate exploits. Experiments show that IoTAEG can complete 20 MIPS/ARM binary files and 8 IoT devices' firmware vulnerability detection and exploit generation. IoTAEG is the first publicly available vulnerability mining and exploit generation system for IoT devices. © 2023 IEEE.

关键词： Internet of things

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HanoiT: Enhancing Context-aware Translation via Selective Context 28th

HanoiT: Enhancing Context-aware Translation via Selective C...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yang, Jian Yin, Yuwei Ma, Shuming Yang, Liqun Guo, Hongcheng Huang, Haoyang Zhang, Dongdong Zeng, Yutao Li, Zhoujun Wei, Furu State Key Lab of Software Development Environment Beihang University Beijing China The University of Hong Kong Hong Kong Microsoft Research Asia Beijing China

ISBN: (纸本)9783031306747

Context-aware neural machine translation aims to use the document-level context to improve translation quality. However, not all words in the context are helpful. The irrelevant or trivial words may bring some noise and distract the model from learning the relationship between the current sentence and auxiliary context. To mitigate this problem, we propose a novel end-to-end encoder-decoder model with a layer-wise selection mechanism to sift and refine the long document context. To verify the effectiveness of our method, extensive experiments and extra quantitative analysis are conducted on four document-level machine translation benchmarks. The experimental results demonstrate that our model significantly outperforms previous models on all datasets via the soft selection mechanism. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural machine translation

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Read Then Respond: Multi-granularity Grounding Prediction for Knowledge-Grounded Dialogue Generation 9th

Read Then Respond: Multi-granularity Grounding Prediction fo...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Du, Yiyang Zhang, Shiwei Wu, Xianjie Yan, Zhao Cao, Yunbo Li, Zhoujun State Key Lab of Software Development Environment Beihang University Beijing China Baidu Inc. Beijing China Tencent Cloud Xiaowei Beijing China

ISBN: (纸本)9783031466632

Retrieval-augmented generative models have shown promising results in knowledge-grounding dialogue systems. However, identifying and utilizing exact knowledge from multiple passages based on dialogue context remains challenging due to the semantic dependency of the dialogue context. Existing research has observed that increasing the number of retrieved passages promotes the recall of relevant knowledge, but the performance of response generation improvement becomes marginal or even worse when the number reaches a certain threshold. In this paper, we present a multi-grained knowledge grounding identification method, in which the coarse-grained selects the most relevant knowledge from each retrieval passage separately, and the fine-grained refines the coarse-grained and identifies final knowledge as grounding in generation stage. To further guide the response generation with predicted grounding, we introduce a grounding-augmented copy mechanism in the decoding stage of dialogue generation. Empirical results on MultiDoc2Dial and WoW benchmarks show that our method outperforms state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval

Cross-Lingual Cross-Modal Consolidation for Effective Multil...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Liu, Jiaheng Yu, Tan Peng, Hanyu Sun, Mingming Li, Ping Cognitive Computing Lab Baidu Research China State Key Lab of Software Development Environment Beihang University China

ISBN: (纸本)9781955917766

Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on a two-stream structure. The visual stream utilizes the visual content in the video to estimate the query-visual similarity, and the subtitle stream exploits the query-subtitle similarity. The final query-video similarity ensembles similarities from two streams. In our work, we propose a simple and effective strategy termed as Cross-lingual Cross-modal Consolidation (C3) to improve mVCMR accuracy. We adopt the ensemble similarity as the teacher to guide the training of each stream, leading to a more powerful ensemble similarity. Meanwhile, we use the teacher for a specific language to guide the student for another language to exploit the complementary knowledge across languages. Extensive experiments on mTVR dataset demonstrate the effectiveness of our C3 method. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Personnel training

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QAAS:quick accurate auto-scaling for streaming processing

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Frontiers of Computer Science 2024年第1期18卷 5-15页

作者： Shiyuan LIU Yunchun LI Hailong YANG Ming DUN Chen CHEN Huaitao ZHANG Wei LI State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

In recent years,the demand for real-time data processing has been increasing,and various stream processing systems have *** the amount of data input to the stream processing system fluctuates,the computing resources required by the stream processing job will also *** resources used by stream processing jobs need to be adjusted according to load changes,avoiding the waste of computing *** present,existing works adjust stream processing jobs based on the assumption that there is a linear relationship between the operator parallelism and operator resource consumption(e.g.,throughput),which makes a significant deviation when the operator parallelism *** paper proposes a nonlinear model to represent operator *** divide the operator performance into three stages,the Non-competition stage,the Non-full competition stage,and the Full competition *** our proposed performance model,given the parallelism of the operator,we can accurately predict the CPU utilization and operator *** with actual experiments,the prediction error of our model is below 5%.We also propose a quick accurate auto-scaling(QAAS)method that uses the operator performance model to implement the auto-scaling of the operator parallelism of the Flink *** to previous work,QAAS is able to maintain stable job performance under load changes,minimizing the number of job adjustments and reducing data backlogs by 50%.

关键词： stream processing performance model auto-scaling

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Minimizing the cost of periodically replicated systems via model and quantitative analysis

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Frontiers of Computer Science 2024年第5期18卷 11-24页

作者： Chenhao ZHANG Liang WANG Limin XIAO Shixuan JIANG Meng HAN Jinquan WANG Bing WEI Guangjun QIN State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China Smart City College Beijing Union UniversityBeijing 100101China

Geographically replicating objects across multiple data centers improves the performance and reliability of cloud storage *** consistent replicas comes with high synchronization costs,as it faces more expensive WAN transport prices and increased *** replication is the widely used technique to reduce the synchronization *** replication strategies in existing cloud storage systems are too static to handle traffic changes,which indicates that they are inflexible in the face of unforeseen loads,resulting in additional synchronization *** propose quantitative analysis models to quantify consistency and synchronization cost for periodically replicated systems,and derive the optimal synchronization period to achieve the best tradeoff between consistency and synchronization *** on this,we propose a dynamic periodic synchronization method,Sync-Opt,which allows systems to set the optimal synchronization period according to the variable load in clouds to minimize the synchronization *** results demonstrate the effectiveness of our *** with the policies widely used in modern cloud storage systems,the Sync-Opt strategy significantly reduces the synchronization cost.

关键词： periodic replication consistency maintenance synchronization cost synchronization strategy

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A method for detecting abnormal users with fake stars 28

A method for detecting abnormal users with fake stars

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28th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2022

作者： Jiang, Jing Li, Hao Liu, Yifan Zhang, Li The State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (纸本)1891706551

In GitHub, users star interesting repositories, and the number of stars is viewed as the significant measure of repository popularity. Some repositories obtain fake stars by unjustified means, which ruin efforts that communities have made stars a valuable indicator, and bring negative impacts in GitHub. Therefore, it is important to stop abusing GitHub stars and detect abnormal users who provide fake stars. In this paper, we first define features from the user dimension and repository dimension. Then we perform differential analysis and find that most of the features show a significant difference between abnormal users and normal users. Next, we propose a method AUDetec for Abnormal User Detection. The method AUDetec uses the decision tree to detect the abnormal users based on two features, including the sum of repositories starred by the user and the median value of the number of days since creation for repositories starred by the user. We evaluate the effectiveness of AUDetec on the data set which contains 120 abnormal users and 240 normal users. The experiment results show that AUDetec has a high performance by achieving an accuracy of 99.86% on average. © 2022 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： Open source software

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