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CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments 30

CLEVR-POC: Reasoning-Intensive Visual Question Answering in ...

引用

Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Abraham, Savitha Sam Alirezaie, Marjan De Raedt, Luc Australian Institute for Machine Learning The University of Adelaide Australia Flybits Labs. TMU Creative AI Hub Toronto Canada Örebro Sweden Department of Computer Science KULeuven Belgium

ISBN: (纸本)9782493814104

The integration of learning and reasoning is high on the research agenda in AI. Nevertheless, there is only a little attention to use existing background knowledge for reasoning about partially observed scenes to answer questions about the scene. Yet, we as humans use such knowledge frequently to infer plausible answers to visual questions (by eliminating all inconsistent ones). Such knowledge often comes in the form of constraints about objects and it tends to be highly domain or environment-specific. We contribute a novel benchmark called CLEVR-POC for reasoning-intensive visual question answering (VQA) in partially observable environments under constraints. In CLEVR-POC, knowledge in the form of logical constraints needs to be leveraged to generate plausible answers to questions about a hidden object in a given partial scene. For instance, if one has the knowledge that all cups are colored either red, green or blue and that there is only one green cup, it becomes possible to deduce the color of an occluded cup as either red or blue, provided that all other cups, including the green one, are observed. Through experiments, we observe that the low performance of pre-trained vision language models like CLIP (≈ 22%) and a large language model (LLM) like GPT-4 (≈ 46%) on CLEVR-POC ascertains the necessity for frameworks that can handle reasoning-intensive tasks where environment-specific background knowledge is available and crucial. Furthermore, our demonstration illustrates that a neuro-symbolic model, which integrates an LLM like GPT-4 with a visual perception network and a formal logical reasoner, exhibits exceptional performance on CLEVR-POC. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge management

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Deep Learning-Based Event Prediction for Text Analysis 14

Deep Learning-Based Event Prediction for Text Analysis

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14th International conference on Information and Communication Technology Convergence, ICTC 2023

作者： Waseem, Muhammad Umer, Qasim Lee, Choonhwa Chung, Sungwook Latif, Zohaib Beijing Institute of Technology School of Computer Science and Technology Beijing China Hanyang University Department of Computer Science Seoul Korea Republic of Changwon University Department of Computer Engineering Changwon Korea Republic of Nazarbayev University Department of Computer Science Astana Kazakhstan

ISBN: (纸本)9798350313277

This paper addresses automatic event prediction from unstructured text, specifically event chains. While current approaches employ LSTM for encoding full chains, learning long-range narrative orders, or learning partial orders and long-range narrative orders, none of them consider writer sentiment. To address this, we propose a deep learning-based approach that incorporates writer sentiment. We pre-process the text, extract events, compute sentiment scores using SentiWordNet, convert events to digital vectors, and feed them along with sentiment scores into a deep learning-based classifier. This classifier uses hidden states for event pair modeling, with each pair having its associated sentiment. Evaluation results show that our approach significantly surpasses state-of-the-art methods with 29.2% accuracy. © 2023 IEEE.

关键词： Deep Learning Event Prediction Sentiment

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Orthogonal-Aware Constraint Neural Network Compression Based on Low-Rank Representation Learning 21

Orthogonal-Aware Constraint Neural Network Compression Based...

引用

21st International conference on Networking, Sensing and Control, ICNSC 2024

作者： He, Yaping Jiang, Linhao Wu, Di College of Computer and Information Science College Southwest University Chongqing China Shapingba Community Health Service Center Chongqing China

ISBN: (纸本)9798350365221

Deep neural networks typically impose significant computational loads and memory consumption. Moreover, the large parameters pose constraints on deploying the model on edge devices such as embedded systems. Tensor decomposition offers a clear advantage in compressing large-scale weight tensors. Nevertheless, direct utilization of low-rank decomposition typically leads to significant accuracy loss. This paper proposes a model compression method that integrates Variational Bayesian Matrix Factorization (VBMF) with orthogonal regularization. Initially, the model undergoes over-parameterization and training, with orthogonal regularization applied to enhance its likelihood of achieving the accuracy of the original model. Secondly, VBMF is employed to estimate the rank of the weight tensor at each layer. Our framework is sufficiently general to apply to other convolutional neural networks and easily adaptable to incorporate other tensor decomposition methods. Experimental results show that for both high and low compression ratios, our compression model exhibits advanced performance. © 2024 IEEE.

关键词： Matrix factorization

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DBIA: DATA-FREE BACKDOOR ATTACK AGAINST TRANSFORMER NETWORKS

DBIA: DATA-FREE BACKDOOR ATTACK AGAINST TRANSFORMER NETWORKS

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IEEE International conference on Multimedia and Expo (ICME)

作者： Lv, Peizhuo Ma, Hualong Zhou, Jiachen Liang, Ruigang Chen, Kai Zhang, Shengzhi Yang, Yunfei Chinese Acad Sci SKLOIS Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Boston Univ Dept Comp Sci Metropolitan Coll Boston MA USA

ISBN: (纸本)9781665468916

Recently, transformer architecture has demonstrated its significance in both Natural Language Processing (NLP) and computer Vision (CV) tasks. Although other network models are known to be vulnerable to the backdoor attack, which embeds triggers in the models and controls the models' behavior when the triggers are presented, little is known about how such an attack performs on the transformer models. In this paper, we propose DBIA, a novel Data-free(1) Backdoor Attack against the CV-oriented transformer networks, leveraging the inherent attention mechanism of transformers to generate triggers and injecting the backdoor using a poisoned substitute dataset. We conducted extensive experiments using three benchmark transformers, i.e., ViT, DeiT, and Swin Transformer, on four mainstream image classification tasks, i.e., ImageNet, CIFAR-10, GTSRB, and Youtube Face. The evaluation results demonstrate that, with fewer resources, our approach can embed backdoors with a high success rate and a low impact on the performance of the victim transformers.

关键词： computer vision

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TFFSQL:Enhancing Graph Neural Network through Text Feature Fusion for Text-to-SQL 24

TFFSQL:Enhancing Graph Neural Network through Text Feature F...

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4th International conference on Artificial Intelligence, Automation and high performance Computing, AIAHPC 2024

作者： He, Yaozhen Huang, Enpei Qi, Rongzhi College of Computer Science and Software Engineering Hohai University China United International College Beijing Normal University China

ISBN: (纸本)9798400710049

In text-to-SQL, generating accurate SQL queries is crucial. Existing methods that rely on Graph Neural Networks (GNN) still fall short in capturing complex semantic and relational information. We propose TFFSQL, which obtains the textual information of natural language questions as node features and integrates them with the original node features. It also adopts segmentation processing to handle long queries, generates vectors for each segment, and merges them with the node structural features, effectively capturing the semantic information of natural language queries and improving the efficiency and accuracy of the model in processing long queries. An cross-graph attention is introduced in the Relational Graph Attention Network (RGAT). Through the cross-graph attention, the model can dynamically adjust attention weights between the original graph and the line graph, thereby capturing rich relational information. Additionally, TFFSQL also combines recursive decoding with the ASTormer decoder for decoding. Experimental results show that our method outperforms existing GNN-based methods on the Spider dataset, significantly improving the accuracy of SQL query generation. © 2024 Copyright held by the owner/author(s).

关键词： Structured Query Language

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ChatASU: Evoking LLM's Reflexion to Truly Understand Aspect Sentiment in Dialogues 30

ChatASU: Evoking LLM's Reflexion to Truly Understand Aspect ...

引用

Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Yiding Wang, Jingjing Luo, Jiamin Zeng, Tao Zhou, Guodong School of Computer Science and Technology Soochow University China No.1 Shizi Street Jiangsu Province Suzhou City China

ISBN: (纸本)9782493814104

Aspect Sentiment Understanding (ASU) in interactive scenarios (e.g., Question-Answering and Dialogue) has attracted ever-more interest in recent years and achieved important progresses. However, existing studies on interactive ASU largely ignore the coreference issue for opinion targets (i.e., aspects), while this phenomenon is ubiquitous in interactive scenarios especially dialogues, limiting the ASU performance. Recently, large language models (LLMs) shows the powerful ability to integrate various NLP tasks with the chat paradigm. In this way, this paper proposes a new Chat-based Aspect Sentiment Understanding (ChatASU) task, aiming to explore LLMs' ability in understanding aspect sentiments in dialogue scenarios. Particularly, this ChatASU task introduces a sub-task, i.e., Aspect Chain Reasoning (ACR) task, to address the aspect coreference issue. On this basis, we propose a Trusted Self-reflexion Approach (TSA) with ChatGLM as backbone to ChatASU. Specifically, this TSA treats the ACR task as an auxiliary task to boost the performance of the primary ASU task, and further integrates trusted learning into reflexion mechanisms to alleviate the LLMs-intrinsic factual hallucination problem in TSA. Furthermore, a high-quality ChatASU dataset is annotated to evaluate TSA, and extensive experiments show that our proposed TSA can significantly outperform several state-of-the-art baselines, justifying the effectiveness of TSA to ChatASU and the importance of considering the coreference and hallucination issues in ChatASU. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Efficient Execution of On-Chip Embedded Software Using Pre-Emulation on Shallow OS 14

Efficient Execution of On-Chip Embedded Software Using Pre-E...

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14th International conference on Mobile Computing and Ubiquitous Network (ICMU)

作者： Kim, Minjung Park, Daejin Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea

ISBN: (纸本)9784907626525

Nowadays, the miniaturization of electronic devices continues. However, as the miniaturization progresses, like both sides of a coin, the available resources become limited because of the difficulty of implementing performance above a certain level on a smaller chip. In this study, we implemented some methods for overcoming these limitations. We propose a scheduling based on deadline. It is possible to pre-emulate the program and predict when the task will finish. In addition, even if the total execution time is the same, high-priority tasks can be completed faster. Also, we implemented a struct bit field to cut back on the total resources downloaded onto the chip. Memory usage is almost 50 % lower than not using a struct bit field whereas the execution time is almost the same.

关键词： Scheduling Memory Usage Emulation Operating System computer Architecture Real-Time

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Three-D Imaging of Urban Layover Scatterers Based on Learned LISTA Algorithm 2

Three-D Imaging of Urban Layover Scatterers Based on Learned...

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2nd IEEE International conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Bai, XueAo Li, Hong Wang, Xiao Nanjing Tech University School of Computer and Information Engineering Nanjing China

ISBN: (纸本)9798331515669

This paper explores a Tomographic Synthetic Aperture Radar (TomoSAR) learning imaging model based on the Learned ISTA (LISTA) algorithm. It addresses the limitations of traditional sparse regularization methods encountered in TomoSAR, particularly their sensitivity to noise and high computational complexity. This study involves unfolding the Iterative Shrinkage Thresholding Algorithm (ISTA) into a neural network structure, allowing for the transformation of preset hyperparameters into learnable parameters, thereby enhancing model adaptability. This paper includes constructing a diverse training set and testing the model against various conditions, including different Signal-to-Noise Ratios (SNRs) and scatterer spacings. The results demonstrate that the LISTA-based TomoSAR imaging algorithm achieves super-resolution performance and reduces computational complexity over traditional methods. © 2024 IEEE.

关键词： Radar imaging

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SentimentGPT: Leveraging GPT for Advancing Sentiment Analysis

SentimentGPT: Leveraging GPT for Advancing Sentiment Analysi...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Kheiri, Kiana Karimi, Hamid Utah State University LoganUT United States

ISBN: (纸本)9798350362480

This study thoroughly examines various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, with more than 22% in the F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to handle these complexities effectively. These findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://***/DSAatUSU/SentimentGPT © 2024 IEEE.

关键词： program processors

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The Effect of Self-Directedness on Learning Outcomes in Distance Learning Courses in higher Education 32

The Effect of Self-Directedness on Learning Outcomes in Dist...

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32nd Annual conference of the European-Association-for-Education-in-Electrical-and-Information-Engineering (EAEEIE)

作者： Myllymaki, Mikko Laine, Sanna Hakala, Ismo Univ Jyvaskyla Kokkola Univ Consortium Chydenius Kokkola Finland

ISBN: (纸本)9789038656908

Distance learning requires students to have different skills than face-to-face learning. The learning environment and the interaction with the teacher and other students are different. Distance learning typically gives students flexibility, s uch a s in terms of their study schedules. For these reasons, in distance learning, it is important for students to be able to independently study, do tasks, and manage their own time. Distance learning also requires increased self-motivation. For this research, master's-level computer science students took the Fisher self-directed learning readiness (SDLR) test to assess their level of self-direction. The results show that the level of student self-direction in the adult distance learning program is very high. The study examined student performance in distance learning courses in relation to SDLR scores. SDLR scores were not found to correlate with academic achievement when success was examined in terms of grades and course completion. The only difference between the different performers was found in the subscale of self-management, which includes time management, systematic thinking, and the ability to take responsibility for the progress of one's own learning. Although the difference was not statistically significant, the results s uggest t hat f actors related to time and daily life management could have an impact on learning outcomes, However, they alone do not explain learning outcomes, and other factors may also be involved.

关键词： Self-directed learning distance learning SDLR computer science learning outcomes

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