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Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception 40

Enhancing Quantitative Reasoning Skills of Large Language Mo...

引用

40th IEEE International Conference on data Engineering, ICDE 2024

作者： Huang, Yuncheng He, Qianyu Liang, Jiaqing Jiang, Sihang Xiao, Yanghua Chen, Yunwen School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China School of Data Science Fudan University China DataGrand Co. LTD. Research Group of Computational and AI Communication Institute for Global Communications and Integrated Media Fudan University China

ISBN: (纸本)9798350317152

Quantities are distinct and critical components of texts that characterize the magnitude properties of entities, providing a precise perspective for the understanding of natural language, especially for reasoning tasks. In recent years, there has been a flurry of research on reasoning tasks based on large language models (LLMs), most of which solely focus on numerical values, neglecting the dimensional concept of quantities with units despite its importance. We argue that the concept of dimension is essential for precisely understanding quantities and of great significance for LLMs to perform quantitative reasoning. However, the lack of dimension knowledge and quantity-related benchmarks has resulted in low performance of LLMs. Hence, we present a framework to enhance the quantitative reasoning ability of language models based on dimension perception. We first construct a dimensional unit knowledge base (DimUnitKB) to address the knowledge gap in this area. We propose a benchmark DimEval consisting of seven tasks of three categories to probe and enhance the dimension perception skills of LLMs. To evaluate the effectiveness of our methods, we propose a quantitative reasoning task and conduct experiments. The experimental results show that our dimension perception method dramatically improves accuracy (43.55%→50.67%) on quantitative reasoning tasks compared to GPT-4. © 2024 IEEE.

关键词： Machine learning

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Multi-Modal Fusion with Semantic Supervision for Radiology Report Generation 26

Multi-Modal Fusion with Semantic Supervision for Radiology R...

引用

26th European Conference on Artificial Intelligence, ECAI 2023

作者： Jia, Xing Xiong, Yun Zhang, Yao Luo, Li Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Public Health Fudan University China

ISBN: (纸本)9781643684369

Radiology report generation, one way of analyzing radiology images, is to generate a textual report automatically for the given image, and it is of great significance to assist diagnosis and alleviate the workload of radiologists. Some report generation methods have been therefore proposed. However, these methods suffer from the problem of low-quality generation, because of the visual and textual bias and training with text similarity oriented objective. To solve this problem, we propose a novel radiology report generation model with multi-modal fusion and semantic supervision, namely MS-Gen. MS-Gen consists of two main components, i.e., the semantic-visual fusion module and the semantic weighted contrastive loss. Specifically, the main idea of the semantic-visual fusion module is to make use of the domain-specific prior knowledge contained in a large pre-trained visual-language model and also the complementary nature between the image and text modalities. Moreover, a novel optimization term, i.e., the semantic weighted contrastive loss, is proposed to guide the optimization process with semantic similarity objective, and further enforce the generated reports with higher clinical accuracy. Extensive experiments conducted on two real datasets of IU X-Ray and MIMIC-CXR demonstrate the effectiveness of MS-Gen. © 2023 The Authors.

关键词： Semantics

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Causal discovery with endogenous context variables 38

Causal discovery with endogenous context variables

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Günther, Wiebke Popescu, Oana-Iuliana Rabel, Martin Ninad, Urmi Gerhardus, Andreas Runge, Jakob Institute of Data Science Jena Germany Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany Dresden Leipzig Germany Technische Universität Dresden Faculty of Computer Science Dresden Germany

Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variables as those variables that indicate this change in causal mechanisms. An example are the causal relations in soil moisture-temperature interactions and their dependence on soil moisture regimes: Dry soil triggers a dependence of soil moisture on latent heat, while environments with wet soil do not feature such a feedback, making it a context-specific property. Crucially, a regime or context variable such as soil moisture need not be exogenous and can be influenced by the dynamical system variables - precipitation can make a dry soil wet - leading to joint systems with endogenous context variables. In this work we investigate the assumptions for constraint-based causal discovery of context-specific information in systems with endogenous context variables. We show that naive approaches such as learning different regime graphs on masked data, or pooling all data, can lead to uninformative results. We propose an adaptive constraint-based discovery algorithm and give a detailed discussion on the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. Numerical experiments demonstrate the performance of the proposed method over alternative baselines, but they also unveil current limitations of our method. © 2024 Neural information processing systems foundation. All rights reserved.

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ECCA:An Efficient Channel-and-Coordinate Attention Module for Image Stitching

ECCA:An Efficient Channel-and-Coordinate Attention Module fo...

引用

2023 China Automation Congress, CAC 2023

作者： Chen, Weikai Yu, Yuanlong Huang, Zhiyong Wu, Zhixin College of Computer and Data Science Science/College of Software Fuzhou University Fuzhou China Southwest Automation Research Institute Sichuan China

ISBN: (纸本)9798350303759

We propose a simple and effective attention module, called Efficient Channel-and-Coordinate Attention (ECCA), which can be applied to CNN-based image stitching models. Traditional image stitching models usually use deep convolutional neural networks, but as the depth increases, the information capacity of the model also increases, leading to unstable region of interest and degraded feature quality. Moreover, for similar features in different positions of two images, the model may mistakenly regard them as the same feature, resulting in incorrect stitching results. To address these issues, we design a multi-head attention mechanism that combines channels and coordinate positions. The module can enable the network to focus on more critical information among the numerous input information and extract higher quality features. Specifically, the module consists of two attention heads: local channel attention head and coordinate attention head. The local channel attention head calculates the correlation between each channel and its adjacent k channels, and the coordinate attention head calculates the correlation between each channel and the global channel according to the feature position in the image. The two attention heads obtain attention weights respectively, and multiply them with the original features to obtain the final output features. Our ECCA module can effectively improve the performance of existing state-of-the-art deep image stitching models. On the WarpedCOCO dataset, we apply the ECCA module to VFISnet and LB-UDHN networks, respectively improving their PSNR values by 0.63 and 1.92, and SSIM values by 5.7% and 5.1%. Code is available at https://***/chenwinkk/ECCA-Network © 2023 IEEE.

关键词： Convolutional neural networks

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Machine Learning Approach for Complaint Prediction in the Telecom Industry 2

Machine Learning Approach for Complaint Prediction in the Te...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Gewaly, Mohamed Mahmoud Saeed, Mahmoud Samy Ammar, Ammar Mohammed FGSSR Cairo University Dept. of Data Science Cairo Egypt Telecom Egypt Cairo Egypt FGSSR Cairo University Dep. of Computer Science Cairo Egypt

ISBN: (纸本)9798350362633

The quality of service of the individual user of telecommunication services is one of the most critical criteria in the telecom industry that affects customer satisfaction. This paper aims to predict customer complaints before escalation and validate current escalated complaints in fixed broadband from telecommunication companies via historical and real-time customer data, including service logs and previous complaints, to optimize resource allocation and maximize customer satisfaction as the challenges are related to the volume of physical complains and churn. The dataset is real-world data provided by a single Egyptian mobile network operator and consists of 12 features pertaining to 100,000 customers. We represent 12 predictive models developed using machine learning algorithms via advanced techniques such as classification algorithms like Random Forest Classifier (RFC), AdaBoost Classifier with a Decision Tree (DT), Logistic Regression (LR), -Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Tree Classifier (DTC), Support Vector Machine (SVM) to analyze various features and patterns within the data. The results show that the AdaBoost with a Decision Tree model did well using a variety of assessment indicators, including Accuracy, Precision, Recall, and F1-score;the model aims to classify customers, indicating their likelihood of raising complaints in the future. The results and insights obtained from this study can provide telecom service providers with valuable guidance for implementing data-driven strategies to improve customer experience and take proactive maintenance that will reduce cost and churn. © 2024 IEEE.

关键词： Customer satisfaction

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PentaPen: Combining Penalized Models to Identify Important SNPs on Whole-genome Arabidopsis thaliana data 24

PentaPen: Combining Penalized Models to Identify Important S...

引用

16th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2024

作者： Kohli, Nikita Tomal, Jabed H. Lin, Wenjun Yan, Yan Program of Data Science Thompson Rivers University KamloopsBC Canada Department of Mathematics and Statistics Thompson Rivers University KamloopsBC Canada School of Computer Science and Technology Algoma University BramptonON Canada School of Computer Science University of Guelph GuelphON Canada

ISBN: (纸本)9798400717666

In the rapidly advancing field of genomics, the identification of Single Nucleotide Polymorphisms (SNPs) plays a crucial role in understanding complex phenotypic *** study introduces "PentaPen", an innovative computational workflow which combines the strengths of five penalized models to achieve improved accuracy in SNP *** compare the performance of PentaPen with existing models, highlighting its advantages in solving problems arising from when the number of predictors exceeds the number of *** model comparison, we provide insights into PentaPen's effectiveness in utilizing all SNPs as input, streamlines data pre-processing, and leverages parallel computation, enabling the workflow a considerable stride in SNP ***, a thorough evaluation and comparison of computational complexities signifies competitive edge of the workflow over individual penalized *** future research directions, we propose applications of PentaPen to plant-specific characteristics and suggest further explorations to assess the robustness of its *** summary, this manuscript presents the genomics community with a tool that combines computational efficiency with high-precision SNP detection, making a strong contribution to the field of genomic research. © 2024 Copyright held by the owner/author(s).

关键词： Genome

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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MULTI-TASK REINFORCEMENT LEARNING WITH MIXTURE OF ORTHOGONAL EXPERTS 12

MULTI-TASK REINFORCEMENT LEARNING WITH MIXTURE OF ORTHOGONAL...

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12th International Conference on Learning Representations, ICLR 2024

作者： Hendawy, Ahmed Peters, Jan D'Eramo, Carlo Department of Computer Science TU Darmstadt Germany Germany Center for Cognitive Science TU Darmstadt Germany Systems AI for Robot Learning Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany

Multi-Task Reinforcement Learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems. To this end, sharing representations plays a fundamental role in capturing both unique and common characteristics of the tasks. Tasks may exhibit similarities in terms of skills, objects, or physical properties while leveraging their representations eases the achievement of a universal policy. Nevertheless, the pursuit of learning a shared set of diverse representations is still an open challenge. In this paper, we introduce a novel approach for representation learning in MTRL that encapsulates common structures among the tasks using orthogonal representations to promote diversity. Our method, named Mixture Of Orthogonal Experts (MOORE), leverages a Gram-Schmidt process to shape a shared subspace of representations generated by a mixture of experts. When task-specific information is provided, MOORE generates relevant representations from this shared subspace. We assess the effectiveness of our approach on two MTRL benchmarks, namely MiniGrid and MetaWorld, showing that MOORE surpasses related baselines and establishes a new state-of-the-art result on MetaWorld. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Spectral Normalized-Cut Graph Partitioning with Fairness Constraints 26

Spectral Normalized-Cut Graph Partitioning with Fairness Con...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Li, Jia Wang, Yanhao Merchant, Arpit School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (纸本)9781643684369

Normalized-cut graph partitioning aims to divide the set of nodes in a graph into k disjoint clusters to minimize the fraction of the total edges between any cluster and all other clusters. In this paper, we consider a fair variant of the partitioning problem wherein nodes are characterized by a categorical sensitive attribute (e.g., gender or race) indicating membership to different demographic groups. Our goal is to ensure that each group is approximately proportionally represented in each cluster while minimizing the normalized cut value. To resolve this problem, we propose a two-phase spectral algorithm called FNM. In the first phase, we add an augmented Lagrangian term based on our fairness criteria to the objective function for obtaining a fairer spectral node embedding. Then, in the second phase, we design a rounding scheme to produce k clusters from the fair embedding that effectively trades off fairness and partition quality. Through comprehensive experiments on nine benchmark datasets, we demonstrate the superior performance of FNM compared with three baseline methods. © 2023 The Authors.

关键词： Embeddings

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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