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arXiv 2023年

作者： Wu, Hongqiu Liu, Linfeng Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Soochow University Suzhou China Harbin Institute of Technology Shenzhen China

Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data. As opposed to constructing increasingly complex logic, this paper probes into the boolean logic, the root capability of a logical reasoner. We find that any pre-trained language models even including large language models only behave like a random selector in the face of multi-nested boolean logic, a task that humans can handle with ease. To empower language models with this fundamental capability, this paper proposes a new self-supervised learning method Curriculum Logical Reasoning (CLR), where we augment the training data with nested boolean logic chain step-by-step, and program the training from simpler logical patterns gradually to harder ones. This new training paradigm allows language models to effectively generalize to much harder and longer-hop logic, which can hardly be learned through naive training. Furthermore, we show that boolean logic is a great foundation for improving the subsequent general logical tasks1 Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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I³: Interactive Iterative Improvement for Few-Shot Action Segmentation

I³: Interactive Iterative Improvement for Few-Shot Action S...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Martina Gassen Frederic Metzler Erik Prescher Lisa Scherf Vignesh Prasad Felix Kaiser Dorothea Koert Interactive AI & Cognitive Models for Human-AI Interaction (IKIDA) TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Department of Computer Science Institute for Intelligent Autonomous Systems TU Darmstadt Germany Department of Law and Economics Chair for Marketing and Human Resource Management TU Darmstadt Germany

Extracting modular segments from raw video demonstrations of high-level actions is important to understand the underlying building blocks for different tasks in human-robot interaction. While (data-hungry) supervised learning approaches for Action Segmentation show good performance when the underlying segments are predefined, their performance degrades when unseen actions are introduced on-the-go as new data samples are scarce. In this regard, Zero-and Few-Shot Learning approaches have shown good performance in generalizing to unseen examples. In Action Segmentation, where each frame needs to be labeled, annotating new data even for a few tasks can become tedious as the number of tasks scale. In this work, we propose Interactive Iterative Improvement $(I^{3})$ for Few-Shot Action Segmentation, a Semi-Supervised Interactive Meta-Learning approach for Zero-Shot Learning on unlabeled videos and Few-Shot Learning on small amounts of labeled videos. $I^{3}$ consists of a Prototypical Network model for frame-wise prediction coupled with a Hidden-Semi-Markov-Model to prevent over-segmentation. The model is iteratively improved in an interactive manner through users’ annotations provided via a webinterface. This is done in a task-agnostic manner that, in theory, can be reused for a number of different actions. Our model provides sequentially accurate segmentations using only a limited amount of labeled data which shows the efficacy of our learning approach. A lower edit distance compared to baselines indicates a lower number of required user edits making it well suited for non-expert users to smoothly provide annotations enabling them to have more control over the learned model.

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A Value-of-Information-based management framework for fog services

A Value-of-Information-based management framework for fog se...

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作者： Poltronieri, Filippo Tortonesi, Mauro Morelli, Alessandro Stefanelli, Cesare Suri, Niranjan Engineering Department University of Ferrara Ferrara Italy Department of Mathematics and Computer Science University of Ferrara Ferrara Italy Florida Institute for Human and Machine Cognition PensacolaFL United States United States Army Research Laboratory AdelphiMD United States

The management of fog computing applications is a challenging task that requires dealing with a dynamic and resource-scarce environment. We argue that approaches leveraging Value-of-Information (VoI) concepts and tools are particularly interesting to support the realization of that objective. This paper describes innovative methodologies and reference models for the service fabric management of fog computing services. First, we formalize the VoI concept and discuss its adoption in fog computing environments. Then, we propose a model that aims at maximizing the allocation of fog services from a value-based perspective. To overcome the complexity of this model, we present two possible solutions (a simplified user-specific VoI model and a distance-based heuristic), and we compare them by adopting meta-heuristics as optimization techniques. Then, we investigate the adoption of a hybrid model, which combines the distance-based heuristic with the simplified user-specific VoI model. Experimental results prove the validity of all presented approaches and highlight the soundness of the distance-based heuristic, which is capable of reaching the 91% performance of the simplified user-specific VoI model in a very short amount of time. This makes it a suitable approach for online optimization of resources in fog computing. © 2021 John Wiley & Sons, Ltd.

关键词： Fog computing

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Dynamic Localisation of Spatial-Temporal Graph Neural Network

arXiv

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arXiv 2025年

作者： Duan, Wenying Guo, Shujun Zhou, Zimu Huang, Wei Rao, Hong He, Xiaoxi Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau China

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy

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Magnetic Resonance Letters 2024年第2期5卷

作者： Sha, Guanchen Liu, Yuefan Cao, Yutong Zhang, Qi Zhang, Yining Chen, Yuanyuan Fan, Qiuyun Cheng, Yue Department of Biomedical Engineering College of Precision Instruments and Optoelectronics Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin300072 China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Department of Medical Imaging Tianjin First Central Hospital Tianjin300192 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China State Key Laboratory of Advanced Medical Materials and Devices Tianjin300072 China

Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N = 51), patients with minimal hepatic encephalopathy (MHE, N = 50), patients with overt hepatic encephalopathy (OHE, N = 51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p © 2024 The Authors

关键词： Diseases

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Surface defect identification using Bayesian filtering on a 3D mesh

arXiv

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arXiv 2025年

作者： Vedove, Matteo Dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025, CC BY-SA.

关键词： Mesh generation

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Surface defect identification using Bayesian filtering on a 3D mesh

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Measurement: Sensors 2025年 38卷

作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

关键词： Mesh generation

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Augmenting Scientific Creativity with Retrieval across Knowledge Domains

arXiv

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arXiv 2022年

作者： Kang, Hyeonsu B. Mysore, Sheshera Huang, Kevin Chang, Haw-Shiuan Prein, Thorben McCallum, Andrew Kittur, Aniket Olivetti, Elsa Human-Computer Interaction Institute CMU PA United States Manning College of Information and Computer Sciences UMASS AmherstMA United States Department of Materials Science and Engineering MIT MA United States

Exposure to ideas in domains outside a scientist’s own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas outside such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can ‘zoom in’ to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration. © 2022, CC BY.

关键词： Search engines

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Federated Learning and Convolutional Neural Networks for Intrusion Detection Systems

Federated Learning and Convolutional Neural Networks for Int...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Malika Abid Mohammed Kamel Benkaddour Mohamed Benouis Computer Science Department Faculty of Sciences and Technology Artificial Intelligence and Information Technology Laboratory (LINATI) University of Kasdi Merbah Ouargla Algeria Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Recently, there has been a growing focus on network traffic threats due to the proliferation of mobile devices and the expanding Internet of Things (IoT). The centralised anomaly detection approach implies sharing the user’s data with a cloud server, which mostly exposes the user to the data breach issue. Given the above issues, this paper proposes a federated learning intrusion detection system (FedIDS) to improve the performance of intrusion detection while protecting user privacy. Motivated by recent exploits into deep learning for intrusion detection, we propose a convolutional neural network (CNN) model to learn and train the record data collected from independent and non-independent identically distributed (Non-IID, IID)data. This work conducts experiments on the NSL-KDD Data set for network intrusion detection. Experimental results show that FedIDS has achieved a competitive advantage over state-of-the-art centralised machine learning methods.

关键词： Performance evaluation Analytical models Privacy Federated learning Intrusion detection Telecommunication traffic Convolutional neural networks

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