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19th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Lu, Qiang Ma, Cheng Wu, Hongfeng Zhang, Gaofeng Yu, Ye School of Computer Science and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9789819623754

Collaborative relationships are becoming more complex, diversified and multi-organization cross, which poses new challenges to collaborative relationship analysis. In order to analyze the effects of different structural features and node attributes on the overall cooperative relationships, and to support the exploration at the global, group and individual levels to promote the comprehensive analysis of cooperative relationships, we propose a visual analysis method based on graph contrast learning(GCL). The first step is to perturb the structural features and node attributes of the collaboration relationships through data augmentation. The second step is to use the graph convolutional network(GCN) to represent the structural features and node attributes as dense vectors in the high-dimensional vector space. The third step is to explore the influence of different structural features and node attributes through classification task outcome indicators. Based on this method, GCL4Vis, an interactive visual analysis system is designed, which is equipped with a variety of visual views to help users analyze the effects of different structural features and node attributes. Finally, this paper verified the usability and effectiveness of the GCL4Vis visual analysis system through specific case studies and structured interviews with users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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visual Language Maps for Robot Navigation

Visual Language Maps for Robot Navigation

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IEEE International conference on Robotics and Automation (ICRA)

作者： Chenguang Huang Oier Mees Andy Zeng Wolfram Burgard University of Freiburg Germany Google Research USA University of Technology Nuremberg Germany

Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-language models pretrained on Internet-scale data (e.g., image captions). While this is useful for matching images to natural language descriptions of object goals, it remains disjoint from the process of mapping the environment, so that it lacks the spatial precision of classic geometric maps. To address this problem, we propose VLMaps, a spatial map representation that directly fuses pretrained visual-language features with a 3D reconstruction of the physical world. VLMaps can be autonomously built from video feed on robots using standard exploration approaches and enables natural language indexing of the map without additional labeled data. Specifically, when combined with large language models (LLMs), VLMaps can be used to (i) translate natural language commands into a sequence of open-vocabulary navigation goals (which, beyond prior work, can be spatial by construction, e.g., “in between the sofa and the TV” or “three meters to the right of the chair”) directly localized in the map, and (ii) can be shared among multiple robots with different embodiments to generate new obstacle maps on-the-fly (by using a list of obstacle categories). Extensive experiments carried out in simulated and real-world environments show that VLMaps enable navigation according to more complex language instructions than existing methods. Videos are available at https://***.

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Transactional Panorama: A Conceptual Framework for User Perception in Analytical visual Interfaces

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PROCEEDINGS OF THE VLDB ENDOWMENT 2023年第6期16卷 1494-1506页

作者： Tang, Dixin Fekete, Alan Gupta, Indranil Parameswaran, Aditya G. Univ Calif Berkeley Berkeley CA 94720 USA Univ Sydney Sydney NSW Australia Univ Illinois Champaign IL USA

Many tools empower analysts and data scientists to consume analysis results in a visual interface. When the underlying data changes, these results need to be updated, but this update can take a long time-all while the user continues to explore the results. Tools can either (i) hide away results that haven't been updated, hindering exploration;(ii) make the updated results immediately available to the user (on the same screen as old results), leading to confusion and incorrect insights;or (iii) present old-and therefore stale-results to the user during the update. To help users reason about these options and others, and make appropriate trade-offs, we introduce Transactional Panorama, a formal framework that adopts transactions to jointly model the system refreshing the analysis results and the user interacting with them. We introduce three key properties that are important for user perception in this context: visibility (allowing users to continuously explore results), consistency (ensuring that results presented are from the same version of the data), and monotonicity (making sure that results don't "go back in time"). Within transactional panorama, we characterize all feasible property combinations, design new mechanisms (that we call lenses) for presenting analysis results to the user while preserving a given property combination, formally prove their relative orderings for various performance criteria, and discuss their use cases. We propose novel algorithms to preserve each property combination and efficiently present fresh analysis results. We implement our framework into a popular, open-source BI tool, illustrate the relative performance implications of different lenses, and demonstrate the benefits of the novel lenses and our optimizations.

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Stunning Doodle: A Tool for Joint visualization and analysis of Knowledge Graphs and Graph Embeddings 19th

Stunning Doodle: A Tool for Joint Visualization and Analysis...

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19th International European Semantic Web conference (ESWC)

作者： Ettorre, Antonia Bobasheva, Anna Michel, Franck Faron, Catherine Univ Cote Azur CNRS INRIA I3S Sophia Antipolis France

ISBN: (纸本)9783031069819;9783031069802

In recent years, the growing application of Knowledge Graphs to new and diverse domains has created the need to make these resources accessible and understandable by users with increasingly diverse backgrounds. visualization techniques have been widely employed as means to facilitate the exploration and comprehension of such data sources. Moreover, the emerging use of Knowledge Graph Embeddings as input features of Machine Learning methods has given even more visibility to this kind of representation, but raising the new issue of understandability and interpretability of such embeddings. In this paper, we show how visualization techniques can be used to jointly explore and interpret both Knowledge Graphs and Graph Embeddings. We present Stunning Doodle, a tool that enriches the classical visualization of Knowledge Graphs with additional information meant to enable the visual analysis and comprehension of Graph Embeddings. The idea is to help the user figure out the logical connection between (1) the information captured by the Graph Embeddings and (2) the structure and semantics of the Knowledge Graph from which they are generated. We detail the use of Stunning Doodle in a real-world scenario and we show how it has been helpful to interpret different Graph Embeddings and to choose the most suitable with respect to a specific final goal.

关键词： Knowledge Graphs visualization Graph Embeddings

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SCDF: A Novel Single-Cell Classification Method Based on Dimension-Reduced data Fusion 18th

SCDF: A Novel Single-Cell Classification Method Based on Dim...

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18th International conference on Intelligent Computing (ICIC)

作者： Fang, Chujie Li, Yuanyuan Wuhan Inst Technol Sch Math & Phys Wuhan 430205 Peoples R China

ISBN: (纸本)9783031138294;9783031138287

Single-cell RNA sequencing (scRNA-seq) technology has recently brought a new insight into identifying and characterizing novel cell types and gene expression patterns. The study of single-cell RNA sequencing is irreplaceable for the exploration of biology and also faces significant challenges. This has led to the emergence of abundant methods and tools that aim to automatically process specific problems associated with scRNA-seq data. However, a prominent feature of scRNA-seq data is noisy and of high dimensionality due to the large-scale expression datasets with hundreds of thousands of genes. So, the raw single-cell datasets are generally processed for dimensionality reduction before being classified for analysis. There are many methods for dimensionality reduction, such as Principal Component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Each method has different effects on the results of single-cell classification. But, choosing an appropriate method for gene expression data to perform dimensionality reduction in scRNA-seq classification is an unsolved problem. This paper integrated seven dimensionality reduction methods to process gene expression profiles based on data fusion and ensemble learning. Moreover, we determined the optimal number of low-dimensional components for each dimensionality reduction method and each dataset before integration. Different from existing dimensionality reduction techniques, the proposed method implements data fusion and ensemble learning schemes that utilize massive weak learners for accurate classification. Our analysis demonstrates that classification using different data representations may capture a more complete data relationship, and integrating various methods to reduce the dimension of the data promotes better performance for single-cell classification.

关键词： Dimensionality reduction Single-cell classification data fusion Ensemble learning scRNA-seq

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data Virtual Machines: Simplifying data Sharing, exploration & Querying in Big data Environments

Data Virtual Machines: Simplifying Data Sharing, Exploration...

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2022 IEEE International conference on Big data, Big data 2022

作者： Chatziantoniou, Damianos Kantere, Verena Antoniou, Nikos Gantzia, Angeliki Aueb Greece Ntua Greece

ISBN: (纸本)9781665480451

Today's analytics environments are characterized by a high degree of heterogeneity in terms of data systems, formats and types of analysis. Many occasions call for rapid, ad hoc, on demand construction of a data model that represents (parts of) the data infrastructure of an organization, including ML tasks. This data model is given to data scientists to play with (express reports, build ML models, explore, etc.) We present a novel graph-based conceptual model, the data Virtual Machine (DVM) representing data (persistent, transient, derived) of an organization. A DVM can be built quickly and agilely, offering schema flexibility. It is amenable to visual interfaces for schema and query management. dataframing, a frequent querying/preprocessing task in analytics applications, is usually carried out by experienced data engineers employing SQL (in the presence of a relational data warehouse) or Python/R: a procedural approach with all the known drawbacks. dataframes over DVMs are expressed declaratively - and visually, via a simple and intuitive tool. This way, non-IT experts can be involved in dataframing. In addition, query evaluation takes place within an algebraic framework with all the known benefits. I.e. a DVM enables the delegation of data engineering tasks to simpler users. We have seen analogous cases in the past, e.g. with the introduction of SQL. Finally, a DVM offers a formalism that facilitates data sharing, data portability and a single view of any entity - because a DVM's node is an attribute and an entity at the same time. In this respect, DVMs can excellently serve as a data virtualization technique, an emerging trend in the industry. We argue that DVMs can have a significant practical impact in today's big data environments. © 2022 IEEE.

关键词： Big data

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Semantic Explanation of Interactive Dimensionality Reduction

Semantic Explanation of Interactive Dimensionality Reduction

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IEEE visualization conference (IEEE VIS)

作者： Bian, Yali North, Chris Krokos, Eric Joseph, Sarah Virginia Tech Blacksburg VA 24061 USA Dept Def Ft Lee VA USA

ISBN: (纸本)9781665433358

Interactive dimensionality reduction helps analysts explore the high-dimensional data based on their personal needs and domain-specific problems. Recently, expressive nonlinear models are employed to support these tasks. However, the interpretation of these human-steered nonlinear models during human-in-the-loop analysis has not been explored. To address this problem, we present a new visual explanation design called semantic explanation. Semantic explanation visualizes model behaviors in a manner that is similar to users' direct projection manipulations. This design conforms to the spatial analytic process and enables analysts better understand the updated model in response to their interactions. We propose a pipeline to empower interactive dimensionality reduction with semantic explanation using counterfactuals. Based on the pipeline, we implement a visual text analytics system with nonlinear dimensionality reduction powered by deep learning via the BERT model. We demonstrate the efficacy of semantic explanation with two case studies of academic article exploration and intelligence analysis.

关键词： Interactive Dimensionality Reduction Projection Explanation Counterfactual Explanation Human-in-the-loop analysis

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Validation of Non-Invasive MEG Techniques for Detecting Hippocampal and visual Cortex Responses to Entropy and Surprise

Validation of Non-Invasive MEG Techniques for Detecting Hipp...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International conference on

作者： Rachele Robbio Marta Suárez-Pinilla Dunja Duran Darya Frank Nicola Moccaldi Elisa Visani Ryan M. Hill Holly Schofield Matthew Brookes Robin Hellerstedt Pasquale Arpaia Marco De Curtis Paola Lanteri Bryan Strange Giulia Varotto Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Laboratory for Clinical Neuroscience Centre for Biomedical Technology Universida d Politécnica de Madrid Madrid Spain Epileptology unit Magnetoencephalography laboratory Fondazione IRCCS Istituto Neurologico Carlo Besta Milan Italy Division of Psychology Communication and Human Neuroscience University of Manchester Manchester UK Sir Peter Mansfield Imaging Centre School of Physics and Astronomy University of Nottingham Nottingham UK Interdepartmental Center for Research on Management and Innovation in Healthcare (CIRMIS) University of Naples Federico II Naples Italy Clinical and Experimental Epileptology unit Fondazione IRCCS Istituto Neurologico Carlo Besta Milan Italy Neurophysiology unit Fondazione IRCCS Istituto Neurologico Carlo Besta Milan Italy

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

Understanding electrophysiological brain activity in response to cognitive tasks is a key focus in neuroscience. Despite advances in brain data acquisition technologies, invasive methods remain the gold-standard to accurately localise electrophysiological activity, particularly in deep brain structures. This study aims to provide preliminary results to validate superconducting quantum interference device (SQUID)-based magnetoencephalography (sMEG) and optically pumped magnetometer-based (OPM)-MEG against previously described gold-standard intracranial electroencephalograpy (iEEG) in detecting hippocampal and visual cortex responses to uncertain visual stimuli. Using two well-established source reconstruction techniques - dynamic Statistical Parametric Mapping (dSPM) and Linearly Constrained Minimum Variance (LCMV) beamformer - this study compared results from time-frequency analysis across different acquisition methods. While hippocampal time-resolved spectral activity detected via dSPM source reconstruction in sMEG data was consistent with previous iEEG findings, other approaches yielded inconsistent results compared to iEEG. These preliminary findings contribute to the understanding of non-invasive detection of cortical and subcortical activity and underscore the need for further methodological improvements in the analysis of non-invasive neurophysiological data to achieve reliable detection and interpretation of brain activity.

关键词： visualization Brain Time-frequency analysis Reconstruction algorithms Electrophysiology Superconducting magnets Entropy Optical pumping Reliability SQUIDs

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Semantic Tracklets: An Object-Centric Representation for visual Multi-Agent Reinforcement Learning

Semantic Tracklets: An Object-Centric Representation for Vis...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Liu, Iou-Jen Ren, Zhongzheng Yeh, Raymond A. Schwing, Alexander G. Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665417143

Solving complex real-world tasks, e.g., autonomous fleet control, often involves a coordinated team of multiple agents which learn strategies from visual inputs via reinforcement learning. Many existing multi-agent reinforcement learning (MARL) algorithms however don't scale to environments where agents operate on visual inputs. To address this issue, algorithmically, recent works have focused on non-stationarity and exploration. In contrast, we study whether scalability can also be achieved via a disentangled representation. For this, we explicitly construct an object-centric intermediate representation to characterize the states of an environment, which we refer to as 'semantic tracklets.' We evaluate 'semantic tracklets' on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment. 'Semantic tracklets' consistently outperform baselines on VMPE, and achieve a +2.4 higher score difference than baselines on GFootball. Notably, this method is the first to successfully learn a strategy for five players in the GFootball environment using only visual data. For more, please see our project page: https://***/SemanticTracklets

关键词： visualization Scalability Semantics Reinforcement learning Task analysis Intelligent robots

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Improving Radiance simulation workflow with process of anti-aliasing and tone-mapping to enhance users’ experience in VR environments 18

Improving Radiance simulation workflow with process of anti-...

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18th IBPSA conference on Building Simulation, BS 2023

作者： Monteoliva, Juan Manuel Bellia, Laura Fragliasso, Francesca Pattini, Andrea CONICET Mendoza Argentina Department of Industrial Engineering the University of Naples "Federico II" Naples Italy

Recently, the use of VR is developing and spreading to be used for different applications: among them the evaluation of luminous environments and their characteristics by potential occupants for visual comfort assessment. Here, an improved simulation workflow based on Radiance use is presented to enhance the visualization quality in lower resolution VR HMDs devices and consequently users’ immersion experience. The method is based on the use of equirectangular projection, digital signal anti-aliasing processing and tone-mapping trough ‘normtiff’ Radiance command. Results demonstrate that the method increases the photorealism of the model, improving users’ experience. © 2023 *** rights reserved.

关键词： Anti-aliasing

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