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ProcessExplorer: Intelligent Process Mining Guidance 17th

ProcessExplorer: Intelligent Process Mining Guidance

Blockchain Forum / Central and Eastern Europe Forum (CEE Forum) / 17th International conference on Business Process Management (BPM)

作者： Seeliger, Alexander Guinea, Alejandro Sanchez Nolle, Timo Muehlhaeuser, Max Tech Univ Darmstadt Telecooperat Lab Darmstadt Germany

ISBN: (纸本)9783030266189;9783030266196

Large amount of data is collected in event logs from information systems, reflecting the actual execution of business processes. Due to the highly competitive pressure in the market, organizations are particularly interested in optimizing their processes. Process mining enables the extraction of valuable knowledge from event logs, such as deviations, bottlenecks, and anomalies. Due to the increase of process complexity in flexible environments, visual exploration is increasingly becoming more challenging. In this paper, we propose ProcessExplorer, an interactive process mining approach to enable fast data analysis and exploration. ProcessExplorer takes an event log as input to automatically suggest subsets of similar process behavior, evaluate each subset, generate interesting insights, and suggest the subsets with the most interesting characteristics. We implemented our approach into an interactive visual exploration system, which we use as part of a user study conducted to evaluate our approach. Our results show that ProcessExplorer can be successfully applied to analyze and explore real-life data sets efficiently.

关键词： Process mining Interactive data exploration data analytics Business intelligence Recommender system

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Using Exploratory data analysis to Support Implementation and Improvement of Education Technology Product 20th

Using Exploratory Data Analysis to Support Implementation an...

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20th International conference on Artificial Intelligence in Education (AIED)

作者： Feng, Mingyu Brenner, Daniel Coulson, Andrew 400 Seaport Ct Redwood City CA 94063 USA 111 Acad Suite 100 Irvine CA 92617 USA

ISBN: (纸本)9783030232078;9783030232061

ST Math is a visual instructional game-based program that builds a deep conceptual understanding of mathematics through rigorous learning and creative problem solving. It is widely adopted in many elementary schools in the US. In this paper, we describe the exploratory data analysis we conducted on system log data of kindergarten students to discover patterns in students' interaction with the system, and to examine productivity and engagement of students with different profiles. The findings informed the implementation of the program in schools as well as improvement of individual games in the program to render more effective student learning.

关键词： ST Math Game-based learning Education technology Exploratory data analysis

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Age-VOX-Celeb: Multi-Modal Corpus for Facial and Speech Estimation

Age-VOX-Celeb: Multi-Modal Corpus for Facial and Speech Esti...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Naohiro Tawara Atsunori Ogawa Yuki Kitagishi Hosana Kamiyama NTT Corporation Japan

Estimating a speaker's age from their speech is more challenging than age estimation from their face because of insufficiently available public corpora. To tackle this problem, we construct a new audio-visual age corpus named AgeVoxCeleb by annotating age labels to VoxCeleb2 videos. AgeVoxCeleb is the first large-scale, balanced, and multi-modal age corpus that contains both video and speech of the same speakers from a wide age range. Using AgeVox-Celeb, our paper makes the following contributions: (i) A facial age estimation model can outperform a speech age estimation model by comparing the state-of-the-art models in each task. (ii) Facial age estimation is more robust against the difference between training and test sets. (iii) We developed cross-modal transfer learning from face to speech age estimation, showing that the estimated age with a facial age estimation model can be used to train a speech age estimation model.

关键词： Training Transfer learning Estimation Signal processing Robustness data models Task analysis

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Multimodal Dialog for Browsing Large visual Catalogs using exploration-Exploitation Paradigm in a Joint Embedding Space 19

Multimodal Dialog for Browsing Large Visual Catalogs using E...

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ACM International conference on Multimedia Retrieval (ICMR)

作者： Bhattacharya, Indrani Chowdhury, Arkabandhu Raykar, Vikas C. Rensselaer Polytech Inst Troy NY 12180 USA Rice Univ Houston TX USA IBM Res Bangalore Karnataka India

ISBN: (纸本)9781450367653

We present a multimodal dialog (MMD) system to assist online customers in visually browsing through large catalogs. visual browsing allows customers to explore products beyond exact search results. We focus on a slightly asymmetric version of a complete MMD system, in that our agent can understand both text and image queries, but responds only in images. We formulate our problem of lshowing the k best images to a userz, based on the dialog context so far, as sampling from a Gaussian Mixture Model (GMM) in a high dimensional joint multimodal embedding space. The joint embedding space is learned by Common Representation Learning and embeds both the text and the image queries. Our system remembers the context of the dialog, and uses an exploration-exploitation paradigm to assist in visual browsing. We train and evaluate the system on an MMD dataset that we synthesize from large catalog data. Our experiments and preliminary human evaluation show that the system is capable of learning and displaying relevant products with an average cosine similarity of 0.85 to the ground truth results, and is capable of engaging human users.

关键词： Multimodal dialog visual browsing common representation learning interactive AI agent multimodal content analysis and retrieval online human-computer interaction

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Computer-Aided Geomorphic Seabed Classification and Habitat Mapping at Punta Licosa MPA, Southern Italy 20th

Computer-Aided Geomorphic Seabed Classification and Habitat ...

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20th International conference on Computational Science and Its Applications (ICCSA)

作者： Violante, Crescenzo CNR Inst Heritage Sci Via G San Felice 8 Naples Italy

ISBN: (纸本)9783030588021;9783030588014

Accurate seafloor maps serve as a critical component for understanding marine ecosystems and are essential for marine spatial planning, management of submerged cultural heritage and hazard risk assessments. In September 2001 the Marine Protected Area (MPA) of Punta Licosa has been mapped using a multibeam echosounder (MBES) and a side scan sonar (SSS) system in support of the Geosed project. Such seabed investigations has allowed for high-resolution bathymetric measurements and acoustic seafloor characterization through backscatter imagery. Based on visual interpretation of the data, the present study utilized a computer-aided seabed classification approach to map marine landform features and seabed composition of the study area. The results were then translated into a complete coverage geomorphologic map of the area to define benthic habitats. Offshore shelf plain make up more than half of the region (52.2%), with the terraces making up another 10.2% of the total area. Slopes make up a cumulative 30.1% of the study area. Scarp features comprise 4.3% while ridge features reach only 3.2% of the total study area. Benefits of the computer-aided seabed classification approach used in this study consisted in a fairly accurate geomorphic classification, while the effectiveness of a semi-automated approach for identifying substrate composition from backscatter data mostly relied on the level of acoustic artefacts present within the survey area.

关键词： Habitat mapping Geomorphology Spatial analysis Multibeam Backscatter DEM

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A Comprehensive analysis of Modern Object Detection Methods for Maritime Vessel Detection 2

A Comprehensive Analysis of Modern Object Detection Methods ...

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conference on Artificial Intelligence and Machine Learning in Defense Applications ii

作者： Spraul, Raphael Sommer, Lars Schumann, Arne Fraunhofer IOSB Inst Optron Syst Technol & Image Exploitat Fraunhoferstr 1 D-76131 Karlsruhe Germany Fraunhofer Ctr Machine Learning Karlsruhe Germany

ISBN: (纸本)9781510639003

Object detection is the basis for several computer vision applications and autonomous functionalities. The task has been studied extensively and since the onset of deep learning detection accuracy have increased significantly. Every year several new models based on convolutional neural networks (CNNs) are developed and released. However, the development is driven by large research datasets, such as ImageNet and MS COCO, which aim to cover a large range of classes and contain very strong biases with respect to object size and position. Thus existing models and design choices are biased towards such situations. More specialized domains, such as that of maritime vessel detection, can have very different requirements and not all mainstream models are equally suited towards this task. Specific challenges of maritime vessel detection in surface-to-surface view are a large variety of object sizes due to distances from the camera but also the large range in different vessel types, atmospheric effects, and strong overlap between objects. Furthermore, the lack of of large training datasets in such specialized domains is a limitation that needs to be considered. Finally, the existing smaller datasets often contain strong biases themselves, as they were usually recorded in a single location with unique visual characteristics and vessel types that may be very distinct from those in other datasets. In this work we analyze the performance of several of the latest state-of-the-art object detectors in the context of maritime vessel detection. We evaluate the detectors on the limited existing public datasets, including the specialized Singapore Maritime dataset and the SeaShips dataset but also ship images included in general object detection datasets, such as MS COCO. We specifically analyze how well existing dataset biases impact the ability of the resulting detectors to generalize. In addition to this, we create our own maritime vessel training data from online sources and investi

关键词： ship detection maritime vessel surface view generalization cross dataset

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面向SWF日志事件流数据的可视分析系统

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数据与计算发展前沿 2021年第4期3卷 3-17页

作者：李玥杨波芦旭熠单桂华中国科学院计算机网络信息中心北京100190 中国科学院大学北京100049

【目的】为解决高性能计算集群的日志数据交互可视分析的问题,提供一套支持多种不同集群系统日志数据的可视分析框架,本文设计并实现了面向SWF日志数据的事件流可视分析系统SWFVis。【方法】SWFVis基于时间事件序列数据可视分析技术,结... 详细信息

【目的】为解决高性能计算集群的日志数据交互可视分析的问题,提供一套支持多种不同集群系统日志数据的可视分析框架,本文设计并实现了面向SWF日志数据的事件流可视分析系统SWFVis。【方法】SWFVis基于时间事件序列数据可视分析技术,结合多维可视分析、关联可视分析等技术,提出了一套针对SWF日志中的时序属性、关联属性、多维属性进行综合可视分析的方法。【结果】针对公开的iPSC/860与ForHLR ii集群产生的真实数据,SWFVis直观展示了日志中整体作业的并发状态、执行情况,并对集群中存在的作业处理模式进行分析。【局限】对于事件流可视化呈现效果,在三维场景中存在的视角遮挡、分布杂乱的现象,未来考虑通过层次聚类等方法实现进一步的优化。【结论】SWFVis提供了针对SWF日志数据的交互式可视分析方法,支持多种计算机集群使用。通过对作业间存在的时序性、关联性进行展示,以及对作业多维属性进行挖掘,可以直观呈现集群工作状态、用户提交行为,并交互式发掘集群作业中存在的处理模式,为集群调度优化提供支撑。

关键词：高性能计算集群时间事件序列事件流可视分析 SWF日志

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iConViz: Interactive visual exploration of the Default Contagion Risk of Networked-Guarantee Loans

iConViz: Interactive Visual Exploration of the Default Conta...

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IEEE Symposium on visual Analytics Science and Technology (VAST)

作者： Zhibin Niu Runlin Li Junqi Wu Dawei Cheng Jiawan Zhang College of Intelligence and Computing Tianjin University Shanghai Jiao Tong University

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks. During economic slowdowns, corporate default may spread like a virus and lead to large-scale defaults or even systemic financial crises. To help financial regulatory authorities and banks manage the risk associated with networked loans, we identified the default contagion risk, a pivotal issue in developing preventive measures, and established iConViz, an interactive visual analysis tool that facilitates the closed-loop analysis process. A novel financial metric, the contagion effect, was formulated to quantify the infectious consequences of guarantee chains in this type of network. Based on this metric, we designed and implemented a series of novel and coordinated views that address the analysis of financial problems. Experts evaluated the system using real-world financial data. The proposed approach grants practitioners the ability to avoid previous ad hoc analysis methodologies and extend coverage of the conventional Capital Accord to the banking industry.

关键词： data visualization visual analytics Risk management Banking Monitoring Tools Task analysis

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visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Visual Analytics for Deep Embeddings of Large Scale Molecula...

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IEEE International conference on Big data (Big data)

作者： Chae, Junghoon Bhowmik, Debsindhu Ma, Heng Ramanathan, Arvind Steed, Chad Oak Ridge Natl Lab Oak Ridge TN 37830 USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9781728108582

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

关键词： visual analytics machine learning HCI molecular dynamics

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Optimizing Cross-Platform data Movement 35

Optimizing Cross-Platform Data Movement

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IEEE 35th International conference on data Engineering (ICDE)

作者： Kruse, Sebastian Kaoudi, Zoi Quiane-Ruiz, Jorge-Arnulfo Chawla, Sanjay Naumann, Felix Contreras-Rojas, Bertty HBKU Qatar Comp Res Inst Ar Rayyan Qatar Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781538674741

data analytics are moving beyond the limits of a single data processing platform. A cross-platform query optimizer is necessary to enable applications to run their tasks over multiple platforms efficiently and in a platform-agnostic manner. For the optimizer to be effective, it must consider data movement costs across different data processing platforms. In this paper, we present the graph-based data movement strategy used by RHEEM, our open-source cross-platform system. In particular, we (i) model the data movement problem as a new graph problem, which we prove to be NP-hard, and (ii) propose a novel graph exploration algorithm, which allows RHEEM to discover multiple hidden opportunities for cross-platform data processing.

关键词： data processing Task analysis Java data models Sparks data structures Query processing

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