Interactive Visual Profiling of Musicians

Jänicke, Stefan; Focht, Josef; Scheuermann, Gerik

doi:10.1109/tvcg.2015.2467620

Cited by 36 publications

(31 citation statements)

References 41 publications

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“…In the field of visualization, researchers have achieved a great deal of success in modeling domain-specific data as a DAG. Typical data includes dynamic relationships between entities [34,50,51], temporal topic data [10,14,47,56], temporal event sequences [54], evolving egocentric network [55], and the information of musicians [23]. Researchers have also developed a set of visualizations to reveal patterns learned from the above data.…”

Section: Related Workmentioning

confidence: 99%

Towards Better Analysis of Deep Convolutional Neural Networks

Liu

Shi²,

et al. 2017

IEEE Trans. Visual. Comput. Graphics

424

276

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Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable.

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Section: Related Workmentioning

confidence: 99%

Towards Better Analysis of Deep Convolutional Neural Networks

Liu

Shi²,

et al. 2017

IEEE Trans. Visual. Comput. Graphics

424

276

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“…Sometimes, even the visualization entirely depends on manually collected data through crowdsourcing (e.g. [WMN*14, RPSF15, HFM16, JFS16]).…”

Section: Data Transformation Aspectsmentioning

confidence: 99%

“…Most often, maps support the analysis of named entities extracted from a text or an entire corpus. Two works illustrate the geographical areas which are associated to persons [Wil15b], for example, by mapping the places of activity of musicians manually extracted from musicological literature in order to support the geospatial comparison of musicians' activity regions [JFS16]. But usually, places mentioned in texts are analysed.…”

Section: Applied Visualization Techniquesmentioning

confidence: 99%

“…Such representations are widely applied in the digital humanities to illustrate the relationships between characters in literary texts [CSV08, Tót13, BB15a, TFK15]. In these graphs, the size of a node can be used to encode the frequency of a character name in the text [BHW11, Pet14], the thickness of an edge [Cob05] (Figure b) or the proximity of the nodes [Poi15, JFS16] can serve to reflect the strength of a relationship and edge style can be used to classify the type of relationship [KOTM13]. As per the aforementioned works, Kochtchi uses a force‐based graph layout to visualize social networks automatically extracted from newspaper articles [KLB14].…”

Section: Applied Visualization Techniquesmentioning

confidence: 99%

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Visual Text Analysis in Digital Humanities

Jänicke

Franzini

Cheema

et al. 2016

Computer Graphics Forum

102

100

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In 2005, Franco Moretti introduced Distant Reading to analyse entire literary text collections. This was a rather revolutionary idea compared to the traditional Close Reading, which focuses on the thorough interpretation of an individual work. Both reading techniques are the prior means of Visual Text Analysis. We present an overview of the research conducted since 2005 on supporting text analysis tasks with close and distant reading visualizations in the digital humanities. Therefore, we classify the observed papers according to a taxonomy of text analysis tasks, categorize applied close and distant reading techniques to support the investigation of these tasks and illustrate approaches that combine both reading techniques in order to provide a multi-faceted view of the textual data. In addition, we take a look at the used text sources and at the typical data transformation steps required for the proposed visualizations. Finally, we summarize collaboration experiences when developing visualizations for close and distant reading, and we give an outlook on future challenges in that research area.While close reading retains the ability to read the source text without dissolving its structure, distant reading does the exact opposite. It aims to generate an abstract view by shifting from observing

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“…Jänicke et al [JFS16] propose the development of an interactive visual profiling system for musicians, which utilizes IV and VA techniques to support users in determining similar musicians. The interface consists of various columns for visualizing the multifaceted data of a musician's CV attributes.…”

Section: Related Workmentioning

confidence: 99%

CV3: Visual Exploration, Assessment, and Comparison of CVs

Filipov

Arleo

Federico

et al. 2019

Computer Graphics Forum

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The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.

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Interactive Visual Profiling of Musicians

Cited by 36 publications

References 41 publications

Towards Better Analysis of Deep Convolutional Neural Networks

Towards Better Analysis of Deep Convolutional Neural Networks

Visual Text Analysis in Digital Humanities

CV3: Visual Exploration, Assessment, and Comparison of CVs

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