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A growing number of longitudinal cohort studies are generating data with extensive patient observations across multiple timepoints. Such data offers promising opportunities to better understand the progression of diseases. However, these observations are usually treated as general events in existing visual analysis tools. As a result, their capabilities in modeling disease progression are not fully utilized. To fill this gap, we designed and implemented ThreadStates, an interactive visual analytics tool for the exploration of longitudinal patient cohort data. The focus of ThreadStates is to identify the states of disease progression by learning from observation data in a human-in-the-loop manner. We propose a novel Glyph Matrix design and combine it with a scatter plot to enable seamless identification, observation, and refinement of states. The disease progression patterns are then revealed in terms of state transitions using Sankey-based visualizations. We employ sequence clustering techniques to find patient groups with distinctive progression patterns, and to reveal the association between disease progression and patient-level features. The design and development were driven by a requirement analysis and iteratively refined based on feedback from domain experts over the course of a 10-month design study. Case studies and expert interviews demonstrate that ThreadStates can successively summarize disease states, reveal disease progression, and compare patient groups.

The availability of massive temporal multivariate data provides unprecedented opportunities to analyze anomaly pattern evolution. However, due to the complicated relationships and dynamic temporality, it is a challenging problem to efficiently reveal and perceive the evolution of abnormal patterns. Existing methods mostly focus on the use of automated algorithms to capture anomalies, which limits the understanding of the evolutionary law. In this paper, we propose a novel visual analytics system, PEVis, to help domain experts extract four types of abnormal evolution patterns and quantitatively analyze the contribution of different features to its occurrence. By aggregating different anomaly detection algorithms, the anomalous data that deviate seriously from other densely distributed points or with small isolated clusters can be identified. A strategy for perceiving temporal multivariate data is proposed to solve the conflict between exploring the global time-varying law and local data distribution in visualization, through aligning dimension reduction space of adjacent timestamp and designing a doughnut glyph to visually display data distribution and feature contributions. Experiments on two real-world datasets, namely air quality index data and consumer price index data, demonstrate that the proposed methods can effectively reveal the seasonal variation of abnormal phenomena.

For many data analysis tasks, such as the formation of well-balanced groups for a fair race or collaboration in learning settings, the balancing between data attributes is at least as important as the actual values of items. At the same time, comparison of values is implicitly desired for these tasks. Even with statistical methods available to measure the level of balance, human judgment, and domain expertise plays an important role in judging the level of balance, and whether the level of unbalance is acceptable or not. Accordingly, there is a need for techniques that improve decision-making in the context of group formation that can be used as a visual complement to statistical analysis. This paper introduces a novel glyph-based visualization, PeaGlyph, which aims to support the understanding of balanced and unbalanced data structures, for instance by using a frequency format through countable marks and salient shape characteristics. The glyph was designed particularly for tasks of relevance for investigation of properties of balanced and unbalanced groups, such as looking-up and comparing values. Glyph-based visualization methods provide flexible and useful abstractions for exploring and analyzing multivariate data sets. The PeaGlyph design was based on an initial study that compared four glyph visualization methods in a joint study, including two base glyphs and their variations. The performance of the novel PeaGlyph was then compared to the best “performers” of the first study through evaluation. The initial results from the study are encouraging, and the proposed design may be a good alternative to the traditional glyphs for depicting multivariate data and allowing viewers to form an intuitive impression as to how balanced or unbalanced a set of objects are. Furthermore, a set of design considerations is discussed in context of the design of the glyphs.

We present a system to visualize multiple time-dependent scalar fields defined on 3D surfaces as they arise from numerical simulations. Domain experts want to understand correlations between multiple scalar fields to verify underlying processes. However, existing visualization techniques require high exploration effort to perceive temporal changes and relations of multiple fields on 3D surfaces. This makes analysis time-consuming and carries a high risk of overlooking regions with specific correlations. Therefore, we introduce a novel concept to facilitate exploration of multiple time-dependent scalar fields simultaneously. The basic idea is to use a novel glyph-based depiction, called skyline visualization, which is inspired by the skyscraper metaphor from information visualization. The skyscrapers are represented by 2D and 3D bar charts, where their floors show the temporal behavior of scalar fields of the corresponding surface region. The user can interact with the skyline visualization to see at which surface areas the scalar fields take on striking values or whether correlations exist between them. Furthermore, the user can immediately see in which regions scalar fields change strongly over time or show strongly inverted behavior. We evaluated our system with several CFD engineers, radiologists and visualization experts. Compared to existing systems, they were able to find regions of interest quickly and with high accuracy.

Table tennis matches consist of many stroke sequences where two players hit the ball interactively and consecutively until one fails to hit the ball. Players usually employ many complicated playing techniques at each stroke in highly antagonistic, variable, and flexible matches. In-depth comparative analyses of players’ stroke sequences are necessary to obtain insights into the technical playing patterns of players. Experts commonly use spreadsheets to browse and compare strokes one by one, and this process is tedious and prone to errors. Statistical analyses are limited to well-defined patterns (e.g., value distribution and relation significance) and fail to present complex and peculiar patterns. We collaborated with experts to dig out soft patterns of stroke sequences and proposed a novel interactive visualization system to present and compare the patterns. The main visualization challenge is to display the multivariate stroke sequence and the spatial variation patterns. We designed a glyph-based pattern view to solve the challenge. These comprehensible visualizations and coordinated views in the system allow efficient comparative analysis of stroke sequence patterns and are highly commended by domain experts, who have identified several new and interesting patterns using the system. We demonstrated the effectiveness and usability of the visualization system through case studies with table tennis experts. .

Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles (Circadian Rhythms), and the degree of regularity of sleep and physical activity have important health ramifications. Ubiquitous devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral patterns. As human behavior is complex and smartphone data is voluminous with many channels (sensor types), it can be challenging to make meaningful observations, detect unhealthy HBR deviations and most importantly pin-point the causes of disruptions. Prior work has largely utilized computational methods such as machine and deep learning approaches, which while accurate, are often not explainable and present few actionable insights on HBR patterns or causes. To assist analysts in the discovery and understanding of HBR patterns, disruptions and causes, we propose ARGUS, an interactive visual analytics framework. As a foundation of ARGUS, we design an intuitive Rhythm Deviation Score (RDS) that analyzes users’ smartphone sensor data, extracts underlying twenty-four-hour rhythms and quantifies their degree of irregularity.​ This score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of HBRs. ARGUS also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state (screen locked, charging), and user-provided or smartphone-inferred ground truth information. This array of visualization overlays in ARGUS enables​ analysts to gain a more comprehensive picture of HBRs, behavioral patterns and deviations from regularity. The design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone sensing. To demonstrate its utility and generalizability, two different datasets were explored using ARGUS and our use cases and designs were strongly validated in evaluation sessions with expert and non-expert users.

9 Pág. Centro de Biotecnología y Genómica de Plantas, As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information., This work was supported by the Grants BioSinT-CM (Y2020/TCS-6555) and CONTEXT (Atracción de Talento Program; 2019-T1/BIO-14053) Projects of the Comunidad de Madrid, MULTI-SYSBIO (PID2020-117205GA-I00) and the Severo Ochoa Program for Centres of Excellence in R&D (CEX2020-000999-S) funded by MCIN/AEI/10.13039/501100011033 and the EPSRC studentship 34000024085 (M.C.)

Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruit growers, such as vintners. Consequently, large amounts of data about the occurrence of D. suzukii have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to the occurrence of D. suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigator for spatio-temporal event predictions, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of our approach in three use cases and an evaluation with more than 30 domain experts. published

In table tennis, tactics specified by three consecutive strokes represent the high-level competition strategies in matches. Effective detection and analysis of tactics can reveal the playing styles of players, as well as their strengths and weaknesses. However, tactical analysis in table tennis is challenging as the analysts can often be overwhelmed by the large quantity and high dimension of the data. Statistical charts have been extensively used by researchers to explore and visualize table tennis data. However, these charts cannot support efficient comparative and correlation analysis of complicated tactic attributes. Besides, existing studies are limited to the analysis of one match. However, one player's strategy can change along with his/her opponents in different matches. Therefore, the data of multiple matches can support a more comprehensive tactical analysis. To address these issues, we introduced a visual analytics system called Tac-Miner to allow analysts to effectively analyze, explore, and compare tactics of multiple matches based on the advanced embedding and dimension reduction algorithms along with an interactive glyph. We evaluate our glyph's usability through a user study and demonstrate the system's usefulness through a case study with insights approved by coaches and domain experts.

Many visual analytics have been developed for examining scientific publications comprising wealthy data such as authors and citations. The studies provide unprecedented insights on a variety of applications, e.g., literature review and collaboration analysis. However, visual information (e.g., figures) that is widely employed for storytelling and methods description are often neglected. We present VIStory, an interactive storyboard for exploring visual information in scientific publications. We harvest a new dataset of a large corpora of figures, using an automatic figure extraction method. Each figure contains various attributes such as dominant color and width/height ratio, together with faceted metadata of the publication including venues, authors, and keywords. To depict these information, we develop an intuitive interface consisting of three components: (1) Faceted View enables efficient query by publication metadata, benefiting from a nested table structure, (2) Storyboard View arranges paper rings—a well-designed glyph for depicting figure attributes, in a themeriver layout to reveal temporal trends, and (3) Endgame View presents a highlighted figure together with the publication metadata. We illustrate the applicability of VIStory with case studies on two datasets, i.e., 10-year IEEE VIS publications, and publications by a research team at CVPR, ICCV, and ECCV conferences. Quantitative and qualitative results from a formal user study demonstrate the efficiency of VIStory in exploring visual information in scientific publications. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s12650-020-00688-1) contains supplementary material, which is available to authorized users.

The Maya employed the k’an |K’AN| glyph in Late Classic (~750 CE) hieroglyphs on murals and polychrome pottery as an adjective meaning precious, yellow. On cacao drinking vessels, the k’an glyph was suggested as a descriptor for a flavoring ingredient, allspice, Pimenta dioica (L.) Merr. (Myrtaceae). However, our previous consensus ethnobotanical fieldwork with Q’eqchi’ Maya healers of Belize revealed another candidate among antidiabetic plants, Tynanthus guatemalensis Donn. Sm. (Bignoniaceae), which was the healers’ top selection for treatment of diabetes and an exceptionally active extract in an antidiabetic assay for inhibition of protein glycation. Traits of T. guatemalensis observed after cross sectioning the liana were: (1) a cross-shaped xylem organization similar to the k’an glyph; (2) an allspice-like aroma; and (3) yellow color. Based on taxonomy and ethnobotany, confirmation of the allspice-like aromatic compound eugenol, and antidiabetic activity, we determined the plant described by the k’an glyph to be T. guatemalensis (chib’ayal in Q’eqchi’), not P. dioica (allspice). In contemporary Q’eqchi’ tradition, the section of the chib’ayal vine with its cross is associated with the eighth day of their Tzolk’in calendar, which is called the “nawal” (energy) of “q’anil” (ripe, full yellow). This day is represented with a different glyph from the k’an glyph, but notably has a cross representing the four cardinal points. The identification of a potent medicinal plant used in the late classic as well as contemporary times may suggest the long-term preservation of traditional medicinal knowledge in Maya culture for pharmacologically significant plants.

Chinese character inpainting is a challenging task where large missing regions have to be filled with both visually and semantic realistic contents. Existing methods generally produce pseudo or ambiguous characters due to lack of semantic information. Given the key observation that Chinese characters contain visually glyph representation and intrinsic contextual semantics, we tackle the challenge of similar Chinese characters by modeling the underlying regularities among glyph and semantic information. We propose a semantics enhanced generative framework for Chinese character inpainting, where a global semantic supervising module (GSSM) is introduced to constrain contextual semantics. In particular, sentence embedding is used to guide the encoding of continuous contextual characters. The method can not only generate realistic Chinese character, but also explicitly utilize context as reference during network training to eliminate ambiguity. The proposed method is evaluated on both handwritten and printed Chinese characters with various masks. The experiments show that the method successfully predicts missing character information without any mask input, and achieves significant sentence-level results benefiting from global semantic supervising in a wide variety of scenes.

Event sequence mining is often used to summarize patterns from hundreds of sequences but faces special challenges when handling racket sports data. In racket sports (e.g., tennis and badminton), a player hitting the ball is considered a multivariate event consisting of multiple attributes (e.g., hit technique and ball position). A rally (i.e., a series of consecutive hits beginning with one player serving the ball and ending with one player winning a point) thereby can be viewed as a multivariate event sequence. Mining frequent patterns and depicting how patterns change over time is instructive and meaningful to players who want to learn more short-term competitive strategies (i.e., tactics) that encompass multiple hits. However, players in racket sports usually change their tactics rapidly according to the opponent's reaction, resulting in ever-changing tactic progression. In this work, we introduce a tailored visualization system built on a novel multivariate sequence pattern mining algorithm to facilitate explorative identification and analysis of various tactics and tactic progression. The algorithm can mine multiple non-overlapping multivariate patterns from hundreds of sequences effectively. Based on the mined results, we propose a glyph-based Sankey diagram to visualize the ever-changing tactic progression and support interactive data exploration. Through two case studies with four domain experts in tennis and badminton, we demonstrate that our system can effectively obtain insights about tactic progression in most racket sports. We further discuss the strengths and the limitations of our system based on domain experts' feedback.

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time. They are common in multimedia applications. Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed. In this paper, we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks. We conclude features of rare categories and two types of anomalies of rare categories. Then we present a novel rare category detection method, called DIRAD, to detect rare category candidates with anomalies. We develop a prototype system called iNet, which integrates two major visualization components, including a glyph-based rare category identifier, which helps users to identify rare categories among detected substructures, a major view, which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes. Evaluations, including an algorithm performance evaluation, a case study, and a user study, are conducted to test the effectiveness of proposed methods.

With the development of neural style transfer and generative adversarial network, the research of text effect style transfer has appeared. The text effect style transfer aims to render text images with style images to produce text effects images. However, for more complex text, the existing methods will generate unrecognizable font images. Therefore, we propose to add morphological methods to the glyph transformation to limit the degree of glyph transformation, and add distance transformation loss when training the texture network to limit the texture transfer, so as to improve the overall transformation effect. Experiments show that, compared with other existing technologies, our proposed method is more suitable for stylizing complex glyph images.

This work is devoted to the comparison of methods for visualization of tensor fields based on the construction of glyph surfaces for visualization of the stress field as a result of the three-dimensional structural analysis. A representative volume of a linear isotropic material was chosen as the analyzed object. Three types of glyph surfaces were considered, the subtypes of these surfaces and the algorithms for their construction were shown. Different methodologies for transforming the original tensor and glyph surfaces are shown, which are best suited for visualizing the structural stress field. The transformation algorithms presented in this work allow to qualitatively visualize tensor fields with negative eigenvalues, which is a fundamental difference from the already known visualization methods and is also fundamental for visualizing stress tensors as results of the three-dimensional structural analysis.

The text rendered by special effects can give a rich visual experience. Text stylization can help users migrate their favorite styles to specified texts, improving production efficiency and saving design cost. This paper proposes a novel text stylization framework, which can transfer mixed text styles, including font glyph and fine decorations, to user-specified texts. The transfer of decorative elements is difficult due to the text is obscured by decorative elements to a certain extent. Our method is divided into three stages: firstly, the position of decorative elements in the image is extracted and retained; secondly, the effects of font glyph and textures other than decorative elements are migrated; finally, a structure-aware strategy is used to reorganize the decorative elements to complete the entire stylization process. Experiments on open source text data sets demonstrated the advantages of our approach over other state- of-the-art style migration methods.

Journalists, government agency and the public may hold different opinions about the same event; these opinions flow within and across multiple social groups. Understanding opinion flow in multiple social groups is conducive to have a quick grasp of the information on social media. Our study aims to explore the opinion flow within or across the pre-defined social groups and the mutual influence of these groups’ views about the same event. First of all, to depict the opinion flow, we propose a topic transition model, and for expressing the interaction of groups’ opinion, we propose a quantitative approach. Secondly, according to 4Ws, we design a visual analytic system to let users analyze public opinion events through our system. The visual interface is tightly coupled with the analytical methods to present a concise summary of the opinion flow of events. A novel forwarding-network slicing diagram is designed to depict the forwarding process. To describe the result of the topic transition model, we also propose a topic transition graph to characterize the opinion flow. And a novel glyph is designed to visualize the characteristic of the influence. Our system provides a variety of interactions to assist users in exploring data, and we demonstrate the functionality of the system through two case studies.

Automatic generation of artistic glyph images is a challenging task that attracts many research interests. Previous methods either are specifically designed for shape synthesis or focus on texture transfer. In this paper, we propose a novel model, AGIS-Net, to transfer both shape and texture styles in one-stage with only a few stylized samples. To achieve this goal, we first disentangle the representations for content and style by using two encoders, ensuring the multi-content and multi-style generation. Then we utilize two collaboratively working decoders to generate the glyph shape image and its texture image simultaneously. In addition, we introduce a local texture refinement loss to further improve the quality of the synthesized textures. In this manner, our one-stage model is much more efficient and effective than other multi-stage stacked methods. We also propose a large-scale dataset with Chinese glyph images in various shape and texture styles, rendered from 35 professional-designed artistic fonts with 7,326 characters and 2,460 synthetic artistic fonts with 639 characters, to validate the effectiveness and extendability of our method. Extensive experiments on both English and Chinese artistic glyph image datasets demonstrate the superiority of our model in generating high-quality stylized glyph images against other state-of-the-art methods., Accepted by SIGGRAPH Asia 2019, code and datasets: https://hologerry.github.io/AGIS-Net/

Permutation exists in various domains such as mathematics, combinatorics, and computer science. Enumerating each permutation, as well as the multivariate information among different items, allows us, for example, to observe distribution, similarity, and dissimilarity of all possible permutations and select a satisfactory permutation or solution. However, the number of permutations increases dramatically along with the number of items in the permutation, which makes it challenging for users to evaluate potential solutions and identify interesting insights. In this paper, we propose PermVizor, a novel and scalable visualization system that aims assisting users exploring the arrangement, distribution, and comparison of permutations. Necessary and comprehensive analysis of requirements is presented for visualization of permutations. PermVizor enables users to explore overall distribution of each permutation with a glyph-based MDS view, investigate statistical information of selected permutations with a parallel coordinates view, and examine detailed arrangement of the items as well the multivariate information among them for each permutation with pixel-based and block-based PermView. Case studies are conducted on classical datasets such as the axis reordering issue in parallel coordinate data and permutation of traveling salesman problem, which shows that PermVizor could facilitate users in exploring unexpected and desired permutations and confirm their finding and decisions in expected permutations.

Learning more about human mobility is crucial for official decision makers and urban planners. Mobility datasets characterize human daily travel behaviors. Most current researches only studied human dynamics from one kind of mobility dataset. However, people may use different means of transportation to different places for different purposes. Besides, the spatial distributions of different types of point of interests (POIs) reflect the land-use types. How to jointly analyze the multi-source mobility datasets and POI information is a great challenge. In this paper, we adopt multi-source datasets, including taxi dataset, public bicycle system dataset and POI dataset, and propose a visual analytics methodology to explore human mobility dynamics insightfully. Two region–feature–time tensors are constructed first, and a tensor decomposition method is employed to classify the mobility patterns automatically. Then, a new POI–mobility glyph is designed to visualize multi-source datasets in a compact manner. Several interactive visual views are also designed to visualize the spatiotemporal patterns from global, regional and locational perspectives. Case studies based on real-world datasets demonstrate the effectiveness of our method, which supports the visual reasoning of trip purposes and mixed urban functions.

Scatterplots require different symbols for different purposes. For presentation, aesthetically pleasing symbols are popular. For analysis, highly discriminable symbols aid pattern detection...

Analysis of game data is used to study player behavior. For puzzle-based games where solutions are usually defined by their action sequences, player behavior can also be studied by their solution complexity. In this paper, we present a visualization system to help learning expert to understand how actions, timing and the resulting strategy change with regard to the solution complexity. To establish a novel perspective into the patterns not only in action choices but also in behavior complexity, we designed an interactive, customized line chart to track how complexity and performance change at each stage of skill acquisition. Specialized glyph system (Strategy Signature) is implemented to find strategy differences easily with simple visual cues. Contextual information can be explored by switching the view modes to see potential links between complexity and raw attributes. Evaluation with expert users shows that the system effectively reduced their time and effort in finding interesting subgroups and gave them unexplored angles of behavior complexity to contemplate player’s skill growth. In summary, this paper illustrates a visualization approach to enable analysis into the subtleties of behavior complexity in video games.

Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding. In this work, we propose ChineseBERT, which incorporates both the {\it glyph} and {\it pinyin} information of Chinese characters into language model pretraining. The glyph embedding is obtained based on different fonts of a Chinese character, being able to capture character semantics from the visual features, and the pinyin embedding characterizes the pronunciation of Chinese characters, which handles the highly prevalent heteronym phenomenon in Chinese (the same character has different pronunciations with different meanings). Pretrained on large-scale unlabeled Chinese corpus, the proposed ChineseBERT model yields significant performance boost over baseline models with fewer training steps. The porpsoed model achieves new SOTA performances on a wide range of Chinese NLP tasks, including machine reading comprehension, natural language inference, text classification, sentence pair matching, and competitive performances in named entity recognition. Code and pretrained models are publicly available at https://github.com/ShannonAI/ChineseBert., To appear at ACL2021

Intuitive visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns in complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by a user-friendly web-based application for creating interactive quantitative pictorial representations of phenotypic data and facilitating the understanding and analysis of image datasets (www.shapography.com). ShapoGraphy enables the user to create a structure of interest as a set of shapes. Each shape can encode different variables that are mapped to the shape dimensions, colours, symbols, and stroke features. We illustrate the utility of ShapoGraphy using various image data, including high dimensional multiplexed data. Our results show that ShapoGraphy allows a better understanding of cellular phenotypes and relationships between variables. In conclusion, ShopoGraphy supports scientific discovery and communication by providing a wide range of users with a rich vocabulary to create engaging and intuitive representations of diverse data types.

Medical named entity recognition is the first step in processing electronic medical records. It is the basis for processing medical natural language text information into medical structured information, which has extremely high research value and application value. In this paper, we have proposed a model that aims to identify various types of named entities such as disease, imaging examination, laboratory examination, operation, drug, and anatomy from Chinese electronic medical record. We construct a fusion Glyph and lexicon model based on BERT. Experimental studies have shown that increasing character-level semantic representation can improve the performance of named entity recognition. In order to boost it, the major measures of our model include: (1) a CNN structure is proposed to capture glyph information. (2) Soft-Lexicon method is introduced to encode lexicon information. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. The experimental results on CCKS2019 dataset showed that the F1 score was 84.64, which was +1.99 higher than the baseline level.

This article contributes a novel visualization method, Missingness Glyph, for analysis and exploration of missing values in data. Missing values are a common challenge in most data generating domains and may cause a range of analysis issues. Missingness in data may indicate potential problems in data collection and pre-processing, or highlight important data characteristics. While the development and improvement of statistical methods for dealing with missing data is a research area in its own right, mainly focussing on replacing missing values with estimated values, considerably less focus has been put on visualization of missing values. Nonetheless, visualization and explorative analysis has great potential to support understanding of missingness in data, and to enable gaining of novel insights into patterns of missingness in a way that statistical methods are unable to. The Missingness Glyph supports identification of relevant missingness patterns in data, and is evaluated and compared to two other visualization methods in context of the missingness patterns. The results are promising and confirms that the Missingness Glyph in several cases perform better than the alternative visualization methods.

Recently, Chinese event detection has attracted more and more attention. As a special kind of hieroglyphics, Chinese glyphs are semantically useful but still unexplored in this task. In this paper, we propose a novel Glyph-Aware Fusion Network, named GlyFN. It introduces the glyphs' information into the pre-trained language model representation. To obtain a better representation, we design a Vector Linear Fusion mechanism to fuse them. Specifically, it first utilizes a max-pooling to capture salient information. Then, we use the linear operation of vectors to retain unique information. Moreover, for large-scale unstructured text, we distribute the data into different clusters parallelly. Finally, we conduct extensive experiments on ACE2005 and large-scale data. Experimental results show that GlyFN obtains increases of 7.48(10.18%) and 6.17(8.7%) in the F1-score for trigger identification and classification over the state-of-the-art methods, respectively. Furthermore, the event detection task for large-scale unstructured text can be efficiently accomplished through distribution.

Chinese Medical Named Entity Recognition (CMNER) is the basic task of information processing and intelligent medical service in Chinese medical field. In order to make full use of the information of characters and words in the text to improve the effect of CMNER, this paper proposes a recognition model based on character-based BiLSTM-CRF, which integrates lexicon and character features. Firstly, in order to make full use of the information of words and word sequence in the text, the C-ExSoftword method is proposed to integrate the lexicon into the model. According to the similarity of Chinese character glyph and morphologically related forms in the field of Chinese medicine, this paper takes the four-corner coding as the character glyph features of Chinese characters, and extracts the morphological features of each word by using the improved Bidirectional Maximum Matching (BDMM) algorithm. Chinese character glyph features and morphological features are integrated into each character vector. Finally, experiments on real data sets show that the proposed model performs better than the character-based and word-based models.

Heap spraying is probably the most simple and effective memory corruption attack, which fills the memory with malicious payloads and then jumps at a random location in hopes of starting the attacker’s routines. To counter this threat, GRAFFITI has been recently proposed as the first OS-agnostic framework for monitoring memory allocations of arbitrary applications at runtime; however, the main contributions of GRAFFITI are on the monitoring system, and its detection engine only considers simple heuristics which are tailored to certain attack vectors and are easily evaded. In this article, we aim to overcome this limitation and propose GLYPH as the first ML-based heap spraying detection system, which is designed to be effective, efficient, and resilient to evasive attackers. GLYPH relies on the information monitored by GRAFFITI, and we investigate the effectiveness of different feature spaces based on information entropy and memory n-grams, and discuss the several engineering challenges we have faced to make GLYPH efficient with an overhead compatible with that of GRAFFITI. To evaluate GLYPH, we build a representative dataset with several variants of heap spraying attacks, and assess GLYPH’s resilience against evasive attackers through selective hold-out experiments. Results show that GLYPH achieves high accuracy in detecting spraying and is able to generalize well, outperforming the state-of-the-art approach for heap spraying detection, NOZZLE. Finally, we thoroughly discuss the trade-offs between detection performance and runtime overhead of GLYPH’s different configurations.

As pictographs, Chinese characters contain latent glyph information, which is often overlooked. In this paper, we propose the FGN (https://githup.com/AidenHuen/FGN-NER), Fusion Glyph Network for Chinese NER. Except for encoding glyph information with a novel CNN, this method may extract interactive information between character distributed representation and glyph representation by a fusion mechanism. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information and interactive information between the neighboring graphs. (2) we provide a method with sliding window and attention mechanism to fuse the BERT representation and glyph representation for each character. This method may capture potential interactive knowledge between context and glyph. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-art performance for Chinese NER. Further, more experiments are conducted to investigate the influences of various components and settings in FGN.

The neat arrangement of Chinese characters is an important factor which affected the visual aesthetics of Chinese characters. Chinese characters have many kinds of glyph centers: the center of convex hull (CCH), the center of boundary rectangle (CBR), the center of circumscribed circle (CCC), the physical gravity center (PGC), and the visual center of calligraphy (VCC). Which center is the best reference point of aesthetic arrangement of Chinese characters? Here, we used the font of stone inscription sets handwritten by Zhenqing YAN in the Tang Dynasty (the Yan font) as a dataset in a visual psychophysics experiment to study how different centers could affect visual aesthetic arrangement of Chinese characters. Results showed that (1) various centers showed different statistical distribution in the arrangement of Chinese characters; (2) the variation of centers in horizontal arrangement was smaller than that of centers in vertical arrangement; (3) the center of the boundary rectangle had the smallest variation range; (4) there was a significantly different arrangement between naive subjects in calligraphy and non-naive subjects. These results suggested that the center of the boundary rectangle of the Chinese characters was the best reference point for the visual aesthetic arrangement of Chinese characters.

Information about the wind situation is crucial for en-route air traffic controllers. In this paper, we compare several glyph designs for showing wind direction and speed by the means of an empirical study. The different designs are based on arrows, wind barbs, and text. During the study, we are measuring response times and accuracy. Moreover, we collect evidence of the applicability of those designs in en-route air traffic control by qualitative feedback from air traffic controllers. Our findings suggest, that the often-used wind barbs are less suited for assessing wind speed and direction. Instead, a combination of arrow and text should be favored

Event Detection is an essential task in information extraction. However, most existing studies on event detection are designed for English text. There is still a lack of efficient algorithm for Chinese event detection, which is expected to be greatly improved. Recent work has shown that enhanced text representation, such as introducing glyph information, can significantly improve downstream tasks in natural language processing. In this paper, we propose a novel method for Chinese Event Detection via Bidirectional Glyph-aware Dynamic Fusion Network, called CED-BGFN. We use two representations: glyph-aware information and pre-trained language model. To integrate the heterogeneous representation modules, we propose a creative fusion network Bidirectional Glyph-aware Fusion Network, named BGFN. Considering the dynamic interaction of the two expressions, BGFN adaptively learns the fusion weights for the downstream event detection task. We conduct extensive experiments to investigate the validity of the proposed method on the ACE 2005 Chinese corpus. Results demonstrate that compared with the previous state-of-the-art methods, our approach obtains transcendent performance in both event trigger identification task and classification task, with an increase of 5.48 (7.46%) and 5.03 (7.1%) in F1-score, respectively.

Components analysis is a common way for the glyph structure research of ancient Chinese characters. Traditional methods require researchers to have a very rich knowledge reserve which is time-consuming and laborconsuming. We proposed a method based on image similarity to study the oracle bone inscriptions components. This method extracted and compressed the component skeleton, and introduced a pixel orientation mechanism, so as to compare the similarity of different components from different characters. The results show that our method can be applied to the components comparison. It will be more effective at processing single simple lines oracle bone inscriptions and for the complex characters the result is slightly worse.

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego&rsquo, s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks.

In this work, we propose a generic visual analytics framework to support tactic analysis based on data collected from racquet sports (such as tennis and badminton). The proposed approach models each rally in a game as a sequence of hits (i.e., events) until one athlete scores a point. Each hit can be described with a set of attributes, such as the positions of the ball and the techniques used to hit the ball (such as drive and volley in tennis). Thus, the mentioned sequence of hits can be viewed as a multivariate event sequence. By detecting and analyzing the multivariate subsequences that frequently occur in the rallies (namely, tactical patterns), athletes can gain insights into the playing styles adopted by their opponents, and therefore help them identify systematic weaknesses of the opponents and develop counter strategies in matches. To support such analysis effectively, we propose a steerable multivariate sequential pattern mining algorithm with adjustable weights over event attributes, such that the domain expert can obtain frequent tactical patterns according to the attributes specified by himself. We also propose a re-configurable glyph design to help users simultaneously analyze multiple attributes of the hits. The framework further supports comparative analysis of the tactical patterns, e.g., for different athletes or the same athlete playing under different conditions. By applying the framework on two datasets collected in tennis and badminton matches, we demonstrate that the system is generic and effective for tactic analysis in sports and can help identify signature techniques used by individual athletes. Finally, we discuss the strengths and limitations of the proposed approach based on the feedback from the domain experts.

Magnetic Resonance Imaging (MRI) technique is very useful for brain tumor diagnosis. However MRI could not find the integrity and location of white matter tracts around the brain tumors. Diffusion Tensor Imaging (DTI) is an emerging technology that gives information about orientation of nervous fiber, anisotropy and their spatial density, with high resolution. DTI is a noninvasive technique, performed by tracking the diffusion of water molecules through brain tissues. Three different visualization techniques of DTI namely scalar indices, tensor glyph, and tractography were used for brain tumor diagnosis and their main characteristics and drawbacks were compared. These analyses were helpful for determining the exact location of brain tumor and the changes in the white matter bundle in affected area. The variation of white matter structure due to brain tumor is analyzed using these three visualization techniques. Parameters influencing the white matter structure were tuned in each technique, which gives more accurate results than the previous studies. The cystic and the solid part of the tumor were accurately located based on the anisotropic and diffusivity parameters. The two different types of white matter pattern are obtained in affected area by extracting the corpus callosum and cingulum bundle.

Youth varieties in Africa such as S’ncamtho, the Ndebele-based youth variety in Zimbabwe, and urban vernaculars interact with urban and modern experiences which offer them new materials and experiences to base their metaphors on compared to older metaphors in the base languages. This paper explores the use of numeral qualities and associations in the conceptualisation and orthographic representation of S’ncamtho metaphors. S’ncamtho is popular with urban youth and this makes social media platforms such as Twitter, Facebook, WhatsApp, SMS and Instagram key in the performance of the youth variety, a performance that includes the creation, use and contraction of metaphors. Numerals offer phonetic attributes which are exploited in S’ncamtho as metaphors for contracting longer words into shorter ones for fast and economic writing on social media. Numerals are also used as frames to create analogies which elicit euphemistic and general S’ncamtho metaphors. Qualities of numerals such as sound, form and association are used to derive S’ncamtho metaphors and a unique numeral aided orthography to represent some of these metaphors in writing. The research deploys relational and attributional tenets of metaphor theory to analyse the numerical mappings in S’ncamtho metaphors.

Calligraphy is an important part of traditional Chinese culture. Authentic calligraphy works are usually preserved on paper, bamboo slips and stone tablets, which are easy to be destroyed and not easily appreciated by most calligraphy lovers anytime and anywhere. Therefore, in order to facilitate the preservation and appreciation of calligraphy works, digital technology and computer technology are used for digital storage, management and service. The time complexity of image recognition algorithm based on traditional method is relatively high, especially the algorithm based on feature matching has low tolerance to Chinese character deformation. Therefore, this paper tries to use deep learning to study the image recognition of Chinese characters. However, some existing data sets of calligraphic images have a small number of corresponding images, which cannot support deep learning network training. In view of this problem, a data augmentation method based on character glyph and stroke characteristics is proposed. This method starts from two aspects of Chinese character glyph and stroke. Chinese character glyph is changed by affine changes of the restricted conditions. The stroke information of Chinese characters is extracted by super pixel and the stroke information is changed to expand the diversity of Chinese characters in the data set. In the process of character recognition, mixup data augmentation algorithm is combined with Inception v4 network which has excellent performance in Imagenet classification tasks?for network recognition training to improve network generalization ability. Experiments show that the algorithm proposed in this paper based on deep learning has a higher recognition effect than previous algorithms, especially in cursive and running recognition. The digital calligraphy knowledge service system based on web terminal and WeChat small program terminal is designed and implemented, which can provide the digital calligraphy knowledge service of calligraphy work segmentation and calligraphy character recognition.

By setting a typeface, each character of the Chinese text can be converted to a glyph pixel matrix. We propose to conduct text classification with such glyph features using bi-directional convolution. Although the pixel embedding can be applied to all languages, it is much more convenient to be used to represent Chinese scripts due to the square shape of Chinese characters. We extract both the forward and backward n-gram features of the text via bi-directional convolutional operations and then concatenate them. A subsequent 1-dimensional max-over-time pooling is applied to the bi-directional feature maps, and then three fully connected layers are used for conducting text classification. The proposed model has a light-weight architecture that only contains a single-layer convolutional neural network. Experiments on several Chinese text classification datasets demonstrate surprisingly excellent results for the training speed and superior performance of the proposed model in comparison with traditional methods.

There is an increasing need to prepare young learners to be Artificial Intelligence (AI) capable for the future workforce and everyday life. Machine Learning (ML), as an integral subfield of AI, has become the new engine that revolutionizes practices of knowledge discovery. Making ML experience accessible to young learners, however, remains challenging due to its high demand for mathematical and computational skills. This research focuses on designing novel learning environments that help demystify ML technologies for K-12 students, and also investigating new opportunities for maximizing ML accessibility through integration with scientific discovery in STEM education. We developed SmileyCluster - a hands-on and collaborative learning environment that utilizes glyph-based data visualization and superposition comparative visualization to assist learning an entry-level ML technology, namely k-means clustering. Findings from an initial case study with high school students in a pre-college summer program show that SmileyCluster leads to positive change in learning ML concepts, methods and sense-making of patterns. Findings of this study also shed light on understanding ML as a data-enabled approach to support evidence-based scientific discovery in K-12 STEM education.

To enhance depth perception and thus data comprehension, additional depth cues are often used in 3D visualizations of complex vascular structures. There is a variety of different approaches described in the literature, ranging from chromadepth color coding over depth of field to glyph-based encodings. Unfortunately, the majority of existing approaches suffers from the same problem: As these cues are directly applied to the geometry’s surface, the display of additional information on the vessel wall, such as other modalities or derived attributes, is impaired. To overcome this limitation we propose Void Space Surfaces which utilizes empty space in between vessel branches to communicate depth and their relative positioning. This allows us to enhance the depth perception of vascular structures without interfering with the spatial data and potentially superimposed parameter information. With this article, we introduce Void Space Surfaces , describe their technical realization, and show their application to various vessel trees. Moreover, we report the outcome of two user studies which we have conducted in order to evaluate the perceptual impact of Void Space Surfaces compared to existing vessel visualization techniques and discuss expert feedback.