Analyzing Visual Mappings of Traditional and Alternative Music Notation

In this paper, we postulate that combining the domains of information visualization and music studies paves the ground for a more structured analysis of the design space of music notation, enabling the creation of alternative music notations that are tailored to different users and their tasks. Hence, we discuss the instantiation of a design and visualization pipeline for music notation that follows a structured approach, based on the fundamental concepts of information and data visualization. This enables practitioners and researchers of digital humanities and information visualization, alike, to conceptualize, create, and analyze novel music notation methods. Based on the analysis of relevant stakeholders and their usage of music notation as a mean of communication, we identify a set of relevant features typically encoded in different annotations and encodings, as used by interpreters, performers, and readers of music. We analyze the visual mappings of musical dimensions for varying notation methods to highlight gaps and frequent usages of encodings, visual channels, and Gestalt laws. This detailed analysis leads us to the conclusion that such an under-researched area in information visualization holds the potential for fundamental research. This paper discusses possible research opportunities, open challenges, and arguments that can be pursued in the process of analyzing, improving, or rethinking existing music notation systems and techniques.Comment: 5 pages including references, 3rd Workshop on Visualization for the Digital Humanities, Vis4DH, IEEE Vis 201

Risk the drift! Stretching disciplinary boundaries through critical collaborations between the humanities and visualization

In this paper, we discuss collaborations that can emerge between humanities and visualization researchers. Based on four case studies we illustrate different collaborative constellations within such cross-disciplinary projects that are influenced as much by the general project goals as by the expertise, disciplinary background and individual aims of the involved researchers. We found that such collaborations can introduce productive tensions that stretch the boundaries of visualization research and the involved humanities fields, often leaving team members "adrift'' trying to make sense of findings that are the result of a mixture of different (sometimes competing) research questions, methodologies, and underlying assumptions. We discuss inherent challenges and productive synergies that these drifts can introduce. We argue that greater critical attention must be brought to the collaborative process itself in order to facilitate effective cross-disciplinary collaborations, and also enhance potential contributions and research impact for all involved disciplines. We introduce a number of guiding questions to facilitate critical awareness and reflection throughout the collaborative process, allowing for more transparency, productive communication, and equal participation within research teams.PostprintPeer reviewe

ADD-up:Visual analytics for augmented deliberative democracy

We demonstrate the first prototype of the ADD-up visual analytics system. The Augmented Deliberative Democracy (ADD-up) project aims to enhance public deliberations by providing argument analytics in real time. The system will ultimately take a stenographic feed of a public deliberation meeting, automatically extract the arguments therein and project visual analytics intended to improve the deliberative quality of the event.publishe

Augmenting Public Deliberations through Stream Argument Analytics and Visualisations

Public deliberations are organised by governments and other large institutions to take the views of citizens around controversial issues. Increasing public demand and the associated burden on public funding make the quality of public deliberation events and their outcomes critical to modern democracies. This paper focuses on technology developed around streams of computational argument data intended to inform and improve deliberative communication in real time. Combining state-of-the-art speech recognition, argument mining, and analytics, we produce dynamic, interactive visualisations intended for non-experts, deployed incrementally in real time to deliberation participants via large screens, hand-held and personal computing devices. The goal is to bridge the gap between theoretical criteria on deliberation quality from the political sciences and objective analytics calculated automatically from computable argument data in actual public deliberations, presented as a set of visualisations which work on stream data and are simple, yet informative enough to make a positive impact on deliberative outcomes

The Impact Of Stress Dependent Permeability Alteration On Gas Based EOR In The Bakken Formation

Effective stress exerted on porous rocks can change and alter reservoir permeability accordingly during reservoir development. The permeability evolution under different reservoir statues will impact oil production and EOR design in the Bakken shale porous media. An accurate permeability model can improve capturing the fluid transport mechanism and create a reliable long-term dynamic fluid forecast via reservoir simulation. This research is focused on studying permeability alteration behavior under different pressure circumstances. The reservoir gradually loses its original pore pressure during production, increasing reservoir net effective stress. Therefore, a reduction in reservoir properties such as permeability or porosity can occur in response to net stress change within the pores due to the withdrawal of the fluids from the reservoir. In contrast, a fluid injection can reduce formation pressure drop and maintain pressure during the development process in tight rock reservoirs. However, physical parameters (e.g., permeability) cannot be fully recovered, and back to its initial value, this nature of rock is characterized as stress sensitivity or hysteresis. Stress-dependent properties are hard to model accurately in reservoir simulation because of the uncertainty associated with the stress-dependent coefficients and correlations. The conventional reservoir simulators use the compressibility concept to consider the change of pore volume, where the rock properties are usually assumed to be insensitive to the evolution of the stress state. However, reservoir compaction and stress changes can significantly impact reservoir management and production performance. In this study, a review of different rock characterizations of the Three forks and Bakken core samples to determine stress dependency of permeability and its hysteresis during pressurizing/ depressurizing rock samples is conducted. Core samples from the Middle Bakken formation in North Dakota for further permeability alteration experiments are utilized. This data will be used to evaluate the permeability behavior with respect to critical pressure known as pressure shock. Also, the data analytic approach to model permeability on a larger scale based on several inputs such as depth, different net confining stress, and porosity is performed. Numerical reservoir simulation using Bakken and Three Forks formation is utilized to integrate permeability pressure correlation in simulation modeling and compare several injection scenarios with non-sensitive permeability models. The results indicate that ignoring the effect of slope discontinuity at a critical effective stress using the same equation for a whole range of data is inaccurate. Indeed, developing permeability-stress correlations cause inapplicable mathematical models and, consequently, erroneous permeability damage prediction. Following this concept, modifying the correlation for two Bakken cores shows that considering the critical points on each hysteresis path could improve the final form of the stress-dependent permeability relationship. Also, machine learning modeling using available lab core data can be used as an alternative method to capture Bakken and Three Forks permeability changes under different net confining stress while incorporating the critical pressure effect. Furthermore, to evaluate the several gas injection scenarios, the timely reservoir pressure change is divided into three distinct regions where critical effective pressure impact and miscibility of gas injection vary based on current reservoir statutes. The results demonstrate that gas injection in these formations is a strong function of fracture/matrix permeability damage. Compared to the model without considering stress-dependent permeability, the cumulative production could reduce because the permeability decreases along with reservoir pressure decline. As a result, considering permeability modeling in numerical simulation can help to understand the role of different injection scenarios and enhance the knowledge for controlling and managing reservoir production by proper operation decisions in unconventional reservoirs

The Future of Interactive Data Analysis and Visualization

The interactive data analysis and visualization (VIS) community has prospered for over thirty years. Generation after generation, the community has evolved its understanding of research problems and, along the way, contributed various techniques, applications, and research methods. While some of the developed techniques have stood the test of time, we will consider what else needs to be remembered or even revitalized from the good old days in this panel. Further, VIS is currently facing exciting times, with great changes and trends within and outside the community. Thus, in this panel, we want to analyze current research trends and discuss our most exciting ideas and directions. Looking ahead, it can already be anticipated that the future of VIS is subject to change. In this panel, we want to map out future research directions for our community. Along these three lines, the guiding theme of our interactive panel will be three types of (provoking) statements: (i) In the good old days, I liked when we did . . . (ii) Currently, a most exciting trend is ... & (iii) In the future, we will be doing . . . Come and join us to reflect on past and present trends, daring a look ahead to an exciting future for the interactive data analysis and visualization community

Which biases and reasoning pitfalls do explanations trigger? Decomposing communication processes in human-AI interaction

Collaborative human–AI problem-solving and decision making rely on effective communications between both agents. Such communication processes comprise explanations and interactions between a sender and a receiver. Investigating these dynamics is crucial to avoid miscommunication problems. Hence, in this article, we propose a communication dynamics model, examining the impact of the sender’s explanation intention and strategy on the receiver’s perception of explanation effects. We further present potential biases and reasoning pitfalls with the aim of contributing to the design of hybrid intelligence systems. Finally, we propose six desiderata for human-centered explainable AI and discuss future research opportunities

Towards agency in human-AI collaboration

Artificial Intelligence (AI) is increasingly evolving from a tool for automating repetitive tasks to an intelligent agent actively engaging in dynamic interactions with humans. As AI becomes more integrated into collaborative contexts, it is essential to examine the factors that shape human–AI interaction. Central to this collaboration is AI agency—the capacity for action and effect—a concept that has remained largely peripheral in existing research. This paper addresses this gap by proposing a comprehensive design space for reasoning about agency in human–AI collaboration. We introduce the high-level perspectives of distribution, modeling, and attribution to outline key dimensions that inform the design of agency in such systems. Our methodology combines a literature review with expert interviews to consolidate existing concepts and surface new insights. To exemplify the capacity of our framework, we reason about three mixed-initiative systems through the lens of our conceptual model. Finally, we identify future directions and critical research gaps in this emerging area

iNNspector: Visual, Interactive Deep Model Debugging

Deep learning model design, development, and debugging is a process driven by best practices, guidelines, trial-and-error, and the personal experiences of model developers. At multiple stages of this process, performance and internal model data can be logged and made available. However, due to the sheer complexity and scale of this data and process, model developers often resort to evaluating their model performance based on abstract metrics like accuracy and loss. We argue that a structured analysis of data along the model's architecture and at multiple abstraction levels can considerably streamline the debugging process. Such a systematic analysis can further connect the developer's design choices to their impacts on the model behavior, facilitating the understanding, diagnosis, and refinement of deep learning models. Hence, in this paper, we (1) contribute a conceptual framework structuring the data space of deep learning experiments. Our framework, grounded in literature analysis and requirements interviews, captures design dimensions and proposes mechanisms to make this data explorable and tractable. To operationalize our framework in a ready-to-use application, we (2) present the iNNspector system. iNNspector enables tracking of deep learning experiments and provides interactive visualizations of the data on all levels of abstraction from multiple models to individual neurons. Finally, we (3) evaluate our approach with three real-world use-cases and a user study with deep learning developers and data analysts, proving its effectiveness and usability.Comment: 41 pages paper, 4 pages references, 3 pages appendix, 19 figures, 2 table

Superoxide Dismutase (SOD) Enzyme Activity Assay in Fasciola spp. Parasites and Liver Tissue Extract

Background: The purpose of this comparative study was to detect superoxide dismutase (SOD) activities in Fasciola hepatica, F. gigantica parasites, infected and healthy liver tissues in order to determine of species effects and liver infection on SODs activity level.Methods: Fasciola spp. parasites and sheep liver tissues (healthy and infected liver tissues), 10 samples for each, were collected, homogenized and investigated for protein measurement, protein detection and SOD enzyme activity assay. Protein concentration was measured by Bradford method and SODs band protein was detected on SDS-PAGE. SODs activity was determined by iodonitrotetrazolium chloride, INT, and xanthine substrates. Independent samples t-test was conducted for analysis of SODs activities difference.Results: Protein concentration means were detected for F. hepatica 1.3 mg/ ml, F. gigantica 2.9 mg/ml, healthy liver tissue 5.5 mg/ml and infected liver tissue 1.6 mg/ml (with similar weight sample mass). Specific enzyme activities in the samples were obtained 0.58, 0.57, 0.51, 1.43 U/mg for F. hepatica, F. gigantica, healthy liver and infected liver respectively. Gel electrophoresis of Fasciola spp. and sheep liver tissue extracts revealed a band protein with MW of 60 kDa. The statistical analysis revealed significant difference between SOD activities of Fasciola species and also between SOD activity of liver tissues (P<.05).Conclusion: Fasciola species and liver infection are effective causes on SOD enzyme activity level