Reviewerly/exHarmony huggingface dataset:
A Cognitive Model for Guiding Automation

A variety of systems and exist for managing human-machine team throughput and effectiveness. One example is autonomous managers (AMs), software that dynamically reallocates tasks to individual members of a team based on their workload and performance. Cognitive models can inform these technologies by projecting performance into the future and enabling “what-if” analyses. For example, would removing a task from an individual whose current performance is low cause them to improve? Conversely, can a team member who is currently performing well handle even more work without dropping performance? In the present study, we develop and validate a cognitive model built in the Adaptive Control of Thought – Rational (ACT-R) cognitive architecture in a novel empirical paradigm: The Intelligence, Surveillance, and Reconnaissance Multi-attribute Task Battery (ISR-MATB). In this task, participants engage in a mock ISR task in which they must integrate information from several subtasks to arrive at a decision about a situation. These tasks include searching visual displays, listening for audio chatter, making decisions based on multiple cues, and remaining vigilant for signals. The tasks are based upon analogous laboratory psychology tasks to improve empirical rigor. Eight participants completed the task under two 30-minute conditions: easy and difficult. The difficult task required searching more complex stimuli in the audio and visual domain than in the easy condition. In addition, subjective workload ratings (NASA-TLX) were collected. We describe the preliminary behavioral and self-report results, as well as the ACT-R model’s fit to the behavioral data. Further, we describe a new method for workload visualization and task decomposition using model-based analyses.
The Removal of Irrelevant Human Factors in a Multi-Review Corpus through Text Filtering

Generating a high-quality explainable summary of a multi-review corpus can help people save time in reading the reviews. With natural language processing and text clustering, people can generate both abstractive and extractive summaries on a corpus containing up to 967 product reviews (Moody et al. 2022). However, the overall quality of the summaries needs further improvement. Noticing that online reviews in the corpus come from a diverse population, we take an approach of removing irrelevant human factors through pre-processing. Apply available pre-trained models together with reference based and reference free metrics, we filter out noise in each review automatically prior to summary generation. Our computational experiments evident that one may significantly improve the overall quality of an explainable summary from such a pre-processed corpus than from the original one. It is suggested of applying available high-quality pre-trained tools to filter noises rather than start from scratch. Although this work is on the specific multi-review corpus, the methods and conclusions should be helpful for generating summaries for other multi-review corpora.
Leveraging Manifold Learning and Relationship Equity Management for Symbiotic Explainable Artificial Intelligence

Improvements in neural methods have led to the unprecedented adoption of AI in domains previously limited to human experts. As these technologies mature, especially in the area of neuro-symbolic intelligence, interest has increased in artificial cognitive capabilities that would allow an AI system to function less like an application and more like an interdependent teammate. In addition to improving language capabilities, next-generation AI systems need to support symbiotic, human-centered processes, including objective alignment, trust calibration, common ground, and the ability to build complex workflows that manage risks due to resources such as time, environmental constraints, and diverse computational settings from super computers to autonomous vehicles.In this paper we review current challenges in achieving Symbiotic Intelligence, and introduce novel capabilities in Artificial Executive Function we have developed towards solving these challenges. We present our work in the context of current literature on context-aware and self-aware computing and present basic building blocks of a novel, open-source, AI architecture for Symbiotic Intelligence. Our methods have been demonstrated effectively in both simulated crisis and during the pandemic. We argue our system meets the basic criteria outlined by DARPA and AFRL providing: (1) introspection via graph-based reasoning to establish expectations for both autonomous and team performance, to communicate expectations for interdependent co-performance, capability, an understanding of shared goals; (2) adaptivity through the use of automatic workflow generation using semantic labels to understand requirements, constraints, and expectations; (3) self healing capabilities using after-action review and co-training capabilities; (4) goal oriented reasoning via an awareness of machine, human, and team responsibilities and goals; (5) approximate, risk-aware, planning using a flexible workflow infrastructure with interchangeable units of computation capable of supporting both high fidelity, costly, reasoning suitable for traditional data centers, as well as in-the-field reasoning with highly performable surrogate models suitable for more constrained edge computing environments. Our framework provides unique symbiotic reasoning to support crisis response, allowing fast, flexible, analysis pipelines that can be responsive to changing resource and risk conditions in the field. We discuss the theory behind our methods, practical concerns, and our experimental results that provide evidence of their efficacy, especially in crisis decision making.
Mixed reality control of a mobile robot via ROS and digital twin

In recent years, mobile robotics has been increasingly used in agricultural production due to technological progress and increasingly powerful IT systems. Today, drones are already being used to spray agricultural areas with pesticides, for example. In the field of mobile robotics, control is currently based primarily on a controller or an app on a smartphone or tablet. If spatial target coordinates are to be specified, which the robot has to control, both systems quickly reach their limits. Using a mobile robot developed for the management of monocultures, this paper explains how spatial target coordinates can be approached with the help of a digital twin, ROS and a mixed reality interface using an MS HoloLens. The fusion of the technologies accordingly opens up new possibilities for a "human-robot interface".When developing an operating concept for the mobile robot, problems quickly arise regarding the determination of coordinates. The explanation for this is: The coordinate systems of the HoloLens and the mobile robot are not identical and, above all, are not known to the user. However, these obstacles can be eliminated by using a Digital Twin, which is displayed on the real robot using the HoloLens. By this measure, the coordinate system of the mobile robot is made known to the mixed reality device and it is possible to send the position coordinates as an offset to the current position of the robot. However, it should be noted that Unity (on the HoloLens) uses the left-hand rule, while ROS (on the robot) uses the right-hand rule. Therefore, the position data must be transformed accordingly.The same applies to the rotations, which must be converted as the equivalent of the coordinate transformation. Afterwards, the converted pose, which is now available in a "base_link" frame, can be transferred to the mobile robot via ROS.For the communication between the mixed reality device and the mobile robot, a ROS Bridge Client, which provides the messages in JSON format, is used. When sending target poses, it is important to note that the current time-stamped must be mandatory included in the message, otherwise the transformation from the "base_link" frame will not be converted to "map" coordinates. The transformation of the pose must also be done for the visual solution of the path planning. For the visualization, a distinction is made between the local and the global path planner A digital twin is superimposed on the real mobile robot for control and path planning. In the course of the project, problems with accuracy or performance have been discovered. In the intended application area - agriculture - the performance is weighted higher after an evaluation, so that slight position inaccuracies are accepted. The paper presents the concept as well as the interaction possibilities between the human and the mobile robot via the digital twin.
Fostering text mining with knowledge graphs: An approach to support business experts in defining domain-specific document sets

Text mining techniques offer an efficient approach to extract information from text-based data, such as online news, for strategic planning tasks like the scenario technique. Data selection and data preprocessing is a crucial step in this process, during which decisive search terms help to sharpen the text corpus for downstream text mining activities. The integration of business knowledge is crucial for the creation of high-quality domain-specific document sets. Manually defining search terms is a time-consuming task for managers. Knowledge graphs accelerate this vital process: Based on a few initial search terms, related domain-specific subgraphs are selected which yield additional search terms that cannot be retrieved via thesauri or mere semantic similarity comparisons. Furthermore, possible biases introduced by subjective expert assessments are avoided and a more objective data selection is realized. The concept is demonstrated in a use case on electric mobility including an online demonstrator (www.digital-scenarios.com).
Evaluation of The Impact of Visual Decoration on The Memorable Effect in Big Data Visualization - The Example of Bar Chart

In recent years, data visualization has been applied to various scenes, and the memory of data visualization has received widespread attention. Some researchers have studied this. However, the effect of visual decoration is polarized in the research community. Some researchers believe that visual decoration can improve memory effect, while others believe that visual decoration can interfere with memory. This paper takes the bar chart as an example to further evaluate the memory effect of visual decoration in visualization, and puts forward four assumptions: ① the visualization using visual decoration has better memory; ② The position of visual decoration will affect the memory effect; ③ The colour of visual decoration will affect the memory effect; ④ The visual position will affect the memory effect under the same visual decoration. The results show that assumptions 1, 2, 3 and 4 are valid, which proves that visual decoration can improve the memory of visualization, and the location, colour and type of visual decoration will affect the memory of visualization.
Detecting and monitoring wandering in AD patients with an integrated device based on Humanitude

Alzheimer is a neurodegenerative type of dementia, that has progressive impairment of cognitive and behavioral functions, most commonly presented above 65 years old and it is divided in three stages. Wandering is the most frightening symptom for familiars and caregivers in the mild stage (second stage of the disease) because it can cause from a minor damage to death. Detecting and monitoring wandering is a complex task and has become a strong research line for several research projects. This paper focuses in proposing, developing and testing a support system for monitoring trajectories to identify direction changes alterations and positioning. The proposed system is called Motion acquisition system + Global positioning system(SAM + GPS). The system integrates an accelerometer, magnetometer, gyroscope and a GPS module. To address this challenge, after the development of the device, a pilot test was conducted with 9 young adult healthy subjects instrumented with the SAM + GPS. Each subject needed to route 2 specific trajectories to prove that the integrated device was able to measure different direction changes and map an accurate trajectory for future wandering detection. It was found that the SAM + GPS had an acceptable error of 12.29% measuring the direction changes in the first trajectory and a 27.44% critical error in the second trajectory. In addition, the device was able to map most of the trajectories with high similarities to an ideal pattern traced with the expected horizontal accuracy of 2.5m of the GPS Neo 6m module. It is worth mentioning that the device had a better performance in outdoor environments than in indoor environments, so in future work, more tests are considered with a larger population sample with the integration of an indoor positioning system and Humanitude methodologyprinciples.
Neurosciences and museum - Museum visit as inclusive, embodied and transformative experience

Following their previous writings and research works, Authors describe very recent experimentations at Museo Nazionale Etrusco di Villa Giulia (Rome) devoted to study the visitors’ behavour and to verify the effectiveness of inclusive and multisensorial communication.Starting from the assumption that access and accessibility to Cultural Heritage are not simply intended as physical approach, and they happen when individuals “appropriate” and “transform” cultural contents, this paper shortly discusses the “Emotion Museology” principles, according to which what moved visitors will be particularly remembered by them, processed and transformed, becoming a very personal asset.Emotions, although difficult to define, are an important element in cognitive processes and are inclusive, as each visitor can empathise with objects and stories. The innovative experiment described by Authors has been conducted in a museum environment with the aid of techniques for detecting the neurophysiological factors of visitors during a visit: a number of experiments have been carried out in recent years on perception mechanisms of a neuro aesthetic nature, but not to indagate the spatial cognition and the role of “atmospherical” conditions.Searching for what all audiences have in common, and not what divides and differentiates them, emotions answer to objects, spaces and communicative stimuli proposed by museums (captions, context, relations). Conversely, differences have also to be considered and “celebrated” as a humanity’s treasure. Then, emotional stimuli can originate very different responses, assuring intimate and individual appropriation processes. From this point of view, the research team aims to relate unconscious responses with cognitive processing of contents: pre visit expectations and “bias” and post visit feedback can support an integrate interpretation of data.In this perspective, and following the seven “Design for All” principles, can be updated referring to cultural accessibility and inclusion, overcoming and abandoning the unrealistic goal of guaranteeing the same experience for different publics, but rather aiming to ensure a fulfilling, lasting and transformative experience for all.
MRI Image Segmentation using Fuzzy C-Means in MATLAB

Image segmentation is challenging because an image has to be partitioned into regions with pixels that contain similar attributes. To accomplish this task, many use MATLAB, Python, or other environments that allow them to set thresholds for the image, to group similar pixels together, or remove undesirable pixels from the image. When approaching the image segmentation problem, many use the fuzzy c-means algorithm to group similar pixels with the same features in an image. Hence, it makes it easier to analyze specific areas in the image, such as a tumor that may be present. This paper presents a tutorial on performing image segmentation using MATLAB and Fuzzy C-Means. Through this tutorial, we examine how fuzzy c-mean is used to solve the image segmentation problem and the methodologies used to properly cluster data.
Visual variable perception in time series dynamic visualization

Visualization facilitates understanding by encoding large amounts of complex information into intuitive formats such as graphics and images. In the design of dynamic visualization of time series, visual coding is more important, because a large number of complex and high-dimensional data will follow over time, but users may not be able to process and filter them in time and effectively. The paper evaluates the cognitive load and performance of users under two time pressures of eight different dynamic visual variables through experiments, so as to allocate more significant coding methods to higher priority information and improve the efficiency of information acquisition. The results show that there are significant differences in cognitive load when using different visual variables, and the cognitive load is lower when using angle, symbol number and area coding. However, there is no significant difference in user performance. There was no significant difference in cognitive load between the two time pressure states, but there was significant difference in accuracy. The accuracy under low time pressure is significantly higher than that under high time pressure.
Enabling active learning experience in XR environments – identifying the design elements for XR learning objects

This research focuses on identifying the design elements for XR learning objects. In this research the data gathered is focusing on the Finnish secondary level education pilot projects which use XR-environments. In this concept paper we present the identified phases for data collection. The identified phases are teacher’s design documents, the planning, implementing phases and reflection phase. Data is collected from teachers that are designing the learning object for XR environment.Extended Reality (XR) is an umbrella term for virtual, mixed and augmented realities. Commonly they’re known as Virtual Reality (VR), Mixed Reality (MR) and Augmented Reality (AR). VR is usually associated into Head Mounted Displays (HMD) and they’re referred as VR-glasses in common language. VR-glasses are basically creating stereoscopic picture and it creates the user feeling like they’re immersed in the 3-dimensional space. (Milgram &amp; Kishino, 1994)XR-environments provides many possibilities to support learning. When XR is used effectively in learning, it increases the participant’s interest in and focus on the learning task. XR makes it possible to look at things that would not otherwise be so well illustrated in the real world. Augmented reality increases students motivation and helps them explore the existing environment (Sotiriou &amp; Bogner 2008). Several studies show that the use of AR in education leads to enhanced learning outcomes (Akçayır &amp; Akçayır 2016).Data is collected nowadays everywhere but there’s a big gap in utilizing the data for the purposes to develop learning and pedagogy. Most of the data applied is for business and management purposes. The term Educational data mining has been firstly mentioned in half of the 2005 and since that the applications and research of this field has been growing. One of the substudy fields of EDM is Learning Analytics which is defined as measuring, collecting, analysing and reporting of data from acquired from learning environments (Larusson &amp; White, 2014; Siemens &amp; Baker, 2013). Learning analytics interprets the collected data from various sources. Data is collected from various learning environments which leave “digital footprints” from student interactions. The data is analysed to make predictions, to visualize learning progress and to make interventions in learning processes. Learning analytics field doesn’t limit only to algorithm based interpretations of learning but it could utilize different techniques to analyze the data properly. (Johnson et al, 2011).
Spatial views in intelligent cabin interaction behavior and touchpoints design

Intelligent cabin is a space that provides users with interaction information during vehicle movement, but in current research, the description model of intelligent cabin interaction design is framed by input-output and focuses only on actions and feedback, but in a multi-touchpoint cabin, the relationship between the interaction medium and the user's spatial location is equally important. Therefore, this paper attempts to refine the spatial description model of interaction touchpoints and interaction behaviors in the cabin based on the existing spatial model-related literature and the cabin interaction design practice to help interaction designers in related fields identify and solve the human-computer relationship issues in the intelligent cabin more comprehensively. The addition of the spatial dimension will expand the concept of interaction touchpoints in the cabin, which will be beneficial to the design of intelligent cabin interaction in both the concept development and design expression stages.
Knowledge Visualization of Earthquake: Impact of Design Format on Readers' Perception and Understanding

To strengthen the seismic intensity and differentiate adaptations to earthquakes in Taiwan, the Central Weather Bureau of the Ministry of Transportation and Communications subdivided the original seismic intensity of 5 Strong and 6 Violent earthquakes of the Seismic Intensity Scale into 5 Lower and 5 Upper, 6 Lower and 6 Upper respectively in 2020.First, we collected 28 infographics from domestic and overseas governmental and private institutions and stock image websites using earthquake intensity as the keyword. Three researchers with design backgrounds classified the infographics into three kinds of types for experimentation: 1) a single-color gradient (semi-circular car dashboard) with an illustration of a cabinet or a house; 2) a 3D geometry (horseshoe type) with illustrations of people and scenes inside and outside the house; 3) a traditional table (horizontal type) with an illustration of a house. The text descriptions of the three infographics were the same. Sixteen respondents (10 of whom had 5-year design background) read through the three infographics sequentially and did a 7-Point Likert Scale Questionnaire. The questions included design form (format, logic, and clarity of information), perception and understanding (distinguishing seismic hazards, perceiving intensity differences, and rapid understanding), risk perception, and personal preference. Finally, semi-structured interviews were to gain a deeper understanding of what factors influence readers' priorities in reading the earthquake intensity infographics.This study found that: 1) a semi-circle speed gauge with one color gradient using light to dark orange had the highest perception and understanding score (mean = 5.19); 2) 3D geometry with horseshoe shape with illustrations and information richness had the highest design form score (mean = 5.13) and highest personal preference ranking; 3) formatting tables with stereotypical image and only one house illustration could not determine the difference had the lowest design form score (mean = 4.65) and lowest perception and understanding score (mean = 4.93) and lowest personal preference ranking. The lowest scores for design form (mean = 4.65), perception and understanding (mean = 4.93), and personal preference were the lowest. In addition, 80% of the respondents were concerned about earthquakes and prepared for them, but more than half would not evacuate quickly during an earthquake. Suggestions for the future are that the earthquake infographics could use color gradients and 3D geometry so that the public can differentiate the danger in different intensity scales.