Human-centric Analysis Research Articles

Vasyl Sukhomlynskyi’s Philosophy of Education: Human-Centred Dimension

Purpose. The basis of the presented study is a methodological and human-centred analysis of the philosophy of education of the outstanding Ukrainian educator Vasyl Sukhomlynskyi as a relevant anthropological-intellectual strategy for understanding and comprehending the educational process in the context of civilisation challenges. This implies a sequential solution to the following tasks: 1) to review the conceptual content and human-centred load of Vasyl Sukhomlynskyi’s pedagogical position in the discourses of philosophical anthropology and social philosophy; 2) to analyse the theoretical knowledge of the anthropo-existential concept of the outstanding teacher in the context of the transformation of Ukrainian education; 3) to determine the main parameters of the cognitive field of Sukhomlynskyi’s philosophy of education in the development of values of patriotic education of young people. Theoretical basis. The dynamic process of Ukraine’s unfolding struggle for the preservation of its nation and territorial integrity brings Sukhomlynskyi’s philosophy of education to a new level of understanding of human life and society. The human-centred dimension in interaction with the philosophy of education allows us to define Sukhomlynskyi’s pedagogical concept in the fullness of its metaphilosophical, existential and axiological content. The relevance of the problem under study is due to the crisis of values in the situation of civilizational transformations, which, as a result of information and digital technologies, have a cumulative impact on all spheres of people’s life and their attitude to the world. In the process of implementing the educational ideas of the Ukrainian teacher, the development and transformation of the individual’s self-awareness takes place in accordance with the intellectual challenges of the modern socio-cultural environment. Originality. It is substantiated that the philosophy of education of Vasyl Sukhomlynskyi largely determines the strategy for the transformation of Ukrainian education both in the context of digitalisation of the educational space and in the situation of a full-scale war against Russian aggression, which opens up new prospects for the implementation of identity dimensions in interaction with a qualitatively different level of national subjectivity. Conclusions. The approval of the conceptual provisions of Sukhomlynskyi’s philosophy of education gives rise to a new type and method of paradigm of thinking and cognition in the implementation of strategies for the transformation of modern education, which determine the qualitative theory of the educational process in the context of human-centrism. It is characterized by the level of modern philosophical reflections, which allows preserving and at the same time changing the system of education and upbringing of the young generation in the context of universal values. On this basis, the philosophy of education of the prominent Ukrainian educator appears as a way of renewing all aspects of human life, which deconstructs itself in the situation of a human-centric dimension.

Towards a method for human-centred analysis of external variety

AbstractAddressing VUCA challenges in product development, the Human-Centred Analysis of External Variety (HAV) method is introduced. Focusing on the early stages of new product development, HAV provides a structured approach, reducing uncertainty through quick market analysis, customer engagement, and goal setting. HAV is illustrated using a cargo bike product family case study and emphasizes its role in aligning human-centred methods with project goals. The first steps of the HAV method are presented, but also the need for further action towards a validated and elaborated method is discussed.

On the interpretability of part-prototype based classifiers: a human centric analysis

Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In addition, previous works have had major issues with following proper experiment design and task representation that limit their reliability and validity. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective that solves these issues. The proposed framework consists of three actionable metrics and experiments. The results of these experiments will reveal important and reliable interpretability related properties of such models. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework.

Multiscale Meets Spatial Awareness: An Efficient Attention Guidance Network for Human Parsing

Human parsing, which aims at resolving human body and clothes into semantic part regions from an human image, is a fundamental task in human-centric analysis. Recently, the approaches for human parsing based on deep convolutional neural networks (DCNNs) have made significant progress. However, hierarchically exploiting multiscale and spatial contexts as convolutional features is still a hurdle to overcome. In order to boost the scale and spatial awareness of a DCNN, we propose two effective structures, named “Attention SPP and Attention RefineNet,” to form a Mutual Attention operation, to exploit multiscale and spatial semantics different from the existing approaches. Moreover, we propose a novel Attention Guidance Network (AG-Net), a simple yet effective architecture without using bells and whistles (such as human pose and edge information), to address human parsing tasks. Comprehensive evaluations on two public datasets well demonstrate that the AG-Net outperforms the state-of-the-art networks.

A Comparative Human-Centric Analysis of Virtual Reality and Dry Lab Training Tasks on the da Vinci Surgical Platform

There is a growing, widespread trend of adopting robot-assisted minimally invasive surgery (RMIS) in clinical care. Dry lab robot training and virtual reality simulation are commonly used to train surgical residents; however, it is unclear whether both types of training are equivalent or can be interchangeable and still achieve the same results in terms of training outcomes. In this paper, we take the first step in comparing the effects of physical and simulated surgical training tasks on human operator kinematics and physiological response to provide a richer understanding of exactly how the user interacts with the actual or simulated surgical robot. Four subjects, with expertise levels ranging from novice to expert surgeon, were recruited to perform three surgical tasks — Continuous Suture, Pick and Place, Tubes, with three repetitions — on two training platforms: (1) the da Vinci Si Skills Simulator and (2) da Vinci S robot, in a randomized order. We collected physiological response and kinematic movement data through body-worn sensors for a total of 72 individual experimental trials. A range of expertise was chosen for this experiment to wash out inherent differences based on expertise and only focus on inherent differences between the virtual reality and dry lab platforms. Our results show significant differences ([Formula: see text]-[Formula: see text]) between tasks done on the simulator and surgical robot. Specifically, robotic tasks resulted in significantly higher muscle activation and path length, and significantly lower economy of volume. The individual tasks also had significant differences in various kinematic and physiological metrics, leading to significant interaction effects between the task type and training platform. These results indicate that the presence of the robotic system may make surgical training tasks more difficult for the human operator. Thus, the potentially detrimental effects of virtual reality training alone are an important topic for future investigation.

Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark.

Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named "Look into Person (LIP)" that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The datasets, code and models are available at http://www.sysu-hcp.net/lip/.

Human factors considerations in designing for infection prevention and control in neonatal care – findings from a pre-design inquiry

Qualitative data collection methods drawn from the early stages of human-centred design frameworks combined with thematic analysis were used to develop an understanding of infection prevention practice within an existing neonatal intensive care unit. Findings were used to generate a framework of understanding which in turn helped inform a baseline approach for future research and design development. The study revealed that a lack of clarity between infection transmission zones and a lack of design attributes needed to uphold infection prevention measures may be undermining healthcare workers’ understanding and application of good practice. The issue may be further complicated by well-intentioned behavioural attitudes to meeting work objectives; undue influences from spatial constraints; the influence of inadvertent and excessive touch-based interactions; physical and/or cognitive exertion to maintain transmission barriers; and the impact of expanding job design and increased workload to supplement for lack of effective barriers.Practitioner Summary: Despite high hand hygiene compliance within a neonatal intensive care unit, healthcare workers expressed concerns about the unit design and infection prevention practice. Early inquiry methods from human-centred design and thematic analysis helped develop a framework to understand how design can be used to aid infection prevention.

Human-centred analysis of the dependencies within sets of proofs

A failure in safety-critical systems, such as vehicles, aircrafts, etc., may endanger human lives. It is crucial that these system behave correctly, in accordance to their specification and fulfilling all the safety-critical properties defined for them. Safety could be guaranteed through the application of analysis techniques, in the form of formal verification and testing. However, verification of a real-size system means having a large number of lemmas, depending on each other. An update in one lemma might require changes in many other lemmas. It would be inefficient to find the existing dependencies out when the proof process is already running. We present a methodology for analysis of the dependencies between lemmas within the provided set of proofs. The application of this methodology to the Isabelle/HOL theories is supported by a tool. The tool provides a quick automated analysis and easy-to-check representation of the results, as well as allows optimisation of the provided set of proofs.

Pose-Guided Human Parsing by an AND/OR Graph Using Pose-Context Features

Parsing human into semantic parts is crucial to human-centric analysis. In this paper, we propose a human parsing pipeline that uses pose cues, e.g., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts. These segment proposals are ranked using standard appearance cues, deep-learned semantic feature, and a novel pose feature called pose-context. Then these proposals are selected and assembled using an And-Or graph to output a parse of the person. The And-Or graph is able to deal with large human appearance variability due to pose, choice of clothing, etc. We evaluate our approach on the popular Penn-Fudan pedestrian parsing dataset, showing that it significantly outperforms the state of the art, and perform diagnostics to demonstrate the effectiveness of different stages of our pipeline.

Parsing Based on Parselets: A Unified Deformable Mixture Model for Human Parsing.

Human parsing, namely partitioning the human body into semantic regions, has drawn much attention recently for its wide applications in human-centric analysis. Previous works often consider solving the problem of human pose estimation as the prerequisite of human parsing. We argue that these approaches cannot obtain optimal pixel-level parsing due to the inconsistent targets between the different tasks. In this work, we directly address the problem of human parsing by using the novel Parselet representation as the building blocks of our parsing model. Parselets are a group of parsable segments which can generally be obtained by low-level over-segmentation algorithms and bear strong semantic meaning. We then build a deformable mixture parsing model (DMPM) for human parsing to simultaneously handle the deformation and multi-modalities of Parselets. The proposed model has two unique characteristics: (1) the possible numerous modalities of Parselet ensembles are exhibited as the "And-Or" structure of sub-trees; (2) to further solve the practical problem of Parselet occlusion or absence, we directly model the visibility property at some leaf nodes. The DMPM thus directly solves the problem of human parsing by searching for the best graph configuration from a pool of Parselet hypotheses without intermediate tasks. Fast rejection based on hierarchical filtering is employed to ensure the overall efficiency. Comprehensive evaluations on a new large-scale human parsing dataset, which is crawled from the Internet, with high resolution and thoroughly annotated semantic labels at pixel-level, and also a benchmark dataset demonstrate the encouraging performance of the proposed approach.

Human-centric analysis and interpretation of time series: a perspective of granular computing

In spite of the truly remarkable diversity of models of time series, there is still an evident need to develop constructs whose accuracy and interpretability are carefully identified and reconciled subsequently leading to highly interpretable (human-centric) constructs. While a great deal of research has been devoted to the design of nonlinear numeric models of time series (with an evident objective to achieve high accuracy of prediction), an issue of interpretability (transparency) of models of time series becomes an evident and ongoing challenge. The user-friendliness of models of time series comes with an ability of humans to perceive and process abstract constructs rather than dealing with plain numeric entities. In perception of time series, information granules (which are regarded as realizations of interpretable entities) play a pivotal role. This gives rise to a concept of granular models of time series or granular time series, in brief. This study revisits generic concepts of information granules and elaborates on a fundamental way of forming information granules (both sets--intervals as well as fuzzy sets) through applying a principle of justifiable granularity encountered in granular computing. Information granules are discussed with regard to the granulation of time series in a certain predefined representation space (viz. a feature space) and granulation carried out in time. The granular representation and description of time series is then presented. We elaborate on the fundamental hierarchically organized layers of processing supporting the development and interpretation of granular time series, namely (a) formation of granular descriptors used in their visualization, (b) construction of linguistic descriptors used afterwards in the generation of (c) linguistic description of time series. The layer of the linguistic prediction models of time series exploiting the linguistic descriptors is outlined as well. A number of examples are offered throughout the entire paper with intent to illustrate the main functionalities of the essential layers of the granular models of time series.

Towards empathy: a human-centred analysis of rationality, ethics and praxis in systems development

Functionalism has long been the dominant paradigm in systems development practice. However, functionalism promotes an innate and immutable instrumental rationality that is indifferent to human values, rights, society, culture and international stability. It, in essence, lacks empathy. Although alternative paradigms have been promoted for decades in the systems development literature to help address this deficit, functionalism remains dominant. This paper reiterates the call for a fundamental paradigm shift away from myopic functionalism and towards a more empathic and human-centred philosophy. It argues that the human-centred tradition offers a philosophically compatible and mature approach that can be practically harnessed for promoting empathy in systems development. The paper investigates the potential of systems development to become truly human-centred using data originally collected as part of a multi-method critical-interpretive study of privacy in information systems development. Multiple methods are used for the data analysis presented, including principal components analysis, hierarchical clustering, Q methodology and descriptive statistics. The multi-method analysis demonstrates that a marked discreteness exists between human-centred sentiments and instrumentally rational ones in systems development praxis. The paper concludes by presenting recommendations on how human-centred values can be practically fostered and engaged to enable greater empathy in contemporary system development and strengthen international stability.

UNDERSTANDING OF HETEROGENEOUS MULTI-STAGE META-PROGRAMS

The paper analyzes an approach to understand heterogeneous meta-programs and multi-stage meta-programs. At the core of the approach is human-centred analysis combined with the Brook’s program cognition theory and the concept of reverse engineering. The use of the approach leads to extracting of higher-level models (graphs representing meta-parameter—meta-function relationship models, feature diagrams and algorithms (processes)) from the correct meta-specifications. The models and processes enable not only to better understand the multi-stage heterogeneous meta-programs but also contribute to their evolution. The paper describes some properties of multi-stage heterogeneous meta-programs. The approach is supported by the case study and complexity evaluation.DOI: http://dx.doi.org/10.5755/j01.itc.41.1.916

A Human-Centred Analysis of Systems Development Goals, Rationality and Focus Using Principal Components Analysis and Hierarchical Clustering

The human-centred approach is a paradigm of systems development that is well suited to improving international stability. This paper consequently investigates whether or not contemporary systems development is, or has the potential to become, truly human-centred. Data originally collected as part of a multi-method critical-interpretive study of privacy in information systems development is used for the analysis. The analysis presented considers the issue of human-centredness from three complementary perspectives: (1) the goals of systems development, (2) the rationality of systems development, and (3) the focus of systems development. Principal components analysis and hierarchical clustering are used to examine the original data in terms of systems development goals, rationality and focus in order to identify the underlying latent variables, factors or clusters therein. The analysis shows clear dyadic polarisations between human-centred goals and rationalities, and more functionalistic goals and rationalities. It also shows a triadic polarisation in systems development focus regarding individual personal issues, organisational issues, and technical issues, and also how the human-centred ideal is not currently being realised in systems development practice. Finally, the paper considers the implications of the findings for human-centred systems development and its pivotal role in fostering international stability.

3.1.2 Human Performance Modeling for Enterprise Transformation

AbstractSatisfaction of current demands for increased efficiency in the use of human resources requires nothing less than a revolutionary transformation in how we structure complex socio‐technical systems. Human systems issues related to transformation of complex socio‐technical systems pose substantive design challenges that must be addressed with human–centric analysis followed by human‐centric design. In this paper, I integrate a cognitive work analysis strategy with a human performance modeling strategy which, when combined, constitutes a systematic, human–centric analysis‐to‐prototype design framework. I have described the cognitive work analysis strategy elsewhere and in this paper focus my discussion on cognitive workflow modeling. I illustrate the requirements of human performance modeling and methods of satisfying them with reference to a Brahms workflow model of time sensitive targeting based in part on a functional abstraction space developed within a cognitive work analysis and use that workflow model to motivate discussion of issues related to human‐centric design for enterprise transformation.