Method illustrations (MIs) play a crucial role in conveying the core ideas of scientific papers, yet their generation remains a labor-intensive process. Here, we take inspiration from human authors' drawing practices and correspondingly propose \textbf{FigAgent}, a novel multi-agent framework for high-quality automatic MI generation. Our FigAgent distills drawing experiences from similar components across MIs and encapsulates them into reusable drawing middlewares that can be orchestrated for MI generation, while evolving these middlewares to adapt to dynamically evolving drawing requirements. Besides, a novel Explore-and-Select drawing strategy is introduced to mimic the human-like trial-and-error manner for gradually constructing MIs with complex structures. Extensive experiments show the efficacy of our method.
Networking is central to the growth and visibility of software engineering research and researchers. However, opportunities and capacities to build such networks are not easily identified and often are unevenly distributed. While networking is often viewed as an individual skill, a researchers workplace, culture and environment significantly influence their motivation and, consequently, the networks they form. This paper explores how factors such as country of residence, immigration status, language, gender, and surrounding context affect researchers' ability to establish professional connections and succeed within the global research ecosystem. Drawing on existing literature and personal experience, this reflective report examines the often-invisible barriers to networking and advocates for a community-driven "expert voice" initiative to acknowledge and address these inequities.
This study aims to develop AR technology of simplified representations based on ISO standards and to quantify the efficiency and contribution of developed AR technology in assisting students in learning mechanical drawing. This research proposed a marker-based AR application development, intended for teaching simplified representations, named Augmented Reality Penyederhanaan Gambar - ARPeGa, and an experimental study to quantify the user experience (UX) using the User Experience Questionnaire (UEQ). A pilot study involving 38 mechanical engineering students was conducted to evaluate the impact of AR involvement on user experience. In addition, the UEQ data analysis tool version 11 was used. The UEQ results showed attractiveness was excellent (1.87), while efficiency, dependability, stimulation, and novelty were good (1.63, 1.60, 1.63, and 1.22 respectively). And perspicuity was categorized as “above average” (1.51). This study’s outcomes demonstrate that using 3D model visualization in the AR application strengthens user experiences to understand simplified representations. Overall, the application has a ‘good’ level in some categories: efficiency, dependability, stimulation, and novelty.
We explore a novel approach to achieving anisotropic thermal photon tunneling, inspired by the concept of parity-time symmetry in quantum physics. Our method leverages the modulation of constitutive optical parameters, oscillating between loss and gain regimes. This modulation reveals a variety of distinct effects in thermal photon behavior and dispersion. Specifically, we identify complex tunneling modes through gain-loss engineering, which include thermal photonic defect states and Fermi-arc-like phenomena, which surpass those achievable through traditional polariton engineering. Our research also elucidates the laws governing the evolution of radiative energy in the presence of gain and loss interactions, and highlights the unexpected inefficacy of gain in enhancing thermal photon energy transport compared to systems characterized solely by loss. This study not only broadens our understanding of thermal photon tunneling but also establishes a versatile platform for manipulating photon energy transport, with potential applications in thermal management, heat science, and the development of advanced energy devices.
This chapter seeks to support software engineering (SE) researchers and educators in teaching the importance of theory as well as the theorizing process. Drawing on insights from other fields, the chapter presents 12 intermediate products of theorizing and what they mean in an SE context. These intermediate products serve different roles: some are theory products to frame research studies, some are theory generators, and others are components of theory. Whereas the SE domain doesn't have many theories of its own, these intermediate products of theorizing can be found widely. The chapter aims to help readers to recognize these intermediate products, their role, and how they can help in the theorizing process within SE research. To illustrate their utility, the chapter then applies the set of intermediate theorizing products to the software architecture research field. The chapter ends with a suggested structure for a 12-week course on theorizing in SE which can be readily adapted by educators.
In the social and organizational sciences, accountability has been linked to the efficient operation of organizations. However, it has received limited attention in software engineering (SE) research, in spite of its central role in the most popular software development methods (e.g., Scrum). In this article, we explore the mechanisms of accountability in SE environments. We investigate the factors that foster software engineers' individual accountability within their teams through an interview study with 12 people. Our findings recognize two primary forms of accountability shaping software engineers individual senses of accountability: institutionalized and grassroots. While the former is directed by formal processes and mechanisms, like performance reviews, grassroots accountability arises organically within teams, driven by factors such as peers' expectations and intrinsic motivation. This organic form cultivates a shared sense of collective responsibility, emanating from shared team standards and individual engineers' inner commitment to their personal, professional values, and self-set standards. While institutionalized accountability relies on traditional "carrot and stick" approaches, such as financial incentives or denial of promotions, grassroots accountability operates on reciprocity with peers and intrinsic motivations, like maintaining one's reputation in the team.
In the last decade, Single-Board Computers (SBCs) have been employed more frequently in engineering and computer science both to technical and educational levels. Several factors such as the versatility, the low-cost, and the possibility to enhance the learning process through technology have contributed to the educators and students usually employ these devices. However, the implications, possibilities, and constraints of these devices in engineering and Computer Science (CS) education have not been explored in detail. In this systematic literature review, we explore how the SBCs are employed in engineering and computer science and what educational results are derived from their usage in the period 2010-2020 at tertiary education. For that, 154 studies were selected out of n=605 collected from the academic databases Ei Compendex, ERIC, and Inspec. The analysis was carried-out in two phases, identifying, e.g., areas of application, learning outcomes, and students and researchers' perceptions. The results mainly indicate the following aspects: (1) The areas of laboratories and e-learning, computing education, robotics, Internet of Things (IoT), and persons with disabilities gather the studies in the review. (2) Researchers highlight the importance of the SBCs to transform the curricula in engineering and CS for the students to learn complex topics through experimentation in hands-on activities. (3) The typical cognitive learning outcomes reported by the authors are the improvement of the students' grades and the technical skills regarding the topics in the courses. Concerning the affective learning outcomes, the increase of interest, motivation, and engagement are commonly reported by the authors.
Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis
et al.
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-grained specifications for ML model behaviors to unite existing efforts towards better ML engineering. We use concrete scenarios (model design, debugging, and maintenance) to articulate capabilities' broad applications across various different dimensions, and their impact on building safer, more generalizable and more trustworthy models that reflect human needs. Through preliminary experiments, we show capabilities' potential for reflecting model generalizability, which can provide guidance for ML engineering process. We discuss challenges and opportunities for capabilities' integration into ML engineering.
Yasunori Yamada, Masatomo Kobayashi, Kaoru Shinkawa
et al.
Early diagnosis of dementia, particularly in the prodromal stage (i.e., mild cognitive impairment, or MCI), has become a research and clinical priority but remains challenging. Automated analysis of the drawing process has been studied as a promising means for screening prodromal and clinical dementia, providing multifaceted information encompassing features, such as drawing speed, pen posture, writing pressure, and pauses. We examined the feasibility of using these features not only for detecting prodromal and clinical dementia but also for predicting the severity of cognitive impairments assessed using Mini-Mental State Examination (MMSE) as well as the severity of neuropathological changes assessed by medial temporal lobe (MTL) atrophy. We collected drawing data with a digitizing tablet and pen from 145 older adults of cognitively normal (CN), MCI, and dementia. The nested cross-validation results indicate that the combination of drawing features could be used to classify CN, MCI, and dementia with an AUC of 0.909 and 75.1% accuracy (CN vs. MCI: 82.4% accuracy; CN vs. dementia: 92.2% accuracy; MCI vs. dementia: 80.3% accuracy) and predict MMSE scores with an $R^2$ of 0.491 and severity of MTL atrophy with an $R^2$ of 0.293. Our findings suggest that automated analysis of the drawing process can provide information about cognitive impairments and neuropathological changes due to dementia, which can help identify prodromal and clinical dementia as a digital biomarker.
Bharadwaj Manda, Shubham Dhayarkar, Sai Mitheran
et al.
Ongoing advancements in the fields of 3D modelling and digital archiving have led to an outburst in the amount of data stored digitally. Consequently, several retrieval systems have been developed depending on the type of data stored in these databases. However, unlike text data or images, performing a search for 3D models is non-trivial. Among 3D models, retrieving 3D Engineering/CAD models or mechanical components is even more challenging due to the presence of holes, volumetric features, presence of sharp edges etc., which make CAD a domain unto itself. The research work presented in this paper aims at developing a dataset suitable for building a retrieval system for 3D CAD models based on deep learning. 3D CAD models from the available CAD databases are collected, and a dataset of computer-generated sketch data, termed 'CADSketchNet', has been prepared. Additionally, hand-drawn sketches of the components are also added to CADSketchNet. Using the sketch images from this dataset, the paper also aims at evaluating the performance of various retrieval system or a search engine for 3D CAD models that accepts a sketch image as the input query. Many experimental models are constructed and tested on CADSketchNet. These experiments, along with the model architecture, choice of similarity metrics are reported along with the search results.
The COVID-19 pandemic illuminated the critical need for flexible mechanical engineering laboratories simultaneously deployable in multiple modalities: face-to-face, hybrid, and remote. A key element in the lesson portfolio of a forward-looking engineering instructor is economical, hands-on, accessible, 'turn-key' lab activities; kits that can be deployed both in brick-and-mortar teaching labs and mailed home to remote learners. The Energy Engineering Laboratory Module pedagogy, described elsewhere, provides an underpinning theoretical framework and examples to achieve these features. In addition, instructional lab kits must demonstrate foundational engineering phenomena while maintaining measurement accuracy and fidelity at reasonable cost. In the energy-thermal-fluid sciences, achieving these conditions presents challenges as kits require energy and matter transport and conversion in real time at scales large enough to reveal measurable phenomena but not so large as to become hazardous to users. This paper presents theoretical underpinning and experimental verification of a fluid mechanics lab experiment appropriate for undergraduate engineering students that 1) meets all the above-described criteria, 2) costs less than $30 in materials, and 3) can be easily mailed to remote learners.
Deficit and redundancies in existing automotive product development hinder a systems engineering based development. In this paper we discuss a methodical procedure to eliminate deficits in the current product development and in turn to enable the introduction of a new systems engineering based development methodology. As the core part of our approach, we discuss how to transform an opaque heterogeneous product development to a homogenous consistent product development taking into account existing disciplines. Our approach paves the way to achieve a process structure that is more amenable to verification and validation. We show the effectiveness of our proposed solution approach on an automotive use case.
Kyle M. Whitcomb, Z. Yasemin Kalender, Timothy J. Nokes-Malach
et al.
There is a significant underrepresentation of women in many Science, Technology, Engineering, and Mathematics (STEM) majors and careers. Prior research has shown that self-efficacy can be a critical factor in student learning, and that there is a tendency for women to have lower self-efficacy than men in STEM disciplines. This study investigates gender differences in the relationship between engineering students' self-efficacy and course grades in foundational courses. By focusing on engineering students, we examined these gender differences simultaneously in four STEM disciplines (mathematics, engineering, physics, and chemistry) among the same population. Using survey data collected longitudinally at three time points and course grade data from five cohorts of engineering students at a large US-based research university, effect sizes of gender differences are calculated using Cohen's d on two measures: responses to survey items on discipline-specific self-efficacy and course grades in all first-year foundational courses and second-year mathematics courses. In engineering, physics, and mathematics courses, we find sizeable discrepancies between self-efficacy and performance, with men appearing significantly more confident than women despite small or reverse direction differences in grades. In chemistry, women earn higher grades and have higher self-efficacy. The patterns are consistent across courses within each discipline. All self-efficacy gender differences close by the fourth year except physics self-efficacy. The disconnect between self-efficacy and course grades across subjects provides useful clues for targeted interventions to promote equitable learning environments. The most extreme disconnect occurs in physics and may help explain the severe underrepresentation of women in "physics-heavy" engineering disciplines, highlighting the importance of such interventions.
Development of scientific and engineering software is usually different and could be more challenging than the development of conventional enterprise software. The authors were involved in a technology-transfer project between academia and industry which focused on engineering, development and testing of a software for optimization of pumping energy costs for oil pipelines. Experts with different skillsets (mechanical, power and software engineers) were involved. Given the complex nature of the software (a sophisticated underlying optimization model) and having experts from different fields, there were challenges in various software engineering aspects of the software system (e.g., requirements and testing). We report our observations and experience in addressing those challenges during our technology-transfer project, and aim to add to the existing body of experience and evidence in engineering of scientific and engineering software. We believe that our observations, experience and lessons learnt could be useful for other researchers and practitioners in engineering of other scientific and engineering software systems.
Concept lattice drawings are an important tool to visualize complex relations in data in a simple manner to human readers. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although those methods are satisfying for some lattices they unfortunately perform poorly in general. In this work we present a novel tool to draw concept lattices that is purely motivated by the order structure.
Power saving is a prevailing concern in desktop computers and, especially, in battery-powered devices such as mobile phones. This is generating a growing demand for power-aware graphics applications that can extend battery life, while preserving good quality. In this paper, we address this issue by presenting a real-time power-efficient rendering framework, able to dynamically select the rendering configuration with the best quality within a given power budget. Different from the current state of the art, our method does not require precomputation of the whole camera-view space, nor Pareto curves to explore the vast power-error space; as such, it can also handle dynamic scenes. Our algorithm is based on two key components: our novel power prediction model, and our runtime quality error estimation mechanism. These components allow us to search for the optimal rendering configuration at runtime, being transparent to the user. We demonstrate the performance of our framework on two different platforms: a desktop computer, and a mobile device. In both cases, we produce results close to the maximum quality, while achieving significant power savings.
It is well-known that every graph with maximum degree 4 has an orthogonal drawing with area at most $\frac{49}{64} n^2+O(n) \approx 0.76n^2$. In this paper, we show that if the graph is 3-connected, then the area can be reduced even further to $\frac{9}{16}n^2+O(n) \approx 0.56n^2$. The drawing uses the 3-canonical order for (not necessarily planar) 3-connected graphs, which is a special Mondshein sequence and can hence be computed in linear time. To our knowledge, this is the first application of a Mondshein sequence in graph drawing.
Patrizio Angelini, Giordano Da Lozzo, Fabrizio Frati
et al.
We give an algorithm to compute a morph between any two convex drawings of the same plane graph. The morph preserves the convexity of the drawing at any time instant and moves each vertex along a piecewise linear curve with linear complexity. The linear bound is asymptotically optimal in the worst case.
Shaheena Sultana, Md. Saidur Rahman, Arpita Roy
et al.
A bar 1-visibility drawing of a graph $G$ is a drawing of $G$ where each vertex is drawn as a horizontal line segment called a bar, each edge is drawn as a vertical line segment where the vertical line segment representing an edge must connect the horizontal line segments representing the end vertices and a vertical line segment corresponding to an edge intersects at most one bar which is not an end point of the edge. A graph $G$ is bar 1-visible if $G$ has a bar 1-visibility drawing. A graph $G$ is 1-planar if $G$ has a drawing in a 2-dimensional plane such that an edge crosses at most one other edge. In this paper we give linear-time algorithms to find bar 1-visibility drawings of diagonal grid graphs and maximal outer 1-planar graphs. We also show that recursive quadrangle 1-planar graphs and pseudo double wheel 1-planar graphs are bar 1-visible graphs.
In this paper we apply a model-driven engineering approach to designing domain-specific solutions for robot control system development. We present a case study of the complete process, including identification of the domain meta-model, graphical notation definition and source code generation for subsumption architecture -- a well-known example of robot control architecture. Our goal is to show that both the definition of the robot-control architecture and its supporting tools fits well into the typical workflow of model-driven engineering development.