Hasil untuk "Drawing. Design. Illustration"

Natalie Friedman, Kevin Weatherwax, Chengchao Zhu

Aesthetics - often treated as secondary to function-guides how people interpret robots' roles. A great deal of robot designs - both real and fictitious - use sleek industrial aesthetics. These feature hard glossy plastics, hiding as much of the underlying mechanical and electrical components as possible, resembling something akin to a nude humanoid figure. This leaves robots as something of a blank slate to which end-users apply coverings to, often based on media of fiction and non-fiction alike. We argue that designers can take cues from fashion to design interaction and set appropriate expectations. Rather than viewing appearance as decoration, we propose that color, texture, and material choices function as interaction signals. These signals can invite or discourage touch, clarify a robot's role, and help align user expectations with a robot's actual capabilities. When done thoughtfully, such cues can create familiarity and legibility; when done poorly, they can lead to wrong expectations. This preliminary paper proposes a framework describing how materials can create explainability by signaling expectations for interaction, task, and environment. We use this framework to do a content analysis of 6 robots.

Yifei Zhou, Thomas Kämpfe, Kai Ni et al.

Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the "memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0 to 85 degrees Celcius at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes.

Raphael Tang, Crystina Zhang, Wenyan Li et al.

In arena-style evaluation of large language models (LLMs), two LLMs respond to a user query, and the user chooses the winning response or deems the "battle" a draw, resulting in an adjustment to the ratings of both models. The prevailing approach for modeling these rating dynamics is to view battles as two-player game matches, as in chess, and apply the Elo rating system and its derivatives. In this paper, we critically examine this paradigm. Specifically, we question whether a draw genuinely means that the two models are equal and hence whether their ratings should be equalized. Instead, we conjecture that draws are more indicative of query difficulty: if the query is too easy, then both models are more likely to succeed equally. On three real-world arena datasets, we show that ignoring rating updates for draws yields a 1-3% relative increase in battle outcome prediction accuracy (which includes draws) for all four rating systems studied. Further analyses suggest that draws occur more for queries rated as very easy and those as highly objective, with risk ratios of 1.37 and 1.35, respectively. We recommend future rating systems to reconsider existing draw semantics and to account for query properties in rating updates.

Patrizio Angelini, Carla Binucci, Giuseppe Di Battista et al.

Unit edge-length drawings, rectilinear drawings (where each edge is either a horizontal or a vertical segment), and rectangular face drawings are among the most studied subjects in Graph Drawing. However, most of the literature on these topics refers to planar graphs and planar drawings. In this paper we study drawings with all the above nice properties but that can have edge crossings; we call them Unit Edge length Rectilinear drawings with Rectangular Faces (UER-RF drawings). We consider crossings as dummy vertices and apply the unit edge-length convention to the edge segments connecting any two (real or dummy) vertices. Note that UER-RF drawings are grid drawings (vertices are placed at distinct integer coordinates), which is another classical requirement of graph visualizations. We present several efficient and easily implementable algorithms for recognizing graphs that admit UER-RF drawings and for constructing such drawings if they exist. We consider restrictions on the degree of the vertices or on the size of the faces. For each type of restriction, we consider both the general unconstrained setting and a setting in which either the external boundary of the drawing is fixed or the rotation system of the graph is fixed as part of the input.

Jiaju Ma, Chau Vu, Asya Lyubavina et al.

One way illustrators engage in disciplined drawing - the process of drawing to improve technical skills - is through studying and replicating reference images. However, for many novice and intermediate digital artists, knowing how to approach studying a reference image can be challenging. It can also be difficult to receive immediate feedback on their works-in-progress. To help these users develop their professional vision, we propose ArtKrit, a tool that scaffolds the process of replicating a reference image into three main steps: composition, value, and color. At each step, our tool offers computational guidance, such as adaptive composition line generation, and automatic feedback, such as value and color accuracy. Evaluating this tool with intermediate digital artists revealed that ArtKrit could flexibly accommodate their unique workflows. Our code and supplemental materials are available at https://majiaju.io/artkrit .

Mohd Faisal Khan, Mukul Lokhande, Santosh Kumar Vishvakarma

Edge-AI applications still face considerable challenges in enhancing computational efficiency in resource-constrained environments. This work presents RAMAN, a resource-efficient and approximate posit(8,2)-based Multiply-Accumulate (MAC) architecture designed to improve hardware efficiency within bandwidth limitations. The proposed REAP (Resource-Efficient Approximate Posit) MAC engine, which is at the core of RAMAN, uses approximation in the posit multiplier to achieve significant area and power reductions with an impact on accuracy. To support diverse AI workloads, this MAC unit is incorporated in a scalable Vector Execution Unit (VEU), which permits hardware reuse and parallelism among deep neural network layers. Furthermore, we propose an algorithm-hardware co-design framework incorporating approximation-aware training to evaluate the impact of hardware-level approximation on application-level performance. Empirical validation on FPGA and ASIC platforms shows that the proposed REAP MAC achieves up to 46% in LUT savings and 35.66% area, 31.28% power reduction, respectively, over the baseline Posit Dot-Product Unit (PDPU) design, while maintaining high accuracy (98.45%) for handwritten digit recognition. RAMAN demonstrates a promising trade-off between hardware efficiency and learning performance, making it suitable for next-generation edge intelligence.

Mohammad Alsager Alzayed, Elizabeth Starkey, Sarah C. Ritter et al.

Designers often rely on their self-evaluations – either independently or using design tools – to make concept selection decisions. When evaluating designs for sustainability, novice designers, given their lack of experience, could demonstrate psychological distance from sustainability-related issues, leading to faulty concept evaluations. We aim to investigate the accuracy of novice designers’ self-evaluations of the sustainability of their solutions and the moderating role of their (1) trait empathy and (2) their beliefs, attitudes and intentions toward sustainability on this accuracy. We conducted an experiment with first-year engineering students comprising a sustainable design activity. In the activity, participants evaluated the sustainability of their own designs, and these self-evaluations were compared against expert evaluations. We see that participants’ self-evaluations were consistent with the expert evaluations on the following sustainable design heuristics: (1) longevity and (2) finding wholesome alternatives. Second, trait empathy moderated the accuracy of self-evaluations, with lower levels of fantasy and perspective-taking relating to more accurate self-evaluations. Finally, beliefs, attitudes and intentions toward sustainability also moderated the accuracy of self-evaluations, and these effects vary based on the sustainable design heuristic. Taken together, these findings suggest that novice designers’ individual differences (e.g., trait empathy) could moderate the accuracy of the evaluation of their designs in the context of sustainability.

Igor Golovachuk, Maria Vorobchuk

Анотація. З появою комп’ютерної техніки розпочався період розвитку нових методів та використання нових технологій для проєктування та розробки ілюстрацій. Доведено, що розробка ілюстративних зображень 3D персонажів для дитячих видань є важливим і динамічним процесом, адже проєктування 3D персонажів для дитячих видань вимагає поєднання творчого підходу, технічної майстерності та розуміння їхнього впливу на підсвідомість дитячої аудиторії. Досліджено специфіку проєктування ілюстративних зображень, структуризовано основні етапи їх розробки та виокремлено функціональні особливості які враховуються при розробці 3D персонажа дизайнером. Мета. Дослідити специфіку проєктування ілюстративних зображень 3D персонажів до дитячих видань та здійснити систематизацію основних етапів, дослідити функціональні особливості, що враховують дизайнери в процесі розробки. Методологія. Застосовано аналітичний, соціокультурний, структурний підходи, а також теоретичні методи дослідження. Результати. Здійснено аналіз літературних джерел за тематикою дослідження і на основі цього систематизовано та описано специфіку проєктування ілюстративних зображень 3D персонажів до дитячих видань: розробка концепції, підбір оптимального програмного забезпечення, моделювання фігури, візуальне представлення, анімація, підготовка персонажу для подальшого інтегрування у дитяче видання. Досліджено, що комп'ютерна графіка надає можливість дизайнерам, ілюстраторам та художникам виражати свою творчу ідею за використання інтерактивних технологій. Таким чином досліджено функціональні особливості, що враховуються дизайнерами в процесі проєктування героїв. Наукова новизна. Систематизовано основні етапи проєктування ілюстративних зображень 3D персонажів до дитячих видань, розкрито їх функціональні особливості не лише у графічному дизайні, а й у інших галузях знань. Практична значущість. Результати дослідження можуть бути використані для подальшого вивчення специфіки проєктування ілюстративних зображень 3D персонажів до дитячих видань, а також слугувати теоретичним та практичним матеріалом при підготовці фахівців з дизайну та при написанні наукових робіт.

Jie Zhou, Chufeng Xiao, Miu-Ling Lam et al.

Animating various character drawings is an engaging visual content creation task. Given a single character drawing, existing animation methods are limited to flat 2D motions and thus lack 3D effects. An alternative solution is to reconstruct a 3D model from a character drawing as a proxy and then retarget 3D motion data onto it. However, the existing image-to-3D methods could not work well for amateur character drawings in terms of appearance and geometry. We observe the contour lines, commonly existing in character drawings, would introduce significant ambiguity in texture synthesis due to their view-dependence. Additionally, thin regions represented by single-line contours are difficult to reconstruct (e.g., slim limbs of a stick figure) due to their delicate structures. To address these issues, we propose a novel system, DrawingSpinUp, to produce plausible 3D animations and breathe life into character drawings, allowing them to freely spin up, leap, and even perform a hip-hop dance. For appearance improvement, we adopt a removal-then-restoration strategy to first remove the view-dependent contour lines and then render them back after retargeting the reconstructed character. For geometry refinement, we develop a skeleton-based thinning deformation algorithm to refine the slim structures represented by the single-line contours. The experimental evaluations and a perceptual user study show that our proposed method outperforms the existing 2D and 3D animation methods and generates high-quality 3D animations from a single character drawing. Please refer to our project page (https://lordliang.github.io/DrawingSpinUp) for the code and generated animations.

Mariona Moncunill-Piñas

El presente número reúne una miscelánea de artículos que, desde perspectivas metodológicas, analíticas, especulativas, críticas y prácticas, abordan distintos modos de hacer y de relacionarse con el mundo desde las artes y el diseño.   

Robin Lecomte, Bernard Yannou, Roland Cahen et al.

This article explores a new approach to anticipate the social impacts of disruptive products, using autonomous vehicles (AVs) as a case study. It highlights the limitations of current methods in predicting the social effects of new products and proposes that futures studies and strategic foresight can provide better techniques. The main hypothesis is that experts in social sciences can anticipate long-term social impacts by considering contextualized future product usages. The authors propose a new model called Representation–Usage–Impact (RUI), which combines expert knowledge from sociology and other fields. The article presents a detailed structure for the model and describes how experts can contribute their knowledge. Sessions were organized with experts to link AV usages with potential social impacts. The results demonstrate that social science experts can identify a wide range of potential long-term social impacts. The article suggests that the RUI model should be integrated and tested into design and decision-making tools to enhance the understanding of product impacts in practical contexts.

Sepehr Tabrizchi, Shaahin Angizi, Arman Roohi

Battery-powered IoT devices face challenges like cost, maintenance, and environmental sustainability, prompting the emergence of batteryless energy-harvesting systems that harness ambient sources. However, their intermittent behavior can disrupt program execution and cause data loss, leading to unpredictable outcomes. Despite exhaustive studies employing conventional checkpoint methods and intricate programming paradigms to address these pitfalls, this paper proposes an innovative systematic methodology, namely DIAC. The DIAC synthesis procedure enhances the performance and efficiency of intermittent computing systems, with a focus on maximizing forward progress and minimizing the energy overhead imposed by distinct memory arrays for backup. Then, a finite-state machine is delineated, encapsulating the core operations of an IoT node, sense, compute, transmit, and sleep states. First, we validate the robustness and functionalities of a DIAC-based design in the presence of power disruptions. DIAC is then applied to a wide range of benchmarks, including ISCAS-89, MCNS, and ITC-99. The simulation results substantiate the power-delay-product (PDP) benefits. For example, results for complex MCNC benchmarks indicate a PDP improvement of 61%, 56%, and 38% on average compared to three alternative techniques, evaluated at 45 nm.

Robert Wille, Lukas Burgholzer, Stefan Hillmich et al.

Quantum computers promise to efficiently solve important problems classical computers never will. However, in order to capitalize on these prospects, a fully automated quantum software stack needs to be developed. This involves a multitude of complex tasks from the classical simulation of quantum circuits, over their compilation to specific devices, to the verification of the circuits to be executed as well as the obtained results. All of these tasks are highly non-trivial and necessitate efficient data structures to tackle the inherent complexity. Starting from rather straight-forward arrays over decision diagrams (inspired by the design automation community) to tensor networks and the ZX-calculus, various complementary approaches have been proposed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.

Ilhan Aslan, Dominik Neu, Daniela Neupert et al.

With today's technologies it seems easier than ever to augment everyday things with the ability to perceive their environment and to talk to users. Considering conversational user interfaces, tremendous progress has already been made in designing and evaluating task oriented conversational interfaces, such as voice assistants for ordering food, booking a flight etc. However, it is still very challenging to design smart things that can have with their users an informal conversation and emotional exchange, which requires the smart thing to master the usage of social everyday utterances, using irony and sarcasm, delivering good compliments, etc. In this paper, we focus on the experience design of compliments and the Complimenting Mirror design. The paper reports in detail on three phases of a human-centered design process including a Wizard of Oz study in the lab with 24 participants to explore and identify the effect of different compliment types on user experiences and a consequent field study with 105 users in an architecture museum with a fully functional installation of the Complimenting Mirror. In our analyses we argue why and how a "smart" mirror should compliment users and provide a theorization applicable for affective interaction design with things in more general. We focus on subjective user feedback including user concerns and prepositions of receiving compliments from a thing and on observations of real user behavior in the field i.e. transitions of bodily affective expressions comparing affective user states before, during, and after compliment delivery. Our research shows that compliment design matters significantly and using the right type of compliments in our final design in the field test, we succeed in achieving reactive expressions of positive emotions, "sincere" smiles and laughter, even from the seemingly sternest users.

Laura Nino, Diana Alarcon , Dana Valentina Cristancho et al.

Workers of community organizations in Amsterdam and Bogota use daily smartphones for personal reasons. However, smartphone apps have been outside their organizational reach. Thus, our paper explores sociotechnical systems’ opportunities to bridge community organizations and female migrants to macrosystems such as healthcare systems. Our selected community organizations, Casa Migrante in Amsterdam and Kilombo Yumma in Bogota, were born out of serving peer migrants from Latin American and Afro-Colombian backgrounds. Their services provide support to female migrants who suffer abuse and poverty. Built on cultural practices and social structures of their own, these organizations live in the periphery of macrosystems. They struggle to be appreciated and integrated into macrosystems and digital networks. Thus, our paper unpeels the core of their services, using asset-based design, and translates their social capital into sociotechnical systems such as smartphone apps. This with the intention to reveal, consolidate and integrate their efforts to wider systems.  

Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Abhishek Moitra et al.

Recent years have seen a paradigm shift towards multi-task learning. This calls for memory and energy-efficient solutions for inference in a multi-task scenario. We propose an algorithm-hardware co-design approach called MIME. MIME reuses the weight parameters of a trained parent task and learns task-specific threshold parameters for inference on multiple child tasks. We find that MIME results in highly memory-efficient DRAM storage of neural-network parameters for multiple tasks compared to conventional multi-task inference. In addition, MIME results in input-dependent dynamic neuronal pruning, thereby enabling energy-efficient inference with higher throughput on a systolic-array hardware. Our experiments with benchmark datasets (child tasks)- CIFAR10, CIFAR100, and Fashion-MNIST, show that MIME achieves ~3.48x memory-efficiency and ~2.4-3.1x energy-savings compared to conventional multi-task inference in Pipelined task mode.

Hongwu Peng, Shaoyi Huang, Shiyang Chen et al.

Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace existing Deep Neural Networks (DNNs). Despite the remarkable triumphs, the prolonged turnaround time of Transformer models is a widely recognized roadblock. The variety of sequence lengths imposes additional computing overhead where inputs need to be zero-padded to the maximum sentence length in the batch to accommodate the parallel computing platforms. This paper targets the field-programmable gate array (FPGA) and proposes a coherent sequence length adaptive algorithm-hardware co-design for Transformer acceleration. Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm. The proposed sparse attention operator brings the complexity of attention-based models down to linear complexity and alleviates the off-chip memory traffic. The proposed length-aware resource hardware scheduling algorithm dynamically allocates the hardware resources to fill up the pipeline slots and eliminates bubbles for NLP tasks. Experiments show that our design has very small accuracy loss and has 80.2 $\times$ and 2.6 $\times$ speedup compared to CPU and GPU implementation, and 4 $\times$ higher energy efficiency than state-of-the-art GPU accelerator optimized via CUBLAS GEMM.

Ram Krishna Ranjan

This text weaves reflections on the (im)possibility of foregrounding caste-subalterns’, specifically Dalits’ experiences and imaginations of the Bengal Famine of 1943. This text is in conjunction with, next to, between, and in/out of the film You deny my living and I defy my death. The film has emerged as a result of a collaborative-performative workshop between two caste-subalterns, initiated and organized in the context of a PhD in Artistic Practice. Just like the workshop, the film is also animated by the desire to complicate the dominant representational realm ascribed to Dalits, which often is either essentialising or reductive. The film explores methodological and aesthetical approaches to go beyond the default imaginaries.   Constructed across multiple modes, genres, fragments, and layers, this text aims to expand, extend, inflect, and build on the key themes explored in the workshop and the film. Some text precedes the workshop and film, some emerged during the process, and some have come afterwards. Mobilising iterative and assemblage-style writing, this text anchors itself in the Bengal Famine of 1943 to critically engage with ideas around ‘critical presence’ and the ‘representation’ of Dalits. The text also aims to explore notions around malnutrition, hunger, starvation, and famine as categories, ‘recovery and representation’ of caste-subaltern histories in the context of famine, opacity and affect as aesthetic choices, and collaborative practices as a method.

Tessira Crawford

This paper shares observations of the limitations of the existing design practice within architecture for the inclusive design of space and architecture. This paper specifically focuses on what could be missing during the study of perception within architecture, among observations of architects’ simplification of phenomenology and tools that visualise phenomena. This paper reflects on an ongoing case study of Matt, a YouTuber diagnosed with retinitis pigmentosa, and the potential bias within today’s architectural design process. Openly acknowledging this bias will begin limiting its potential, encouraging the observation of others as unique – inclusively studying perception in architecture – and proposing the assessment of theories, methods and tools in architectural phenomenology – continuously testing their inclusivity to eliminate notions of an ideal.

Christian Pilato, Stanislav Bohm, Fabien Brocheton et al.

High-Performance Big Data Analytics (HPDA) applications are characterized by huge volumes of distributed and heterogeneous data that require efficient computation for knowledge extraction and decision making. Designers are moving towards a tight integration of computing systems combining HPC, Cloud, and IoT solutions with artificial intelligence (AI). Matching the application and data requirements with the characteristics of the underlying hardware is a key element to improve the predictions thanks to high performance and better use of resources. We present EVEREST, a novel H2020 project started on October 1st, 2020 that aims at developing a holistic environment for the co-design of HPDA applications on heterogeneous, distributed, and secure platforms. EVEREST focuses on programmability issues through a data-driven design approach, the use of hardware-accelerated AI, and an efficient runtime monitoring with virtualization support. In the different stages, EVEREST combines state-of-the-art programming models, emerging communication standards, and novel domain-specific extensions. We describe the EVEREST approach and the use cases that drive our research.