Identitas visual penting dalam pemasaran sebagai upaya membangun <em>brand </em>agar bertahan dalam jangka panjang serta dapat bersaing dengan kompetitor. Romansa Steak sebagai <em>brand </em>baru dalam bidang usaha steak ayam belum memiliki identitas visual khususnya logo. Tujuan penelitian ialah untuk merancang logo yang mempresentasikan <em>brand </em>Romansa Steak. Penelitian ini berjenis kualitatif, dengan teknik pengumpulan data melalui studi literatur, wawancara terhadap pihak <em>brand</em> dan observasi visual terhadap identitas visual beberapa <em>brand</em> steak sejenis serta terhadap tren desain. Logo dirancang menggunakan pendekatan <em>design thinking</em> yang spesifik untuk desain komunikasi visual oleh Ambrose dan Harris dengan 7 tahapan proses yakni <em>Define, Research, ideate, Prototype, Select, Implement, Learn</em> dengan melibatkan peneliti sebagai tim desain serta pemilik <em>brand</em>. Hasil penelitian menunjukkan bahwa dalam menciptakan identitas visual logo yang paling tepat bagi Romansa Steak ialah dengan menitikberatkan pada <em>brand name</em> dan <em>brand tagline</em> “teman romantismu makan <em>steak</em>” yang divisualisasikan melalui gambar<em> steak</em> ayam untuk mengenalkan identitas sebagai restoran <em>steak </em>ayam; simbol jari membentuk <em>love</em> untuk merepresentasikan konsep romantis; <em>tone </em>warna hangat yang mewakili kehangatan suasana; dan jenis huruf yang klasik dan <em>bold </em>memperkuat kesan restoran <em>steak</em>.<strong></strong>
Issue No. 16 of the journal diségno marks the handover of a scientific publication founded in 2017 and which has been running for 8 years. A balance sheet full of events and transformations, which now sees a mature journal witnessing the scope of a scientific disciplinary sector with strong ties to Area 08 - Civil Engineering and Architecture and beyond. [read more]
Design requirements for moving parts in mechanical assemblies are typically specified in terms of interactions with other parts. Some are purely kinematic (e.g., pairwise collision avoidance) while others depend on physics and material properties (e.g., deformation under loads). Kinematic design methods and physics-based shape/topology optimization (SO/TO) deal separately with these requirements. They rarely talk to each other as the former uses set algebra and group theory while the latter requires discretizing and solving differential equations. Hence, optimizing a moving part based on physics typically relies on either neglecting or pruning kinematic constraints in advance, e.g., by restricting the design domain to a collision-free space using an unsweep operation. In this paper, we show that TO can be used to co-design two or more parts in relative motion to simultaneously satisfy physics-based criteria and collision avoidance. We restrict our attention to maximizing linear-elastic stiffness while penalizing collision measures aggregated in time. We couple the TO loops for two parts in relative motion so that the evolution of each part's shape is accounted for when penalizing collision for the other part. The collision measures are computed by a correlation functional that can be discretized by left- and right-multiplying the shape design variables by a pre-computed matrix that depends solely on the motion. This decoupling is key to making the computations scalable for TO iterations. We demonstrate the effectiveness of the approach with 2D and 3D examples.
There needs to be innovation in improving the quality of elementary school students' health awareness to help overcome health problems through education. Therefore, the researchers conducted a study entitled Thematic-based story illustrations as a means of healthy living literacy for elementary school students. The research method that will be used in this study is the ADDIE model Research And Development Method. This research will be conducted in elementary schools in Songgom Lor, Songgom District, and Brebes Regency. Researchers carry out stages (1) Analysis: needs analysis to determine the right problems and solutions to determine student competencies; (2) Design: determine specific competencies, methods, teaching materials, and learning strategies; (3) Development: produce programs and teaching materials that will be used in learning programs; (4) Implementation: carrying out learning programs by implementing learning program designs or specifications; (5) Evaluation: evaluating learning programs and evaluating learning outcomes. The research objectives include (1) theoretically examining the concept of healthy living with care for the environment; (2) developing instruments for healthy living skills with care for the environment; (3) identifying healthy living behaviors with care for the environment for elementary school students and teachers; and (4) developing thematic-based story illustrations as media for healthy living literacy.
Elias Perdomo, Alexander Kropotov, Francelly Cano
et al.
Emulating chip functionality before silicon production is crucial, especially with the increasing prevalence of RISC-V-based designs. FPGAs are promising candidates for such purposes due to their high-speed and reconfigurable architecture. In this paper, we introduce our Makinote, an FPGA-based Cluster platform, hosted at Barcelona Supercomputing Center (BSC-CNS), which is composed of a large number of FPGAs (in total 96 AMD/Xilinx Alveo U55c) to emulate massive size RTL designs (up to 750M ASIC cells). In addition, we introduce our FPGA shell as a powerful tool to facilitate the utilization of such a large FPGA cluster with minimal effort needed by the designers. The proposed FPGA shell provides an easy-to-use interface for the RTL developers to rapidly port such design into several FPGAs by automatically connecting to the necessary ports, e.g., PCIe Gen4, DRAM (DDR4 and HBM), ETH10g/100g. Moreover, specific drivers for exploiting RISC-V based architectures are provided within the set of tools associated with the FPGA shell. We release the tool online for further extensions. We validate the efficiency of our hardware platform (i.e., FPGA cluster) and the software tool (i.e., FPGA Shell) by emulating a RISC-V processor and experimenting HPC Challenge application running on 32 FPGAs. Our results demonstrate that the performance improves by 8 times over the single-FPGA case.
Sahil Goyal, Abhinav Mahajan, Swasti Mishra
et al.
Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production. An automated way of evaluating their quality becomes critical. Towards this end, we introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs. Further, our approach can suggest modifications to these designs to improve its visual appeal. To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting. Our exhaustive quantitative and qualitative analysis of our approach against baselines adapted for the task (including recent Multimodal LLM-based approaches) brings out the efficacy of our methodology. We hope our work will usher more interest in this important and pragmatic problem setting.
In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is based on modeling and performance simulation, is crucial for reducing building energy costs. However, the field of green building encompasses a broad range of specialized knowledge, which involves significant learning costs and results in low decision-making efficiency. Many studies have already applied artificial intelligence (AI) methods to this field. Based on previous research, this study innovatively integrates large language models with DGBD, creating GreenQA, a question answering framework for multimodal data reasoning. Utilizing Retrieval Augmented Generation, Chain of Thought, and Function Call methods, GreenQA enables multimodal question answering, including weather data analysis and visualization, retrieval of green building cases, and knowledge query. Additionally, this study conducted a user survey using the GreenQA web platform. The results showed that 96% of users believed the platform helped improve design efficiency. This study not only effectively supports DGBD but also provides inspiration for AI-assisted design.
This workshop proposal focuses on best practices in UI/UX design for AI applications aimed at children, emphasising safety, engagement, and ethics. It aims to address the challenge of measuring the safety, trustworthiness, and reliability of interactions between children and AI systems. Through collaborative discussions, participants will explore effective design strategies and ethical guidelines while developing methodologies for assessing the safety and reliability of AI interactions with children. This proposal seeks to foster responsible and child-centered AI design practices within the CHI community.
Many studies indicate the importance of including the instructional illustrations (pictures, drawings, concrete objects …etc.) in childhood education learning materials and employing them in a way that suits the psychological and cognitive levels of young children. In this context, the current study aimed to develop a list of standards to be considered and adopted in designing instructional illustrations, and to reveal the perceptions of childhood teachers about the extent to which these standards are considered in instructional illustrations used in children’s learning materials. The participants were childhood education teachers in the Jordanian region of Irbid, who were randomly selected. Two hundred thirty-four teachers completed the questionnaire online. The scale consisted of a total of 34 items distributed over four dimensions. The results showed that the scores of teachers’ estimation about employing design standards in the instructional illustrations used in childhood education came at low levels, ranging from average to low, and did not reach high ratings. The study also revealed that there is an impact attributed to teaching experience on teachers’ perceptions about the extent to which these standards are employed in instructional illustrations, while there is no impact of gender, academic qualification, or the classes taught by the teachers.
Laurence De Backer, Renata Enghels, Patrick Goethals
Recent years have witnessed the development of the Metaphor Identification Procedure (MIP/VU), a step-by-step protocol designed to identify metaphorically-used words in discourse. However, MIP(VU)‘s merits notwithstanding, the procedure poses a problem to scholars intending to use its output as the basis for a semantic field analysis involving a quantitative component. Depending on the research question, metaphor analysts may be interested in chunks of language situated above the procedure’s standardized level of analysis (i.e., the lexical unit or lexeme), including phrases and sentences. Yet, attempts to decenter the method’s exclusive focus on metaphor-related words have been the target of critique, among others on the grounds of their lack of clear unit-formation guidelines and, hence, their inconsistent unit of analysis and measurement. Drawing on data derived from a Spanish-language US-based newspaper’s coverage of the migration program known as DACA (Deferred Action for Childhood Arrivals), this article describes challenges that analysts can run into when attempting to use a dataset containing atomized metaphor-related words as the input for subsequent quantitative semantic analyses. Its main methodological contribution consists in a proposal and illustration of three possible methods to extend the existing MIP(VU)-protocol in such a way as to allow it to capture metaphorical strings, on top of lexemes, in a reliable and systematic manner. The first two methods are procedural, and entail formulating a-priori grouping-directives based on the research question(s). One departs from semasiological criteria (Method 1) and the other takes an onomasiological approach (Method 2). The third method works bottom-up, involving the ad hoc grouping of lexemes and adding a descriptive parameter meant to keep track of grouping-decisions made by the analyst, thereby safeguarding transparency at all times.
In order to humanize forecasting, communities have been proposed to activate and enlarge a collective ability of foresight. To better understand how communities relate to collective foresight abilities, this article untangles its critical modes, roles and social media involved. Based on a fine-grained analysis of 10 community practices, we uncovered the abilities of capturing, conceiving and designing foresights enacted in the distinct modes of creative, user and strategic communities. Discoveries included the novel abilities of conceiving foresights, a new groundbreaker role for strategic designers and specific activities of social media listening with regard to future interests. Grounded on the prime findings, we propose a framework with propositions that shape further theory development on community abilities of designing foresights. Further research directions are outlined.
ABSTRACT Qualitative research is designed to generate in-depth and subjective findings to build theory. Combined with the quantitative bias of top-tier journals, many qualitative researchers do not utilise the full-benefits of their adopted methodologies. This makes it challenging for qualitative researchers to publish their work at this level. Therefore, this current paper is intended to help academics and postgraduate researchers to be aware of the mistakes that can be made when undertaking qualitative research. These include very small (and weak) sample sizes, single-source studies, and poorly-written data analysis processes. This investigation contributes to the extant literature by suggesting some ways to improve the credibility of these methodologies, like accessing the correct informants, reporting on larger (or richer) samples, conducting pilot studies, clearly presenting qualitative data, and triangulation. By drawing upon a range of best practices from the broader commercial literature, some illustrations are offered on how to effectively undertake qualitative research.
This article provides an empirical illustration of current disjunctive globalization dynamics, especially with regard to the transition from embodied to disembodied globalization. After an overview of a new typology of globalization designed to capture these dynamics, we introduce ABCKID (a pseudonym) and other home-based, virtual, and transnational educational platforms as cases. We illustrate how offshore English teaching conducted on these platforms took over migrated English teachers as the dominant form of foreigner-taught English learning in China before and during the COVID-19 pandemic. Drawing on in-depth interviews, surveys, and online data, we argue in this article that this transition from embodied to disembodied globalization has been disorganized, uneven, and multidirectional. In particular, we document how the rise of digital platforms brought the global educational job market to formerly marginalized US social groups such as stay-at-home moms, military wives, and freelance artists who had been left out of the domestic labor market and migration-based global job market. Disjunctive globalization creates new forms of global inequality among contractors and produces in individual contractors a divided, “unhappy consciousness.” Such complex and contradictory work experience of these formerly marginalized social groups is the micro-level manifestation of the macro-level shift from embodied to disembodied globalization.
This paper presents a computational approach to growing artificial societies (agent-based simulations) as an explicit, accessible, and systematic tool to visualize and generate insights and new questions about Paulo Freire’s concepts of oppression and emancipation. These models do not make claims of validity or prediction, instead, their value is to structure our thinking and support our understanding. Here, I use computational social simulations as generative allegories to reflect upon the role of designers in participatory, co-design, and social design contexts. The paper shows how Freirean ideas can help reframe design as a pedagogical craft based on dialogue and collective inquiry.
Мета дослідження. Проаналізувати художні прийоми (метафори, гіперболи, асоціації і т.п.), які застосовуються для розкриття протилежних за змістом тем – героїв і ворогів. Встановити зв’язок поставлених завдань із завданнями, сформульованими у навчальній програмі дисципліни «Дизайн-проєктування». Методи дослідження. Порівняльний та мистецтвознавчий аналіз. Наукова новизна. Введення у науковий обіг раніше недослідженого обсягу візуальних даних, створених творчими особистостями у критичній ситуації, а саме – зразків плакатної діяльності студентів і викладачів Київського національного університету культури і мистецтв та Київського університету культури у період активних військових дій. Аналіз здійснювався на основі корпусу творів, зроблених у групі «Креативний супротив КНУКІМ/КУК». Висновки. Новітні часи визначили нових героїв і антигероїв, а також і певні правила їхньої візуальної інтерпретації. Героїчні образи подаються через сакралізацію, монументальність образу, виявлення рис їхньої національної ідентичності та спорідненості із «стовпами нації», національними ідеалами. Спостерігається наголошення на укоріненості в історичній традиції, наприклад, війська часів Київської Русі або українського козацтва, повстанського руху тощо.
Трактування антигероїв коливається у діапазоні від демонізації або дегуманізації до карикатурності, втілення в образах певних тварин, подання як комічних персонажів. Персоніфікація ворожих сил у образі очільника Російської федерації далі ієрархічно розповсюджується на окупаційне військо, придворних пропагандистів, а згодом і на всю Росію як державу, і власне російське народонаселення, як таке, що у своїй більшості підтримало так звану «спецоперацію». Стилістика виконання плакатних творів, запозичуючи деякі елементи із системної пари «герой- антигерой» у радянському плакаті, знаходить і використовує сучасні прийоми і засоби графічних рішень, залучаючи арсенал новітніх технічних засобів, які дозволяють використовувати сучасні комп’ютерні технології.
In this paper, we present an energy-efficient SNN architecture, which can seamlessly run deep spiking neural networks (SNNs) with improved accuracy. First, we propose a conversion aware training (CAT) to reduce ANN-to-SNN conversion loss without hardware implementation overhead. In the proposed CAT, the activation function developed for simulating SNN during ANN training, is efficiently exploited to reduce the data representation error after conversion. Based on the CAT technique, we also present a time-to-first-spike coding that allows lightweight logarithmic computation by utilizing spike time information. The SNN processor design that supports the proposed techniques has been implemented using 28nm CMOS process. The processor achieves the top-1 accuracies of 91.7%, 67.9% and 57.4% with inference energy of 486.7uJ, 503.6uJ, and 1426uJ to process CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively, when running VGG-16 with 5bit logarithmic weights.
Approximate circuits trading the power consumption for the quality of results play a key role in the development of energy-aware systems. Designing complex approximate circuits is, however, a very difficult and computationally demanding process. When deploying approximate circuits, various error metrics (e.g., mean average error, worst-case error, error rate), as well as other constraints (e.g., correct multiplication by 0), have to be considered. The state-of-the-art approximation methods typically focus on a single metric which significantly limits the applicability of the resulting circuits. In this paper, we experimentally investigate how various error metrics and their combinations affect the reduction of the power consumption that can be achieved. To this end, we extend evolutionary-driven techniques that allow us to effectively explore the design space of the approximate circuits. We identify principal limitations when complex error constraints are required as well as important correlations among the error metrics enabling the construction of circuits providing the best-known trade-offs between the power reduction and combined error constraints.