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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2023年第2期70卷 784-790页

作者： Luo, Jianxi Singapore Univ Technol & Design Data Driven Innovat Lab Singapore 487372 Singapore

The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized and often confused with several other data-related phenomena. This article defines and crystalizes "DDI" as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization, data-based innovation, and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, I elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process and present a process-based taxonomy of different DDI approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact DDI.

关键词： Technological innovation Creativity Patents Companies Uncertainty Taxonomy Government Artificial intelligence creativity data science innovation process machine learning uncertainty

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designing the future of the fourth industrial revolution

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JOURNAL OF ENGINEERING design 2023年第10期34卷 779-785页

作者： Luo, Jianxi Singapore Univ Technol & Design Data Driven Innovat Lab 8 Somapah Rd Singapore 487372 Singapore

The Fourth Industrial Revolution (4IR) looms large on the horizon. It is characterised by the fusion of cyber, physical, and human realms, with the heart of it powered by artificial general intelligence (AGI). This revolution brings profound economic, societal, and ethical implications for humanity. Addressing its intricate challenges necessitates a shift toward higher-order and higher-dimensional empathy and creativity for humanity-centred design innovations. Traditional design thinking and methodologies, while once effective, now fall short amidst the complexities of the 4IR. Future designers must adeptly wield creative artificial intelligence for humanity-centred design and execute robust entrepreneurship and leadership to ensure a human-led design process towards the future.

关键词： The fourth industrial revolution evolutionary ecosystem design thinking artificial general intelligence entrepreneurship leadership

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TOWARD ARTIFICIAL EMPATHY FOR HUMAN-CENTERED design: A FRAMEWORK

TOWARD ARTIFICIAL EMPATHY FOR HUMAN-CENTERED DESIGN: A FRAME...

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ASME International design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC-CIE) / 49th design Automation Conference (DAC)

作者： Zhu, Qihao Luo, Jianxi Singapore Univ Technol & Design Data Driven Innovat Lab Singapore Singapore

ISBN: (纸本)9780791887318

In the early stages of the design process, designers explore opportunities by discovering unmet needs and developing innovative concepts as potential solutions. From a human-centered design perspective, designers must develop empathy with people to truly understand their needs. However, developing empathy is a complex and subjective process that relies heavily on the designer's empathetic capability. Therefore, the development of empathetic understanding is intuitive, and the discovery of underlying needs is often serendipitous. This paper aims to provide insights from artificial intelligence research to indicate the future direction of AI-driven human-centered design, taking into account the essential role of empathy. Specifically, we conduct an interdisciplinary investigation of research areas such as data-driven user studies, empathetic understanding development, and artificial empathy. Based on this foundation, we discuss the role that artificial empathy can play in human-centered design and propose an artificial empathy framework for human-centered design. Building on the mechanisms behind empathy and insights from empathetic design research, the framework aims to break down the rather complex and subjective concept of empathy into components and modules that can potentially be modeled computationally. Furthermore, we discuss the expected benefits of developing such systems and identify current research gaps to encourage future research efforts.

关键词： Empathy Human-Centered design Artificial Intelligence data-Driven design

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MATlab Simulation, and FPGA Implementation of the DRLSE Segmentation Algorithm

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TRAITEMENT DU SIGNAL 2025年第2期42卷 719-727页

作者： Hamadi, Fatma Z. Hamidatou, Mohamed L. Hamami-Mitiche, Latifa Belkacem, Sabrina Ait Bouzid, Bouchra Natl Polytech Sch ENP Dept Elect Algiers 16000 Algeria Natl High Coll Data Proc Syst Design Methods Lab Algiers 16000 Algeria

This work focuses on using level set curves for medical image segmentation through the DRLSE (Distance Regularization Level Set Evolution) algorithm, recognized for its effectiveness and adaptability. Traditional systems face limitations in computation time and efficiency when implementing this algorithm. To overcome these challenges, FPGA (Field-Programmable Gate Arrays) are used for their parallelism and low resource consumption. The objective is to optimize medical image segmentation by implementing the DRLSE algorithm on FPGA while ensuring efficient resource and computation time management. The Algorithm was first simulated in MATlab and tested on a database of brain, breast, and other medical images, demonstrating its robustness and flexibility. The results validate the effectiveness of the DRLSE algorithm and highlight the advantages of the FPGA in terms of speed and precision. Despite the limited documentation on implementing DRLSE on FPGA Our approach is distinguished by the use of DDR memory, which provides increased capacity to overcome the limitations of BRAM memory. Parameter optimization ensures better performance and efficient management of hardware resources. This work underscores the potential of FPGA-based implementations for accelerating computationally intensive tasks like medical image segmentation while maintaining high accuracy and efficiency.

关键词： DRLSE algorithm FPGA implementation level sets MATlab simulation medical image segmentation

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Do Sentence-Level Sentiment Interactions Matter? Sentiment Mixed Heterogeneous Network for Fake News Detection

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第4期11卷 5090-5100页

作者： Zhang, Hao Li, Zonglin Liu, Sanya Huang, Tao Ni, Zhouwei Zhang, Jian Lv, Zhihan Cent China Normal Univ Natl Engn Lab Educ Big Data Wuhan Peoples R China Uppsala Univ Fac Arts Dept Game Design Uppsala Sweden

With the proliferation of fake news, the spread of misleading information can easily cause social panic and group polarization. Many existing methods for detecting fake news rely on linguistic and semantic features extracted from the content of the news. Some existing approaches focus on sentiment analysis for fake news detection, but the sentiment changes and sentence-level emotional interactions in news classification are not fully analyzed. Fortunately, we observe that in long-form news, the change and mutual influence of sentiment between sentences are different. To extract the features of sentiment interaction between sentences in the article, we propose a graph attention network-based model that combines both sentiment and external knowledge comparison to meet the needs of fake news classification. We obtain the contextual sentiment representation and entity representation of the sentence through the heterogeneous network and the emotion interaction network and obtain the change of the sentiment vector through the emotion comparison network. We compare the entity vectors in the context with those corresponding knowledge base (KB)-based, combine them with the contextual semantic representation of the sentence, and finally input them into the classifier. In experiments, our model performs well in both single and multiclass classification, achieving the state-of-the-art accuracy on existing datasets.

关键词： Fake news Feature extraction Semantics Internet Social networking (online) Sentiment analysis Knowledge engineering Fake news detection heterogeneous graph knowledge graph sentiment analysis sentiment comparison text classification

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design and validate a wide-bandwidth, high-performance tunable metamaterial based on cultural-innovation shell materials and its sensing applications

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MATERIALS RESEARCH EXPRESS 2024年第3期11卷 035802页

作者： Wu, Wei Huang, Ge Zhong, Min South China Normal Univ Sch Creat Design Guangzhou 516625 Guangdong Peoples R China Hezhou Univ Hezhou 542899 Peoples R China Guangxi Univ Key Lab Big Data Innovat Design Nanning Guangxi Peoples R China

The application of metamaterials in controllable thermal emission devices is an interesting field. However, most of the demonstrated thermal emitters required continuous consumption of external energy (electrical or thermal) to provide an effective thermal emissivity. Here, a metamaterial containing phase change materials Ge2Sb2Te5 (GST) and shell materials with controllable thermal emission power was proposed and measured. Based on the completely amorphous state of the GST layer, an emissivity of 0.212 at wavelength 7.11 mu m was achieved by this this metamaterial, while a thermal emission band (with an average amplitude of 0.857 and a bandwidth of 6.16 mu m) was excited for the crystalline state. Moreover, numerous thermal emission states were excited by this metamaterial based on the intermediate states between completely amorphous and crystalline states of the GST layer. Tunability of the thermal emission window was obtained by this metamaterial sample. The temperature sensitivity of this metamaterial thermal emitter was 341 nm degrees C-1. By increasing the thickness of the GST or shell layers, the thermal emission performance of the metamaterial was enhanced. Since the phase transition of GST does not require the continuous consumption of external energy, the metamaterial has the potential to be used in the development of low-power heat emitters, as well as temperature sensors.

关键词： metamaterials metasurface thermal emission tunable phase change material GST

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A mobile e-learning application for enhancement of basic mathematical skills in visually impaired children

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UNIVERSAL ACCESS IN THE INFORMATION SOCIETY 2024年第3期23卷 1091-1101页

作者： Shoaib, Muhammad Khan, Shakeel Fitzpatrick, Donal Pitt, Ian Univ Coll Cork Sch Comp Sci & Informat Technol Cork Ireland Data Lab Sols Lahore Pakistan Natl Disabil Author Ctr Excellence Universal Design Dublin Ireland

Although smartphones are equipped with accessibility functions, they still pose significant problems for visually impaired people. Sometimes these functions cannot fulfil the expectations of users. Early mobile devices had physical buttons and a keypad, and visually impaired users could navigate around the keypad using the tactile markers on the buttons. However, the lack of tactile markers makes it much more difficult to operate a touchscreen device. This paper describes an e-learning platform that is designed to improve the accessibility of smartphone applications for students who are visually impaired but have some useful vision. A User-Centered design approach was used to develop an effective solution for visually impaired students. A study was conducted during the development of the described platform, and the results showed that our suggested design improves task completion time as compared to the initial version. Participants also expressed higher levels of satisfaction when using the improved design of this platform. The modified design was also assessed using the Mobile Application Rating Scale (MARS), and the results indicate that it is quite reliable and rated well among visually impaired children. Furthermore, developers can use our suggested design guidelines such as clear navigation, color contrast, immediate feedback, icon arrangements, button and text size in the development of new applications.

关键词： Smartphone Visually impaired Learning Mathematical skills Traditional mobile

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Making EULA Great Again: A Novel Nudge Mechanism to Improve Readability, User Attention and Awareness 20th

Making EULA Great Again: A Novel Nudge Mechanism to Improve ...

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20th International Conference on Information Systems Security

作者： Bin Zahid, Shamir Bristy, Aishwarya Ghost Oli, Md Musfikur Hasan Fahim, Md Rumee, Sarker Tanveer Ahmed Zaber, Moinul Islam Univ Dhaka Dept Comp Sci & Engn Dhaka 1000 Bangladesh Univ Dhaka Data & Design Lab Dhaka 1000 Bangladesh

ISBN: (纸本)9783031800191;9783031800207

Software End User License Agreements (EULAs) are notoriously dense legal documents that users often skim over without fully understanding their implications, including privacy-related clauses. This research proposes a novel approach to improve the readability and privacy awareness of EULAs by implementing a nudge tool to improve user attention and comprehension of EULA contents. Through natural language processing technique, privacy-sensitive keywords within EULAs are identified and visually emphasized for users. By drawing attention to EULA clauses related to data collection, sharing, and security, users can make more informed decisions about their privacy when agreeing to software terms. Detailed user experiments involving 173 participants are performed to evaluate the effectiveness of the proposed approach demonstrating significant improvements in comprehension and awareness of privacy implications compared to traditional EULAs. The developed nudge tool is released and made freely available for the interesting readers (https://***/data-and-design-lab/EULA-plugin/).

关键词： EULA Privacy Feedback Education Nudge

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Dynamic Pricing for Client Recruitment in Federated Learning

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第2期32卷 1273-1286页

作者： Wang, Xuehe Zheng, Shensheng Duan, Lingjie Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai 519082 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore

Though federated learning (FL) well preserves clients' data privacy, many clients are still reluctant to join FL given the communication cost and energy consumption in their mobile devices. It is important to design pricing compensations to motivate enough clients to join FL and distributively train the global model. Prior pricing mechanisms for FL are static and cannot adapt to clients' random arrival pattern over time. We propose a new dynamic pricing solution in closed-form by constructing the Hamiltonian function to optimally balance the client recruitment time and the model training time, without knowing clients' actual arrivals or training costs. During the client recruitment phase, we offer time-dependent monetary rewards per client arrival to trade off between the total payment and the FL model's accuracy loss. Such reward gradually increases when we approach to the recruitment deadline or have greater data aging, and we also extend the deadline if the clients' training time per iteration becomes shorter. Further, we extend to consider heterogeneous client types in training data size and training time per iteration. We successfully extend our dynamic pricing solution and develop an optimal algorithm of linear complexity to monotonically select client types for FL. Finally, we also show robustness of our solution against estimation error of clients' data sizes, and run numerical experiments to validate our results.

关键词： Federated learning dynamic pricing incentive mechanism incomplete information

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Group commonality graph: Multimodal pedestrian trajectory prediction via deep group features

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PATTERN RECOGNITION LETTERS 2025年 192卷 36-42页

作者： Zhou, Di Gao, Ying Li, Hui Liu, Xiaoya Lin, Qinghua Qingdao Univ Sci & Technol Sch Data Sci Qingdao 266061 Peoples R China Guangxi Minzu Univ Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Pedestrian trajectory prediction is a challenging task in domains such as autonomous driving and robot motion planning. Existing methods often focus on aggregating nearby individuals into a single group, while neglecting individual differences and the risks of unreliable interactions. Therefore we propose a novel framework termed group commonality graph, which comprises a group feature capture network and a spatial-temporal graph sparse connected network. The previous network can group and pool pedestrians based on their characteristics, capturing and integrating deep features of the group to generate the final prediction. The subsequent network learns pedestrian motion patterns and simulates their interactive relationships. The framework not only addresses the limitations of overly simplistic aggregation methods but also ensures reliable interactions with sparse directionality. Additionally, to evaluate the effectiveness of our model, we introduce a new evaluation metric termed collision prediction error, which incorporates map environment information to assess the comprehensiveness of multimodal prediction results. Experimental results on public pedestrian trajectory prediction benchmark demonstrate that our method outperforms the state-of-the-art methods.

关键词： Pedestrian trajectory prediction Group commonality Map information Autonomous driving

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