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IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality

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arXiv 2022年

作者： Lyu, Yan Lu, Hangxin Lee, Min Kyung Schmitt, Gerhard Lim, Brian Y. School of Computer Science and Engineering Southeast University Nanjing China Future Cities Laboratory Singapore-ETH Centre Singapore Department of Architecture ETH Zurich Switzerland School of Information The University of Texas at Austin United States School of Computing National University of Singapore Singapore Singapore

With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users. Instead, tackling fairness must be inclusive and involve domain experts with specialized tools and workflows. Thus, domain-specific visualizations are needed for algorithmic fairness. Furthermore, while much work on AI fairness has focused on predictive decisions, less has been done for fair allocation and planning, which require human expertise and iterative design to integrate myriad constraints. We propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages explanations of causal attribution (Why), contrastive (Why Not) and counterfactual reasoning (What If, How To) to aid domain experts to assess and alleviate unfairness in allocation problems. We apply the framework to fair urban planning for designing cities that provide equal access to amenities and benefits for diverse resident types. Specifically, we propose an interactive visual tool, Intelligible Fair City Planner (IF-City), to help urban planners to perceive inequality across groups, identify and attribute sources of inequality, and mitigate inequality with automatic allocation simulations and constraint-satisfying recommendations. We demonstrate and evaluate the usage and usefulness of IF-City on a real neighborhood in New York City, US, with practicing urban planners from multiple countries, and discuss generalizing our findings, application, and framework to other use cases and applications of fair allocation. Copyright © 2022, The Authors. All rights reserved.

关键词： Urban planning

On the Outage Performance of Drones-Aided Cooperative D2D communications systems

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On the Outage Performance of Drones-Aided Cooperative D2D co...

Computer Communications and Intelligent Systems (I3CIS), International Conference on

作者： Widad Belaoura Khalida Ghanem Mohummed Zeeshan Shakir Department Engineering of Electrical Systems Université M'Hamed Bougara Boumerdes Boumerdes Algeria System Architecture Laboratory Center of development of advanced techniques (CDTA) Algiers Algeria School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley UK

The advanced Device-to-Device (D2D) system is recognized as one of the key emerging technologies to support the increasing wireless data traffic of fifth generation (5G) systems and beyond. In this paper, we aim to study a novel cooperative D2D communications concept to improve the communication between the D2D users. In this model, multiple drones are included in the network and operate as half duplex amplify-and-forward (AF) relays scheme and the Maximal Ratio Combining (MRC) method is employed at the receiver side to combine the relayed signals. The effect of various parameters on the outage probability is investigated, including drone altitude placement, number of drones, and transmission power. Simulation results indicate that the outage probability of the cooperative D2D solution is greatly improved whenever an additional drone is added to the cooperative system.

关键词： Wireless communication Simulation System performance Cooperative systems Receivers Probability Device-to-device communication

Catalysts of choice: Unraveling the dynamics of housing preferences across diverse societal contexts – Evidence from literature

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Journal of Engineering Research

Journal of engineering Research 2024年

作者： Seyda Emekci Ali Abbas Buse Akçay Pehlivanoğlu Duygu Savur Gülsüm Taç Ankara Yıldırım Beyazıt University Faculty of Fine Arts and Architecture Architecture Department Ankara Turkiye University of East London School of Architecture Computing And Engineering Department of Engineering & Construction London UK

Housing preferences worldwide are shaped by a myriad of factors, reflecting the diverse and dynamic nature of societal needs and individual choices. The process of evaluating available housing options is intricately linked to underlying motivating factors, shaping individual preferences and decisions in response to evolving circumstances and environments. This comprehensive study delves into the intricate dynamics of housing preferences, examining the interplay of temporal-spatial relationships, psychological factors, social-cultural dimensions, and political-economic aspects. Employing bibliometric-based citation analysis techniques and social network analysis, the research offers a nuanced exploration of scholarly landscapes from 1967 to 2022 across prominent databases—Web of Science, Scopus, and Science Direct. This article undertakes a comprehensive analysis of the preferences within contemporary housing systems, offering insights into diverse dwelling choices and their determinants. The examination encompasses multifaceted considerations, contributing to an understanding of the intricate dynamics influencing housing preferences in varying societal contexts. The article also informs policymakers, urban planners, and researchers about the intricate dynamics influencing housing preferences worldwide, providing valuable guidance for addressing the evolving housing needs of diverse populations.

关键词： Housing preferences Temporal-spatial relationships Psychological factors Social-cultural dimensions Housing policies Dwelling choices Bibliometric analysis

Higher education assessment practice in the era of generative AI tools

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arXiv 2024年

作者： Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Sharma, Hemlata Olayinka, Olakunle School of Architecture Technology & Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computing & Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Department of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Department of Computing Sheffield Hallam University SheffieldS1 2NU United Kingdom

The higher education (HE) sector benefits every nation’s economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE. © 2024, CC BY.

关键词： Project management

Utilization of Operation Research Techniques and Innovative Testing Methods for Sustainable Construction Materials

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Utilization of Operation Research Techniques and Innovative ...

IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Oluwadare Joshua Oyebode Sunday Apeh Samuel-Soma M. Ajibade Chukwudulue Jedidiah Anomnakachukwu Imade Princess Okokpujie Akinniyi Tunde Akinyemi Adedotun Adetunla Olusola Anthony Ibitoye Stephen Gekwu Udeagbara Civil and Environmental Engineering Department Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Architecture Afe Babalola University Ado-Ekiti Ekiti-State Nigeria Department of Mechanical Engineering Sciences University of Johannesburg South Africa Department of Finance Afe Babalola University Ado-Ekiti Ekiti State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Innovative technology and local content cannot be achieved without appropriate techniques and testing methods to realize sustainable civil infrastructure in a challenging economy. This paper examines the utilization of operation research techniques and innovative testing methods for sustainable construction materials. Pertinent literature and empirical studies were combined to explore the theoretical foundations and real-world applications of operation research methodologies and novel testing methods for sustainable construction. Findings revealed that operation research techniques play a significant role in maximizing the consumption of sustainable construction materials. In the construction sector, ultrasonic testing (UT) is a popular non-destructive testing (NDT) method for evaluating the characteristics and structural integrity of materials like composites, steel, and concrete. Radiographic testing (RT), Computed tomography (CT), digital image correlation (DIC), and laser scanning are Ekiti State, Nigeria beneficial. A non-invasive geophysical technique called ground-penetrating radar (GPR) is used to find and map subsurface features, utilities, and irregularities in soil and building materials. Despite the advancements in operation research techniques and innovative testing methods for sustainable construction materials, several challenges persist, and future directions warrant attention to further enhance their effectiveness and applicability in construction projects. In conclusion, integrating operation research techniques and innovative testing methods represents a transformative opportunity to build a more sustainable future for the construction industry and society. Supportive policies and regulations can drive the adoption of sustainable practices and innovative technologies in the construction industry.

关键词： Operations research Correlation Digital images Computed tomography Maintenance Sustainable development Optimization Civil engineering Testing Construction industry

Mortar: Morphing the Bit Level Sparsity for General Purpose Deep Learning Acceleration

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Mortar: Morphing the Bit Level Sparsity for General Purpose ...

Asia and South Pacific Design Automation Conference

作者： Yunhung Gao Hongyan Li Kevin Zhang Xueru Yu Hang Lu School of Electronics Engineering and Computer Science Peking University Beijing China State Key Lab of Computer Architecture Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Shanghai Integrated Circuits R&D Center Co. Ltd Shanghai China

Vanilla Deep Neural Networks (DNN) after training are represented with native floating-point 32 (fp32) weights. We observe that the bit-level sparsity of these weights is very abundant in the mantissa and can be directly exploited to speed up model inference. In this paper, we propose Mortar, an off-line/on-line collaborated approach for fp32 DNN acceleration, which includes two parts: first, an off-line bit sparsification algorithm to construct the target formulation by “mantissa morphing”, which maintains higher model accuracy while increasing bit-level sparsity; second, the associating hardware accelerator architecture to speed up the on-line fp32 inference through manipulating the enlarged bit sparsity. We highlight the following results by evaluating various deep learning tasks, including image classification, object detection, video understanding, video & image super-resolution, etc.: We (1) increase bit-level sparsity up to 1.28~2.51x with only a negligible -0.09~0.23% accuracy loss, (2) maintain on average 3.55% higher model accuracy while increasing more bit-level sparsity than the baseline, (3)and our hardware accelerator outperforms up to 4.8x over the baseline, with an area of 0.031 mm 2 and power of 68.58mW.

关键词： Deep learning Training Mortar Superresolution Software algorithms Inference algorithms Software

A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey

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arXiv 2024年

作者： Sohail, Shahab Saquib Himeur, Yassine Kheddar, Hamza Amira, Abbes Fadli, Fodil Atalla, Shadi Copiaco, Abigail Mansoor, Wathiq School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates LSEA Laboratory Department of Electrical Engineering University of Medea 26000 Algeria Department of Computer Science University of Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data’s quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL’s background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions. © 2024, CC BY.

关键词： Object detection

Influence of database noises to machine learning for spatiotemporal chaos

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arXiv 2021年

作者： Yang, Yu Qin, Shijie Liao, Shijun Center of Advanced Computing School of Naval Architecture Ocean and Civil Engineering Shanghai Jiaotong University China School of Physics and Astronomy Shanghai Jiaotong University China

A new strategy, namely the "clean numerical simulation" (CNS), was proposed (J. Computational Physics, 418:109629, 2020) to gain reliable/convergent simulations (with negligible numerical noises) of spatiotemporal chaotic systems in a long enough interval of time, which provide us benchmark solution for comparison. Here we illustrate that machine learning (ML) can always give good enough fitting predictions of a spatiotemporal chaos by using, separately, two quite different training sets: one is the "clean database" given by the CNS with negligible numerical noises, the other is the "polluted database" given by the traditional algorithms in single/double precision with considerably large numerical noises. However, even in statistics, the ML predictions based on the "polluted database" are quite different from those based on the "clean database". It illustrates that the database noises have huge influences on ML predictions of some spatiotemporal chaos, even in statistics. Thus, we must use a "clean" database for machine learning of some spatiotemporal chaos. This surprising result might open a new door and possibility to study machine learning. © 2021, CC BY.

关键词： Database systems