A Use of Form-Based Code for Volumetric Morphology of High-density Cities

© 2018 IEEE. This paper presents the experimental use of Form-Based Code (FBC), an alternative approach to urban planning and regulation, for volumetric morphology towards more flexible and predictable development in high-density cities. Quantitative analysis of the Tsim Sha Tsui (TST) area of Hong Kong results in a workflow for using FBC in high-density contexts; combining transect matrix redefining, the integration of multiple variables, and parametric modelling. Findings indicate that a newly-defined transect matrix with multifarious types and subtypes enables the extension of the FBC study to encompass high-density conditions. Secondly, that FBC has the capacity for the assembly of multi-level regulations more suitable for volumetric urban forms. Thirdly, that the parametric regulation method of FBC can visualize and enumerate scenarios that conventional paper-based regulation cannot. The findings also suggest increasing the awareness of urban forms, rather than rigid land-use concerns, can be a critical influence when designers are pursuing sustainable communities within crowded contexts

Influence of impervious surface area and fractional vegetation cover on seasonal urban surface heating/cooling rates

The urban thermal environment is impacted by changes in urban landscape patterns resulting from urban expansion and seasonal variation. In order to cope effectively with urban heat island (UHI) effects and improve the urban living environment and microclimate, an analysis of the heating effect of impervious surface areas (ISA) and the cooling effects of vegetation is needed. In this study, Landsat 8 data in four seasons were used to derive the percent ISA and fractional vegetation cover (FVC) by spectral unmixing and to retrieve the land surface temperature (LST) from the radiative transfer equation (RTE). The percent ISA and FVC were divided into four different categories based on ranges 0-25%, 25-50%, 50-75%, and 75-100%. The LST with percent ISA and FVC were used to calculate the surface heating rate (SHR) and surface cooling rate (SCR). Finally, in order to analyze the heating effect of ISA and the cooling effect of vegetation, the variations of LST with SHR and SCR were compared between different percent ISA and FVC categories in the four seasons. The results showed the following: (1) In summer, SHR decreases as percent ISA increases and SCR increases as FVC increases in the study area. (2) Unlike the dependence of LST on percent ISA and FVC, the trends of SHR/SCR as a function of percent ISA/FVC are more complex for different value ranges, especially in spring and autumn. (3) The SHR (heating capacity) decreases with increasing percent ISA in autumn. However, the SCR (cooling capacity) decreases with increasing FVC, except in summer. This study shows that our methodology to analyze the variation and change trends of SHR, SCR, and LST based on different ISA and FVC categories in different seasons can be used to interpret urban ISA and vegetation cover, as well as their heating and cooling effects on the urban thermal environment. This analytical method provides an important insight into analyzing the urban landscape patterns and thermal environment. It is also helpful for urban planning and mitigating UHI

Mapping Volumetric Urban Space: A Critical Development Analysis of Multi- level Morphology of High-dense Cities

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On the use of Machine Learning methods in rock art research with application to automatic painted rock art identification

Rock art is globally recognized as significant, yet the resources allocated to the study and exploration of this important form of cultural heritage are often scarce. In areas where numerous rock art sites exist, much of the rock art is unidentified and therefore remains, unrecorded and unresearched. Manually identifying rock art is time-consuming, tedious, and expensive. Therefore, it is necessary to automate many processes in rock art research, which can be accomplished by Machine Learning. Artificial Intelligence (AI) and Machine Learning (ML) can greatly facilitate rock art research in many ways, such as through Object Recognition and Detection, Motif Extraction, Object Reconstruction, Image Knowledge Graphs, and Representations. This article is a reflective work on the future of ML for rock art research. As a proof-of-concept, it presents a machine learning method based on recent advances in deep learning to train a model to identify images with painted rock art (pictograms). The efficacy of the proposed method is shown using data collected from fieldwork in Australia. Furthermore, our proposed method can be used to train models that are specific to the rock art found in different regions. We provide the code and the trained models in the supplementary section

Artificial intelligence for visually impaired

The eyes are an essential tool for human observation and perception of the world, helping people to perform their tasks. Visual impairment causes many inconveniences in the lives of visually impaired people. Therefore, it is necessary to focus on the needs of the visually impaired community. Researchers work from different angles to help visually impaired people live normal lives. The advent of the digital age has profoundly changed the lives of the visually impaired community, making life more convenient. Deep learning, as a promising technology, is also expected to improve the lives of visually impaired people. It is increasingly being used in the diagnosis of eye diseases and the development of visual aids. The earlier accurate diagnosis of the eye disease by the doctor, the sooner the patient can receive the appropriate treatment and the better chances of a cure. This paper summarises recent research on the development of artificial intelligence-based eye disease diagnosis and visual aids. The research is divided according to the purpose of the study into deep learning methods applied in diagnosing eye diseases and smart devices to help visually impaired people in their daily lives. Finally, a summary is given of the directions in which artificial intelligence may be able to assist the visually impaired in the future. In addition, this overview provides some knowledge about deep learning for beginners. We hope this paper will inspire future work on the subjects

A Hybrid Framework for Lung Cancer Classification

Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84% ± 0.156% (accuracy), 99.84% ± 0.153% (precision), 99.84% ± 0.156% (sensitivity), and 99.84% ± 0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods

Cross-layer access control in publish/subscribe middleware over software-defined networks

When technologies of software-defined networks (SDNs) provide a chance to improve the quality of service (QoS) of publish/subscribe middlewares, new chances are also arising for adversaries to attack the networks and the middlewares. We here propose a cross-layer access control solution to protect the publish/subscribe middleware over SDNs. Applications over a publish/subscribe middleware interact by an indirect, anonymous and multicast event communication paradigm, where we hope that the applications, the middleware, and the underlying network collaborate to realize the access control of reading/writing events. The key issue is how to use the flow matching capability of SDN switches to efficiently and securely enforce complex authorization policies that include multiple conjunction and disjunction structures. It is required to resist against the collusion attacks of SDN controllers and subscribers when the middleware/network is partially delegated to enforce the authorization policies of publishers. In our cross-layer solution, a policy representation method is presented to encode authorization policies into flow entries with high data compression and security, and a two-party computation method is presented to carry out secret sharing for defeating malicious SDN controllers and subscribers. Finally, our solution is evaluated to show its effectiveness

H∞ control of LPV systems with randomly multi-step sensor delays

This paper is concerned with the H∞ control problem for a class of linear parameter-varying (LPV) systems with randomly multi-step sensor delays. A mathematical model which describes the randomly multi-step sensor delayed measurements for LPV systems is established. An improved Lyapunov functional is proposed to determine the asymptotically mean-square stability of the closed-loop system which depends on the varying parameters. The obtained full-order parameter-dependent dynamic feedback controller guarantees the considered system to be asymptotically mean-square stable and to satisfy the modified H∞ performance for all possible delayed measurements. An extended cone complementarity linearization method (CCLM) is used to solve the constrained linear matrix inequality (CLMI). Simulation results illustrate the effectiveness of the proposed method

Divide-and-conquer large scale capacitated arc routing problems with route cutting off decomposition

The capacitated arc routing problem is a very important problem with many practical applications. This paper focuses on the large scale capacitated arc routing problem. Traditional solution optimization approaches usually fail because of their poor scalability. The divide-and-conquer strategy has achieved great success in solving large scale optimization problems by decomposing the original large problem into smaller sub-problems and solving them separately. For arc routing, a commonly used divide-and-conquer strategy is to divide the tasks into subsets, and then solve the sub-problems induced by the task subsets separately. However, the success of a divide-and-conquer strategy relies on a proper task division, which is non-trivial due to the complex interactions between the tasks. This paper proposes a novel problem decomposition operator, named the route cutting off operator, which considers the interactions between the tasks in a sophisticated way. To examine the effectiveness of the route cutting off operator, we integrate it with two state-of-the-art divide-and-conquer algorithms, and compared with the original counterparts on a wide range of benchmark instances. The results show that the route cutting off operator can improve the effectiveness of the decomposition, and lead to significantly better results especially when the problem size is very large and the time budget is very tight