Search results for: spatial semantic

Authors: Marina Yurievna Aleksandrova

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Research of the Holocaust retains value not only for history but also for sociology and psychology. One of the most important fields of study is how people were coping during and after this traumatic event. The aim of this paper is to identify the main contexts of the topic of love and to determine which contexts are more characteristic for different groups of victims of the Holocaust (gender, nationality, age). In this research, transcripts of interviews with Holocaust victims that were collected during 1946 for the "Voices of the Holocaust" project were used as data. Main contexts were analyzed with methods of network analysis and latent semantic analysis and classified by gender, age, and nationality with random forest. The results show that love is articulated and described significantly differently for male and female informants, nationality is shown results with lower values of quality metrics, as well as the age.

Keywords: Holocaust, latent semantic analysis, network analysis, text-mining, random forest

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Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

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Authors: Yu Chen

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In line with existing philosophical approaches, this research proposes a conceptual model with an innovative spatial vision and aesthetic principles for Artificial Intelligence (AI) application in brand advertising. The model first identifies the major constituencies in contemporary advertising on three spatial levels—physical, psychological, and symbolic. The model further incorporates the relationships among AI, aesthetics, branding, and advertising and their interactions with the major actors in all spaces. It illustrates that AI may follow the aesthetic principles-- beauty, elegance, and simplicity-- to reinforce brand identity and consistency in advertising, to collaborate with stakeholders, and to satisfy different advertising objectives on each level. It proposes that, with aesthetic guidelines, AI may assist consumers to emerge into the physical, psychological, and symbolic advertising spaces and helps transcend the tangible advertising messages to meaningful brand symbols. Conceptually, the research illustrates that even though consumers’ engagement with brand mostly begins with physical advertising and later moves to psychological-symbolic, AI-assisted advertising should start with the understanding of brand symbolic-psychological and consumer aesthetic preferences before the physical design to better resonate. Limits of AI and future AI functions in advertising are discussed.

Keywords: AI, spatial, aesthetic, brand advertising

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Authors: Dania Aljouhi

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September 26th, 2017, is a historic date for all women in Saudi Arabia. On that day, Saudi Arabia announced the decree on allowing Saudi women to drive. With the advent of vision 2030 and its goal to empower women and increase their participation in Saudi society, we see how Saudis’ Twitter users deliberate the 2017 decree from different social, cultural, religious, economic and political factors. This topic bridges social media 'Twitter,' gender and social-cultural studies to offer insights into how Saudis’ tweets reflect a broader discourse on Saudi women in the age of social media. The present study aims to explore the meanings and themes that emerge by Saudis’ Twitter users in response to the 2017 royal decree on women driving. The sample used in the current study involves (n= 1000) tweets that were collected from Sep 2017 to March 2019 to account for the Saudis’ tweets before and after implementing the decree. The paper uses semantic and thematic network analysis methods to examine the Saudis’ Twitter discourse on the women driving issue. The paper argues that Twitter as a platform has mediated the discourse of women driving among the Saudi community and facilitated social changes. Finally, framing theory (Goffman, 1974) and Networked framing (Meraz & Papacharissi 2013) are both used to explain the tweets on the decree of allowing Saudi women to drive based on # Saudi women-driving-cars.

Keywords: Saudi Arabia, women, Twitter, semantic network analysis, framing

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Yuzuru Mitsui, Takashi Ikegami

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The exponents of Taylor’s law in deterministic chaotic systems are computed, and their meanings are intensively discussed. Taylor’s law is the scaling relationship between the mean and variance (in both space and time) of population abundance, and this law is known to hold in a variety of ecological time series. The exponents found in the temporal Taylor’s law are different from those of the spatial Taylor’s law. The temporal Taylor’s law is calculated on the time series from the same locations (or the same initial states) of different temporal phases. However, with the spatial Taylor’s law, the mean and variance are calculated from the same temporal phase sampled from different places. Most previous studies were done with stochastic models, but we computed the temporal and spatial Taylor’s law in deterministic systems. The temporal Taylor’s law evaluated using the same initial state, and the spatial Taylor’s law was evaluated using the ensemble average and variance. There were two main discoveries from this work. First, it is often stated that deterministic systems tend to have the value two for Taylor’s exponent. However, most of the calculated exponents here were not two. Second, we investigated the relationships between chaotic features measured by the Lyapunov exponent, the correlation dimension, and other indexes with Taylor’s exponents. No strong correlations were found; however, there is some relationship in the same model, but with different parameter values, and we will discuss the meaning of those results at the end of this paper.

Keywords: chaos, density effect, population dynamics, Taylor’s law

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Authors: Agata Twardoch

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The processes of commodification of real estate and changes in housing markets have led to a situation where the prices of free market housing in European capitals are significantly higher than the purchasing value of average wages. This phenomenon has many negative social and spatial consequences. At the same time, the attractiveness of real estate as an asset makes these processes progress. Out of concern for sustainable social development, city authorities apply solutions to balance the burdensome effects of codification of housing. One of them is a social provision for housing investments. The article presents a comparative study of solutions applied in selected European capitals, on the example of Warsaw, Paris, London, Berlin, Copenhagen, and Vienna. The study was conducted along with works on expert report for the master plan for Warsaw. The forms of commissions applied in Local Planning Acts were compared, with particular reference to spatial solutions. The results of the analysis made it possible to determine common features of the solutions applied and to establish recommendations for further practice. Major findings of the study indicate that requirement of social provision is achievable in spatial planning documents. Study shows that application of social provision in private housing investments is a useful tool in housing policy against commodification.

Keywords: affordable housing, housing provision, spatial planning, sustainable social development

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Authors: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

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Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Silvia Lorenzo, Hongmian Gong

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With the influx of research assessing the economic impact of the global financial crisis of 2007-8, a spatial analysis based on empirical data is needed to better understand the spatial significance of the financial crisis in New York, a key international financial center also considered the origin of the crisis. Using spatial statistics, the existence of financial clusters specializing in credit and securities throughout the New York metropolitan area are identified for 2000 and 2010, the time period before and after the height of the global financial crisis. Geographically Weighted Regressions are then used to examine processes underlying the formation and movement of financial geographies across state, county and ZIP codes of the New York metropolitan area throughout the 2000s with specific attention to tax regimes, employment, household income, technology, and transportation hubs. This analysis provides useful inputs for financial risk management and public policy initiatives aimed at addressing regional economic sustainability across state boundaries, while also developing the groundwork for further research on a spatial analysis of the global financial crisis.

Keywords: financial clusters, New York, global financial crisis, geographically weighted regression

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Authors: Mina Samizadeh

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In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems.

Keywords: keyword extraction, n-gram extraction, text summarization, topic clustering, semantic analysis

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Authors: Bourama Mane, Ibrahima Fall, Mamadou Samba Camara, Alassane Bah

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At the level of the National Statistical Institutes, there is a large volume of data which is generally in a format which conditions the method of publication of the information they contain. Each household or business data collection project includes a dissemination platform for its implementation. Thus, these dissemination methods previously used, do not promote rapid access to information and especially does not offer the option of being able to link data for in-depth processing. In this paper, we present an approach to modeling these data to publish them in a format intended for the Semantic Web. Our objective is to be able to publish all this data in a single platform and offer the option to link with other external data sources. An application of the approach will be made on data from major national surveys such as the one on employment, poverty, child labor and the general census of the population of Senegal.

Keywords: Semantic Web, linked open data, database, statistic

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Authors: Bedilu A. Ejigu, Eshetu Wencheko, Kiros Berhane

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Anaemia is a condition in which the haemoglobin concentration falls below an established cut-off value due to a decrease in the number and size of red blood cells. The current study aimed to assess the spatial pattern and identify predictors related to anaemia using the third Ethiopian demographic health survey which was conducted in 2010. To achieve this objective, this study took into account the clustered nature of the data. As a result, multilevel modeling has been used in the statistical analysis. For analysis purpose, only complete cases from 15,909 females, and 13,903 males were considered. Among all subjects who agreed for haemoglobin test, 5.49 %males, and 19.86% females were anaemic. In both binary and ordinal outcome modeling approaches, educational level, age, wealth index, BMI and HIV status were identified to be significant predictors for anaemia prevalence. Furthermore, it was noted that pregnant women were more anaemic than non-pregnant women. As revealed by Moran's I test, significant spatial autocorrelation was noted across clusters. The risk of anaemia was found to vary across different regions, and higher prevalence was observed in Somali and Affar region.

Keywords: anaemia, Moran's I test, multilevel models, spatial pattern

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Authors: Natalia M. Nepomniashchikh

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The research focuses on Russian and English linguoculturemes Caucasian mountaineer and Scottish Highlander, the effort of comparative-contrastive analysis was made. In order to reach the aim, the analysis of the vocabulary definitions of the concepts under consideration was taken, which made it possible to build the lexical-semantic fields of both lexical items in Russian and English. This stage of research helped to turn to the linguistic-cultural fields construction. To build these fields, literary pieces containing the concepts under consideration and the items directly related to them were taken from the works about the Caucasus mountains and mountaineers living there by M. Yu. Lermontov and the ones by W. Scott devoted to the Scottish Highlands and their inhabitants. All collected data was systematized in schemes and tables reflecting the differences and intercrossing areas.

Keywords: lexemes, lexical items, lexical-semantic field, linguistic-cultural field, linguoculturemes

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Authors: W. J. Strydom, K. Puren

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Place-making is viewed here as an empowering process in which people represent, improve and maintain their spatial (natural or built) environment. With the above-mentioned in mind, place-making is multi-dimensional and include a spatial dimension (including visual properties or the end product/plan), a procedural dimension during which (negotiation/discussion of ideas with all relevant stakeholders in terms of end product/plan) and a psychological dimension (inclusion of intrinsic values and meanings related to a place in the end product/plan). These three represent dimensions of place-making. The purpose of this paper is to explore these dimensions of place-making in a case study of a local community in Ikageng, Potchefstroom, North-West Province, South Africa. This case study represents an inclusive process that strives to empower a local community (forcefully relocated due to Apartheid legislation in South Africa). This case study focussed on the inclusion of participants in the decision-making process regarding their daily environment. By means of focus group discussions and a collaborative design workshop, data is generated and ultimately creates a linkage with the theoretical dimensions of place-making. This paper contributes to the field of spatial planning due to the exploration of the dimensions of place-making and the relevancy of this process on spatial planning (especially in a South African setting).

Keywords: community engagement, place-making, planning theory, spatial planning

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Authors: Xin Wang, Chaoqun Ma, Zheng Yao

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Although green technology innovation faces new opportunities and challenges in the digital era, its theoretical research remains limited. Drawing on the attention-based view, this study employs the spatial Durbin model to investigate the impact of local government digital attention and digital industrial agglomeration on green technology innovation across 30 Chinese provinces from 2011 to 2021, as well as the spatial spillover effects present. The results suggest that both government digital attention and digital industrial agglomeration positively influence green technology innovation in local and neighboring provinces, with digital industrial agglomeration exhibiting a positive moderating effect on this direct local and indirect spatial spillover relationship. The findings of this study provide a new theoretical perspective for green technology innovation research and hold valuable implications for the advancement of the attention-based view and green technology innovation.

Keywords: local government digital attention, digital industrial agglomeration, green technology innovation, attention-based view

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Authors: Sun Gang

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A variety of functional spaces are distributed on the vast mountain waterfront. Their functional positioning presents a spontaneous form of settlement space, and the construction features show a passive impact on the natural environment. As the precious heritage of inheriting human civilization and promoting historical culture, the traditional settlement space in mountainous areas is also the local expression of landscape pattern pattern gene. Under the impact of rapid urban construction and the stimulation of the transformation of social consumption demand, the original texture, scale and ecology of the traditional mountain settlement space, especially the historical and cultural settlement space, have been affected, and the decline of characteristics hinders the development. This paper selects Anju Ancient Town, the fourth largest ancient city in China, which is located in the city of mountains and waters as the research object, and combines spatial analysis and other methods to study the characteristics and causes of its spatial morphology, analyze the internal logic in its formation and development process, build a genetic analysis map, explore the possibility of settlement inheritance and development, and provide reference for the construction, protection and inheritance of traditional mountain settlements.

Keywords: mountain traditional settlement, historical and cultural settlement space, spatial form, spatial gene

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Authors: Youngji Yoo, Seung Hwan Park, Daewoong An, Sung-Shick Kim, Jun-Geol Baek

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The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns.

Keywords: semiconductor, wafer bin map, feature extraction, spatial point patterns, contour map

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Authors: Navid Nasrolah Mazandarani, Faezeh Mohammadi Tahrodi, Jr., Norshida Ujang, Richard Jan Pech

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Human evolution has designed us to be dependent on social and natural settings, but designed of our modern cities often ignore this fact. It is evident that high-rise buildings dominate most metropolitan city centers. As a result urban parks are very limited and in many cases are not socially responsive to our social needs in these urban ‘jungles’. This paper emphasizes the functions of urban morphology in supporting socialization in Lake Garden, one of the main urban parks in Kuala Lumpur, Malaysia. It discusses two relevant theories; first the concept of users’ experience coined by Kevin Lynch (1960) which states that way-finding is related to the process of forming mental maps of environmental surroundings. Second, the concept of social activity coined by Jan Gehl (1987) which holds that urban public spaces can be more attractive when they provide welcoming places in which people can walk around and spend time. Until recently, research on socio-spatial behavior mainly focused on social ties, place attachment and human well-being; with less focus on the spatial dimension of social behavior. This paper examines the socio-spatial behavior within the spatial structure of the urban park by exploring the relationship between way-finding and social activity. The urban structures defined by the paths and nodes were analyzed as the fundamental topological structure of space to understand their effects on the social engagement pattern. The study uses a photo questionnaire survey to inspect the spatial dimension in relation to the social activities within paths and nodes. To understand the legibility of the park, spatial cognition was evaluated using sketch maps produced by 30 participants who visited the park. The results of the sketch mapping indicated that a spatial image has a strong interrelation with socio-spatial behavior. Moreover, an integrated spatial structure of the park generated integrated use and social activity. It was found that people recognized and remembered the spaces where they engaged in social activities. They could experience the park more thoroughly, when they found their way continuously through an integrated park structure. Therefore, the benefits of both perceptual and social dimensions of planning and design happened simultaneously. The findings can assist urban planners and designers to redevelop urban parks by considering the social quality design that contributes to clear mental images of these places.

Keywords: spatial structure, social activities, sketch map, urban park, way-finding

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Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

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In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

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Authors: Salehi Zahabi Saleh, Rajabi Hosaien, Rasaneh Samira

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Yttrium-90 (90Y) radioembolisation(RE) is increasingly used for the treatment of patients with unresectable primary or metastatic liver tumours. Image-based approaches to assess microsphere distribution after RE have gained interest but are mostly hampered by the limited imaging possibilities of the Isotope 90Y. Quantitative 90Y-SPECT imaging has limited spatial resolution because it is based on 90Y Bremsstrahlung whereas 90Y-PET has better spatial resolution but low sensitivity. As a consequence, new alternative methods of visualizing the microspheres have been investigated, such as MR imaging of iron-labelled microspheres. It was also shown that MRI combines high sensitivity with high spatial and temporal resolution and with superior soft tissue contrast and thus can be used to cover a broad range of clinically interesting imaging parameters.The aim of the study in this article was to investigate the capability of MRI to measure the intrahepatic microsphere distribution in order to quantify the absorbed radiation dose in RE.

Keywords: radioembolisation, MRI, imaging, dosimetry

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Authors: Z. Liu, K. Cao

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Strategic spatial planning, which is taken as an effective and competitive way for the governors of the city to improve the development and management level of a city, has been blooming in recent years all over the world. In the context of globalization and informatization, strategic spatial planning must transfer its focus on three different levels: global, regional and urban. Internal and external changes in environmental conditions lead to new advances in strategic planning both theoretically and practically. However, such advances or changes respond differently to cities on account of different dynamic mechanisms. This article aims at two cities of Tianjin in China and Melbourne in Australia, through a comparative study on strategic planning, to explore the differentiation of mechanisms in urban planning making. By comparison and exploration, the purpose of this article is to exhibit two different planning worlds between western and Chinese in a new way nowadays.

Keywords: differentiation, Tianjin China, Melbourne Australia, strategic planning

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Authors: Armineh Nourbakhsh, Sameena Shah, Carolyn Rose

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Layout-aware language models have been used to create multimodal representations for documents that are in image form, achieving relatively high accuracy in document understanding tasks. However, the large number of parameters in the resulting models makes building and using them prohibitive without access to high-performing processing units with large memory capacity. We propose an alternative approach that can create efficient representations without the need for a neural visual backbone. This leads to an 80% reduction in the number of parameters compared to the smallest SOTA model, widely expanding applicability. In addition, our layout embeddings are pre-trained on spatial and visual cues alone and only fused with text embeddings in downstream tasks, which can facilitate applicability to low-resource of multi-lingual domains. Despite using 2.5% of training data, we show competitive performance on two form understanding tasks: semantic labeling and link prediction.

Keywords: layout understanding, form understanding, multimodal document understanding, bias-augmented attention

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Authors: Liu Junzhu

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In contemporary China,city is expanding with an amazing speed. And because of the unexpected events’ interference, spatial structure could change itself in a short time, That will lead to the new urban district livingness and unfortunately, this phenomenon is very common.On the one hand,it fail to achieve the goal of city planning, On the other hand,it is unfavourable to the sustainable development of city. Bill Hillier’stheory Space Syntax shows organzation pattern of each space,it explains the characteristics of urban spatial patterns and its transformation regulation from the point of self-organization in system and also, it gives confirmatory and predictive ways to the building and city. This paper used axial model to summarize Hangzhou City’s special structure and enhanced comprehensive understanding of macroscopic space and environment, space structure,developing trend, ect, by computer analysis of Space Syntax. From that, it helps us to know the operation law in the urban system and to understand Hangzhou City’s spatial pattern and indirect social effect it has mad more clearly, Thus, it could comply with the tendency of cities development in process and planning of policy and plan our cities’ future sustainably.

Keywords: sustainable urban design, space syntax, spatial network, segment angular analysis, social inclusion

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Authors: Thomas Ousterhout

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The purpose of this study was to investigate if incorporating meaningful pictures of gestures and facial expressions in short sentences of text could supplement the text with enough semantic information to produce and N400 effect when probe words incongruent to the picture were subsequently presented. Event-related potentials (ERPs) were recorded from a 14-channel commercial grade EEG headset while subjects performed congruent/incongruent reaction time discrimination tasks. Since pictures of meaningful gestures have been shown to be semantically processed in the brain in a similar manner as words are, it is believed that pictures will add supplementary information to text just as the inclusion of their equivalent synonymous word would. The hypothesis is that when subjects read the text/picture mixed sentences, they will process the images and words just like in face-to-face communication and therefore probe words incongruent to the image will produce an N400.

Keywords: EEG, ERP, N400, semantics, congruency, facilitation, Emotiv

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Authors: Novee Lor C. Leyso, Maylin C. Palatino

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Under-five malnutrition continues to persist in the Philippines, particularly in the island Province of Marinduque, with prevalence of some forms of malnutrition even worsening in recent years. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon as key in analyzing patterns of geographic variation, identification of community-appropriate programs and interventions, and focused targeting on high-risk areas. Using data from a province-wide household-based census conducted in 2014–2016, this study aimed to determine and evaluate spatial clusters of under-five malnutrition, across the province and within each municipality at the individual level using household location. Malnutrition was defined as weight-for-age z-score that fall outside the 2 standard deviations from the median of the WHO reference population. The Kulldorff’s elliptical spatial scan statistic in binomial model was used to locate clusters with high-risk of malnutrition, while adjusting for age and membership to government conditional cash transfer program as proxy for socio-economic status. One large significant cluster of under-five malnutrition was found southwest of the province, in which living in these areas at least doubles the risk of malnutrition. Additionally, at least one significant cluster were identified within each municipality—mostly located along the coastal areas. All these indicate apparent geographical variations across and within municipalities in the province. There were also similarities and disparities in the patterns of risk of malnutrition in each cluster across municipalities, and even within municipality, suggesting underlying causes at work that warrants further investigation. Therefore, community-appropriate programs and interventions should be identified and should be focused on high-risk areas to maximize limited government resources. Further studies are also recommended to determine factors affecting variations in childhood malnutrition considering the evidence of spatial clustering found in this study.

Keywords: Binomial model, Kulldorff’s elliptical spatial scan statistic, Philippines, under-five malnutrition

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Authors: Khalil Khan, Ikram Syed, Muhammad Ehsan Mazhar, Iran Uddin, Nasir Ahmad

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Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method (FPL) which divides a given image into its constitutes parts is proposed in this paper. A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. The testing phase is performed with two semantic segmentation methods, i.e., pixel and super-pixel based segmentation. In pixel-based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixel only – as a result, the same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68 % and 93.45% respectively.

Keywords: face labeling, semantic segmentation, classification, face segmentation

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Authors: Alicia Muñoz, Pedro Oyola, Cristian Henriquez

Abstract:

Pollution in Santiago de Chile has a spatial variability due to different factors, including meteorological parameters and emission sources. Socioenvironmental aspects are also significant for pollution in the canopy layer since it influences the type of edification, vegetal mass proportion and other environmental conditions. This study analyzes spatially urban pollution in Santiago, specifically, from the bike path perspective. Bike paths are located in high traffic zones, as consequence, users are constantly exposed to urban pollution. Measurements were made at the higher polluted hour, three days a week, including three transit regimes, on the most polluted month of the year. The environmental parameters are fine particulate matter (Model 8520, DustTrak Aerosol Monitor, TSI), temperature and relative humidity; it was also considerate urban parameters as sky view factor and vegetal mass. Identification of an environmental injustice will be achieved with a spatial modeling, including all urban factors and environmental mediations with an economic index of population.

Keywords: canopy layer, environmental injustice, spatial modeling, urban pollution

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Authors: William Michael Short

Abstract:

Semantic Web’ technologies such as the Unified Medical Language System Metathesaurus, SNOMED-CT, and MeSH have been touted as transformational for the way users access online medical and health information, enabling both the automated analysis of natural-language data and the integration of heterogeneous healthrelated resources distributed across the Internet through the use of standardized terminologies that capture concepts and relationships between concepts that are expressed differently across datasets. However, the approaches that have so far characterized ‘semantic bioinformatics’ have not yet fulfilled the promise of the Semantic Web for medical and health information retrieval applications. This paper argues within the perspective of cognitive linguistics and cognitive anthropology that four features of human meaning-making must be taken into account before the potential of semantic technologies can be realized for this domain. First, many semantic technologies operate exclusively at the level of the word. However, texts convey meanings in ways beyond lexical semantics. For example, transitivity patterns (distributions of active or passive voice) and modality patterns (configurations of modal constituents like may, might, could, would, should) convey experiential and epistemic meanings that are not captured by single words. Language users also naturally associate stretches of text with discrete meanings, so that whole sentences can be ascribed senses similar to the senses of words (so-called ‘discourse topics’). Second, natural language processing systems tend to operate according to the principle of ‘one token, one tag’. For instance, occurrences of the word sound must be disambiguated for part of speech: in context, is sound a noun or a verb or an adjective? In syntactic analysis, deterministic annotation methods may be acceptable. But because natural language utterances are typically characterized by polyvalency and ambiguities of all kinds (including intentional ambiguities), such methods leave the meanings of texts highly impoverished. Third, ontologies tend to be disconnected from everyday language use and so struggle in cases where single concepts are captured through complex lexicalizations that involve profile shifts or other embodied representations. More problematically, concept graphs tend to capture ‘expert’ technical models rather than ‘folk’ models of knowledge and so may not match users’ common-sense intuitions about the organization of concepts in prototypical structures rather than Aristotelian categories. Fourth, and finally, most ontologies do not recognize the pervasively figurative character of human language. However, since the time of Galen the widespread use of metaphor in the linguistic usage of both medical professionals and lay persons has been recognized. In particular, metaphor is a well-documented linguistic tool for communicating experiences of pain. Because semantic medical knowledge-bases are designed to help capture variations within technical vocabularies – rather than the kinds of conventionalized figurative semantics that practitioners as well as patients actually utilize in clinical description and diagnosis – they fail to capture this dimension of linguistic usage. The failure of semantic technologies in these respects degrades the efficiency and efficacy not only of medical research, where information retrieval inefficiencies can lead to direct financial costs to organizations, but also of care provision, especially in contexts of patients’ self-management of complex medical conditions.

Keywords: ambiguity, bioinformatics, language, meaning, metaphor, ontology, semantic web, semantics

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