Suggesting Assess Queries for Interactive Analysis of Multidimensional Data

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The ${{\sf assess}}$assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube;(ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance;(iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.

A hybrid approach based on linguistic analysis and fuzzy logic to ensure the surveillance of people having paranoid personality disorder towards Covid-19 on social media

This paper presents a supervised learning method for paranoid detection in French tweets. A classifier uses four groups of features (textual, linguistic, meta-data, timeline) that exploit a hybrid approach. This approach uses information obtained from the text of tweets by applying Natural Language Processing (NLP) techniques to analyse them, such as morphological analysis, syntactic analysis and sentence embedding. Thus, information about the user such as the number of followers and the number of shared posts. Besides, information about tweets such as the number of symbols and the number of hashtags. Moreover, information about the publication date of tweets such as the number of postings in the morning. Finally, statistical techniques to combine and filter the different types of features extracted from the previous steps in order to calculate the distance between the training corpus (the labelled data) and the test corpus (unlabelled data). In addition, the state mentioned statistical techniques are used for detecting the writing style of patients. In general, our method benefits from different types of features and preserves the principle of relativity by taking advantage of fuzzy logic. Our results are encouraging with an accuracy of 78% for the detection of paranoid people and 70% for the detection of the behaviour of these people towards Covid-19. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Influences of obstacle factors on the transmission trends of respiratory infectious diseases in indoor public places

Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmissions among individuals. However, most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles' spatial elements on epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor by introducing the Space Syntax. Secondly, two spreading epidemic indicators (i.e., daily new cases and people's average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden particles, which accurately forecasted the disease spreading between individuals. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, structural equation modeling was employed to examine the effects of obstacle factors on epidemic transmissions. Finally, three obstacle-related guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles, dividing the obstacle into more sub-ones, and placing obstacles evenly distributed in space. © 2022 Elsevier Ltd

Forecasting district-wide pedestrian volumes in multi-level networks in high-density mixed-use areas

This paper is concerned with improvements in the forecasting of pedestrian flows in multilevel pedestrian networks in high-density urban environments. 3D network topology measures are combined with land-use data, and validated against extensive pedestrian counts, to provide both evidence for the applicability of network analysis in tropical metropolises, as well as a calibrated tool for urban planners. The research focuses on four areas in Singapore. These areas have in common that they all are prominent transport hubs, but differ in surrounding land-use types and dominant network topology (e.g. indoor, outdoor, above ground, below ground, at grade). Multi-level pedestrian networks were drawn based on OpenStreetMap, include sidewalks on both sides of major roads for a radius up to 2 kilometres from the site centroids. Spatial network analysis was performed using sDNA which allows vertical networks to generate measures describing the spatial configuration of the network. Subsequently, pedestrian counts were conducted during three consecutive days. In total, counts were conducted at more than 250 locations in 2018 and 2019, well before the global COVID19 pandemic. Pedestrian flows are set against a series of variables, including pedestrian attractors and generators (e.g. shops, offices, hotels, dwellings), and variables describing the spatial configuration of the network, using advanced regression models. Our results show that betweenness metrics (i.e. space syntax choice) combined with land-use yield high predictive power. Dependent on the study site, network metrics based on angular distance outperform those based on metric distance or perceived link distance. This research demonstrates that is necessary to account for the multi-level nature of networks, and that indoor flows through private developments cannot be neglected, in particular when planning for integrated transport developments. The paper concludes with recommendations and implications for practice. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

Resilience of virtual and real shops under the impact of Covid19 epidemic: an empirical study on the distribution of breakfast service in Beijing

The breakfast service is an important part of local vitality which are traditionally provided by restaurants and street vendors in real space. The booming virtual economy and delivery service provide alternative type. With the outbreak of COVID-19, both the temporary lock-down of many streets and the reduction of travelling have great impact on breakfast service at the beginning of 2020. During this epidemic period, what kind of breakfast service suffers more, if the location matters, these became interesting questions. This paper presents a comparative study on the central city area (160km2) of Beijing before and after the impact of Covid19. Based on two site surveys in 2019 July and 2020 September, over 3000 breakfast service are mapped in 6 categories (Chain restaurant, subcontracted breakfast service, fixed vendor stance, mobile vendor stance, supermarket and bakery) in real space. Cell phone data of 2018 and 2020 are also used to provide other factors such as employment/residential densities and distances of commuting. Additionally, social media data of breakfast distribution from Dazhongdianping.com are collected to study how service in real and virtual space overlap. In general, it can be found that the space with dominant accessibility has stronger resilience. Breakfast services in an advantageous position are more likely to expand new opportunities through the network platform in virtual space. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

Social inclusion through the urban lens: a comparative analysis of neighbourhoods of residential racial homogeneity and heterogeneity in Cape Town, South Africa

The world is increasingly urbanising, more than half of the global population live within cities. The impact of COVID-19 is having devastating effects. The United Nations estimates that the pandemic will most likely elevate poverty and inequalities at a global scale. The World Bank's twin goals of ending extreme poverty and promoting prosperity and the United Nations' Sustainable Development Goals have deemed inclusive, resilient, and sustainable cities as global imperatives. Despite wide recognition, building inclusive cities remains a challenge. Many studies of social inclusion are conducted at an individual or household scale, with little emphasis on the interaction between human dynamics and the spatial characteristics of cities. This article proposes a data driven framework for examining urban social inclusion through the profiling of neighbourhoods by combining spatial network measurements, transport, land use and socio-economic indicators in Cape Town, South Africa. The spatial unit of the neighbourhood is considered an important building block within cities and has especially historically important social and cultural connotations in South Africa. The results show that there are 4 types of neighbourhoods, Economically disadvantaged and marginalised, Affluent and exclusive, Semi residentially heterogeneous and Residentially heterogeneous. Neighbourhoods with increased residential racial heterogeneity, additionally, have access to higher levels of mixed land use, transport, and global closeness centrality. Furthermore, neither extremely high nor low-income neighbourhoods are found to be related to racial heterogeneity. The results enable the profiling and comparison of neighbourhoods, and it is envisioned that this evidence-based approach could support policy makers and urban planners within decision making processes. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

A comparative study of graph structures, traversability movement and exhibition strategy in museums during Covid-19

The global pandemic of COVID-19 has posed challenges in relation to how buildings re-open to use, particularly buildings attracting large numbers of visitors, such as museums and galleries. As these institutions started to reopen across the UK and internationally, a number of social distance measures were adopted in order to safely bring people into their premises and access their collections. Building on Bill Hillier's theorical model of spatial types and spatial structures (2019), we explore the spatial-curatorial changes implicated in the re-opening of five British museums (The National Gallery, The Tate Britain, Tate Modern, British Museum and The Wallace Collection in London) and one American museum (The MoMA, New York). Our purpose is not to provide practical solutions, but to set the search for spatial approaches to the reopening of museums within a theory of spatial structure in space syntax and inform the design future of public buildings. We present a model of a three-layered spatial system, interfacing the global and local structure of these buildings. We argue that the presence of intersecting cycles of movement in spatial layouts determines their capability for adapting to the one-way routes imposed by the pandemic. The spatial organisation of the display is a second factor influencing the reopening strategies, either limiting or optimising available spatial sequences to meet curatorial criteria. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

The Future of Innovative Workplaces A socio-spatial investigation of interaction patterns in accelerator and incubator workplaces

The Covid-19 pandemic was a catalyst towards shaping the future of the workplace. This shit from a physical workplace to a virtual one was an eye-opener to many organizations, especially innovative workplaces (incubators, accelerators, coworking spaces, and FabLabs) to adapt to a more flexible mode of working. This resulted in second-guessing the importance of the physical environment and its influence on shaping organizational culture. The aim of this study is to build an understanding of the parameters needed to shape decisions made towards changing the mode of working in innovative workplace. For this purpose, this research investigates the major influences that lead to the emergence of organizational cultures, from organizational goals, spatial influences, social structure, and technology. Using analytical and empirical methods of research to assess human behavior in the physical environment of accelerator and incubator programs, calculated decisions can be made in implementing new modes of working to innovative workplaces without impacting their organizational culture. Founders Factory, an accelerator and incubator program in London, was used as a case study. The research investigated the company's organizational goals, social structure, and workspace. The results suggest that interaction patterns in accelerator and incubator workplaces are driven by organizational goals and common social ties, but space plays a major factor in spontaneous face-to-face interactions allowing for easier communication patterns. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

Essay Test Based E-Testing Using Cosine Similarity Vector Space Model

The covid-19 pandemic has been pushing the development of online learning systems in Indonesia. In online learning, computer-based essay tests and assessments have an essential role. Essay test systems are designed to mimic the concept of essay tests without being computer-based. The answer from the lecturer is compared to the response from the student. The TF-IDF (Term Frequency -Inverse Document Frequency) cosine similarity is used. It is one of the methods of information re-gathering systems. The process in this model consists of two types: 1) creating a corpus/ inverted file, and the second is cosine similarity (CS) for calculating the similarity of the user's answers with the lecturer's. Creating a corpus/inverted file involves several stages like data collection, parsing sentences into terms, stoplist, weighting with IDF, and term weighting using TF-IDF. The cosine similarity process consists of parsing users' answers, weighting users' answers using TF-IDF, and finding cosine similarity values of users' answers with lecturers' answers using the vector space model. The highest cosine similarity value is taken to give the user's answer points. Testing the Essay Test system produces excellent grades. The tests were done Mean Squared Error (MSE) values resulted in an average MSE value of 3.28 from three students. © 2022 IEEE.

Developing Computing Competencies Without Restrictions

The information society represents a great revolution. Computing programming is a relevant competence nowadays for everybody, regardless of educational background. However, traditional programming languages consider syntax barriers that complicate their adoption and usefulness for beginners. Python is an exception for its open-source, cross-platform nature and syntax simplicity, which facilitate the development of algorithmic thinking and dissemination of programming solutions. Several Python extensions support modern functionalities such as web development, videogame, and machine learning, making it one of the most used programming languages. Google Colab or Colaboratory facilitates the online learning and development of Python solutions. This article presents positive academic experiences of Chilean students of majors from two Chilean universities, a traditional university in the north and a private university in the middle of Chile, using Google Colab to develop programming competencies remotely for the Covid pandemic. We highlight the promising results obtained for basic programming and operating system programming subjects, which motivate us to use Python and Google Colab widely, not only in university contexts. We expect to continue developing programming competencies using Google Colab and Python. The main limitation encountered in this experience is the internet connection requirements for online education. However, it does not represent an issue for education in developing and developed countries. Google Colab permits the development of highly demanded competencies worldwide at home, only with internet access and a web browse, an excellent motivation for learning for all students regardless of age and academic level.

Cyber-Physical Verification of Intermittently Powered Embedded Systems

Intermittently powered embedded systems are a foundational and growing component of the Internet-of-Things. It is essential to rigorously prove these systems’correctness because they arise both in safety-critical applications and applications where quality-of-service is essential to social good. Such proofs are challenging because they are simultaneously cyber-physical and time-sensitive: correctness is affected by physical properties that change with time. This paper introduces a new general-purpose formal verification approach for cyber-physical properties of intermittent systems. We define a high-level modeling and specification language for intermittent systems, define its formal semantics, and prove that the language reduces to hybrid games, enabling application of existing theorem-proving software. Cold storage for COVID vaccines serves as a running example;we provide a machine-checked proof that safe temperatures are maintained under suitable assumptions. The crux of our proof approach is to identify power and timing assumptions under which sufficient power is available to complete time-sensitive tasks. Orthogonal to approaches that prove new guarantees on power or timing, our work rigorously shows which power and timing assumptions are needed for cyber-physical correctness. IEEE

Standardized Risk Mitigation Measurement in Extended Reality Environments Utilizing the IEEE Experience API (xAPI) Standard

Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored. © 2022 IEEE.

Lessons from the Distribution Pattern of Urban Parks and Factors that Contribute to Control COVID-19 Outbreak in Neighborhood Design

The novel coronavirus 2 (SARS-CoV-2) is spreading over South Asia at alarming rates. In Sri Lanka, urban cities like Colombo with high population densities are challenged to control the spread of the virus due to the unprecedented clustering of people in public parks. However, allowing access to such spaces is a vital requirement to ensure the health and wellbeing of the neighborhood. Since the gatherings and crowding in open parks are inevitable, the risk of being infected is irrepressible. Thus, it is of paramount importance to study the physical distribution of parks and playgrounds in dense neighborhoods of the Colombo metro area to evaluate their user attraction and risk index, in order to reinvent strategies to ease the pandemic outbreak. This study investigates five main variables: neighborhood density (ND), park values (PV), user mobility (UM), park capacity (PC) and park extent (PE) of 34 public parks and playgrounds located within the Colombo metro area. Each variable was visualized using Geographical Information System and space syntax and further correlated using SPSS software. PVs are designed to numerically interpret user attraction towards selected settings. From the relationship between each variable with park values, it was identified that large-scale parks with multifunctional facilities attract more users than small pockets of neighborhood playgrounds. Thus, study findings strongly suggest that parks and playgrounds should be distributed in more isolated pockets to absorb the threshold of park users within a neighborhood bubble, while discouraging visitors from outside the bubble. Through fragmenting the large scale municipal and district parks and facilitating the local parks of less than 0.05 square kilometers, the attraction of users can be controlled. Overall, moderation of facilities can reduce 41% of the total attraction to parks. The even distribution of facilities encourages more utilization of neighborhood, community, and pocket parks, which leads to the creation of neighborhood pockets. The study suggests that suitable planning and design recommendations regarding park profiles can encourage small neighborhood parks to promote livability through easing the outbreak. © 2021 IEEE.

Estimation of Pedestrian Flows in Urban Context: A Comparison Between the Pre and Post Pandemic Period

The Covid-19 pandemic, within a few months, radically changed the organization of daily life on a global scale;this has affected all aspects related to everyday life such as home-to-work or not home-to the work trips, accessibility of destination, recreational activities and so on. The need to reduce coronavirus transmission, especially indoors, has imposed the “social or physical distancing” that has required administrations to reorganize roads and sidewalks for public use both to tackle this crisis and to prepare for the future pandemic challenges. Following a previous extensive study devoted to the analysis and prediction of pedestrian flows in urban area in the city of Cassino, a new experimental campaign has been recently designed and carried out in order to validate the previous methodology and/or to highlight new trends in urban pedestrian activities. Comparison between pre-pandemic and post-pandemic data and calibrated models provided an interesting insight on the pedestrian behavioral impacts of emergency measures undertaken during pandemic. It is believed that obtained results may provide a useful knowledge for urban planners and designers to retrofit urban spaces taking into account the new pedestrian attitudes to mobility induced by the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Suggesting assess queries for interactive analysis of multidimensional data

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube;(ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance;(iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users. IEEE

Improving the Virtual Educational Platforms for the Deaf and Dumb under the Covid-19 Pandemic Circumstances

Arabic Sign Language (ArSL) is a language of communication between deaf dumb people in Arab countries. This study focuses on establishing a real-time speech conversion service into ArSL by 3D animation videos, to facilitate the learning process in virtual educational platforms. This study to help the deaf dumb students in distant learning under the Covid-19 pandemic. This study applied on the TEAMS platform. A database was created containing more than 550 ArSL videos from the Al-tarjuman application and connected it with the TEAMS platform. A Python was used to link the System units. After studying some related works that were concerned with the language of the deaf dumb, we found that some studies do not support real-time translation, has lack Arabic sign language translation, has limited DB, resulting in a low level of system performance, and use of high-cost devices such as gesture motion sensors 'Kinect and Leap Motion'. As future work, we will increase the words in the DB to improve the results. We advise researchers to contribute by working on merging the videos retrieved from the DB to make an integrated serial video. Also, adding a syntactic analysis stage to extract the sentence structure. © 2022 IEEE.

Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks

Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (w-GSTL) formulas. For learning w-GSTL formulas, we introduce a flexible w-GSTL formula structure in which the user’s preference can be applied in the inferred w-GSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible w-GSTL formula structure. We initially train a neural network to learn the w-GSTL operators, and then train a second neural network to learn the parameters in a flexible w-GSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, support vector machine, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods. IEEE