Stacking-based visualization of trajectory attribute data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well

Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

This book promotes occupants as a focal point for the design process

Comparative Performance of Data Mining Techniques for Cyberbullying Detection of Arabic Social Media Text

Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the Arabic language. Data Mining techniques are often used to solve and detect this problem. In this study, different data mining algorithms were used to detect cyberbullying in Arabic texts.. Our study was conducted The Bullying datasets consisted of 26,000 comments written in Arabic and were collected from kaggle.com, the Cyber_2021 dataset consisted of 13,247 comments collected via github.com, and the Data 2022 dataset consisted of 47,224 comments collected via Instagram. Various extraction features CountVectorizer and Tf-Idf were used Accuracy, precision, recall, and the F1 score were used to evaluate classifier performance. In the study, Bagging Classifier achieve high results of Bullying dataset from Kaggle Accuracy 96.04, F1-Score 95.98, Recall 96.04, Precision 95.95, SVC model gave the highest results of  Cyber_2021 dataset from Github an Accuracy 98.49, F1-Score 98.49, Recall 98.49, Precision 98.50, while Data 2022 dataset from (Instagram) achieving an Accuracy of 77.51, F1-Score 76.60, Recall 77.51, and Precision 77.24. Were achieved for Tf-Idf Vectorizer. Tf-Idf  Vectorizer the best to all results than count Vectorizer

Annoyancetech Vigilante Torts and Policy

The twenty-first century has ushered in demand by some Americans for annoyancetech devices—novel electronic gadgets that secretly fend off, punish, or comment upon perceived antisocial and annoying behaviors of others. Manufacturers, marketers, and users of certain annoyancetech devices, however, face potential tort liability for personal and property damages suffered by the targets of this “revenge by gadget.” Federal, state, and local policymakers should start the process of coming to pragmatic terms with the troubling rise in the popularity of annoyancetech devices. This is an area of social policy that cries out for thoughtful and creative legislative solutions

RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

Measuring the impact of COVID-19 on heritage sites in the UK using social media data

The COVID-19 pandemic has had a profound impact on almost all aspects of society. Cultural heritage sites, which are deeply intertwined with the tourism industry, are no exception. The direct impacts of the virus on the population, as well as indirect impacts, such as government-mandated measures including social distancing, face coverings, and frequent temporary closures of sites, have greatly impacted visitor experiences at heritage sites. To quantitatively evaluate the impact of these measures from the perspective of visitors, we collected 1.4 millions visitor reviews from the Google Maps platform for 775 heritage sites. We analyzed visiting rates using the number of online reviews as a proxy and adopt state-of-the-art natural language processing techniques to more deeply understand visitor perception of preventive measures put in place to control the spread of COVID-19. Our findings reveal that even if visitor focus on COVID-19 has significantly decreased, there may still be notable difference between actual and expected number of reviews, suggesting that visitor involvement (e.g., number of visitors) for cultural heritage sites, especially urban indoor sites, needs more time to recover. Our findings further show that most comments by visitors to sites were associated with negative sentiment toward restricted access, but recognized the necessity of other safeguarding measures (e.g., social distancing and the requirement for face coverings). Moreover, they exhibited negative sentiment towards staff or other visitors who did not adhere to these measures. We make specific recommendations for heritage sites to adapt to the COVID-19 pandemic and a more general observation that the method used to gather information from online reviews in this paper will be effective in measuring visitor perceptions towards specific aspects of heritage sites, particularly in capturing changes in perception before and after unexpected or disruptive events at heritage sites