305 research outputs found
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe
Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Ranking
To provide real-time parking information, existing studies focus on
predicting parking availability, which seems an indirect approach to saving
drivers' cruising time. In this paper, we first time propose an on-street
parking recommendation (OPR) task to directly recommend a parking space for a
driver. To this end, a learn-to-rank (LTR) based OPR model called OPR-LTR is
built. Specifically, parking recommendation is closely related to the "turnover
events" (state switching between occupied and vacant) of each parking space,
and hence we design a highly efficient heterogeneous graph called ESGraph to
represent historical and real-time meters' turnover events as well as
geographical relations; afterward, a convolution-based event-then-graph network
is used to aggregate and update representations of the heterogeneous graph. A
ranking model is further utilized to learn a score function that helps
recommend a list of ranked parking spots for a specific on-street parking
query. The method is verified using the on-street parking meter data in Hong
Kong and San Francisco. By comparing with the other two types of methods:
prediction-only and prediction-then-recommendation, the proposed
direct-recommendation method achieves satisfactory performance in different
metrics. Extensive experiments also demonstrate that the proposed ESGraph and
the recommendation model are more efficient in terms of computational
efficiency as well as saving drivers' on-street parking time
Home Security Alarm Using Wemos D1 And HC-SR501 Sensor Based Telegram Notification
Abstract—Home Security Alarms in today's modern society only use CCTV that can only see the person without any notification that goes into the cellphone in dealing with the theft that occurred. To help the community in dealing with the theft that enters the house, a Home Security Alarm was made using WEMOS D1 and HC-SR501 Sensor with Telegram Notification. The whole tool is divided into several parts which consist of HC-SR501 Sensor, WEMOS D1 and Buzzer. This tool works when the WEMOS D1 microcontroller processes the pear sensor as a motion detector and buzzer as a sound alarm if motion is detected, then the notification automatically enters into the Telegram Application, With this tool can monitor directly if anyone enters the house while being left
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
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