Category: IEEE Intelligent Systems

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

The existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic. It is urgent to find an approach to collecting the high-resolution real-time status of unconnected road users. This paper introduces a new-generation light detection and ranging (LiDAR) enhanced connected infrastructures that actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The LiDAR data processing procedure, including background filtering, object clustering, vehicle recognition, lane identification, and vehicle tracking, is presented in this paper. The performance of the proposed data processing procedure is evaluated with the field collected data.

Sentiment classification and sarcasm detection are both important natural language processing tasks. Sentiment is always coupled with sarcasm where intensive emotion is expressed. Nevertheless, most literature considers them as two separate tasks. We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa. We show that these two tasks are correlated, and present a multitask learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multitask learning setting. Our method outperforms the state of the art by 3-4% in the benchmark dataset.

There has been growing interest in harnessing Artificial Intelligence (AI) to improve situational awareness for disaster management. However, to the authors' best knowledge, few studies have focused on socio-economic recovery. Here, as a first step toward investigating the possibility of developing an AI-based method for detecting socio-economic recovery, this study provides fundamental insights about the correlations between public sentiment on social media and socio-economic recovery activities as reflected in market data. Our result shows multiple correlations between sentiment on social media and the socio-economic recovery activities involved in restarting daily routines. Conventional socio-economic recovery indicators, such as governmental statistical data, have a significant time lag before publishing. Therefore, by taking advantages of the real timeliness and the effectiveness of seizing communication trends of massive social media data, using public sentiment on social media can improve situational awareness in recovery operations.

Social media plays a vital role in information sharing during disasters. Unfortunately, the overwhelming volume and variety of data generated on social media make it challenging to sieve through such content manually and determine its relevancy. Most automated approaches to classify crisis data for relevancy are based on classic statistical features. However, such approaches do not adapt well to situations when applied on a new crisis event, or to a new language that the model was not trained on. In crisis situations, training a new model for particular crises or languages is not a viable approach. In this paper, we introduce a hybrid semantic-statistical approach for classifying data with regards to relevancy to a given crisis. We demonstrate how this approach outperforms the baselines in scenarios where the model is trained on one type of crisis and language and tested on new crisis types and additional languages.

Enterprise resilience plays an important role to prevent business services from disruptions caused by human-induced disasters such as failed change implementations and software bugs. Traditional expert-centric approach has difficulty to maintain continued critical business functions because the disasters can often only be handled after their occurrence. This paper introduces a data-analytics approach, which leverages system monitoring data for the enterprise resilience. With the power of data mining and machine learning techniques, we build an intelligent business analytics system to detect the potential disruptions proactively, and to assist the operational team for enterprise resilience enhancement. We demonstrate the effectiveness of our approach on a real enterprise system monitoring dataset in simulation.

The articles in this special section provide a forum on the burgeoning topic of artificial intelligence (AI) for disaster management and resilience.

Presents the table of contents for this issue of the publication.