Driver classification is used recently for vehicle anti-burglary and fake driver accounts based on driving behavior. Anti-burglary is a challenging problem as it leans on external devices to defend against vehicle theft. Several researchers analyzed the driving behavior to identify drivers, but they faced several challenges to produce a stable model for the cold start problem and for medium-long sequences. In addition, some approaches had an unpleasant performance when the action space increased (> 2 drivers). In this paper, we propose a novel approach named OnlineDC (Online Driver Classification), which leverages temporal driving behavior to identify a human subject behind the wheel. Our method utilizes the Gated Recurrent Unit (GRU) and the ResNet with the Squeeze-Excite blocks (SE) to analyze the long-short term patterns of driving behaviors. Moreover, we fostered the performance by building and applying the Feature Generation (FG) algorithm to extract spectral, temporal, and statistical features from the sensing data of vehicles. We conducted extensive experiments to show how our approach outperformed state-of-the-art baseline methods. The results also showed that our solution could resolve the cold-start problem for short patterns.

Ahstract-A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to a huge burst of help requests made in a very short period. To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule a rapid deployment of volunteers to rescue victims in dynamic settings. The core concept is to quickly identify victims and volunteers from social network data and then schedule rescue parties with an adaptive learning algorithm. This framework performs two key functions: 1) identify trapped victims and volunteers, and 2) optimize the volunteers' rescue strategy in a complex time-sensitive environment. The proposed ResQ algorithm can speed up the training processes through a heuristic function which reduces the state-action space by identifying a set of particular actions over others. Experimental results showed that the proposed heuristic multi-agent reinforcement learning based scheduling outperforms several state-of-art methods, in terms of both reward rate and response times.

Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions 'joy' and 'negative'. This work is one of the first attempts to predict relapses using massive social media (Reddit.com) data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.

It is of paramount importance to know the situations of people who undergone disaster events and be aware of their updates, yet it is not an easy job to accomplish in the chaos of a disaster. To facilitate advanced disaster relief organization and efficient supplement distribution, we develop NiPred, a social media based need prediction prototype that predicts needs for victims across the affected area. NiPred first extracts problems and concerns posted by victims of disaster-hurricane, in our case study; then displays the statistics to offer an overview for awareness and further analysis; and last, predicts the needs such as "diaper", "boat", "canoe" and "shelter" etc. for disaster relief planning.

Water is a critical natural resource that has significant impacts on human living and society. Growing population and energy consumption exacerbate the scarcity of water and our ability to manage this resource. This demonstration paper presents WaterScope, a smart and connected platform for water resource management, which integrates multiple data sources such as water level data, social media data, and water related articles. Furthermore, the tool enables forecasting underground water levels, identifying water concerns, sharing knowledge and expertise among stakeholders, and thus bringing new insights to our understanding and insights of the water supplies and resource management. The prototype engages water stakeholders who face problems of similar nature but deal with the problem in an ad-hoc and isolated manner. The interactive WaterScope platform targets creating an interconnected virtual community that aims to improve water supply resilience.

Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point and solar radiation to accurately predict wind speeds. The prediction performance is extensively assessed using real data collected in West Texas, USA. The experimental results show that the proposed MSLSTM can preferably capture and learn uncertainties while output competitive performance.

Social media has become a popular data source to track and analyze societal events. Targeted domains such as election, civil unrest, and spreading disease all require a natural language normalization tool capable of extracting information pertinent to these domains accurately. Due to the unstructured language, short-length messages, casual posting styles, and homonyms, it is technically difficult and labor-intensive to remove barriers that may lead to inaccurate analysis. Because the fact that typos or other symbolic representations of sentiment may lead to lower frequency of term appearance, language preprocessing becomes critical and necessary to improve social media text reasoning. We propose a novel unsupervised preprocessing approach to enhance text understanding quality and illustrate this approach using one specific domain, flu shot reasoning. The proposed approach relies on a database of synonyms and opposite words and an algorithm to transform negative sentences into its affirmative form. In this form, the features and opinions are reflected accurately via transforming parts of speech. For instance, features are presented as nouns and opinions are presented as verbs or adjectives. The algorithm also corrects words if they are not correctly written and normalizes them to increase its frequency of appearance. The effectiveness of our algorithm is evaluated on the tweets dataset to answer why people are reluctant to take flu shots.

Social media provided a significant communication platform for rescuing people when Harvey hit Houston area. In this devastating flooding crisis, the overloaded official emergency institutes were not able to respond quickly due to the burst of call for help in a very short period of time. In this circumstance, many volunteers and people who needed help often post their information on social media such as Twitter and Facebook. How to organize volunteers smartly and efficiently to help people is an extremely challenging and significant problem considering the constraints of volunteer's time slots, urgent priorities, etc. In this paper, we propose three rescue scheduling algorithms to explain how to provide victims timely help by the volunteers on social media.