We introduce deep Markov spatio-temporal factorization (DMSTF), a deep generative model for spatio-temporal data. Like other factor analysis methods, DMSTF approximates high-dimensional data by a product between time-dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower-dimensional latent variables that we infer using stochastic variational inference. The innovation in DMSTF is that we parameterize weights in terms of a deep Markovian prior, which is able to characterize nonlinear temporal dynamics. We parameterize the corresponding variational distribution using a bidirectional recurrent network. This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering, or perform factor analysis in the presence of a control signal. Our experiments, which consider simulated data, fMRI data, and traffic data, demonstrate that DMSTF outperforms related methods in terms of reconstruction accuracy and can perform forecasting in a variety domains with nonlinear temporal transitions.

On-street parking is convenient, but has many disadvantages: on-street spots come at the expense of other road uses such as traffic lanes, transit lanes, bike lanes, or parklets; drivers looking for parking contribute substantially to traffic congestion and hence to greenhouse gas emissions; safety is reduced both due to the fact that drivers looking for spots are more distracted than other road users and that people exiting parked cars pose a risk to cyclists. These social costs may not be worth paying when off-street parking lots are nearby and have surplus capacity. To see where this might be true in downtown Vancouver, we used artificial intelligence techniques to estimate the amount of time it would take drivers to both park on and off street for destinations throughout the city. For on-street parking, we developed (1) a deep-learning model of block-by-block parking availability based on data from parking meters and audits and (2) a computational simulation of drivers searching for an on-street spot. For off-street parking, we developed a computational simulation of the time it would take drivers drive from their original destination to the nearest city-owned off-street lot and then to queue for a spot based on traffic and lot occupancy data. Finally, in both cases we also computed the time it would take the driver to walk from their parking spot to their original destination. We compared these time estimates for destinations in each block of Vancouver's downtown core and each hour of the day. We found many areas where off street would actually save drivers time over searching the streets for a spot, and many more where the time cost for parking off street was small. The identification of such areas provides an opportunity for the city to repurpose valuable curbside space for community-friendly uses more in line with its transportation goals.

Current autonomous driving systems are composed of a perception system and a decision system. Both of them are divided into multiple subsystems built up with lots of human heuristics. An end-to-end approach might clean up the system and avoid huge efforts of human engineering, as well as obtain better performance with increasing data and computation resources. Compared to the decision system, the perception system is more suitable to be designed in an end-to-end framework, since it does not require online driving exploration. In this paper, we propose a novel end-to-end approach for autonomous driving perception. A latent space is introduced to capture all relevant features useful for perception, which is learned through sequential latent representation learning. The learned end-to-end perception model is able to solve the detection, tracking, localization and mapping problems altogether with only minimum human engineering efforts and without storing any maps online. The proposed method is evaluated in a realistic urban driving simulator, with both camera image and lidar point cloud as sensor inputs. The codes and videos of this work are available at our github repo and project website.

Although DST has many advantages in representing and dealing with uncertainty, but it is limited by some hypotheses and constraints that are hardly satisfied in some situation [3-6]. There are two main aspects. First, in DST a frame of discernment (FOD) must be composed of mutually exclusive elements, which is called the FOD's exclusiveness hypothesis. Second, in DST the sum of basic probabilities or belief m(.) in a basic probability assignment (BPA) must be 1 (or basic probabilities can not be assigned to elements outside the FOD), which is called the BPA's completeness constraint. To overcome the above-mentioned limitations in DST, a new generalization of DST, called D number theory (DNT), has been proposed in recently [7, 8] for the fusion of uncertain information with non-exclusiveness and incompleteness. The theory of DNT stems from the concept of D numbers [9-16], and aims to build a more sophisticated framework for representing and reasoning with uncertain information similar to DST from a generic setmembership perspective, in which DNT relaxes the exclusiveness constraint of elements in FOD and completeness assumption of BPA in DST.

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities until some predefined invocation depth. These tools require more search time as the invocation depth increases. Since the number of smart contracts is increasing exponentially, it is difficult to analyze the contracts using these traditional tools. Recently a machine learning technique called Long Short Term Memory (LSTM) has been used for binary classification, i.e., to predict whether a smart contract is vulnerable or not. This technique requires nearly constant search time as the invocation depth increases. In the present article, we have shown a multi-class classification, where we classify a smart contract in Suicidal, Prodigal, Greedy, or Normal categories. We used Average Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), which is a variant of LSTM, to perform classification. We reduced the class imbalance (a large number of normal contracts as compared to other categories) by considering only the distinct opcode combination for normal contracts. We have achieved a weighted average Fbeta score of 90.0%. Hence, such techniques can be used to analyze a large number of smart contracts and help to improve the security of these contracts.

We consider crowdsourced labeling under a worker-task specialization block model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to the tasks of unmatched types. We design an inference algorithm that recovers binary task labels (up to any given recovery accuracy) by using worker clustering and weighted majority voting. The designed inference algorithm does not require any information about worker types, task types as well as worker reliability parameters, and achieve any targeted recovery accuracy with the best known performance (minimum number of queries per task) for any parameter regimes.

Disaster prediction is one of the most critical tasks towards disaster surveillance and preparedness. Existing technologies employ different machine learning approaches to predict incoming disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. Therefore, additional sources of warnings are required for accurate prediction. We consider social media as a supplementary source of knowledge in addition to historical environmental data. However, social media posts (e.g., tweets) is very informal and contains only limited content. To alleviate these limitations, we propose the combination of semantically-enriched word embedding models to represent entities in tweets with their semantic representations computed with the traditionalword2vec. Moreover, we study how the correlation between social media posts and typhoons magnitudes (also called intensities)-in terms of volume and sentiments of tweets-. Based on these insights, we propose an end-to-end based framework that learns from disaster-related tweets and environmental data to improve typhoon intensity prediction. This paper is an extension of our work originally published in K-CAP 2019 [32]. We extended this paper by building our framework with state-of-the-art deep neural models, up-dated our dataset with new typhoons and their tweets to-date and benchmark our approach against recent baselines in disaster prediction. Our experimental results show that our approach outperforms the accuracy of the state-of-the-art baselines in terms of F1-score with (CNN by12.1%and BiLSTM by3.1%) improvement compared with last experiments

College students are enrolled at each semester with either part time or full time status. While most of the students keep an overall constant enrollment status during their education period, some of them may frequently change their status between full time and part time from one semester to the next. The goal of this research is to exploit the historic patterns to estimate and categorize students$'$ strategy in three different groups of part time, full time and mixed, investigate the educational features of each group and compare their performance. Enrollment strategy refers to the student$'$s mindset for enrollment plan and in one way can be captured from the student$'$s historic enrollment status. Data is collected from the University of Central Florida from 2008 to 2017 and Hidden Markov Model is applied to identify different types of student strategy. Results show that students with Mixed Enrollment Strategy (MES) have features (ex. time to graduation and graduation and halt enrollment ratio) and performances (ex. cumulative GPA) relatively between students with Full time Enrollment Strategy (FES) and students with Part time Enrollment Strategy (PES).

Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy. However, it has been shown that LECs such as deep neural networks (DNN) are not robust and adversarial examples can cause the model to make a false prediction. The paper considers the problem of efficiently detecting adversarial examples in LECs used for regression in CPS. The proposed approach is based on inductive conformal prediction and uses a regression model based on variational autoencoder. The architecture allows to take into consideration both the input and the neural network prediction for detecting adversarial, and more generally, out-of-distribution examples. We demonstrate the method using an advanced emergency braking system implemented in an open source simulator for self-driving cars where a DNN is used to estimate the distance to an obstacle. The simulation results show that the method can effectively detect adversarial examples with a short detection delay.