The DEBS 2020 grand challenge

The ACM DEBS 2020 Grand Challenge is the tenth in a series of challenges which seek to provide a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The focus of the ACM DEBS 2020 Grand Challenge is on Non-Intrusive Load Monitoring (NILM). The goal of the challenge is to detect when appliances contributing to an aggregated stream of voltage and current readings from a smart meter are switched on or off. NILM is leveraged in many contexts, ranging from monitoring of energy consumption to home automation. This paper describes the specifics of the data streams provided in the challenge, as well as the benchmarking platform that supports the testing of the solutions submitted by the participants

Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes

The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score

Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction

The Rise of the Democratic Socialists of America: A Qualitative Analysis of the Contributing Factors to Insurgent Mobilization

Although there are other more conventional means through which aggrieved populations can voice their concerns, social movements have long served as important vehicles for articulating and advancing a group\u27s interests and claims. Indeed, some of the most significant developments in the history of the modern era are bound up with social movements. As a result, social movement analysts are interested in understanding the protests, conflicts, and other forms of resistance that have challenged the prevailing social order. Scholarly interest in collective action has engendered a proliferation of empirical studies, igniting a series of theoretical debates. These debates are animated around concerns regarding movement emergence, the significance of formal organizations, and the role of elites in social movements. Contemporary movement scholars have underscored the ubiquitous presence of social movements in modern society as an exemplar of their continuing significance as vital agents in generating social change. The Bush administration’s neoconservative foreign policy, the Great Recession, and persistent social inequalities once again inspired the launch of a myriad of protest activities. Inspired in part by Bernie Sanders’ runs for the presidency, a new wave of activists mobilized under the banner of democratic socialism as tens of thousands of them joined the Democratic Socialists of America. This dissertation argues that Sanders, along with likeminded politicians, represents an opportunity for the growth and development of a unique version of socialism in the United States