Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications. © International Building Performance Simulation Association, 2022

Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response

Most governments employ a set of quasi-standard measures to fight COVID-19, including wearing masks, social distancing, virus testing, contact tracing, and vaccination. However, combining these measures into an efficient holistic pandemic response instrument is even more involved than anticipated. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision making and the differing national implementations of the response mechanism. In this article, through simple games, we show the effect of individual incentives on the decisions made with respect to mask wearing, social distancing, and vaccination, and how these may result in sub-optimal outcomes. We also demonstrate the responsibility of national authorities in designing these games properly regarding data transparency, the chosen policies, and their influence on the preferred outcome. We promote a mechanism design approach: It is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism;moreover, there is no one-size-fits-all solution when designing an effective solution. © 2022 held by the owner/author(s). Publication rights licensed to ACM.

Semantic Watermarks for Detecting Cheating in Online Database Exams

Due to the COVID-19 pandemic, we were forced to conduct two exams for a database course as online exams. An essential part of the exams was to write non-trivial SQL queries for given tasks. In order to ensure that cheating has a certain risk, we used several techniques to detect cases of plagiarism. One technique was to use a kind of "watermarks” in variants of the exercises that are randomly assigned to the students. Each variant is marked by small discrimination points that need to be included in submitted solutions. Those markers might go through undetected when a student decides to copy a solution from someone else. In this case, the student would reveal to know a "secret” that he cannot know without the forbidden communication with another student. This can be used as a proof for plagiarism instead of just a subjective feeling about the likelihood of similar solutions without communication. We also used a log of SQL queries that were tried during the exam. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

Epidemic Forecasting with a Data-Centric Lens

The recent COVID-19 pandemic has reinforced the importance of epidemic forecasting to equip decision makers in multiple domains, ranging from public health to economics. However, forecasting the epidemic progression remains a non-trivial task as the spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics and environmental conditions, etc. Research interest has been fueled by the increased availability of rich data sources capturing previously unseen facets of the epidemic spread and initiatives from government public health and funding agencies like forecasting challenges and funding calls. This has resulted in recent works covering many aspects of epidemic forecasting. Data-centered solutions have specifically shown potential by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This tutorial will explore various data-driven methodological and practical advancements. First, we will enumerate epidemiological datasets and novel data streams capturing various factors like symptomatic online surveys, retail and commerce, mobility and genomics data. Next, we discuss methods and modeling paradigms with a focus on the recent data-driven statistical and deep-learning based methods as well as novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some open problems found across the forecasting pipeline. © 2022 Owner/Author.