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   Trucco, Elisa M. ; Yang, Songshan ; Yang, James J. ; Zucker, Robert A. ; Li, Runze ; Buu, Anne ( January 2020 , Journal of Youth and Adolescence)
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   Pakravan, Mohammad H. ; MacCarty, Nordica ( June 2020 , Energies)

   null (Ed.)

   Understanding and integrating the user’s decision-making process into product design and distribution strategies is likely to lead to higher adoption rates and ultimately increased impacts, particularly for those products that require a change in habit or behavior such as clean energy technologies. This study applies the Theory of Planned Behavior (TPB) in design for global development, where understanding the tendency to adopt beneficial technologies based on parsimonious approaches is critical to programmatic impact. To investigate robustness and applicability of behavioral models in a data scarce setting, this study applies TPB to the adoption of biomass cookstoves in a sample size of two remote communities in Honduras and Uganda before and after a trial period. Using multiple ordinal logistic regressions, the intention to adopt the technology was modeled. Results quantify the influence of these factors on households’ intentions to cook their main meals with improved cookstoves. For example, the intention of participants with slightly stronger beliefs regarding the importance of reducing smoke emissions was 3.3 times higher than average to cook more main meals with clean cookstoves. The quantitative method of this study enables technology designers to design and develop clean technologies that better suit user behavior, needs, and priorities. In addition, the data driven approach of this study provides insights for policy makers to design policies such as subsidies, information campaigns, and supply chains that reflect behavioral attributes for culturally tailored clean technology adoption initiatives. Furthermore, this work discusses potential sources of bias and statistical challenges in data-scarce regions, and outlines methods to address them. 

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3. DeepIntent: Deep Icon-Behavior Learning for Detecting Intention-Behavior Discrepancy in Mobile Apps

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4. Detecting Aberrant Linking Behavior in Directed Networks [Detecting Aberrant Linking Behavior in Directed Networks]

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   Hochbaum, Dorit ; Spaen, Quico ; Velednitsky, Mark ( January 2019 , Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management)

   Agents with aberrant behavior are commonplace in today’s networks. There are fake profiles in social media, malicious websites on the internet, and fake news sources that are prolific in spreading misinformation. The distinguishing characteristic of networks with aberrant agents is that normal agents rarely link to aberrant ones. Based on this manifested behavior, we propose a directed Markov Random Field (MRF) formulation for detecting aberrant agents. The formulation balances two objectives: to have as few links as possible from normal to aberrant agents, as well as to deviate minimally from prior information (if given). The MRF formulation is solved optimally and efficiently. We compare the optimal solution for the MRF formulation to existing algorithms, including PageRank, TrustRank, and AntiTrustRank. To assess the performance of these algorithms, we present a variant of the modularity clustering metric that overcomes the known shortcomings of modularity in directed graphs. We show that this new metric has desirable properties and prove that optimizing it is NP-hard. In an empirical experiment with twenty-three different datasets, we demonstrate that the MRF method outperforms the other detection algorithms. 

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