Improved prediction of personalized health outcomes -- such as sleep quality and stress -- from multimodal lifelog data could have meaningful clinical and practical implications. However, state-of-the-art models, primarily deep neural networks and gradient-boosted ensembles, sacrifice interpretability and fail to adequately address the significant inter-individual variability inherent in lifelog data. To overcome these challenges, we propose the Subject-Adaptive Sparse Linear (SASL) framework, an interpretable modeling approach explicitly designed for personalized health prediction. SASL integrates ordinary least squares regression with subject-specific interactions, systematically distinguishing global from individual-level effects. We employ an iterative backward feature elimination method based on nested $F$-tests to construct a sparse and statistically robust model. Additionally, recognizing that health outcomes often represent discretized versions of continuous processes, we develop a regression-then-thresholding approach specifically designed to maximize macro-averaged F1 scores for ordinal targets. For intrinsically challenging predictions, SASL selectively incorporates outputs from compact LightGBM models through confidence-based gating, enhancing accuracy without compromising interpretability. Evaluations conducted on the CH-2025 dataset -- which comprises roughly 450 daily observations from ten subjects -- demonstrate that the hybrid SASL-LightGBM framework achieves predictive performance comparable to that of sophisticated black-box methods, but with significantly fewer parameters and substantially greater transparency, thus providing clear and actionable insights for clinicians and practitioners.

Software has eaten the world with many of the necessities and quality of life services people use requiring software. Therefore, tools that improve the software development experience can have a significant impact on the world such as generating code and test cases, detecting bugs, question and answering, etc. The success of Deep Learning (DL) over the past decade has shown huge advancements in automation across many domains, including Software Development processes. One of the main reasons behind this success is the availability of large datasets such as open-source code available through GitHub or image datasets of mobile Graphical User Interfaces (GUIs) with RICO [112] and ReDRAW [267] to be trained on. Therefore, the central research question my dissertation explores is: In what ways can the software development process be improved through leveraging DL techniques on the vast amounts of unstructured software engineering artifacts? We coin the approaches that leverage DL to automate or augment various software development task as Intelligent Software Tools.

This is a preview of subscription content, access via your institution. The 14 full papers and 5 short papers presented were carefully reviewed and selected from 42 submissions.

Karl Aberer, Ebrahim Bagheri, Marya Bazzi, Rumi Chunara, Ziv Epstein, Fabian Flöck, Adriana Iamnitchi, Diana Inkpen, Maurice Jakesch, Kyraki Kalimeri, Elena Kochkina, Ugur Kursuncu, Maria Liakata, Yelena Mejova, George Mohler, Daniela Paolotti, Jérémie Rappaz, Manon Revel, Horacio Saggion, Indira Sen, Panayiotis Smeros, Katrin Weller, Sanjaya Wijeratne, Christopher C. Yang, Fattane Zarrinkalam The Association for the Advancement of Artificial Intelligence’s 2021 International Conference on Web and Social Media was held virtually from June 8-10, 2021. There were 8 workshops in the program: Data for the Wellbeing of Most Vulnerable, Emoji 2021: International Workshop on Emoji Understanding and Applications in Social Media, Information Credibility and Alternative Realities in Troubled Democracies, International Workshop on Cyber Social Threats (CySoc 2021), International Workshop on Social Sensing (SocialSens 2021): Special Edition on Information Operations on Social Media, Participatory Development of Quality Guidelines for Social Media Research: A Structured, Hands-on Design Workshop, Mediate 2021: News Media and Computational Journalism, Mining Actionable Insights from Social Networks: Special Edition on Healthcare Social Analytics.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

The 15th International Conference on AI and Law (ICAIL 2015) will be held in San Diego, California, USA, June 8-12, 2015, at the University of San Diego, at the Kroc Institute, under the auspices of the International Association for Artificial Intelligence and Law (IAAIL), an organization devoted to promoting research and development in the field of AI and law with members throughout the world. The conference is held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and with ACM SIGAI (the Special Interest Group on Artificial Intelligence of the Association for Computing Machinery).