Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations with perceived defects in physical appearance. In this extended analysis, we employ multiple machine learning approaches to predict treatment outcomes -- specifically treatment response and remission -- with an emphasis on interpretability to ensure clinical relevance and utility. Across the various models investigated, treatment credibility emerged as the most potent predictor, surpassing traditional markers such as baseline symptom severity or comorbid conditions. Notably, while simpler models (e.g., logistic regression and support vector machines) achieved competitive predictive performance, decision tree analyses provided unique insights by revealing clinically interpretable threshold values in credibility scores. These thresholds can serve as practical guideposts for clinicians when tailoring interventions or allocating treatment resources. We further contextualize our findings within the broader literature on BDD, addressing technology-based therapeutics, digital interventions, and the psychosocial determinants of treatment engagement. An extensive array of references situates our results within current research on BDD prevalence, suicidality risks, and digital innovation. Our work underscores the potential of integrating rigorous statistical methodologies with transparent machine learning models. By systematically identifying modifiable predictors -- such as treatment credibility -- we propose a pathway toward more targeted, personalized, and ultimately efficacious interventions for individuals with BDD.

Geographic web sources such as OpenStreetMap (OSM) and knowledge graphs such as Wikidata are often unconnected. An example connection that can be established between these sources are links between streets in OSM to the persons in Wikidata they were named after. This paper presents StreetToPerson, an approach for connecting streets in OSM to persons in a knowledge graph based on relations in the knowledge graph and spatial dependencies. Our evaluation shows that we outperform existing approaches by 26 percentage points. In addition, we apply StreetToPerson on all OSM streets in Germany, for which we identify more than 180,000 links between streets and persons.

The principle of maximum entropy (MaxEnt) provides a well-founded methodology for commonsense reasoning based on probabilistic conditional knowledge. We show how to calculate MaxEnt distributions in a first-order setting by using typed model counting and condensed iterative scaling. Further, we discuss the connection to Markov Logic Networks for drawing inferences.

The principle of maximum entropy (MaxEnt) constitutes a powerful formalism for nonmonotonic reasoning based on probabilistic conditionals. Conditionals are defeasible rules which allow one to express that certain subclasses of some broader concept behave exceptional. In the (common) probabilistic semantics of conditional statements, these exceptions are formalized only implicitly: The conditional (B A)[p] expresses that if A holds, then B is typically true, namely with probability p, but without explicitly talking about the subclass of A for which B does not hold. There is no possibility to express within the conditional that a subclass C of A is excluded from the inference to B because one is unaware of the probability of B given C. In this paper, we apply the concept of default negation to probabilistic MaxEnt reasoning in order to formalize this kind of unawareness and propose a context-based inference formalism. We exemplify the usefulness of this inference relation, and show that it satisfies basic formal properties of probabilistic reasoning.

Languages, Compilers, and Tools for Embedded Systems, (Stockholm, Sweden, Apr.

Our Watch-Bot watches what a human is currently doing, and uses our unsupervised learning model to detect the human's forgotten actions. Once a forgotten action detected (put-milk-backto-fridge in the example), it points out the related object (milk in the example) by the laser spot in the current scene. Andrew Dalton in Engadget called it "a sort of robo-sentry." Watch-Bot is designed to keep an eye on tasks in the home or office and remind you if one of those tasks is still not done--not with a beep, not with a soothing companion-like voice, but with a laser pointer to nab the object still needing attention. Evan Ackerman in IEEE Spectrum said Watch-Bot can independently learn your household activity patterns in order to come up with its unfinished task reminders. Core components of Watch-Bot are a 3D sensor (a Kinect, in this case), a camera that can pan and tilt, a laptop, and laser pointer, said IEEE Spectrum.