This article proposes a method for an artificial agent to behave in a social manner. Although defining proper social behavior is difficult because it differs from situation to situation, the agent following the proposed method adaptively behaves appropriately in each situation by empathizing with the surrounding others. The proposed method is achieved by incorporating empathy into active inference. We evaluated the proposed method regarding control of autonomous mobile robots in diverse situations. From the evaluation results, an agent controlled by the proposed method could behave more adaptively socially than an agent controlled by the standard active inference in the diverse situations. In the case of two agents, the agent controlled with the proposed method behaved in a social way that reduced the other agent's travel distance by 13.7% and increased the margin between the agents by 25.8%, even though it increased the agent's travel distance by 8.2%. Also, the agent controlled with the proposed method behaved more socially when it was surrounded by altruistic others but less socially when it was surrounded by selfish others.

While mobile applications (i.e., apps) are becoming capable of handling various needs from users, their increasing access to sensitive data raises privacy concerns. To inform such sensitive behaviors to users, existing techniques propose to automatically identify explanatory sentences from app descriptions; however, many sensitive behaviors are not explained in the corresponding app descriptions. There also exist general techniques that translate code to sentences. However, these techniques lack the vocabulary to explain the uses of sensitive data and fail to consider the context (i.e., the app functionalities) of the sensitive behaviors. To address these limitations, we propose DescribeCtx, a context-aware description synthesis approach that trains a neural machine translation model using a large set of popular apps, and generates app-specific descriptions for sensitive behaviors. Specifically, DescribeCtx encodes three heterogeneous sources as input, i.e., vocabularies provided by privacy policies, behavior summary provided by the call graphs in code, and contextual information provided by GUI texts. Our evaluations on 1,262 Android apps show that, compared with existing baselines, DescribeCtx produces more accurate descriptions (24.96 in BLEU) and achieves higher user ratings with respect to the reference sentences manually identified in the app descriptions.

Mobile apps have been an indispensable part in our daily life. However, there exist many potentially harmful apps that may exploit users' privacy data, e.g., collecting the user's information or sending messages in the background. Keeping these undesired apps away from the market is an ongoing challenge. While existing work provides techniques to determine what apps do, e.g., leaking information, little work has been done to answer, are the apps' behaviors compatible with the intentions reflected by the app's UI? In this work, we explore the synergistic cooperation of deep learning and program analysis as the first step to address this challenge. Specifically, we focus on the UI widgets that respond to user interactions and examine whether the intentions reflected by their UIs justify their permission uses. We present DeepIntent, a framework that uses novel deep icon-behavior learning to learn an icon-behavior model from a large number of popular apps and detect intention-behavior discrepancies. In particular, DeepIntent provides program analysis techniques to associate the intentions (i.e., icons and contextual texts) with UI widgets' program behaviors, and infer the labels (i.e., permission uses) for the UI widgets based on the program behaviors, enabling the construction of a large-scale high-quality training dataset. Based on the results of the static analysis, DeepIntent uses deep learning techniques that jointly model icons and their contextual texts to learn an icon-behavior model, and detects intention-behavior discrepancies by computing the outlier scores based on the learned model. We evaluate DeepIntent on a large-scale dataset (9,891 benign apps and 16,262 malicious apps). With 80% of the benign apps for training and the remaining for evaluation, DeepIntent detects discrepancies with AUC scores 0.8656 and 0.8839 on benign apps and malicious apps, achieving 39.9% and 26.1% relative improvements over the state-of-the-art approaches.