Recommender systems have become essential tools in many application areas as they help alleviate information overload by tailoring their recommendations to users' personal preferences. Users' interests in items, however, may change over time depending on their current situation. Without considering the current circumstances of a user, recommendations may match the general preferences of the user, but they may have small utility for the user in his/her current situation.We focus on designing systems that interact with the user over a number of iterations and at each step receive feedback from the user in the form of a reward or utility value for the recommended items. The goal of the system is to maximize the sum of obtained utilities over each interaction session. We use a multi-armed bandit strategy to model this online learning problem and we propose techniques for detecting changes in user preferences. The recommendations are then generated based on the most recent preferences of a user. Our evaluation results indicate that our method can improve the existing bandit algorithms by considering the sudden variations in the user's feedback behavior.

Self-adaptive systems (SAS) automatically mitigate environmental changes and unexpected system issues at run time by adapting towards optimal configurations that enable continual requirements satisfaction. The increasing proliferation of SASs presents engineering challenges that reflect issues experienced by non-adaptive systems, more specifically, ensuring that continuing assurance for software artifacts is provided. In particular, ensuring that requirements traceability links are appropriately managed at run time in SASs can be an error-prone procedure and may require significant effort from a requirements engineer. Natural language processing (NLP) techniques have been used to recover broken or missing traceability links efficiently between requirements and other artifacts, however, performing traceability link recovery can introduce significant overhead for SASs. Specifically, the state-space explosion of possible combinations of environmental states, system parameters, and expressed behaviors can lead to states in which no traceability link exists, thereby necessitating recovery. This paper proposes Adaptive Requirements Traceability (ART), a conceptual framework for handling traceability recovery in terms of SASs. We motivate this framework with an illustrative example in the networking domain.

JERUSALEM – Israeli Prime Minister Benjamin Netanyahu on Tuesday warned Iran and its Lebanese Shiite proxy, the militant Hezbollah group, that Israel "knows how to defend itself and how to pay back its enemies." Netanyahu's remarks came in response to Hezbollah leader Hassan Nasrallah's threats to retaliate against an Israeli airstrike in Syria that killed two Hezbollah members. Netanyahu said he heard the threats saying: "I suggest that Nasrallah relax." He also sent a message to Iranian Gen. Qassem Soleimani, whom Israel accuses of masterminding a drone attack from Syria that it thwarted with its airstrike. "Be careful with your words and even more so be careful with your actions," Netanyahu said.