The effectiveness of human-robot interaction often hinges on the ability to cultivate engagement - a dynamic process of cognitive involvement that supports meaningful exchanges. Many existing definitions and models of engagement are either too vague or lack the ability to generalize across different contexts. We introduce IM HERE, a novel framework that models engagement effectively in human-human, human-robot, and robot-robot interactions. By employing an effort-based description of bilateral relationships between entities, we provide an accurate breakdown of relationship patterns, simplifying them to focus placement and four key states. This framework captures mutual relationships, group behaviors, and actions conforming to social norms, translating them into specific directives for autonomous systems. By integrating both subjective perceptions and objective states, the model precisely identifies and describes miscommunication. The primary objective of this paper is to automate the analysis, modeling, and description of social behavior, and to determine how autonomous systems can behave in accordance with social norms for full social integration while simultaneously pursuing their own social goals.

This is the accepted author's version of the paper. The final published version is available in the Proceedings of the 2016 I EEE International Conferences on Ubiquitous Intelligence and Computing (UIC), Advanced and Trusted Computing (ATC), Scalable Compu ting and Communications (ScalCom), Cloud and Big Data Computing (CBDCom), Internet of People (IoP), and Smart World Congress (SmartWorld). Distributed and Intelligent Systems Research Group University of Westminster London, United Kingdom Leszek.Sliwko@my.westminster.ac.uk Distributed and Intelligent Systems Rese arch Group University of Westminster London, United Kingdom V.S.Getov@ westminster.ac.uk Abstract -- This paper presents the Accurate Google Cloud Simulator (AGOCS) - a novel high - fidelity Cloud workload simulator based on parsing real workload traces, whic h can be conveniently used on a desktop machine for day - to - day research. Our simulation is based on real - world workload traces from a Google Cluster with 12.5K nodes, over a period of a calendar month. The framework is able to reveal very precise and detai led parameters of the executed jobs, tasks and nodes as well as to provide actual resource usage statistics. The system has been implemented in Scala language with focus on parallel execution and an easy - to - extend design concept. The paper presents the det ailed structural framework for AGOCS and discusses our main design decisions, whilst also suggesting alternative and possibly performance enhancing future approaches. The framework is available via the Open Source GitHub repository. Correctly characterizing user behavior is of utmost importance when modeling Cloud workloads [14, 19] . Cloud workloads have been researched thoughtfully and are relativ ely wel l understood [15, 16, 24, 27]; however there have been limited attempts to accurately simulate Cloud workloads with consideration of detailed t ask parameters and constraints [6, 7, 10, 13], especially with consideration of workload scheduling [25]. Evaluat ing the performance of distributed applications and services without unrestricted access to existing Cloud environments is a very difficult task, which can also be addressed via simulation. Existing Cloud simulators do succeed in representing high - view inf rastructure parameters (i.e.

OpenAI, an Artificial Intelligence (AI) expert team, has been showing off its ground-breaking AI photo generator DALL-E for more than a year. The first generation model was capable of creating realistic and artistic images from a text description. Some of the best include an avocado-shaped armchair, Darth Vader fishing in the Arctic, and many more. Now, a group of enquiring coders asked DALL-E, 'What will be the last selfies on Earth?' The outcomes are truly terrifying. The AI generator's sample photos depict apocalyptic ghost towns, a person battered with battle scars, and thick smoke in the background, most likely caused by a nuclear bomb explosion.

Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.

Chinese-based drone manufacturer DJI said Wednesday it complies with government requests to hand over data collected by unmanned aerial vehicles, the New York Times reported. This is a standard requirement for any business working in the country, but DJI stated it could provide data from drones flown in Hong Kong if requested to do so by the Chinese government. If the Chinese government requests data from a particular drone, DJI will notify the user, company spokesman Zhang Fanxi said in a press briefing held in Shenzhen, the mainland industrial city near Hong Kong. "We are constantly having communications with our government and related departments. We have made suggestions to regulators and given them our advice, and said that we're willing to share our data," Zhang said.