Current visual question answering (VQA) models tend to be trained and evaluated on image-question pairs in isolation. However, the questions people ask are dependent on their informational needs and prior knowledge about the image content. To evaluate how situating images within naturalistic contexts shapes visual questions, we introduce CommVQA, a VQA dataset consisting of images, image descriptions, real-world communicative scenarios where the image might appear (e.g., a travel website), and follow-up questions and answers conditioned on the scenario. We show that CommVQA poses a challenge for current models. Providing contextual information to VQA models improves performance broadly, highlighting the relevance of situating systems within a communicative scenario.

Not even in the digital world can it be denied that since time immemorial there has always been a dynamic interaction between technology and organization, where the former never can deterministically deliver a certain result if it is dissonant with the latter. As a starting point for the article, it is shown how there are still remarkable "trends" of organization even today, where selected organizational models not always are based on the most appropriate model given a certain context, technology or a specific goal (so-called contingency model), but instead is following the discourse of what other companies, researchers or consultants currently see as the most "modern". Parallel to becoming increasingly clear how the digitalization of recent years now is exploding into a new era of data disruption, it is argued that we are seeing exactly the same process in the data area as well -- with an increasingly strong trend from monolithic setups in data lakes, to a more distributed data setup adapted to local stewardship, so-called data mesh. The article draws attention to how in this context it becomes even more hopeful how we now can see the first indications of a more fruitful approach than the "latest trend" in the field, where we in a similar way as within organizational research can discern the first embryo to a contingency model also for data setups. The article argues that this will most likely be one of the most crucial prerequisites for a data-disruptive success in 2022 and beyond.

Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applications increases, these objects are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings and have access to online contextual information. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. Hence, IoT applications must be equipped with self-adaptation techniques to manage their run-time uncertainties locally or in cooperation with each other. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in human-centered environments. In this paper, we have considered the Smart Home as the case study of smart environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home'In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments.

The belief that AI dominance is imperative for economic development, military control, and strategic competitiveness has accelerated AI development initiatives across countries. The release of national strategic plans has been accompanied by billions of dollars in investment as well as concrete policies to attract relevant talent and technology. In our previous post "How different countries view artificial intelligence", we presented a snapshot of governments' planning for AI, based on our analysis of 34 national strategic AI plans. Our post covered the description of AI plans and categorized countries based on their coverage of various related concepts. In this post, we extend details about what accounts for the variation in countries' AI plans.