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AI for In-Vehicle Infotainment Systems
Published in Josep Aulinas, Hanky Sjafrie, AI for Cars, 2021
In order for the user action predictions in the infotainment system domain to be useful, the recommender system deployed also needs to be context aware. In spoken or written language, context (which we can generally define as the surrounding words) helps us understand the meaning of a word better. In the case of recommender systems, context is any affiliated information including time, location and even weather etc. – that might be relevant for making better recommendations for the user. The automatic phone number suggestion use-case mentioned at the beginning of this section is one example of these context-aware recommender systems in action. However, adding context increases both the dimensionality and sparsity of the system’s model [77]. In other words, since the system becomes significantly bigger and more complex, training it to perform well requires a lot more data to “fill it in”.
Opportunistic Networks in Disaster Management
Published in Ankur Dumka, Sandip K. Chaurasiya, Arindam Biswas, Hardwari Lal Mandoria, A Complete Guide to Wireless Sensor Networks, 2019
Ankur Dumka, Sandip K. Chaurasiya, Arindam Biswas, Hardwari Lal Mandoria
Forwarding of data in opportunistic networks is more challenging as compared to its traditional counterparts. Moreover, the more fundamental question in message forwarding in opportunistic network is to figure out the node to which message can be transported, with an objective of maximizing message delivery and minimizing the delay. A number of forwarding solutions have already been defined by the researcher community, each differing in the degree of knowledge or contextual information. Here, the context of the user is defined as current system state surrounding the user. More illustratively, context of the user can be defined in terms of location, temperature, etc. (referred to as physical context); in terms of the available resources and services (i.e., system attributes); and in terms of preferences, habits, etc. of the user (i.e., user context) (Dey, 2001). The available set of opportunistic forwarding protocols is classified into three major categories on the basis of amount of contextual information being utilized in the solution. They are no context, partial context, and full context forwarding protocols.
Internet of Things: A Context-Awareness Perspective
Published in Lu Yan, Yan Zhang, Laurence T. Yang, Huansheng Ning, The Internet of Things, 2008
Davy Preuveneers, Yolande Berbers
Many authors initially defined context information by enumerating types of information related to the user or application environment that seemed relevant. The term context was first used by Schilit and Theimer [32] to refer to “location, identities of nearby people and objects, and changes to these objects.” Brown, Bovey, and Chen [6] have defined context as “location, identities of the people around the user, the time of day, season, temperature, etc.” Ryan, Pascoe, and Morse [31] referred to context as “the user’s location, environment, identity, and time.” Dey [10] listed “the user’s emotional state, focus of attention, location and orientation, date and time, objects and people in the user’s environment” as elements being part of the definition of context. As the use of enumerations to describe context was too limited to analyze whether certain information could be classified as context, Dey et al. [1] provided the following more general and widely accepted definition that encompasses the previous ones: Context is any information that can be used to characterize the situation of entities (i.e., whether a person, place or object) that are considered relevant to the interaction between a user and an application, including the user and the application themselves. Context is typically the location, identity and state of people, groups and computational and physical objects.
Artificial Intelligence and Human Resources Management: A Bibliometric Analysis
Published in Applied Artificial Intelligence, 2022
P.R. Palos-Sánchez, P. Baena-Luna, A. Badicu, J.C. Infante-Moro
Three clusters or groups of content are highlighted: Cluster 1 (AI in HRM): In this first cluster, the AI tools being applied in HRM are addressed to highlight big data and machine learning. With big data, this might support decision-making processes, since large amounts of varied data from various sources can be quickly analyzed, resulting in a stream of actionable knowledge (Caputo et al. 2019). As for machine learning, the last decade has accelerated its use and applicability owing to the availability and variety of data (Hamilton and Davison 2022). This type of learning provides systems with the ability to learn (Soleimani et al. 2022) and mimic human skills (Bolander 2019). Machine learning can learn from the current context and generalize what it has learned to a new context. There are many organizations that, despite not comprehensively using AI in HRM, use this type of algorithm (Nankervis et al. 2021).Cluster 2 (Digital Recruitment): It is the use of ICTs to attract potential candidates, keep them interested in the organization during the selection processes, and influence their employment choice decisions (Johnson et al. 2021). Pillai and Sivathanu (2020) point out how talent acquisition has become a crucial function for HR managers, with organizations going to great lengths to attract the best talent.
A Relief from Mental Overload in a Digitalized World: How Context-Sensitive User Interfaces Can Enhance Cognitive Performance
Published in International Journal of Human–Computer Interaction, 2023
Paula Gauselmann, Yannick Runge, Christian Jilek, Christian Frings, Heiko Maus, Tobias Tempel
Here, contexts are understood as a “sense-giving environment” for a (given) nucleus, i.e., a context tries to represent relevant information items and their relations describing the given situation. Such a nucleus can be an activity (e.g., writing a scientific paper), an event (a meeting) or an information item itself (a PDF document, email, etc.). Because of the dynamics of situations, a context evolves over time. The context of a large research task (later containing many documents, links, notes, etc.), for example, could spawn from a small context having only an email calling for participation as its nucleus. The information items are represented using an explicit knowledge description formalism allowing for describing the item, its content, and metadata as well as the relationship to the object in the focus of the current situation. Introducing explicit contexts, so-called Context Spaces (Jilek et al., 2018), to the Semantic Desktop lays the foundation for higher automation. By user activity tracking (e.g., Jilek et al., 2018) as well as explicit feedback by the user (like tagging an item or dragging it to a certain context, etc.), the system learns more about the user’s world. The system is also able to check whether certain topics are still relevant (semantic network being stimulated recently), items of the context are still used, or whether a follow-up meeting has been scheduled, etc. If none of this was the case, it could, by means of Managed Forgetting (Jilek et al., 2018, 2019) condensate, reorganize or archive the given context automatically, thus reducing users’ cognitive load when revisiting their digital workspace.
A knowledge-based model for context-aware smart service systems
Published in Journal of Information and Telecommunication, 2022
Thang Le Dinh, Thanh Thoa Pham Thi, Cuong Pham-Nguyen, Le Nguyen Hoai Nam
In the information systems field, context is defined as any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application (Abowd et al., 1999). Contexts can be classified as computing context, physical context, time context, and user context (Chen & Kotz, 2000).