About this Research Topic
Human needs motivate the improvement of computing paradigms. Examples of this include ubiquitous computing showing higher mobility, cloud computing providing better functionality, and social computing offering better interactivity. Each of these examples points out an implicit or explicit need or expectation from humans, and attempts to realize these needs though specific approaches. However, humans may look for more with the development of technology. A chatting robot is one such example, as this robot is expected to maintain relationships with our social contacts when we do not have enough space and/or time to do so. To this end, it continuously interacts with our contacts by simulating our thinking patterns, behaviors, and other relevant information. Of course, there are many similar studies in this field, which invites the introduction of a new computing paradigm, ‘Anticipatory Computing.’ This computing paradigm indicates a field related to technology designed and able to anticipate specific user needs. It is also used together with new or wearable technology to perform an action in anticipation of a user’s request or to make a suggestion to the user. This is not only an instance of artificial intelligence, but also an advancement (i.e., prediction plus action). It can also be considered as key to developing well-being within society, and a way to achieve the ideal of “serve before you ask.” This phenomenon will become an opportunity to raise challenging issues within the field of computer science.
Considering the invaluable crowd intelligence residing in the social network and big data content, opportunities continue to emerge to enable promising smart applications to meet individual needs, create company business models, and promote smart life development. However, the nature of big data also poses fundamental challenges from multiple perspectives to techniques and applications that rely on social big data. These include algorithm effectiveness, computation speed, energy efficiency, user privacy, server security, data heterogeneity, and system scalability. In this Research Topic, we aim to invite original contributions that tackle challenges and issues relating to exploiting deep neural networks or machine learning methods for building anticipatory systems. The main goal is to collect manuscripts reporting the latest advances on the technologies, algorithms, models, standards, and applications in this field.
This Research Topic calls for original manuscripts describing the latest developments, trends, and solutions in deep neural networks or machine learning methods in anticipatory systems. We specifically seek to invite the following article types: Original Research, Systematic Review, Methods, Review, Mini Review, Hypothesis and Theory, Perspective, and Brief Research Report.
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Topics of interest include, but are not limited to:
• Deep / machine learning methods for fake news detection, social networks, opinions & sentiments analysis;
• Deep / machine learning methods for data preprocessing, text clustering & classification, computer vision & pattern recognition;
• Human behavior and user interface for human-centered anticipatory computing;
• Human participatory and social sensing for human-centered anticipatory computing;
• The applications of personality and social psychology (e.g., interaction and persuasion) to anticipatory systems;
• Artificial intelligence: trust, security, and privacy;
• Artificial intelligence and mental processes in human-centered anticipatory computing.
Considering the invaluable crowd intelligence residing in the social network and big data content, opportunities continue to emerge to enable promising smart applications to meet individual needs, create company business models, and promote smart life development. However, the nature of big data also poses fundamental challenges from multiple perspectives to techniques and applications that rely on social big data. These include algorithm effectiveness, computation speed, energy efficiency, user privacy, server security, data heterogeneity, and system scalability. In this Research Topic, we aim to invite original contributions that tackle challenges and issues relating to exploiting deep neural networks or machine learning methods for building anticipatory systems. The main goal is to collect manuscripts reporting the latest advances on the technologies, algorithms, models, standards, and applications in this field.
This Research Topic calls for original manuscripts describing the latest developments, trends, and solutions in deep neural networks or machine learning methods in anticipatory systems. We specifically seek to invite the following article types: Original Research, Systematic Review, Methods, Review, Mini Review, Hypothesis and Theory, Perspective, and Brief Research Report.
.
Topics of interest include, but are not limited to:
• Deep / machine learning methods for fake news detection, social networks, opinions & sentiments analysis;
• Deep / machine learning methods for data preprocessing, text clustering & classification, computer vision & pattern recognition;
• Human behavior and user interface for human-centered anticipatory computing;
• Human participatory and social sensing for human-centered anticipatory computing;
• The applications of personality and social psychology (e.g., interaction and persuasion) to anticipatory systems;
• Artificial intelligence: trust, security, and privacy;
• Artificial intelligence and mental processes in human-centered anticipatory computing.
Keywords: Artificial Intelligence, Anticipatory Systems, Machine Learning, Deep Learning, Human-Media Interaction
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
