Rapid growth in the Internet of Things (IoT) has resulted in a massive growth of data generated by these devices and sensors put on the Internet. Physical-cyber-social (PCS) big data consist of this ...IoT data, complemented by relevant Web-based and social data of various modalities. Smart data is about exploiting this PCS big data to get deep insights and make it actionable, and making it possible to facilitate building intelligent systems and applications. This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing. Their synergistic use is expected to power future progress in building intelligent systems and applications for rapidly expanding markets in multiple industries. Over the next two years, this column on IoT will explore many challenges and technologies on intelligent use and applications of IoT data.
Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the ...physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration. Keywords: Machine learning, Internet of Things, Smart data, Smart City
Over the past several decades, the incidence of early-onset colorectal cancer (EOCRC; in patients <50 years old) has increased at an alarming rate. Although robust and scientifically rigorous ...epidemiological studies have sifted out environmental elements linked to EOCRC, our knowledge of the causes and mechanisms of this disease is far from complete. Here, we highlight potential risk factors and putative mechanisms that drive EOCRC and suggest likely areas for fruitful research. In addition, we identify inconsistencies in the evidence implicating a strong effect of increased adiposity and suggest that certain behaviours (such as diet and stress) might place nonobese and otherwise healthy people at risk of this disease. Key risk factors are reviewed, including the global westernization of diets (usually involving a high intake of red and processed meats, high-fructose corn syrup and unhealthy cooking methods), stress, antibiotics, synthetic food dyes, monosodium glutamate, titanium dioxide, and physical inactivity and/or sedentary behaviour. The gut microbiota is probably at the crossroads of these risk factors and EOCRC. The time course of the disease and the fact that relevant exposures probably occur in childhood raise important methodological issues that are also discussed.
In this article, I introduce the exciting paradigm of citizen sensing enabled by mobile sensors and human computing - that is, humans as citizens on the ubiquitous Web, acting as sensors and sharing ...their observations and views using mobile devices and Web 2.0 services.
We present a theoretically motivated design perspective, challenges, and applications of next-generation artificial intelligence (AI) systems. We envision systems with greater capabilities for ...meaningful human interaction, including socially adaptive behavior that incorporates personalization and sensitivity to social context and intentionality. Personalized knowledge graphs combining generic, common-sense, and domain-specific knowledge with both sociocultural values and norms and individual cognitive models provide a foundation for building humanity-inspired AI systems.
Technology plays an increasingly important role in facilitating and improving personal and social activities, engagements, decision making, interaction with physical and social worlds, insight ...generation, and just about anything that humans, as intelligent beings, seek to do. The term computing for human experience (CHE) captures technology's human-centric role, emphasizing the unobtrusive, supportive, and assistive part technology plays in improving human experience. Here, the authors present an emerging paradigm called physical-cyber-social (PCS) computing, supporting the CHE vision, which encompasses a holistic treatment of data, information, and knowledge from the PCS worlds to integrate, correlate, interpret, and provide contextually relevant abstractions to humans. They also outline the types of computational operators that make up PCS computing.
Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains ...undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.
Scene understanding is a key technical challenge within the autonomous driving domain. It requires a deep semantic understanding of the entities and relations found within complex physical and social ...environments that is both accurate and complete. In practice, this can be accomplished by representing entities in a scene and their relations as a knowledge graph (KG). This scene knowledge graph may then be utilized for the task of entity prediction, leading to improved scene understanding. In this paper, we will define and formalize this problem as Knowledge-based Entity Prediction (KEP). KEP aims to improve scene understanding by predicting potentially unrecognized entities by leveraging heterogeneous, high-level semantic knowledge of driving scenes. An innovative neuro-symbolic solution for KEP is presented, based on knowledge-infused learning, which 1) introduces a dataset agnostic ontology to describe driving scenes, 2) uses an expressive, holistic representation of scenes with knowledge graphs, and 3) proposes an effective, non-standard mapping of the KEP problem to the problem of link prediction (LP) using knowledge-graph embeddings (KGE). Using real, complex and high-quality data from urban driving scenes, we demonstrate its effectiveness by showing that the missing entities may be predicted with high precision (0.87 Hits@1) while significantly outperforming the non-semantic/rule-based baselines.
The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The ...SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.
Web services are becoming a major technology for deploying automated interactions between distributed and heterogeneous applications, and for connecting business processes. Service mashups indicate a ...way to create new Web applications by combining existing Web resources utilizing data and Web APIs. They facilitate the design and development of novel and modern Web applications based on easy-to-accomplish end-user service compositions.
PDF
