Pramod Anantharam

Physical-Cyber-Social Computing: An Early 21st Century Approach

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 technologys 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.

Semantic Gateway as a Service Architecture for IoT Interoperability

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet[1]. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between the things. This paper proposes a gateway and Semantic Web enabled IoT architecture to provide interoperability between systems, which utilizes established communication and data standards. The Semantic Gateway as Service (SGS) allows translation between messaging protocols such as XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of broadly accepted specifications such as W3Cs Semantic Sensor Network (SSN) ontology for semantic annotations of sensor data provide semantic interoperability between messages and support semantic reasoning to obtain higher-level actionable knowledge from low-level sensor data.

Comparative trust management with applications: Bayesian approaches emphasis

Trust relationships occur naturally in many diverse contexts such as collaborative systems, e-commerce, interpersonal interactions, social networks, and semantic sensor web. As agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update becomes critical. There is a need to find online substitutes for traditional (direct or face-to-face) cues to derive measures of trust, and create efficient and robust systems for managing trust in order to support decision-making. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics or computation in many situations. We motivate the trust problem, explain the relevant concepts, summarize research in modeling trust and gleaning trustworthiness, and discuss challenges confronting us. The goal is to provide a comprehensive broad overview of the trust landscape, with the nitty-gritties of a handful of approaches. We also provide details of the theoretical underpinnings and comparative analysis of Bayesian approaches to binary and multi-level trust, to automatically determine trustworthiness in a variety of reputation systems including those used in sensor networks, e-commerce, and collaborative environments. Ultimately, we need to develop expressive trust networks that can be assigned objective semantics.

Extracting City Traffic Events from Social Streams

Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology-enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services, such as traffic, public transport, water supply, weather, sewage, and public safety, as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance-level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over 4 months from the San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.

Some trust issues in social networks and sensor networks

Trust and reputation are becoming increasingly important in diverse areas such as search, e-commerce, social media, semantic sensor networks, etc. We review past work and explore future research issues relevant to trust in social/sensor networks and interactions. We advocate a balanced, iterative approach to trust that marries both theory and practice. On the theoretical side, we investigate models of trust to analyze and specify the nature of trust and trust computation. On the practical side, we propose to uncover aspects that provide a basis for trust formation and techniques to extract trust information from concrete social/sensor networks and interactions. We expect the development of formal models of trust and techniques to glean trust information from social media and sensor web to be fundamental enablers for applying semantic web technologies to trust management.

Trust model for semantic sensor and social networks: A preliminary report

Trust is an amorphous concept that is becoming Increasingly important in many domains, such as P2P networks, E-commerce, social networks, and sensor networks. While we all have an intuitive notion of trust, the literature is scattered with a wide assortment of differing definitions and descriptions; often these descriptions are highly dependent on a single domain or application of interest. In addition, they often discuss orthogonal aspects of trust while continuing to use the general term “trust”. In order to make sense of the situation, we have developed an ontology of trust that integrates and relates its various aspects into a single model.

Topical anomaly detection from Twitter stream

In this paper, we spot topically anomalous tweets in twitter streams by analyzing the content of the document pointed to by the URLs in the tweets in preference to their textual content. Existing approaches to anomaly detection ignore such URLs thereby missing opportunities to detect off-topic tweets. Specifically, we determine the divergence of claimed topic of a tweet as reflected by the hashtags and the actual topic as reflected by the referenced document content. Our approach avoids the need for labeled samples by selecting documents from reliable sources gleaned from the URLs present in the tweets. These documents are used for comparison against documents associated with unknown URLs in incoming tweets improving reliability, scalability and adaptability to rapidly changing topics. We evaluate our approach on three events and show that it can find topical inconsistencies not detectable by existing approaches.

Trust networks: Interpersonal, sensor, and social

Trust relationships occur naturally in many diverse contexts such as ecommerce, interpersonal interactions, social networks, sensor web, etc. As agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update become critical. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics or computation in many situations. In this beginners level tutorial, we motivate the trust problem, explain the relevant concepts, summarize research in modeling trust and gleaning trustworthiness, and discuss challenges confronting us in this process.

Knowledge-Driven Personalized Contextual mHealth Service for Asthma Management in Children

Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.

Semantic, Cognitive, and Perceptual Computing: Paradigms That Shape Human Experience

Unlike machine-centric computing, in which efficient data processing takes precedence over contextual tailoring, human-centric computation provides a personalized data interpretation that most users find highly relevant to their needs. The authors show how semantic, cognitive, and perceptual computing paradigms work together to produce actionable information.