Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

Matrix Factorization Techniques for Recommender Systems

We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

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Membership Inference Attacks Against Machine Learning Models

Quality of sleep is an important attribute of an elder's health state and its assessment is still a huge issue. The sleep posture is a significant feature to evaluate the quality of sleep, and how to detect elder's sleep posture is a key challenge in elder-care community. This paper proposes an unobtrusive sleep postures detection solution and introduces pressure sensor matrix to monitor the elder's sleep posture in bed. Based on the proposed sleep detection system, the processing methods of experimental data and the classification approaches for sleep posture detection are also discussed.

Unobtrusive sleep posture detection for elder-care in smart home

In this paper, we propose a peer-to-peer approach to derive and obtain additional context data from low-level context data that may be spread over multiple domains in pervasive computing environments. In this system, peers are self-organized into a semantic peer- to-peer network as the underlying communication substrate. Context reasoning is done in a distributed fashion through logical reasoning according to a set of user-defined rules. Both pull and push services are supported in the system to enable message exchange during the reasoning process. We present our design concepts, and prove the effectiveness of our system through the prototype evaluation.

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Peer-to-Peer Context Reasoning in Pervasive Computing Environments

With the pervasiveness of sensors, Internet-enabled devices, mobile phones, Internet applications and services, and wireless communication, a new field of social and community intelligence (SCI) has emerged quite quickly. SCI aims to reveal individual and group behaviors, social interactions, and community dynamics by mining the digital traces that people leave while interacting with Web applications, static infrastructure, and mobile and wearable devices. Author Daqing Zhang and colleagues provide an introduction to this field and highlight the various technologies.

Social and Community Intelligence: Technologies and Trends

With the development of advanced mobile devices and social network services, mobile social network (MSN) has become popular in recent years. This paper we present a new perspective of MSN, the opportunistic MSN, which aims to enhance spontaneous interaction/communication among people that opportunistically encounter in the physical world, without any infrastructure support. We present the motivation of opportunistic MSN, explore its underpinning and the key concepts. Further, we describe our ongoing practice regarding to this emerging research area.

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Enhancing spontaneous interaction in opportunistic mobile social networks

Piggyback crowdsensing (PCS) is a novel energy- efficient mobile crowdsensing paradigm that reduces the energy consumption of crowdsensing tasks by leveraging smartphone app opportunities (SAOs). This article, based on several fundamental assumptions of incentive payment for PCS task participation and spatial-temporal coverage assessment for collected sensor data, first proposes two alternating data collection goals. Goal 1 is maximizing overall spatial-temporal coverage under a predefined incentive budget constraint; goal 2 is minimizing total incentive payment while ensuring predefined spatial-temporal coverage for collected sensor data, all on top of the PCS task model. With all of the above assumptions, settings, and models, we introduce CrowdMind -- a generic incentive allocation framework for the two optimal data collection goals, on top of the PCS model. We evaluated CrowdMind extensively using a large-scale real-world SAO dataset for the two incentive allocation problems. The results demonstrate that compared to baseline algorithms, CrowdMind achieves better spatial-temporal coverage under the same incentive budget constraint, while costing less in total incentive payments and ensuring the same spatial-temporal coverage, under various coverage/incentive settings. Further, a short theoretical analysis is presented to analyze the performance of Crowd- Mind in terms of the optimization with total incentive cost and overall spatial-temporal coverage objectives/constraints.

Daqing Zhang

Papers

Unobtrusive sleep posture detection for elder-care in smart home

Peer-to-Peer Context Reasoning in Pervasive Computing Environments

Social and Community Intelligence: Technologies and Trends

Enhancing spontaneous interaction in opportunistic mobile social networks

Near-Optimal Incentive Allocation for Piggyback Crowdsensing