A Home Security System Based on Smartphone Sensors

Several new smartphones are released every year. Many people upgrade to new phones, and their old phones are not put to any further use. In this paper, we explore the feasibility of using such retired smartphones and their on-board sensors to build a home security system. We observe that door-related events such as opening and closing have unique vibration signatures when compared to many types of environmental vibrational noise. These events can be captured by the accelerometer of a smartphone when the phone is mounted on a wall near a door. The rotation of a door can also be captured by the magnetometer of a smartphone when the phone is mounted on a door. We design machine learning and threshold-based methods to detect door opening events based on accelerometer and magnetometer data and build a prototype home security system that can detect door openings and notify the homeowner via email, SMS and phone calls upon break-in detection. To further augment our security system, we explore using the smartphone’s built-in microphone to detect door and window openings across multiple doors and windows simultaneously. Experiments in a residential home show that the accelerometer- based detection can detect door open events with an accuracy higher than 98%, and magnetometer-based detection has 100% accuracy. By using the magnetometer method to automate the training phase of a neural network, we find that sound-based detection of door openings has an accuracy of 90% across multiple doors

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

TCP in the Internet of Things: from ostracism to prominence

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.TCP has traditionally been neglected as a transport-layer protocol for the Internet of Things (IoT). However, recent trends and industry needs are favoring TCP presence in IoT environments. In this article, we describe the main IoT scenarios where TCP will be used. We then analyze the historically claimed issues of TCP in the IoT context. We argue that, in contrast to generally accepted wisdom, most of those possible issues fall in one of the following categories: i) are also found in well-accepted IoT end-to-end reliability mechanisms, ii) can be solved, or iii) are not actual issues. Considering the future prominent role of TCP in the IoT, we provide recommendations for lightweight TCP implementation and suitable operation in such scenarios, based on our IETF standardization work on the topic.Postprint (author's final draft

Automatic fine-grained area detection for thin client systems

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The HiSCORE concept for gamma-ray and cosmic-ray astrophysics beyond 10\,TeV

Air-shower measurements in the primary energy range beyond 10 TeV can be used to address important questions of astroparticle and particle physics. The most prominent among these questions are the search for the origin of charged Galactic cosmic rays and the so-far little understood transition from Galactic to extra-galactic cosmic rays. A very promising avenue towards answering these fundamental questions is the construction of an air-shower detector with sufficient sensitivity for gamma-rays to identify the accelerators and large exposure to achieve accurate spectroscopy of local cosmic rays. With the new ground-based large-area (up to 100 square-km) wide-angle (Omega ~ 0.6-0.85 sr) air-shower detector concept HiSCORE (Hundred*i Square-km Cosmic ORigin Explorer), we aim at exploring the cosmic ray and gamma-ray sky (accelerator-sky) in the energy range from few 10s of TeV to 1 EeV using the non-imaging air-Cherenkov detection technique. The full detector simulation is presented here. The resulting sensitivity of a HiSCORE-type detector to gamma-rays will extend the energy range so far accessed by other experiments beyond energies of 50 - 100 TeV, thereby opening up the ultra high energy gamma-ray (UHE gamma-rays, E > 10 TeV) observation window.Comment: 31 pages, 15 figures, accepted by Astroparticle Physics, DOI information: 10.1016/j.astropartphys.2014.03.00

Recognition decision-making model using temporal data mining technique

An accurate and timely decision is crucial in any emergency situation. This paper presents a recognition decision making model that adopts the temporal data mining approach in making decisions. Reservoir water level and rainfall measurement were used as the case study to test the developed computational recognition-primed decision (RPD) model in predicting the amount of water to be dispatched represented by the number of spillway gates. Experimental results indicated that new events can be predicted from historical events. Patterns were extracted and can be transformed into readable and descriptive rule based form

DAMEWARE - Data Mining & Exploration Web Application Resource

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.Comment: User Manual of the DAMEWARE Web Application, 51 page