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Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better...
During the recent decade we have experienced a rise of popularity of sensors capable of collecting large amounts of data. One of most popular types of data collected by sensors is time series composed of sequences of measurements taken over time. With low cost of individual sensors, multivariate time series data sets are becoming common. Examples can include vehicle or machinery monitoring, sensors...
Recurrent drift, as a specific type of concept drift, is characterised by the appearance of previously seen concepts. Therefore, in those cases the learning process could be saved or at least minimized by applying an already trained classification model. In this paper we propose Fuzzy-Rec, a framework that is able to deal with recurrent concept drifts by means of a repository of classification models...
Effective prediction of unobservable degradation can assist to schedule preventive maintenance and reduce unexpected downtime for realistic industrial systems. In this paper, an extended time-/condition-based framework is proposed for the Probability Density Function (PDF) prediction of unobservable industrial wear. Furthering our earlier work of unobservable degradation estimation, a stage-based...
Medical literature have recognized physical activity as a key factor for a healthy life due to its remarkable benefits. However, there is a great variety of physical activities and not all of them have the same effects on health nor require the same effort. As a result, and due to the ubiquity of commodity devices able to track users' motion, there is an increasing interest on performing activity...
Recent years have witnessed a series of occupy protest events all over the world. Detecting and monitoring these events is an important and challenging task in social science research and also can provide reference for government's emergency management. Existing methods mainly solve this problem by document clustering techniques. This paper proposes a novel graph-based occupy protest event detection...
Anomaly detection systems rely on machine learning techniques to model the normal behavior of the system. This model is used during operation to detect anomalies due to attacks or design faults. Ensemble methods have been used to improve the overall detection accuracy by combining the outputs of several accurate and diverse models. Existing Boolean combination techniques either require an exponential...
This paper describes, and illustrates using documented applications, a general framework methodology for wide-area forest and land use mapping and change detection using Synthetic Aperture Radar (SAR) remote sensing. Consideration is given to implementation of the SAR-based methodology using both commercial and free/open-source software. Our experience shows that constructing a complete processing...
Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. It will be a daunting task for system administrators to manually keep track of the execution status of a large number of virtual machines all the time. Anomaly prediction is an effective approach to enhancing availability and reliability of Cloud infrastructures...
As information technology improves, the Internet is involved in every area in our daily life. When the mobile devices and cloud computing technology start to play important parts of our life, they have become more susceptible to attacks. In recent years, phishing and malicious websites have increasingly become serious problems in the field of network security. Attackers use many approaches to implant...
In this paper a Distribution State Estimator (DSE) tool suitable for real-time monitoring in poorly characterized low voltage networks is presented. An Autoencoder (AE) properly trained with Extreme Learning Machine (ELM) technique is the “brain” of the DSE. The estimation of system state variables, i.e., voltage magnitudes and phase angles is performed with an Evolutionary Particle Swarm Optimization...
In this paper, we propose Segugio, a novel defense system that allows for efficiently tracking the occurrence of new malware-control domain names in very large ISP networks. Segugio passively monitors the DNS traffic to build a machine-domain bipartite graph representing who is querying what. After labelling nodes in this query behavior graph that are known to be either benign or malware-related,...
Patient monitoring is an important part of the overall treatment plan for hospital in-patients. However, monitoring is often time consuming for hospital staff. Staff must either remain in a patient's room, check in on the patient with frequent intervals or remotely monitor the patient via video surveillance. Constant monitoring may be disruptive to the patient as he or she attempts to rest. Furthermore,...
The automatic identification of appliances through the analysis of their electricity consumption has several purposes in Smart Buildings including better understanding of the energy consumption, appliance maintenance and indirect observation of human activities. Electric signatures are typically acquired with IoT smart plugs integrated or added to wall sockets. We observe an increasing number of research...
The German federal constitutional court ruled, in 2009, that elections had to have a public nature. EasyVote, a promising hybrid electronic voting system for conducting elections with complex voting rules and huge ballots, meets this requirement. Two assumptions need to hold, however. The first is that voters will verify the human-readable part of the EasyVote ballot and detect discrepancies. Secondly,...
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between the human brain and the external environment. Recently, multiple access (MA) methods in telecommunications have been introduced into the system design of BCIs and showed their potential in improving BCI performance. This study investigated the feasibility of...
This paper proposed a method to monitor systolic blood pressure (BP) variability without using a cuff during the daytime. In this method, BP variability of long-term and short-term were separated and estimated respectively from features of phoplethysmograph (PPG) through the use of a frequency filter. Then, total variability was obtained from the combination of long-term and short-term. BP by using...
This paper presents a medication adherence monitoring system for pill bottles based on a wearable inertial sensor. Signal templates corresponding to the two actions of twist-cap and hand-to-mouth are created using a camera-assisted training phase. The act of pill intake is then identified by performing a moving window dynamic time warping in real-time between signal templates and the signals acquired...
Malicious software and especially botnets are among the most important security threats in the Internet. Thus, the accurate and timely detection of such threats is of great importance. Detecting machines infected with malware by identifying their malicious activities at the network level is an appealing approach, due to the ease of deployment. Nowadays, the most common communication channels used...
In-house automatic activity detection is highly important toward the automatic evaluation of the resident's cognitive state. However, current activity detection systems suffer from the demand for on-site acquisition of large amounts of ground truth data for training purposes, which poses a major obstacle to their real-world applicability. In this paper, focusing on resident location trajectory-based...
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