This paper presents a novel framework for high-level activity analysis based on late fusion using multi-independent temporal perception layers. The method allows us to handle temporal diversity of ...high-level activities. The framework consists of multi-temporal analysis, multi-temporal perception layers, and late fusion. We build two types of perception layers based on situation graph trees (SGT) and support vector machines (SVMs). The results obtained from the multi-temporal perception layers are fused into an activity score through a step of late fusion. To verify this approach, we apply the framework to violent events detection in visual surveillance and experiments are conducted by using three datasets: BEHAVE, NUS–HGA and some videos from YouTube that show real situations. We also compare the proposed framework with existing single-temporal frameworks. The experiments produced results with accuracy of 0.783 (SGT-based, BEHAVE), 0.702 (SVM-based, BEHAVE), 0.872 (SGT-based, NUS–HGA), and 0.699 (SGT-based, YouTube), thereby showing that using our multi-temporal approach has advantages over single-temporal methods.
Assessment of energy consumption behaviour plays an important role in designing demand reduction programs by utility companies. Knowledge of appliance activities in a household aids in conducting the ...energy consumption behaviour assessment for a community load. Non-intrusive load monitoring (NILM) is a tool that can help in identifying the appliance activities. In this paper, a Modified Factorial Hidden Markov Model (MFHMM) based NILM framework is proposed, which models dependencies among appliance operating states and differential appliance operating states by considering differential in power consumption profiles over time. All the appliances are modelled as individual load models using the Hidden Markov Model (HMM). The appliance operating states are obtained with the application of an iterative k-means clustering algorithm. The aggregated power consumption profile is divided into segments using an optimization-based change-point detection (CPD) algorithm. The NILM problem is solved for each of the segments, and the obtained solution is corrected based on the voltage profile at the aggregated load point. The approach of segmentation and efficient identification of appliance operating states make the model less time complex. Simulations are carried out on publicly available datasets named AMPds, REDD, and UK-DALE. The efficacy of the proposed framework over existing frameworks is evident from the simulation results.
Eye-trackers are a popular tool for studying cognitive, emotional, and attentional processes in different populations (e.g., clinical and typically developing) and participants of all ages, ranging ...from infants to the elderly. This broad range of processes and populations implies that there are many inter- and intra-individual differences that need to be taken into account when analyzing eye-tracking data. Standard parsing algorithms supplied by the eye-tracker manufacturers are typically optimized for adults and do not account for these individual differences. This paper presents gazepath, an easy-to-use R-package that comes with a graphical user interface (GUI) implemented in Shiny (RStudio Inc
2015
). The gazepath R-package combines solutions from the adult and infant literature to provide an eye-tracking parsing method that accounts for individual differences and differences in data quality. We illustrate the usefulness of gazepath with three examples of different data sets. The first example shows how gazepath performs on free-viewing data of infants and adults, compared to standard EyeLink parsing. We show that gazepath controls for spurious correlations between fixation durations and data quality in infant data. The second example shows that gazepath performs well in high-quality reading data of adults. The third and last example shows that gazepath can also be used on noisy infant data collected with a Tobii eye-tracker and low (60 Hz) sampling rate.
Recent technology advancement has resulted in optimistic view toward the practicability of wireless sensor networks (WSNs) in the context of Internet of Things (IoT) and Cyber Physical Systems (CPS). ...However, to realize their full benefits in a broad range of commercial applications, there are still many technical hitches that need to be overcome. In this paper, we address three vital technical issues in a WSN: (1) distributed event detection, (2) distributed parameter estimation, and (3) network's robustness. We make use of a recent development in social networks called small world characteristics and propose novel fault-resilient distributed detection and estimation methods over a small world WSN (SW-WSN). In particular, a small world WSN has been developed by mounting antenna arrays on sensor nodes for the purpose of beamforming. A low-complexity optimization problem for beamforming is formulated by introducing a new parameter Flow between node pairs. Additionally, a new beamforming algorithm is also proposed which optimizes this flow, leading to optimal beam parameters. The proposed method yields a lower average path length and a higher average clustering coefficient of the network. Experiments are conducted using simulations and real node deployments over a WSN testbed. Analysis and experimental results obtained demonstrate that the proposed SW-WSN model achieves faster convergence rates for both distributed detection and distributed estimation while being resilient to node failures when compared to results obtained using state-of-the-art methods.
The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is ...becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.
•Visualization and analysis of OSA detection techniques is necessary.•Visualization makes the algorithm trustworthy to the user.•It has the potential to reduce burden and guide diagnosis for Sleep technician.
As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. ...Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models.
At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal ...structure characteristics of feature vector. In addition, a single classifier is difficult to ensure the accuracy of classification. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature expectation subgraph calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of a single classifier by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. Second, we project expectation subgraphs on the sparse vector to combine with a support vector classifier to calibrate the results of a linear support vector classifier. Finally, the experiments on a common dataset named UCSDped1 and a coal mining video dataset in comparison with some existing works demonstrate that the performance of the proposed method is better than several the state-of-the-art approaches.
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