466 research outputs found

    A data-oriented approach for outlier detection

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    In this paper, characteristics of data obtained from the sensors (used in OpenSense project) are identified in order to build a data-oriented approach. This approach consists of application of Class Outliers: Distance Based (CODB) and Hoeffding tree algorithms. Subsequently, machine learning models were built to detect outliers in a sensor data stream. The approach presented in this paper may be used for developing methodologies for data-oriented outlier detection

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment

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    [EN] Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province, China.Sajjad, M.; Ul Haq, I.; Lloret, J.; Ding, W.; Muhammad, K. (2019). Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment. IEEE Transactions on Industrial Informatics. 15(12):6541-6550. https://doi.org/10.1109/TII.2019.2921652S65416550151

    Automated Intruder Detection from Image Sequences using Minimum Volume Sets

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    We propose a new algorithm based on machine learning techniques for automatic intruder detection in surveillance networks.  The algorithm is theoretically founded on the concept of minimum volume sets.  Through application to image sequences from two different scenarios and comparison with some existing algorithms, we show that it is possible for our proposed algorithm to easily obtain high detection accuracy with low false alarm rates

    Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

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    With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

    Data science applications to connected vehicles: Key barriers to overcome

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    The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor

    Temporal Mapping of Surveillance Video for Indexing and Summarization

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    This work converts the surveillance video to a temporal domain image called temporal profile that is scrollable and scalable for quick searching of long surveillance video by human operators. Such a profile is sampled with linear pixel lines located at critical locations in the video frames. It has precise time stamp on the target passing events through those locations in the field of view, shows target shapes for identification, and facilitates the target search in long videos. In this paper, we first study the projection and shape properties of dynamic scenes in the temporal profile so as to set sampling lines. Then, we design methods to capture target motion and preserve target shapes for target recognition in the temporal profile. It also provides the uniformed resolution of large crowds passing through so that it is powerful in target counting and flow measuring. We also align multiple sampling lines to visualize the spatial information missed in a single line temporal profile. Finally, we achieve real time adaptive background removal and robust target extraction to ensure long-term surveillance. Compared to the original video or the shortened video, this temporal profile reduced data by one dimension while keeping the majority of information for further video investigation. As an intermediate indexing image, the profile image can be transmitted via network much faster than video for online video searching task by multiple operators. Because the temporal profile can abstract passing targets with efficient computation, an even more compact digest of the surveillance video can be created

    Object Tracking in Vary Lighting Conditions for Fog based Intelligent Surveillance of Public Spaces

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    With rapid development of computer vision and artificial intelligence, cities are becoming more and more intelligent. Recently, since intelligent surveillance was applied in all kind of smart city services, object tracking attracted more attention. However, two serious problems blocked development of visual tracking in real applications. The first problem is its lower performance under intense illumination variation while the second issue is its slow speed. This paper addressed these two problems by proposing a correlation filter based tracker. Fog computing platform was deployed to accelerate the proposed tracking approach. The tracker was constructed by multiple positions' detections and alternate templates (MPAT). The detection position was repositioned according to the estimated speed of target by optical flow method, and the alternate template was stored with a template update mechanism, which were all computed at the edge. Experimental results on large-scale public benchmark datasets showed the effectiveness of the proposed method in comparison with state-of-the-art methods
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