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Titlebook: Collaborative Computing: Networking, Applications and Worksharing; 13th International C Imed Romdhani,Lei Shu,Deze Zeng Conference proceedi

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A Short Review of Constructing Noise Map Using Crowdsensing Technology by using mobile smart phones in a cost-effective manner is being widely used in the city and industrial plants. In this short paper, the state-of-the-art crowdsensing-based noise map applications are first summarized. Furthermore, open research challenges associated with building up noise map are h
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Research of Positioning Tracking on Dynamic Target Based on the Integral Complementing Algorithmod based on integral compensation algorithm. The discrete digital quantity is quantized and transformed into a continuous analog quantity, and the tracking direction is controlled by the angular rate control function. In the process of tracking, if the motion state of the target changes, the control
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LWTP: An Improved Automatic Image Annotation Method Based on Image Segmentationneighbor-based methods such as TagProp are successful methods which have been used for image annotation. However, these methods focus more on weights based on the distances between the images and their neighbors, and ignore the weights of the different labels which can co-occur in the same image. In
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AXE: Objects Search in Mobile Volunteered Services mobile volunteered services emerging. As far as we know, little work has been done on the search for mobile spatial textual objects, even though considerable researches have been done on moving objects query and spatial keyword query. In this paper, we study the problem of searching for mobile spa
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Modeling the Impacts of WiFi Signals on Energy Consumption of Smartphones experiments are conducted to quantitatively study the phone energy impacts by normal WiFi signals. As the experimental results show, the weaker the signal strength is, the faster the phone energy dissipates. To quantitatively describe such impacts, we construct a time-based signal strength-aware en
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A Human-Machine Collaborative Detection Model for Identifying Web Attacksns perform better than machines in attack recognition, while suffer from low bandwidth. In this paper, we adopt a collaborative detection model, based on machine learning augmented with human interaction to detect web attacks. We leverage human knowledge to continuously optimize the detection model
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