With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.

Day 35 bifurcation bifurcation Decay Decay Decay Decay budding budding Decay Decay bifurcation bifurcation budding budding bifurcation bifurcationFigure 1: (Top) Dishlia growth time lapse point cloud over 5 weeks, with classified organs and detected budding, bifurcation and decay events. (Bottom) The extracted events are then used to bring a static plant model to life with both motion and growth. AbstractStudying growth and development of plants is of central importance in botany. Current quantitative are either limited to tedious and sparse manual measurements, or coarse image-based 2D measurements. Availability of cheap and portable 3D acquisition devices has the potential to automate this process and easily provide scientists with volumes of accurate data, at a scale much beyond the realms of existing methods. However, during their development, plants grow new parts (e.g., vegetative buds) and bifurcate to different components -violating the central incompressibility assumption made by existing acquisition algorithms, which makes these algorithms unsuited for analyzing growth. We introduce a framework to study plant growth, particularly focusing on accurate localization and tracking topological events like budding and bifurcation. This is achieved by a novel forward-backward analysis, wherein we track robustly detected plant components back in time to ensure correct spatio-temporal event detection using a locally adapting threshold. We evaluate our approach on several groups of time lapse scans, often ranging from days to weeks, on a diverse set of plant species and use the results to animate static virtual plants or directly attach them to physical simulators.

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With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most accessible and practical approaches to localize a mobile user. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization framework with WiFi fingerprints for multi-building and multi-floor localization. We propose a novel classification model by combining Stacked Auto-Encoder (SAE) with one-dimensional CNN. The SAE can be used to extract key features more precisely from sparse Received Signal Strength (RSS) data, and the CNN can be trained to effectively achieve high success rates in the localization phase. We evaluate CNNLoc with state-of-the-arts as benchmarks on the UJIIndoor-Loc dataset and Tampere dataset. CNNLoc shows its excellence in both building-level and floor-level classifications and outperforms the existing solutions with 100% success on building success rate and an average success rate over 95% on floor-level localization.

Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards and with Internet-of-Things (IoT) devices, due to the fast popularization of them. Because of the characteristic of fast overwhelming, it is important to make fast as well as accurate response to DDoS attacks, and the realtime performance can be even more important to prevent and legitimate the attacks. Among the methods proposed by researchers, the entropy-based detection method provides a sensitive and reliable performance. However, the balance between computational complexity and recognition accuracy remains a challenge. In this paper, we propose a detection method that consists of 3 main parts in different aspects: a sliding time window to fasten the entropy calculation, a single-directional filter to realize early detection during the DDoS progress but not after the crash, and a quintile deviation check algorithm to optimize the detection result. These will eventually lead to a real-time and high-efficient performance to recognize IoT DDoS attacks as soon as possible.

and USC. We thank Erik Lillethun for expert proofreading. Nageeb Ali gratefully acknowledges Microsoft Research for their financial support and hospitality, and financial support from the NSF (SES-1127643). Doug Bernheim gratefully acknowledges financial support from the NSF (SES-0137129) The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Flower blooming is a beautiful phenomenon in nature as flowers open in an intricate and complex manner whereas petals bend, stretch and twist under various deformations. Flower petals are typically thin structures arranged in tight configurations withheavy self-occlusions. Thus, capturing and reconstructing spatially and temporally coherent sequences of blooming flowers is highly challenging. Early in the process only exterior petals are visible and thus interior parts will be completely missing in the captured data. Utilizing commercially available 3D scanners, we capture the visible parts of blooming flowers into a sequence of 3D point clouds. We reconstruct the flower geometry and deformation over time using a template-based dynamic tracking algorithm. To track and model interior petals hidden in early stages of the blooming process, we employ an adaptively constrained optimization. Flower characteristics are exploited to track petals both forward and backward in time. Our methods allow us to faithfully reconstruct the flower blooming process of different species. In addition, we provide comparisons with state-of-the-art physical simulation-based approaches and evaluate our approach by using photos of captured real flowers.or image-based measurements taken at sparse intervals. Such workflows are tedious, prone to measurement bias and difficult to scale to large-scale observations, both in space and time.Advances in affordable three-dimensional (3D) acquisition devices now provide new opportunities in capturing and modelling 3D real-life phenomena in time. For instance, Li et al. [LFM*13] proposed a method for detecting bifurcations of indoor plants from 4D point cloud data. The problem of reconstructing 4D sequences of blooming flowers, however, is considerably more challenging. During the flower opening, inner petals, which were previously occluded, appear and commence an intricate deformation process. Throughout this process, petals may stretch, bend and finally shrink and wrinkle, while possibly colliding with other flower structures.