• Rural Digital Europe
• Article close
• HK close

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices&rsquo privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

The concept of the Internet of Things is rapidly becoming a reality, with many applications being deployed within industrial and consumer sectors. At the ‘thing’ level—devices and inter-device network communication—the core technical building blocks are generally the same as those found in wireless sensor network implementations. For the Internet of Things to continue growing, we need more plentiful resources for building intelligent devices and sensor networks. Unfortunately, current commercial devices, e.g., sensor nodes and network gateways, tend to be expensive and proprietary, which presents a barrier to entry and arguably slows down further development. There are, however, an increasing number of open embedded platforms available and also a wide selection of off-the-shelf components that can quickly and easily be built into device and network gateway solutions. The question is whether these solutions measure up to built-for-purpose devices. In the paper, we provide a comparison of existing built-for-purpose devices against open source devices. For comparison, we have also designed and rapidly prototyped a sensor node based on off-the-shelf components. We show that these devices compare favorably to built-for-purpose devices in terms of performance, power and cost. Using open platforms and off-the-shelf components would allow more developers to build intelligent devices and sensor networks, which could result in a better overall development ecosystem, lower barriers to entry and rapid growth in the number of IoT applications.

High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc. Recent advances in learning-based approaches have accomplished unprecedented accuracy in recovering unclothed human shape and pose from single images, thanks to the availability of powerful statistical models, e.g. SMPL, learned from a large number of body scans. In contrast, modeling and recovering clothed human and 3D garments remains notoriously difficult, mostly due to the lack of large-scale clothing models available for the research community. We propose to fill this gap by introducing Deep Fashion3D, the largest collection to date of 3D garment models, with the goal of establishing a novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. In addition, each garment is randomly posed to enhance the variety of real clothing deformations. To demonstrate the advantage of Deep Fashion3D, we propose a novel baseline approach for single-view garment reconstruction, which leverages the merits of both mesh and implicit representations. A novel adaptable template is proposed to enable the learning of all types of clothing in a single network. Extensive experiments have been conducted on the proposed dataset to verify its significance and usefulness. We will make Deep Fashion3D publicly available upon publication. 23 pages, 9 figures. For project page, see https://kv2000.github.io/2020/03/25/deepFashion3DRevisited/

Abstract The International Organization for Standardization (ISO)/Commission International de l’Eclairage (CIE) Standard General Skies is a set of standard skies that can describe, mathematically, the distribution of the luminance of a sky. Since the introduction of such standard, several methods for sky type classification based on the ISO/CIE standard types have been proposed. However, each of these methods could lead to a different result. Based on sky luminance measurement and through sky type classification in Dammam city in Saudi Arabia, a new procedure is proposed that can help reach a more accurate result.

For nearly half a century, parents in China have faced compulsory quotas allowing them to have no more than one or two children. A great debate in recent years over the impact of this program on China’s population continues in PLOS ONE with the publication of Gietel-Basten et al. (2019). The core question concerns how much higher China’s birth rates might have been had birth quotas not been enacted and enforced. Gietel-Basten et al. argue that the selection of such comparators in recent studies may reflect subjective choices. They profess to avoid such subjectivities by using what they present to be a more scientific, objective, and transparent statistical approach that calculates a weighted average of birth rates of countries with other characteristics similar to China’s. Yet the authors make subjective choices regarding the non-fertility characteristics used to form their comparators which leads to an underestimation of the impact of birth planning. Moreover, their visual presentation, which focuses on the two key sub-phases of the birth program, underrepresents its overall impact. Their comparators suggest that China’s population today would be just 15 million more had it not enacted any birth restrictions since 1970 (one percent above its current population) and that in the absence of one-child limits, which began in 1979, China’s population would be 70 million less. At the same time, the authors acknowledge that the one-child program has had numerous negative consequences. It seems fair to ask how such consequences could result if the program had no significant impact on childbearing decisions.

Authors acknowledge support from a NASA Earth and Space Sciences fellowship to Sophie Goliber (18-EARTH18F-323) and terminus tracers everywhere. Niels J. Korsgaard was supported by the Programme for Monitoring of the Greenland Ice Sheet (PROMICE). Michael Wood was supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Technology, administered by the Universities Space Research Association under contract with NASA. James M. Lea is supported by a UKRI Future Leaders Fellowship (grant no. MR/S017232/1). Dominik Fahrner acknowledges support for this study through the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments (grant no. EP/L015927/1). Tavi Murray is funded by the Leverhulme Trust Research Leadership scheme F/00391/J and the UK NERC NE/G010366/1. Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet. Peer reviewed

The electric fault arc, particularly the series arc, leads to plenty of electrical fire. The limitations of t previous studies include: (1) most existing research focused on gaseous atmospheres rather than solids materials, which is contrary to the actual demand; and (2) the studied external heat sources were restricted to cone heaters and flames, while the electric arc was seldom studied. To overcome these limitations, we developed an experimental platform to investigate the flame behavior when cable insulation material was ignited by the fault arc. We proposed a flame-extracting and noise-reduction algorithm to process the enormous number of photos shot by the high-speed camera. The main obtained findings were: (1) the appropriate size of the structuring element plays an essential role in filtering the flame region in the photos, too small a size resulted in the wrong recognition of incandescent particles, while too large a size made a jagged distortion; (2) the mean flame area increased as the system load grew; (3) The flame size became more prominent, and the flame appeared more frequently in specific locations when the system load increased. The in-depth understanding of flame behavior provided by this work will help to optimize the design of electric systems and disaster prevention reduction.