Understanding the soil and water conservation (SWC) impact of steep‐slope agricultural practices (e.g. terraces) has arguably never been more relevant than today, in the face of widespread ...intensifying rainfall conditions. In Italy, a diverse mosaic of terraced and non‐terraced cultivation systems have historically developed from local traditions and more recently from the introduction of machinery. Previous studies suggested that each type of vineyard configuration is characterised by a specific set of soil degradation patterns. However, an extensive analysis of SWC impacts by different vineyard configurations is missing, while this is crucial for providing robust guidelines for future‐proof viticulture. Here, we provide a unique extensive comparison of SWC in 50 vineyards, consisting of 10 sites of 5 distinct practices: slope‐wise cultivation (SC), contour cultivation (CC), contour terracing (CT), broad‐base terracing (BT) and oblique terracing (OT). A big‐data analysis approach of physical erosion modelling based on high‐resolution LiDAR data is performed, while four predefined SWC indicators are systematically analysed and statistically quantified. Regular contour terracing (CT) ranked best across all indicators, reflecting a good combination of flow interception and homogeneous distribution of runoff and sediment under intense rainfall conditions. The least SWC‐effective practices (SC, CC, and OT) were related to vineyards optimised for trafficability by access roads or uninterrupted inter‐row paths, which created high upstream‐downstream connectivity and are thus prone to flow accumulation. The novel large‐scale approach of this study offers a robust comparison of SWC impacts under intense rainstorms, which is becoming increasingly relevant for the sustainable future management of such landscapes.
This paper presents a multi-objective optimization model for a green supply chain management scheme that minimizes the inherent risk occurred by hazardous materials, associated carbon emission and ...economic cost. The model related parameters are capitalized on a big data analysis. Three scenarios are proposed to improve green supply chain management. The first scenario divides optimization into three options: the first involves minimizing risk and then dealing with carbon emissions (and thus economic cost); the second minimizes both risk and carbon emissions first, with the ultimate goal of minimizing overall cost; and the third option attempts to minimize risk, carbon emissions, and economic cost simultaneously. This paper provides a case study to verify the optimization model. Finally, the limitations of this research and approach are discussed to lay a foundation for further improvement.
Social media contains a massive amount of information, which provides researchers and practitioners with an invaluable source of data to conduct research from end-users' perspectives, in order to ...influence firm strategic choices. Although an extensive amount of research has been developed in B2C and B2B marketing context, few social media studies take a dive into potential linkages between external and internal marketing contexts in an industry specific paradigm. This study aims to bridge B2C and B2B social media marketing, by adopting the outside-in perspective as theoretical lens. Using a large-scale dataset, collected from a micro-blogging site, and consumer-oriented information assembled from multiple sources, we empirically examine the inter-relationship between firm-generated messages, consumer digital engagement, and firm sales performance in the movie industry. Theoretically, this study builds upon the outside-in perspective and extends the current knowledge of the outside-in perspective to the social media context. It also bridges the B2C and B2B marketing literature by demonstrating that the insight garnered from B2C social media interactions should be integrated into the B2B firm interactions, communications, and decision makings. Managerially, this study provides movie practitioners with important implications.
•Consumer digital engagement measurements can be considered good outside-in performance metrics•Consumer digital engagementas triggered by varying types of FGC mediates the impact of FGC on firm sales performance differentially•Insight from B2C social media communications can be value-added and should be integrated into B2B firm dynamics and decision making processes
A Survey on Large-Scale Machine Learning Wang, Meng; Fu, Weijie; He, Xiangnan ...
IEEE transactions on knowledge and data engineering,
06/2022, Volume:
34, Issue:
6
Journal Article
Peer reviewed
Open access
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual ...classification, and recommender systems. However, most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. This issue calls for the need of Large-scale Machine Learning (LML), which aims to learn patterns from big data with comparable performance efficiently. In this paper, we offer a systematic survey on existing LML methods to provide a blueprint for the future developments of this area. We first divide these LML methods according to the ways of improving the scalability: 1) model simplification on computational complexities, 2) optimization approximation on computational efficiency, and 3) computation parallelism on computational capabilities. Then we categorize the methods in each perspective according to their targeted scenarios and introduce representative methods in line with intrinsic strategies. Lastly, we analyze their limitations and discuss potential directions as well as open issues that are promising to address in the future.
Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full ...potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.
•We introduce an approach to a content analysis of geotagged photos for CES uses.•By using automated tags and a network analysis, themes of the photos were grouped.•This method allowed to distinguish ...CES- and non-CES-related photos.•This approach can provide spatial information about socio-cultural uses.•Our approach is applicable for crowd-sourced photos available in other regions.
The volume of accessible geotagged crowdsourced photos has increased. Such data include spatial, temporal, and thematic information on recreation and outdoor activities, thus can be used to quantify the demand for cultural ecosystem services (CES). So far photo content has been analyzed based on user-labeled tags or the manual labeling of photos. Both approaches are challenged with respect to consistency and cost-efficiency, especially for large-scale studies with an enormous volume of photos. In this study, we aim at developing a new method to analyze the content of large volumes of photos and to derive indicators of socio-cultural usage of landscapes. The method uses machine-learning and network analysis to identify clusters of photo content that can be used as an indicator of cultural services provided by landscapes. The approach was applied in the Mulde river basin in Saxony, Germany. All public Flickr photos (n = 12,635) belonging to the basin were tagged by deep convolutional neural networks through a cloud computing platform, Clarifai. The machine-predicted tags were analyzed by a network analysis that leads to nine hierarchical clusters. Those clusters were used to distinguish between photos related to CES (65%) and not related to CES (35%). Among the nine clusters, two clusters were related to CES: ‘landscape aesthetics’ and ‘existence’. This step allowed mapping of different aspects of CES and separation of non-relevant photos from further analysis. We further analyzed the impact of protected areas on the spatial pattern of CES and not-related CES photos. The presence of protected areas had a significant positive impact on the areas with both ‘landscape aesthetics’ and ‘existence’ photos: the total number of days in each mapping unit where at least one photo was taken by a user (‘photo-user-day’) increased with the share of protected areas around the location. The presented approach has shown its potential for reliable mapping of socio-cultural uses of landscapes. It is expected to scale well with large numbers of photos and to be easily transferable to different regions.
Abstract
English is one of the world’s universal language. With the frequent exchange of our country with the world, the society has a higher demand for English talents. According to the analysis of ...big data, the reform of the traditional teaching method and the cultivation of the comprehensive English talent are the goal of the English education in our country, including the multi-dimensional interactive teaching mode, the limitation of the traditional mode, the practice teaching activity and the training of the students’ language expression ability. To provide students with a good language learning environment is the function of the interactive teaching mode which should be promoted in our education 1.
Football is the most popular sport in the world with four billion fans all over the world. Reportedly, the violence incidence rates are high during or after the matches. The violent or destructive ...behavior carried out by a person or player, who watches or plays the game in the stadium is known as football hooliganism. To prevent or control the violence, a real time violence detection system is exclusively needed to monitor the behavior of the crowd and players to take necessary action before the violence is about to happen. Even it is necessary for the system to find whether the attack is non-intentional or intentional in the game. In this paper, a real time violence detection system is proposed which processes the huge input streaming data and recognize the violence with human intelligence simulation. The input to the system is the enormous amount of real time video streams from different sources which is processed in Spark framework. In the Spark framework, the frames are separated and the features of individual frames are extracted by using HOG (Histogram of Oriented Gradients) function. Then the frames are labeled based on features as violence model, human part model and negative model, which are used to train the Bidirectional Long Short-Term Memory (BDLSTM) network for recognition of violence scenes. The bidirectional LSTM can access the information both in forward and reverse direction. Thus the output is generated in context to both past and future information. The network is trained with the violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. Moreover to make the model robust to violence detection, we have created a dataset with 410 video clips having non-violence scenes and 409 video clips having violence scenes, acquired from the football stadium. The performance of this model is validated and it proves the sturdiness of the system with an accuracy of 94.5 percentage in recognizing the violent action.
This paper presents a new approach to analyzing measurement records from industrial processes. The proposed methodology is based on the model of contextual processing and uses big data from ...experimental process tomography datasets. Electrical capacitance tomography is used for monitoring noninvasive flow and for data acquisition. The measurement data are collected, stored, and processed to identify process regimes and process threats. A specific physical modification was introduced into the pneumatic conveying flow rig in order to study flow behavior under extreme conditions, extending the available knowledge base. A support vector machine was applied for data classification. This study illustrates how contextual processing can facilitate data interpretation and opens the way for the development of methods for detecting pre-emergency flow patterns.
In this paper, we present the problem formulation and methodology framework of Super Resolution Perception (SRP) on smart meter data. With the widespread use of smart meters, a massive amount of ...electricity consumption data can be obtained. Smart meter data is the basis of automated billing and pricing, appliance identification, demand response, etc. However, the provision of high-quality data may be expensive in many cases. In this paper, we propose a novel problem - the SRP problem as reconstructing high-quality data from unsatisfactory data in smart grids. Advanced generative models are then proposed to solve the problem. This technology makes it possible for empowering existing facilities without upgrading existing meters or deploying additional meters. We first mathematically formulate the SRP problem under the Maximum a Posteriori (MAP) estimation framework. The dataset namely Super Resolution Perception Dataset (SRPD) is designed for this problem and released. A case study is then presented, which performs SRP on smart meter data. A network namely Super Resolution Perception Convolutional Neural Network (SRPCNN) is proposed to generate high-frequency load data from low-frequency data. Experiments demonstrate that our SRP models can reconstruct high-frequency data effectively. Moreover, the reconstructed high-frequency data can lead to better appliance identification results.
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