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Towards the target of image retouching tasks, the three-dimension LookUp Table (3D LUT) is a discrete 3D lattice that represents the color mapping to correct the inconsistent color of images produced from varying devices and environments. We design a network named Dual Branch LUT-aware Network (DualBLN), which innovatively introduces the information representing the pixel value mapping in 3D LUT into the real-time retouching procedure, learning the adaptive weights and the multiple 3D LUTs with strong representation capability. Specifically, we elaborate two branches to conduct feature extraction of the raw image and 3D LUTs, which consider the detail of the dual inputs, then generate the precise weights to fuse LUTs for graceful retouching. To integrate the information of the image and the 3D LUTs meticulously, we adopt bilinear pooling to alleviate the conflict between both features when fusing them from two branches of the network, which avoids the feature distortion caused by concatenation or summation directly. In addition, to meet the needs of portrait photo retouching (PPR) for portrait area priority (HRP), we propose Human Region Mask Generator (HRMG) module to solve the shortcomings of the current PPR task that requires the manual marking of portrait region, which achieve the PPR task more conveniently and efficiently. Extensive experiments verify the effectiveness of our method, e.g., the PSNR metric receives over 0.6 gains on high-resolution and 1.4 gains on the human-centered region at most compared with prior state-of-art works. • We design a new network to use look-up table carefully for image retouching. • We use bilinear pooling for feature fusion to improve robustness of the model. • We propose a human region segmentation module by fusing multi-scale feature. • Vast experiments on many datasets prove the efficacy of our proposed method. [ABSTRACT FROM AUTHOR]

Accurate forecasting of natural gas consumption (NGC) plays an important role in energy supply, energy trading, economic effects and environmental sustainability. NGC forecasts can be used to adjust production and supply plans to improve gas efficiency and reduce carbon emissions and supply chain waste. This paper reviews the research progress on NGC in the past decade, analyzes the typical characteristics of different forecasting strategies, and highlights 163 studies in terms of the technical aspects of feature processing methods, data decomposition methods, forecasting models and optimization algorithms. It also systematically elaborates the application of statistical models, machine learning models, grey models, logistic regression and their combinations in predictive models. Bibliometric methods are also utilized to dissect research hotspots and summarize cutting-edge trends in the field. It is worth mentioning that in the terms of hybrid model structures, the application and performance of various model structures are described and evaluated. In this paper, the future development is discussed from spatiotemporal characteristics, studying reasonable data decomposition layers and fusion models, considering potential data privacy issues, and developing artificial intelligence-supporting models and interpretable frameworks. This paper is expected to provide a multi-technology reference for natural gas forecasting and help researchers to select and develop more accurate forecasting techniques and strategies. • Bibliometric analysis of natural gas consumption forecasting. • Typical features of different forecasting strategies are reviewed. • Characteristics and applications of forecasting models and assisted forecasting techniques are outlined. • The application and performance of various hybrid model structures are described and evaluated. • Existing problems and future research directions in natural gas consumption forecasting are discussed. [ABSTRACT FROM AUTHOR]

• Revealing research hotspot and corresponding characteristics by bibliometric methods. • Discovering underlying laws behind data by constructing scientific knowledge graph. • Summarizing potential research hotspots by keyword co-occurrence cluster graph. • Summarizing cutting-edge trends by keyword co-occurrence cluster graph. • Discussing the main open problems in depth and proposing corresponding solutions. With the advent of the era of big data, the recommendation system has become an effective solution to the problem of information overload. This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system. To this end, firstly, classify statistics and feature analysis of valid literature data. Secondly, use VOSviewer software to construct various different scientific knowledge graph to discover valuable knowledge. Thirdly, according to keyword co-concurrence graph conclude five main hotspots of current research about recommendation system and discover five main directions that have potential value in research field of recommendation system. Finally, deeply explore five main key issues and propose corresponding solutions. [ABSTRACT FROM AUTHOR]

• In this paper, we revisit the CL-PDP scheme proposed by Wang et al. in 2017 since their protocol is attacked in recent work of Liao et al. We modify their original scheme and hence obtain a twisted one. • We also find out Want et al.'s security model is not proper because they merged the CSP and KGC into one entity. In our new security model, we prove that the twisted HKWWC-protocol is secure. Certificateless provable data possession (CL-PDP) protocol is an important tool to check the integrity of data outsourced to cloud service provider (CSP) since it is not necessary to consider the certificate management and key escrow problems. In 2017, He et al. proposed an efficient CL-PDP protocol (HKWWC-protocol, for short) with an additional good property: Privacy protection from the verifier Appl. Math. Comput., vol. 314, pp. 31-43. However, recently, Liao et al. illustrates that the HKWWC-protocol is completely insecure under their suggested attack (LLON-attack) since CSP can return a forged proof that is able to pass the verification of the verifier, which shows that the HKWWC-protocol will become completely useless. In this paper, we revisit the original HKWWC-protocol and try to rescue it. After our modification, the twisted new protocol can be immune to the LLON-attack. More importantly, we find that the original He et al.'s security model cannot depict the practical scene at all because they viewed CSP and KGC (key generation center) as one entity in their system. Hence, we redefine a new security model and prove the twisted HKWWC-protocol is secure under our new security model. Performance analysis shows that our proposed protocol is still efficient and practical. [ABSTRACT FROM AUTHOR]