Context Modeling for Ranking and Tagging Bursty Features in Text Streams

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. ? 2010 ACM.EI

The Invention relates to a Tobacco Silk Big Data and AI Development Platform and its Application

In recent years, big data and AI technology has been widely used, and the traditional manufacturing industry has also obtained a huge space for technological transformation and upgrading. For tobacco silk production, due to its abundant sensors in the production line and high degree of automation, it has unique advantages in the application of big data and AI technology; At the same time, due to the complexity of its production process and technology, the application of big data and AI technology is facing challenges. Therefore, this paper designed a graphical silk making big data and AI development platform to accelerate the application of big data and AI technology in tobacco processing and manufacturing industry

Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

In Internet of Vehicles (IoV), data sharing among vehicles is essential to improve driving safety and enhance vehicular services. To ensure data sharing security and traceability, highefficiency Delegated Proof-of-Stake consensus scheme as a hard security solution is utilized to establish blockchain-enabled IoV (BIoV). However, as miners are selected from miner candidates by stake-based voting, it is difficult to defend against voting collusion between the candidates and compromised high-stake vehicles, which introduces serious security challenges to the BIoV. To address such challenges, we propose a soft security enhancement solution including two stages: (i) miner selection and (ii) block verification. In the first stage, a reputation-based voting scheme for the blockchain is proposed to ensure secure miner selection. This scheme evaluates candidates' reputation by using both historical interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among the active miners, a newly generated block is further verified and audited by the standby miners. To incentivize the standby miners to participate in block verification, we formulate interactions between the active miners and the standby miners by using contract theory, which takes block verification security and delay into consideration. Numerical results based on a real-world dataset indicate that our schemes are secure and efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio

NegDL: Privacy-Preserving Deep Learning Based on Negative Database

In the era of big data, deep learning has become an increasingly popular topic. It has outstanding achievements in the fields of image recognition, object detection, and natural language processing et al. The first priority of deep learning is exploiting valuable information from a large amount of data, which will inevitably induce privacy issues that are worthy of attention. Presently, several privacy-preserving deep learning methods have been proposed, but most of them suffer from a non-negligible degradation of either efficiency or accuracy. Negative database (\textit{NDB}) is a new type of data representation which can protect data privacy by storing and utilizing the complementary form of original data. In this paper, we propose a privacy-preserving deep learning method named NegDL based on \textit{NDB}. Specifically, private data are first converted to \textit{NDB} as the input of deep learning models by a generation algorithm called \textit{QK}-hidden algorithm, and then the sketches of \textit{NDB} are extracted for training and inference. We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets demonstrate that the accuracy of NegDL could be comparable to the original deep learning model in most cases, and it performs better than the method based on differential privacy

Mechanism of In-Situ Catalytic Cracking of Biomass Tar over Biochar with Multiple Active Sites

Biomass tar is the bottleneck in the development of efficient utilization of biomass syngas. The in-situ catalytic cracking biomass tar with multi-active biochar is investigated in a two-stage fluidized bed-fixed bed reactor. It indicates that adding H2O or CO2 is found to improve the homogeneous and heterogeneous cracking of biomass tar. Activation of biochar by H2O or CO2 impacted the morphology of biochar surface and distribution of metal species. H2O or CO2 affects the creation and regeneration of pore structures, influencing the biochar structure and dynamical distribution of alkali and alkaline earth metal species (AAEMs), which ensure enough surface active sites to maintain the catalytic activity of biochar. The tar cracking into low-quality tar or small-molecule gas may be catalyzed by K, while the combination of tar with biochar would be promoted by Ca. The volatilizations of K and Ca, due to their reaction with volatiles, are to a large extent in accordance with their valences and boiling points. The subsequent transformation from the small aromatic ring systems to the larger ones occurs due to the volatile-biochar interaction. During tar cracking over biochar, K and Ca act as the active sites on biochar surface to promote the increase of active intermediates (C▬O bonds and C▬O▬K/Ca)