In this paper, we focus on examining the effects of Ad-context on the click-Through rate (CTR) for the online advertising. Many researches have shown that ad-context congruity is a key factor to CTR, but the features ...
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Recently there have been growing interests in the applications of wireless sensor networks. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an ef...
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Nowadays, WSMO (Web Service Modeling Ontology)1 has received great attention of academic and business communities, since its potential to achieve dynamic and scalable infrastructure for web services is extracted. Ther...
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In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queri...
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In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queries are issued by mobile units(laptop,cell phone,etc.)which access the data objects of database servers by wireless *** inherent properties of mobile computing environment such as mobility,limited wireless bandwidth,frequent disconnection,make skyline queries more *** show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach,called efficient distributed skyline based on mobile computing(EDS-MC).In EDS-MC,a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication,network delay and query response *** conduct extensive experiments in a simulated mobile database system,and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.
NBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to weight sparse bag-of-n-grams repre...
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MBR (Minimum Bounding Rectangle) has been widely used to represent multimedia data objects for multimedia indexing techniques. In kNN search, MINDIST and MINMAXDIST was the most popular pruning metrics employed by MBR...
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This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key ...
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This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.
Multi-party conversation (MPC) bring unprecedented challenges due to the complex scenarios involving multiple speakers and crisscrossed utterance relationships. Existing models for MPC face several key challenges: fir...
Multi-party conversation (MPC) bring unprecedented challenges due to the complex scenarios involving multiple speakers and crisscrossed utterance relationships. Existing models for MPC face several key challenges: firstly, they often ignore the logical structures in dialogs, compromising intent understanding. Secondly, these models overlook individual differences in speakers’ linguistic styles, potentially leading to inconsistent responses. Lastly, the frequent changes in utterance content and constant shifts in speakers significantly increase the difficulty of extracting key information from background noise. To address these challenges, we designed the L ogical O ptimization and S emantic D ecoupling F ramework (LOSDF). Our framework utilizes multi-party attention to manage the contextual information of different speakers over time, effectively handling complex information flow. By rewriting speakers’ utterances, we reduce semantic errors and enhance consistency. Additionally, our information decoupling module distinguishes semantic intent from noise, improving logical reasoning. Evaluations on the Molweni, FriendsQA and DailyDialog datasets show our method outperforms existing models, improving F1 scores by 2.1%, 1.2% and 2.3% respectively. Extensive ablation studies further validate the effectiveness.
keyword Search Over Relational databases (KSORD) enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In...
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keyword Search Over Relational databases (KSORD) enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN (Classification, Learning And Selection of Candidate Network) is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks (CNs) from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models (CNLMs). The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used (NAU) queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.
The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no...
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