The Experts below are selected from a list of 133524 Experts worldwide ranked by ideXlab platform
Xiaolong Wang - One of the best experts on this subject based on the ideXlab platform.
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app relationship calculation an Iterative Process
International Conference on Data Engineering, 2016Co-Authors: Ming Liu, Xiangnan Zhao, Chinyew Lin, Xiaolong WangAbstract:Today, plenty of apps are released to help users make the best use of their mobile phones. Facing the large amount of apps, app retrieval and app recommendation are extensively adopted to help users obtain their favorite apps. To acquire the high-quality retrieval or recommending results, it needs to obtain the accurate app relationship calculating results in advance. Unfortunately, recent methods are conducted mostly depending on user's log or app's contexts, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many deep relationships other than similarity, e.g., one app needs another app to cooperate to fulfill its work. Obviously, app's reviews contain user's viewpoint. They are useful to help dig deep relationship between apps. Therefore, to calculate relationship between apps via reviews, we propose an Iterative Process by combining review similarity calculation and app relationship calculation together.
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app relationship calculation an Iterative Process
IEEE Transactions on Knowledge and Data Engineering, 2015Co-Authors: Ming Liu, Xiangnan Zhao, Chinyew Lin, Xiaolong WangAbstract:Today, plenty of apps are released to enable users to make the best use of their cell phones. Facing the large amount of apps, app retrieval and app recommendation become important, since users can easily use them to acquire their desired apps. To obtain high-quality retrieval and recommending results, it needs to obtain the precise app relationship calculating results. Unfortunately, the recent methods are conducted mostly relying on user's log or app's description, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many general relationships other than similarity, such as one app needs another app as its tool. These relationships cannot be dug via user's log or app's description. Reviews contain user's viewpoint and judgment to apps, thus they can be used to calculate relationship between apps. To use reviews, this paper proposes an Iterative Process by combining review similarity and app relationship together. Experimental results demonstrate that via this Iterative Process, relationship between apps can be calculated exactly. Furthermore, this Process is improved in two aspects. One is to obtain excellent results even with weak initialization. The other is to apply matrix product to reduce running time.
Ming Liu - One of the best experts on this subject based on the ideXlab platform.
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app relationship calculation an Iterative Process
International Conference on Data Engineering, 2016Co-Authors: Ming Liu, Xiangnan Zhao, Chinyew Lin, Xiaolong WangAbstract:Today, plenty of apps are released to help users make the best use of their mobile phones. Facing the large amount of apps, app retrieval and app recommendation are extensively adopted to help users obtain their favorite apps. To acquire the high-quality retrieval or recommending results, it needs to obtain the accurate app relationship calculating results in advance. Unfortunately, recent methods are conducted mostly depending on user's log or app's contexts, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many deep relationships other than similarity, e.g., one app needs another app to cooperate to fulfill its work. Obviously, app's reviews contain user's viewpoint. They are useful to help dig deep relationship between apps. Therefore, to calculate relationship between apps via reviews, we propose an Iterative Process by combining review similarity calculation and app relationship calculation together.
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app relationship calculation an Iterative Process
IEEE Transactions on Knowledge and Data Engineering, 2015Co-Authors: Ming Liu, Xiangnan Zhao, Chinyew Lin, Xiaolong WangAbstract:Today, plenty of apps are released to enable users to make the best use of their cell phones. Facing the large amount of apps, app retrieval and app recommendation become important, since users can easily use them to acquire their desired apps. To obtain high-quality retrieval and recommending results, it needs to obtain the precise app relationship calculating results. Unfortunately, the recent methods are conducted mostly relying on user's log or app's description, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many general relationships other than similarity, such as one app needs another app as its tool. These relationships cannot be dug via user's log or app's description. Reviews contain user's viewpoint and judgment to apps, thus they can be used to calculate relationship between apps. To use reviews, this paper proposes an Iterative Process by combining review similarity and app relationship together. Experimental results demonstrate that via this Iterative Process, relationship between apps can be calculated exactly. Furthermore, this Process is improved in two aspects. One is to obtain excellent results even with weak initialization. The other is to apply matrix product to reduce running time.
Badreddine Ounnas - One of the best experts on this subject based on the ideXlab platform.
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wave concept Iterative Process method for electromagnetic or photonic jets numerical and experimental results
IEEE Transactions on Antennas and Propagation, 2015Co-Authors: Noemen Ammar, H Baudrand, Taoufik Aguili, B Sauviac, Badreddine OunnasAbstract:In this paper, we report the numerical and experimental observations of electromagnetic or photonic jets created by a planar metallic waveguide filled with dielectric and terminated by an elliptical tip. The theoretical framework of the study is based on the wave concept Iterative Process (WCIP) method formulated in the spatial domain. The analyzed structure is excited by the fundamental transverse electric mode of the waveguide. The accuracy and efficiency of our program are investigated; the results of the radiated power density show a good agreement compared with those given by finite element method (FEM) simulation. The tangential electric field is computed along the elliptical tip interface and in its vicinity, i.e., in the exterior area. The parameters of the electromagnetic jet phenomenon are investigated according to the geometrical and physical characteristics of the proposed structure. Our numerical simulations are in good agreement with the measurements, indicating that the electromagnetic jet can also be obtained in the microwave field.
Edwin Vedejs - One of the best experts on this subject based on the ideXlab platform.
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a two stage Iterative Process for the synthesis of poly oxazoles
Organic Letters, 2005Co-Authors: Jeffery M Atkins, Edwin VedejsAbstract:[reaction: see text]. Methodology has been developed to prepare bis-oxazoles via a two-stage Iterative Process. The sequence begins with C(2)-chlorination of a lithiated oxazole using hexachloroethane. Generation of the C(2)-C(4)(') bond by S(N)Ar substitution with TosMIC anion, followed by conversion to the heterocycle in a one-pot reaction with glyoxylic acid monohydrate, affords the desired bis-oxazole in good yield and purity. The two-stage Process allows for efficient synthesis of a tris-oxazole and the first Iterative preparation of a tetra-oxazole.
Xiangnan Zhao - One of the best experts on this subject based on the ideXlab platform.
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app relationship calculation an Iterative Process
International Conference on Data Engineering, 2016Co-Authors: Ming Liu, Xiangnan Zhao, Chinyew Lin, Xiaolong WangAbstract:Today, plenty of apps are released to help users make the best use of their mobile phones. Facing the large amount of apps, app retrieval and app recommendation are extensively adopted to help users obtain their favorite apps. To acquire the high-quality retrieval or recommending results, it needs to obtain the accurate app relationship calculating results in advance. Unfortunately, recent methods are conducted mostly depending on user's log or app's contexts, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many deep relationships other than similarity, e.g., one app needs another app to cooperate to fulfill its work. Obviously, app's reviews contain user's viewpoint. They are useful to help dig deep relationship between apps. Therefore, to calculate relationship between apps via reviews, we propose an Iterative Process by combining review similarity calculation and app relationship calculation together.
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app relationship calculation an Iterative Process
IEEE Transactions on Knowledge and Data Engineering, 2015Co-Authors: Ming Liu, Xiangnan Zhao, Chinyew Lin, Xiaolong WangAbstract:Today, plenty of apps are released to enable users to make the best use of their cell phones. Facing the large amount of apps, app retrieval and app recommendation become important, since users can easily use them to acquire their desired apps. To obtain high-quality retrieval and recommending results, it needs to obtain the precise app relationship calculating results. Unfortunately, the recent methods are conducted mostly relying on user's log or app's description, which can only detect whether two apps are downloaded, installed meanwhile or provide similar functions or not. In fact, apps contain many general relationships other than similarity, such as one app needs another app as its tool. These relationships cannot be dug via user's log or app's description. Reviews contain user's viewpoint and judgment to apps, thus they can be used to calculate relationship between apps. To use reviews, this paper proposes an Iterative Process by combining review similarity and app relationship together. Experimental results demonstrate that via this Iterative Process, relationship between apps can be calculated exactly. Furthermore, this Process is improved in two aspects. One is to obtain excellent results even with weak initialization. The other is to apply matrix product to reduce running time.
