Dual compressible hybrid quantum secret sharing schemes based on extended unitary operations

A crucial issue with hybrid quantum secret sharing schemes is the amount of data that is allocated to the participants. The smaller the amount of allocated data, the better the performance of a scheme. Moreover, quantum data is very hard and expensive to deal with, therefore, it is desirable to use as little quantum data as possible. To achieve this goal, we first construct extended unitary operations by the tensor product of n, n ≥ 2, basic unitary operations, and then by using those extended operations, we design two quantum secret sharing schemes. The resulting dual compressible hybrid quantum secret sharing schemes, in which classical data play a complementary role to quantum data, range from threshold to access structure. Compared with the existing hybrid quantum secret sharing schemes, our proposed schemes not only reduce the number of quantum participants, but also the number of particles and the size of classical shares. To be exact, the number of particles that are used to carry quantum data is reduced to 1 while the size of classical secret shares also is also reduced to l−2 m−1 based on ((m+1, n′)) threshold and to l−2 r2 (where r2 is the number of maximal unqualified sets) based on adversary structure. Consequently, our proposed schemes can greatly reduce the cost and difficulty of generating and storing EPR pairs and lower the risk of transmitting encoded particles.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

The Behavior of Hydroxyl Units of Synthetic Goethite and its Dehydroxylated Product Hematite

The behavior of the hydroxyl units of synthetic goethite and its dehydroxylated product hematite was characterized using a combination of Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) during the thermal transformation over a temperature range of 180-270 degrees C. Hematite was detected at temperatures above 200 degrees C by XRD while goethite was not observed above 230 degrees C. Five intense OH vibrations at 3212-3194, 1687-1674, 1643-1640, 888-884 and 800-798 cm(-1), and a H2O vibration at 3450-3445 cm(-1) were observed for goethite. The intensity of hydroxyl stretching and bending vibrations decreased with the extent of dehydroxylation of goethite. Infrared absorption bands clearly show the phase transformation between goethite and hematite: in particular. the migration of excess hydroxyl units from goethite to hematite. Two bands at 536-533 and 454-452 cm(-1) are the low wavenumber vibrations of Fe-O in the hematite structure. Band component analysis data of FTIR spectra support the fact that the hydroxyl units mainly affect the a plane in goethite and the equivalent c plane in hematite.

Study on microwave dielectric properties of epoxy resin mixtures used for rapid product development

This paper presents a preliminary study on the dielectric properties and curing of three different types of epoxy resins mixed at various stichiometric mixture of hardener, flydust and aluminium powder under microwave energy. In this work, the curing process of thin layers of epoxy resins using microwave radiation was investigated as an alternative technique that can be implemented to develop a new rapid product development technique. In this study it was observed that the curing time and temperature were a function of the percentage of hardener and fillers presence in the epoxy resins. Initially dielectric properties of epoxy resins with hardener were measured which was directly correlated to the curing process in order to understand the properties of cured specimen. Tensile tests were conducted on the three different types of epoxy resins with hardener and fillers. Modifying dielectric properties of the mixtures a significant decrease in curing time was observed. In order to study the microstructural changes of cured specimen the morphology of the fracture surface was carried out by using scanning electron microscopy.

Exploring the effectiveness of virtual product placement on recall and recognition of new brands

This paper investigates the effectiveness of virtual product placement as a marketing tool by examining the relationship between brand recall and recognition and virtual product placement. It also aims to address a gap in the existing academic literature by focusing on the impact of product placement on recall and recognition of new brands. The growing importance of product placement is discussed and a review of previous research on product placement and virtual product placement is provided. The research methodology used to study the recall and recognition effects of virtual product placement are described and key findings presented. Finally, implications are discussed and recommendations for future research provided.