Neural networks have been an active research area for decades. However, privacy bothers many when the training dataset for the neural networks is distributed between two parties, which is quite common nowadays. Existing cryptographic approaches such as secure scalar product protocol provide a secure way for neural network learning when the training dataset is vertically partitioned. In this paper, we present a privacy preserving algorithm for the neural network learning when the dataset is arbitrarily partitioned between the two parties. We show that our algorithm is very secure and leaks no knowledge (except the final weights learned by both parties) about other party's data. We demonstrate the efficiency of our algorithm by experiments on real world data.
In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network's performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of largesized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network's performance. The analytical results are corroborated with Monte-Carlo simulations.Index Terms-Reconfigurable intelligent surface (RIS), unmanned aerial vehicle (UAV), outage probability, nultiuser downlink communication, outdated channel state information (CSI), imperfect CSI, RIS selection schemes. communications.The integration of UAV and RIS has recently come up as an important framework to enhance the energy efficiency and spectrum efficiency in B5G and 6G communications [24]-[30]. In [27], an algorithm has been proposed to jointly design active and passive beamforming and UAV's trajectory in RISassisted UAV networks. While the secrecy rate maximization problem has been formulated in [28] for jointly optimizing the transmit power, UAV trajectory, and RIS phase shifts, an iterative algorithm has been proposed in [30] to maximize the sum-rate of the RIS-assisted UAV orthogonal frequencydivision multiple access (OFDMA) communication systems. Recently, considering practical models and designing a robust beamforming in RIS-assisted communications becomes very important. Accordingly, in [31], an algorithm for robust beamforming design has been proposed for RIS-aided communication where a multi-antenna access point serves a singleantenna user with the aid of an RIS. Similar to [31], the authors in [32] have jointly optimized hybrid access point energy beamforming, time allocation, ...
The information explosion on the Internet has placed high demands on search engines. Despite the improvements in search engine technology, the precision of current search engines is still unsatisfactory. Moreover, the queries submitted by users are short, ambiguous and imprecise. This leads to a number of problems in dealing with similar queries. The problems include lack of common keywords, selection of different documents by the search engine and lack of common clicks etc. These problems render the traditional query clustering methods unsuitable for query recommendations. In this paper, we propose a new query recommendation system. For this, we have identified conceptually related queries by capturing users’ preferences using click-through graphs of web search logs and by extracting the best features, relevant to the queries, from the snippets. The proposed system has an online feature extraction phase and an offline phase in which feature filtering and query clustering are performed. Query clustering is carried out by a new tripartite agglomerative clustering algorithm, Query-Document-Concept Clustering, in which the documents are used innovatively to decouple queries and features/concepts in a tripartite graph structure. This results in clusters of similar queries, associated clusters of documents and clusters of features. We model the query recommendation problem in four different ways. Two models are non-personalized and personalized content-ignorant models. Other two are non-personalized and personalized content-aware models. Three similarity measures are introduced to estimate different kinds of similarities. Experimental results show that the proposed approach has better precision, recall and F-measure than the existing approaches.
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