A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually *** enrich the services in mobile communications,developers have combined Web ...
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A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually *** enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a *** emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of *** a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage ***,the question of how to determine the most suitable mashups from big data has become a challenging *** this paper,we propose a mashup recommendation framework from big data in mobile networks and the *** proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix *** employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the *** also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization *** then crawl through a real-world large mashup dataset and perform *** experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.
Silicon is one of the main gangue components in iron ore,usually in the form of quartz and *** studies have shown that SiO_(2) has a two-sided effect on the consolidation of pellets during high-temperature oxidation r...
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Silicon is one of the main gangue components in iron ore,usually in the form of quartz and *** studies have shown that SiO_(2) has a two-sided effect on the consolidation of pellets during high-temperature oxidation roasting of ***,it is very difficult to capture the structural evolution and migration mechanisms during high-tem-perature roasting process by existing experimental ***,the influence of SiO_(2) on the consolidation behavior of magnetite was studied through a series of roasting experiments and molecular dynamic *** results show that the consolidation index and particle growth index decrease with the increase in SiO_(2) content in the *** 1573 K,the liquid phase promotes the recrystallization growth of hematite at high *** dynamic study shows that it is difficult for quartz SiO_(2) to form sintering neck with Fe_(2)O_(3).When the calcination temperature is higher than 1400 K,the Fe_(2)O_(3)-Fe_(2)SiO_(4) system produces a considerable sintering neck structure after *** atomic migration ability of Fe_(2)SiO_(4) is much higher than that of Fe_(2)O_(3).The higher atomic migration ability of Fe_(2)SiO_(4) is the main reason for the formation of the sintering neck.
To reduce key disagreement rate and increase key generation rate, this paper proposes a lightweight and robust shared secret key extraction scheme from atmospheric optical wireless channel. A conception of grouping sa...
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Purpose-Path planning is an important part of UAV mission *** main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization(PSO)such as easy to fall into the local optimum,so t...
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Purpose-Path planning is an important part of UAV mission *** main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization(PSO)such as easy to fall into the local optimum,so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality ***/methodology/approach-Firstly,the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV ***,the standard PSO is improved,and the improved particle swarm optimization with multi-strategy fusion(MFIPSO)is *** method introduces class sigmoid inertia weight,adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation ***,MFIPSO is applied to UAV path ***-Simulation experiments are conducted in simple and complex scenarios,respectively,and the quality of the path is measured by the fitness value and straight line rate,and the experimental results show that MFIPSO enables the UAV to plan a path with better ***/value-Aiming at the standard PSO is prone to problems such as premature convergence,MFIPSO is proposed,which introduces class sigmoid inertia weight and adaptively adjusts the learning factor,balancing the global search ability and local convergence ability of the *** idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle *** addition,the Cauchy perturbation is used to avoid the algorithm from falling into local ***,the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself,which improves the accuracy of the evaluation model.
In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of acc...
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In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE
From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing *** achieve a prec...
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From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing *** achieve a precise quantification of the quantumness introduced by this channel,we examine its uncertainties,which encompass both statedependent and state-independent ***,for qudit systems,we provide general formulas for these *** analyze the uncertainties associated with standard quantum teleportation when induced by isotropic states,Werner states,and X-states,and we elucidate the correlation between these uncertainties and the parameters of the specific mixed *** findings demonstrate the validity of quantifying these uncertainties.
This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of f...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.
The rapid development of ISAs has brought the issue of software compatibility to the forefront in the embedded *** address this challenge,one of the promising solutions is the adoption of a multiple-ISA processor that...
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The rapid development of ISAs has brought the issue of software compatibility to the forefront in the embedded *** address this challenge,one of the promising solutions is the adoption of a multiple-ISA processor that supports multiple different ***,due to constraints in cost and performance,the architecture of a multiple-ISA processor must be carefully optimized to meet the specific requirements of embedded *** exploring the RISC-V and ARM Thumb ISAs,this paper proposes RVAM16,which is an optimized multiple-ISA processor microarchitecture for embedded devices based on hardware binary translation *** results show that,when running non-native ARM Thumb programs,RVAM16 achieves a significant speedup of over 2.73×with less area and energy consumption compared to using hardware binary translation alone,reaching more than 70%of the performance of native RISC-V programs.
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