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The automatic pulmonary segmentation for chest X-ray(CXR) plays an important role in assisting diagnosis. Many deep learning methods have the problems of high computational complexity and low segmentation accuracy, which hinder the application to clinical workstations. Therefore, this paper proposes a lightweight multiple attention network(LMA-Net), which improved U-Net by using the progressive dilated convolution(PDC) for lightweight. A reinforced channel attention(RCA) and a multiscale attention(MSA) are embedded in the decoder to further improve the network segmentation performance. We fuse four types of pulmonary disease CXR from the COVID-QE-Ex dataset to generate a multi-source heterogeneous dataset. Effectiveness of LMA-Net is shown by achieving Intersection over Union(IoU) of 96.28%, Dice of 96.95%, Average symmetric surface distance(ASSD) of 13.11mm and Hausdorff Distance 95th percentile( $HD95$ ) of 81.12mm, respectively. It can be seen that lightweight of LMA-Net is achieved according to parameter(Param) of 2.89M and floating-point operations(FLOPs) of 2.64G. This method can effectively improve segmentation performance and speed.

Medical image segmentation is a challenging and popular task in the field of medical image processing in recent decades. Most of the current mainstream segmentation networks are based on convolutional neural networks (CNNs) methods. Among them, encoding and decoding structures based on U-Net architecture and skip connection mechanism have made great progress in medical segmentation. However, these networks come with increased complexity and training difficulty as the accuracy of network segmentation continues to increase, and their ability remains to be improved for extracting feature information in specific information-intensive structure segmentation tasks, such as brain tumors. In addition, the high training cost raises the application threshold of medical image segmentation. To address these issues, we introduce a frequency representation approach that can effectively reduce the loss of feature during encoding and decoding of segmentation networks. Then a tokenized multi-layer perceptron (MLP) method is introduced to learn the space information. Frequency representation and tokenized MLP can greatly reduce the parameters and computational effort while achieving more accurate and efficient medical image segmentation. Therefore, a multi-level lightweight U-Net segmentation network named MLU-Net is proposed to perform segmentation tasks of medical images quickly. In brain tumor segmentation experiments under equivalent preprocessing conditions, our network achieves substantial efficiency gains with parameter and computational workload reductions to 1/39 and 1/61 of U-Net’s, while simultaneously demonstrating superior performance, enhancing the Dice and Intersection over Union (IoU) metrics by 3.37% and 3.30%, respectively. In addition, we perform experiments on dermatologic data and still achieve segmentation performance that outperforms comparable networks. These experiments show that the proposed network is characterized by lightweight and high accuracy, which is contributing to the exploration of clinical medicine scenarios.

In the field of neuroscience, the electroencephalogram (EEG) is a crucial indicator of emotion. The EEG emotion recognition method based on domain adaptation (DA) has good objectivity and high time resolution and is the preferred method to study the brain’s response to emotional stimuli. However, due to the obvious instability of EEG emotion characteristics, it is difficult to predict the emotion corresponding to EEG signals of cross-subjects by a model that combines all source domains into a single source. In order to solve the problem of cross-subject emotion analysis, we propose an EEG emotion recognition net with a cross-subject multi-source adaptive method (MASTF-net), where EEG features of different subjects are regarded as different domains. Through analyzing the invariance of the target domain and the uniqueness of the source domain, this method realizes the emotional analysis of different objects according to the spatio-temporal images and frequency domain information. First, features of EEG image are extracted from frequency and time dimensions. Secondly, combined with the serialized EEG frequency characteristics of local brain regions, independent classification module are established for different domains to recognize the emotion feature distribution of different subjects. In addition, a feature extraction method of differential entropy(DE) data of EEG is proposed based on frequency band division, which can provide stable feature input for our network structure. Finally, experiments are conducted on the SEED dataset. The experimental results show that our method has better classification accuracy in the experiment on the problem of cross multiple subjects. MASTF-net is superior to other relevant methods and models in multi-source domain. On the issue of cross subject emotion analysis, the highest accuracy of our method can reach $88.19\%$ .

Federated learning (FL) is now considered a critical method for breaking down data silos. However, data encryption can significantly increase computing time, limiting its large-scale deployment. While hardware acceleration can be an effective solution, existing research has largely focused on a single hardware type, which hinders the acceleration of FL across the various heterogeneous hardware of the participants. In light of this challenge, this paper proposes a novel FL acceleration framework that supports diverse types of hardware. Firstly, we conduct an analysis of the key elements of FL to clarify our accelerator design goals. Secondly, a unified acceleration framework is proposed, which divides FL into four layers, providing a basis for the compatibility and implementation of heterogeneous hardware acceleration. After that, based on the physical properties of three mainstream acceleration hardware, i.e., GPU, ASIC and FPGA, the architecture design of corresponding heterogeneous accelerators under the framework is detailed. Finally, we validate the effectiveness of the proposed heterogeneous hardware acceleration framework through experiments. For specific algorithms, our implementation achieves a state of the art acceleration effect compared to previous work. For the end-to-end acceleration performance, we gain $12\times $ , $7.7\times $ and $2.2\times $ improvement on GPU, ASIC and FPGA respectively, compared to CPU in large-scale vertical linear regression training tasks.

Winding deformation caused by short-circuit is one of the main causes of transformer damage. Even if the transformer successfully withstands one short-circuit impact, accumulative effect of multiple short-circuit impacts will cause sudden permanent deformation of the winding. Internal winding deformation is a hidden fault due to the visual blocking of the shell of transformer, so it is extremely important to measure the accumulative effect and analyze its influence. In this paper, a SFSZ7-31500/110 transformer was used for short-time multiple short-circuit impact tests. The measurability of accumulative effect was determined by calculating the difference between measured value and reference value of the acceleration of the time-domain vibration acceleration signal, and then a time-frequency matrix was constructed based on Wigner-Ville Distribution (WVD) time-frequency analysis. Based on Fuzzy C-Means (FCM) clustering and the singular values of the matrix, the membership degree of winding mechanical state was calculated, and the influence degree of the accumulative effect was quantitatively analyzed. Results show the method can reflect the deformation trend and degree of winding before the impedance change rate. Research provides some reference for the mechanical state evaluation and fault early warning of windings, and also has a guiding significance for the safe and stable operation of transformers.

Aiming at the problems of uneven illumination of underwater image caused by supplementary illumination in deep-sea and night waters, image noise, low contrast and color deviation caused by suspended particles in water, a new underwater image enhancement method under non-uniform illumination is proposed. The heterogeneous feature fusion module is designed to fuse different levels and different levels of features, so as to improve the overall perception ability of the network to detail information and semantic information. Secondly, a new feature attention mechanism is designed to improve the traditional channel attention mechanism, and the improved channel attention and pixel attention mechanism are added to the heterogeneous feature fusion process to strengthen the ability of the network to extract pixel features with different turbidity. Then, the dynamic feature enhancement module is designed to adaptively expand the receptive field to improve the adaptability of the network to the image distortion scene and the ability of model conversion, and strengthen the network’s learning of the region of interest. Finally, the color loss function is designed, and the absolute error loss and structural similarity loss are jointly minimized to correct the color deviation on the basis of maintaining the image texture. A multi-scale feature extraction module is designed to extract different levels of features at the beginning of the network, and the output results are obtained through the convolution layer with jump connection and the attention module. The experimental results on several data sets show that this method can have good results in processing synthetic underwater images and real underwater images, and can better restore the image color and texture details compared with the existing methods. It conforms to the characteristics of human vision, and the visual effect is better than the existing underwater image enhancement algorithms.

The effect of seed power on the inter-modal four-wave mixing (IM-FWM) effect in a distributed side-pumped fiber amplifier is investigated for the first time, to the best of our knowledge. By using a seed source smaller than 70 W, the effect of seed power on the IM-FWM is studied experimentally. Surprisingly, it is found that the IM-FWM effect becomes stronger with the weaker seed. To analyze the physics behind this result, a numerical model is introduced, and the numerical results are in good agreement with the experimental observations. It is revealed that the IM-FWM enhancement is induced by the gain competition between the signal light and the anti-Stokes light of the IM-FWM. It is also found that there is an optimized seed power for suppressing the IM-FWM effect. The experimental and numerical results which are presented in this work can provide significant guidance for understanding the IM-FWM in fiber lasers and amplifiers.

With the fast proliferation of device sensing and computing, crowed sensing has become the building block of the Internet of things. Consequently, various data collection and incentive mechanisms are investigated for people-centric services. In this paper, we have investigated the problem of privacy-aware people-centric IoT service based on a tailored auction approach. We applied a bi-tier differential privacy methodology on the data collected from crowdsensing IoT devices. A corresponding pricing scheme is also proposed to ensure the property of incentive compatibility, precise service data, and anonymized query results. Comparing to traditional privacy-aware auction schemes which only focus on the cost, our corresponding precise privacy-aware auction scheme provides a tailored IoT service based on the customers’ request. The proposed trial query technique is able to provide a precise assessment of service quality, thus improves the efficiency of the people-centric IoT service. The customer could enjoy the convenience of service evaluation before making a bid, while the actual service data is anonymized to guarantee the service providers’ interests. We evaluate the proposed bi-tier differential privacy schema for auction-based service by conducting extensive simulations. The experimental results show that our proposed method yields higher data utility and accuracy for the IoT service customers with privacy concerns.

The anonymity of the Internet used to be considered as an encouraging factor that helped learners engage in online learning. However, academic studies on anonymity have found that its effect on learning is context-dependent or mixed. In this research, we focused on massive open online course (MOOC) learners’ preference for personal name disclosure in their screen names as a predictor of their final achievement levels (FALs) at the end of a course including 2606 active learners. We conducted two studies, one to examine the associations between these two variables and one to demonstrate how such associations can be utilized in MOOC FAL prediction. We found that MOOC learners who included personal names in their MOOC screen names significantly outperformed other learners in their FALs (p < 0.001). We also found that screen name preference improved FAL prediction accuracy utilizing natural language processing and proper machine learning technologies. The error rate was reduced to 4.03% by a random forest algorithm with an appropriate feature combination: the personal name disclosure indicator (PNDI), quiz scores, number of replies, and exam scores. The results are potentially useful for the development of an early intervention to provide different types of help to students who prefer to disclose personal names and those who do not. The practical effects of these interventions will be examined in the future. In addition, whether the course difficulty level or course type affects the associations between personal name disclosure and FAL will also be examined.

The inter-modal four-wave mixing (IM-FWM) and transversal mode instabilities (TMI) are experimentally studied in high-power distributed side-pumped fiber amplifiers. To the best of our knowledge, we have made the first demonstration of TMI and IM-FWM which can be suppressed simultaneously by increasing the bending diameter. Besides, the experimental results show that the counter-pumping scheme is beneficial to suppress both of IM-FWM and TMI, when comparing with co-pumping scheme. Furthermore, these two effects cannot be observed when the M2 factor is lower than 1.3, which can give a condition to estimate whether these two effects should be considered or not. The pertinent study can provide some guidance for understanding TMI and IM-FWM in the high-power fiber laser and amplifier.

Social computing, exploiting utilization of advanced computational techniques to overcome typical problems in social science, has been a more visualized conception in academia. However, existing researches still suffer from two aspects of challenges: 1) lack of reliable multi-source data acquisition and management; 2) absence of high-performance algorithmic approaches. Fortunately, some newly-emerged cross-discipline technologies offer more opportunities to enhance conventional solutions. For the former, characterized by its property of information collection and integration, Internet of Things (IoT) can be introduced to produce a novel architecture named Internet of Social Computing Things (IoSCT). For the latter, specific neural network models can be set up to manipulate complicated calculation. Thus, taking the issue of sustainability prediction as objective situation, deep neural network-embedded Internet of Social Computing Things (NeSoc) is proposed in this paper. Firstly, IoSCT is put forward as bottom support platform, guaranteeing comprehensive resource involvement of social computing. Secondly, a hybrid neural network mechanism is formulated and embedded into IoSCT for centralized modeling. Finally, a series of experiments are conducted on a real-world dataset to evaluate performance of the proposed NeSoc.

A quantum secure direct communication (QSDC) protocol based on the hyper-entangled state is proposed to improve the efficiency of eavesdropping detection in an improved “ping-pong” protocol, in which we use the hyper-entangled state to detect eavesdropping during the quantum communication. In the security analysis, we use the entropy theory method to calculate the amount of information and compare the detection strategy of two QSDC protocols quantitatively by comparing the constraint between the maximal amount of information that the eavesdropper can get, and the probability that the eavesdropper is detected. The probability of being detected will be 50% in the original QSDC protocol which based on the Bell states if the eavesdropper attempts to get all the information, however, in the proposed protocol this probability can reach 75%, which indicates that the proposed protocol is more secure.

A quantum secure direct communication (QSDC) protocol is presented. In the proposed protocol, the omega state is introduced to detect the eavesdropping during the quantum communication, eavesdropping behaviors will change the state of the omega state, and the sender Alice and the receiver Bob detect eavesdropping through the state measurement results. The relationship between the amount of information that the eavesdropper gets and the probability of being detected is also given. Compared with the original QSDC protocol based on the Bell state, when the same amount of information is obtained, the eavesdropper must face a higher detection probability in the proposed protocol, and the eavesdropper mostly can obtain 0.676 (bit) of information. The security analysis is also given, and the result indicates that the proposed protocol is more secure. A simulation based on the law of large number and the Monte Carlo method is also given, and the mean square error is introduced to describe the similarity between the simulation data and the theoretical value. The simulation result indicates that the proposed security in an ideal environment and the security analysis are correct, and the proposed protocol is more secure by sending more quantum particles in detecting eavesdropping.

The roadside deployed light detecting and ranging (LiDAR) has been a solution to fill the data gap for the transition period from the unconnected-vehicles environment to the connected-vehicles system. For the roadside LiDAR system, background filtering is an initial but important step. This paper presented a raster-based method for background filtering with roadside LiDAR data. The proposed method contains four major parts: region of interest (ROI) selection, rasterization, background area detection, and background array generation. The location of the background points was stored in a 3D array. The performance of the raster-based method was tested with the data collected at different scenarios. The comparison to the state-of-the-art also confirmed the robustness of the proposed method.

A new k-anonymous method which is different from traditional k-anonymous was proposed to solve the problem of privacy protection. Specifically, numerical data achieves k-anonymous by adding noises, and categorical data achieves k-anonymous by using randomization. Using the above two methods, the drawback that at least k elements must have the same quasi identifier in the k-anonymous data set has been solved. Since the process of finding anonymous equivalence is very time consuming, a two-step clustering method is used to divide the original data set into equivalence classes. First, the original data set is divided into several different sub-datasets, and then the equivalence classes are formed in the sub-datasets, thus greatly reducing the computational cost of finding anonymous equivalence classes. The experiments are conducted on three different data sets, and the results show that the proposed method is more efficient and the information loss of anonymous dataset is much smaller.

Connected-vehicle system is an important component of smart cities. The complete benefits of connected-vehicle technologies need the real-time information of all vehicles and other road users. However, the existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic since there are still many unconnected vehicles and pedestrians on the roads. Therefore, it is urgent to find an approach to collect the high-resolution real-time status of unconnected road users. When it is difficult for all vehicles, pedestrians, and bicyclists to broadcast their real-time status in the near future, enhancing the traffic infrastructures to actively sense and broadcast each road user's status is an intuitive solution to fill the data gap. This paper introduces a new-generation LiDAR-enhanced connected infrastructures that can actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The system architecture, the LiDAR data processing procedure, the data communication, and the first pilot implementation at an intersection in Reno, Nevada are included in this paper. This research is the start of the new-generation connected infrastructures serving connected/autonomous vehicles with the roadside LiDAR sensors. It will accelerate the deployment of the connected network for the smart cities to improve traffic safety, mobility, and fuel efficiency.

The composition and content of dissolved gases in transformer oil are closely related to the type of faults in the transformer and the severity of potential hazards. Dissolved gas analysis (DGA) in insulating oil is an important way to monitor the state of transformer equipment. CO, CH4, and C2H2 are one of the dissolved gases in the transformer oil. Based on the density functional theory, the optimal adsorption site of the transition metal atom Pt on the surface of WSe2, one of the typical layered transition metal disulfides (LTMDs), is determined in the beginning. Attaining the adsorption behavior of these three gases on the surface of Pt-WSe2. The optimal structure of gas adsorption, charge transfer, adsorption energy, electronic density of states (DOS), deformation charge density (DCD), and frontier orbital are analyzed. As an electron acceptor, Pt-WSe2 attracts electrons from all three gas molecules. The adsorption type of CO and C2H2 molecules is chemisorption, whose adsorption effect is strong. The CH4 adsorption is physical adsorption, whose adsorption effect is weak. The adsorption of all the three gas molecules leads to an increase in the bandgap of the Pt-WSe2, that is, the increase in the resistivity.

For many data mining applications under the Internet of Things (IoT) environments, the attitude and the position of a rigid target are indispensable and hidden information to be dug. The term Rigid Body Localization (RBL) refers to simultaneously estimation of the position and the attitude of a rigid target. The RBL framework which adopts only one single base station (BS) is considered in this paper for IoT applications. Several wireless sensor nodes with known topology information are fixed on the surface of the rigid target. The single BS fuses the angle of arrival (AoA) measurements from the nodes with the topology information for the RBL purpose. In this paper, we propose a two-stage RBL method to efficiently fusing the aforementioned two pieces of information. Firstly, we built the maximum likelihood estimator (MLE) of the information fusion and adopted the modified Newton's iteration algorithm (mNIA) to determine the wireless node position; then we used the unit quaternion (UQ) algorithm for estimating the relative position and attitude with respect to the predetermined reference state, which completed the RBL task. Finally, we evaluated the proposed RBL performance in terms of the root mean squared error (RMSE), convergence success rate, as well as the computation costs. Simulation results showe that the proposed mNIA-based RBL algorithm can achieve a finer RBL performance with obviously higher speed and 100 percent success convergence rate, comparing with existing heuristic methods.

Most of existing community detection algorithms group nodes with more connections into the same community, and they are more concerned with links within the community. However, the weak ties between different communities are also important, because they can reflect the relationships between different communities, including helpful, friendly or negative, and adverse. Few studies focus on weak ties, although they are important. In this paper, we propose a novel sign prediction model based on the nodes features in the network, including the Jaccard similarity and the ratio of the negative degrees of all nodes, and the autoencoder technology that self-defines its loss function with the features of the communities. The proposed model maps the original network to a low-dimensional space so that the weak ties can be represented by low-dimensional vectors. We conduct experiments on the Epinions and Slashdot datasets and find that the proposed model outperforms the challenging state-of-the-art graph embedding methods in the sign prediction of weak ties in terms of accuracy and F1 score measurement.

Community detection is a classic and essential task in complex network analysis which aims at understanding their structural properties and functional organization. Community detection based on overlapping nodes has become one of the most popular methods in recent years. But, how to define the overlapping nodes in the real network is an important job. In this paper, we use the speaker-listener label propagation algorithm to find overlapping nodes. Second, we proposed a new metric which is based on local and global attributes to measure the node influence so as to evaluate the overlapping nodes’ importance. Community detection is realized with the $k$ -lowest-influence overlapping nodes deleting. Then, we assign the removed nodes into specific communities by voting to find the final community structure. The voting strategy is based on choosing the specific communities containing most of their neighbors. Finally, the extensive experiments on real-world networks demonstrate that our proposed method improves the quality of community detection methods and show both the effectiveness and efficiency of the method.

With the fast development of social networks, more and more data has been generated. Finding useful information among these data is important. Subgraph matching is the method that can be used in social networks for social search, recommender systems, and so on. In this paper, we choose five typical graph matching methods which are VF2, SPath, TurboISO, BoostIso, and RI to value their performance and scalability in social networks. These methods are verified by three social network data sets whose node’s number vary from thousands to millions. According to the experiments, we can find that VF2 and RI is applicable for rather small graphs while SPath performs better in large graph when average degree of graph is small. When graph with high average degree, TurboISO and BoostIso performs better. What is more, we also compare the efficiency of different matching order when searching. The order choosing strategy proposed by RI is better.

Caches are used to improve the performance of the internet, and to reduce the latency of data access time and the low speed of repeated computing processes. Cache replacement is one of the most important issues in a caching system; therefore, it must be coordinated with the caching system to minimize the access latency and maximize the hit rate or byte hit rate. In this paper, we presented a novel caching replacement algorithm named Weighted Greedy Dual Size Frequency (WGDSF) algorithm, which is an improvement on the Greedy Dual Size Frequency (GDSF) algorithm. The WGDSF algorithm mainly adds weighted frequency-based time and weighted document type to GDSF. By increasing the above two weighted parameters, WGDSF performs fairly well at keeping popular objects in the cache and replacing rarely used ones. Our experiment shows that this algorithm has a better hit rate, byte hit rate and access latency than state-of-the-art algorithms, such least Recently Used, least Frequently Used, and GDSF.

Renewable energy resources (RESs) have been considered to be a promising solution for providing clear and economical power in isolated islands. Due to the low predictive nature, conventional unit commitment with a deterministic optimization algorithm is no long suitable for grids with high RES scale. This paper proposes a coordinated multi-time scale robust scheduling framework for isolated system with energy storage units. The framework decomposes the adjustable dispatch process into three coordinated stages and specifies the operational policy of thermal and hydraulic units. We formulate mathematical models for each dispatch stage and add additional constraints for inter-stage coordination. Then, we develop a multi-stage robust solution based on Bender's decomposition and a cutting plane algorithm. Finally, we test the proposed framework with real data from Nanji Island, China. Compared with traditional scheduling, robust schedule approach can significantly improve the “robustness”of grids and allow higher RES penetration level.

With the significant growth in the scale of data, an increasing amount of training data is available in many machine learning tasks. However, it is difficult to ensure perfect labeling with a large volume of training data. Some labels can be incorrect, resulting in label noise, which could lead to deterioration in learning performance. A common way to address label noise is to apply noise filtering techniques to identify and remove noise prior to learning. Multiple noise filtering approaches have been proposed. However, almost all existing works focus on only mislabeled training data and ignore the existence of unlabeled data. In fact, unlabeled data are common in many applications, and their values have been extensively studied and recognized. Therefore, in this paper, we explore the effective use of unlabeled data to improve the noise filtering performance. To this end, we propose a novel noise filtering algorithm called enhanced soft majority voting by exploiting unlabeled data (ESMVU), which is an ensemble-learning-based filter that adopts a soft majority voting strategy. ESMVU provides a systematic way to measure the value of unlabeled data by considering different aspects, such as label confidence and the sample distribution. Finally, the effectiveness of the proposed method is confirmed by experiments and comparison with other methods.

Cache memory plays an important role in improving the performance of web servers, especially for big data transmission, which response time is constrained. It is necessary to use an effective method, such as web cache. Because an outstanding cache replacement algorithm can not only reduce the users access time but also improve the performance of the system. The traditional used weighting replacement policy does not consider the size parameter, hence, it may perform poorly while the datasets are larger. In this paper, we propose a novel, high-performance cache replacement algorithm for the web cache, named weighting size and cost replacement policy (WSCRP) bases on the weighting replacement policy. The algorithm recalculates the objects weight with adding the cost attribute in the cache, then orders the weight. Additionally the influence of various factors on the Web object as frequency, time, and cost value are considered. When the cache space cannot satisfy the new request object, the replacement policy WSCRP replaces the largest weighting and cost object. The experiments show that proposed algorithm has higher

Due to advancements in the development of wireless medical sensing devices and wireless communication technologies, the wireless body area network (WBAN) has become an eminent part of e-healthcare systems. WBAN uses medical sensors to continuously monitor and collect the physiological parameters of a patient's health and send them to a remote medical server through a portable digital assistance (PDA)/mobile. Due to limitations in communication, such as power, storage, and the computational capabilities of sensors, data aggregation techniques are used to reduce the communication overhead in real-time data transmission in WBAN. However, since the WBAN transmits sensitive health data, data security and data privacy are a major concern. In this paper, we propose a secure privacy-preserving data aggregation (SPPDA) scheme based on bilinear pairing for remote health monitoring systems to improve data aggregation efficiency and data privacy. Our proposed SPPDA scheme utilizes the homomorphic property of the bilinear ElGamal cryptosystem to perform privacy-preserving secure computation and combines it with the aggregate signature scheme, enabling data authenticity/integrity in the WBAN. The proposed SPPDA scheme is proved to be semantically secure under the decisional bilinear Diffie-Hellman assumption. Security analysis demonstrates that our proposed scheme preserves data confidentiality, data authenticity, and data privacy; it also resists passive eavesdropping and replay attacks. A performance evaluation based on simulation results and a comparison of computational cost with related schemes show that data aggregation and batch verification at the PDA significantly reduce communication and transmission overhead and support efficient computation at the remote server.