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As an emerging joint learning model, federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’ original data. However, a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model, especially in partially homomorphic cryptosystems. In this paper, we propose a Practical and Efficient Privacy-preserving Federated Learning (PEPFL) framework. First, we present a lifted distributed ElGamal cryptosystem for federated learning, which can solve the multi-key problem in federated learning. Secondly, we develop a Practical Partially Single Instruction Multiple Data (PSIMD) parallelism scheme that can encode a plaintext matrix into single plaintext for encryption, improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem. In addition, based on the Convolutional Neural Network (CNN) and the designed cryptosystem, a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent (MGD). Finally, we evaluate the security and performance of PEPFL. The experiment results demonstrate that the scheme is practicable, effective, and secure with low communication and computation costs.

Advances in deep learning have propelled the evolution of multi-view clustering techniques, which strive to obtain a view-common representation from multi-view datasets. However, the contemporary multi-view clustering community confronts two prominent challenges. One is that view-specific representations lack guarantees to reduce noise introduction, and another is that the fusion process compromises view-specific representations, resulting in the inability to capture efficient information from multi-view data. This may negatively affect the accuracy of the clustering results. In this article, we introduce a novel technique named the “contrastive attentive strategy” to address the above problems. Our approach effectively extracts robust view-specific representations from multi-view data with reduced noise while preserving view completeness. This results in the extraction of consistent representations from multi-view data while preserving the features of view-specific representations. We integrate view-specific encoders, a hybrid attentive module, a fusion module, and deep clustering into a unified framework called AMCFCN. Experimental results on four multi-view datasets demonstrate that our method, AMCFCN, outperforms seven competitive multi-view clustering methods. Our source code is available at https://github.com/xiaohuarun/AMCFCN.

Traffic sign detection is a critical task in the autonomous driving. Ordinary networks cannot obtain satisfactory results in traffic sign detection because the size distribution of traffic signs are extremely unbalanced. To overcome this challenge, this paper proposed an improved YOLOv7-Tiny object detection model. Firstly, a path connection strategy was proposed to enhance small-scale feature representation. Compared to the original FPN connection strategy, it adds a path that leads out of the backbone and connects into the Feature Pyramid Network(FPN). Secondly, we proposed a new down-sampling module—-Slice-Sample. By slicing, the size of the feature map is reduced and subsequently, the weights of the sliced feature map channels are assigned using the channel attention mechanism. It can reduce the loss of feature information. Additionally, a module for detecting attention was proposed to address the aliasing effect found in the fusion of different scales. This channel attention mechanism not only focuses on the correlation of neighboring channels, but also employs two branches to increase the model’s ability to extract information from the feature map. Experiments on the German Traffic Sign Detection Benchmark (GTSDB) showed that the improved model can achieve more remarkable performance than yolov7-tiny. Our method achieved 93.47% mean average precision (mAP) surpassing the yolov7-tiny’s 7.48%, and the frames per second (FPS) value is maintained at 67.5. Besides, our method is superior to other lightweight models on the GTSDB. To demonstrate the generalizability of our approach, we tested it on the Tsinghua-Tencent 100K dataset (TT100K) without tuning and obtained 66.29% mAp surpassing the yolov7-tiny’s 7.59%. In addition, the number of parameters of improved YOLOv7-Tiny is about 23.29 M.

Abstract The diversity of consumer demand makes the manufacturing industry shift from the production mode of small variety and large batch to the production mode of multi-variety and small batch, which leads to the increase of production cost and the decrease of production efficiency, especially the increase of energy consumption. In this paper, the energy-aware scheduling problem of two-stage flexible flow shop based on group technology is studied, in which the process differences of jobs in the two stages are inconsistent. To solve the problem of unbalanced load of machines caused by group technology, this paper uses a dynamic splitting strategy to balance the load of machines on the basis of increasing the setup energy consumption as little as possible. Considering that the research problem is a multi-objective optimization problem, this study develops an improved multi-objective scatter search algorithm. According to the characteristics of the problem, the coding scheme, decoding scheme, diversity generation method and combination method are designed. The experimental results show that the group technology can effectively reduce energy consumption, and the dynamic split strategy can balance the load of machines effectively. Moreover, the results show the proposed method is better than the existing methods in terms of obtaining better solutions.

The mutual influence between information and infectious diseases during the spreading process is becoming increasingly prominent. To elucidate the impact of factors such as higher-order interactions, interpersonal distances, and asymptomatic carriers on the coupled propagation of information and infectious diseases, a novel coupled spreading model is constructed based on a two-layer complex network, where one layer is a higher-order network and another layer is a weighted network. The higher-order interactions in information propagation are characterized using a 2-simplex, and a sUARU (simplicial unaware-aware-removed-unaware) model is employed to articulate information propagation. The inter-individual social distances in disease propagation are represented by the weights of a weighted network, and an SEIS (susceptible-exposed-infected-susceptible) model is utilized to describe disease propagation. The dynamic equations of coupled spreading are formulated utilizing the microscopic Markov chain approach. An analytical expression for the epidemic threshold is obtained by deriving it from the steady-state form of the dynamic equations. Comprehensive simulations are conducted to scrutinize the dynamic characteristics of the coupled spreading model. The findings indicate that enhancing the effects of higher-order interactions in information propagation and increasing inter-individual social distances both lead to higher outbreak thresholds and greater spreading of diseases. Additionally, a stronger infectivity among asymptomatic carriers and an extended incubation period are favorable for the outbreak and spread of an epidemic. These findings can provide meaningful guidance for the prevention and control of real-world epidemics.

An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the CSI. The direct path distance estimated by the CSI is utilized to construct a position set consisting of multiple positions and a corresponding distance database containing multiple distance vectors. The error parameters of the weighted combinations of different distance vectors are calculated iteratively to evaluate the impact of different K-values and weights on the positioning accuracy. The proposed algorithm can achieve high-precision trilateration positioning by adaptively selecting the K-value and weight. A typical 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to simulate the positioning performance. The simulation results reveal that the mean error of the CSI-based AWKNN algorithm achieves 1.84 cm, with a root mean square error (RMSE) of 2.13 cm. Compared with the CSI-based least squares (LS) method, the CSI-based nonlinear LS method, and the CSI-based WKNN method, the average error of this method is decreased by 29%, 16%, and 17%, whereas the RMSE is reduced by 35%, 14%, and 19%.

Energy conservation and emission reduction is a critical task for China's aluminum industry. During the last decade, the high temperature superconducting (HTS) technology has been successfully demonstrated to have great potential for energy conservation in this field. Different from the present AC induction heater, this HTS direct current (DC) induction heater is expected to significantly improve the energy efficiency of preheating aluminium billets before extrusion process. This paper thoroughly presents the development of the world's first commercial megawatt HTS DC inducting heater based on 2nd generation HTS tapes. Firstly, the commercial design of this 1MW HTS DC induction heater including design specifications, energy efficiency and commercial potential are analyzed carefully. Subsequently, the manufacture and operation of three core parts including HTS magnet with extra-large diameter, iron core structure and electrical driving system, are introduced. A low cost and practical driving system consisting of motors and gearboxes is developed to solve the problem of peak electromagnetic torque at low rotation. Meanwhile, the heating quality of aluminium billets (i.e., temperature distribution along the radial and axial direction) can be accurately controlled by an adjustable air gap structure. Finally, the present technique challenges and corresponding solutions of this commercial HTS DC induction heaters are summarized and proposed. Compared with an energy efficiency of 45-60% in the MW-class conventional heater, the energy efficiency of this HTS DC induction heater can achieve up to 80.6% based on test results. This work can also provide guidance for the design and optimization of HTS DC induction heaters.

Being sensitive to synchronization errors, symbol timing synchronization is a key factor affecting the performance of orthogonal frequency division multiplexing (OFDM) -based visible light communication (VLC) systems. A novel timing synchronization method based on odd-even symmetry for direct current biased optical OFDM (DCO-OFDM) is investigated in this paper. The proposed method produces an excellent pulse-like timing metric, resulting in a sharper peak at the correct place, while the values of other parts are negligible. The performance of the proposed timing synchronization approach is assessed by measuring the root mean squared error (RMSE) and mean absolute error (MAE) of timing offset estimation and bit error rate (BER) performance by computer simulation. The simulation results show that this novel symbol timing synchronization approach is superior to the typical methods for DCO-OFDM, such as the Park method and Guerra method, in various channel environments.

To preserve the privacy of data uploaded on the cloud, it is widely accepted to encrypt the data before uploading it. This leads to the challenge of data analysis, especially association rule mining while protecting data privacy. As one of the solutions, homomorphic encryption is presented allowing encrypted data processing without decryption. In particular, the twin-cloud structure is frequently applied in the privacy-preserving association rule mining schemes based on asymmetric homomorphic encryption, which contradicts the reality that most of the practical applications applied the single cloud server. However, the existing related single cloud server schemes suffer from privacy leakage problems. To fill this gap in the literature, in this paper, we first present a universal secure multiplication protocol with the single cloud server using the garbled circuit and additive homomorphic encryption. Based on this multiplication protocol, we construct the inner product protocol, comparison protocol, frequent itemset protocol, and the final association rule mining protocol that is secure against privacy leakage. Finally, we give the theoretical security analysis of the proposed protocols and show its performance analysis.

The efficiency of novel superconducting direct current (DC) induction heaters can be up to 80-90%. This is much higher than the 40-50% of conventional alternating current induction heaters. Induction heaters are used to preheat aluminum billets in extrusion pressing. Such devices use a driving motor to rotate the billet. A peak in the electromagnetic torque appears at low rotation speeds (of the motor). Therefore, this peak electromagnetic torque is of particular interest when designing the driving system, which is one of the main challenges for megawatt-class superconducting DC induction heaters. In this paper, the peak electromagnetic torque characteristics of different sized billets were analyzed. A numerical 3D finite element method model was built to analyze the peak electromagnetic torque and resistive heating power behavior of aluminum billets. The results obtained from simulations and experiments match well. The effect of the following five relative parameters, on electromagnetic torque and resistive heating power, were studied: billet diameter, billet length, magnetic flux density, billet material, and electrical conductivity. The peak electromagnetic torque increases nonlinearly with increasing billet diameter, up to 640 mm. The time elapsed between the peak to stable torque decreases nonlinearly with increasing magnetic flux density. The effects of billet material (copper, titanium alloy and stainless steel) on torque were analyzed and compared. These results are likely to guide more cost-effective and practical designs for the billet driving system of superconducting DC induction heaters.

In real-time applications, the information system may often evolves over time. Dynamic information processing is one of main challenges in the research fields related to information processing. Rough sets theory is an excellent information processing tool which has been applied in dynamic information processing researches in recent years. Dominance-based rough sets approach is an outstanding generation of rough sets theory, which can process the ordered information related to the problems of multi-criteria decision analysis and multi-criteria sort. In this article, we investigate some new strategies to unleash the performance of updating approximations in dominance-based rough sets approach further. An original concept of coarse boundary of approximations is given, which can be applied to prune more unnecessary computation in updating approximations further. Then a new incremental approach for updating approximations under a dominance relation is proposed and the corresponding algorithm is designed. A numeric illustration shows the feasibility of the approach. By extensive experimental estimation, the performance of the approach outperforms that of its counterpart on the computational time.

Hesitant fuzzy sets, as an extension of fuzzy sets to deal with uncertainty, have attracted much attention since its introduction, in both theory and application aspects. The present work is focused on the interval-valued hesitant fuzzy sets (IVHFSs) to manage additional uncertainty. Now that distance and similarity as a kind of information measures are essential and important numerical indexes in fuzzy set theory and all their extensions, the present work aims at investigating distance and similarity measures in the IVHFSs and then employing them into multiple attribute decision making application. To begin with, II-type generalized interval-valued hesitant fuzzy distance is firstly introduced in the IVHFS, along with its properties and its relationships with the traditional Hamming-Distance and the Euclidean distance. Afterwards, another interval-valued hesitant fuzzy Lp distance based on Lp metric is proposed and its relationship with the Hausdorff distance is discussed. In addition, different from most of similarity measures with dependent on the corresponding distances, a new similarity measure based on set-theoretic approach for IVHFSs is introduced and its properties are discussed; especially, a relative similarity measure is proposed based on the positive ideal IVHFS and the negative ideal IVHFS. Finally, we describe how the IVHFS and its relative similarity measure can be applied to multiple attribute decision making. A numerical example is then provided to illustrate the effectiveness of the proposed method.

As the capacity of the power grid continues to expand, high-level fault currents might be caused during a contingency, and the problem of short-circuit current over-limitation is imminent. The high-temperature superconducting (HTS) fault current limiter (FCL) is an effective method to solve this problem. In this paper, a transient numerical model for the process of limiting current in the inductive FCL is proposed. The model is based on the coupling of multiphysics finite element simulation and a circuit model. The voltage source is used as input, which can simulate the macroscopic characteristics in the process of limiting current, such as the voltage and current waveforms, and can also simulate microscopic characteristics, such as temperature, magnetic field, and electrodynamic force distribution. The short-circuit experimental data of an air core inductive superconducting fault current limiter (SFCL) prototype was compared with the simulation results to verify the reliability of the simulation.

We focus our discussion on the uncertainty measures of vague soft sets. We propose axiomatic definitions of similarity measure and entropy for vague soft sets. Furthermore, we present a new category of similarity measures and entropies for vague soft sets. The basic properties of these measures are discussed and the relationships among these measures are analyzed.