Two-party private set intersection (PSI) plays a pivotal role in secure two-party computation protocols. The communication cost in a PSI protocol is normally influenced by the sizes of the ...participating parties. However, for parties with unbalanced sets, the communication costs of existing protocols mainly depend on the size of the larger set, leading to high communication cost. In this paper, we propose a low communication-cost PSI protocol designed specifically for unbalanced two-party private sets, aiming to enhance the efficiency of communication. For each item in the smaller set, the receiver queries whether it belongs to the larger set, such that the communication cost depends solely on the smaller set. The queries are implemented by private information retrieval which is constructed with trapdoor hash function. Our investigation indicates that in each instance of invoking the trapdoor hash function, the receiver is required to transmit both a hash key and an encoding key to the sender, thus incurring significant communication cost. In order to address this concern, we propose the utilization of a seed hash key, a seed encoding key, and a Latin square. By employing these components, the sender can autonomously generate all the necessary hash keys and encoding keys, obviating the multiple transmissions of such keys. The proposed protocol is provably secure against a semihonest adversary under the Decisional Diffie–Hellman assumption. Through implementation demonstration, we showcase that when the sizes of the two sets are 28 and 214, the communication cost of our protocol is only 3.3% of the state-of-the-art protocol and under 100 Kbps bandwidth, we achieve 1.46x speedup compared to the state-of-the-art protocol. Our source code is available on GitHub: https://github.com/TAN-OpenLab/Unbanlanced-PSI.
The fault diagnosis of rolling bearings is a critical technique to realize predictive maintenance for mechanical condition monitoring. In real industrial systems, the main challenges for the fault ...diagnosis of rolling bearings pertain to the accuracy and real-time requirements. Most existing methods focus on ensuring the accuracy, and the real-time requirement is often neglected. In this paper, considering both requirements, we propose a novel fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping, named logistic-ELM. First, we identify 14 kinds of time-domain features from the original vibration signals according to mechanical vibration principles and adopt the sequential forward selection strategy to select optimal features from them to ensure the basic predictive accuracy and efficiency. Next, we propose the logistic-ELM for fast fault classification, where the biases in ELM are omitted and the random input weights are replaced by the chaotic logistic mapping sequence which involves a higher uncorrelation to obtain more accurate results with fewer hidden neurons. We conduct extensive experiments on the rolling bearing vibration signal dataset of the Case Western Reserve University bearing data centre. The experimental results show that the proposed approach outperforms existing state-of-the-art comparison methods in terms of the predictive accuracy, and the highest accuracies are 100%, 99.71%, 98%, 100%, 100%, and 100%, respectively, in seven separate sub data environments. Moreover, in terms of the runtime cost, the experimental results indicate that the proposed logistic-ELM can predict the fault in 40 ms with a high accuracy, up to 21-1858 times more rapid than existing methods based on support vector machine, convolutional neural network and multi-scale entropy. Other experiments of fault diagnosis of the rolling bearings under four different loads also indicate that the logistic-ELM can adapt to different operation conditions with high efficiency. The relevant code is publicly available at
https://github.com/TAN-OpenLab/logistic-ELM
.
With the rapid development of online social networks (OSN), the influence of rumor propagation on social life raises great concern. Traditional rumor-propagation models, which do not fully consider ...the features of OSN, are not suitable for use in OSN. In this paper, we focus on discovering a pattern of rumor-propagation phenomena in OSN, and propose a novel rumor-propagation model, inspired by a ball elastic-collision model, called the elastic collision-based rumor-propagation model (ECRModel). We investigate the dynamics of ball elastic collisions, which is similar to the dynamics of rumor propagation between nodes in OSN. We adopt the parameter relationships of the elastic collision model and apply them to rumor propagation in social networks. In the ECRModel, we do not directly adopt the node classification categories of "Ignorants, Spreaders, and Stiflers", but divide the user nodes into three types: 1) inactive and never spread rumors; 2) active and spread rumors forward; and 3) inactive but have previously spread rumors. We mathematically model node interaction attributes, and analyze the spreading probabilities and the steady state, considering both individual perspectives with detailed attributes and integral perspectives with node-state densities. At last, we conduct a series of simulations, and the results verify the correctness of the analytical results. We further investigate the effects of detailed properties on rumor propagation, such as average out-degree of OSN, rumor confusingness degree, and each node's comprehensive influence.
With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete ...information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high precision, capturing the underlying symmetry in data. With the experimentally measured data generated from the balanced homodyne detectors, we compare the degradation information about quantum noise squeezed states predicted by the reconstruction and characteristic models; both are in agreement with the empirically fitting curves obtained from the covariance method. Such a ML-QST with direct parameter estimations illustrates a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, advanced gravitational wave detectors, to macroscopic quantum state generation.
Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait ...sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios.
During online social networks (OSNs), popularity prediction uncovers the final size of online content based on the observed cascade, which has been the critical technology for online recommendation, ...viral marketing, and rumor detection. Recently, representation learning could help to infer the mapping between the dynamic cascade and the final popularity efficiently, and has been a new research paradigm for popularity prediction. However, those methods are vulnerable to structure disturbance when lack of fine-grained supervision, as only the dynamic cascade is used. Therefore, we propose a novel trend and cascade based spatiotemporal evolution network (TCSE-Net), which preserves the distinguishable structure pattern while eliminating potential noise, via aligning and fusing the temporal popularity and cascade. To be specific, we first leveraged the Long-Short Term Memory (LSTM) and recurrent graph convolutional network (GCN) to learn the trend representation and the corresponding cascade representation respectively. Meanwhile, we represent node with it’s layer, thereby the hierarchy is preserved in cascade representation through GCN. Then, both trend and cascade representations are aligned in time sequence and selectively assembled by a set of shared parameters for popularity prediction. The extensive experimental results show that our TCSE-Net outperforms state-of-the-art baselines on two real datasets. Related code will be publicly available on
https://github.com/TAN-OpenLab/TCSE-Net
.
Recently rumors have been rapidly propagated while the Internet has been extensively developed. Research shows that highly credible comments with a distinct stance have worthy information. In this ...paper, we attempt to combine user credibility and user stance to capture worthy comments during the information-dissemination process to detect rumors. We propose a User Stance Bi-Directional Graph Attention Networks (USBGAT) model to extract accurate information for rumor detection based on high credibility users with strong stance, and diminish ineffectively neutral comments. Specifically, we take user features and user stance as a component of the node features, with multiviews features of tweets content engaged. Then, we use bidirectional graph attention networks (GAT) to capture the high-level representation of the rumor. Furthermore, we reweight the node features according to users' stances. Extensive experiments on two datasets: Pheme and Weibo show that our model is superior to the state-of-the-art models, especially in the early rumor detection. Our code and data are available at https://github.com/TAN-OpenLab/USB-GAT
In order to leverage the full power of quantum noise squeezing with unavoidable decoherence, a complete understanding of the degradation in the purity of squeezed light is demanded. By implementing ...machine-learning architecture with a convolutional neural network, we illustrate a fast, robust, and precise quantum state tomography for continuous variables, through the experimentally measured data generated from the balanced homodyne detectors. Compared with the maximum likelihood estimation method, which suffers from time-consuming and overfitting problems, a well-trained machine fed with squeezed vacuum and squeezed thermal states can complete the task of reconstruction of the density matrix in less than one second. Moreover, the resulting fidelity remains as high as 0.99 even when the antisqueezing level is higher than 20 dB. Compared with the phase noise and loss mechanisms coupled from the environment and surrounding vacuum, experimentally, the degradation information is unveiled with machine learning for low and high noisy scenarios, i.e., with the antisqueezing levels at 12 dB and 18 dB, respectively. Our neural network enhanced quantum state tomography provides the metrics to give physical descriptions of every feature observed in the quantum state with a single scan measurement just by varying the local oscillator phase from 0 to 2π and paves a way of exploring large-scale quantum systems in real time.
In electronic commerce, the supply of peculiar commodity is not adequate to users' requirements. Many users are inclined to use malicious software to order scarce commodities instead of legal ...purchasing processes. To solve this problem, designers of E-commerce websites use CAPTCHA to distinguish if the purchase request is applied by human rather than software. It does not work because malicious software (malware) can identify various CAPTCHA by specific function. So websites attempt to use more complex CAPTCHA to resist malware, however, users cannot identify it either. As a result, using CAPTCHA is not a perfect method to deal with distinguishing problems. In this paper, we propose a novel dynamic authentication CAPTCHA to enhance security and overcome limitations existing in static scheme. Our system can distinguish human from software by the negotiation between host and mobile terminal. The security analysis shows that the method we proposed can resist known types of attacks efficiently.
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