In this letter, we present an adaptive weighted transfer subspace learning (AWTSL) method for cross-database speech emotion recognition (SER), which can efficiently eliminate the discrepancy between source and target databases. Specifically, on one hand, a subspace projection matrix is first learned to project the cross-database features into a common subspace. At the same time, each target sample can be represented by the source samples by using a sparse reconstruction matrix. On the other hand, we design an adaptive weighted matrix learning strategy, which can improve the reconstruction contribution of important features and eliminate the negative influence of redundant features. Finally, we conduct extensive experiments on four benchmark databases, and the experimental results demonstrate the efficacy of the proposed method.

Generative Adversarial Networks (GANs) are one of the most successful learning principles of generative models and were wildly applied to many generation tasks. In the beginning, the gradient penalty (GP) was applied to enforce the discriminator in GANs to satisfy Lipschitz continuity in Wasserstein GAN. Although the vanilla version of the gradient penalty was further modified for different purposes, seeking a better equilibrium and higher generation quality in adversarial learning remains challenging. Recently, DRAGAN was proposed to achieve the local linearity in a surrounding data manifold by applying the noised gradient penalty to promote the local convexity in model optimization. However, we show that their approach will impose a burden on satisfying Lipschitz continuity for the discriminator. Such conflict between Lipschitz continuity and local linearity in DRAGAN will result in poor equilibrium, and thus the generation quality is far from ideal. To this end, we propose a novel approach to benefit both local linearity and Lipschitz continuity for reaching a better equilibrium without conflict. In detail, we apply our synchronized activation function in the discriminator to receive a particular form of noised gradient penalty for achieving local linearity without losing the property of Lipschitz continuity in the discriminator. Experimental results show that our method can reach the superior quality of images and outperforms WGAN-GP, DiracGAN, and DRAGAN in terms of Inception Score and Fréchet Inception Distance on real-world datasets.

In the online removable knapsack problem, a sequence of items, each labeled with its value and its size, is given one by one. At each arrival of an item, a player has to decide whether to put it into a knapsack or to discard it. The player is also allowed to discard some of the items that are already in the knapsack. The objective is to maximize the total value of the knapsack. Iwama and Taketomi gave an optimal algorithm for the case where the value of each item is equal to its size. In this paper we consider a case with an additional constraint that the capacity of the knapsack is a positive integer N and that the sizes of items are all integral. For each positive integer N, we design an algorithm and prove its optimality. It is revealed that the competitive ratio is not monotonic with respect to N.

The performance of conventional direction of arrival (DOA) methods is susceptible to the uncertainty of acoustic velocity in the underwater environment. To solve this problem, an underwater DOA estimation method with L-shaped array for wide-band signals under unknown acoustic velocity is proposed in this paper. The proposed method refers to the idea of incoherent signal subspace method and Root-MUSIC to obtain two sets of average roots corresponding to the subarray of the L-shaped array. And the geometric relationship between two vertical linear arrays is employed to derive the expression of DOA estimation with respect to the two average roots. The acoustic velocity variable in the DOA estimation expression can be eliminated in the proposed method. The simulation results demonstrate that the proposed method is more accurate and robust than other methods in an unknown acoustic velocity environment.

In this paper, we address the problem of finding a representation of a subtree distance, which is an extension of a tree metric. We show that a minimal representation is uniquely determined by a given subtree distance, and give an O(n2) time algorithm that finds such a representation, where n is the size of the ground set. Since a lower bound of the problem is Ω(n2), our algorithm achieves the optimal time complexity.

This paper investigates the joint problem of user association and spectrum allocation in satellite-terrestrial integrated networks (STINs), where a low earth orbit (LEO) satellite access network cooperating with terrestrial networks constitutes a heterogeneous network, which is beneficial in terms of both providing seamless coverage as well as improving the backhaul capacity for the dense network scenario. However, the orbital movement of satellites results in the dynamic change of accessible satellites and the backhaul capacities. Moreover, spectrum sharing may be faced with severe co-channel interferences (CCIs) caused by overlapping coverage of multiple access points (APs). This paper aims to maximize the total sum rate considering the influences of the dynamic feature of STIN, backhaul capacity limitation and interference management. The optimization problem is then decomposed into two subproblems: resource allocation for terrestrial communications and satellite communications, which are both solved by matching algorithms. Finally, simulation results show the effectiveness of our proposed scheme in terms of STIN's sum rate and spectrum efficiency.

This letter studies a biobjective optimization problem in binary associative memories characterized by ternary connection parameters. First, we introduce a condition of parameters that guarantees storage of any desired memories and suppression of oscillatory behavior. Second, we define a biobjective problem based on two objectives that evaluate uniform stability of desired memories and sparsity of connection parameters. Performing precise numerical analysis for typical examples, we have clarified existence of a trade-off between the two objectives.

Integral cryptanalysis is one of the most powerful attacks on symmetric key block ciphers. Attackers preliminarily search integral characteristics of a target cipher and use them to perform the key recovery attack. Todo proposed a novel technique named the bit-based division property to find integral characteristics. Xiang et al. extended the Mixed Integer Linear Programming (MILP) method to search integral characteristics of lightweight block ciphers based on the bit-based division property. In this paper, we apply these techniques to the symmetric key block cipher KASUMI which was developed by modifying MISTY1. As a result, we found new 4.5-round characteristics of KASUMI for the first time. We show that 7-round KASUMI is attackable with 263 data and 2120 encryptions.

Although end-to-end based speech recognition research for Mandarin-English code-switching has attracted increasing interests, it remains challenging due to data scarcity. Meta-learning approach is popular with low-resource modeling using high-resource data, but it does not make full use of low-resource code-switching data. Therefore we propose a two-fold cross-validation training framework combined with meta-learning approach. Experiments on the SEAME corpus demonstrate the effects of our method.

The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.

For the increasing demands of computation, heterogeneous multicore architecture is believed to be a promising solution to fulfill the edge computational requirement. In FPGAs, the heterogeneous multicore is realized as multiple soft processor cores with custom processing elements. Since FPGA is a resource-constrained device, sharing the hardware resources among the soft processor cores can be advantageous. A few research works have focused on the resource sharing between soft processors, but they do not study how much FPGA logic is minimized for a different pipeline processor. This paper proposes the microarchitecture of four, and five stage pipeline processors that enables the sharing of functional units for execution among the multiple cores as well as sharing the BRAM ports. We then investigate the performance and hardware resource utilization for a four-core processor. We find that sharing different functional units can save the LUT usage to 31.7% and DSP usage to 75%. We analyze the performance impact of sharing from the simulation of the Embench benchmark program. Our simulation results indicate that for some cases the sharing improves the performance and for other configurations worst-case performance drop is 16.7%.

AONT (All-or-Nothing Transform) is a kind of (n, n)-threshold secret sharing scheme that distributes a message m into a set of n shares such that the message m can be reconstructed if and only if n shares are collected. At CRYPTO 2000, Desai proposed a simple and faster AONT based on the CTR mode of encryption (called CTRT) and proved its security in the ideal cipher model. Though AES-128, whose key length k = 128 and block length l = 128, can be used in CTRT as a block cipher, AES-256 and AES-192 cannot be used due to its intrinsic restriction of k ≤ l. In this paper, we propose an extended CTRT (for short, XCTRT) suitable for AES-256. By thoroughly evaluating all the tricky cases, we prove that XCTRT is secure in the ideal cipher model under the same CTRT security definition. Also, we discuss the security result of XCTRT in concrete parameter settings. For more flexibility of key length, we propose a variant of XCTRT dealing with l

The differential uniformity, the boomerang uniformity, and the extended Walsh spectrum etc are important parameters to evaluate the security of S (substitution)-box. In this paper, we introduce efficient formulas to compute these cryptographic parameters of permutation polynomials of the form xrh(x(2n-1)/d) over a finite field of q=2n elements, where r is a positive integer and d is a positive divisor of 2n-1. The computational cost of those formulas is proportional to d. We investigate differentially 4-uniform permutation polynomials of the form xrh(x(2n-1)/3) and compute the boomerang spectrum and the extended Walsh spectrum of them using the suggested formulas when 6≤n≤12 is even, where d=3 is the smallest nontrivial d for even n. We also investigate the differential uniformity of some permutation polynomials introduced in some recent papers for the case d=2n/2+1.

To solve the problem of metamerism in the color reproduction process, various spectral reflectance reconstruction methods combined with neural network have been proposed in recent years. However, these methods are generally sensitive to initial values and can easily converge to local optimal solutions, especially on small data sets. In this paper, we propose a spectral reflectance reconstruction algorithm based on the Back Propagation Neural Network (BPNN) and an improved Sparrow Search Algorithm (SSA). In this algorithm, to solve the problem that BPNN is sensitive to initial values, we propose to use SSA to initialize BPNN, and we use the sine chaotic mapping to further improve the stability of the algorithm. In the experiment, we tested the proposed algorithm on the X-Rite ColorChecker Classic Mini Chart which contains 24 colors, the results show that the proposed algorithm has significantly better performance compared to other algorithms and moreover it can meet the needs of spectral reflectance reconstruction on small data sets. Code is avaible at https://github.com/LuraZhang/spectral-reflectance-reconsctuction.

Noninvasive recognition is an important trend in diabetes recognition. Unfortunately, the accuracy obtained from the conventional noninvasive recognition methods is low. This paper proposes a novel Diabetes Noninvasive Recognition method via the plantar pressure image and improved Capsule Network (DNR-CapsNet). The input of the proposed method is a plantar pressure image, and the output is the recognition result: healthy or possibly diabetes. The ResNet18 is used as the backbone of the convolutional layers to convert pixel intensities to local features in the proposed DNR-CapsNet. Then, the PrimaryCaps layer, SecondaryCaps layer, and DiabetesCaps layer are developed to achieve the diabetes recognition. The semantic fusion and locality-constrained dynamic routing are also developed to further improve the recognition accuracy in our method. The experimental results indicate that the proposed method has a better performance on diabetes noninvasive recognition than the state-of-the-art methods.

A Mach-Zehnder optical modulator with the tunable multimode interference coupler was fabricated using Ti-diffused LiNbO3. The modulation extinction ratio could be voltage controlled to maximize up to 50 dB by tuning the coupler. Optical single-sideband modulation was also achieved with a sideband suppression ratio of more than 30 dB.

Feature selection based on particle swarm optimization is often employed for promoting the performance of artificial intelligence algorithms. However, its interpretability has been lacking of concrete research. Improving the stability of the feature selection method is a way to effectively improve its interpretability. A novel feature selection approach named Interpretable Particle Swarm Optimization is developed in this paper. It uses four data perturbation ways and three filter feature selection methods to obtain stable feature subsets, and adopts Fuch map to convert them to initial particles. Besides, it employs similarity mutation strategy, which applies Tanimoto distance to choose the nearest 1/3 individuals to the previous particles to implement mutation. Eleven representative algorithms and four typical datasets are taken to make a comprehensive comparison with our proposed approach. Accuracy, F1, precision and recall rate indicators are used as classification measures, and extension of Kuncheva indicator is employed as the stability measure. Experiments show that our method has a better interpretability than the compared evolutionary algorithms. Furthermore, the results of classification measures demonstrate that the proposed approach has an excellent comprehensive classification performance.

Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.

Currently, unmanned aerial vehicles (UAV) have been widely used in many applications, such as in transportation logistics, public safety, or even in non-terrestrial networks (NTN). In all these scenarios, it is an important issue to model channel behavior between the UAV and the user equipment (UE) on the ground. Among these channel features, a critical parameter that dominates channel behavior is the probability of the line-of-sight (LOS), since the statistical property of the channel fading can be either Ricean or Rayleigh, depending on the existence of LOS. Besides, with knowledge of LOS probability, operators can design approaches or schemes to maximum system performance, such as the serving coverage, received signal to noise ratio (SNR), or the bit error rate (BER) with the limited transmitted power. However, the LOS UAV channel is likely difficult to acquire or derive, as it depends on the deployment scenario, such as an urban or rural area. In this paper, we generated four different scenarios defined by the ITU via the ray tracing simulator. Then, we used the spatial geometric relation and the curve fitting approach to derive the analytic models to predict the probability of the UAV LOS channels for different scenarios. Results show that our proposed relationships yield better prediction results than the methods in the literature. Besides, an example of establishing UAV self-awareness ability for the deployed environment via using proposed models is also provided in this paper.

The final exponentiation affects the efficiency of pairing computations especially on pairing-friendly curves with high embedding degree. We propose an efficient method for computing the hard part of the final exponentiation on the KSS18 curve at the 192-bit security level. Implementations indicate that the computation of the final exponentiation is 8.74% faster than the previously fastest result.