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Highly uniform and tight adhering of Fe3O4 particles on carbon fiber film (Fe3O4/CFF) is achieved through a simple in-situ thermal oxidation method. Particularly, 3D CFF with interconnected structure can shorten transfer path and buffer the volume expansion during charge-discharge cycling. Herein, the obtained Fe3O4/CFF anode exhibits a stable cycling performance and excellent high rate capability. The cell delivers a reversible capacity of 1 711 mAh·g-1 at a current density of 100 mA·g-1 after 100 cycles. Even at a high rate density of 2 A·g-1, the specific capacity also can maintain 1 034 mAh·g-1 after 100 cycles. The simplified fabrication is featured with low-cost and this binder-free perspective holds great potential in mass-production of high-performance metal oxide electrochemical devices. [ABSTRACT FROM AUTHOR]

The fourth international conference on Web information systems and applications (WISA 2007) has received 409 submissions and has accepted 37 papers for publication in this issue. The papers cover broad research areas, including Web mining and data warehouse, Deep Web and Web integration, P2P networks, text processing and information retrieval, as well as Web Services and Web infrastructure. After briefly introducing the WISA conference, the survey outlines the current activities and future trends concerning Web information systems and applications based on the papers accepted for publication. [ABSTRACT FROM AUTHOR]

WISA 2006 has received 581 submissions and has accepted 65 papers for publication of this issue. These papers are involved in 8 research areas, including Web Information Mining and Retrieval, Semantic Web and Intelligent Web, Web Data Management and Information Integration, Web Application Framework and Architecture, Web Information Security, Web Services and Workflow Models, Text Processing and Decision Support, and Grid and Networking Technology. This paper gives an introduction to previous WISA conferences and a survey on the papers to be published in this issue. [ABSTRACT FROM AUTHOR]

In recent years, self-supervised learning has achieved great success in areas such as computer vision and natural language processing because it can mine supervised signals from unlabeled data and reduce the reliance on manual labels. However, the currently generated self-supervised signals are either neighbor discrimination or self-discrimination, and there is no model to integrate neighbor discrimination and self-discrimination. Based on this, this paper proposes Fu-Rec that integrates neighbor-discrimination contrastive learning and self-discrimination contrastive learning, which consists of three modules: (1) neighbor-discrimination contrastive learning, (2) self-discrimination contrastive learning, and (3) recommendation module. The neighbor-discrimination contrastive learning and self-discrimination contrastive learning tasks are used as auxiliary tasks to assist the recommendation task. The Fu-Rec model effectively utilizes the respective advantages of neighbor-discrimination and self-discrimination to consider the information of the user's neighbors as well as the user and the item itself for the recommendation, which results in better performance of the recommendation module. Experimental results on several public datasets demonstrate the effectiveness of the Fu-Rec proposed in this paper. [ABSTRACT FROM AUTHOR]

The Product Sensitive Online Dirichlet Allocation model (PSOLDA) proposed in this paper mainly uses the sentiment polarity of topic words in the review text to improve the accuracy of topic evolution. First, we use Latent Dirichlet Allocation (LDA) to obtain the distribution of topic words in the current time window. Second, the word2vec word vector is used as auxiliary information to determine the sentiment polarity and obtain the sentiment polarity distribution of the current topic. Finally, the sentiment polarity changes of the topics in the previous and next time window are mapped to the sentiment factors, and the distribution of topic words in the next time window is controlled through them. The experimental results show that the PSOLDA model decreases the probability distribution by 0.160 1, while Online Twitter LDA only increases by 0.069 9. The topic evolution method that integrates the sentimental information of topic words proposed in this paper is better than the traditional model. [ABSTRACT FROM AUTHOR]

In this paper, we first give the definition of the Euclidean sums of linear codes, and prove that the Euclidean sums of linear codes are Euclidean dual-containing. Then we construct two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of the Reed-Solomon codes, and two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of linear codes generated by Vandermonde matrices over finite fields. Moreover, these optimal asymmetric quantum errorcorrecting codes constructed in this paper are different from the ones in the literature. [ABSTRACT FROM AUTHOR]

In recent years, numerical weather forecasting has been increasingly emphasized. Variational data assimilation furnishes precise initial values for numerical forecasting models, constituting an inherently nonlinear optimization challenge. The enormity of the dataset under consideration gives rise to substantial computational burdens, complex modeling, and high hardware requirements. This paper employs the Dual-Population Particle Swarm Optimization (DPSO) algorithm in variational data assimilation to enhance assimilation accuracy. By harnessing parallel computing principles, the paper introduces the Parallel Dual-Population Particle Swarm Optimization (PDPSO) Algorithm to reduce the algorithm processing time. Simulations were carried out using partial differential equations, and comparisons in terms of time and accuracy were made against DPSO, the Dynamic Weight Particle Swarm Algorithm (PSOCIWAC), and the Time-Varying Double Compression Factor Particle Swarm Algorithm (PSOTVCF). Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO. Regarding processing time, PDPSO is 40% faster than PSOCIWAC and PSOTVCF and 70% faster than DPSO. [ABSTRACT FROM AUTHOR]

As a product of the combination of information and energy technology, the energy internet is enormous and complex, and the absence of security safeguards at any aspect of it can cause incalculable damage. Aiming at the problems that Energy Internet terminals are difficult to be trusted and the integrity of massive terminals cannot be guaranteed, this paper designs and implements a set of comprehensive credibility measures and security protection schemes, isolates various malicious operations, and ensures that the system is always in a credible state. Specifically, we develop a secure TF card control program in the Android terminal application layer to realize the overall security scheme. Experimental results prove that this security protection scheme can effectively detect illegal application packages in energy Internet terminal devices, resist malicious programs, and protect key data from theft at the same time, achieving security protection in the "thing-to-thing" interconnection scenario of the Energy Internet. [ABSTRACT FROM AUTHOR]

This paper presents a design scheme of wire-line telephone system using self-interference (SI) cancellation technology in co-frequency co-time full-duplex (CCFD) system to realize absolute secure communication at the physical layer. This scheme can hide the target signal by skillfully releasing the high-power artificial noise to the whole link at the receiving node, and then make use of the receiver's knowledge of the SI signal to achieve high dB SI cancellation with the help of analog domain SI cancellation technology in CCFD domain, so that the signal-to-noise ratio (SNR) received by the eavesdropper at any position of the link is far lower than that of the legitimate receiver, so as to realize the absolutely secure communication in the sense of Wyner principle. This paper not only puts forward the specific design scheme of absolutely secure communication telephone, but also analyzes the calculation of security capacity under different eavesdropping positions, different SI cancellation capability and different system parameters according to Shannon theory. [ABSTRACT FROM AUTHOR]

The program construction process is based on rigorous mathematical reasoning, which leads to a fully correct algorithmic program via step-by-step refinement of the program specifications. The existing program construction methods' refinement process is partly based on individual subjective speculation and analysis, which lacks a precise guidance method. Meanwhile, efficiency factors have usually been ignored in the construction process, and most of the constructed abstract programs cannot be run directly by machines. In order to solve these problems, a novel program construction method for the sequence statistical class algorithms based on bidirectional scan induction is proposed in this paper. The method takes into account the efficiency factor and thus improves the Morgan's refinement calculus. Furthermore, this paper validates the method's feasibility using an efficiency-sensitive sequential statistics class algorithm as a program construction example. The method proposed in this paper realizes the correctness construction process from program specifications to efficient executable programs. [ABSTRACT FROM AUTHOR]

The pre-earthquake ionospheric anomalies in Wenchuan, China (21°-41°N, 93°-113°E) are studied and analyzed using the sum‐ mer nighttime data from 2005 to 2008 measured by DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions) satellite detectors ICE (Internet Communications Engine), IAP (In Application Programming), and ISL (Interior Switching Link). In this paper, we take the 12 May 2008 Wenchuan earthquake as an example, use the spatial gridding method to construct the background field over the epicenter, analyze the background characteristics of very low frequency (VLF) electric field components, low-energy particle parameters, and plasma parameters, and define the perturbation intensity index of each parameter before the earthquake to extract each pa‐ rameter anomaly in both space and time dimensions. The results show that the background values of some ionospheric parameters in the Wenchuan area are related to spatial distribution. Moreover, anomalous enhancement of low-frequency electric field power spectral density, H+ concentration, He+ concentration and ion concentration with different intensities and anomalous weakening of ion temperature were extracted in the fifteen days before the Wenchuan earthquake. After filtering the data to exclude external interference, such as solar ac‐ tivity, this paper concludes that there is some connection between these anomalies and the Wenchuan earthquake. [ABSTRACT FROM AUTHOR]

The medical monitoring system is widely used. In the medical monitoring system, each user only possesses one piece of data logging that participates in statistical computing. Specifically in such a situation, a feasible solution is to scatter its statistical computing workload to corresponding statistical nodes. Moreover, there are still two problems that should be resolved. One is how the server takes advantage of intermediate results obtained through statistical node aggregation to perform statistical computing. Statistical variable decomposition technique points out the direction for statistical projects. The other problem is how to design an efficient topological structure for statistical computing. In this paper, tree topology was adopted to implement data aggregation to improve aggregation efficiency. And two experiments were done for time consumption of statistical computing which focuses on encrypted data aggregation and encrypted data computing. The first experiment indicates that encrypted data aggregation efficiency of the scheme proposed in this paper is better than that of Drosatos' scheme, and the second indicates that improving computing power of the server or computational efficiency of the functional encryption scheme can shorten the computation time. [ABSTRACT FROM AUTHOR]

Finite-time generalized synchronization and parameter identification of chaotic systems with different dimensions are studied in this paper. Based on finite-time stability theory, adaptive controllers and parameter identification rules are proposed to realize generalized synchronization of chaotic systems and identify accurately all their unknown parameters in finite time. At the same time, the obtained synchronization time formula can estimate the required longest time for realizing the generalized synchronization of chaotic systems, which solely depends on the initial values of chaotic systems and is easily calculated in numerical simulation. At the last, some numerical examples are presented to illustrate the effectiveness and correctness of the obtained conclusions. [ABSTRACT FROM AUTHOR]

The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bambara-French Facebook comments. We develop four Long Short-term Memory (LSTM)-based models and two Convolutional Neural Network (CNN)-based models, and use these six models, Naïve Bayes, and Support Vector Machines (SVM) to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %. [ABSTRACT FROM AUTHOR]

In this paper, we present a distributed multi-level cache system based on cloud storage, which is aimed at the low access efficiency of small spatio-temporal data files in information service system of Smart City. Taking classification attribute of small spatio-temporal data files in Smart City as the basis of cache content selection, the cache system adopts different cache pool management strategies in different levels of cache. The results of experiment in prototype system indicate that multi-level cache in this paper effectively increases the access bandwidth of small spatio- temporal files in Smart City and greatly improves service quality of multiple concurrent access in system. [ABSTRACT FROM AUTHOR]

The Hankel transform is widely used to solve various engineering and physics problems, such as the representation of electromagnetic field components in the medium, the representation of dynamic stress intensity factors, vibration of axisymmetric infinite membrane and displacement intensity factors which all involve this type of integration. However, traditional numerical integration algorithms cannot be used due to the high oscillation characteristics of the Bessel function, so it is particularly important to propose a high precision and efficient numerical algorithm for calculating the integral of high oscillation. In this paper, the improved Gaver-Stehfest (G-S) inverse Laplace transform method for arbitrary real-order Bessel function integration is presented by using the asymptotic characteristics of the Bessel function and the accumulation of integration, and the optimized G-S coefficients are given. The effectiveness of the algorithm is verified by numerical examples. Compared with the linear transformation accelerated convergence algorithm, it shows that the G-S inverse Laplace transform method is suitable for arbitrary real order Hankel transform, and the time consumption is relatively stable and short, which provides a reliable calculation method for the study of electromagnetic mechanics, wave propagation, and fracture dynamics. [ABSTRACT FROM AUTHOR]

In this paper, two kinds of chaotic systems are controlled respectively with and without time-delay to eliminate their chaotic behaviors. First of all, according to the first-order approximation method and the stabilization condition of the linear system, one linear feedback controller is structured to control the chaotic system without time-delay, its chaotic behavior is eliminated and stabilized to its equilibrium. After that, based on the first-order approximation method, the Lyapunov stability theorem, and the matrix inequality theory, the other linear feedback controller is structured to control the chaotic system with time-delay and make it stabilized at its equilibrium. Finally, two numerical examples are given to illustrate the correctness and effectiveness of the two linear feedback controllers. [ABSTRACT FROM AUTHOR]

This paper focuses on Gauss principle of least compulsion for relative motion dynamics and derives differential equations of motion from it. Firstly, starting from the dynamic equation of the relative motion of particles, we give the Gauss principle of relative motion dynamics. By constructing a compulsion function of relative motion, we prove that at any instant, its real motion minimizes the compulsion function under Gaussian variation, compared with the possible motions with the same configuration and velocity but different accelerations. Secondly, the formula of acceleration energy and the formula of compulsion function for relative motion are derived because the carried body is rigid and moving in a plane. Thirdly, the Gauss principle we obtained is expressed as Appell, Lagrange, and Nielsen forms in generalized coordinates. Utilizing Gauss principle, the dynamical equations of relative motion are established. Finally, two relative motion examples also verify the results' correctness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel shear gradient operator by combining the shear and gradient operators. The shear gradient operator performs well to capture diverse directional information in the image gradient domain. Based on the shear gradient operator, we extend the total variation (TV) norm to the shear total variation (STV) norm by adding two shear gradient terms. Subsequently, we introduce a shear total variation deblurring model. Experimental results are provided to validate the ability of the STV norm to capture the detailed information. Leveraging the Block Circulant with Circulant Blocks (BCCB) structure of the shear gradient matrices, the alternating direction method of multipliers (ADMM) algorithm can be used to solve the proposed model efficiently. Numerous experiments are presented to verify the performance of our algorithm for non-blind image deblurring. [ABSTRACT FROM AUTHOR]

In the era of intelligent economy, the click-through rate (CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors (point-level), while ignoring the relationships between elements in item embedding vectors (union-level). The Lambda layer-based Convolutional Sequence Embedding (LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC (Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we consider the eigenvalue problem of the singular differential equation... ...And we have made a detailed characterization of the weak solution space. Furthermore, the existence of the minimum eigenvalue and the fundamental gap are provided. [ABSTRACT FROM AUTHOR]

This paper is concerned with radially positive solutions of the k-Hessian equation involving a Matukuma-type source... [ABSTRACT FROM AUTHOR]

Image semantic segmentation is an essential technique for studying human behavior through image data. This paper proposes an image semantic segmentation method for human behavior research. Firstly, an end-to-end convolutional neural network architecture is proposed, which consists of a depth-separable jump-connected fully convolutional network and a conditional random field network; then jump-connected convolution is used to classify each pixel in the image, and an image semantic segmentation method based on convolutional neural network is proposed; and then a conditional random field network is used to improve the effect of image segmentation of human behavior and a linear modeling and nonlinear modeling method based on the semantic segmentation of conditional random field image is proposed. Finally, using the proposed image segmentation network, the input entrepreneurial image data is semantically segmented to obtain the contour features of the person; and the segmentation of the images in the medical field. The experimental results show that the image semantic segmentation method is effective. It is a new way to use image data to study human behavior and can be extended to other research areas. [ABSTRACT FROM AUTHOR]

In order to improve the performance and service life of the Leningrader seal of the Stirling engine piston rod, interference, preload and friction coefficient were taken as influencing factors, and the curved surface response method was adopted to reduce the contact stress of sealing surface and von Mises stress of the sealing sleeve as the response index, with the optimization goal of reducing wear and extending life. The above three key parameters are analyzed and optimized, the influence of each parameter on the sealing performance and service life is obtained, and the best combination scheme of the three is determined. The results show that the interaction between pretightening force and interference fit has the greatest impact on contact stress. The interaction between interference fit and friction coefficient has the most significant effect on von Mises stress. The optimized parameters can reduce the maximum contact stress and maximum von Mises stress of the sealing sleeve by 26.3% and 20.6%, respectively, under a media pressure of 5-9 MPa. Test bench verification shows that the leakage of the optimized sealing device in 12 h is reduced by 0.44 cc·min-1 (1 cc=1 cm³). The wear rate of the sealing sleeve is 1.08% before optimization and 0.45% after optimization, indicating that the optimized parameters in this paper are effective. [ABSTRACT FROM AUTHOR]

Cracks, potholes, and other defects often occur on infrastructure such as bridges, among which cracks are one of the most frequent defects. They have diverse shapes and are difficult to detect. Traditional manual inspection methods are inefficient and have low accuracy, while automated inspection machines are bulky and inconvenient to carry and use. Based on the shortcomings of existing detection technologies, this paper proposes a portable structural surface crack detection system based on the Android platform using a portable handheld image acquisition device. The system captures cracks on the structure's surface and obtains high-definition crack images. Then, these images are transmitted to portable smartphone terminals through Wi-Fi. Next, the image is pre-processed using weighted averaging, grayscale linear transformation, and adaptive median filtering. Then, the improved Canny edge detection algorithm is applied to identify crack information, and the edge segmentation algorithm is used to determine the crack width. Finally, based on camera calibration, the pixels are converted into the length data required for actual measurement. The results show that the system is easy to operate, and it not only has crack storage and tracking functions, but also can effectively measure the crack width on the surface of components. The measurement accuracy of this system reaches the sub-pixel level, and in actual testing, compared with the crack width gauge, the maximum relative error does not exceed 6.25%. [ABSTRACT FROM AUTHOR]

Let A be an arbitrary square matrix, then equation AXA =XAX with unknown X is called Yang-Baxter matrix equation. It is difficult to find all the solutions of this equation for any given A. In this paper, the relations between the matrices A and B are established via solving the associated rank optimization problem, where B =AXA = XAX, and some analytical formulas are derived, which may be useful for finding all the solutions and analyzing the structures of the solutions of the above Yang-Baxter matrix equation. [ABSTRACT FROM AUTHOR]

Shor in 1994 proposed a quantum polynomial-time algorithm for finding the order r of an element a in the multiplicative group Zn*, which can be used to factor the integer n by computing gcd(ar/2 ± 1,n), and hence break the famous RSA cryptosystem. However, the order r must be even. This restriction can be removed. So in this paper, we propose a quantum polynomial-time fixed-point attack for directly recovering the RSA plaintext M from the ciphertext C, without explicitly factoring the modulus n. Compared to Shor's algorithm, the order r of the fixed-point C for RSA(e, n) satisfying Cer=C(modn) does not need to be even. Moreover, the success probability of the new algorithm is at least 4φ(r)/π²r and higher than that of Shor's algorithm, though the time complexity for both algorithms is about the same. [ABSTRACT FROM AUTHOR]

The Walsh transform is an important tool to investigate cryptographic properties of Boolean functions. This paper is devoted to study the Walsh transform of a class of Boolean functions defined as g(x) = f(x)Tr1n (x) + h(x)Tr1n (δx), by making use of the known conclusions of Walsh transform and the properties of trace function, and the conclusion is obtained by generalizing an existing result. [ABSTRACT FROM AUTHOR]

Although many classical IP geolocation algorithms are suitable to rich-connected networks, their performances are seriously affected in poor-connected networks with weak delay-distance correlation. This paper tries to improve the performances of classical IP geolocation algorithms by finding rich-connected sub-networks inside poor-connected networks. First, a new delay-distance correlation model (RTD-Corr model) is proposed. It builds the relationship between delay-distance correlation and actual network factors such as the tortuosity of the network path and the ratio of propagation delay. Second, based on the RTD-Corr model and actual network characteristics, this paper discusses about how to find rich-connected networks inside China Internet which is a typical actual poor-connected network. Then we find rich-connected sub-networks of China Internet through a large-scale network measurement which covers three major ISPs and thirty provinces. At last, based on the founded rich-connected sub-networks, we modify two classical IP geolocation algorithms and the experiments in China Internet show that their accuracy is significantly increased. [ABSTRACT FROM AUTHOR]

Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra (Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate. [ABSTRACT FROM AUTHOR]

Block chain is widely used in the financial field for its characteristics of decentralization, anonymity and trust. Electronic money payment is an important application hotspot. Ring signature is widely used in strong anonymous authentication such as electronic cash and electronic voting because of its unconditional anonymity, spontaneity and flexible group structure. Among them, the correlation ring signature can prove whether two signatures are issued by the same person without revealing the identity of the real signer. Therefore, the signature right breach can be avoided based on the premise of guaranteeing anonymity, such as repeated voting, electronic money repetition cost and so on. Most of the existing correlation ring signature security is based on the discrete logarithm problem, and most of the schemes result in the degradation of anonymity because of strong association. These methods do not apply to the block chain electronic currency transaction scene with strong anonymity. Therefore, this paper first proposes a blockchain-based electronic currency transaction security model. The model ensures not only the anonymity of both parties but also a certain degree of traceability. In order to support these two characteristics, this paper proposes an association ring signature method based on large integer decomposition problem. This method has strong anonymity and can be applied to blockchain scenes. On the other hand, it can be converted into group signatures in specific scenarios. It is therefore traceable. Finally, the adaptive selection message of the scheme and the unforgeability under the selection of public key attack are proved under the random oracle model. [ABSTRACT FROM AUTHOR]

With the rapid development of software technology, software vulnerability has become a major threat to computer security. The timely detection and repair of potential vulnerabilities in software, are of great significance in reducing system crashes and maintaining system security and integrity. This paper focuses on detecting three common types of vulnerabilities: Unused_ Variable, Use_of_Uninitialized_Variable, and Use_After_ Free. We propose a method for software vulnerability detection based on an improved control flow graph (ICFG) and several predicates of vulnerability properties for each type of vulnerability. We also define a set of grammar rules for analyzing and deriving the three mentioned types of vulnerabilities, and design three vulnerability detection algorithms to guide the process of vulnerability detection. In addition, we conduct cases studies of the three mentioned types of vulnerabilities with real vulnerability program segments from Common Weakness Enumeration (CWE). The results of the studies show that the proposed method can detect the vulnerability in the tested program segments. Finally, we conduct manual analysis and experiments on detecting the three types of vulnerability program segments (30 examples for each type) from CWE, to compare the vulnerability detection effectiveness of the proposed method with that of the existing detection tool CppCheck. The results show that the proposed method performs better. In summary, the method proposed in this paper has certain feasibility and effectiveness in detecting the three mentioned types of vulnerabilities, and it will also have guiding significance for the detection of other common vulnerabilities. [ABSTRACT FROM AUTHOR]

Key-recovery technology is often used by an adversary to attempt to recover the cryptographic key of an encryption scheme. The most obvious key-recovery attack is the exhaustive key-search attack. But modern ciphers often have a key space of size 2128 or greater, making such attacks infeasible with current technology. Cache-based side channel attack is another way to get the cryptographic key of an encryption scheme, but there are random noises in side channel attack. In order to reduce random errors, it is advisable to repeat the key recovery process many times. This paper is focused on the way to improve the key recovery accuracy by dealing with the key sequences obtained from the repeated Cache-based side channel attacks. To get the real key, private key bits from side channel attack are collected firstly. And then the key sequences are aligned using sequence alignment algorithms based on dynamic programming. The provided method of key recovery is universal, which is not limited to any cryptographic algorithm. The experiment shows that the proposed method has a good performance and a high availability when the error rate of the collected key bit is within a reasonable range. [ABSTRACT FROM AUTHOR]

The classical definition of Steiner symmetrizations of functions are defined according to the Steiner symmetrizations of function level sets and the layered representation of functions. In this paper, the definition is not only transformed into Steiner symmetrizations of one-dimensional parabolic functions, but also depends on the Steiner symmetrizations of the level sets of log-concave functions. To this end we prove Steiner's inequality on layering functions in the space of log-concave functions. [ABSTRACT FROM AUTHOR]

This paper considers the one- and two-dimensional risk models with a non-stationary claim-number process. Under the assumption that the claim-number process satisfies the large deviations principle, the uniform asymptotics for the finite-time ruin probability of a one-dimensional risk model are obtained for the strongly subexponential claim sizes. Further, as an application of the result of onedimensional risk model, we derive the uniform asymptotics for a kind of finite-time ruin probability in a two dimensional risk model sharing a common claim-number process which satisfies the large deviations principle. [ABSTRACT FROM AUTHOR]

Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification. Although this technique is widely used in multi-label classification problems, batch learning deals with most issues, which consumes a lot of time and space resources. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets. However, existing online learning research has done little to consider correlations between labels. On the basis of existing research, this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples. We evaluate the performance of the proposed algorithm on several public datasets. Experiments show the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we construct three classes of Clifford subsystem maximum distance separable (MDS) codes based on Reed-Solomon codes and extended generalized Reed-Solomon codes over finite fields Fq for specific code lengths. Moreover, our Clifford subsystem MDS codes are new because their parameters differ from the previously known ones. [ABSTRACT FROM AUTHOR]

In this paper, a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed. The algorithm employs a kernel function with a linear growth term to derive the search direction, and by introducing new technical results and selecting suitable parameters, we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods. Furthermore, numerical results illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, {zn}n=1∞ acts as an interpolating sequence for Qp⋂H∞. An analytic function f is constructed, and f(zn)=∑jλjfzj(zn)=λn,n=1,2,⋯ for any {λn}∈l∞, where f and fzj belong to Qp⋂H∞. As a result, the study achieves a comparable outcome for Fp,p-2,s⋂H∞. [ABSTRACT FROM AUTHOR]

Federated learning is an emerging distributed privacypreserving framework in which parties are trained collaboratively by sharing model or gradient updates instead of sharing private data. However, the heterogeneity of local data distribution poses a significant challenge. This paper focuses on the label distribution skew, where each party can only access a partial set of the whole class set. It makes global updates drift while aggregating these biased local models. In addition, many studies have shown that deep leakage from gradients endangers the reliability of federated learning. To address these challenges, this paper propose a new personalized federated learning method named MpFedcon. It addresses the data heterogeneity problem and privacy leakage problem from global and local perspectives. Our extensive experimental results demonstrate that MpFedcon yields effective resists on the label leakage problem and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous parties. [ABSTRACT FROM AUTHOR]

In this paper, we obtained complete qth-moment convergence of the moving average processes, which is generated by m-WOD moving random variables. The results in this article improve and extend the results of the moving average process. m-WOD random variables include WOD, m-NA, m-NOD and m-END random variables, so the results in the paper also promote the corresponding ones in WOD, m-NA, m-NOD, m-END random variables. [ABSTRACT FROM AUTHOR]