Equal pay is an essential component of gender equality, one of the Sustainable Development Goals of the United Nations. Using resume data of over ten million Chinese online job seekers in 2015, we study the current gender pay gap in China. The results show that on average women only earned 71.57% of what men earned in China. The gender pay gap exists across all age groups and educational levels. Contrary to the commonly held view that developments in education, economy, and a more open culture would reduce the gender pay gap, the fusion analysis of resume data and socio-economic data presents that they have not helped reach the gender pay equality in China. China seems to be stuck in a place where traditional methods cannot make further progress. Our analysis further shows that 81.47% of the variance in the gender pay gap can be potentially attributed to discrimination. In particular, compared with the unmarried, both the gender pay gap itself and proportion potentially attributed to discrimination of the married are larger, indicating that married women suffer greater inequality and more discrimination than unmarried ones. Taken together, we suggest that more research attention should be paid to the effect of discrimination in understanding gender pay gap based on the family constraint theory. We also suggest the Chinese government to increase investment in family-supportive policies and grants in addition to female education.

The adversarial example presents new security threats to trustworthy detection systems. In the context of evading dynamic detection based on API call sequences, a practical approach involves inserting perturbing API calls to modify these sequences. The type of inserted API calls and their insertion locations are crucial for generating an effective adversarial API call sequence. Existing methods either optimize the inserted API calls while neglecting the insertion positions or treat these optimizations as separate processes. This can lead to inefficient attacks that insert a large number of unnecessary API calls. To address this issue, we propose a novel reinforcement learning (RL) framework, dubbed GAME-RL, which simultaneously optimizes both the perturbing APIs and their insertion positions. Specifically, we define malware modification through IAT (Import Address Table) hooking as a sequential decision-making process. We introduce an invalid action masking and an auto-regressive policy head within the RL framework, ensuring the feasibility of IAT hooking and capturing the inherent relationship between factors. GAME-RL learns more effective evasion strategies, taking into account functionality preservation and the black-box setting. We conduct comprehensive experiments on various target models, demonstrating that GAME-RL significantly improves the evasion rate while maintaining acceptable levels of adversarial overhead.

The relentless advancement of wireless mobile communication technology, evolving from 1G to 5G and now venturing into 6G, has prompted an exploration of massive random-access (MRA) techniques to accommodate the anticipated massive communication demands. This paper delves into the domain of MRA technology, garnering increasing attention in 6G Internet of Things (IoT) networks. Based on extensive prior research on MRA, this paper analyzes the impact of repetitions and post-encoding processing on MRA performance. Two scenarios are considered: the ideal post-encoding processing scenario with the maximum processing gain, where each repetition is treated as independent; and the identical post-encoding processing scenario with the most basic processing gain, where all repetitions undergo the same processing steps. Through theoretical derivations, performance bounds for MRA with joint decoding across all actual IoT devices and all repetitions are established for both scenarios. The special scenario concerning performance bounds, where the repetition number is 1, aligns with existing results. Numerical evaluations have confirmed the accuracy of the theoretical derivations and further demonstrated that, as the number of repetitions increases, the performance disparity between the ideal post-encoding processing scenario and the identical post-encoding processing scenario widens. The performance bounds of other post-encoding processing schemes lie between the ideal and the identical post-encoding processing. These findings emphasize optimizing post-encoding processing schemes to bring actual performance closer to the ideal post-encoding processing scenario.

In order to build a low-risk Fund of Funds (FOF), from the perspective of correlation, the principal component factor is used to improve the traditional risk parity model. Principal component analysis is used to decompose the underlying assets and generate unrelated principal component factors, and then the authors can construct a principal component risk parity portfolio. The proposed empirical results based on China’s mutual fund market show that the performance of principal component risk parity portfolio (PCRPP) is better than that of equal weight portfolio (EWP) and traditional risk parity portfolio (RPP). That is to say, not only the PCRPP in this paper has much lower risk than EWP and RPP, but also slightly better than EWP and RPP in terms of average return. Moreover, the study of dividing the underlying assets shows that the PCRPP in this paper is not sensitive to the underlying assets. The PCRPP in this paper is better than EWP and RPP for both the better performing funds and the worse performing funds. In addition, the empirical results on dynamic portfolio adjustments show that it is not appropriate to adjust asset allocation too frequently when the expected rate of return is calculated using the arithmetic mean.

Many existing fine-grained sentiment analysis (FGSA) methods have problems such as easy loss of fine-grained information, difficulty in solving polysemy and imbalanced sample categories. Therefore, a Transformer based FGSA method for Weibo comment text is proposed. Firstly, the RoBERTa model with knowledge augmentation was used to dynamically encode the text so as to solving the polysemy issue. Then, BiLSTM is used to effectively capture bidirectional global semantic dependency features. Next, Transformer is used to fuse multi-dimensional features and adaptively strengthen key features to overcome the problem of fine-grained information loss. Finally, an improved Focal Loss function is utilized for training to solve the issue of imbalanced sample categories. As demonstrated by the experimental outcomes on the SMP2020-EWECT, NLPCC 2013 Task 2, NLPCC 2014 Task 1, and weibo_senti_100k datasets, the suggested method outperforms the alternatives for advanced comparison methods.

In this study, a novel hybrid model for share price index futures forecasting named WT-ARIMALSTM is proposed. In this hybrid model, share price index futures are decomposed to extract data characteristics at different time scales by the wavelet transform and the ARIMA-LSTM model are applied to predict the close price of futures. The findings of the study are as follows. 1) The DWT hybrid model and the MODWT hybrid model have higher forecasting accuracy than some commonly used forecasting models under the three metrics of MAE, MAPE and RMSE. The DWTARIMA-LSTM model has better forecasting performance when the forecasting performance in different markets and the operational efficiency of the method are combined. 2) The DWT method is more applicable than the MODWT method in forecasting models of futures closing price series; the approximate signals obtained from the DWT decomposition have lower volatility and can better characterise the original signals. 3) The LSTM model has better prediction performance for noisy residual series, while the ARIMA model has better prediction performance for less noisy approximate signals. 4) Based on the forecasting results, a timing trading strategy is constructed that can maintain a robust return performance under different market conditions, especially on the risk side with significant advantages. In addition, this work examines the impact of the unexpected event of the COVID-19 epidemic on the forecasting performance of the model, and the results show that the model can adapt to different data structures to achieve more robust forecasting performance. This work provides insights into the integration of deep learning methods with econometric methods in the field of asset pricing.

Person re-identification aims to retrieve pedestrians with the same identity across different cameras. However, current methods increase attention to interfering regions when dealing with complex backgrounds and occlusion, especially in the presence of similar interfering features. To enhance the robustness of the model, we propose the Reverse Pyramid Attention Guidance (RPAG) network, using a reverse pyramid structure to learn features at multiple granularities. To mitigate the impact of occlusion, we introduce the Similar Feature Filtering (SFF) attention module at the pixel level, using graph convolution to adaptively select occluded regions, thereby enhancing retrieval accuracy by filtering out irrelevant parts. Combining the reverse pyramid structure with the pixel-level attention module strengthens adaptability to complex scenes, guides multi-granularity feature learning, and effectively handles various occlusion scenarios. RPAG achieved Rank-1 accuracies of 96.2%, 93.2%, 88.7%, and 73.2% on the Market1501, DukeMTMC-ReID, MSMT17, and Occluded-Duke datasets, respectively.

With the widespread application of machine learning techniques in malware detection, researchers have proposed various adversarial attack methods to generate adversarial examples (AEs) of malware, thereby evading detection. Previous studies have shown that the reinforcement learning (RL) framework can enable black-box attacks by performing a sequence of function-preserving operations, which produces functional evasive malware samples. However, it is difficult to obtain the useful guidance and feedbacks from the environment for agent training in the black-box scenario, which results in the RL framework being unable to learn the effective evasion policy. In this paper, we propose the Shapley prior and establish a prior-guidance-based RL framework, namely PSP-Mal, to generate AEs against Portable Executable (PE) malware detectors. Our framework improves on existing methods in three aspects: 1) We explore feature effects of the black-box model by computing Shapley values and further propose the Shapley prior to represent the expected impact of operations. 2) A novel prioritized experience utilization mechanism is established regarding the Shapley prior guidance in the RL framework. 3) The actions are expanded into item-content pairs and we use the Thompson sampling to choose effective content, which helps to reduce randomness and ensure repeatability. We compare the attack performance of our framework with other methods, and experimental results demonstrate that our algorithm is more effective. The evasion rates of PSP-Mal against the LightGBM models trained on EMBER and SOREL-20M reach 76.88% and 72.03%, respectively.

Under modern high-tech conditions, the importance of personnel authorization security is increasingly prominent. A set of safe authorization method can ensure that human resources are properly distributed to each unit, thus providing reliable guarantee for the successful completion of tasks. However, current personnel authorization are still paper-based or verbal, prone to errors or inconsistencies, and difficult to verify. In this paper, a method of digital Tiger Mark authorization based on PKI system is proposed, using modern cryptography technology to provide support for the security and reliability of personnel authorization. This method realizes fine authorization, and the authorization can be verified. The method uses digital certificates to assign people's identities to their respective roles, and uses encryption algorithms to enforce access control policies and prevent unauthorized access. The feasibility of this method is verified by us in a simulated cross-domain task environment.

Neighbor discovery is the procedure to establish a first contact between two wireless devices. For duty-cycled low-power devices, energy consumption is closely related to neighbor discovery latency. Actually, in recent protocols, such as Bluetooth low energy (BLE) or ANT+, neighbor discovery latency is determined by the parameters used by the devices, such as advertising interval, scan window, scan interval, and so on. A fundamental problem of the BLE-like protocol is that the exact relation between parameters and discovery latency has not been fully analyzed. In this article, we propose a Euclidean-division-based low-complexity precise analytical approach that can derive the mathematical expressions of both worst-case latency and average latency for any parameter groups. It is confirmed by simulation results that our solution can make highly accurate predictions about the value of latencies. Simulation results also show that the proposed solution has an extremely low complexity. Moreover, we derive the lower bound of latency for given duty cycles, which provides useful guidelines for the choice of energy-efficient parameter groups for BLE.

Image semantic segmentation is one of the core tasks for computer vision. It is widely used in fields such as unmanned driving, medical image processing, geographic information systems, and intelligent robots. Aiming at the problem that the existing semantic segmentation algorithm ignores the different channel and location features of the feature map and the simple method when the feature map is fused, this paper designs a semantic segmentation algorithm that combines the attention mechanism. First, dilated convolution is used, and a smaller downsampling factor is used to maintain the resolution of the image and to obtain its detailed information. Secondly, the attention mechanism module is introduced to assign weights to different parts of the feature map, which reduces the accuracy loss. The design feature fusion module assigns weights to the feature maps of different receptive fields obtained by the two paths and merges them together to obtain the final segmentation result. Finally, through experiments, it was verified on the Camvid, Cityscapes, and PASCAL VOC2012 data sets. Mean intersection over union (MIoU) and mean pixel accuracy (MPA) are used as metrics. The method in this paper can make up for the loss of accuracy caused by downsampling while ensuring the receptive field and improving the resolution, which can better guide the model learning. And the proposed feature fusion module can better integrate the features of different receptive fields. Therefore, the proposed method can significantly improve the segmentation performance compared to the traditional method.

In order to improve the accuracy of hierarchical network security situational awareness data fusion and shorten the fusion time, this paper proposes a hierarchical network security situational awareness data fusion method in cloud computing environment. The hierarchical model is established to obtain the hierarchical structure of data fusion. Hierarchical network security situational awareness data are collected and processed in parallel by cloud computing technology. According to the data collection results, the similarity between security events is calculated by using the clustering idea, and the similar security events are merged to achieve the purpose of removing redundant events. The hierarchical network security situational awareness data is fused by grey relational analysis. Finally, the simulation results show that the accuracy of data fusion of this method is high, up to 98%, and the fusion time is short, the longest is 13 s. Compared with the comparison method, this method has a better performance, indicating that this method is suitable for data fusion of hierarchical network security situation awareness.

With the continuous and rapid development of the Internet, high-bandwidth services emerge gradually, which puts forward the requirement of high bandwidth and large traffic on the network. High-bandwidth services usually occupy a large amount of transmission bandwidth. However, due to the limited resources of a single network node in the power local wireless communication network, it cannot meet the multi-dimensional resource requirements of high-bandwidth services. Therefore, resource coordination among multiple nodes is required. To solve the above problems, this paper proposes an integrated allocation method of communication, storage and computing (CSC) resources oriented to maximizing network throughput. Firstly, an integrated allocation model of storage and computing resources oriented to maximizing throughput is established. On the basis of this model, an integrated allocation method of CSC resources aiming at minimizing delay and maximizing network throughput is proposed. Solve the resource allocation problem of large-bandwidth services in the local converged network, increase network throughput, and improve the utilization of network resources.

It is a key point to find out the actual privileges of network users in network security risk assessment. The Privilege dependency graph (PDG) provides an effective way to reason the actual privileges of network users from their initial privileges. The existing User Actual Privilege reasoning method is time-consuming and not suitable for large-scale networks. This paper introduces a fast User Actual Privilege reasoning framework based on PDG reduction. The core idea is to reduce the size of the graph as much as possible before the actual privilege reasoning. Three different nodes merged scenarios are proposed and discussed, as well as the influences of different execution sequences and execution times. Networks of different sizes were simulated to validate the effectiveness and scalability of their method. The experimental results show that the proposed method can decrease the time of User Actual Privilege reasoning by over 25% in large-scale networks.

Adversarial example generation on Synthetic Aperture Radar (SAR) images is an important research area that could have significant impacts on security and environmental monitoring. However, most current adversarial attack methods on SAR images are designed for white-box situations by end-to-end means, which are often difficult to achieve in real-world situations. This article proposes a novel black-box targeted attack method, called Shallow-Feature Attack (SFA). Specifically, SFA assumes that the shallow features of the model are more capable of reflecting spatial and semantic information such as target contours and textures in the image. The proposed SFA generates ghost data packages for input images and generates critical features by extracting gradients and feature maps at shallow layers of the model. The feature-level loss is then constructed using the critical features from both clean images and target images, which is combined with the end-to-end loss to form a hybrid loss function. By fitting the critical features of the input image at specific shallow layers of the neural network to the target critical features, our attack method generates more powerful and transferable adversarial examples. Experimental results show that the adversarial examples generated by the SFA attack method improved the success rate of single-model attack under a black-box scenario by an average of 3.73%, and 4.61% after combining them with ensemble-model attack without victim models.

Occupational segregation is widely considered as one major reason leading to the gender discrimination in labor market. Using large-scale Chinese re-sume data of online job seekers, we uncover an interesting phenomenon that occupations with higher proportion of men have smaller gender wage gap measured by the female-male ratio on wage. We further show that the sever-ity of occupational segregation in China is low both overall and regionally, and the inter-occupational discrimination is much smaller than the intra-occupational discrimination. That is to say, Chinese women do not face large barriers when changing their occupations. Accordingly, we suggest Chinese women a new way to narrow the gender wage gap: to join male-dominated occupations. Meanwhile, it is worth noticing that although the gender wage gap is smaller in male-dominated occupations, it does not mean that the gender discrimination is smaller there.

In the IoT environment, in order to solve the problem that cache nodes can’t be effectively utilized and respond efficiently, this paper proposes a hierarchical cooperative caching strategy based on edge computing. Firstly, based on the designed IoT three-tier architecture model, the data communication model is established in combination with equipment request process. Secondly, node collaborative cache is realized by optimizing particle swarm optimization algorithm. To prevent particles from falling into local optimal solutions, Metropolis criterion is introduced to disturb solution space. In the simulation experiment, it is verified that the algorithm in this paper can reduce the network delay.

组织内部网络不仅面临着外部攻击者的威胁,同时也面临以破坏组织网络结构、内部信息资料窃取以及各种诈骗手段为主的内部威胁.内部威胁因为其多元化、伪装性强等特点,对组织机构内部造成了严重影响,因此对于内部威胁发现检测方法的研究变得非常有必要.本文首先对内部威胁进行了描述,重点针对内部威胁发现检测方法的现实意义进行了论述.同时将现有的内部威胁发现检测方法分为3类:基于异常行为的检测方法、基于审计日志异常的检测方法和其他检测方法,分别介绍了现有3类方法的研究现状,并对它们的研究进展进行了总结、归纳和分析.最后对内部威胁发现检测方法的未来研究方向进行了展望.

Although the current Domain Name System (DNS) has been able to satisfy the use of network services, there are still many challenges in the future development of the Internet. The centralized management of traditional domain name management systems has many risks, and cannot defend against Distributed Denial of Service (DDoS) attacks and single points of failure. As a decentralized tool, blockchain provides innovative ideas for the improvement of domain name management systems. Starting from the existing network resolution system and combining the application of cross-chain communication in DNS, this paper proposes a domain name resolution service architecture model based on the master-slave chain, adopts a Multi-Sig Notary Schemes to achieve cross-chain communication, and proposes based on the master-slave chain consensus algorithm (MSBFT) of the PBFT algorithm. In a single blockchain, this paper uses the VRF algorithm to select the master node. Finally, the simulation experiment designed in this paper tests the throughput and analysis delay of the analytical model, compares the analytical delay of the main chain and slave chain, and analyzes the advantages of the model.

In recent years, social media is becoming the main channel for people to obtain news, but also promotes the spread of fake news. Under the trend of rich media of social media, fake news gradually change from single text to multi-modal form, so multi-modal fake news detection is receiving more and more attention. First, we combine the pre-trained model Robustly Optimized Bert Pretraining Approach (RoBERTa) and other methods such as Bi-directional LSTM (BiLSTM) and UER by using the text and OCR information. The above-mentioned methods are trained as part of our ensemble model, together with semi-supervised training, are weighted to generate our final results. In the multi-modal model, we use RoBERTa and ResNet to extract text and image features respectively, and use Light Gradient Boosting Machine (LightGBM) to classify them. Finally, we fuse text-based and multimodal-based results and take the best-performing one. In the competition, our weighted average F1 score has reached 0.7428, achieving 6th place in FACTIFY.