Underwater acoustic target recognition traditionally relies on feature engineering, wherein features are extracted through time-frequency transformations and fed into classifiers for target recognition. However, the noise distribution is uneven and lacks computational efficiency when using recognition models such as Transformers. To this end, this paper proposes a novel Multi-Scale Lightweight Adaptive Time Series Acoustic Network (MS-LATSANet), which is able to directly extract target discriminative information from raw signals without the need for complex feature engineering processing. Specifically, the network utilizes multi-scale time windows to capture different frequency components of the time series. In addition, MS-LATSANet employs modified discrete Fourier analysis with time domain background equalization to enhance feature representation, and uses adaptive thresholds to suppress noise. The network also introduces a global-local block to strengthen the understanding of temporal information. Extensive experimental results demonstrate that MS-LATSANet outperforms existing state-of-the-art models and exhibits stronger generalization performance under different signal-to-noise ratio scenarios.

Segment anything model (SAM) has received significant attention owing to its outstanding segmentation performance. However, it may still face security threats from adversarial examples. Since SAM interactively realizes the prediction of target areas according to user-specified prompts (e.g., points), adversarial examples generated by existing end-to-end attack methods usually exhibit limited attack performance when faced with different point prompts. To this end, we propose a cross-point adversarial attack method based on feature neighborhood disruption against SAM, called CP-FND attack. CP-FND aims to generate adversarial examples capable of effectively deceiving SAM under different user-specified point prompts. Specifically, CP-FND forces the intermediate feature of adversarial examples to be similar to the designed disruption features without relying on any specified point prompt. Subsequently, the continuity and relevance of contextual features are disrupted, thereby fooling SAM and suppressing its predicted masks. Extensive experiments demonstrate that CP-FND achieves superior cross-point adversarial attack performance against SAM compared to state-of-the-art methods.

The conventional convolutional neural network (CNN) model has limitations in terms of global modeling ability, as it can only extract local features and is susceptible to noise interference. Due to repeated downsampling, small target features are easily lost in the deeper layers of the network. On the other hand, Transformer models are renowned for their exceptional global modeling capabilities; however, for the specific task of polyp images, effective results necessitate local feature extraction. Therefore, when reconsidering the relationship between local and global aspects within CNN and self-attention models, we propose a parallel relevant attention module with a Transformer structure for feature extraction and fusion. By combining channel attention and spatial attention mechanisms, we obtain semantic information from the lowest-level features to determine target feature locations accurately. Additionally, our step-by-step feature fusion module integrates shallow feature information into deeper layers through a CNN structure to capture more detailed target features comprehensively. Finally, these detailed target features are combined with underlying semantic information to achieve precise target segmentation.

Due to their flexibility and agility, unmanned aerial vehicles (UAVs) offer a promising approach to cluster planning within wireless sensor networks (WSNs). However, the limited battery capacity of a single UAV limits its application in many situations, such as searching in wild areas. In this article, we propose a computational scheme of cooperative path planning for heterogeneous UAVs based on Voronoi diagrams and intelligent swarm optimization algorithm. In this article: 1) Voronoi diagrams are used to model the field environment according to the radar sensor position; 2) an improved$K$-medoids algorithm based on the maximum empty circle property of the Voronoi diagram (Vor-$K$-medoids) is proposed to complete the reconnaissance UAVs (RUAVs) domain cooperative search; and 3) a hyperbolic tangent heuristic function intelligent optimization algorithm is proposed to calculate the minimum risk path for the attack UAV (AUAV) according to the characteristics of the attack mission. The simulation results show that the proposed scheme integrates the properties of the Voronoi diagram, clustering algorithm, and path planning algorithm commendably. Compared with the traditional ant colony optimization (ACO), under the same number of iterations, the probability of obtaining the optimal track is improved by 14%, and the running time is shortened by 50.87%.The proposed scheme offers a practical and cost-effective approach for efficiently searching areas within large-scale radar sensors in real-world scenarios.

Acoustic waves are forms of energy that propagate through various mediums. They can be represented by different modalities, such as auditory signals and visual patterns. The two modalities are often described as one-dimensional waveform in the time domain and two-dimensional spectrogram in the frequency domain. Most acoustic signal processing methods use single modal data for input and training models. This poses a challenge for black-box adversarial attacks on audio signals because the input modality is also unknown to the attacker. In fact, there currently exist no methods that explore the cross-modal transferability of adversarial perturbation. This paper investigates the cross-modal transferability from waveform to spectrogram. We argue that the data distributions in the sample space with the different modalities have mapping relations and propose a novel decision-based cross-modal and cross-medium adversarial attack method. Specifically, it generates an initial example with cross-modal attack capability by combining random natural noise, then iteratively reduces the perturbation to enhance its invisibility. It incorporates the constraints of the spectrogram sample space while iteratively optimizing adversarial perturbations for black-box audio classification models. The perturbation is imperceptible to humans, both visually and aurally. Extensive experiments demonstrate that our approach can launch attacks on classification models for sound waves and spectrograms that share the same audio signal. Furthermore, we explore the cross-medium capability of our proposed adversarial attack strategy that can target processing models for acoustic signals propagating in air and seawater. The proposed method has preeminent invisibility and generalization compared to other methods.

Recently, contrastive learning for sequential recommendation has demonstrated its powerful ability to learn high-quality user representations. However, constructing augmented samples in the time domain poses challenges due to various reasons, such as fast-evolving trends, interest shifts, and system factors. Furthermore, the F-principle indicates that deep learning preferentially fits the low-frequency part, resulting in poor performance on high-frequency tasks. The complexity of time series and the low-frequency preference limit the utility of sequence encoders. To address these challenges, we need to construct augmented samples from the frequency domain, thus improving the ability to accommodate events of different frequency sizes. To this end, we propose a novel Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation (CFIT4SRec). We treat the embedding representations of historical interactions as "images" and introduce the second-order Fourier transform to construct augmented samples. The components of different frequency sizes reflect the interest trends between attributes and their surroundings in the hidden space. We introduce three data augmentation operations to accommodate events of different frequency sizes: low-pass augmentation, high-pass augmentation, and band-stop augmentation. Extensive experiments on four public benchmark datasets demonstrate the superiority of CFIT4SRec over the state-of-the-art baselines. The implementation code is available at https://github.com/zhangyichi1Z/CFIT4SRec.

With the rapid development of high-speed mobile network technology and high-precision positioning technology, the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social Networks. Researchers can mine users’ trajectories in Location-based Social Networks to obtain sensitive information, such as friendship groups, activity patterns, and consumption habits. Therefore, mobile users’ privacy and security issues have received growing attention in Location-based Social networks. It is crucial to strike a balance between privacy protection and data availability. This paper proposes a differential privacy trajectory protection method based on latent interest community detection (DPTP-LICD), ensuring strict privacy protection standards and user data availability. Firstly, based on the historical trajectory information of users, spatiotemporal constraint information is extracted to construct a potential community strength model for mobile users. Secondly, the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory, and their priorities are assigned based on a popularity model. A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data unusable. Finally, to prevent privacy leakage, we add Laplace and exponential noise in generating preferred hot spots and recommending user interest points. Security and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’ privacy from disclosure.

With intelligent terminal devices’ widespread adoption and global positioning systems’ advancement, Location-based Social Networking Services (LbSNs) have gained considerable attention. The recommendation mechanism, which revolves around identifying similar users, holds significant importance in LbSNs. In order to enhance user experience, LbSNs heavily rely on accurate data. By mining and analyzing users who exhibit similar behavioral patterns to the target user, LbSNs can offer personalized services that cater to individual preferences. However, trajectory data, a form encompassing various sensitive attributes, pose privacy concerns. Unauthorized disclosure of users’ precise trajectory information can have severe consequences, potentially impacting their daily lives. Thus, this paper proposes the Similar User Discovery Method based on Semantic Privacy (SUDM-SP) for trajectory analysis. The approach involves employing a model that generates noise trajectories, maximizing expected noise to preserve the privacy of the original trajectories. Similar users are then identified based on the published noise trajectory data. SUDM-SP consists of two key components. Firstly, a puppet noise location, exhibiting the highest semantic expectation with the original location, is generated to derive noise-suppressed trajectory data. Secondly, a mechanism based on semantic and geographical distance is employed to cluster highly similar users into communities, facilitating the discovery of noise trajectory similarity among users. Through trials conducted using real datasets, the effectiveness of SUDM-SP, as a recommendation service ensuring user privacy protection is substantiated.

With the advent of intelligent technology, the users of spatio-temporal crowdsourcing and their participation in the crowdsourcing tasks continue to increase exponentially. This poses new challenges to the crowdsourcing field. One of the core research areas of spatio-temporal crowdsourcing is task assignment. Most of the existing research on task assignment is focused on offline optimal task assignment, where, the platform has already learned all the information about workers and tasks beforehand. However, these studies cannot obtain good results in real-world situations. At the same time, online task assignment problems often result in local optimal assignment. To solve these problems, more attention needs to be paid to online task assignments and the arrival time of workers. This paper proposes an Online Bilateral Assignment (OBA) problem based on the online assignment model. The competitive ratio of the Greedy algorithm is analyzed according to the OBA problem model. Also, another solution to the OBA problem according to the Greedy algorithm, the Improved-Baseline algorithm, is proposed. Additionally, a Bilateral Online Priority Reassignment algorithm (BOPR) is proposed. The BOPR algorithm realizes real-time task/worker assignment through the bilateral assignment as a solution for online task assignment. In order to guarantee the number of matching tasks, a priority queue is designed in the BOPR algorithm. Considering the waiting time deadlines of tasks and workers and the error rate for priority ranking, it avoids tasks and workers waiting too long and assigns each task to the best possible extent. On this basis, a two-stage assignment strategy is designed for unsuccessful tasks, which could minimize the error rate of the task and significantly improve the efficiency of task assignment. Finally, through experiments on real data sets, the algorithm's performance in terms of global utility value and the number of matches is evaluated.

Deep learning technology (a deeper and optimized network structure) and remote sensing imaging (i.e., the more multisource and the more multicategory remote sensing data) have developed rapidly. Although the deep convolutional neural network (CNN) has achieved state-of-the-art performance on remote sensing image (RSI) scene classification, the existence of adversarial attacks poses a potential security threat to the RSI scene classification task based on CNN. The corresponding adversarial samples can be generated by adding a small perturbation to the original images. Feeding the CNN-based classifier with the adversarial samples leads to the classifier misclassify with high confidence. To achieve a higher attack success rate against scene classification based on CNN, we introduce the projected gradient descent method to generate adversarial remote sensing images. Then, we select several mainstream CNN-based classifiers as the attacked models to demonstrate the effectiveness of our method. The experimental results show that our proposed method can dramatically reduce the classification accuracy under untargeted and targeted attacks. Furthermore, we also evaluate the quality of the generated adversarial images by visual and quantitative comparisons. The results show that our method can generate the imperceptible adversarial samples and has a stronger attack ability for the RSI scene classification.

At present, with the popularization of intelligent equipment. Almost every smart device has a GPS. Users can use it to obtain convenient services, and third parties can use the data to provide recommendations for users and promote relevant business development. However, due to the large number of location data, there are serious data sparsity problems in the data uploaded by users. At the same time, with great value comes great danger. Once the user’s location information is obtained by the attacker, severe security issues will be caused. In recent years, a lot of researchers have studied the recommendation of point of interests (POIs) and the privacy protection of location. Yet, few of them have explored both together, which induces some drawbacks on the combination of them. This paper combines POI recommendation with a privacy protection mechanism. Besides providing user with POI recommendation service, it also protects the privacy of user’s location. We proposed a POI recommendation model with privacy protection mechanism, termed POI recommendation model for community groups based on privacy protection (CGPP‐POI). This model can ensure the recommendation accuracy and reduce the leakage of user location information via taking advantages of the characteristics of location. At the same time, it deals with the problem of poor recommendation performance caused by sparse data. In addition, through the expansion of location, random and other methods are used to protect the user’s real check‐in information. First, the data processed at the terminal satisfied local differential privacy. At the same time, we use the data to build a recommendation model. Then, we use a community of user in the model to improve the availability of these disturbed data, explore the relationship between users, and expand check‐ins within the community. Finally, we provide the POI recommendations to users. Based on the traditional evaluation criteria, we adopted four metrics, i.e., accuracy, recall rate, coverage rate, and popularity in evaluation part, where intensive experiments conducted on real datasets Gowalla and Brightkite demonstrate that our approach outperforms the baseline methods significantly.

The development of 5G technology has driven the rise of e‐commerce, social networking, and the Internet of Things. Under the high‐speed transmission, the data volume increases, and the user demand also changes. Personalized customization has become the mainstream trend of network development. However, as the speed of the Internet increases, a series of problems also arise. The increase in data volume results in a reduction of bandwidth, a growth of the central processor’s pressure, and a higher risk of data leakage. A search system and a recommendation platform are the tools to improve people’s search efficiency. However, providing personalized recommendations to different users according to their needs is still an urgent problem. Simultaneously, the big data volume means that attackers can also get more information. They can use background knowledge and various reasoning methods to deduce the user’s private information using nonprivate items. In this paper, the solutions to safe and reliable recommendation services are the main problem explored. Based on this idea, this paper proposed short‐term dynamic recommendation model based on local differential privacy (SDRM‐LDP). This model uses a small amount of user information to construct short‐term user preference behaviors and provides recommendations for users based on the similarity between items. We consider that an attacker uses nonprivate items to derive privacy items. Therefore, we randomly replace the original data in the same category. At the same time, the local differential privacy (LDP) is added to the privacy item query to make the private data available and protect the privacy information. In this paper, two real‐world datasets, ML‐100K and ML‐10M, are used for experiments. Experimental results show that the results of SDRM‐LDP are superior to other models.

The rapid development of the Global Positioning System (GPS) devices and location‐based services (LBSs) facilitates the collection of huge amounts of personal information for the untrusted/unknown LBS providers. This phenomenon raises serious privacy concerns. However, most of the existing solutions aim at locating interference in the static scenes or in a single timestamp without considering the correlation between location transfer and time of moving users. In this way, the solutions are vulnerable to various inference attacks. Traditional privacy protection methods rely on trusted third‐party service providers, but in reality, we are not sure whether the third party is trustable. In this paper, we propose a systematic solution to preserve location information. The protection provides a rigorous privacy guarantee without the assumption of the credibility of the third parties. The user’s historical trajectory information is used as the basis of the hidden Markov model prediction, and the user’s possible prospective location is used as the model output result to protect the user’s trajectory privacy. To formalize the privacy‐protecting guarantee, we propose a new definition, L&A‐location region, based on k‐anonymity and differential privacy. Based on the proposed privacy definition, we design a novel mechanism to provide a privacy protection guarantee for the users’ identity trajectory. We simulate the proposed mechanism based on a dataset collected in real practice. The result of the simulation shows that the proposed algorithm can provide privacy protection to a high standard.

Video-based behavior recognition is a challenging research topic. The three dimensional convolution neural network (3D CNN) is effectively adopted to capture features from videos directly. 3D CNN is extended by two-dimensional convolution neural network, in which a time dimension is added. 3D CNN is better than two-dimensional convolution network in expressing effective motion information, and it has certain advantages. In order to make better use of the valuable features extracted from the original video information, only stacked RGB frame data sets can be used as the input of network. Ensembling 3D CNN framework for video recognition is proposed in the paper. Firstly, the pre-training model of Sports-1M is initialized firstly, and a 3D convolution neural network based on multi-level feature fusion is constructed. . The final high-dimensional feature combination is obtained by fusing multiple convolution features. Then 3D convolutional neural network based on ensemble learning is proposed to increase motion information, enrich motion features and enhance the robustness of single feature representation. Three incomplete training data sets are obtained by Bagging algorithm. To get different networks, three data sets are employed to train three 3D convolution neural networks respectively, and the output of the three networks is integrated. The output features of the three networks are input into the SVM classifier through the Stacking algorithm and the final results are obtained. The integration effects of different ensemble methods are compared. The experimental results show that the method of this work can improve recognition accuracy on UCF-101 data set effectively.

In this paper, an effective differential evolution is proposed with binary strategy to solve feature selection problem. Firstly, a new binary mutation operator and a binary crossover operator are designed. The two-stage adaptive strategy is constructed in the mutation operator to generate new individuals to improve the diversity of the population. Then, the adaptive cross-parameter selection based on individual is developed in the crossover operator to fully exploit each potential optimal individual. Finally, six benchmark data sets are adopted to evaluate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm significantly improves the classification accuracy, reduction rate and time cost.

Due to the fast increase of mobile traffic, most mobile network operators face the congestion issue in licensed spectrum bands. Several telecommunication vendors and operators propose to expand LTE service to the unlicensed spectrum bands to relieve the traffic congestion. However, LTE in unlicensed spectrum may interfere with Wi-Fi communications in the same bands and cause significant decrease in the quality of service of Wi-Fi. In this paper, we propose a novel mechanism that enables negotiations between two different wireless technologies (Wi-Fi and LTE), which ensures fair spectrum sharing between Wi-Fi and LTE-Unlicensed (LTE-U) in the same bands. We formulate the co-existence of Wi-Fi and LTE-U as a constrained optimization problem, and we solve the problem. We evaluate the performance of the proposed scheme via NS-3 simulations. The simulation results show that our approach can effectively improve the overall channel utilization and reduce the interference between Wi-Fi and LTE-U.

The recommender system is mainly used in the e-commerce platform. With the development of the Internet, social networks and e-commerce networks have broken each other’s boundaries. Users also post information about their favorite movies or books on social networks. With the enhancement of people’s privacy awareness, the personal information of many users released publicly is limited. In the absence of items rating and knowing some user information, we propose a novel recommendation method. This method provides a list of recommendations for target attributes based on community detection and known user attributes and links. Considering the recommendation list and published user information that may be exploited by the attacker to infer other sensitive information of users and threaten users’ privacy, we propose the CDAI (Infer Attributes based on Community Detection) method, which finds a balance between utility and privacy and provides users with safer recommendations.

Internet-of-Things (IoT) are everywhere in our daily life. They are used in our homes, in hospitals, deployed outside to control and report the changes in environment, prevent fires, and many more beneficial functionality. However, all those benefits can come of huge risks of privacy loss and security issues. To secure the IoT devices, many research works have been conducted to countermeasure those problems and find a better way to eliminate those risks, or at least minimize their effects on the user's privacy and security requirements. The survey consists of four segments. The first segment will explore the most relevant limitations of IoT devices and their solutions. The second one will present the classification of IoT attacks. The next segment will focus on the mechanisms and architectures for authentication and access control. The last segment will analyze the security issues in different layers.

To address computational complexity of winner determination in combinatorial auction, a new co-evolutionary algorithms is developed based on combining mixed mutation with self-organization optimization for finding high quality solutions quickly. Mixed mutation strategy can select adaptively mutation operators which are suitable for discrete space to maintain population diversity, self-organization optimization makes the search to jump out of local optima. This paper investigates two combination methods of mixed mutation and self-organization optimization, the results of experiment show the better performance of the second way (MMSEO2) that self-organization optimization is added to mixed mutation strategy set as a pure mutation operator. We compare the proposed algorithm with current well-known approximate algorithms for winner determination problem, and demonstrate that the proposed algorithm MMSEO2 produces competitive results and finds better solutions than other algorithms for large problem sizes.

Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.