Abstract Operating system (OS) security is paramount in ensuring the integrity, confidentiality, and availability of computer systems and data. This research manuscript presents a comprehensive investigation into the multifaceted domain of OS security, aiming to enhance understanding, identify challenges, and propose effective solutions. The research methodology integrates diverse approaches, including an extensive exploration for available knowledge process mechanics, empirical data collection, case studies investigations, experimental analysis, comparative studies, qualitative analysis, synthesis, and interpretation. Through various experimental perspectives, theoretical foundations, historical developments, and current trends in OS security are also explored. Empirical data collection involves gathering insights from publicly available reports, security advisories, case studies, and expert interviews to capture real-world perspectives and experiences. Case studies illustrate practical implications of security strategies, while experimental analysis evaluates the efficacy of security measures in controlled environments. Comparative studies and qualitative analysis provide insights into strengths, limitations, and emerging trends in OS security. The synthesis and interpretation of the findings offer actionable insights for improving OS security practices, policy recommendations, and providing towards future research directions. This research contributes to advancing knowledge in OS security and informs the development of effective strategies to safeguard computer systems against evolving threats and vulnerabilities.

Abstract Recommended in this paper, because the existing single objective experience is poor, and the recommended model in a large difference of targets under the complex relationship of joint optimization and the conflict caused by faults, this paper proposes a personalized recommendation based on the deep learning multi-objective optimization algorithm, the estimated probability of users on the individual behavior as a model to study target, Multiple objectives are integrated into a model for learning. Firstly, the embedding layer is used to change the feature vectors, so that the bottom layer of the model shares the same feature embedding. Secondly, the factorization machine and deep learning are used to construct high-low order feature interaction. Then, the gating network and multilevel expert network constructed by a fully connected neural network are used to learn the characteristic relationship of user behavior. Finally, make connections between goals. Through experiments on public and real datasets, The results show that the multi-objective model proposed in this paper has better co-optimization performance and increases the AUC value by 0.1% compared with advanced personalized recommendation models such as MMoE and ESMM, to achieve the ultimate goal of increasing the prediction accuracy and improving user satisfaction.

Abstract In the context of rapid developments in drone technology, the significance of recognizing and detecting low-altitude unmanned aerial vehicles (UAVs) has grown. Although conventional algorithmic enhancements have increased the detection rate of low-altitude UAV targets, they tend to neglect the intricate nature and computational demands of the algorithms. This paper introduces ATD-YOLO, an enhanced target detection model based on the YOLOv5s architecture, aimed at tackling this issue. Firstly, a realistic low-altitude UAV dataset is fashioned by amalgamating various publicly available datasets. Secondly, a C3F module grounded in FasterNet, incorporating Partial Convolution (PConv), is introduced to decrease model parameters while upholding detection accuracy. Furthermore, the backbone network incorporates an Efficient Multi-Scale Attention (EMA) module to extract essential image information while filtering out irrelevant details, facilitating adaptive feature fusion. Additionally, the universal upsampling operator CARAFE (Content-aware reassembly of features) is utilized instead of nearest-neighbor upsampling. This enhancement boosts the performance of the feature pyramid network by expanding the receptive field for data feature fusion. Lastly, the Slim-Neck network is introduced to fine-tune the feature fusion network, thereby reducing the model’s floating-point calculations and parameters. Experimental findings demonstrate that the improved ATD-YOLO model achieves an accuracy of 92.8%, with a 31.4% decrease in parameters and a 28.7% decrease in floating-point calculations compared to the original model. The detection speed reaches 75.37 frames per second (FPS). These experiments affirm that the proposed enhancement method meets the deployment requirements for low computational power while maintaining high precision.

Abstract In order to meet the needs of military, road construction, multimedia industry and other aspects, UAVs are gradually given more functions. As the basic function of UAV applications, the fixed-point delivery problem model has higher and higher accuracy requirements. However, in the actual scene, the UAV delivery problem is affected by the interaction of various factors such as flight height, air resistance, and dive angle, which makes it difficult to achieve high stability and high hit accuracy. This paper will analyze the complex motion model based on the fixed-point delivery of explosives by UAV, study the relationship between the stability of UAV delivery and the hit accuracy, and analyze the influence of relevant parameters on the problem by using modeling. In this paper, a multivariate nonlinear continuous time change model is proposed, and a continuous time slice discretization idea operation model is introduced to approximate the time slice splitting inside the UAV launch motion. Secondly, the design quantified evaluation index reaction the initial velocity of the explosive, the launch Angle, the height off the ground and other parameters to analyze the model. Finally, the best scheduling strategy is obtained and verified by using the idea of variable traversal and trial- and-error simulation. The experimental results show that the variation of UAV flying height, speed, depression and other interference factors is consistent with the prediction of score and hit accuracy, according to the environment setting of this model, when the UAV is 300 meters above the ground and 290 meters away from the target horizontal position, the delivery speed is 250m/s, and the pitch angle is about 27°, the fixed-point delivery of explosives is the best strategy.

Abstract During the training of target recognition models based on Radar Cross Section (RCS) data, a persistent challenge arises in sampling due to the inherent difficulty in acquiring a sufficient number of samples. This scarcity of data poses a significant impediment to the effective training of models, resulting in diminished accuracy in target recognition. To address this issue, this article proposes a target classification method based on RCS data under small sample conditions. The approach adopts the fundamental concept of Model-Agnostic Meta-Learning (MAML) to train the target recognition model, enhancing the structure of MAML model. An hourglass-shaped convolution layer is introduced to the input layer, with an additional convolution layer preceding the output layer, and a switch to a central loss function. To substantiate the efficacy of the improved MAML model, comprehensive comparative analyses are conducted with benchmark models, including MAML, ResNet 18-layers, Long Short-Term Memory (LSTM), among others. Experimental results conclusively demonstrate the superior performance of the refined MAML model in target recognition under conditions of limited samples, attaining an average prediction accuracy of 85.62%. This signifies a noteworthy 5-percentage-point improvement compared to the baseline model prior to the introduced enhancements.

Abstract In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor fusion SLAM algorithm for indoor robots was proposed. Aiming at the mismatch problem of the traditional ICP algorithm in the front end of the lidar SLAM, the algorithm adopts the PL-ICP algorithm that is more suitable for the indoor environment, and uses the extended Kalman filter to fuse the wheel odometer and IMU to provide the initial motion estimation value. Then, during the mapping phase, the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to compensate for the lack of vertical field of view in the 2D lidar mapping. The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33% compared to a single wheeled odometer, providing a higher initial iteration value for the PL-ICP algorithm. At the same time, fusion mapping compensates for the shortcomings of a single two-dimensional lidar mapping, and constructs an environmental map with more complete environmental information.

Abstract This study introduced a novel energy enhancement approach for Wireless Sensor Networks (WSNs) by leveraging the shortest path transmission technique to minimize energy consumption and extend the network’s lifetime. Unlike traditional methods that heavily relied on cluster heads (CHs) for data transmission, our model proposed a non-cluster-based routing algorithm, utilizing Dijkstra’s algorithm to identify the most energy-efficient paths for data transmission. Simulation results, based on varying node densities (100, 200, and 300 nodes) within a 200x200 network area, demonstrated the effectiveness of our approach. Our findings indicated a significant reduction in energy consumption, with the network lifetime extending to approximately 100,000 rounds, surpassing traditional LEACH-based and other related protocols. This enhancement not only promised a sustainable WSN deployment but also offered a scalable solution adaptable to different network sizes and configurations.

Abstract This study introduces a technique for facial recognition according to capsule networks. The system utilizes the advantages of capsule networks to model the face features in the image hierarchically, and realizes the efficient recognition of faces. First of all, we know the difference between the capsule network and the convolutional neural network through the study of the operating principle and the structure of the capsule network. Secondly, the Capsule Network is realized through deep research on the algorithm for dynamic routing and the internal operating principle of the capsule. Finally, by conducting experiments on the face dataset and optimizing it with the Adam optimization algorithm as well as the boundary loss and reconstruction loss, the capsule network is promoted to learn more robust feature representations to obtain better face recognition results. The experiments show that the face recognition system based on capsule network can reach 93.5% correct rate of evaluation on WebFace dataset, which achieves a high recognition accuracy. The final results demonstrate the feasibility and effectiveness of capsule networks for face recognition.

Abstract Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.

Abstract GreatFree is a generic distributed programming language to develop various distributed systems over the Internet-oriented computing environment. The fundamental characters of GreatFree are shaped by three essential techniques, including the message-passing, the physical-machine-visible, and the thread-visible. More important, GreatFree is equipped with three additional distinguished mechanisms, i.e., the distributed primitives, the distributed common patterns, and the distributed threads on the application level, which are sufficient to turn GreatFree into a generic distributed programming technology. To the best of our knowledge, compared with any others, GreatFree is the first one to achieve the goal. Thereafter, GreatFree is capable of exploiting distributed computing resources flexibly to adapt to any heterogeneous environments with a uniform solution. It indicates that GreatFree represents the common principles existed in various complicated computing circumstances over the Internet. That inspires that GreatFree is a proper technology to build a new concept of cloud computing environment, i.e., the cloud-side operating system, which dominates diverse distributed computing resources upon the common principles of GreatFree. Such a system is a generic development and running environment for any distributed systems. Without doubt, within the environment, GreatFree is the unique choice to program any distributed systems in a scalable manner.