This article presents methods for the automated security analysis of Android applications, which can be used to search for cryptographic vulnerabilities, vulnerabilities in third-party software components, authentication, and authorization, as well as to identify the storage and transmission of sensitive information in clear text. The accuracy of searching for the given types of vulnerabilities using automated vulnerability search tools and a software prototype is analyzed.

This paper presents a method for semantic data analysis by complex-valued matrix decomposition. The method is based on the quantum model of contextual decision-making, according to which observable probabilities are generated by the qubit states, representing the subjective meaning of the contexts relative to the basis decision. In the simplest three-context case, one of these qubits is decomposed into a superposition of the remaining two, mathematically encoding semantic relations between the three contexts. For use in data analysis, this model is translated to the matrix form, in which the rows and columns correspond to the contexts and samples of the experiment. The observable real-valued data then emerge from a complex-valued amplitude matrix, decomposed to a product of a real basis matrix and complex-valued matrix of superposition coefficients. This decomposition reveals stable process-semantic relations between the considered contexts, not captured by other methods of analysis. As a result, the data are approximated with higher precision and fewer parameters than singular and nonnegative matrix decompositions, truncated to the same dimension. The model is experimentally tested in the descriptive and prognostic regimes. The result opens prospects for development of nature-like computational architectures on novel logical grounds.

This paper presents a study of the application of large language models (LLMs) to predict events based on LLM agents—autonomous systems that use LLMs for reasoning, decision making, and interaction with the environment. Various architectures of LLM agents are analyzed: cooperative systems (ChatDev, MetaGPT), multiagent debates (MAD, ChatEval), agents for web tasks (WebAgent, WebVoyager), and simulation agents (Generative Agents, EconAgent). Particular attention is paid to the features of predictive modeling based on LLMs, where classical approaches (regression, time series) are replaced by agent-based modeling and predictive engineering. This article presents the results of an experiment on predicting the outcome of a selected conflict using an LLM agent (Mistral, DeepSeek) and the retrieval-augmented generation (RAG) approach based on data from analytical agencies, opinion leaders, and news sources. The convergence of forecast estimates of polarized sources is revealed and requirements for forecasting systems are formulated: weighting sources according to expert significance, filtering neutral data, and sample balancing. Requirements are put forward for the selection of data assessed by simulation LLM agents.

The high-precision time-frequency is a crucial strategic resource for any country, serving as the bedrock for national defense construction and economic operations. Global Navigation Satellite Systems (GNSS) provides timing service through satellite, which has the characteristics of all-weather, high-precision and wide coverage. It has become an indispensable means of timing service. However, GNSS signal can be disrupted under certain conditions, leading to service interruptions and preventing users from obtaining accurate time information. This paper introduces a time-frequency broadcasting system utilizing the BeiDou Navigation Satellite System (BDS) signal system in scenarios when GNSS is unavailable. By acquiring and calculating the BDS ephemeris data, the system calculates the BDS coordinate information according to the local time in real-time, simulates the satellite trajectory, adjusts the time of broadcasting according to the local position input, and generates the corrected satellite navigation message information. The time-frequency broadcasting system can provide real-time timing service for BDS navigation terminals within the coverage area. The timing accuracy of the system is better than 6.3 nanoseconds, the project implementation is feasible, and the application range is wide. In the case of no GNSS, the system can also provide emergency timing service for BDS navigation terminals within the coverage area.

The fingerprint-based biometric technology is vulnerable to several kinds of attacks. One of the simplest attacks to carry out on the fingerprint sensor is the presentation attack. Numerous fingerprint presentation attack detection (FPAD) strategies have been put out in recent years. These FPAD techniques have yielded promising results on cross-material datasets. However, when training and testing datasets come from different domains (sensors), the performance of the FPAD approach can degrade by up to 30%. Therefore, to achieve a consistent performance, a robust FPAD approach must learn domain-independent features. We have developed an unsupervised divergence-based domain adaptation (UDDA) method with an adaptive loss function (ALF) to minimize the domain shift in FPAD. The ALF integrates domain divergence loss (DDL) and classification loss. In a cross-sensor scenario, the ALF helps learn domain-invariant features and provides reliable classification of real and fraudulent fingerprints. Experimental results demonstrate that the proposed UDDA approach reduces the cross-sensor average classification error (ACE) by 19.94% on LivDet 2015 and 19.23% on LivDet 2017.

Efficient and flexible autonomous obstacle avoidance motion capabilities have become an urgent and practical requirement for robots in the current production life. Aiming at the problem that the joint vibration caused by excessive impact affects the quality of work when the robotic arm is operating, an optimal spraying trajectory planning method for the robotic arm is proposed. In this paper, we take the four-degree-of-freedom robotic arm as the research object, use five times nonuniform B spline functions to construct the trajectory of the robotic arm, construct the multiobjective optimization function of time and impact, optimize the trajectory based on the multiobjective particle swarm optimization algorithm, and then get the required solution from the Pareto front-end through the normalization of the objective weighting function. Real robots are used for experimental verification, and the improved multiobjective optimization particle swarm algorithm, PAD-MOPSO is used to achieve the effect of multiobjective optimization of time and impact, and the displacement, velocity, acceleration, and torque during the motion process are within the constraint range.

one of the important features of information retrieval systems is ranking. Ranking performs the function of ranking search results based on relevance to the user’s query. Methods developed in state-of-the-art research still require multiple iterations. In this paper, we proposed to use zone partitioning strategies for computing web page rank parameters in retrieval systems, which implements iterative calculation for only some randomly selected subgraphs (zone). The zone approach is based on the idea to use multiple neural networks to classify rank data in graph-based structures. The crawled web pages are fragmented into three distinct zones. The core zone is used for training graph convolutional network, in this zone, the labels are known. It is covered with an undiscovered zone, where classifiers label node parameters. The most interesting part is the intersection zone, which represents the set of nodes and edges that belong to more than one undiscovered zone. The experiments show that the probability of classifying the true labels in the intersection zones via aggregating the results of multiple classifiers in some cases is higher than in undiscovered zones.

The recommender system (RS) utilizes collective user opinions to forecast customer preferences. RS can be vulnerable to malicious information attacks. Introducing deceitful “shilling” profiles, such as push and nuke attacks, into the RS can promote inappropriate products. The introduction of these attacks results in inaccurate product recommendations. Because of their openness, Recommender systems are vulnerable to the injection of several fraudulent profiles, which might manipulate their predictions. Conventional detection methods rely on artificial characteristics derived from a single category of user-generated data. These methods need to comprehensively capture detailed user-item interactions, resulting in decreased accuracy when detecting diverse attacks. This study utilizes the modified density peak clustering algorithm to create a well-defined cluster using customer review information. The integration of collaborative filtering and content-based filtering methods has enabled a more advanced recurrent neural network algorithm to more precisely identify these hazards within hybrid recommendation systems. Characteristics obtained through varying degrees of corruption are ultimately incorporated into feeble classifiers capable of detecting attacks. Numerous assaults can be identified using the proposed method, as demonstrated by experimental testing on the Amazon dataset. In the realm of electronic commerce platforms, the recommended approach proves to be more effective for those experiencing rapid expansion in both product offerings and consumer base.

The continuous progress of modern science and technology pushes the development of agricultural machinery in the direction of automation, and the design of agricultural machinery has become an important means to improve the efficiency and quality of agricultural production. In order to achieve accurate and efficient transplanting operations, the study carried out a corresponding design of the seedling picking claw device of the transplanting machine, and at the same time, in view of the control requirements of the device, a double closed-loop control algorithm based on the position-velocity proportional-integral-derivative was introduced to achieve the precise positioning control of the opening and closing of the seedling picking claw. Results show that the motor plate of the seedling picking claw with the control strategy proposed by the Institute reaches and stabilizes at the desired displacement in about 0.1 s, which reduces the response time by 0.2 and 0.12 s, respectively, compared with the traditional PID control method and the fuzzy PID control method. It shows that the transplanting machine device designed by the Institute has a good effect of practical application, which helps to increase the transplanting precision as well as efficiency, and provides important technical support for the development of modern agriculture. Provide important technical support for the development of modern agriculture.

Lattice-based cryptography is widely embraced for its strong security and versatility, particularly in postquantum public key (PK) systems. The incorporation of an aggregate signature scheme consolidates numerous signatures into a concise cryptographic signature, thereby enhancing verifiability. However, traditional methods encounter security vulnerabilities as quantum computing capabilities evolve, resulting in performance challenges for real-time and resource-intensive applications. This research introduces a solution, called postquantum cryptography and the enhancement of identity encryption (PQCEIE), aimed at addressing blockchain security by leveraging the round optimal lattice-based multisignature scheme (ROLMSS) against quantum attacks. The study significantly enhances efficiency in terms of time and storage, with reduced overall complexity. It achieves minimal encryption time (5 ms), decryption times (4.9 ms), key generation (80 ms) for 16 attributes, and execution time (0.07s) compared to conventional approaches.